Strategy Implementation and Performance of Mobile-Commerce in Kenya’S Commercial Banks

STRATEGY IMPLEMENTATION AND PERFORMANCE OF MOBILE-COMMERCE IN KENYA’S COMMERCIAL BANKS

MARYLYN DOREEN MURIU

UNITED STATES INTERNATIONAL UNIVERSITY - AFRICA

FALL 2016

STRATEGY IMPLEMENTATION AND PERFORMANCE OF MOBILE-COMMERCE IN KENYA’S COMMERCIAL BANKS

MARYLYN DOREEN MURIU

A Dissertation Report Submitted to the Chandaria School of Business in Partial Fulfilment of the Requirement for the Degree of Doctor of Business Administration (DBA)

UNITED STATES INTERNATIONAL UNIVERSITY- AFRICA

FALL 2016

STUDENT’S DECLARATION I, the undersigned, declare that this is my original work and has not been submitted to any other college, institution or university other than the United States International University - Africa in Nairobi for academic credit.

Signed______Date: ______

Marylyn Doreen Muriu (ID 640161)

This dissertation has been presented for examination purposes with our approval as the appointed supervisors.

Signed______Date: ______

Dr. Joseph Ngugi Kamau

Signed______Date: ______Dr. Zachary Mosoti

Signed______Date: ______

Dean Chandaria School of Business

Signed______Date: ______

Deputy Vice Chancellor, Academic and Students Affairs

iii

COPYRIGHT All rights reserved. No part of this dissertation report may be photocopied recorded or otherwise reproduced, stored in retrieval system or transmitted in any electronic or mechanical means without prior permission of USIU-A or the author. Marylyn Doreen Muriu ©2016

ABSTRACT The purpose of this study was to determine the influence of strategy implementation on performance of mobile commerce (M-Commerce) in Kenya’s commercial banks. This study was based on the view that effective strategy implementation influences performance. The main objective of this study was to establish the strategy implementation influence of leadership, structure, information systems, human resources and strategy communication on m-commerce performance in Kenya’s commercial banks. The motivation of this study, was drawn from the fact that many banks in Kenya were investing heavily on the new technology m-commerce without much evidence of the success as demonstrated by other m-commerce service providers such as the mobile network operators in Kenya. Globally, mobile commerce initiatives have generated a lot of hype though some have ended up stagnating or failing completely. Strategy implementation failures have been estimated at between 50 and 80 percent. The high rate of failure is what motivated this study combined with the fact that there is limited research on strategy implementation and on m-commerce performance. In addition, the absence of a clear success in the m-commerce strategy in Kenya’s commercial banks, formed the basis for the need to establish the relationship between strategy implementation factors and m-commerce performance. The study adopted a multi-theoretical approach of the following theories: Resource Based View, Activity Theory, Expectancy Theory and was anchored on Agency Theory. Stratified random sampling technique was used to pick a sample of 133 managers from the target population of 200 from the commercial banks operating in Kenya at the end of December 2015. Data collection was done using structured questionnaires. For data collection method, the researcher recruited research assistants who dropped and picked the questionnaires from the banks. The response rate achieved was 84.76 percent. The research approach utilized positivism research philosophy and descriptive research design. The inferential analysis used in this study, was composed of factor analysis, correlation analysis, Chi square analysis, Analysis of Variance (ANOVA) and Structural Equation Modeling (SEM). The major findings of the study were that strategy implementation variables: organizational leadership, organizational structure, information system and strategic communication were positively and significantly correlated with m-commerce performance. This therefore means the variables influence strategy implementation and m-commerce performance. The study findings also, demonstrated that there was insignificant relationship between human resource variable and m-commerce performance. The influence of human resource on m- commerce performance was negative and statistically insignificant. The conclusion was that the four variables: organizational leadership, organizational structure, information system and strategic communication influence m-commerce performance and that the human resources in the banking industry were no longer rare, inimitable but could be substituted and therefore does not influence strategy implementation and m-commerce performance. This is because bank staff are mobile in nature and that the skill sets that are important for strategy implementation may be lacking. The results will inform banks and key stakeholders on key variables that support the appropriate strategy implementation, the risk of not adopting the right strategy and highlight the opportunity in using m- commerce to provide services to new segments of customers that are currently not served by the current banking services. Additional contribution of this study is the development of measurement parameters of m-commerce performance in commercial banks.

ACKNOWLEDGEMENT

I am most grateful first to my Lord and Savior Jesus Christ for granting me the ability to undertake this study. I sincerely thank my supervisor Dr. Joseph Ngugi Kamau and Dr. Zachary Mosoti from the Chandaria School of Business, for their patience and guidance throughout my research process. I extend my appreciation to my colleagues DBA two, for their encouragement and Dr. Michael Ndwiga for his support in data collection and guidance. I would like to thank my children, who cheered me all the way, despite long hours of absence from family. I also wish to extend special acknowledgement to my mother who taught me resilience, determination, patience and encouraged me to keep moving on. Finally, I am most grateful to my dear husband for his tremendous support throughout my academic journey. He has been my cheer leader, my flag bearer and this study was his brain child and motivation. To you all, without your support and encouragement, I would not have pursued this great dream. To you all and the readers of this thesis, may God bless and fulfil your dreams.

DEDICATION

I dedicate this doctoral study to my children, to them, I desire that they learn that with God’s help, they will achieve every dream they dare to dream.

vii

TABLE OF CONTENTS

STUDENT’S DECLARATION ...... iii COPYRIGHT ...... iv ABSTRACT ...... v ACKNOWLEDGEMENT ...... vi DEDICATION...... vii TABLE OF CONTENTS ...... viii LIST OF TABLES ...... xi LIST OF FIGURES ...... xv ABBREVIATIONS AND ACRONYMS ...... xvi CHAPTER ONE ...... 1 1.0 INTRODUCTION...... 1 1.1 Background of the Study ...... 1 1.2 Statement of the Problem ...... 12 1.3 Purpose of the Study ...... 13 1.4 Specific Objectives ...... 13 1.5 Hypotheses ...... 14 1.6 Justification of the Study ...... 15 1.7 Scope of the Study ...... 16 1.8 Definitions of Terms ...... 17 1.9 Chapter Summary ...... 19 CHAPTER TWO ...... 20 2.0 LITERATURE REVIEW ...... 20 2.1 Introduction ...... 20 2.2 Theoretical Review ...... 20 2.3 Conceptual Framework ...... 54 2.4 Empirical Review...... 65 2.5 Development of the Hypothesis ...... 97 2.6 Chapter Summary ...... 97 CHAPTER THREE ...... 99

3.0 RESEARCH METHODOLOGY ...... 99

3.1 Introduction ...... 99

viii

3.2 Research Philosophy ...... 99 3.3 Research Design...... 101 3.4 Target Population ...... 102 3.5 Sampling Design ...... 102 3.6 Data Collection Methods ...... 104 3.7 Research Procedures ...... 105 3.7.2 Reliability of the Instruments...... 106 3.7.3 Validity of the Instruments ...... 107 3.8 Data Analysis Methods ...... 109 3.9 Chapter Summary ...... 111 CHAPTER FOUR ...... 112

4.0 RESULTS AND FINDINGS ...... 112

4.1 Introduction ...... 112 4.2 General Information ...... 112 4.2.2 Normality Test ...... 118 4.3 Influence of Organizational Leadership on M-Commerce Performance ...... 127 4.4 Influence of Organizational Structure on M-Commerce Performance ...... 137 4.5 Influence of Information System on M-Commerce Performance ...... 144 4.6 Influence of Human Resources on M-Commerce Performance ...... 150 4.7 Influence of Strategic Communication on M-Commerce-Performance ...... 157 4.8 Influence of Market Turbulence on M-Commerce Performance ...... 164 4.9 Influence of Technological Turbulence on M-Commerce Performance ...... 169 4.11 Chapter Summary ...... 177 CHAPTER FIVE ...... 180

5.0 SUMMARY, DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS .. 180

5.1 Introduction ...... 180 5.2 Summary ...... 180 5.3 Discussion of Results ...... 181 5.4 Conclusions ...... 192 5.5. Recommendations ...... 196

REFERENCES ...... 199

Appendices ...... 228

Appendix 1: Questionnaire ...... 228

Appendix 2: Factor Analysis, Kurtosis and sem Modeling ...... 234

Appendix 3: List of Kenya’s Commercial Banks as at December 31, 2015 ...... 241

Appendix 4: Research Authorization ...... 242

Appendix 5: Nacosti Permit Letter ...... 243

LIST OF TABLES

Table 1.1: Growth of Financial Touch Points ...... 10 Table 2.2: Noble’s (1999) Strategic Framework ...... 47 Table 2.3 Comparison of Implementation Models ...... 52 Table 2.4 Summary of Selected Studies ...... 95 Table 3.1 Population, Sample Size and Precision Error ...... 104 Table 4.1 Response Rate ...... 113 Table 4.2 Respondents by Age Group ...... 114 Table 4.3 Position in the Bank ...... 114 Table 4.4 Educational Level ...... 115 Table 4.5 Work Place Experience ...... 116 Table 4.6 Bank Tier Group ...... 116 Table 4.7 Account Transfers ...... 117 Table 4.8 Pay Bill Merchants...... 117 Table 4.9 Mobile Banking Services ...... 118 Table 4.10 Value Add Products ...... 118 Table 4.11Assessment of Normality ...... 120 Table 4.12 Convergent Validity for Constructs ...... 124 Table 4.13 Cross Loading ...... 126 Table 4.14 VIF values for Constructs ...... 127 Table 4.15 Frequency Distribution for Organizational Leadership Influence ...... 128 Table 4.16 Mean and Standard Deviation for Organizational Leadership Influence ...... 129 Table 4.17 KMO and Bartlett’s Test for Organizational Leadership Influence ...... 130 Table 4.18 Factor Analysis for Influence of Organizational Leadership ...... 130 Table 4.19 Organizational Leadership –Coefficient Alpha ...... 131 Table 4.20 Reliability Test for Organizational Leadership ...... 131 Table 4.21 Correlation between Organizational Leadership and M-Commerce Performance ...... 132 Table 4.22 Chi Square Test on Organizational Leadership Influence ...... 132 Table 4.23 ANOVA Between Organizational Leadership and M-Commerce Performance ...... 133 Table 4.24 Relationship between Organizational Leadership and M-Commerce ...... 134

Table 4.25 Moderated Relationship between Organizational Leadership and M-Commerce Performance ...... 136 Table 4.26 Frequency Distribution for Organizational Structure ...... 138 Table 4.27 Organizational Structure Mean and Standard Deviation Statistics ...... 139 Table 4.28 Reliability Test for Organizational Structure ...... 140 Table 4.29 KMO and Bartlett’s Test for Organizational Structure Influence ...... 140 Table 4.30 Factor Loadings for Organizational Structure ...... 141 Table 4.31 Correlation between Organizational Structure and M-Commerce Performance ...... 141 Table 4.32 Chi Square Test of Organizational Structure Influence on M-Commerce Performance ...... 142 Table 4.33 ANOVA between Organization Structure and M-Commerce Performance . 142 Table 4.34 Relationship between Organizational Structure and M-Commerce ...... 143 Table 4.35 Relationship between Organizational Structure and M-Commerce Performance ...... 144 Table 4.36 Frequency Distribution for Information System ...... 145 Table 4.37 Frequency Distribution for Information System ...... 146 Table 4.38 KMO and Bartlett’s Test for Information System ...... 146 Table 4.39 Component Matrix for Influence of Information Systems ...... 147 Table 4.40 Reliability Test for Information Systems...... 147 Table 4.41 Correlation between Information Systems and M-Commerce ...... 148 Table 4.42 Chi Square Test on Information System Influence ...... 148 Table 4.43 ANOVA Between Information System and M-Commerce Performance ..... 149 Table 4.44 Relationship between Information System and M-Commerce Performance 150 Table 4.45 Frequency Distribution for Human Resources Influence on M-Commerce Performance...... 152 Table 4.46 Mean and Standard Deviation for Human Resources Influence...... 153 Table 4.47 KMO and Bartlett’s Test for Human Resources Influence...... 153 Table 4.48 Human Resource Component Matrix ...... 154 Table 4.49 Reliability Test for Human Resources Items ...... 154 Table 4.50 Correlation between Human Resources and M-Commerce Performance ..... 155 Table 4.51 Chi Square Test on Human Resource Influence ...... 155 Table 4.52 ANOVA between Human Resources and M-Commerce Performance ...... 156 Table 4.53 Relationship between Human Resources and M-Commerce Performance ... 157 xii

Table 4.54 Frequency Distribution of Strategy Communication ...... 158 Table 4.55 KMO and Bartlett’s Test for Strategic Communication ...... 159 Table 4.56 Strategic Communication Component Matrix for Strategic Communication159 Table 4.57 Reliability Test for Strategic Communication Items ...... 160 Table 4.58 Correlation Between Strategic Communication and M-Commerce Performance ...... 161 Table 4.59 Chi Square Test on Strategic Communication Influence...... 161 Table 4.60 ANOVA Between Strategic Communication and M-Commerce ...... 162 Table 4.61 Relationship between Strategic Communication and M-Commerce ...... 163 Table 4.62 Frequency Distribution of Market Turbulence ...... 165 Table 4.63 Mean and Standard Deviation for Market Turbulence ...... 166 Table 4.64 KMO and Bartlett’s Test for Market Turbulence ...... 166 Table 4.65 Market Turbulence Component Matrix ...... 167 Table 4.66 Reliability Test for Market Turbulence Items ...... 167 Table 4.67 Correlation Between Market Turbulence and M-Commerce Performance ... 168 Table 4.68 Chi Square Test on Market Turbulence Influence ...... 168 Table 4.69 ANOVA between Market Turbulence and Strategic Management ...... 169 Table 4.70 Relationship between Market Turbulence and M-Commerce Performance . 169 Table 4.71 Frequency Distribution for Technological Turbulence ...... 171 Table 4.72 Mean and Standard Deviation for Technological Turbulence ...... 172 Table 4.73 KMO and Bartlett’s Test for Technological Turbulence ...... 172 Table 4.74 Technological Turbulence Component Matrix ...... 173 Table 4.75 Reliability Test for Technological Turbulence ...... 173 Table 4.76 Correlation Between Technological Turbulence and M-Commerce Performance ...... 174 Table 4.77 Chi Square Test on Technological Turbulence Influence ...... 174 Table 4.78 ANOVA Between Technological Turbulence and M-Commerce ...... 175 Table 4.79 Relationship between Technological Turbulence and M-Commerce Performance ...... 175 Table 5.1 Organizational Leadership and M-Commerce Performance ...... 183 Table 5.2 Organization Structure and m-commerce performance ...... 185 Table 5.3 Relationship between Information System and M-Commerce Performance .. 186 Table 5.4 Relationship between HR and M-Commerce Performance ...... 188 Table 5.5 Relationship between Strategic Communication and M-Commerce ...... 189 xiii

Table 5.6 Moderating Influence of Market Turbulence on all Variables ...... 191 Table 5.7 Moderating Influence of Technological Turbulence on all Variables ...... 192

xiv

LIST OF FIGURES

Figure 1.1: Markets Best Prepared for Mobile Payment Adoption ...... 8 Figure 2.1: Basic Expectancy Model ...... 32 Figure 2.2: The Core of an Activity Theory ...... 37 Figure 2.3: Eight “S” Strategy Model ...... 43 Figure 2.4: Strategy Implementation Framework and Key Variable ...... 52 Figure 2.5: Conceptual Framework ...... 56 Figure 2.6: Operational Framework ...... 64 Figure 2.7: Evolution of M-Commerce ...... 69 Figure 4.1: Respondents by Gender ...... 115 Figure 4.2: Path Coefficients ...... 136 Figure 4.3: T values ...... 137 Figure 4.4: Path Coefficients for the Moderated Model. (MT and TT) ...... 138 Figure 4.5: T Values for the Moderated Model. (MT and TT) ...... 139

ABBREVIATIONS AND ACRONYMS

AT Activity Theory CA Competitive Advantage CFA Confirmatory Factor Analysis CR Composite Reliability G Generation GoK Government of Kenya HR Human Resources IS information Systems IT information Technology MC-P Mobile Commerce Performance MT Market Turbulence OL Organizational Leadership OS Organizational Structure PCA Principal Component Analysis PDA Personal Digital Assistant R-A Resource-Advantage RBV Resource Based View SC Strategic Communication TT Technological Turbulence MLQ Multifactor Leadership Questionnaire USSD Unstructured Supplementary Service Data

xvi

CHAPTER ONE

1.0 INTRODUCTION

1.1 Background of the Study

Strategic management is the set of decisions and actions used to formulate and implement plans that will provide a competitively superior fit between the organization and its environment so as to achieve organizational goals (Prescott, 1986). Strategy plays a very important role in the existence of an organization. However, without effective implementation, no business strategy can succeed. Strategy implementation is more difficult than strategy making (Hrebiniak, 2006). Continuous poor strategy execution weakens the strategy formulated (Bonoma & Crittenden, 2008). There are many definitions and frameworks for strategy formulation, however there is little scientific knowledge about the actual realization of strategy once planned.

Most research takes a general approach (Weernink, 2014). Neilson, Martin and Powers (2008), state that a brilliant strategy, great product or innovative technology can put an organization on the competitive position, however, it is only the execution that can keep the organization competitive. Blahová and Knápková (2011), added that formulation and implementation of business strategies is often associated with top management or shareholders. They however show that most companies struggle with implementation because they focus on structural reorganization which according to them produces only short-term gains and neglect the drivers of effectiveness and information flow.

A study by Balogun, (2006); (Hrebiniak), 2006; Saunders et al., (2009), most organizations experience serious challenges in implementing their intended strategies. Cater and Pucko, (2010), indicates that 80 percent of firms have the right strategies, but only 14 percent manage to implement them well. Hrebiniak (2006), argued that while strategy formulation is difficult, executing strategy is even more difficult. Supporting similar studies, Zaribaf and Bayrami (2010), state that most executives in organizations spend time, energy and money in formulating a strategy, but do not provide sufficient input to implement it adequately. Strategic management literature has focused primarily on planning and strategy formulation and neglected strategy execution and its importance (Smith, 2011). According to Bossidy, Charan and Burck, (2002), the focus on strategy planning and formulation and the neglect of implementation, and the neglect of

implementation, is a problem to strategic management because people view strategy implementation as the tactical side of business. Some leaders delegate implementation, while they focus on perceived “bigger” issues. Bossidy (2002), argues that this approach to strategy implementation is wrong and that implementation is not just tactics, but a discipline and a system. This discipline therefore has to be built into a company’s strategy, its goals, and its culture. Therefore, the leader of the organization must be deeply engaged in it, and cannot afford to delegate this, but they can delegate its substance. Dawes, (2013), states that a culture of fast, effective decision-making and action throughout the organization enables true flexibility and responsiveness. Dawes, (2013), argue that in a rapidly changing business landscape, executing the right decisions better than competition is the key to success.

Dion, Allday, Lafforet, Derain and Lahiri, (2007); Kaplan and Norton, (2001), report that strategy implementation failure rates is high at between 50 percent and 90 percent. Candido and Santos, (2008), also show that strategy has received much attention but indicate that the focus of most studies has been the making and developing of strategic decisions. Neilson, Martin, and Powers, (2008) in their reviews of strategy, indicate that companies have expertise in developing strategies, but frequently fail to successfully implement them.

1.1.1 Strategy Implementation

Strategy implementation is how strategy is put into action. According to the definition of strategy implementation by Pearce and Robinson (2013), strategy implementation is the sum total of the activities and choices required for the execution of a strategic plan. It is the process by which objectives, strategies and policies are put into action through the development of programs, budgets and procedures. No matter how creative the formulated strategy is, if it is incorrectly implemented, the organization does not benefit. In the current competitive environment, there is an increasing recognition of the need for more dynamic approaches to formulating as well as implementing strategies (Hough, Thompson, Arthur, Strickland, & Gamble, 2011). Successful strategy development may depend on several factors such as market positioning, business vision, industry competitive analysis, however, successful strategy execution depends on working with and through others.

This is achieved through instilling discipline, strengthening and building competitive capabilities and recompensing people in a strategy for getting things done (Hough et al., 2011). Similarly, Charles and Gareth (2007), are of the opinion that strategy implementation is tougher, more time-consuming in comparison to crafting strategy because of the wide array of managerial activities that have to be attended to, the many steps that managers ought to systematically take, and the number of issues that must be worked out. It takes follow-through and perseverance to get a variety of initiatives launched and moving and to integrate the efforts of many different work groups into a smoothly functioning whole. Depending on how much consensus building and organizational change is involved, the process of implementing strategy can take several months to several years. It takes still longer to achieve real proficiency in executing the strategy (Hough et al., 2011).

According to Hough and Thompson, (2011), executing strategy is a job for the whole management teams not just a few senior managers. Top level managers have to rely, on the active support and cooperation of middle and lower managers to push strategy changes into functional areas and operating units and to see that the organization operate in accordance with the strategy on a daily basis. Hough et al., (2011), also indicate that middle and lower level managers are instrumental in getting their juniors to consistently improve on how strategy-critical value chain activities are being performed. And in producing the operating results that allow company performance targets to be met. They state that “well-conceived policies and procedures support strategy execution” (Hough et al., 2011). The success of strategy is driven by collaborative human effort and, by internal compound of co-operation and raw brainpower. Firms are not just economic entities, but social entities, in which conscious deliberations by individuals and teams can achieve extraordinary results (Koch, 2011).

Central to every business is the adoption of a strategy that would enhance an organization’s overall performance, strengthen its long-term competitive position to gain competitive advantage over competition. A competitive advantage is the most reliable approach for profitability. Organizations tend to earn significantly higher profits when they enjoy competitive advantage as opposed to when it is constrained by competitive disadvantage (Hough et al., 2011). Based on the above background on strategic management and strategy

implementation, this study sought to investigate the relationship between strategy implementation and m-commerce performance in Kenya’s commercial banks. Banking sector is one of the oldest systems that has evolved over time based on consumption and demand of financial services. The consumption of services has consistently changed in size and method of transaction but the core business has remained constant, with the exception of the fact that innovation has transformed banking resulting in a bank operational changes (Shekhar, 2005).

1.1.2 Global Perspective of Strategy Implementation

Globally, strategy implementation is taking a new shape by incorporating firm’s functional areas such as marketing, human resource management and information systems into the implementation process (Naranjo-Gill & Hartmann, 2006). Well- accepted factors of strategy implementation such as structure, culture or organizational processes are developing as new trends and many organizations are emphasizing them (Olson, Slater, Tomas & Hult, 2005). The success of implementation is attached to moving strategy from the boardroom to back offices and the market places using the discipline of project management (Longman & Mullins, 2004).

All these, are organizational ways of responding to environmental turbulence in the attempt to dislodge organizations from hierarchical structures and embracing more modular forms of structures (Balogun & Johnson, 2004). Olson et. al., (2005), echoed the importance of a firm’s structure and processes in strategy implementation. Another significant feature in the global strategy implementation is the reporting of studies in detailed socio-economic contexts in particular countries for example in China (Wu, Chou, & Wu, 2004) and in developing economies such as Latin America in (Brenes, Mena & Molina, 2007). The concept and practice of strategy implementation has been adopted worldwide and across various sectors because of its perceived contribution to organizational effectiveness (Strickland & Thompson, 2007).

Despite the fact that numerous studies globally acknowledge that strategies frequently fail because of inappropriate implementation, it is still apparent that strategy implementation has received less research attention than strategy formulation (Li, Guohui & Eppler, 2008). Strategy implementation does not have a universally accepted definition but, Li et al., (2008), identified three distinct conceptions for strategy implementation and outlines them as a process perspective, as a behaviour perspective and as a combination of the

process perspective and behaviour perspective to form a third approach they labelled hybrid perspective.

According to Al-Ghamdi (1998), problems associated with strategy implementation process include managers’ failure to anticipate training requirements for employees, a disconnect between performance during implementation with overall reward system of the organization and a mismatch between time required for implementing the plans which should have been considered during strategy planning. Cascella (2002), states that there are a few managers able to implement plans because more emphasis is based on formulation process instead of its implementation. In addition, Snow and Hrebiniak (1980), are of the view that the longer the time for execution of plans the more the chances of failure of plans. Hansen and Boyd (1998), identified implementation problems such as failing to periodically alter the plan, deviation from actual objectives and lack of confidence about success.

1.1.3 Africa Perspective on Strategy Implementation

Globalization and internalization of world markets have shaken the economic dynamics of African countries and being driven by the private sector becoming the predominant player in wealth creation. A research by Smith, (2011), concluded that the task of strategy implementation is primarily an operations-driven activity, revolving around the management of people and business processes. The well rewarded, motivated and trained a team is, would result in a successful strategy implementation. In addition, research shows that owners, managers and employees of different groups have divergent views on building a capable organization necessary for strategy implementation (Allio, 2005). Hence the need to ensure that efforts for building a capable organization needs to take into account the viewpoints of a diverse workforce.

A study by Fourie and Jooste (2009), on strategy implementation in South African organizations, provided the view that strategy implementation is more important than strategy formulation and that the ability to implement a strategy is more important than the ability to formulate a strategy. In the same study, the resultant perception is that strategy implementation is more difficult than strategy formulation, and that poor strategy implementation results in a high failure rate of planned initiatives.

1.1.4 Kenyan Strategy Perspective

Kenyan market environment is experiencing rapid changes making managers in organizations and the government to re-examine and change views on market orientation in order to remain competitive. (Mwando & Muturi, 2016). In the last decade, most of the external changes that have occurred globally and within Africa have influenced strategic approach in organizations in Kenya. Kenya has been affected by international trends such as the recession, globalization and competition (Aosa 2011). The promulgation of Kenya’s constitution in 2010, set a new framework of governance that sought to devolve power and development from centralized government to a de-centralized governance of 47 counties which serve as the basis for national governance and planning (Nyanjom, 2011). This has changed strategies for both the government and organizations in the country seeking to meet the needs of the government (Mwando & Muturi, 2016).

In view of these therefore, it is expected that organizations in Kenya would turn to strategic management as a way of securing competitive advantage. However, the study by Aosa (2011), revealed that there were variations in strategy practices in Kenya between foreign companies and local companies. According to the research, foreign companies focused more on the strategic management process than the local companies. Accordingly, the local companies were heavily oriented to financial plans and focusing more on cash flow projections and extended budgeting. The differences are based on the fact that foreign companies were subsidiaries of bigger international companies and would therefore have more of their strategy plans drawn from their home countries. Their activities were therefore supported and influenced by their parent companies.

Most organizations in Kenya emphasize democratic leadership in an attempt to maximize participation, involvement of staff and to empower the decision making process (Mulube, 2009). However, the challenge is to establish if effective strategy implementation is attainable. Mintzberg (2004) is of the opinion that effective strategy implementation depends on the learning and development of employee environment who are the implementers.

1.1.5 Commercial Banks

A commercial bank is defined as a financial institution that accepts money on deposit from the public that is repayable on demand or at the expiry of a fixed period (The

Banking Act of Kenya 2010). The accepting from members of the public of money on current account and payment and acceptance of cheques and the employing of money held on deposit or in current account, or any part of the money, by lending investment or in any other manner for the account and at the risk of the person so employing the money.

1.1.6 M-Commerce Strategy in Commercial Banks

Globally, banks are experiencing competition from Mobile Network Operators (MNOs). In the developed countries, several mobile money payment initiatives have developed and despite promising high returns, some have either stagnated or failed (Gaur, & Ondrus, 2013). This is attributed to changing technology and customer preferences with studies indicating that traditional banks could have up to 35 percent of their revenues taken by MNOs (Insights, 2014). Research also indicates that the mobile payment solutions have a history of tried and failed solutions. However, there are great future promises for mobile payments though with uncertain possibilities and potential for new technology innovations (Dahlberg, Guo & Ondrus, 2015). In half a decade, mobile has revolutionized the way people buy, bank and make payments. The battle for market share between the MNO’s and the banks is beginning to bite the banks. The banks have the ability and the imperative to take advantage of the opportunity. Banks are in a unique position to provide consumers with a connected m-commerce experience, while defending traditional services and driving new revenue streams (Insights, 2014).

The penetration of global mobile phones reached 99 percent in quarter one of 2015. Smart phones which drive m-commerce accounted for 75 percent of all mobile phones sold in the first quarter of 2015 in comparison to 65 percent during the first quarter of 2014 (Carson, Godor, Kersch, Kälvemark, Lemne, & Lindberg (2015). Ericsson estimates that around 40 percent of all mobile phone subscriptions are smartphones. The implication of this is that the convenience of an electronic gadget in people’s pocket with data collected digitally, means that financial services are becoming accessible to the customer. The property of mobile phone being available everywhere provides a persuasive business case and is an influential factor in the adoption of m-payment systems, more so, when a bigger number of a population is not banked (Contini, Crowe, Merritt, Oliver & Moth, 2011; FINsights, 2008; Pandy 2014) this applies to both the developed and developing countries. A global view of markets best prepared for mobile payments places Kenya at number four after Singapore, Canada and U.S. as indicated in figure 1.1.

Figure 1.1: Markets Best Prepared for Mobile Payments Adoption Source: Monitise, 2014

Since the introduction of M-Pesa in 2007, there has been a rapid uptake of mobile-money services which was buoyed by the low level of financial access in Kenya. (Aker & Mbiti, 2010). Mobile telephony has brought new possibilities in terms of financial access to the un-banked in Kenya. Kenya is at position four in preparedness for mobile payment adoption, an indication that this expected to affect the banking landscape and therefore the banks’ approach to this innovation is critical to its growth, sustainability and performance.

Africa witnessed an increase from 0.5 mobile phones per 100 people to 43 between 1998 and 2009 (Kahm, Edensvard, & Nelson, 2014). This fast growth has enabled real-time connectivity between cities and remote rural areas and provided new payment systems and basic banking services. Sub-Saharan Africa (SSA) has approximately 1 billion people with 70-80 percent of its population unbanked, with banks being small in absolute and relative size and therefore presenting a growth potential for banking (Eric, 2014). The banking industry in SSA is characterized by modest reach in terms of providing financial services to the population (Eric, 2014). The growth of mobile phone-based banking has provided opportunities for banks to expand their operational reach (Kahm, Edensvard, & Nelson, 2014).

The banking landscape in Kenya is becoming volatile because of competition from alternative financial service providers that seek to disintermediate the banks. In Kenya, an estimated 39.7million adults (CAK, 2014), have mobile phones. Mobile phones and mobile technology driven gadgets such as tablets and high end smart phones are used to send and receive e-mails, shop conveniently from any place. However, with the abundance of access to mobile phones, consumers are not utilizing mobile commerce technology fully (Sreenivasan & Noor, 2011), causing questions as to why this is the case.

The launch of smartphones, has the potential to revolutionize the way people interact with the phones, finances and the market. The m-commerce innovation links banks, telecommunications companies and other service providers. This makes Banks and Mobile Network Operators (MNOs) consider the potential opportunities and challenges associated with M- commerce and therefore exploring new ways to collaborate (Accenture, 2012). According to Accenture (2012), the winners in the M-Commerce landscape, are those who would be able to provide their customers with incentives to adopt a new type of technology such as a digital wallet instead of remaining in their credit card comfort zone. Accenture’s view is that banks and MNOs that extend their vision beyond basic payments to develop service benefits that end users would find useful, would place them in the best position to succeed.

The entry of mobile money service providers to the Kenyan banking sector is seen as a move that will drive mobile banking in the country more rapidly and would help catapult it to the level or higher than that of the western countries by skipping some of the steps in the technology roadmap development (CAK, 2014). This is evidenced by the increase in mobile money subscriptions which was 26.3 million, and mobile money transfer agents numbering 158,727. A total of 227.3 million M-Commerce transaction made in the second quarter (April-June 2016), translating to a value of Kshs 404.1 Billion (CAK, 2016). Players in the mobile money transaction business are Safaricom, Airtel, Equitel, Orange Telkom, Tangaza Pesa and MobiKash.

The growth of mobile money services is believed to be the cause of decline of bank electronic payment cards (CBK, 2015). For example, between January 2015 and April 2015, transactions made through electronic payment cards stood at KShs. 427.6 billion, a marginal increase of 0.66 percent from KShs. 424.8 billion transacted in the same period

in 2014 (CBK, 2015). In addition to that, the government is pushing for cashless payments, both card and mobile money using agents who in turn are making developments to improve their product offerings while partnering with firms to ease payment processes in order to gain competitive advantage in the market (CBK, 2015).

According to (Eric 2014), Sub Saharan Africa is behind the rest of the world in developing new technologies in the banking sector. However, in Kenya, the Mobile Network Operators (MNOs): Airtel, Orange and Safaricom and other mobile money service providers are re-shaping the landscape in terms of mobile banking. Safaricom started in 2007 with M-Pesa (M for mobile, pesa for money in Kiswahili), a mobile-phone based money transfer service that has scaled up partnerships with banks and other institutions for a variety of transactions. Since the Kenyan launch of M-Pesa in 2007, the service has gained great traction locally and internationally, raising the country’s profile as a mobile money service success story. By April of 2015, Safaricom had signed an agreement with MTN of South Africa allowing Safaricom to send money through their mobile phones to recipients in 7 African countries namely: Kenya, Tanzania, DRC and Mozambique, and MTN Mobile Money customers in Rwanda and Zambia (The East African, April 2015).

Mobile money services have metamorphosed from a send-money only to a service that is now used in paying bills, money remittances from abroad, sending or withdrawing money to and from bank accounts, payments of fees to schools, hospitals, utility firms and other organizations, shopping at retail outlets, purchase of airtime and data bundles. Growth of mobile money agents, bank agents, agent outlets and money transfer services (FSD, 2015), as indicated in table 1.1, illustrates that there is great potential in the banking industry and therefore the institution that would embrace the change would be successful.

Table 1.1: Growth of Financial Touch Points 2013 2015

Mobile Money Agents 49,417 68141

Bank Agents 8,083 13,428

Money Transfer Services 2,484 3,778

Development of Finance Service Providers 57 91

Source: FSD Kenya (2015).

1.1.7 Studies on Implementation of Innovations

The innovations resulting in the mobile money in the telecommunication is seen as a huge success. However, studies carried by Accenture (2014) on implementation of digital innovation indicate that speed, skills and culture present a challenge. From the study, 60 percent of the respondents felt that established financial services players would survive and thrive in the digital future. Whereas others were of the opinion that the industry would become disaggregated, others felt that the traditional banks would lose market share, revenue, and that margins would fall. The Accenture study proposes two possibilities: digital change in the banking sector is digitally disrupted or digitally reimagined. In the digitally disrupted, banks continue with a product-based sales approach rather than improving customer experience resulting in a lack of motivation to deal with legacy applications (Accenture, 2014). The banks in this group compete for a diminishing share of wallet. These banks continue to depend on their traditional business model with the expectation that their strategies will remain successful. The second groups of banks are the digitally reimagined who embrace innovations at the business model. The focus of these banks is on making a customer’s life easier and learns how to use collaboration whilst adjusting business models to delight customers. Banks in this category see themselves as having short term advantages in infrastructure and customer data, but no long term right to exist without converting this into services that solve emerging digital consumer frustrations (Julian, 2014). From the above background on the digitally disrupted and digitally reimagined opportunities in the banking sector, a majority of the Kenyan banks could be said to be adopting digitally reimagined approach. Most banks are rolling out M-Commerce with the expectations that this will drive customer growth, reduce operating costs, gain leverage over competitors and ultimately increase revenue earnings over the medium to long term (Fintech 2015). With m-commerce strategy banks pursue strategic target that is aimed at increasing organizational performance and enables the organization to gain competitive advantage (Accenture 2012). In Kenya, commercial banks are adopting m-commerce with low success rate as compared to the success of M-Pesa and this could be attributed to various barriers in m-commerce strategy implementation, for which this study seeks to investigate.

1.2 Statement of the Problem

Strategy implementation is a key element of strategic management process but several organizations are faced with challenges in implementing intended strategies (Balogun, 2006; Hrebiniak, 2006; Saunders et.al., 2009). Several studies indicate that successful strategy implementation is achieved through leadership, structure, information systems, human resources and strategic communication (Thompson, Gamble & Strickland 2011; Arooj Azhar, 2012). A study by Brinkschröder (2014), on challenges of strategy implementation in relation to firm performance, reviewed an interplay of strategy, structure and behavior. The study concluded that the challenges would be resolved through dialogue and communication to achieve effective strategy implementation.

While there are several studies undertaken on m-commerce, the studies have not addressed strategy implementation. A study by Guptal and Vyas (2014), addressed benefits and drawbacks of m-commerce in India. The results showed a low adoption with the conclusion of a need for help from the Indian Government to accelerate growth through building of infrastructure to internet connectivity and creating awareness. Altaher (2012), investigated m-commerce service system implementation in banks in Jordan and concluded that developing countries have to apply the technology acceptance model in order to help bank staff accept the new services.

In Malawi, Saidi (2010), carried out a study on implementation of m-commerce and concluded that the implementation challenges include technical, (limited infrastructure), business related (huge capital investments), low levels of user acceptance and lack of regulatory policies to regulate mobile payment systems. In Kenya, a study by Gitau and Nzuki (2014) on determinants of m-commerce adoption by online consumers, concluded that perceived ease of use, cost, trust, personal innovativeness social influence and demographic variables influence the adoption of m-commerce by online users. There is evidence also in the developing countries that explosion of mobile devices is accelerating m-commerce growth, yet, even with the high penetration rate of mobile phones, low adoption rate of m-commerce is observed (Gitau & Nzuki, 2014).

Kenya’s banking industry is undergoing rapid technological changes and has one of the most successful mobile banking models in Africa – the M-Pesa which caters for more than 70 percent of Kenya’s adult population (KPMG, 2015). A study by European Investment Bank (EIB), (2015, indicates that sub-Saharan African (SSA) financial

systems are underdeveloped and the region’s banking sectors are inefficient at financial mediation. The World bank’s database (2015), indicate that only 34 percent of adults in SSA were banked in 2014, thus access to finance in SSA is among the lowest in the world. Despite the poor financial access, SSA leads the world in mobile money accounts; while it is estimated that only 2 percent of adults worldwide have a mobile money account, in SSA 12 percent of the population have a mobile account (EIB, 2015). The dawn of mobile technology and other innovations have enabled access to banking services to a broader customer base allowing services to be provided to lower income households residing in rural locations through affordable cellular technology (African Development Bank, 2012).

Kenya’s prudential guidelines introduced in 2012 have improved risk management practices. The CBK’s Risk Management Guidelines on strategic risks monitors responsiveness to industry changes that could significantly impact on the achievement of a bank’s vision and strategic objectives, but, it does not provide any support for strategy implementation of m-commerce. From a policy perspective, this study addressed a policy gap by investigating the influence of strategy implementation on m-commerce performance. M-commerce being a new innovation, is expected to present more challenges in implementation. Factors

hindering or enhancing its implementation, have not been identified in any of the research reviewed and therefore, this study is expected to close the gap on the study of m- commerce strategy implementation. Most of the reviewed literature on m-commerce strategy have focused on consumer end adoption of m-commerce and m-commerce security. This study focused on implementation of m-commerce in the banking sector.

1.3 Purpose of the Study

The study investigated strategy implementation and M-commerce performance in Kenya’s Commercial Banks.

1.4 Specific Objectives

The specific objectives of this study were;

1.4.1 To assess the influence of organizational leadership on m-commerce performance in Kenya’s commercial banks.

1.4.2 To explore the influence of organizational structure on m-commerce performance in Kenya’s commercial banks.

1.4.3 To determine how information systems influence m-commerce performance in commercial banks in Kenya.

1.4.4 To analyze the influence of human resources on m-commerce performance in Kenya’s commercial banks.

1.4.5 To explore the influence of strategic communication on m-commerce performance in Kenya’s commercial banks.

1.4.6 To assess the moderating effect of market turbulence has on the relationship between strategy implementation variables and m-commerce performance in Kenya’s commercial banks

1.4.7 To determine the moderating effect technological turbulence has on the relationship between strategy implementation variables and m-commerce performance in Kenya’s commercial banks

1.5 Hypotheses

The null hypotheses of this study are as follows:

1.5.1 Organizational leadership does not significantly influence m-commerce performance in Kenya’s commercial banks. 1.5.2 Organizational structure does not significantly influence m-commerce performance in Kenya’s commercial banks. 1.5.3 There is no significant relationship between information systems and m-commerce performance in Kenya’s commercial banks. 1.5.4 There is no significant relationship between human resources and m-commerce performance in Kenya’s commercial banks. 1.5.5 Strategic communication does not significantly affect m-commerce performance in Kenya’s commercial banks.

1.5.6 Market turbulence does not significantly moderate the relationship between strategy

implementation and m-commerce performance in Kenya’s commercial banks.

1.5.7 Technological turbulence does not significantly moderate the relationship between strategy implementation and m-commerce performance in Kenya’s commercial banks.

1.6 Justification of the Study The industry comprises of private and public shareholders including association with technology vendor providers, insurance and government. The acquisition and implementation of m-commerce have great implications for the operations of banking. However, despite the potential benefits to be gained from the use of M-Commerce, the technology has not been fully adopted by Banks in Kenya. There is no formal framework that exists to support m-commerce. This limitation is attributed to the absence of enabling legislation and low utilization of digital transactions in the country. Generally, financial institutions have been reluctant to establish payment gateways. There has been a call for banks and the government to implement online payment processing systems and mapping systems that would facilitate m-commerce transactions. This study, therefore sought to fill the knowledge gap by identifying obstacles of m-commerce strategy implementation contributing to empirical evidence on the influence of effective strategy implementation on m-commerce performance in Kenya’s banking sector. The study will be important to the following constituents:

1.6.1 Commercial Bank Strategy Leaders

The constituents for the study, include the senior management and strategy implementers in Kenya’s commercial banks. The study provides the senior management with the perceptions of their subordinates regarding their leadership influence in strategy implementation. The study will help the senior management understand the factors that hinder successful strategy implementation and help them design appropriate intervention programs to enhance strategy implementation and positively drive the strategy implementation process.

1.6.2 Central Bank of Kenya (CBK)

The study will benefit Kenya’s banking regulator CBK, by getting the insights on the need to focus on strategic risk management in the area of strategy implementation. The study will assist in helping policy makers provide policies that enhance secure and efficient payment systems in order to deliver value to customers. This proactivity on the

regulators part can provide governments with the opportunity to enhance access to financial services especially for the unbanked and under-banked population in Kenya.

1.6.3 Scholars

Knowledge gap reduction is viewed as a benefit from this study because strategy implementation is an under-researched area in the field of strategic management. Academia and business strategists, in majority of cases, focus more on strategy formulation than on strategy implementation. This study will be important for scholars to see the insights of strategy implementation which should give them the impetus to change their orientation to strategy implementation.

1.6.4 The Kenyan Government

The study of strategy implementation issues is expected to help the Kenyan Government which seeks to leverage on technology to improve financial access and inclusion. Through this study, the government of Kenya would be able to use this study’s information to support its strategy as a global leader in mobile money as part of 2030 vision. The study will help the government determine incentives such as taxation that would support the banking sector’s ability to grow financial services and in turn assist the government in facilitating financial access to the unbanked.

1.6.5 Policy Stakeholders

Policy stakeholder in this context would include other constituent policy administrators whose input affects the operations of both the banks and the mobile network operators. These would include and not limited to: Communication Authority of Kenya (CAK), Competition Authority of Kenya, National Treasury, the parliamentarians and Kenya Bankers Association. A better understanding of factors supporting or hindering strategy implementation will enable these policy makers formulate policies that would support effective strategy implementation of M-Commerce for the benefit of the country.

1.7 Scope of the Study

The study was limited to factors that are internal to commercial banks in Kenya and would therefore not investigate external factors that could affect strategy implementation in Kenya or outside Kenya. The rationale for undertaking internal evaluation is because this is an under-researched area and studying it is expected to provide valuable

contribution to existing literature in strategy implementation. In addition, the external factors are assumed to affect all banks equally, hence the central focus was the internal issues which vary from bank to bank.

1.8 Definitions of Terms

1.8.1 Strategic Leadership: Lussier and Achua (2007), define strategic leadership as a person’s ability to think strategically, to participate, envision and work with others in initiating changes that does create a sustainable future. A lack of strategic leadership by the top management of a sustainable future. A lack of strategic leadership by the top management of an organization is a major barrier to effective strategy implementation (Kaplan & Norton, 2004; Hrebiniak, 2005).

1.8.2 Organizational Structure Organizational structure is defined as the formal framework by which job tasks are divided, grouped, and coordinated. It helps people pull together in their activities and promote effective strategy implementation (Pearce & Robinson, 2013).

To achieve strategic objectives, organizational structure coordinates and integrates tasks executed by all employees at all levels, and across all divisions and functions (Hill & Jones 2009).

1.8.3 Information Systems: Information system (IS) can be defined as a set of interrelated components that collect, process, store and distribute information to support decision making and control in an organization. IS supports, coordination and helps managers analyze problems and create new products (Mcnurlin, Sprague, & Bui, 2009).

1.8.4 Human Resources: Human Resource (HR), can be defined as people, labour, intellectual capital, human capital, or employees of an organization (Boudreau & Ramstad, 2007). How HR is organized in an organization becomes critical to strategic success and competitive advantage of an organization (Gabcanova, 2011). Human function should be positioned and designed as strategic business partner that leads in both strategy formulation and implementation (McKnight, 2005).

1.8.5 Strategy Communication: Communication is defined as the process of transferring information from one person to another through understandable symbols or signals (Lekovic & Berber, 2014). Strategic communication is the purposeful use of communication by organizations to implement its mission (Arnold, 2011). Communication links business’ overall picture and individuals and departmental roles to enhance the clarity of the strategy and motivate staff to support successful implantation (Rajasekar, 2014).

1.8.6 Mobile Commerce: According to Tiwari and Buse (2006), Mobile commerce is defined as transactions that involve ownership transfer of rights to use goods and services, that is initiated or completed through mobile access to computer mediated networks with the help of electronic device M-commerce refers to the different types of business transactions that are conducted on mobile devices using wireless networks. M-commerce can also be defined as the use of Personal Ditigal Assitants (PDAs) and network to access information and conduct transactions that result in the transfer of value in exchange for information, goods and services (Saidi, Azad & Noorudin, 2010).

1.8.7 Performance: Performance management connects information to monitor the execution of business strategy and help organizations achieve their goals. Performance management involves creating strategy and plans, monitoring the execution of those plans, and adjusting activity and objectives to achieve strategic goals. Integrated data and metrics, provide a measurement framework to gauge the effectiveness of strategic and management processes (Eckerson, 2009).

1.8.8 Commercial Banks: A commercial bank a financial institution dealing in money, it accepts deposits of money from the public to keep them in its custody for safety. Commercial bank function as a mobiliser of saving in an economy. A bank is, therefore like a reservoir into which flow the savings, the idle surplus money of households and from which loans are given on interest to businessmen and others who need them for investment or productive uses (The Banking Companies Act of India).

1.8.9 Strategic Implementation: Strategy implementation is defined as the sum total of the activities and choices required for the execution of a strategic plan. It is the process by which objectives, strategies and policies are put into action through the development of programs, budgets and procedures (Pearce & Robinson, 2013).

1.9 Chapter Summary

This chapter presented the study background providing an ample picture of the study and sets out the foundations for the following chapters. The chapter provided an overview of the research issues; problem, objectives, hypotheses and justification of the study. The scope and delimitations are discussed and definitions of terms used in the study presented. Chapter two presents the theoretical and empirical literature review with the purpose of identifying a gap in strategy implementation and develops a model using existing literature. Chapter three presents the research methodology used to collect the data and test the hypotheses. Chapter 4 presents findings and results of the research. The final chapter, chapter five presents the study discussions, conclusions and recommendations.

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Introduction

This chapter reviewed literature on the concepts of strategy implementation and its influence on mobile commerce performance in commercial banks in Kenya. The literature review presents the conceptual framework and reviews the relationship between organizational leadership, organizational structure, information systems, human resources, strategy communication and mobile commerce performance measured through the following elements; growth of new applications, growth of m-commerce users, growth of bank accounts and growth of savings. Gaps in literature are identified and the chapter ends with a summary of recent studies on the relationship between strategy implementation and m-commerce performance.

2.2 Theoretical Review

A theory refers to a logical statement or group of statements, supported by evidence with the view of explaining an occurrence (Kombo & Tromp, 2009, Smyth, 2004). A theoretical framework is composed of concepts that are held together by definitions and reference in scholarly literature and provide a structure that supports the theory guiding the research study (Swanson, 2013). The development of a theoretical framework requires presentation of alternative theories that may challenge the chosen theory, while considering the limitations associated with the chosen theory (Corvellec, 2013).

According to Hrebianiak, (2006), strategy implementation for any management team is a difficult task, especially implementing it throughout an organization. Noble, (1999), stated that strategy implementation is more of a craft, rather than a science, with its previous history describing it as fragmented and extensive. Li, Guohui and Epler (2008), aver that in majority of cases, after a all-inclusive strategy decision has been formulated, major difficulties arise during the strategy implementation process. Studies concede that strategies fail because of insufficient implementation, yet strategy implementation has received less research attention in comparison to strategy formulation (Li, et. al., 2008). To simplify research from a complex subject, several theories were used to examine the established theoretical foundations of strategy implementation. Specifically, the section

reviews the agency theory, resource based view theory and expectancy theory as the relevant theories in explaining the foundation of strategy implementation.

2.2.1 Agency Theory

The agency theory was first formulated in the academic economics literature in the early 1970s (Ross 1973, Jensen & Meckling, 1976) and this spread into business schools and management literature, becoming a new buzzword in business (Zajac & Westphal, 2004). Agency relationship is defined as a contract under which one or more person (principals) engage another person (the agent) to perform some service on their behalf which involves delegating some decision making authority to the agent (Jensen & Meckling, 1976). The expectation of the principal is that the agent will act in the best interest of the principal. However, due to opportunistic behaviour, the agent may not necessarily act in that way (Padilla, 2002). As a result, institutions adopted new policies, aligning incentives, discouraging self-interest behaviour by managers, and reducing agency costs. (Zajac & Westphal, 2004).

Jensen and Meckling (1976), identify managers as the agents, who are employed to work for maximizing the returns to the shareholders, who are the principals. The agency theory is a result of the explored risk sharing among individuals and groups during 1960-1970 (Eisenhardt, 1989). The agency theory is a management approach where a person (agent) acts on behalf of another (the Principal) and is supposed to advance the principal’s goals (Laffont, Jean, & David, 2002). Under the agency relationship, both the agents and the principals are assumed to be motivated solely by self-interest. Thus when principal agents delegate some decision making responsibility to the agents, agents use their power to promote their own well-being by choosing actions which may or may not be in the best interest of principals (Barnea, Haugen & Sanbet, 1985; Bomwich, 1992; Chowdhury, 2004).

The agency theory applies to this study which is characterized by a large number of stakeholders that are involved in strategy implementation. Agency theory in this study was used to examine the theory contribution to the strategy implementation based on the relationship between the principal who delegates work to an agent (manager) who implements strategy. Agents may take actions, such as strategy implementation that would have significant effect on performance of an organization. When two cooperating parties have differing attitudes toward a job and thus have different objectives in mind,

the agent will then seek to achieve his or her own objective and this may be difficult for the principal to observe the agents work. Therefore, it can be assumed that the agent will act in his or her own interest rather than the principal’s (Bender, 2011). The agency theory advances both the principals’ and the agents’ interests in an institution. The interests should be merged to enable corporate objectives of the organization through the agent. Laffort and Martimost (2002), contends that the agency theory of strategic management is very crucial since the action chosen by a particular individual (the agent) affects several other parties (including the principals). Therefore, the agents’ role in strategic implementation is key to the success of the strategy implementation. The firm is often characterized as a nexus of both explicit and implicit contracts linking the management and all its stakeholders. The agency theory is important for explaining the relationship between the shareholders and the agents they appoint to implement strategy so as to maximize the returns (Davis, Schoorman & Donaldson, 1997).

According to the Agency Theory, proper synergy between the management and its stakeholders enables the achievement of common goals. Agency problems arises when, the desires of the principal and the desires of the agent conflict. Therefore, in strategy implementation, the cooperation of the management and the strategy implementers is key to the success of strategy implementation. The Agency Theory is also seen as the central approach to managerial behaviour. Rugman and Verbeke (2008), says that the Agency Theory is used in the managerial literature as a theoretical framework for structuring and managing contract, which is among the emerging issues in strategic management. Agency theory therefore, explains the policies introduced by organizations to manage behaviour of principals and agent’s relationships in performance contracting in management.

An example of violation of powers bestowed on agents was demonstrated through the Enron Company in the US which led to the loss of billions of dollars by the owners (Li, 2010). A local example in Kenya is the Goldenberg Scandal of the early 1990s, where the country lost millions of Kenya Shillings resulting in devaluation of the local currency against the hard currency (Bosire, 2005). In both cases, the agents were working for their own interests and not the stakeholders’ interests. Laffont (2002), states that it is difficult for shareholders to exercise effective control of management interests between managers and owners.

To manage the potential for agency/principal issues, scholars’ contribution has been the introduction of agency theory on risk, outcome uncertainty, incentives, and information systems. Agency theory can be used to evaluate agent/principal relationship in strategy implementation and this is supported by empirical evidence (Eisenhardt, 1985). Eisenhardt describes the risk-sharing problem as one that arises when cooperating parties have differing attitudes toward risk. Agency theory broadened this risk-sharing literature to include agency problem that occurs when cooperating parties have different goals and division of labor (Jesen & Meckling 1976: Ross, 1973). The agency structure is applicable in a variety of settings such as regulatory policy to micro level phenomenon such as blame, impression management and expression of self-interest. Frequently, agency theory is applied to organizational phenomena such as compensation (e.g., Conlon & Parks, 1988; Einsenhardt, 1985), Ownership and financing structures (e.g., Argawal & Mandelker, 1987; Zenger, 1988). Generally, the domain of agency theory is relationships that mirror the basic agency structure of a principal and an agent who are engaged in cooperative behaviour, but have differing goals and differing attitudes toward risk (Eisenhardt, 1989).

Agency Theory has developed along two lines: positivist and principal-agent (Jensen, 1983). The two streams share the contract between the principal and the agent. They also share common assumptions about people, organizations, and information. Positivist Agency Theory focuses on identifying situations in which the principal and agent are likely to have conflicting goals and then describing the governance mechanisms that limit the agent’s self-serving behaviour. Characteristic of the principal-agent paradigm involves careful specification of assumptions, which are followed by logical deduction and mathematical proof. Principal-agent stream has a broader focus and greater interest in general, theoretical implications. In contrast, the positivist writers have focused almost exclusively on the special case of the owner/CEO relationship in the large corporation. The principal-agent research includes many more testable implications. The two streams are complementary: Positivist theory identifies various contract alternatives, and principal-agent theory indicates which contract is the most efficient under varying levels of out-come uncertainty, risk aversion, information and other variables.

Agency theory supports the importance of incentives and self-interest in organizational thinking (Perow, 1986). Its main aim is to illustrate that organizations are based on self- interest. Agency theory also emphasizes the importance of a common problem structure

across research topics. Mintzberg, Joseph, and James (2003) state that the source of strategies in an organization, is the agency theory because the agents are charged with the responsibility of strategic formulation by the organizations’ key stakeholders who control the organization. Gibbons (2003), says that the agency theory rests on the firm’s shareholders as the principal and the CEO as the agent. He, however, uses this context to propose that this scenario can be moderated by analyzing a chain of command in organizations. The chain of command is structured in the following order; Principal, Supervisor and Agent. It is from this argument that Otunga (2011), observes that the chain of command comprises of corporate strategy, strategic business units’ level tactical level and operational level where each one is in charge of every level of strategy formulation as an agent. He states that the efforts of all agents would be influenced by sound management skills in regard to resources apportioned to their individual levels which would enhance synergy that leads to successful implementation of strategy and the achievement of competitive advantage.

Otunga (2011) argues that Agency theory is superior to all other theories of strategic management since they all depend on the agents in the entire process of strategic management in achieving organizational success. Ackermann and Collin (2004), contend that Agency Theory is a major component in strategy as it is influenced by agents. The agents’ personal skills and personality highly influence strategy. Ackermann and Collin (2004), further state that the most important outcome of strategy is the management of the link between the competing demands of different stakeholders (agent and principal), concluding that stakeholders determine the ability of an organization to achieve its aspirations.

Agency Theory and Principal Agent refer to the potential difficulties that arise when two parties engaged in a contract have different goals and different levels of information (Lipsey, 1983; Eisenhardt, 1989; Lange, 2005). Agency Theory assumes that “agents are autonomous and are prone to maximizing their own interests at the expense of principals” (Sharma, 1997). This brings out the assumption that there is a general goal conflict between the principal and the agent. Another problem is that of information asymmetry between the two parties. This exists when one party to a transaction holds relevant information, but is unable or unwilling to transfer this information to the other party.

The agency theory perceives goal conflict as the departure of agents from the interests of the principal. Hence the solution to this agency problem is to come up with incentives that will align the interests of agents with those of the principal. That is to keep the agent from dodging by paying him/her well (Shapiro, 2005). Despite the hype and uptake of Agency Theory, Agency Theory has become a cottage industry that explores every permutation and combination of agency experience in the corporate form. Because the reviews on agency theory is largely empirical, it relaxes some of the assumptions of agency theory, turning dichotomies into continuous variables, stimulating abstract categories, and places inquiry in at least some limited context. The frequent area of literature reviews has been on incentive alignment. Empirical studies consider the types and trade-offs between behaviour-oriented (salary) and outcome-oriented (piece rates, commissions, bonuses, equity ownership and other devices that link compensation to shareholder wealth) compensation (Shapiro, 2005).

Agency Theory has applicability, explanatory power and is solution focus. Agency Theory is applicable to a broad field of enquiry and has extended to agency relationships in a wide range of contexts and pervade both markets and organizations (Sorrell, O’Malley, Schleich, & Scott, 2004.) Proponents urge the adoption of an Agency Theory perspective when investigating problems that exist in relationships that have a principal- agent structure and therefore the rationale for using this relationship in this study. Another strength identified by Agency Theory proponents is the Theory’s explanatory power. By focusing on the principal-agent relationship, the contribution of this Theory provides logical predictions about what rational individuals may do if placed in such relationship (Wright, Mukherji, & Kroll, 2001). As a result, Agency theory “Provides a unique realistic, and empirically testable perspective in problems of cooperative effort” (Eisenhardt, 1989). This strength of the Theory can be used to estimate the level of strategy implementation affected in principal agent problem situation.

Agency Theory is focused on solutions to principal agent problems. The major contribution of Agency Theory is that economic inefficiency is inevitable in principal- agent relationships. The Theory turns naturally to considering the ways in which the relationship between agents and principals can be made more efficient. The strengths of Agency Theory are on the fact that it also focuses on improving the contracts between parties. Thus the theory attempts to identify ways in which the relationship is aligned to the interests of principal and agent as much as possible (Wright, Mukherji, & Kroll,

2001). Despite the good insights that Agency Theory provides into issues such as principal agent problems, the Theory has many critics (Mitncik, 1992; Lubatkin, 2005 & Sorrell et al., 2004). The critiques of agency theory revolve around the following issues: its ability to adequately portray real world situations, and the completeness of the Theory.

Some scholars argue that Agency Theory is not able “to explain the complexities of real- world organizations” (Lubatkin, 2005). They maintain that much of Agency Theory literature “employs complex and highly formalized mathematical models whose utility in explaining real-world organizational arrangements must be questioned” (Sorrell et. al., 2004). These limitations arise because the theory is reductionist and makes three appropriate assumptions (Lubatkin, 2005): that opportunism is pervasive, whereas, people are motivated by more than just money, that they also have needs for achievement, responsibility, recognition and that they are capable of a full range of actions, varying from self-serving with guile to owner-serving to altruistic; that participants in the principal-agent relationship are portrayed as simply “dispassionate individuals, whereas people are more complex; and that information asymmetry is pervasive, whereas this may not always be the case.

Another criticism of Agency Theory is that the Theory is as yet incomplete. Sharma (1997), notes that the persistence of agency problems in many sectors of the economy raises questions about the completeness or even appropriateness of the mainstream agency perspective. However, despite these criticisms, the Theory can provide sufficient insight into issues relevant to this study. Agency Theory arguably serve as an attempt to review the relationships between senior management and strategy implementers. The Agency Theory only shows a relationship between owners and managers with flaws resident with the agent in their deception and misappropriation of funds, citing as examples the Enron Company (Laffont, 2002). However, it is noteworthy to realize that the agency problem is that the agent is most likely serving many masters, many of them with conflicting interests. Their challenge is how to maneuver through tangled loyalties he or she owes to many different principals and how to negotiate through their competing interests and sometimes irreconcilable differences. It becomes difficult to honour the preferences of one when doing so means that one would undermine the interests of another (Shapiro, 2005).

2.2.2 Resource Based View Theory

The resource based view (RBV)theory suggests that organizations are gifted with resources such as staff, technology, organizational routines and other assets. The theory submits that for an organization to have competitive advantage over its competitors, it needs to prioritize the acquisition of unique resources and capabilities (Barney 2002). The resource-based view (RBV) theory explains that valuable and rare organization resources can be difficult to replicate, and thus leading to sustained advantages in organizational performance (Alavi, Wahab, Muhamad, & Shirani, 2014). The RBV emphasizes the organization’s resources as the fundamental determinant of competitive advantage. Two of RBV’s assumptions are that firms within an industry or in a strategic group could be heterogeneous with respect to the kind of resources that they control. Secondly, it assumes that resource heterogeneity is long lasting because the resources used to implement firms’ strategies are not perfectly mobile across firms and are difficult to accumulate and imitate. Heterogeneous resources are considered the necessary conditions that to contribute to competitive advantage (Barney, 1991; Peteraf & Barney, 2003). Barney, 1991; Conner, 1991; Teece, Pisano & Shuen, 1997), argue that RBV is an efficiency-based explanation of performance differences. The achievement of performance is attributed to resources having intrinsically different levels of efficiency, in the sense that they enable firms to deliver greater benefits to the organization. For resources to be a source of competitive advantage, the resources ought to be rare, valuable and not imitable or substitutable (Barney, 1991).

This theory is important to the study because strategy implementation depends on the resources and capability of an organization; it implies that effective application of resources in implementing strategies could influence M-Commerce performance in Kenya’s commercial banks. The development of human resources and talent in organizations has become critical in an increasingly knowledge-based globalizing economy. In the last 50 years, the resource-based view (RBV) of strategic management has attracted wide academic and managerial attention. Drawing on RBV literature, this paper analyses the interrelationships between RBV and strategy implementation. It examines aspects of RBV that critically determine the firm’s capability to implement strategy by integrating the relevant theoretical and empirical evidence. The importance of RBV is through its rapid diffusion throughout the strategy literature. Theoretically, RBV addresses the fundamental question of why firms are different and how they achieve and

sustain competitive advantage. The RBV literature suggests that firm’s sustainability of competitive advantage come from building on the resource endowment and core competencies of the organization (Kostopoulos, 2003). Conceptually and empirically, resources are the foundation for attaining and sustaining competitive advantage and eventually high performance for the organization (Ismail, Raduan, Uli, & Abdullah 2011).

The resource based view is considered to be relevant to competitive advantage. RBV contributes to the understanding of competitiveness of an organization (Amis, 2003). Technical efficiency in management terms is brought about by the building – up of management capabilities through RBV (Hitt, 2013). The RVB model assumes that each organization is a collection of unique resources and capabilities. Accordingly, organizations’ performances are attributed to their unique resources and capabilities rather than the industry’s structural characteristics. RVB also assumes that firms acquire different resources and develop unique capabilities based on how they combine and use the resources; that resources and capabilities are not highly mobile across organizations and that the differences in resources and capabilities are the basis of competitive advantage (Hitt, 2013). Not all of an organization’s resources and capabilities have the potential to be the foundation for a competitive advantage. This potential is realized when resources and capabilities are valuable, rare costly to imitate and non-substitutable. Notably, resources are valuable when they allow a firm to take advantage of opportunities or neutralize threats in its external environment. Kraaijenbrink, (2010) is of the view that the uniqueness of its resources and capabilities is the basis of an organization’s strategy and its ability to earn above average returns. Research indicates that both the industry and environment and an organization’s internal assets affect the organization’s performance over time (Ndofar, 2006).

Several critics have been labeled against RBV, with Newbert, (2007) arguing that resource combinations are more likely to explain performance than single resources. However, Kraaijenbrink, Spender and Groen, (2010) argue that for the understanding of complementarity and substitutability of resources in a firm, there is need to consider the organizational level as well. Grant (1996), argued that it is not resources themselves that generate competitive advantage, but the managerial capabilities to integrate these resources and he developed a hierarchy of integration capabilities from the level of individual resources to the level of organizational capabilities. Other supporters of this

view include (Kraaijenbrink & Wijnhoven, 2008; Stieglitz & Heine, 2007; Teece, 2007). What is brought out here however is that complex interdependencies exist between multiple levels of strategy (Kor & Leblebici, 2005). The perspective taken in this study is that of Grant’s work which provides a promising starting point for further developing the RBV with a more refined concept of resource.

Another limitation of RVB is associated with the unit of analysis. Where individual resource is used, this is considered inappropriate (Foss, 1998). Foss (1998), however argues that the choice of unit of analysis may be legitimized, when the relevant resources are sufficiently well-defined and free-standing. He further argued that it is the way resources are clustered, how they interplay and fit into the system that is important to the understanding of competitive advantage. Bridoux, (2004) argued that RBV has focused on internal resources at the expense of external factors that does influence firm performance. He opines that strategic managers, should have resources as the basis of competitive strategy. Other critics (Foss, Foss, Klein, & Klein, 2007), argue that the practical assessment and evaluation of resources involves subjectivism, knowledge creation and entrepreneurial judgement. This study adopts their view for purposes of this investigation taking their assumptions as supportive of the writers’ views in regards to the subject matter of strategy implementation of M-Commerce in the banking sector.

Another critique of RBV is a study by Gizawi (2014) in favour of Dynamic Capabilities Theory (DCT), who is of the opinion that RBV theory is conceptually vague and redundant, with limited focus on the mechanisms by which resources actually contribute to competitive advantage. He emphasizes the fact that RBV does not explain how these mechanisms operate. He proposed that competitive advantage would be attributed to those companies that were able to react rapidly and flexibly to product innovation, while simultaneously possessing the capacity to manage firm specific capabilities in such a way as to effectively coordinate and redeploy internal and external competencies. His conclusion was that the Dynamic Capability Theory is important in the academic field, because of the amount of literature available and the empirical studies that have been conducted. However, he highlights that there are several issues with the approach, although it has been argued that these issues may resolve themselves with time as the research domain evolves.

Resource-Advantage (R-A), is a new theory of competition which focuses on implications for managers and public policy makers. Proponents of this theory indicates that the R-A Theory is grounded in empirical research which shows that variation between firms account for 45-58% of firm profitability compared with industry effects of around 8-10%. Their view therefore is that the key strategic task of managers is to create and nurture the resources and core competencies of the firm, rather than simply to decide which industries to compete in. R-A maintains that strategy is about creating core competencies and other strategic resources so that the firm can positively influence its environment (O’Keeffe Mavondo & Schroder, 1998).

Proponents of other approaches to HR include Pisano (2015), who argued that dynamic capabilities need to be aligned to the strategic ability to identify and select capabilities that lead to competitive advantage. The study focus was on the problem of how competition in product markets and competition to create capabilities are linked. This was on the premise of dynamic capabilities which are the capability to identification, selection and creation. They argue that firms compete in product markets, creation of technological, operational and organizational capabilities that provide them with advantage in the markets. He also points out that the decisions about product market entry and position, and decisions about capability creation are intimately linked. He therefore points out that the job of a capabilities –based theory of strategy should be to provide conceptual and practical insights about these links. He points out that more specifically, a capability-based theory of strategy should identify the choices available to firms and the consequence of those choices under different competitive circumstances.

Pisano (2015), argues that a firm possesses a collection of capabilities that span a continuum from highly general-purpose to highly application-specific. That a robust capability-based theory of strategy should provide guidance to managers about the nature of these constraints (e.g. which choices are economically feasible?) and the implications of their capability decisions across a broad range of competitive circumstances. Conclusion was that the dynamic capabilities research has not delivered despite intensive effort by many scholars. In addition, he states that the existing dynamic capabilities literature by focusing on firms’ generalized capacity for adoption to change, does not provide much insight about strategic choices.

The RBV’s critics notwithstanding, this study still finds the RBV theory still applicable in the current research context. This is because RVB was used to define resources that banks use to implement market strategies to improve performance (Wernerfelt, 1984; Kraaijenbrink et al., 2010). In addition, it the study also look at capabilities which are referred to as an organization’s superior way of deploying its assets, tangible or intangible, to perform some task or activity to improve performance (Schreyogg & Kliesch-Eberl, 2007; Amit & Schoemaker, 1993).

2.2.3 Expectancy Theory

Based on the work of Vroom (1964), Expectancy Theory suggested that people will only carry out a task with the expectation that their action will help them achieve a required result. The theory deals with motivation and management. Expectancy theory assumes that behavior is a result of choosing among alternatives with the purpose of maximizing pleasure and minimizing pain. Vroom (1964), suggested that the relationship between people’s behavior at work and their goals which lead to their performance is based on individual factors such as personality, skills, knowledge, experience and abilities. Employees have differing goals and could be said to be motivated at the belief that there would be a positive correlation between their performance and their effort (Vroom, 1964). Vroom (1964), believed that favourable performance will result in a desirable reward, and that the reward would satisfy an important need. Thus the longing to satisfy the need would make the employee effort worthwhile. Expectancy theory is adopted for this study because it is a theory of management behaviour that promotes employee commitment to organizational goals and standards. Thus greater commitment leads to increased productivity and therefore, expectancy theory can be used to show managers how to enhance the value of employees’ work and promote the perception that they can be successful and earn ensuing rewards (Quick, 1988).

Based on studies done on motivation, it is noted that motivation is the driving force behind all human efforts and is essential to all human achievements. Expectancy theory as an aspect of management, occupies a very important place in business (Parijat & Bagga, 2014). Expectancy theory is relevant in strategy implementation which requires a large number of stakeholders that are motivated by different interests in relation to strategy implementation. The Expectancy Theory is useful for explaining facts related to employees, which highlight the fact that employees have personal goals for which they

work for. These personal goals are what justifies the reason they work in organizations (Parijat & Bagga, 2014). The personal goals can be fulfilled by organizational rewards or work outcomes. Personal goals are important in determining the extent to which organizational rewards fulfil an employee’s personal goals and how attractive the rewards are to the employees.

Vroom (1964), outlined four assumptions for the expectancy theory and these are: one; that employees join organizations because they have their own individual motivations, needs and past experiences. This assumption forms the basis for an employee’s reaction to the organization. Assumption number two is based on the fact that individual’s behaviour is a result of conscious choice. In addition, the third assumption considers that people look for different things from an organization and these include; personal development, a good salary, job security and challenge. The final assumption is that in order to optimize outcomes for themselves, employees would choose from alternatives. The above assumptions have been grouped into three elements: Valence, Expectancy and Instrumentality. The expectancy theory therefore assumes that a person is motivated to the degree that they believe performance is a result of their effort. In addition, that performance would result in reward that is instrumentality and that the value of the reward is highly positive and that is valence. Figure 2.1 illustrates the expectancy model.

Figure 2.1: Basic Expectancy Model Source: Lunenburg (2011)

As illustrated in figure 2.1, valence was defined by Vroom as an effective orientation toward particular outcomes. According to Vroom, (1964), valence is the confidence level that an employee has while expecting a particular outcome based on their actions and behaviour. Therefore, what an employee expects (expectancy), becomes the outcome that the employee anticipates in return for his actions or behaviour. Instrumentality is defined as the qualifications and abilities an employee has to perform the work necessary to

produce a desirable outcome. An outcome is positively valent when the person prefers attaining it to not attaining it. On the other hand, an outcome has a valence of zero when the person is indifferent to attaining or not attaining it, and it is negatively valent when the person prefers not attaining it to attaining it. The explanation that indicates there can be a discrepancy between the anticipated satisfaction from an outcome (valance) and the actual satisfaction from an outcome is referred to as value (Lee, 2007).

Expectancy theory explains the reasons why managers make decisions and asserts that behaviours are driven by a function of expectancy. In other words, it is the probability of getting a desired outcome, and the value attached to the desired outcome (Chen, Ellis & Suresh, 2014). Expectancy theory is relevant for this study because strategy implementation can be positioned as the pivotal behavioural choice which then can be used to advance factors that indirectly affect the adoption of implementation activities through expectancy. Expectancy theory attempts to identify relationships among variables in a dynamic state which affect individual behaviour, hence it is a process theory. The expectancy theory therefore, is relevant in strategy implementation which also is a process of putting into action plans already made. In addition, individuals participating in strategy implementation, are thinking and reasoning people who have beliefs and anticipations in relation to their future lives, which can be understood by the cognitive theory of motivation in expectancy theory (Steers & Porter, 1975).

While there are several authors who have studied human behaviour, theorists abound from Maslow (1954) to McGregor (1959), to Herzberg (1960), yet most theories are complex and difficult to translate into managerial practice. It would be inadequate in a short span of time to try and demonstrate to managers for example how Maslow (1954) hierarchical model of needs can organizations solve employee performance problems. However, Expectancy theory is more practical, simple, is a mainstream psychology and has been in existence for a long time. In addition, it is easy to apply and works it works because it is an explanation of human behaviour (Quick, 1988).

Expectancy theory is relevant to strategy implementation because from a management perspective, the expectancy theory has important implications for motivating employees. According to Lunenburg, 2011, the expectancy theory highlights the motivation of employees in relation to performance to reward and reward valences. The review of motivation theories whose attempt was to explain staff workplace motivation, revealed

that expectancy theory focuses on mental experiences that motivate people and their interrelations.

2.2.4 Activity Theory

In this study, activity theory was used as a unit of analysis to explore strategy implementation by banks. Activity theory has few applications in the strategy and organization field. However, the absence of formal inquiry into the use of activity theory in exploring strategy issues in the past is seen to have changed in recent years as a framework to examine various organizational and strategic phenomena (Blackler, 2009). Researchers use activity theory to understand the relationships among people participating in activities, the tools people use to accomplish their activities and the processes they have to go through to accomplish their tasks. In addition, researchers use activity theory to understand how historical and social forces shape the way people participates in activities and how change effects activities. Three important goals of activity theory include: accounting for aspects of a system to better understand the nature of activity, analyzing how the parts of a system work together to better anticipate participants’ needs, goals and isolating problems to develop solutions (Kaine & Wardle, 2000).

Activity Theory started in the 1920’s by the Russian Psychologists Vygotsky, Rubinshtein (Kaptelinin, Kuutti, Bannon, 1995; Leont’ev, 1978) and his student Leontiev who developed a conceptual framework for a complete theory of human activity (Leontiev, 1981). The theory is a philosophical framework that allows the study of different forms of human practice. The practice can be viewed as a developmental process where both individual and social levels are interlinked. Activity Theory can be used to provide a broad conceptual framework that can be used to describe the structure, development and context of tasks that are supported by a computerized system. Activity Theory offers the possible integration of many theories and concepts, thus helping to maintain conceptual integrity in terms of designs, evaluation and usage. Activity Theory consists of 5 principles: The hierarchical structure of activity, object-orientedness, internalization/externalization, mediation and development. In activity theory, the unit of analysis is an activity directed at an object which motivates activity, giving it a specific direction. Activities are composed of goal-directed actions that must be undertaken to fulfill the object and for this study, the goal-directed action is strategy implementation.

According to this theory, actions are conscious, and different actions may be undertaken to meet the same goal (Kaptelinin, 1997).

Activity Theory (AT) is a theoretical framework for the analysis and understanding of human interaction through their use of tools and artefacts. Activity Theory is relevant in conditions that have significant historical and cultural context and where the participants, their purposes and their tools are in a process of rapid and constant change, such as in the banking industry. The key concept of Activity Theory arises through an understanding of human consciousness as it has been shaped by experience and the subjectivity of human awareness. (Hashim & Jones, 2007).

“Activity theory is a conceptual framework based on the idea that an activity is primary, that doing precedes thinking, that goals, images, cognitive models, intentions, and abstract notions like “definition” and “determinant” grow out of people doing things” (Morf & Weber, 2000). Activity Theory can be broken into the analytical components of subject, tool and object, in this study, the subject is the strategist in the bank (manager) being studied, the object (m-commerce performance) is the intended activity, and the tool is the mediating device (Strategy Implementation) by which the action is executed (Hasan, 1998). Activity Theory has been applied in a variety of fields such as psychology, education, management, culture and information systems, fields which in general incorporate approaches involving human activity. Researchers recognize this theory as being holistically rich in terms of understanding how people do things together with the assistance of tools in dynamic environments (Crawford & Hasan, 2006; Hakkinen & Korpela, 2006; Huang & Chen, 2007).

Activity theory is the oldest and most developed, stretching back to 1920’s. The theory has core concepts that are fundamental for studies of technology (Nardi, 1999). In Activity theory the logical concept of “Activity” is what people do, so that Activity Theory provides a framework suitable for the analysis of everyday human work where information and communications technologies make a strategic contribution (Hasan, 2002). A number of research scholars have used the Activity Theory framework to areas such as organizational theory, organizational learning, organizational memory and organizational sense-making (Hasan, 2002). This study therefore extended this work into strategy implementation because the theory is based on the concept that human activity is an interactive relationship between subject (person) and object (purpose). In this case the

subject is the bank staff and the purpose is strategy implementation. According to Leont’ev (1974), activity is composed of subject, actions and operations. A subject is a person or a group engaged in an activity. An object is held by the subject and motivates activity, giving it a specific direction. Actions are goal directed processes that must be undertaken to fulfil the object. Activity Theory holds that the constituents of activity are not fixed but can dynamically change as conditions change.

According to Kuutti (1996), Activity Theory is a philosophy and cross disciplinary framework for studying different forms of human practices and offers a set of concepts, structures and terms that are eminently suited to research undertaken within the communities of practice. Blackler (1993), gives reasons for adopting an Activity Theory approach by stating that it offers a way of synthesizing and developing various notions of knowledge, organizations and management and it deals with the chaotic problems encountered at the strategic level in organizations, by attributing significance to incoherency and dilemma. There are three Generations of Activity Theory (Engestrom, 1996, 2001) the first is Vygotsky’s mediated action triangle, the second generation activity theory is attributed to Leontieve’s work that emphasized the collective nature of human activity, and the third generation activity theory is the application of activity systems analysis in developmental research where the investigator often takes a participatory and interventionist role in the participants’ activity to help participants experience change.

This first generation approach is borrowed heavily from Vgotsky’s concept of mediation. The approach represents the way in which Vygotsky brought together cultural artefacts with human actions in order to allocate relationships with the other components of an activity system and the individual/social dualism (Engestrom 1987). During this period studies tended to focus on individuals. The second generation covers the study of artifacts as integral and inseparable components of man’s functioning, but Engeström, (1999), argues that the focus of the study of mediation should be on its relationship with the other components of an activity system, in other words, examination of systems of activity at macro level as opposed to micro level concentration on the individual actor or agent operating with tools. This observation is relevant in strategy implementation, as the function is only effected by cross functional teams, and not as individuals. The third generation also views the tool as joint activity or practice as the unit of analysis for activity theory, it looks at the structure holistically and not per individual.

According to activity theory, human activity is purposeful and could be carried out by sets of actions through use of tools which can be physical or psychological. The psychological tools include language which basically is the collaborative human activity, and is mainly experienced through communication. Activity theory illustrates the relationship between the human (doer) and the object being done which in this study is strategy implementation and the outcomes would be the m-commerce performance. The core of an activity theory is illustrated in figure 2.2.

Figure 2.2: The Core of an Activity Theory source: Vygotsky, (1978)

Strategy implementation uses different tools and artefacts as illustrated by activity theory in order to achieve an effective strategy implementation. As such, activity theory provides a language and a set of frameworks for making sense of a situation through observation, interviews and other methods. Activity theory becomes a lens for research as it takes activity theory as a unit of analysis where activity would be defined by the relationship between the subject and the object, which can also be stated as “who is doing what for what purpose” (Vygotsky, 1978).

2.2.5 Comparison of Strategy Implementation Theories

The agency theory is a management approach where a person (agent) acts on behalf of another (the Principal) and is supposed to advance the principal’s goals. The resource based view is founded on the belief that organizations perform better when they, control long lasting heterogeneous strategic resources. Expectancy Theory is of the view that employees’ motivation is based on how much an individual wants a reward (Valence), and that the effort will lead to the expected performance (Expectancy). Activity theory facilitates understanding of team unity, and is important in studying existing groups, whose communication is mediated largely through electronic systems. The implications of these theories on successful strategy implementation are based on; one in agency

theory, the senior management representing the interest of the shareholders and the board well, in resource based view, the identification and acquisition of unique resources, in expectancy theory, the identification of appropriate motivation of staff and in activity theory, understanding how to harness teamwork. Summary of strategy implementation theories are presented in table 2.1.

Table.2.1: Comparison of Strategy Implementation Theories

Theory Assumptions Variables Supporting Limitation/weakness Agency Managers are prone to work Leadership: Represents the principal and the Relies on human nature and Principle for their own interests at the organization’s interest. motivation, fails to distinguish Agent Theory expense of principals HR: Responsible for conformity of organization’s other factors than opportunistic plans by the leader and the agent behaviour. Resource Firm’s resources are the HR: The development of human resources and RBV is firm-centred analysis Based View fundamental determinants of talent in organizations is critical to its success. and ignores industry dynamics. Theory competitive advantage and Leadership: management capabilities integrate Individual resources taken as performance. resources from individual to organizational relevant units of analysis are capabilities to generate competitive advantage. inappropriate. Expectancy That employees act out of Leadership: Promotes employee commitment to Employee may be motivated by Theory self-interest and their desire organizational goals and standards other factors for reward. Motivation may not be correlated with performance in some organizations Activity Lens with which to Structure: sets out who is doing what, why and Activities are not usually as Theory understand human activity how Communication: Language/tools used by simple as the theory presents it subject for collaborative purposes to be. Information Systems- the framework can be used to analyze tasks that are supported by computerized systems Information System as tools to aid the performance of a particular activity or practice. Source: Author (2017)

2.2.6 Models of Strategy Implementation

There are several strategy implementation models that have been adopted by companies globally. Although strategy implementation has been a subject of many studies (Huber, 2011), many theoretical models have been developed, but strategic management theory is still establishing their usability and their associated implications (Hitt, Freeman, Harrison, 2006). The models discussed in this section are the 8 “S”, Bourgeios and Brodwin implementation models, Noble’s strategic framework and Okumus strategy implementation Model.

2.2.6.1 The Eight “S” Model

The 8 “S” model is adopted for this study, because of the value the model adds to strategy implementation. The 8 “S” model was developed by Higgins, (2005) as a revision of the McKinsey 7 S’s model originally developed in 1980 by the consulting company McKinsey and Company (Pascale & Athos, 1981). The McKinsey 7S Framework was created as a recognizable and easily remembered model in business. The 7S variables referred to as levers by the authors, all begin with the letter ‘S’. It is this original model that Higgins (2005), modified and named as the 8 “S” model. The purpose of 8 “S” model is to allow management to effectively and efficiently manage the cross functional implementation of strategies. The execution of strategies in all organizations, including the banks is an important activity determining the progress of an organization. According to Higgins (2005), strategy implementation involves aligning key organizational functions and factors with the selected strategy. Executives need to align the cross functional organizational factors: structure, system and processes, leadership style, staff, resources and shared values with the new strategy so that the strategy adopted can succeed (Higgins, 2005).

The 8 “S” model focuses on the factors that support an effective strategy implementation (Higgins, 2005). The 8 “S” model is based on the concept introduced by Peters and Waterman (1982). Higgins (2005), supplemented the model by adding an eighth element – the Strategic Performance, which is important to mangers’ view of the results achieved. The basic assumption of 8 “S” model, is that the implementation of different strategies requires various types of organizational structures, systems, management styles, staff, resources and shared values. According to Higgins (2005), the 8 “S” model is divided into two parts, the seven contextual “S”s and strategic performance. All the factors falling in

the Contextual “7” S’s must be aligned to achieve best possible strategic performance. The organization’s arrows point in the same direction, indicating they should be aligned with one another. The model 8S is presented in figure 2.4.

Context (Aligned) Strategic Performance

Figure 2.3: Eight “S” Strategy Model Source: Higgins, (2005).

Description of Eight S’s according to Higgins is as follows: the first “S” stands for Strategy and Purpose. Organization’s purpose is achieved through strategies formulated. Components of strategic purpose include strategic intent, vision, focus, mission, goals and strategic objectives. The second “S” represents structure and consists of five parts; jobs, authority to carry out the jobs, logical grouping of the jobs, managers span of control and mechanism of coordination. Therefore, when executing a business strategy, the decisions made involve how an organization is structured. This would include the kind of jobs to be completed, authority to do the jobs, grouping of jobs into departments and divisions, and control of the chosen structure. Thirdly, system and processes are the enablers of an organization to execute daily activities. This component is about the formal and informal

procedures used in an organization to manage information systems, planning systems, budgeting and resource allocation systems, quality control systems and reward systems. The fourth “S” is style which refers to leadership or management approach demonstrated by the leaders or mangers when relating to junior staff or other employees. “S” number five generally, refers to staff who are organization’s employees needed and details of the required backgrounds and skills essential to achieve the organization’s strategic purpose. This factor also covers strategy aspects such as staff training, career management and promotion of employees. The sixth “S”, is re-sources which mainly are the organization’s assets and includes people, money, technology and other management system elements. The seventh “S”, is shared values and relates to organizational culture. These are the values shared by organizational staff which is different and diverse from the other organizations. The last “S” represents strategic performance and is the derivative of the other seven ‘S’s. Strategic performance resides in an organization as a total part of the whole. Performance can be measures in various ways.

According to Higgin’s model, organizational strategy is roofed in the Eight S’s and by applying this model, the leaders and the managers involved in strategy can foresee changes that are to be made within the organization in order to make strategy workable. Higgins states that the model serves as a road map for implementation and helps uncover the causes of failure during implementation.

2.2.6.2 Bourgeios and Brodwin Implementation Models

This section presents five implementation models discussed by Bourgeios and Brodwin (1984) and are; commander model, organizational model, collaborative model, cultural model and cresive model. The nature of these models are relevant to the banking sector.

2.2.6.2.1 The Commander Model

According to the commander model, the strategic leader is the thinker and the planner. The leader therefore concentrates on the strategy formulation or supervises a team to do so. The leader does not take an active role in the implementation process, but passes on the implementation of the identified strategy to his/her subordinates. This is a common practice in many banks in Kenya where the CEOs are seen to be more involved directly in the strategy formulation with the senior management team and in many cases, does not

participate in the implementation, because of busy schedules. According to (Barnat, 2014), the commander approach has the strategic leader concentrating on formulating the strategy, applying rigorous logic and analysis. In majority of Kenyan banks, this is the case, where the CEO with top management and the Board of Governors focus on the development of the strategy. The strategic leader employs tools such as growth/share matrices, experience curves, industry and competitive analysis. In the commander approach, the leader is not an active participant in implementing the strategy because he is more of the thinker/planner rather than the doer. This helps the senior management to make difficult day-to-day decisions from strategic perspective. The three conditions that must exist for the commander approach to succeed are first, that the leader must employ enough power to command implementation; or the strategy itself does not pose much threat to the current management otherwise implementation would have to be resisted. Secondly, timely and accurate information must be available and the environment must be reasonably stable to allow it to be assimilated. Thirdly, if the strategist is not the leaders, he/she should be protected from personal biases and any other influences that might affect the content of the plan.

The disadvantage of the commander approach is that it can reduce employee motivation. That is if the leader fashions the belief that it is only the strategies developed at the top that are acceptable, this may earn him/her an extremely unmotivated, un-innovative group of employees. The factors that contribute to the attractiveness of the commander approach are (i), it offers a valuable perspective to the chief executive, (ii), divides the strategic management task into two stages “thinking “and “doing” this allow the leader to reduce the number of factors that have to be considered all at the same time. (iii), when the approach is structured in such a way that the focus is on the quantitative, objective elements of a situation, rather than with core subjective and behavioral considerations, then the young managers would prefer the commander approach. And finally, this kind of approach is popular because it allows managers own the process and feel that they are shaping the destiny of an organization with their decisions, hence they would feel empowered.

2.2.6.2.2 The Organizational Change Approach

The organizational change model approach to strategy implementation, focuses on the structure, staff, information and reward system. This approach considers 4 of the five

constructs in this study. The organizational change approach has been seen in several banks where the banks approach to change of strategy involves re-organizing, retrenching staff, adopting enhanced core banking systems and developing attractive reward systems. According to the organizational approach, the starting point of strategy implementation is the reorganization of human resources in order to facilitate an effective strategy implementation. In this case the strategist’s role becomes architectural, that is designing administrative systems for effective strategy implementation.

This approach is deemed more effective than commander approach as it can be used to implement more difficult strategies because it uses several behavioural science techniques. The techniques include demonstrations to communicate desired new activities rather than using words, and focus on important needs that the organization is already aware of and have solutions presented by people that have high credibility in the organization. The disadvantage of this approach is that managers are not able to keep up with the rapid changes in the market place. The approach does not work well in uncertain or rapidly changing conditions. It also imposes the strategy in “top down” approach, causing demotivation problems among staff.

2.2.6.3.3 The Collaborative Model Approach

In the collaborative model, the management is involved in the strategic decision making and the leader engages group dynamics and brainstorming to get the views of managers in the strategy decision-making process. The collaborative approach is seen as a practice in the banking sector. The management incorporates the staff from cross functional groups to access important information and to get a buy in from staff on planned activities. The management also uses their managers to cascade the strategy implementation and thus by sharing bank strategy direction, the bank staff become “owners” of the strategy. This facilitates staff alignment with the bank strategy. This approach overcomes the limitations that are experienced in the commander and organizational change approach model in that the information contributed by the managers closer to the operations is captured. It is by giving these teams a forum to express their point of view that increases the quality and timeliness of the information incorporated in the strategy. The degree to which participation is allowed, enhances commitment to the strategy, as it improves chances of efficient implementation. Despite its positive aspects based on more commitment that

were missing in the earlier approaches, the collaborative approach could also result in a poorer strategy.

Risks could result from the negotiated approach as the strategy would be more conservative and less visionary than one developed by the senior management team or by a single person. In addition, the negotiation process can take so much time that an organization could easily miss out on opportunities as it may fail to react fast enough to changing environments. A criticism of the Collaborative Approach is that it does not in real sense take the collective decisions from the teams, but the senior management more often would retain a centralized control. The approach therefore ends up maintaining the difference between the thinkers and the doers and thus fails to draw on the full ownership of the process throughout the organization.

2.2.6.3.4 The Crescive Approach

The crescive model approach differs from the other 3 approaches discussed above. In the crescive approach, the strategy comes upwards from the bottom. Strategy therefore becomes the sum of all the proposals that surface throughout the year from lower level managers and others at the bottom of the organization. The leader acts as a judge and evaluates all the strategy proposals and makes a selection from the proposals received. The leader in the Crescive approach, encourages subordinates to develop, champion and implement sound strategies on their own. This approach is seen as a noble approach that would facilitate more ideas in a setting such as in commercial banks. This is because the junior staff are constantly in touch with the customers and would therefore contribute value towards bank strategy. The difference between the crescive approach and the other three approaches (command, change and collaborative), is that the strategy moves upward from the doers who are generally the sales people, engineers, production workers and lower middle-level managers. Secondly, the strategy adopted is the sum of all the individual proposals that are raised in the course of the year. Thirdly, the role of the senior management becomes that of shaping the employees’ premises- that is clarifying what should be supportable as strategic projects. Fourthly, the top management or the chief executive functions more as a judge by evaluating the proposals that are received than as a master strategist.

According to Brodwin and Bourgeois (1984), the use of crescive approach works better for managers of large, complex and diversified organizations. Their rationale is that in

these organizations, the strategic leader cannot know and understand all the strategic and operating situations, facing each business unit in the organization. Brodwin and Bourgeois (1984), argue that if strategies are to be formulated and implemented effectively, the leader must give up some control to spur opportunism and achievement. The approach therefore suggests some generalizations concerning how the chief executive of the large divisionalized firm ought to help the organization generate and implement sound strategies. Their recommendation includes the following elements; maintenance of openness of the organization to new and discrepant information, articulation of a general strategy that would guide the firm’s growth, manipulation of systems and structures to encourage bottom-up strategy formulation, and usage of logical incrementalism approach to select from among the strategies which emerge.

The advantages of crescive approach is that it encourages middle-level managers to formulate effective strategies and gives them opportunity to carry out the implementation of their own plans. Strategies developed by staff and managers would be closer to the strategic opportunity and operationally sound and readily implementable.

2.2.6.4 Noble’s Strategic Framework

This study finds Noble’s implementation framework relevant to the study because the independent variables of this study are akin to Nobles’ strategy implementation factors used to develop his framework. The factors identified by Noble (1999), include organizational structure, Leadership, communications, goals, and incentives which also forms the basis for this study. The strategy framework by Noble (1999), is organized around four key stages of implementation that is: pre-implementation, organizing the implementation effort, managing implementation process, maximizing cross-functional performance. The implementation phases consist of five managerial levers: leadership, organization structure, goals, communication and incentives. Noble opines that the management of these factors changes in every stage and that considering these factors with each major phase provides a useful way to improve implementation. Noble’s strategic framework is illustrated in table 2.3.

Table 2.2: Noble’s (1999) Strategic Framework

STAGES

LEVERS Pre-Implementation Organizing the Managing the Maximizing Cross-Functional Implementation Implementation Performance Efforts Process

Leadership Develop Employees Establish a Ensure that leaders Balance visible and charismatic knowhow and “Champion” who has show equal leadership with a maintenance appreciation of multiple both official cross- attention to all of autonomy for functional – functional areas functional authority functional-level level implementation efforts and general respect in concerns the firm.

Organization Ensure that functional Establish a formal Ensure equal Temporarily suspend key structure areas have the slack implementation unit representation by implementation team members resources needed to and ensure its visibility all affected normal responsibilities to allow contribute to an throughout the firm functional areas them to focus on the important important effort effort

Goals Ensure that all managers Introduce goals of the Maintain the Develop and focus on common are aware of strategic strategy being flexibility to adapt goals to encourage cross-

goals of the firm implemented fit within goals based on functional cohesiveness firm’s broader strategic environmental vision changes

Communication Maintain regular cross- Discuss and resolve Update Communicate implementation functional important details early implementation progress across the entire communications to in the process team frequently on organization to foster buy-in. foster understanding and progress and appreciation changes in the process

Incentives Reward the development Develop time and Adjust incentives as Establish visible and consistent of cross-functional skills performance based strategy and cross-functional rewards for incentives for environmental successful implementation implementation team conditions change efforts. while lessening during traditional functional implementation incentives.

2.2.6.5 Okumu’s Strategy Implementation Model

Okumus strategy model (2001) outlines factors affecting strategy implementation. It looks at issues this study is focusing on. Factors such as organizational structure, people, resource allocation, organizational culture, organizational learning, communication, monitoring and outcome, issues that this study is analyzing. Outcome for this study will be the m-commerce performance. All the factors under investigation for influence of strategy in this study are factors that Okumu has also identified in his model as impacting strategy implementation.

Okumus (2001) framework is an extension of Pettigrew’s (1985) implementation variables. The implementation variables according to Okumus, 2001, are grouped into four categories. The first is content which deals mainly with strategic decision, and are multiple project implementations. This is followed by the context and it includes the internal context that is the organization structure, organization culture, organization learning and the external context which incorporates the environmental uncertainty in the general and task environment. The third category is the process which includes operational planning, resources allocation, people, communication, monitoring and feedback which are all external partners. The final variable is the outcome which includes tangible and intangible outcomes of the project and this is presented in figure 2.5.

Figure 2.4: Strategy Implementation Framework and Key Variable Source: Okumus 2001

2.2.6.5 Summary of the Differences between Strategy Implementation Models

The selected implementation models have specific differences as summarized in Table 2.4. The 8-S model is a revised 7-S framework. The 7S model posits that organizations’ strategy must be approached holistically in order to achieve effective strategy. The organizational 7 elements must be aligned with each other for the organization to succeed, and that organizations must fit or align with the external environment. The framework proposes seven factors that managers need to take account of for implementation to succeed. The factors are: style, structure, strategy, systems, staff, skills and shared values. However, the 7-S framework has been criticized because it does not show how all the factors are interrelated. The 8-S model used in this study is a revised 7-S framework. The difference is that the skills component of the McKinsey’s framework is substituted by resources. Strategic performance is also added to the framework to help focus on strategy implementation process. The commander model, is mainly top-down led strategy implementation, while the organizational model, focuses on the inner contents of the organization such as the structure, staff, and rewards. The collaborative model, involves

top management in the process, but the crescive model is a bottom up strategy implementation process. Noble’s strategic implementation framework has four key stages of implementation and the proper management of these factors changes in every stage and when well-managed, provide efficiency in strategy implementation. Okumu’s strategy implementation model is an extension of Pettigrew’s (1985) implementation variables and is grouped into four with the main focus being internally.

Table 2.3 Comparison of Implementation Models

Model Characteristics Strengths Weakness 8S Similar to 7S; however, in Enables management to effectively Frequent and rapid changes in the Higgins 8S, the skills and efficiently manage the cross business environment, makes the component is substituted by functional implementation of alignment process a challenge. (from resources and added a strategic strategies the 8S Model ) performance to help focus on strategy execution process Commander Model The leader is the thinker and Managers focus on strategy Strategy implementers are not the planner, he/she does not formulation only. involved in the formulation of the take an active role in Creates ownership in the leader strategy and therefore they become implementing strategy. It and makes some managers feel demotivated employs a top down approach like heroes as they shape the destiny of thousands. Organizational Change The strategic leader may Uses behavioural tools, focuses on Imposes strategies in a “top-down” Model change structure, staff, the organization, includes staffing, management resulting in information and reward structure, resources and is used to motivational issues. Managers are not systems in order to have implement difficult strategies. informed on changes occurring strategy implemented More effective than the within the environment. effectively Commander Approach Model. Collaborative Model Concentrates on group There is ownership hence gaining May result in poorer strategy decision-making at senior level more commitment The negotiated aspect may result in and involves top management more conservative and less visionary in the formulation process to strategy. ensure commitment. The process is time consuming at the expense of rapid emerging oppotunities Crescive Model The leader encourages the Empowers subordinates to Financially demanding to the subordinates to develop, formulate and implement organization champion and implement strategies on their own Strategies that are formulated may sound strategies on their own. not be proper since these are

Process is upward from the formulated by the middle and lower implementers to top management management teams. The CEO acts as a judge, rather than as a master strategist. Developed around four key Simplifies the strategy It is inward looking at the expense of Noble’s Strategic Framework stages of implementation: Pre- implementation process to two rapid external environmental implementation, Organizing, dimensions: the structural view changes. Process and cross functional and the interpersonal process view. performance Implementation variables A shift away from environmental Any inconsistency with one factor Okumus Model grouped into four: Content, scanning, formulation, influences the other factors and, this Context-(internal & external), implementation, evaluation and affects, the success of the process and outcome (tangible control of strategy to a holistic implementation process. & intangible outcomes of interpretation of strategy project) formulation and implementation.

Source: Author (2017)

2.3 Conceptual Framework

Conceptual framework is a system of, concepts, assumptions, beliefs, expectations and theories that inform and support the research. Conceptual framework is a key part of the design of the study and explains graphically the main factors, concepts, variables and the presumed relationships between them (Swanson, 2013). A concept unlike a theory, does not need to be discussed to be understood (Smyth, 2004), but when clearly articulated, a conceptual framework has potential usefulness as a tool to assist a researcher make meaning of subsequent findings. This forms the agenda for the discussion to be scrutinized, tested, reviewed and reformed as a result of investigation as it explains the possible connections between variables (Smyth, 2004).

The conceptual framework of this study focused on the relationship between variables that influence the implementation of M-Commerce in Kenya’s commercial banks. The independent variables in this study is strategy implementation, represented by organizational leadership (OL), organizational structure (OS), information systems (IS), human resources (HR) and strategy communication (SC). The conceptual framework addressed the relationship between the independent variables and M-Commerce performance. The dependent variable was represented by growth of new applications, growth of M-Commerce users, growth of bank accounts, and growth of savings, which provided the results of strategy implementation, and therefore measured the m-commerce performance of the banks. The study variables are illustrated in figure 2.6.

Independent Variables Dependent Variable

Organizational Leadership H1

Organizational Structure H2

M-Commerce Performance Information Systems H3

Human Resources H H H 6 7 4

Strategic Communication

Market Technological Turbulence Turbulence

Figure 2. 5 Conceptual Framework Source: Author 2017

2.3.1 Mobile Commerce

Mobile Commerce is the buying and selling of goods and services through wireless handheld devices. Mobile Commerce is the trading of an item of economic value such as goods, services, information or money between two or more entities through a mobile device such as a mobile or smart phone, personal digital assistant (PDAs) or emerging mobile equipment such as dashtop mobile devices (Kurkovsky, 2007). M-commerce applications include mobile: advertisement, payments, ticketing, banking and entertainment. The focus of this study was be on mobile banking with a mention of mobile payments in the descriptive section. Mobile payments are both remote and physical mobile payments. The remote mobile payments are payments for digital good or physical goods via a mobile web enabled retailer. On the other hand, physical mobile payments are payments made in a physical environment in the same way cash or a plastic debit/credit card is used (Kurkovsky, 2007). M-Commerce services were first introduced in 1997 when Coca Cola installed the first two mobile phone enabled vending machines

in Helsinki in Finland. They were able to send mobile payments to the vending machines via SMS text messages. In addition, within the same year, they introduced the banking service. The M-commerce server was developed in late 1997 by Kevin Duffey at logica (Chaitanya, 2013).

2.3.2 Organizational Leadership and M-Commerce Performance

The study evaluated the influence of leadership on M-commerce performance. The most important task of leadership is to align its vision with the organization’s goals and objectives in order for the organization to compete in an environment effectively and to train and motivate the staff to achieve the organization’s vision. Leaders act as the link that relates the organizations vision with strategic management (Azhar, Ikram Rashid & Saqib, 2013). The role of leadership is expected to provide vision, mission and priorities of the bank. The leaders are expected to create an environment that nurtures a cohesive leadership team that in-turn can trickle down so that the entire reporting lines are closely intertwined and working towards the same goals. The areas that the study seeks to evaluate are leadership’s mission and ability to inspire others to join them, the leaders’ ability to create strong organizations through staff involvement in strategy implementation, leaders’ flexibility in allowing bank staff to make mistakes without being penalized heavily, the leaders’ interpersonal skills and ability to be tolerant with bank staff.

2.3.3 Organization Structure and M-Commerce Performance

Organization’s structure expresses how people are ordered and how jobs are distributed and coordinated (Mintzberg, 2009). Researchers argue that organizational structure is a combination of job positions and their relationships to each other and their responsibilities for the processes and sub-process deliverables (Gerwin & Kolodny, 1992; Greenberg, 2011; Long, Ajagbe, Nor & Suleiman, 2012). On the other hand, organizational performance is the ability to accomplish its aims through the use of resources in a well- structured method (Daft, 2001). In this study, organization’s structure was examined to evaluate how an organizational structure influences strategy implementation and M- Commerce performance in Kenya’s commercial banks. It is perceived that a bank’s structure can inhibit or promote strategy implementation and resulting effective or ineffective strategy implementation. A study by Maduenyi, Oke, and Fadeyi (2015), indicated that organizational structure has an impact on organization’s performance and

that organizational structure affect the behaviour of employees in the organization. Their conclusion was that the performance of an organization largely depends on the structure of the organization.

A key factor therefore to the efficiency of a structure is the relationship between the top executives, senior management, middle managers and the rest of the staff. The efficiency will depend on how the senior management maintain their relationships and workflow instruction. Structure defined departmental structure and reporting lines. The design of an organization was expected to determine how it performs. Improvement of organizational performance would depend on the willingness of the management to change their design. The structure is expected to support the business strategy implementation. An effective organizational design considers the following components: leadership, decision making, people work processes and systems. The design of an organization is shaped by the function it performs, the location of each function and the authority of each function within the entire body of the organization. Decentralization of autonomy and centralization of control are indicators of a good structure design. To measure the efficiency of structures in the banks, the five components that were examined together with the functions they perform were; the location of each function and the authority of each function within its domain.

2.3.4 Information Systems and M-Commerce Performance

Organizational information systems (IS) was initially seen as a support function, but with the increased dynamism in the business environment, IS should be viewed more from a strategic standpoint working to maximize its use for competitive advantage. (Gaines, Hoover, Foxx, Matuszek & Morrison, 2013). Information system is an interrelated component that collects, processes, stores and distributes information to support decision making and control in an organization (Ernst & Young, 2014). The evaluation of the role of organizational information systems in this study is to determine how information systems influence M-Commerce performance in Kenya’s commercial banks.

The organizations which developed before the computer age were inefficient, slow to change and less competitive. Organizational information systems are changing this and creating flatter organizations with fewer levels of management, with lower level employees being granted access to more information for decision-making, which previously were made by senior managers only. With the information systems, managers

can manage and control more workers spread over greater distances. Information system contain information data about both internal and external customers. Information system is an organizational and management solution, based on information technology to a challenge posed by the environment. Information system provide communication and analytical power that firms need for conducting trade and managing businesses effectively. Information is data that has been fashioned into a form that is meaningful and useful for consumption and use by an organization. Because of the declining cost of computer technology and increased use of internet, Information systems play an important role in strategy implementation.

2.3.5 Human Resource and M-Commerce Performance

In the fast changing competitive environment, human resources are a very important source of competitive advantage. Human Resource is viewed as the main contributor to sustained competitive advantage because HR develops competencies that are firm specific. (Caliskan, 2010). The way an organization manages its HR has a significant relationship with the organization’s results, which is also supported by resource based view (Caliskan, 2010). An organizations competitive advantage in resource-based view, is a function of resource capabilities (Barney, 1991; Peteraf, 1993; Wernerfelt, 1984). The main aim of HR variable in this study is to investigate the influence of HR on m- commerce performance in commercial banks in Kenya. Human resources are the linkage between the overall strategic activities which includes strategy implementation and m- commerce performance in commercial banks. This is because every activity that needs to be undertaken would be driven by the staff, who are the human resources in the organization. It is presumed that the way an organization manages its human resources would have a relationship with the organization’s performance.

Human resources are considered the most important resources in an organization, with competition raging in all sectors. How people are managed is important because all other resources can be easily copied, but the human resource is important in that how they are managed would determine the performance, sustainability and competitive advantage of any organization (Pfeffer, 1994). It is also perceived that the relationship between resourcing, training and development and staff involvement in strategy implementation would affect business strategies and therefore the performance of an organization in this case being the m-commerce performance. This study presupposes that the policies

operational in any organization’s human resource function, influences staff in the following areas; skills set, attitudes, behaviours and therefore these become a link to the performance of the organization (Delery & Doty, 1996). This study presupposes that observation and implementation of HR policies, motivates employees and in turn results in optimal performance in an organization. The relationship between HR and organizational performance is illustrated in figure 2.7.

2.3.6 Strategy Communication and M-Commerce Performance

Communication is a process of transmitting information from one person to another and acts as a medium or a means through which performance can be achieved (Banihashemi, 2011). Communication is a critical determinant in directing, mobilizing and encouraging the workforce towards the achievement of the firm’s goals and objectives (Stephen, 2011). Strategy communication was investigated to establish its influence on M- Commerce performance. Strategy involves change of a current status in any organization. This therefore would need to be communicated to all members of an organization. The strategy communication enables an organization to align the extent and scope of the change and approaches of implementation with the values and principles as stated in the developed strategy document (Nkemdima, 2015). In majority of cases, organization’s focus in strategy implementation does not include a comprehensive communication strategy. The purpose for evaluating strategy communication in this study is based on the understanding that how organizations utilize their communications strategy would help create and sustain strategic advantages (Lekovic & Berber, 2014). Communication strategy needs to be placed on equal footing with all the other business agendas and innovative plans and policies that have been championed by the organization. Communication planers need to appreciate the fact that organizations are made up of social, communicative human beings who can be motivated to achieve things together once they understand that it is to their benefit to cooperate and communicate effectively together.

Excellent communication influences strategy implementation (Cushaman & King, 1997). Therefore, this study sought and tested the role of communication in successful strategy implementation in line with previous scholarly conclusions. It investigated corporate communication and bank’s ability to create and disseminate its strategy to the business. The study evaluated communication from the overall banks’ vision and mission,

communication of the overall bank strategy, individuals and departmental roles and internal communication. Strategy implementation effectiveness was expected to be affected positively by communication especially interpersonal, and unwritten when a team is cohesive. Another reason for evaluating communication is because few scholars have investigated the link between corporate communication and strategy.

2.3.7 Market Turbulence and M-Commerce Performance

Market turbulence is considered as the amount of change in number, structure, composition of customers and their preferences. Market turbulence is driven mainly by changes in economic stability, continuous change of customer needs and envisioned technological change (Kohli & Jaworski, 1990). The preceding statements are reflective of the market in which commercial banks operate in Kenya. Based on this therefore, the study sought to investigate the influence of market turbulence on m-commerce performance. A turbulent environment critically impacts an organizations’ operations and thus it is important to understand the influence of a market turbulence on m-commerce performance. In environments where market turbulence is high, customers change fast in composition and preferences resulting in uncertain demands on services and products, at times leading to product or service obsolescence (Su & Peng, 2013). This study attempts to understand the influence the market turbulence would have on m-commerce performance.

According to Calantone, Garcia and Droge, (2003), they point out that when market turbulence is low, stability in the composition of customers and their preferences enables organizations gain greater return and longer life cycle for products. Accordingly, therefore, understanding of the actual market turbulence, enables organizations anticipate the kind of changes that can take place in customer composition and therefore identify potential customers and their composition for targeting (Song, DiBenedetto & Nason, 2007). Further Dickson, (1992), Hunt & Morgan, (1995); Slater & Naver, (1995), argue that in high market turbulence organizations intensify gathering and utilization of market information in order to adopt to changes adequately. The argument is based on the assumption that under such conditions, the market environment represents excellent learning capability, which would earn the organization competitive advantage.

Garrett, (2016), defines turbulence in an organizational context as an unpredictable and swift changes in its external or internal environments that affect performance. For the

internal organization, he opines that the internal events would limit their effect to the organizations in which they occur, but that external activities would have a wide reaching effect, and would affect all organizations within an industry sector. Because of the unpredictability of the market, this study evaluated the market unpredictability in relation to commercial banks m-commerce performance.

2.3.8 Technological Turbulence and M-Commerce Performance

Technological turbulence is defined as the degree to which technology changes over time within an industry, in areas such as production and in process, in addition to the product itself, including new product technologies (Jaworski & Kohli 1993; Moorman & Miner 1997; Trkman & McCormack, 2009). The study sought to investigate the influence of technological turbulence on m-commerce in Kenya’s commercial banks. This was based on the hypothesis that there was a relationship between technological turbulence and m- commerce performance. According to Neil, Singh and Pathak (2014), technology capability is a key determinant of an organizational competitive advantage. He argued that technology capability denotes the application of superior knowledge and skills by being able to develop new and preferred ways of conducting business.

Product lifecycle is shortened by technological turbulence, making it difficult for businesses to maintain expected customer-service levels. This calls for greater collaboration among stakeholders in a supply chain (Simatupang & Sridharan, 2002). It is argued that organizations operating in environment with high technological turbulence would experience constant and rapid changes in technology altering competition (Levine & Prietula, 2012). This study sought to investigate if this would also apply in the commercial banks environment. It is also opined that conditions of high technological turbulence, would necessitate organizations to adopt more rapid responses such as product adaptations or modifications to satisfy changeable customer needs as well as preferences (Jaworski & Kohli, 1993).

2.3.9 Operational Framework

To measure the constructs of the study, composite variables were used. The study had seven (7) latent variables and twenty-three (23) composite variables. The study operational framework is illustrated in figure 2, while Table 2.5 provides a summary operational framework.

Independent Technological Turbulence Variables  Technology rapidly changing  Opportunities for growth  Technological Dependent Variable Breakthroughs Organizational Leadership  Contributes to m-commerce  Technological growth developments  Flexible in facilitating staff contribution H  Leadership style supports m- 1 commerce  Influence the overall m- commerce performance M-Commerce Performance Organizational Structure  Eases decision making H  Influences growth of new  Growth of New 7 m-commerce applications. H2 Applications  Influences m-commerce  Growth of m- growth  Influences positively the commerce users overall m-commerce  Growth of bank

accounts Information Systems  IS Drives Growth of m-  Growth of savings commerce applications  Contributes positively to Source:the Author overall performance H of the bank 3

Human Resources  Specialized skilled staff  Talented staff drive the H overall m-commerce 6 performance H 4

Strategic Communication  SC of Vision Supports growth H  SC of Mission linked to m- 5 commerce  SC of Strategy Lead to overall M-Commerce Marketing Turbulence performance  Competition in the banking sector  Stable customer demand  Introduction of frequent products and service  Changing business environment  Customers seek new products and services continuously Figure 2.6 Operational Framework

Table 2.4 Construct Measurement

Construct Construct Definition Relationships Hypothesized Measured Items Organizational Ability of leaders to align and link the OL M-CP  M-commerce growth, Leadership organization’s vision with strategy and  Flexibility (OL) motivate staff to achieve the  Leadership style organization’s vision  Leadership Influence Organizational The order with which people are OS M-CP  Eases of decision making Structure (OS) organized and how jobs are distributed  Growth of new m-commerce and coordinated for efficiency. applications.  M-commerce growth  The overall m-commerce performance Information A set of interrelated components that IS M-CP  Growth of m-commerce applications Systems (IS) collect, process, store and distribute  The overall performance of the bank information to support decision making and control in an organization Human The people, labour, intellectual capital, HR M-CP  Specialized skilled staff Resources (HR) human capital or employees of an  Talented staff drive the overall m- organization. commerce performance Strategic Purposeful use of communication by SC M-CP  SC of Vision Supports growth Communication orgnaizations to implement its  SC of Mission linked to m-commerce (SC) mission.  SC of Strategy Lead to overall M- Commerce performance Marketing The amount of change in number, MT M-CP  Competition in the banking sector Turbulence structure, composition of customers  Stable customer demand (MT) and their preferences  Frequency of new products and service  Changing business environment  Continuous customer demands Technological The degree to which technology TT M-CP  Technological rapid changes Turbulence changes over time within an industry.  Opportunities for growth

(TT)  Technological Breakthroughs  Technological developments  Revolutionizing banking Mobile Mobile Commerce (M-Commerce), is Dependent Variable  Growth of New Applications Commerce the use of wireless hand held device to  Growth of m-commerce users Performance conduct commercial transactions  Growth of bank accounts (M-CP) online.  Growth of savings

Source: Author 2017

2.4 Empirical Review

This section presents a critical review of recent studies on strategy implementation and performance. Literature on m-commerce performance is limited and therefore the literature used to represent performance in this study is literature on organizational performance. The section starts with an overview of the development of m-commerce. The subsequent sections were presented in line with the research objectives.

2.4.1 Performance of Mobile Commerce

Research in the area of M-Commerce is scanty and therefore still lacks standardized parameters for measuring m-commerce performance (Salameh & Hassan, 2015). Most research studies approach m-commerce from the consumer perspective (Martin, 2012). Literature abound on mobile commerce context (Ballocco, Mogre, & Toletti, 2009; Mallat & Tuunainen, 2008). However, the extant literature on m-commerce has focused on analysing the consumer, and research from the perspective of the firm remain scarce (Martin, 2012). Few studies explore predecessor’s and determinants of mobile commerce acceptance or perceived performance of mobile commerce from the organization’s perspective (Martin, 2012). This makes it a challenge to gain relevant knowledge on internal processes which firms undergo to adopt mobile commerce or the means available to them for implementing this new technology (Salo, Sinisalo & Karjaluoto, 2008).

Organizations’ managers will only engage in m-commerce once they perceive a positive return in terms of performance. Studies by Wu et al 2003, indicate that there is a positive relationship between electronic commerce acceptance and firm performance. This study therefore sought to investigate the relationship between strategy implementation and m- commerce performance in order to determine the factors that lead an organization to achieve high performance in m-commerce. Previous studies evaluating performance in banks, used financial performance measurements ratios such as return on asset (ROA), return on equity (ROE), net interest margin (NIM) Oyewole, Abba, El-maude, & Gambo, 2013; Ngumi, 2013; Ruparelia, 2015).

Measuring performance of any business requires identification and focus on the areas that make a business successful and hence the decision to measure performance in those areas (Nibusinessinfo.co.uk 2016). In the absence of an existing framework of measuring m- commerce performance, this study proposed a model for measuring the performance of

M-Commerce in the banking sector using the areas that would signify success as being the following variables; growth of new applications, growth of M-commerce users, growth of bank accounts and growth of savings. This study used organizational performance in attempting to build a framework for future studies on m-commerce performance.

2.4.2 M-Commerce Historical Development and Overview

Mobile commerce was born in 1997 when two mobile-phones enabled Coca Cola vending machines were installed in the Helsinki area in Finland. In the same year the first mobile- phone based banking service was commissioned in 1997 by Merita Bank of Finland. Subsequently in 1998, the first sales of digital content was downloaded to mobile phones when the first commercial downloadable ringtones were started by Radiolinja of Finland, (now part of Elisa Oyj). In 1999, two m-commerce commercial platforms namely (Smart Money in the Philippines and NTT DoCoMo’s I-Mode Internet service in Japan were launched Golden and Regi. (2013). A detailed chronology of telecommunication evolution to M-Commerce is illustrated in figure 2.8.

4 G 2001 3 G /2002

2.5 G 2001 All Late /2002 network elements 2G 90s/200 M- are digital, 1990s 0 GPRS Comme 1 G Higher GSM Mcomm rce Bandwidt 1970/8 erce h lower 1 G 0

1960 Wireles costs 1 G IMTS s Analogu Cellular 1897 1st e AT&T Wireless 1946 Contact

Figure 2.7 Evolution of M-Commerce Source; compiled by author

The first generation 1G was introduced in 1946 by AT&T Bell as the first commercial mobile phone. In 1960s AT& T developed the Improved Mobile Telephone Services (IMTS). In Late 1970s and early 1980s, microprocessor technology and improvements in cellular network infrastructure led to wireless telecommunications systems. The Second generation 2G was transitioned into from 1G in early 1990s through the Global System for Mobile Communication (GSM). This had the following features: More global compatible telecommunication network, less costly, roaming was made possible and incorporated voice communication. 2G therefore could send and receive short messages (SMS) and was enabled to facilitate mobile internet browsing via the Wireless Applications Protocol (WAP). A limitation that existed with the 2G network was its inability to handle data as it was primarily voice centred. In late 1990s and early 2000s, a General Packet Radio Service (GPRS) was developed to fill the gap. GPRS had higher transmission rates and an always on-connectivity. From figure 10 above the mobile telecommunication operator’s business model has been evolving over the years based on continuous improvements of each technological generation. This was primarily driven by gaps identified after each development. It can therefore be said that the mobile operator’s/technology experts have constantly changed the business model for competitive advantage. This has resulted in the introduction of m-commerce service. M- commerce presents opportunities for revenue generation through mobile banking,

ticketing (airlines, long-distance transport), general purchasing and monitoring of stocks and shares using the mobile phone. The latest technology is the 4G whose capability encompassed multimedia, data transfer, video streaming, video telephony, and access to the internet in addition to voice communication.

M-commerce has become potentially important for a wide range of industries, including telecommunications, information technology, finance, retail, media and end users. The 3G and 4G technologies have provided platforms that have expanded beyond telecommunication to all other sectors including the banking sector. According to (Nysveen, Pedersen & Skard, 2015), several reviews on mobile services have been carried out by several researchers such as (Ngai & Gunasekaran, 2007, Shankar & Balasubramanian, 2009; Shankar,Venkatesh, Hofacker, & Naik, 2010). However, the last review on mobile services was published by Nysveen et al., (2010) who also identified literature gaps that should be guiding future research on mobile services. Reviews of previous studies by Nysveen et. al., (2010), reveal that most qualitative studies focused on successful adoption, while quantitative studies (which were also the largest), focused on success criteria through the adoption approach. They state that a few articles focused on synergies between mobile channels and other channels. Much of the recent success of mobile services are attributed to specialized application and not the generic mobile internet services (West & Mace, 2010). Research also indicates that literature considers adoption of mobile services as the main measure of success but Nysveen et al., (2010), suggests that future research should focus more on the possible effects of using mobile services, because mobile services can influence customers’ behaviour. The same opinion is also held by (Davis & Sjtos, 2009; Pihlstrom & Brush, 2008).

2.4.2 Overview of Strategy Implementation

Strategy implementation and execution are often used interchangeably creating substantial confusion on the distinction between the two (MacLennan, 2011). Hrebiniak (2006) for example states that “Formulating strategy is difficult. Making strategy work- executing or implementing it throughout the organization –is even more difficult” (Thompson & Strickland 2003). It is difficult to distinguish the two terms from both a theoretical point of view and from the literary meaning of the word. However, for this study, strategy implementation was the main term used to describe the concept.

Despite many studies undertaken in strategy implementation, it is apparent from the above statements, that there is no universally accepted definition of strategy implementation. This study used a citation by Stonich (1982) view as cited in MacLennan (2011), which distinguishes strategy formulation and implementation by proposing that planning is about where the firm is going, and on the other hand, implementation is about how to get there. For a start, this separation of concepts is important, but it does not give an accurate definition. Three distinct conceptions of the term have been identified by Li, Guohui and Eppler, (2008). The first concept concentrates on a process perspective taking strategy implementation as a sequence of well-planned consecutive steps. The second approach takes strategy implementation as a series of actions often parallel and examines them from a behaviour perspective. Some scholars combine the two perspectives (Process and behaviour or action perspective), to form a third approach labelled hybrid perspective (Li, et al., 2008). They define strategy implementation as a “dynamic, iterative and complex process, comprising a series of decisions and activities by employees and managers influenced by a number of interrelated external and internal factors to turn strategic plans into reality in order to achieve strategic objectives (Li, et al., 2008).

2.4.3 Organizational Leadership and M-Commerce Performance

Leadership management determines the performance of an organization’s strategy implementation (Ogunmokun et al., (2005). Ogunmokun et al., (2005), established that the extent to which a leader carries out their strategic implementation activities is related to the level of their organizational performance. Respondent employees from organizations with high level of performance mentioned that their organizations made tremendous changes to their structure, that they communicated to their employees when and how the strategies would be implemented; that they offered incentives for employees to execute the strategies effectively and assigned able people responsibilities for implementing these strategies compared to organizations with low level of performance. This therefore demonstrates that success of organizations is mainly attributed to effective leadership.

The elements of leadership and organizational culture are interrelated enabling an organization to portray the values and beliefs of its leaders as they are the ones who shape the cultural characteristics of the organization. As the organization changes and its culture develops the new culture shapes the leader and influences his or her actions in time (Popa,

2012). It is also stated that the behaviour of the leader of a company with the employees of the company is one of the major reasons for the company’s success (Karamat, 2013). In the empirical study on the impact of leadership on organizational performance, Karamat (2013), presented that leadership behaviour is a very important factor for the growth and development of organizations in the service sector, with the conclusion that there is a strong impact of leadership behaviours on the overall performance of an organization.

While using linear regression model in his study to evaluate the relationship between personal values balance and organizational differentiation of a group of Brazilian executives of several organizations, Bruno (2008) established that the executives need to be trained in terms of leadership skills, so that they have more flexibility of styles and to be able to make use of the appropriate leadership style depending on the situation. Lieberson and O’Connor (1972), indicated that leadership has a strong effect on profit margins than other restrictions even though they argued that it was possible to overlook far more important environmental influences by emphasizing on the effect of leadership. Leadership varies greatly between goals and objectives of a particular organization. Brenes, Mena and Molina (2007), pointed out that five key dimensions were the contributory factors to successful implementation of business strategy. The dimensions are the strategy formulation process, systematic execution, implementation control and follow-up, CEO’s leadership and suitable, motivated management and employees, and corporate governance which includes board and shareholders, who lead the change.

On relationships between leadership and organizational performance, reviews by Jing and Avery (2008) concluded that many scholars have examined the effectiveness of leadership paradigms and behaviours and aver that the existing research on leadership- performance relationship have some unresolved problems, including methodological problems. The studies indicate that some of the problems relate to the quality of performance measurement as researchers do not use the same measurements. Some use financial measurements or non-financial measurements, rather than employing both kinds of measures in order to enhance the validity of the research. According to Jing and Avery, (2008), in using only one parameter such as the financial measure only, they ignore the interrelationship between financial performance and customer satisfaction and employee satisfaction. Both financial measurements and non-financial measurements of performance are essential in order to enhance research validity.

The results from a research undertaken by Flanigan, Stewardson, Dew, Fleig-Palmer and Reeve, (2013), seeking to understand the impact of leadership on organizational outcomes, examined the effect of transformational and transactional leadership behaviours on financial measures of success at a branch office of an industrial distribution branch office. Using validated instrument measuring transformational and transactional leadership, the multifactor Leadership Questionnaire (MLQ) survey, the results demonstrated that the higher the managers believed they practiced transformational leadership, the higher their annual branch sales and margins increased. On the other hand, when branch managers’ subordinate’s perception was that their leader had more transactional style, the branch sales were lower.

The results therefore suggest that transformational leadership is more effective than transactional leadership, and that the perception of subordinate staff has both positive and negative implication on performance. Regression analysis done on this research revealed that there was positive relationship between the independent variables (leadership style) and sales (year-over-year profit margin performance). The regression also showed the association between leader-reported and follower-reported leadership scale ratings and sales. Leader-reported transformational leadership was positively related to year-over- year sales performance. The results show that for every unit increase in transformational leadership qualities, there was a predicted increase in sales. Sales were reported in year- over-year percentage change, this therefore meant that sales increased on average, 4.4 percent annually as leaders self-reported transformational skills increased by one unit, as measured by the Multifactor Leadership Questionnaire (MLQ). The regression also revealed a negative relationship between sales and leadership when the followers perceive their leader to be more transactional in nature.

According to Boston Consulting Group (BCG) (2011), Organizational and people capabilities drive performance and facilitate strategy. Leadership alignment and effectiveness is deep within the organization. According to BCG, leadership produces high-performance teams of individual leaders who drive urgency and direction. They argue that pipeline is stocked with future leaders whose skills are matched to future needs and the middle managers embrace and translate the strategy. Leadership behaviour impacts performance because high performance leadership teams understand that collective and individual behaviour casts a positive or negative shadow across the entire organization. Employees take their cues on what is important and how to behave from

their leaders, such that negative behaviour at the top creates negative behaviours among the lower management, adversely impacting performance and productivity. People watch the leader’s behaviour for clues as to what is accepted and what is not. One of the greatest obligations of leadership is integrity between words and deeds (Childress, 2009).

A compilation of several studies on leadership by DuBrin, (2013), illustrated that leaders affect organizational performance. Boards of directors of an organization replace the leader with the intention of having the new leader reverse performance. According to studies done by the Centre on Leadership and Ethics at Duke University about executive leadership based on 205 executives from public and private companies, it was concluded that they affect performance, but only if the leader is perceived to be responsible and inspirational or promotes an environment in which employees have a sense of responsibility for the entire organization. Another study analysed 200 management techniques as employed by 150 companies over 10 years. The results established that CEOs influence 15 percent of the total variance (influencing factors) in a company’s profitability. The same study also found that the industry in which a company operates also accounts for 15 percent of the variance in profitability. In conclusion the study suggested that the choice of a CEO leader is as important as a strategic decision to be undertaken. Another research on overview of management succession over a 20-year period provided support that leadership had an impact on organizational performance. A consistent relationship was found between who was in charge and how well an organization performed as measured by a variety of indicators. Using different methodologies, the different studies had the same conclusion that changes in leadership are followed by changes in company performance. And like the above CEO study, this study also concludes that statistical analyses suggest that the leader might be responsible for between 15 percent of a firm’s performance.

Crow and Lockhart (2016), investigated the link between company owners and company managers with the view of establishing if this relationship enhances organizational performance. The study involved top management and members of the board of directors. Their findings were that where both the board and the management were involved in the organization’s strategy development, monitoring of implementation, and when the board and management practiced governance, there was enhanced performance. The two CEO’s interviewed stated to control the process of strategy development.

Strategy implementation is an important component of the strategic management process. The ability to implement a strategy is viewed as significantly more important than strategy formulation, and that strategy implementation, rather than strategy formulation is the key to superior organizational performance. However, with a high failure rate of strategy implementation documented, there exists many barriers to effective strategy implementation. A lack of leadership especially at the top of the organization was identified as one of the major barriers to effective strategy implementation. On the other hand, strategic leadership is viewed as a key driver to effective strategy implementation (Joose & Fourie, 2009).

Leader-member exchange (LMX) theory, challenges the belief that leaders should interrelate with and have the same association with every group member. The theory addresses the issue that people are largely different and need to be treated as such. Therefore, the LMX theory makes the dyadic relationship between a leader and a subordinate the focal point of the leadership process (Dansereau, Graen, & Haga, 1975; Graen & Cashman, 1975). A leader who develops quality relationships with all members of staff, would make everyone feel like they are in the group creating a sense of belonging and encouraging participation towards a common goal (Setley, Dion, & Miller 2013), investigated various styles of leadership and its relationship to the LMX relationship. Setley et. al., (2013) used the LMX theory within the dyadic conceptualization of leadership, to investigate if various leadership styles are related to the LMX as perceived by the subordinate. The findings were that the leadership styles that focused on developing high relationships with staff, resulted in higher quality LMX even from the perspective of the subordinate. This positive outlook supports effectiveness of the organization. On the converse, it was also found that a major focus on task oriented behaviour if not sufficiently balanced with relationship-oriented behaviour, would not have a significant positive impact on the quality of the LMX or subordinate goal commitment.

A study by Jamali, Marthandan, Khazaei, Samadi and Fie (2015), sought to understand the potential factors influencing adoption of electronic commerce in Iranian family SMEs. According to the study, organizational support interpreted as CEO’s perception of the availability of support within the organization, was seen as a factor that contributed to electronic commerce adoption by Iranian family SMEs. The organizational support viewed the active involvement of the CEO, support from directors of the board, and

involvement of employees as a contributing factors to e-commerce adoption. The contribution of the CEO was seen as the extent to which a CEO involves himself with the employees in the organization mainly in advise, control roles within the organization. According to their study, the findings were organizational support had direct effect on e- commerce adoption in Iranian family SME.

2.4.4 Organization Structure and M-Commerce Performance

A study on the relationship between organizational structure and organizational performance in a semiconductor industry, indicated that there was a significantly positive relationship between the two items. (Hsiao, Weng & Shih-Chin 2008). Using a conceptual model of Teixeira, Koufteros and Peng (2012), in their study to explore how organizational structure would enhance supply chain integration and how supply chain integration was related to manufacturing organization performance, the study found out that organizations that integrated with customers and suppliers had a great positive performance in regards to product innovation, quality, delivery time, flexibility, and cost performance.

Dalton, Daily, Ellstrand, and Johnson, (1998), employed an empirical model in the quest to assess the relationship between specific board composition configurations or board leadership structures and firm financial performance. They concluded that there was no substantive relationship between board composition and financial performance. Their analyses were based on firm size, the nature of the performance indicators, and board composition which provide no evidence of influences of these variables too. The evidence derived from the analyses for board leadership structure and financial performance has the same character, i.e., there was no evidence of a meaningful relationship.

Using Tobin's Q as an approximation of market valuation, Yermack (1996) presented evidence consistent with theories that small boards of directors in organizations are more effective. In a sample of 452 large U.S. industrial corporations between 1984 and 1991, the study showed that companies with small boards exhibit more favourable values for financial ratios, and provide stronger chief executive performance incentives from compensation and the threats of being dismissed.

Kyereboah-Coleman (2007) examined the effect of corporate governance on the performance of firms in Africa by using both market and accounting based performance

measures. The findings from the study show that large and independent company boards improve firm value, and that combining the positions of chief executive and board chair has an adverse effect on the organization’s performance. He also found that the tenure of a chief executive enhances a firm’s profitability while intensified board activity negatively affects profitability. Finally, the study suggested that for enhanced performance of corporate organizations, a clear separation of the positions of chief executive and board chair should be maintained, and the use of independent audit units enhanced.

Mang’unyi (2011), looked into ownership structure and corporate governance and its effects on performance of firms in Kenya with reference to banks. The study revealed that there was no significant difference between ownership structure and financial performance, and between banks ownership structure and corporate governance practices. Further results from the study showed that there was great difference between corporate governance and financial performance of banks.

Lee (2008), examined the effect of equity ownership structure on firm finance performance in South Korea. The study focused on two dimensions of ownership structure: ownership concentration (shares owned by majority shareholders) and identity of owners (foreign investors and institutional investors). The findings were that firm performance measured by the accounting rate of return on assets generally improved as ownership concentration increased, but the effects of foreign ownership and institutional ownership were insignificant. Lee (2008) concluded that there was a strong relationship between ownership concentration and firm performance, stating that firm performance peaks at intermediate levels of ownership concentration. The study provided empirical evidence supporting the role ownership structure plays in performance, and thus offering insights to policy makers interested in improving corporate governance systems in an emerging economy.

The business structure and strategy of a bank are deemed to be a very important element in the assessment of a bank’s capacity to perform in the future according to a study by European Central Bank (2010). According to the European Central Bank (2010), sustainable indicators constructed on the basis of economic capital models and financial planning frameworks within the banks may be of important use. They conclude that a good performance measurement framework should incorporate more forward-looking

indicators and be less prone to manipulation from the markets. According to this study, other ways of measuring banks’ performance requires a deeper analysis of the way banks run their business and make use of their stress-testing results, or even enhancement of their high-level discussions with supervisors on consistency between performance and business strategy. Other measures would include reassessment of the risk function with respect to its independence and the available tools and an adequate level of risk awareness at the top-tier management level. This is expected to create an opportunity for regulators to address these issues with bank managers (European Central Bank 2010).

A study by Ferri, Kalmi and Kerola, (2010), using a panel of over 300 banks for 15 years from 19 countries, demonstrated the impact of ownership structure on performance in European banking. They used return on assets (profitability), loan losses (loan quality), and cost-to-income ratio (efficiency). Their rationale for using these measures were that these were standard set of performance variables in banking. Their results contradicted the belief that shareholder ownership is superior to stakeholder ownership in banking. They concluded that there were no significant differences in profitability across ownership classes. They found that co-operatives and publicly owned savings banks outperformed commercial retail banks in terms of cost efficiency and loan losses. They state that there was diversity within the stakeholder-owned banks. The study indicated that diversity of ownership structure is a universal feature of the European banking industry. Together with profit-maximizing commercial banks, most European countries host a significant sector of stakeholder banks such as co-operative banks and or non-profit savings banks. According to them the impact of such diversity is under-researched and hence the need for this study to provide further empirical studies to close the gap.

2.4.5 Informational Systems and M-Commerce Performance

Information systems in the last decade have assumed an increasingly strategic role in organizations. IS helps organizations to conduct their daily activities, functions properly and supports decision making (Altameem, Aldrees & Alsaeed, 2014). In an article by Altameem et al., (2014), they confirm that in the last 10 years, there has been an increase in recognition of information system and its role in the strategy of organizations. In their study they aver that information systems can be regarded as a strategic resource in an organization as it provides the following opportunities; competitive advantage, improvement of productivity and performance and enables new ways of managing and

organizing and developing new businesses. In their article, they conclude that most organizations are in agreement that information systems are important strategic organizational resource that provides strategic advantage and raises organizational performance. They demonstrate from their study that the organization which had a better information system had less problems with their chosen hardware and had smoother and more effective implementation of their plans.

In the period 1990s, scholars questioned the ability of information technology to contribute a firm’s profitability, a phenomenon referred to as the “productivity paradox” (Brynjolfsson, 1993). Studies that have come after these earlier studies have revealed that there are significant effects of IT investments on the productivity and profitability of a firm (Brynjolfsson & Hitt, 1996). Subsequent studies have demonstrated the continual importance of IT to the creation of business value and competitive advantage (Melville, Kraemer, & Gurbaxani 2004). The challenge in having concrete solutions on the impact of IT on firm performance has been linked to the inability to know how to measure dependent variable, that is the IT impact, and how to measure productivity and profitability (Jacks, Palvia, Schilhavy & Wang, 2011). Scholars focusing on IT have generally been driven by a desire to understand how and to what extent the application of IT within firms leads to improved organizational performance (Melville et al., 2004). Other scholars Tallon and Kraemer (2007), are of the opinion that lack of robust firm- level measures and absence of objective measures of IT’s impact are the main challenge in understanding IT’s contribution to firm performance. Based on this, they recommended the use of perceptual measures to measure ITs impact. While Melville et al., (2004) avers that IT like other firm resources does contribute to competitive advantage, Carr (2003) contradicts this by showing that IT resources are commoditized and are no longer hard to imitate or are rare, stating that some IT assets are not difficult to duplicate, such as infrastructure and networking components.

A study by Kim, Song, and Triche, (2015), demonstrates that service innovation literature is growing but lacks frameworks for the management of service innovation. Their study investigated the relationship between internal firm resources and relational capabilities, and how they interact and evolve to generate better service innovation in a dynamic environment. Their main purpose was to provide a framework for future service innovation research in the context of a dynamic environment. In the study discussion, the highlights were on the fact that Resource Based View (RBV) was a static theory and

seemed not to address how firms integrate resources and capabilities in a dynamic environment. The proposal was the use of dynamic capabilities theory. According to the study, the use of dynamic capabilities framework (DCF) was based on the fact that DCF emphasizes on the role of managerial capabilities rather than firm resources which is the main assumption in the RBV. In addition, DCF focuses on the inimitable cross- departmental combination of resources.

According to Kim et al. (2015), though there is a huge number of literature using the RBV of the firm in innovation research, there is little research with solid theoretical backing using the RBV in the context of service innovation. For theoretical framework, Kim et al., (2015) used the RBV and their conclusion was that their study applies most readily to firms providing technology-oriented services, such as firms in telecommunication industry, cloud-hosting providers, and IT consulting companies. Their discussion focused on managerial capabilities that manipulate the resources as well as integrate external resources. They concluded that their study needs to be combined with dynamic capabilities with external resources based on the firm’s current resources in order to generate better service innovation. They are of the view that their framework guides firms to reject inefficient service innovations and suggests how service innovations need to be evolved.

A study by Christensen (1997), sought the reasons why great firms fail. He used the Hard Disk Drive Industry study, terming it as a very dynamic industry with changes in technology, global scope, market structure and vertical integration being unrelenting, very persistent and rapid. The dynamism the Hard Disk Drive Industry, is comparable to the situation in the banking sector and therefore the adoption of this situation to this study brings relevant insights. Christensen (1997), in his study of the hard disk drive industry, highlighted that firms that succeeded, were those that listened responsively to their customers and invested aggressively in technology, products, and manufacturing capabilities in the process, satisfied their customers’ next-generation needs.

Christensen (1997), noted that the incumbent firms were the leading innovators in sustaining innovations in the industry’s history and that there were a few of the disruptive technologies which tumbled the industry’s leaders. Examples included the architectural innovations that shrunk the size of computer drives which offered less of what customers in established markets wanted, which were only valued in emerging markets, far from and

unimportant to the mainstream market. Thereafter, the 8-inch drive makers found that, by adopting sustaining innovations, they were able to increase capacity of their products at a rate of more than 40 percent per year. Consequently, they were able to provide the capacities required for lower-end mainframe computers, growing significantly the unit volumes so that the cost per megabyte of 8inch drives declined below other competing 14-inch drives, and creating apparent competitive advantage.

The conclusion of the study was that whereas the incumbent established firm’s technological prowess in leading sustaining innovations, the firms that led the industry in developing and adopting disruptive technologies were the new entrants to the industry and not the incumbent leaders. The reasoning was that the incumbent organizations were innovative, customer focused, and aggressive in their approach to sustaining innovations but were not as successful as the smaller new starters. The challenge of the incumbent firms was that they were not flexible enough to focus on the downward vision and mobility. The established firms were focusing only on their existing customers allowing therefore new entrant firms to topple the incumbent industry leaders with disruptive technology (Christensen, 1997).

According to Saeidi (2014), in his study on the role of accounting information systems on organizational environment from the perspective of top managers, the use of accounting information systems does not significantly improve the organizational performance of managers. Further, Lipaj and Davidavičienė (2013), in their theoretical study sought to identify tangible and intangible benefits of Information Systems implementation and its influence on business performance and established that information systems can help in identifying and resolving the existing problems and weaknesses of a company by bringing a lot of direct and indirect benefits, therefore increasing the financial stability of a company. Contrary to this study is one by Kharuddin, Ashhari, and Nassir, (2010) from their regression model, which investigated the impact of accounting information system on firm performance of Malaysian SMEs, their study revealed that SMEs that use accounting information systems do increase their firm performance and hence they suggested that SMEs should take the opportunity to acquire accounting information systems to make them more competitive.

A study by Chou, Chuang, and Shao (2014), using a panel data from 30 Organizations of Economic Cooperation and Development (OECD) countries over the period of 10 years

to empirically test hypotheses on information technology (IT) and Total Factor Productivity (TFP) link was carried out. The impact of IT on TFP was assessed through an integrative framework of IT-induced externalities and IT-leveraged innovation, with the aim of reconciling the prediction by neoclassical growth theory with recent observed evidence. They argue that computerization had reshaped the competitive landscape into a network economy with IT-induced externalities that benefit all stakeholders. In addition, they suggest that IT is a platform technology that can leverage innovations to enhance the technological level of production process. They assert that IT-induced externalities and IT-leveraged innovations exert positive impacts on TFP, suggesting IT plays a pivotal role than input consumption and accumulation that neoclassical growth theory assumes for IT. The study illustrated that the measures of IT capital that includes computer hardware, software and telecommunication equipment provided instruments for examining externalities from IT usage while most prior studies mainly focused on computer hardware. The study also highlighted future research gaps to measure ITs contributions at the macro level more accurately and policy makers to cultivate ITs positive impacts on TFP to help sustain long-term economic growth.

Najjar, Hug, Aghazadeh and Hafeznezami (2012), carried out a study whose purpose was to investigate the extent that IT could be used and their effect on firm performance. They used data from 108 small-to-medium sized organizations both in service and in manufacturing. Factor Analysis and MANOVA analysis were employed to analyse the relationships and to find out the optimum points (interaction among the types of IT and types of business process re-engineering BPR and their effect on firm performance. The results indicated that organizations that adapt high technology alone or BPR alone cannot achieve the same result as the organization that benefits from interdependency between IT and (BPR). The organizations that use interdependency of IT in BPR impacts business positively.

Eruemegbe, (2015), in her study on the effect of information and communication technology (ICT) on Organization Performance in Banking industry, found that ICT in the banking services had a positive effect in the development and growth of the organization. From the results, she concluded that ICT leads to efficient and effective performance of banks and leads to competitive advantage over others and thus increases banks profitability.

Aliyu, and Tasmin (2012), in their study on the impact of information and communication technology on banks’ performance, reviewed scholarly and organizational literature regarding the impact of ICT on banks’ performance by examining if banks had successfully achieved effective and high level customer service delivery, through online delivery channel. They concluded that the usage of ICT could lead to lower costs, but the effect on profitability remained inconclusive, owing to the possibility of ICT effects that arise as a result of consistence of high demand of skilled workforce, issues of increasing demand to meet customer’s expectations, trustworthiness of the information system and competition in financial services. From the reviews in their study, there were perceptions that ICT was not significantly accepted by consumers, and that other researchers also found that banks were not also competent in adopting the new technology. The study recommended that further research need to be carried out in different locations for comparison with the previous findings.

A study by Binuyo and Aregbeshola, (2014), was done on the impact of information and communication technology (ICT) and Information and Communication Technology Cost Efficiency (ICTE) on commercial bank performance in South Africa. Annual data of 22 years published by Bankscope-world banking information source was used. From the findings of the study they concluded that the use of ICT increased return on capital employed as well as return on assets of the South African banking industry. In addition, the study results also conclude that the impact of ICTCE on performance of banks was more than that of ICT investment. It was therefore noted that the most contribution to performance came from information and communication technology cost efficiency compared to investment in information and communication technology. Their recommendation was that banks emphasize policies that would enhance proper utilization of existing ICT equipment rather than additional investment.

Monyoncho, (2015) in her study on relationship between banking technologies and financial performance of commercial banks in Kenya, assessed the influence of ATMs, debit and credit cards, mobile banking and internet banking on the financial performance of commercial banks in Kenya. The study concluded that adoption of technologies had a positive influence on the performance of commercial banks in Kenya. The study recommended that commercial banks should continue investing in ICT.

A similar study was done by Aduda and Kingoo, (2012): Gichungu, (2015), seeking the relationship between performances as measured by return on assets and investments in e- banking. They concluded that there was a positive relationship between e-banking and bank performance. Dasgupta, Sarkis, Srinivas and Talluri (1999), undertook a study to evaluate impact of information technology on firm productivity in both manufacturing and service industries. They used a sample of 85 manufacturing and 77 service firms, with a methodology of various data devnvelopment analysis models and non-parametric statistical technique referred to as the Kruskall-Wallis (KW) test. KW test is derived from Mann-Whitney test (Conover, 1980). The difference between the two is that Mann- Whitney test analyzes two samples, while the KW test analyzes two or more independent samples. Their hypothesis was that increasing information technology investment has a positive impact on firm performance in the manufacturing sector. Their results indicated that productivity in the service and manufacturing sectors lagged as investments increased, contrary to their expectation that the information technology investment would have a significant positive effect on firm performance.

A study was carried out by Olugbode, Elbeltagi, Simmons and Biss (2008), on information systems and firm performance and profitability, using a case study approach, for the company Beale and Cole. The results indicated that the organization’s operating practices, business system and ICT infrastructure improved the operational processes and efficiency of the company. This consequently, led to reduced operating and transaction costs, increased turnover and enhanced profitability. The new integrated IT system became a strategic tool for Beale and Cole which thereafter served to sustain growth and maintain competitive advantage making the company one of the leading building services company in the South West region.

A study by Jacks, Palvia, Schilhavy and Wang (2011), sought understanding from several organization-level studies on impact of IT on organizational performance. The study methodology was a Meta-analysis of IS literature from 2001 to 2009. The framework used, categorized measures of the impact of IT into productivity, intangible benefits and profitability, while the antecedents of IT impact were categorized into IT resources, IT capabilities, IT/business alignment and external factors. From the findings of the meta- analysis of 76 articles over a nine-year period (from 2001-2009), and the work of Devaraj and Kohli (2003), they were able to provide a comprehensive 20-year snapshot of the landscape of IT value research. The findings resulted in a framework of three major

dependent variables (profitability, productivity, and intangible benefits) and four major independent variables (Business alignment, resources, capabilities and external factors. Accordingly, they recommended further research into the dependent variable of information systems and IT research.

A study by Makadok (2001) in his study “Toward a Synthesis of the Resource-Based and Dynamic Capability Views of Rent Creations”, found that there was no statistically significant relationship between information technology competence and firm performance. Another study by Ray, Muhanna and Barney (2005) in their study on “Information Technology and Performance of the Customer Service Process: A Resource- Based Analysis”, found that there were no direct effects of any of the three different information technology resources, thus the technical skills of information technology unit, managers’ technology knowledge, and information technology spending, on the performance of the customer service process, which would lead to the overall firm performance.

2.4.6 Human Resources and M-Commerce Performance

Human resources are the employees or workers, or the personnel of a business entity. They are regarded as a significant asset because of their skills and abilities. The resource- based view (RBV) theory illustrates that valuable and rare organizational resources can be difficult to replicate, and thus leading to sustained advantages in organizational performance (Alavi, Wahab, Muhamad & Shirani, 2014). In their study, RBV highlighted the link between strategy, internal resources and performance of an organization. The application of the theory on the relationship between employee and organization, led to the conclusion that positive work perceptions was the rationale of employee doing something in return to preserve the positive relationship with the organization. Methodology employed was structural equation modelling as a statistical method of research.

Based on a study by Vermeeren, Kuipers and Steijn, (2014) who sought to find out if leadership style made a difference, linking HRM, Job satisfaction and organizational performance. The mediating variable between organizational performance and HRM, was the influence of a supervisor’s leadership style on implementation of Human Resource (HR) practices. Vermeeren et al., (2014), used previous studies with a quantitative research approach and performed tests using structural equation modelling (SEM). They

concluded that human resource (HR) practices and organizational performance were weakly related in the private sector. This they attributed to the fact that public organizations are more likely than private organizations to have strong commitment to staff training, trade union, and workforce participation in decision making, promotion of equal opportunities and a concern for the welfare of employees to meet their personal and family needs. Based on the data used by the scholars, they conclude that a positive relationship exists between HRM and organizational performance in the public sector.

Riaz (2015), undertook a study to determine the impact of High Performance Work Systems (HPWS) on organizational performance. The assumption was that HPWS results in staff motivation, job satisfaction and organizational citizenship behaviours, which would result in better organizational performance. The study concluded that the relationships between the firm and staff were attributed to positive impact on the firm’s performance deriving support from social exchange theory.

A study by Pratono and Mahmood (2015), explored the relationship between organizational performance and organizational resources, based on small business (SME) in Indonesia. The study was a contribution to the development of resource-based theory, utilizing the role of dynamic capability. Their findings were that there was no direct effect of business orientation on organizational performance. That reward alone was not sufficient to drive excellent performance, but that other factors such as marketing capability, employee compensation, business cycle time frame and risk aversion (Andersén 2010) would transform the reward philosophy to performance.

Huselid (1995) used Tobin’s Q method in his study to comprehensively evaluate the links between Human Resource practices and firm performances. He showed that these practices have an economically and statistically significant impact on both intermediate employee outcomes (such as turnover and productivity) and corporate financial performance. In his attempt to examine the importance of a firm’s relationship between human resource management and financial performance, Cetin (2010) performed correlation and regression analyses and established that Human resource management does not have any statistically significant effect on a firm’s financial performance. The effect of human resource on financial performance is dwarfed by both marketing and manufacturing performances.

Sels, Winne, Delmotte, Macs, Facms & Forrier (2006), contradicted Celtin (2010) study by stating that there exists a strong effect of intensive Human Resource Management on the profitability of small and medium sized companies while using the structural equation modelling on his study to determine the influence of HRM on operational and financial performances of SMEs. Bussin and Modau (2015) used correlation and multiple regression analyses to argue that it is evident that the relationship between Human Resource remuneration and organisational financial performance has been experiencing a decline. This was attributed to the 2008 global financial crisis due to a shift away from performance-related elements of remuneration contracts by company chief executives, creating a disconnect between CEO remuneration and organisational performance.

Using the descriptive statistics method, Khalumba (2012) in her study to determine the influence of Human Resource Management Practices on the Financial Performance of Commercial Banks in Kenya concluded that the major human resource management practices that affected the financial performance of commercial banks included human resource planning, recruitment and selection, reward management, training and development, career planning and employee relations. Ojo, (2015), in his study on impact of strategic human resource corporate performance in selected Nigerian banks, sought to establish if there was a relationship between strategic human resource practice and corporate performance in the Nigerian banking industry. The study concluded that the strategic human resources practices enhance corporate performance and that there was a positive relationship that existed between strategic human resource practice and corporate performance.

A study on human resource development in relation to performance of the banking industry in Ogun State Nigeria, revealed that significant positive relationship between expenditure on human development and financial performance existed. More than 50 percent of financial performance indicators were attributed to human capital expenditure. The same results also indicated that training programmes have positive effect on the performance of staff. In addition, the results also showed that the new generation banks spent more on human resource development compared to old generation banks. The old generation banks’ trainings and duration were low and most trainings were in-house. (Ololade, Eleyowo, Abiodun, & Olalekan, 2015).

The HRM environment can be an important determinant of productivity in the service sector than in the manufacturing sector, given the much larger share of total production costs accounted for by employment and the much more extensive direct contact between employees and customers in services (Bartel, 2004). Delery and Doty, (1996) in their survey of senior human resource executives in U.S. banks, sourcing for information on human resource policies used for loan officers, used a cross-sectional framework that ignored the role of bank fixed assets and found a positive correlation between bank’s returns on assets and equity and the existence of profit-sharing and employment security for loan officers, controlling for the size and age of the bank.

Another study by Harker and Hunter (2000), indicated that the management of a well aligned technology, human resources, and other assets, plays an important role in the banking industry. This author however discredits previous studies firstly because they were done cross-sectionally, and secondly because the study was done at the headquarters’ level. Their argument is that the managers at the headquarters level can affect the banks performance but much of the banks activities occur at the branch level, hence he focused his study at the branch level. The conclusion of the study was that branch-level performance in the banking industry can be influenced by specific human resource management-related actions.

Most of previous studies focused on relationship between HR and employee performance (Shitsama 2011; Kuvaas & Dysvick, 2010; Shitsama, 2011 & Muli, 2014). Other empirical research also on the relationship between human resource management and firm performance focused mainly on manufacturing. Bartel, (2004), extended his study to the service industry with the justification that it had the majority of workers and studied the banking sector, from a branch level perspective and concluded that the incentive dimension of high performance work system had a positive and statistically significant relationship with branch level performance, which was observed through sales of loans, which was strong under different stipulations. The fixed effects indicated a positive effect of quality of communications, an opportunity to participate in the dimension of a high performance work system.

2.4.7 Strategy Communication and M-Commerce Performance

Strategy Employee Communication (SEC), was a review of strategic communication in the context of work organization from the point of view of employees (Juholin, Aberg &

Aula, 2014). In their study, they argue that previous studies focused on the role of top management and communications managers and undermined the role of the other managers. The aim of their study was to discuss how communication models match the demands of present working life and strategic employee communication and whether the dialogic approach had some contribution to it. They proposed a shift towards an active employee role in strategic organizational communications and they provided three perspectives: a call for a more proactive model of management, the motivation for a more comprehensive use of models of communication and the need for a new model of strategic employee communication, arguing that this bears a repositioning of the concept of organizational communication. They conclude that the basic qualities of responsible dialogue and their relation to former communication models should be examined further and that the role of communications management and HRD should be focused on.

Other studies term communication as consisting of building an effective communication system, defining strategic core messages, and communicating them to a grass root level in such a manner that strategic goals are met Davis, (1962); Likert, (1967) and Grunig, (1992). According to Hallahan, Holzhausen, van Ruler, Vercic and Sriramesh, (2007), Strategic communication has been defined as a paradigm for analysing communication as consisting of purposeful activities of organizations having a mission and targets. Their main purpose therefore was to maintain favourable reputations with all stakeholders in order to harness the strategic interests of the organization.

A study by Florea (2014), emphasized the fact that communication is a skill required in any organization looking to achieve performance in the current dynamic environment. In this study, an organization must have a relevant communication strategy and leaders who can implement the communication strategy. In the study of two organizations, they sought to find out the strengths and weaknesses of communication process with the intention of gaining competitive advantage. The study concluded that there was need to communicate effectively with employees if organizations needed to obtain high-quality products to satisfy customers and to motivate employees.

A study by Henderson, Cheney and Weaver (2014), sought to examine organizational identities and issues related to strategic communication, based on the world’s largest marketer of Kiwifruit and Fonterra, a dairy company. The study was based on a public debate about the uses of Genetically Modified (GM) technologies on farm products. The

study examined the value and policy positions held by managers, farmers, growers and by each organization as an entity in communication with the government, wholesale markets, interest groups and consumers. The study demonstrated the importance of the role of organizational identity in strategic communication and management issues. The Kiwifruit organization, Zespri, embraced a cautious public policy position, while dairy organization Fonterra strongly advocated for the commercial development of GM products. The research utilized a discussion guide and focus groups, and the analysis was based on critical-interpretive approach.

The conclusion of the study was that organizational members would possibly draw on different value premises as they create or defend their identities in relation to GM issues. The study also added that strategic communication in an organization involves explicit and implicit value premises, identifications and representation of identities at all levels. In terms of issues of strategic communication by the management, it was stated that construction of both external and internal communication needs to reflexively represent multiple value premises and manage multiple organizational identities. Further, they point out that strategic communication aims to reconcile perceived differences in policy between the organizational and what members identify with such as professional, social or cultural values that are conflicting and changing. The study reveals the roles of employee identification and organizational identity formation in strategic communication and issues of organizational management while dealing with controversial public policies. The findings show the importance of making clear connections between the “voice of the organization” represented by management issues and members’ identifications and that members may be taken into account in the development of strategic communication.

Defining distinctiveness, a study by Evans, (2015), investigated how views on organizational competition correspond with organizational identity discourse. The study focus was on strategic communication and technological themes. The study sought to provide insights into strategy and communication by examining views between departments in organizations, as well as among organizations. In addition, they sought to find out how competitor networks differ among public media organizations, and among people in similar roles in different organizations. Data was collected through purposive interviews, aimed at four people in each of the 14 organizations targeted. The conclusion of the study showed that the way people talk about competitors brighten perceptions of organizational identity and that this affects how value is placed on strategic choices, for

example in the expenditures of resources. In addition, the study findings were that the theme of distinctiveness has a role in defining organizational identity, in the context of competition and strategic group identity. The study demonstrated that views about competitors provide insights to organizational identity and results in information with regard to strategy which if not focused on, can potentially hamper innovation.

An in-depth discourse on strategic communication was done by Hallan, Holtzhausen, van Ruler, Verčič and Sriramesh (2007). They examined the nature of strategic communication and identified key aspects of communication. The study was based on panel discussion which involved journal’s editors and international scholars at the international Communication Association in New York. The authors opine that in the current competitive and complex world, organizations seek for attention, affinity, admiration and loyalty from customers, employees, donors and investors, governments and the public at large. Communication therefore becomes a deliberate approach to reach the audience with the organization’s goals. The researcher picked one of the discussions in the article by Hallan et. al., (2007) that was relevant to the study. They argue that communication is an essential part of study in strategic management, and rejecting the idea that communication is a study of relationships. Hallan et. al., (2007) l argues that strategic communication focuses on how an organization communicates across the organizational endeavours. They point out that strategic communication is critical to an organization and that it emphasises how an organization functions as a social actor to advance its mission. They show that a big number of corporations in Europe, South Africa, Australia, New Zealand, and North America use strategic Communication to describe their units and the services they offer.

Hallan et al., (2007), also points out that organizations use different methods to influence the behaviours of their staff which range from what they know, how they feel and the ways they act relative to the organization. Another argument, is that people do not differentiate between the various forms of communication an organization might use, but that the organization should drive communications activities from a strategic and integrative perspective (Hallahan, 1999). Internal communication is vital for an organizational management and success, and this was alluded to in the research to understand corporate communication, organizational communication and public relations literature (Invernizzi, Biraghi & Romenti, 2012). The study’s purpose was to discuss the strategic role of internal communication in the development and management of

organizations based on previous studies on entrepreneurial organization. The study methodology was a review of internal communication literature which had been carried out with the intention of systematizing it according to Entrepreneurial Communication Paradigm (ECP). ECP is a framework used to interpret the strategic role of communication in light of entrepreneurial organization Theories. The study used entrepreneurial organizational theories to identify what should be considered strategic in an organization, and thus how internal communication support the identified strategic dimensions. Their findings were that internal communication support the organization in three critical ways; first in developing stronger links with the organizational context and by aligning the role of internal communication adequately, it sustains the organization by helping the organization to stay in touch with its organizational base. Internal communication becomes the gateway and the channel to get closer to employees’ expectations and opinions. This is supported by (Cheney, 2006; Shockley-Zalaback, 2009). The involvement of staff in large corporate decision making, boosts organizational validity and employee’s consensus, and in turn nurture the achievement of the strategic goals of the organization. Secondly, the internal communication, expands the ability of the organization to have staff on board. This therefore would support the organization to build a network among staff who are strongly committed to the organizational goals and vision, and thus translating them into organization’s ambassadors. Thirdly, that internal communication supports managers in helping them develop and carry out their roles more effectively. This is more so when the role of the internal communication bears the organization’s vision and gives voice to the leaders’ decisions, thus assisting them spread the vision within the organization and aiding in gaining staff approval.

A study by Gaither (2012), sought to evaluate the effect of internal communication practices on employee engagement for Kosair Children Hospital (KCH). This was done by testing three constructs: employee forums, weekly meetings and communication portal page. The purpose was to establish if the implementation of internal communication practices would have a significant impact on employee engagement scores at KCH and secondly, when KCH is compared to peer institutions within Norton Health care, whether KCH would demonstrate greater increase in mean engagement scores from 2010 to 2011 as a result of the changed communication practices. The study findings were that there was no significant increase in employee engagement for KCH as a result of the interventions. The report states that there were improvements in mean scores from 2010

to 2011, but the scores were not statistically significant. The illustrations were that the previous mean scores were at 4.0, while the top possible score was 5.0, and therefore there was little room for improvement at a significant level. In regards to the second evaluation, KCH did not have a significant increase in mean engagement scores, though KHC was the only hospital that had a 3 percent increase in their engagement score.

2.4.8 Moderating Effect of Market Turbulence on M-Commerce Performance

Market turbulence is referred to as the changes in tastes and preferences of consumers and the composition of an entire market. A study by Kumar, Subraamanian and Yauger, (1998), sought to establish if market turbulence moderated the relationship between market orientation and performance in the health care industry, where Market Orientation (MO) is the technique or the ability to understand and satisfy customer’s needs (Deshpande, Farley & Webster, 1998). They undertook a survey of 159 hospitals and used multiple regression analyses. The results showed that market turbulence moderated the relationship between market orientation for four (4) out of five (5) performance measures; return on capital, success of new services, success in retaining patients and success in controlling expenses. However, the results also indicated that market turbulence did not moderate the relationship between market orientation and growth in revenue.

A study by Slater and Narver (1994), found that there was no moderation by market turbulence on MO performance. Morah, Wilson and Tzempelikos, (2014), avers that this contradicts the marketing theory whose assumption was that consumer tastes and preferences dictate the consumer buying behaviour. Morah, et al., (2014), in their study on the moderation effects on the MO-performance connubial relationship in a developing world perspective, hypothesized the following: MT moderates the MO- profitability(performance) relations and MT moderates the MO-market share (performance) relations. The results of their findings were that when MT was introduced to moderate the MO-profitability relationship, the relationship was not statistically significant and the relationship was not supported. The introduction of MT to moderate the MO-market share relationship also indicated insignificant relationship and the hypothesis was not supported. The conclusion therefore was that the moderation effect of market turbulence does not hold true for profitability and market share.

In their study, Choi and Lee (2014), investigated the relationship between market orientations and firm innovativeness with the moderating effects of technological and market turbulences. The analysis was done using regression analysis and data was collected from 326 Korean manufacturing SMEs. Their findings were that market turbulence negatively moderated the relationship between market orientation and firm innovativeness.

Kohli and Jaworski (1990), reviewed literature of the previous 35 years with the purpose of theory construct and propositions. The research was based on the lack of a framework for understanding the implementation of the marketing concept. They employed a purposive theoretical sampling plan for their data collection so as to have both marketing and non-marketing manager respondents in consumer, Industrial and service industries. They sampled both large and small organizations. Market turbulence was taken as a moderator, with the role of influencing the desirability of a market orientation. The study hypothesized that: with a greater market turbulence, there was a stronger relationship between the market orientation and the performance of business. The assumption is that when there is a fixed set of customers who have stable preferences, then a market orientation would have little effect on performance because there would be little adjustments required in the marketing mix. The study findings did not support this hypothesis. The results indicated that new products did not originate from the customer, especially in the high-technology industry and that the organizations need to balance between initiating research as well as focusing on customer/market driven products.

Slater and Narver (1994), undertook another study based on the study of Kohli and Jaworski (1990), reviewing the moderation effect of the competitive environment on market orientation performance relationship. Their purpose was to test whether a competitive environment would influence the form and effectiveness of a business’s market-orientation. In their study, they tested hypotheses suggested by Kohli and Jaworski regarding the environmental moderator effects. They however highlight that Kohli and Jaworski’s theory was grounded in executives’ perceptions because, they interviewed executives. The objective of Slater and Narver (1994), was to test whether the executive theory was accurate. They changed the hypothesis as follows: “The greater the extent of market turbulence, the greater the positive impact of market orientation on performance”.

2.4.9 Moderating Effect of Technology Turbulence

Perez- Nordtveldt, Mukherjee and Kedia (2015), undertook a study on the relationship between the effectiveness and efficiency of cross-border knowledge gained from International Business Affiliates (IBAs), and recipient firm’s performance. They suggested that there was a direct and positive effect of effectiveness and efficiency in learning gained from the IBA on firm performance which was moderated by technological turbulence. On the other hand, (Slater & Narver, 1994, Kirca, Jayachndran & Bearden, 2005) found no supporting evidence of moderation by Technological turbulence.

A review of previous studies by Morah et al., (2014), found that some studies showed that there was positive effect of MO on performance (Gaur, Vasudevan & Gaur, 2011, Hau, Evangelista & Thuy, 2013). Other researchers contend that there was non-direct effect of MO on performance, but that the relationship was moderated by environmental factors. In Morah et al., (2014), review of studies by Pulendran, Speed and Widing (2000), they aver that this study supported the moderating role of market turbulence on performance. They found that the study by Rose and Shoham (2002), supported the fact that technological turbulence moderated performance. According to Morah, et al., researchers comprising Slater and Narver, 1994; Kirca, Jayachandran & Bearden, 2005), found no support for the moderation by market turbulence or technological turbulence on performance.

The study by Morah et al., (2014), hypothesized the following: that TT moderates the MO-profitability (performance) relationship and that TT moderates the MO-market share performance relationship. Morah et al., (2014), found that TT negatively moderated the effect of MO on profitability. On the second hypothesis of TT on market share, their finding was that the moderated relationship was insignificant and therefore the hypothesis was not supported. Morah et al., (2014), stated that their findings were similar to MO’s empirical literature (Rose & Shoham, 2002), which states that TT weakens the MO-sales and profit relationships. However, Morah et al states that their findings contradict studies by (Subramanian & Gopalakrishn, 2001). Morah et al., (2015) attributing the results of their finding to the high cost of technological innovation that is prevalent in the Nigerian markets. Morah et al., (2015) avers that although the moderation influence of variables on MO profitability and market share were mixed, their study was inconsistent with Cano, Carrillat and Jaramillo’s (2004), and their assertion was that the relationship between MO

and business performance was positive and consistent worldwide. Morah et al., (2015) findings were similar to other Western countries studies. Their conclusion therefore was that Nigeria business resembled the developed countries. From their study they suggested that the similarity with the developed countries could be attributed to the western influence in Nigeria, which has driven the change in business landscape and customer lifestyle.

In the study by Kohli and Joworski (1990), they hypothesized that: “The greater the technological turbulence, the weaker the relationship between a market orientation and business”. This was not supported as there was no evidence of effects of technological turbulence on the market orientation-performance relationship. They hold that in industries characterized by technological stability, market orientation is important, but in industries with rapidly changing technology, the market-orientation may not be important because in these industries, major innovations will be developed by research and development efforts outside the industry.

Another study by Joworski and Kohli (1992), on the moderating effects of technological turbulence, market turbulence, and competitive intensity, on the market orientation- performance relationship, they report that they did not find evidence of the environmental factors affecting the strength of the relationship. Their findings were that none of the environmental moderators on the relationship between market orientation and performance was significant. The research report stated that because market turbulence meant changing strategies based on changing customer needs, they expected a negative relationship with performance.

Table 2.4 Summary of Selected Studies

Authors Title Objectives Methodology Results

Li, Y., Making Strategy Work: A To review the factors that A survey of 60 articles 9 crucial factors for strategy Guohui, S. literature review on the factors enable or impede on strategy implementation, two approaches of & Eppler influencing strategy effective strategy implementation aggregating and relating relevant J.M (2008) implementation implementation. between 1984-2007 factors

Jing, F.F & Missing links in understanding To clarify the Reviews of existing Research into the leadership- Avery, the relationship between relationship between literature performance relationship is not C.G. leadership and organizational leadership paradigms and conclusive because of issues such (2008) performance organizational as methodological problems based performance. on measurement parameters.

Lin, H-L Organizational structure and Examination of the Sample: 154 Different acquisition strategies (2014) Acculturation in Acquisitions; strategy-structure and acquisitions in require different levels of structure perspectives of congruence strategy-culture that Taiwanese electronics as well as different degree of theory and task would facilitate superior and information acculturation interdependence performance sector.

Bartel A.P. Human Resource Management Investigation of Longitudinal survey of A positive and statistically (2004) & Organizational Performance: relationship between operations of a large significant relationship with branch Evidence from Retail Bnking HRM and establishment bank and extension to performance as measured by sales performance the service sector of loans.

Altaher, A. M-Commerce Service Systems Investigation of Sample: 249 Developing countries have to apply (2012) Implementation technology acceptance respondents in several the technology acceptance model, model statistical in order Jordan banks. in order to help bank staff accept to find new ways that can new services. support involved Managers play a significant role in employees and allows influencing the mobile services in

them to understand the banks through social interaction. m-commerce services in Jordan banks.

Cherotich, Financial Innovations and To establish the effect of Census of all 44 There is a strong relationship M.K., Performance of Commercial financial innovations on banks. Secondary data between financial innovations and San,W. Banks in Kenya financial performance of was used. financial performance. Shisia, A. commercial banks in Financial innovations positively & Kenya affect financial performance. MUtung’u, C. (2015)

Martin S. S Factors Determining Firms’ To analyze factors that Sample: 125 Spanish Technological competence and (2012) Perceived Performance of impact firms’ perceived firms. Structural customer value exert a direct and Mobile Commerce performance in M- Equation Modeling positive effect on performance. Commerce.

Hsiao, J. The relationship between To discuss the impacts of Questionnaire survey, Organizational structure and supply Weng M.C Organizational Structure, organizational structure Target: Semiconductor chain management has significantly (2008) Supply Chain Management, and and supply chain Industry. Factor positive impact on supply chain Organizational Performance: a management on analyses, regression a management Study on the Semi-conductor organizational and canonical Industry performance, correlation analyses

Lekovic, The relationship between To explore the Purposive research, Statistical significant differences B., & Communication Practice and differences between statistical techniques, between private and local Berbe, N. Organizational Performance in private and local descriptive statistics, companies with clear (2014). Organizations from Europe organizations’ chi square and t-test. communication. performances in the context of the existence and development of communication practices.

2.5 Development of the Hypothesis. While several literatures abound on mobile commerce context (Ballocco, Mogre, & Toletti, 2009; Mallat & Tuunainen, 2008), the main focus on m-commerce has been on analyzing the consumer. Studies from the perspective of the firm are limited (Martin, 2012). Previous studies explored predecessor’s and determinants of mobile commerce acceptance or perceived performance of mobile commerce from the organization’s perspective (Martin, 2012). Studies by Wu et. al., (2003), indicated that there was a positive relationship between electronic commerce acceptance and firm performance. Studies that evaluated performance in banks, analyzed financial performance measurements such as ratios; return on asset (ROA), return on equity (ROE), net interest margin (NIM) Oyewole, Abba, El-maude, & Gambo, (2013); Ngumi, (2013); Ruparelia, (2015). There is no existing framework of measuring m-commerce performance. This study sought to propose a model for measuring the performance of m-commerce in the banking sector using the following variables: growth of new applications, growth of m- commerce users, growth of bank accounts and growth of savings. This study therefore used organizational performance in attempting to build a framework for future studies on m-commerce performance. This study therefore sought to investigate the relationship between strategy implementation and m-commerce performance in order to determine the factors that lead an organization to achieve high performance in m-commerce.

2.6 Chapter Summary This chapter discussed literature on strategy implementation theories and models, and the relationship between strategy implementation and m-commerce performance. Theoretical literature review covered the following; Agency Theory, Resource Based View Theory, Expectancy Theory and Activity Theory. Agency theory is premised on the assumption that the top management’s interests are different from the strategy implanters’ (manager’s) interests. Resource based view theory is premised on the assumption that an organization would have competitive advantage over its competitors through the acquisition of unique resources and capabilities, while expectancy theory is premised on the assumption that employees have different sets of goals and can be motivated if they believe that there will be a positive correlation between efforts and performance. Secondly, the literature reviews also presented the models of strategy implementation which were: 7S of Mackinsey, Higgins’ 8S Model, Bourgeios and Brodwin Modelsand Okumus Model. These models were of differing orientations in terms of their perception

of effective strategy implementation factors. The conceptual framework used in this study links strategy implementation variables (organizational leadership, organizational structure, information systems, human resources and strategic communication) with M- Commerce Performance (growth of new applications, growth of m-commerce users, growth of bank accounts, growth of savings) and moderating variables (Technological Turbulence and Market Turbulence).

The empirical literature review presents existing evidence on the relationship between strategy implementation and performance. The existing literature shows that empirical studies examining the relationship between strategy implementation and performance have generated mixed results. Some results demonstrate a positive effect of strategy implementation on performance, while others report a negative association between strategy implementation and performance. All the literature reviewed showed that previous scholars concentrated on financial measures in evaluating organizational performance. Studies on carried out on m-commerce were mainly from the consumer perspective, on adoption and security issues. From the literature reviewed also, it has been found that few studies have been done on innovations and m-commerce in the banking sector, but have not been linked to strategy implementation and performance. This study therefore intends to fill the identified gaps in literature by studying the influence of strategy implementation on m-commerce performance in Kenya’s Commercial Banks.

CHAPTER THREE

3.0 RESEARCH METHODOLOGY

3.1 Introduction

In this chapter, research methodology used for data collection and to test the hypotheses is presented. Research methodology is a tool that explains the technical procedures used in the collection, processing and analysis of data stated in a manner that is understood by target audience (Zikmund, Babin, Carr & Griffin, 2010). The chapter contains the following: research philosophy, research design, target population, sampling design and data collection methods, research procedure, reliability and validity.

3.2 Research Philosophy

According to Galliers, (1991), research philosophy is the belief on how data about a phenomenon is gathered, analyzed and used. Saunders et al., (2009), refers to research philosophy as an over-arching term relating to the development of knowledge and the nature of that knowledge. Various philosophies of research approach encompass: Epistemology (what is known to be true) and doxology (what is believed to be true). Science becomes the process of transforming doxa to episteme. Saunders et al., (2012) defined research philosophy as a way in which data of a certain phenomenon is gathered and analyzed. Three types of research philosophy were identified: interpretivism realism and positivism.

Positivism research philosophy can be defined as approaches of research that employ empirical methods, making use of quantitative analysis, or develop logical calculi to build formal explanatory theory (Saunders et al., 2009). The positivism philosophy assumes that reality is unchanging and can be witnessed and defined without altering the phenomenon itself (Cooper & Schindler, 2010). The proponents of this research philosophy argue that a researcher can test existing theories by observing social realities and can make predictions. The rationale for using this approach is that the observations are deemed to be repeatable and predictions can be made on the basis of previously observed and explained realities and their inter-relationships. To test the hypotheses, the researcher developed appropriate measurements of people and system behaviors.

Interpretivism research philosophy assumes that people develop the meaning of a condition or phenomena based on their memories, experiences and expectations. Interpretivist contend that reality can be fully understood through the subjective interpretation and intervention. Interpretivist believe that the study of phenomena in their natural environment is key to the interpretivist philosophy. The interpretations of a reality would vary from person to person because people have different memories, experiences and expectations. Thus proponents of interpretivism emphasize on the need to understand factors that influence people enabling them to understand reality. Though this research philosophy enables the researcher to understand the reality behind the current situation as it is highly contextual thereby limiting its ability to generalize findings (Eriksson & Kovalainen, 2008).

Realism research philosophy, relates to scientific enquiry. What realism philosophy believes is that realism is that what the sense expresses as reality is the truth: that objects have an existence independent of human mind. This therefore means that realism is opposed to idealism, the theory which states that only the mind and its contents exist (Saunders et al., 2009). Saunders et al., (2012) noted that realism research philosophy assumes that reality exists independent of human consciousness. This implies that peoples’ perception of reality is determined by social objects that are internal and external to them. Therefore, it requires an understanding of factors that influence peoples’ behavior and views in order to understand their socially constructed meanings and interpretation.

This study is based on positivism research philosophy since positivism research philosophy assumes that one can test hypothesis without interfering with the phenomenon itself (Saunders et al., 2012). According to Cohen and Crabtree (2006), a positivist approach to research is based on knowledge gained from positive verification of observable experience rather than introspection or intuition. Bryman (2001) and Levin (1997). The current study examined the relationship between strategy implementation and M-Commerce performance in Kenya’s commercial banks without meddling with the banks’ strategy implementation. This would enable one to not only generalize the findings but also predict future relationships.

100

3.3 Research Design

Research design can be defined as the general plan of how the research questions were managed by the researcher. (Saunders et al., 2012). It is the plan for selecting the sources and types of information used to answer research questions. Research design provides a general plan of answering questions such as; to what extent does leadership, organizational structure, information systems, human resources and strategy communication in strategy implementation affect performance of M-Commerce in commercial banks in Kenya. The importance of research design is that it formulates the proper framework within which the research work is carried out.

Three main types of research designs exist and these are; descriptive, exploratory and explanatory research designs. The descriptive research design attempts to answering questions such as what, when who, or how much is the problem. This research design provides a narrative of the problem. On the other hand, explanatory research design seeks to establish causal relationship between variables while exploratory research design seeks to find new insights into a phenomenon. The study used cross-sectional survey, descriptive and explanatory design. According to Sekaran and Bougie (2009), there is no singe appropriate design, therefore the use of these two designs was to achieve the optimal results.

The study sought to establish the relationship between M-Commerce performance and strategy implementation in commercial banks. The researcher identified explanatory research design as an appropriate methodology for the study. The rationale for using explanatory design for this study was to obtain information about the link between strategy implementation variables and the dependent variable. To further explain the cause and effect of the relationship between the independent and dependent variables, causal research design was also used. As opposed to the information observation through the descriptive research design, causal research design assists in deciphering whether the relationship is causal through the analysis of data collected. Causal research aids in understanding which variables are the cause and which variable are the effect and helps to determine the nature of the relationship between the causal variables and the effect to be predicted. In the study, the causal research was used to complement the descriptive research design. This is because descriptive research design aims to build the overall picture to identify, describe and provide quantitative image of the study, but does not

101

provide the answer to the “why” of the actual happening. The study used descriptive research design to answer the what, who, when and how regarding m-commerce strategy implementation. The rationale for using descriptive research design, was because the study gathered quantitative data that described the constraints to strategy implementation in Kenya’s commercial banks.

3.4 Target Population Population would be defined as a group of actions or things, individuals who have a common discernable characteristic (Mugenda & Mugenda, 2008). The target population, also termed as the unit of observation, for this study were the 40 licensed commercial banks in operation as at the ending of December 2015. The rationale for using all the operating commercial banks was to ensure that all banks’ views were represented in the final results. In addition, by selecting all the 40 commercial banks, the non-response rate would be limited. Commercial banks in Kenya was chosen for the study because in comparison to other m-commerce users, commercial banks are adopting m-commerce as a strategy in a structured, preplanned and deliberate design and employing mobile channels with a clear business focus. Commercial banks are using m-commerce to generate additional revenues by offering value-added, innovative mobile financial services, whilst retaining and extending their base of technology-savvy customers (Tiwari, Buse & Herstatt, 2006). The units of analysis for this study comprise of five managers from different management levels of all the 40 commercial banks in Kenya. The rationale for choosing the selected managers was because they were responsible for the strategy implementation and this team is assumed to have a clear understanding of how strategy implementation would influence M-Commerce performance. In addition, these are the staff that monitor the implementation in their various business units.

3.5 Sampling Design

According to Statistics Canada 2015, sample design is a method of selecting experimental or subset of units from a target population that can be used to make inferences about the entire population. The choices in sample design are influenced by among other factors; the desired level of precision, detailed information required, availability of the correct sampling frames, variables for stratification, sample selection, the methods of estimation to be used, resources and budget. The study sampling design entails the following: sampling technique, sample size and sampling frame.

102

3.5.1 Sampling Frame

Sampling frame consists of all elements or population that the researcher wishes to study (Saunders et al., 2012). The sampling frame of this study consisted of all Kenya’s commercial banks licensed by Central Bank of Kenya and operational as at December 2015. According to Panneerselvam (2006), sampling frame is a complete list of all members or units of the population from which each sampling unit is selected. The focus of this study was the strategy implementation senior management estimated at 200 taking 5 respondents from each commercial bank. The justification for using all commercial banks as a sampling frame was based on the availability and ease of access to the banks. In addition, the research used quantitative research design and probability sampling technique which would allow the study to make statistical inferences which can be generalized to other sectors.

3.5.2 Sampling Techniques

A sampling technique can be termed as the process through which the units of the sample are selected. Sampling techniques can broadly be divided into two categories namely; probability or non-probability sampling. For probability sampling, all elements have equal chances of being selected while non-probability sampling indicates that estimating the probability of an element being included in a sample is difficult. Types of probability sampling include stratified random sampling, simple random sampling, systematic random sampling, cluster random sampling and mixed /multi-stage random sampling. Non-probability sampling techniques include purposive sampling, self-selection sampling, convenience sampling, quota sampling snowball sampling (Saunders et al., 2012). This study used stratified sampling techniques and selected managers from the following levels: Senior management, middle-level management and lower level management. Stratified sampling technique was used because the respondents were from three different management levels in the bank. This therefore means the respondents from the different levels within the target population, would have different perceptions regarding strategy implementation and m-commerce performance. The rationale of using stratification is because stratification reduces error of estimation and provides better results. Strata stratified sample produces results that are reliable if measurements within the strata are is different within themselves and homogeneous internally (Barreiro &

103

Albandoz, 2001). Stratifying data in a detailed careful manner, improves precision for a study estimates of population qualities (Fiendber, 2003).

3.5.3 Sample Size

A sample is a subdivision of the population (Saunders et al., 2012). Literature shows that there are various formula for calculating sample size, however each formula has its own advantages and disadvantages. This study adopts sample size formula developed by Yamane (1967) since it is easy to use and is scientific. (Yamane, 1967) sample size formula requires the researcher to know the target population and the precision error to be used. This study identified a population of 133 and used a precision error of 0.05. Thus the sample size for this study is 133 as shown in table 3,1 and in the equation 3.1.

N n = . … … … … … … … … … … … . . … … … … … … … … … … … … … … … … . .3.1 [1 + N(e2)]

Where; n is the sample size,

N is the target population and e is the precision error.

Based on Yamane (1967) formula, a target population 200 and a precision error of 0.05 (95% confidence), the sample size was estimated as 133. Thus the sample size for this study was 133 as shown in table 3.1 and in the equation 3.2.

Table 3.1 Population, Sample Size and Precision Error

Population Size Precision of Error Sample Size

200 0.05 133

200 n = ≅ 133. … … … … … … … … … . . … … … … … … … … . .1.2 [1 + 200(0.052)] 3.6 Data Collection Methods

Data collection method is defined by Burns and Gove (2003) as a logical way of gathering information pertinent to specific research objectives or questions. Burns and Grove 2003, advocate that with regard to the study, data can be collected in several

104

methods with consideration to the attainment of the research objectives being accomplished. The data for this study was administered through a questionnaire. A questionnaire is concrete, definitive, and the questions are readily determined, and are presented in exactly the same phrasing, language and in the same order to all respondents (Cooper and Schindler 2014), Use of questionnaire is preferred because it is simple to administer and relatively cheaper to analyse in contrast to unstructured questionnaire.

3.7 Research Procedures

The research procedure is how the study data was collected. Data collection using questionnaire, was achieved through the recruitment of three qualified research assistants who undertook the data collection. The research assistants were trained on best methods of data collection and how to approach the bank managers. The research assistants were fully supported in terms of finances to facilitate their transport, telephone credit and lunch. Primary data was collected using the questionnaires administered to senior management of the banks. The research assistants made follow-up of the questionnaires till they were all collected. The subsequent section illustrates the procedures followed during the data collection.

3.7.1 Pilot Study

Pilot study is described as a replica and rehearsal of the main study Kombo and Tromp (2009) and Kothari (2004). The pilot testing aided the researcher to ensure that the questionnaire obtained the required results (Dawson, 2002). In essence, pilot studies are a smaller version of a larger study that is conducted to prepare for the main study (Polit, Beck & Hungler, 2003). Polit et al., (2003), gives the purpose of the pilot as to test protocols, data collection instruments, sample recruitment strategies and not to test the study hypothesis. The purpose of the pilot study is to provide any advance warning of possibilities where certain types of research techniques could fail. The process of piloting the study can therefore be of value for testing the feasibility of the instruments and the process itself. The pilot study being smaller than the main study, means the number of units and the duration is less in comparison to the main study. Before the main data was collected, reliability was tested through 25 respondents, who were randomly selected from commercial banks. These bank staff were not included in the final study sample to avoid response bias by completing the same questionnaire twice. According to Cooper and Schilder, (2011), 5 to 10 percent of the target sample is required for the pilot test and

105

therefore fourteen was adequate for this study. The pilot study consisted of participants similar to those that had been expected to participate in the main study. Pilot study pre- tested the instruments to determine their clarity and possible unseen problems which would have occured during the actual study. It included the analysis of the data collected in accordance with the procedures laid down for the main study. After the questionnaires were received, they were coded and the data entered into a computer package where data validation was conducted. The collected data was analysed using Statistical Package for Social Sciences (SPSS) version 22 to test the reliability and validity of the research instrument in gathering the data required for purposes of the study. The pilot results indicated that the questionnaire was reliable and therefore, the researcher commenced on data collection for the main survey.

3.7.2 Reliability of the Instruments

Reliability is the measure of the degree to which the research instrument yields the same results of data, after repeated trials (Mugenda & Mugenda (2008). The purpose of developing and validating the research tool, is mainly to reduce error in the measurement process. Reliability estimates are used to evaluate the stability of measures administered at different times to the same individuals or to different individuals using the same standards. Stability of measurement, is determined by administering a test at different points in time to the same individuals and determining the correlation or strength of association of the two sets of scores. Notably, the timing of the second administration is very important especially when the tests are administered repeatedly (Meeker & Escobar, 2014). The internal consistency, reliability of the questionnaire was estimated using Cronbach alpha. Cronbach’s alpha is a function of the average inter-correlations of items and the number of items in the scale. Holding all things constant, the greater the number of items in a summated scale, the higher Cronbach’s alpha tends to be and thus when a single item is used to measure a construct, the results would not be optimal, hence the data become unreliable. Therefore, multiple items to measure a construct helps in the determination of reliability of measurement and, improves the reliability or precision of the measurement (Saunders et al., 2014)

The values for Cronbach’s alpha for strategy implementation and m-commerce performance sub constructs; organizational leadership, organizational structure, information system, human resource and strategy communication were 0.737, 0.694,

106

0.780, 0.681 and 0.891 respectively. All the Cronbach’s alpha values were equal or greater than 0.7. This finding suggests that m-commerce strategy implementation was reliable. The Cronbach’s alpha for technological turbulence sub constructs; technological turbulence and market turbulence were 0.668 and 0.778 which are equal or greater than 0.7. This result suggests that technological turbulence and market turbulence were reliable. The Cronbach’s alpha for m-commerce performance was 0.957. These values are equal or greater than 0.7 indicating that firm performance was reliable. According to Götz et al., (2010), indicator reliability validates that the indicator variance is explained by its reflective constructs.

3.7.3 Validity of the Instruments

Validity refers to the accuracy and meaningfulness of inferences made based on results obtained (Mugenda & Mugenda, 2008). This study adopted both construct validity and content validity to measure the validity of the instruments used. To test for construct validity, the questionnaire had sets of questions in different sections to ensure that each section could assess information for particular objectives and linked to the conceptual framework. Content validity enables data being collected to be reliable in representing the specific content of a particular concept. An instrument that would yield valid data was designed and then exposed to subjects of similar samples; inferences were then made and compared to the existing theories. Content validity was established on three levels, where the researcher considered each item to see if it contained real representation of the desired content and to see if it measured what it was supposed to measure. The instruments of the research were presented to a selected teaching staff from USIU-A business school to ensure content clarity (Schutt, 2012). The study tested for content validity by administering the questionnaire to random students and professors in the field of strategic management; to evaluate the applicability, clarity, relevance, meaning and appropriateness of the content, for and adequacy of construction of the instrument from a research perspective. The respondents provided invaluable information which was evaluated and incorporated to enhance content and was used to improve the questionnaire boosting the probability of achieving relevant answer and information for the study.

3.7.4 Administration of the Instruments

The researcher supervised and supported the research assistants in booking appointments with the managers of the banks and sent the research assistants to drop and pick the

107

questionnaires. In addition, the researcher supervised data entry and validated data in SPSS to ensure high quality data was collected.

Questionnaires were used for primary data collection of strategy implementation and m- commerce performance in commercial banks in Kenya. Questionnaires are defined by Schwab (2005) as measuring instruments that are used to ask individuals to answer a set of questions. To this end, the study used questionnaire to gather information from the respondents. The questionnaire was divided into three sections as follows; demographic information of the respondents, m-commerce strategy implementation and m-commerce performance. The questionnaire used five point Likert scale where the lowest point indicated small extent while the highest point indicated large extent.

Questionnaires were used because the required respondents were many and widely dispersed. To reach all the respondents through face to face interviews would take extremely long for the study to be completed. The use of questionnaire is deemed to be cheaper than personal intervieing and facter in achieving the target respondents (Mathers, Fox & Hunn, 2009). Questionnaires are good tools for acquiring information on public knowlegde and perceptions. Questionnaire was used because as a tool, is a fundamental instrument which does provide valuable information with respect to any topic under investigation (Bulmer, 2004).

3.7.5 Ethical Considerations To comply with the ethical standards for the study, the researcher obtained research clearance and a written permission from United States International University-Africa to conduct the research. According to Belmont Report of 1974, there are three basic ethical principles relevant to research and these are; respect for persons, beneficence and justice. For the individual’s respect, the researcher ensured that individuals were treated as autonomous agents, the respondents were provided with basic information about the study to gain their consent to participate by completing the questionnaire. In compliance with the ethical principle of beneficence, that is minimizing harm and maximizing benefits, the design of the questionnaire was done in such a way that it did not pose any psychological, professional or physical risk to the respondent. The researcher ensured that the respondent had received a full disclosure of the nature of the study, the risks, benefits and alternatives, with an extended opportunity to ask questions. The researcher ensured that

108

the respondents answers could not lead to losing their jobs or jeopardizing their position in the bank in any way. The researcher maximized the potential benefits emanating from the research, whilst minimizing any potential doubt or concerns from the respondents.

3.8 Data Analysis Methods

Data analysis tested the hypotheses and presented the interpretation in a systematic way (Saunders et al., 2012). The study utilized the inferential and descriptive analytical techniques.

3.8.1 Descriptive Analysis

The inclusion of descriptive data analysis had a sole purpose of relating various patterns of the important variables. Descriptive analysis is mainly viewed as a preliminary phase for quantitative analysis (Trochim, 2006). Descriptive analysis was used in this study to analyze quantitative data by use of means, mode, standard deviation and frequency distribution.

3.8.2 Inferential Analysis

The inferential analysis used in this study, was composed of factor analysis, correlation analysis, Chi square analysis, Analysis of Variance (ANOVA) and Structural Equation Modeling (SEM). Data analysis methods are discussed in the next sections.

3.8.2.1 Factor Analysis

Factor analysis is a process for gauging the existence of linearly correlated variables to unobservable factors (Cooper & Schindler, 2014). Factor analysis was used to reduce the items of each dimension of strategy implementation and m-commerce performance into few but items that were strongly related. Principal component analysis with varimax rotation method was used to identify the components that were heavily loaded to the construct. The measure of sampling adequacy (Kaiser Meyer Oklin (KMO) and Bartlett’s Sphericity test was used to estimate the appropriateness of factor analysis in this study. The values of KMO greater than 0.5 indicated that the construct could be factor analyzed while a significant Chi square for Bartlett’s test confirmed the need to conduct factor analysis (Greene, 2012). The constructs that were factor analyzed were; leadership, organizational structure, information systems, human resources and strategy communication in strategy implementation and m-commerce performance. The heavily

109

loaded items were later used to generate summated scores that were used in correlational analysis and SEM.

3.8.2.2 Correlation Analysis

Green (2012) defines correlation analysis as a process of evaluating the relationship between variables. Correlation analysis was used to test the strength and direction of the relationship between organizational leadership, organizational structure, information systems, human resources, strategic communication, and m-commerce performance.

3.8.2.3 Analysis of Variance and Chi Square Test

According to Greene (2012), ANOVA is used to test the mean difference between two groups. The study conducted ANOVA to test the relationship between each dimension of strategy implementation and m-commerce performance. The study conducted ANOVA for organizational leadership, organizational structure, information systems, human resources, strategic communication on m-commerce performance. To find out the influence of each dimension of strategy implementation on m-commerce performance, the study used SEM (Greene, 2012). The study also utilized Chi Square analysis to determine the association between organizational leadership, organizational structure, information systems, human resources, strategic communication, and m-commerce performance in Kenya’s commercial banks

3.8.2.4 Structural Equation Model

Structural Equation Modelling (SEM) is a linear, cross-sectional statistical modeling technique. SEM is represented by factor analysis, path analysis and regression. SEM was used in this study to determine validity of the model as a confirmatory technique rather than as an exploratory technique. SEM takes into account the interrelations between variables and produces estimates that are unbiased. This study estimated the equations where m-commerce performance was the dependent variable while the independent variables were organizational leadership, organizational structure, information systems, human resources, strategic communication, technology and market turbulence and their interactions. The coefficients of each path in the SEM indicated the magnitude of the effect of the particular dimension of strategy implementation on m-commerce performance and the t values for each coefficient were used to test the statistical

110

significance. The t values greater than or equal to 1.96 indicated that the coefficient was statistically significant.

3.9 Chapter Summary

Chapter four presented the study research methodology. The chapter identified positivism research philosophy, explanatory and descriptive research designs as the philosophy and design used. In addition, the chapter highlighted target population, data collection methods, sampling design, and research procedures. Finally, the chapter presented the methodology for data analysis. The study identified factor analysis, correlation analysis, Chi Square analysis, ANOVA and SEM as methods that were used for data analysis.

111

CHAPTER FOUR

4.0 RESULTS AND FINDINGS

4.1 Introduction

Chapter four present results of the statistical analysis and findings based on each of the study objectives. This chapter presents the analyses conducted to test the conceptual model and reports the results of this study. Descriptive statistics was used to analyze the data and presented using tables, charts and figures. This section contains the details of: response rate, sample characteristics, presentation of data analysis, interpretation, measurement and structural model estimation using PLS regression and discussion of findings. Organization of data presentation is based on objectives and respective hypotheses tested.

4.2 General Information

The key purpose of the study was to establish the relationship between M-Commerce Performance and strategy implementation in Kenya’s Commercial Banks. The factors under this study were organizational leadership, organization structure, information system, human resources and strategy communication. The study sought to determine the direction, strength and significance of the relationships between strategy implementation and m-commerce performance.

To collect the primary data, 210 questionnaires were issued and 178 were returned which represented 84.76 percent response rate. The study had sought to collect data from 210 managers from all 40 commercial banks that were licensed and in operation as at the end of December 2015. Ahmed and Uchida (2005), undertook a study in the banking industry in Japan and Bangladesh and had a response rate of 80.2 percent. In addition, according to Graham (2005), a response rate above 50 percent of the total sample size is justified for gathering of sufficient data that can be generalized to represent the opinions of respondents in the target population of the study. This study’s response rate of 84.76 percent was therefore deemed acceptable. Details of the respondents is presented in the subsequent sections:

112

Table 4.1 Response Rate

Sample size Percentage (%) Returned questionnaires 178 84.76 Un-returned questionnaires 32 15.24 Total 210 100

4.2.1 Demographic Characteristics 4.2.1.1 Gender The study sought to determine the response rate by gender and the study found out that female response rate was slightly higher at 52 percent (n=70), while the male response rate was 48 percent (n=64).

Respondents by Gender

Male , 48

Female, 52

Figure 4.1 Respondents by Gender

4.2.1.2 Respondents by Age Group The study found that age group 30-39 years (n=87) represented 49 percent while ages 40- 49 (n=74) represented 42 percent, age 21-29 where (n=9), was 5 percent and above 50 years where (n=8), represented 4 percent. Staff above 50 years was a small percentage and this can be attributed to the banks’ periodic realignments which typically result in the exit of older employees through either individual Voluntary Early Retirement (VER) or employer initiated early retirement. The findings suggest that majority of bank staff

113

involved in strategy implementation are individuals that are below 50 years of age as indicated in table 4.2.

Table 4.2 Respondents by Age Group

Main Factor Factor Level Frequency Percentage (%) Age 21-29 years 9 5 30-39years 87 49 40-49years 74 42 Above 50 years 8 4

The study found that 95 percent of the respondents were above 30 years and this indicates that the managers who responded to the questionnaire, had significant experience and therefore the feedback received from them can be relied on for accurate decision making.

4.2.1.3 Position in the Bank Out of the total respondents, 91 percent were top management and middle management. The entry level management was low at 9 percent. The study findings indicate that strategy implementation cuts across different levels in the bank, with the majority being in the top and middle management. The findings also imply that the information gathered was authentic and could be relied upon to make justifiable judgement. A breakdown of respondents by position in the bank is illustrated in table 4.3.

Table 4.3 Position in the Bank

Main Factor Factor Level Frequency Percentage (%) Position in the Bank Top management 76 44 Middle management 82 47 Entry Level Management 16 9

114

4.2.1.4 Educational Level The study evaluated respondent’s educational levels and the findings showed that a majority of the respondents at 66 percent had attained a master’s degree, 24 and 8 percent had attained bachelor’s and doctorate degrees respectively, while only 1 percent had a diploma and below. The high educational levels can be attributed to respondent’s desire to upskill themselves in order to be competitive in the current workplaces that are mainly driven by information, global competition and knowledge. The high level of education is seen as a desired tool that enables staff to have relatively high accepted level of knowledge that is expected to enable them drive the strategy agenda in the banks. This tends to support the continual changes in workplaces as organizations and staff strive to remain competitive. Table 4.4 provides educational levels of the respondents.

Table 4.4 Educational Level

Main Factor Factor Level Frequency Percentage (%) Education Level Doctorate Degree 15 8 Master’s Degree 118 66 Bachelor’s Degree 43 24 Diploma and below 2 1

4.2.1.5 Work Place Experience The study sought to understand the work place experience of the respondents based on duration of work and found that 57 percent had worked between 6-10 years and 28 percent had worked for the period between 11-20 years. On the other hand, only 15 percent have work experience of under 5 years. The finding suggest that majority of the respondents have been in the bank during the growth period in the industry which has continued since 2003. The respondents that have joined banks in the last 6-10 years, shows that the banks have been recruiting more staff to oversee the technological and other market driven changes in the industry. Details are presented in table 4.5.

115

Table 4.5 Work Place Experience

Tier Group Frequency Percent (%) Tier 1 30 17 Tier 2 53 30 Tier 3 95 53

4.2.1.6 Bank Tier Group

In the quest to establish the number of respondents based on the bank tiers, finding was that most respondents were from tier 3 at 53 percent while tier two and tier one had response rates of 30 and 17 percent respectively as indicated in table 4.6.

Table 4.6 Bank Tier Group

Main Factor Factor Level Frequency Percentage (%) Work experience 0-5 years 26 15 6-10 years 100 57 11-20 years 50 28

4.2.1.7 Account Transfers Results on table 4.5 show responses on statements regarding cash account transfers using the phone. The highest at 83 percent are respondents who use the phone to transfer cash from their individual accounts to another bank. This finding illustrates that the respondents are using the phone for convenience, the phone shortens the process of having to withdraw cash from one bank and go to another bank to deposit for various purposes including depositing for parents or dependents. Seventy percent is second in size are the respondents who use their phones to transfer cash from their individual bank accounts to purchase airtime. Respondents who use the phone to transfer cash from their own bank account to their phone and to another person’s phone were 50 and 40 percent respectively. The findings demonstrate that the usage of phone for account transfers at between 40 and 83 percent is high and this could be attributed to the convenience the process provides. The respondents being bank staff could be termed as a busy group of people and therefore, the use of the phone provides convenience. Table 4.7. presents the results.

116

Table 4.7 Account Transfers

Account transfers Yes (%) No (%) From own bank account to own phone 50 50 From own bank account to another person’s phone 40 60 From own bank account to another bank 83 17 From own bank account to Airtime top up 70 30

4.2.1.8 Pay Bill Merchants Respondents who use the phones to pay their bills are highest for DSTV at 60 percent followed by Zuku at 45 percent and KPLC, Jumia Shopping, and Nairobi water at 37, 36 and 31 respectively. From the findings, usage of the phone is not highly used, however, the usage by DSTV respondents at 60 percent, is an indication that utility payments using the phone has a high potential. Table 4.8 presents the details of pay bill merchants.

Table 4.8 Pay Bill Merchants

Pay Bill merchants Yes (%) No (%)

DSTV 60 40

KPLC 37 63

Jumia Shopping 36 64

GO TV 24 76

Star Times 26 74

Nairobi Water 31 69

Zuku 45 55

4.2.1.9 Mobile Banking Services On the mobile banking services, 59 percent of the respondents are aware and use the information alert on financial services. 58 percent use agent banking services and 60 percent of the respondents do not use the phone to load any of their bank cards. These findings illustrate opportunities lost by the bank in that the respondents not using the phone to load their cards, are representative of the bank customers. This could be attributed to lack of awareness or lack of trust in the processes. Details of mobile banking services are presented in table 4.9.

117

Table 4.9 Mobile Banking Services

Yes (%) No (%) Agent Banking 58 42 Information Alert 59 41 Load Card 40 60

4.2.1.10 Value Add Products Of all the value add products in table 4.9, usage response rate was low at below 50 percent. This could be attributed to lack of awareness of the products or the perception of the respondents on the products being an actual value add to them as individuals. The most utilized of the value add products are other market stock and performance of the bank shares at 48 and 47 percent respectively. When this behavior is generalized to the external population, it would mean that customers may not be aware of the products and therefore may not utilize the existing value add products. Usage of value add products is presented in table 4.10. Table 4.10 Value Add Products

Yes (%) No (%) Personal Finance Management 42 58 Monitoring and Transfer Services 41 59 Performance of the Bank shares 47 53 Other Market Stock 48 52 Forex 45 55 Business news 29 71

4.2.2 Normality Test

Normality test is a prerequisite evaluation for statistical tests because normal data is an underlying assumption in parametric testing. Statistical tests make an objective judgement of normality. The normality test for this study was done by examining its skewness and kurtosis (Kline, 2005). Normality of response variable enables the application of statistical analysis such as partial least squares estimation methods. Test for normality was done as part of exploratory data analysis. This process was to aid in the selection of appropriate estimation method in SEM. A variable with an absolute skew-index value

118

more than 3.0 is extremely skewed and on the other hand, a Kurtosis index greater than 8.0 is an extreme Kurtosis (Kline, 2005). If the distribution of scores on variables does not deviate significantly from normality, the partial least squares estimation of SEM can be applied (Hair et al, 2006). According to Cunningham (2008), an index smaller than an absolute value of 2.0 for skewness and an absolute value of 7.0 is the least violation of the assumption of normality. The results of the normality test indicated skewness and kurtosis as shown in table 4.11.

119

Table 4.11Assessment of Normality

Variabl Skewnes Kurtosi Min Max c.r. c.r. e s s OL1 2 5 -0.274 -1.442 0.547 1.437 OL2 3 5 -0.977 -5.14 4.525 11.901 OL3 2 5 -0.675 -3.549 4.761 12.522 OL4 3 5 0.173 0.911 2.908 7.648 OS1 3 5 0.123 0.645 -0.049 -0.128 OS2 3 5 0.116 0.608 1.728 4.543 IS3 3 5 0.107 0.565 0.336 0.884 IS6 3 5 -0.006 -0.029 -0.137 -0.362 HR1 3 5 -0.702 -3.691 -0.497 -1.306 HR2 3 5 0.449 2.364 -0.17 -0.446 SC2 2 5 -0.968 -5.091 3.692 9.709 SC4 2 5 -0.777 -4.087 3.417 8.985 SC5 2 5 -0.866 -4.555 2.934 7.716 MC-P4 1 5 -0.97 -5.102 1.356 3.567 MC-P5 1 5 -0.469 -2.468 0.724 1.903 MC-P6 1 5 -0.362 -1.903 0.819 2.153 MT3 2 5 -0.145 -0.763 0.803 2.111 MT6 2 5 -0.505 -2.655 0.411 1.08 TT4 3 5 0.011 0.059 -0.044 -0.117 TT6 1 5 -0.913 -5.856 0.819 2.153

The values of Skew and Kurtosis were calculated for the distribution of scores for all seven latent variables in the study. None of the values exceeded the absolute values of 2 for skewness and 7 for Kurtosis indices. This implied that the data set had a normal distribution of scores as the variables did not deviate significantly from normality, therefore the assumption of normality was satisfied. Based on this therefore, the researcher could progress to utilize the data with the assumption that the normality represented by the manifest variables implied that the unobserved variables in the study would also be normally distributed (Hair et al., 2006).

4.2.3 Exploratory Factor Analysis

Exploratory factor analysis was used to purify the study’s construct measures and tested for reliability analysis using SPSS 22. The raw measures were purified and tested for reliability and validity by running a series of tests. The original assessment was the unidimensional of measures. To assess construct unidimensional scales and to identify the structure of the measurement or outer model for the items in the study, the exploratory

120

factor analysis was performed. In addition, exploratory factor analysis was performed to achieve measure purification and refine the variables into the most effective number of factors. This was therefore followed by the reliability analysis. Principal component analysis (PCA) was used to refine each of the study construct. For each construct, factor loadings above 0.5 were retained for each principle component extracted (Hair et al., 2010). The items were assessed to determine their ability to be retained for factor analysis. The following three indicators were used: measure of Sampling Adequacy Kaiser Meyer-Olin (KMO), Barlett’s Test of Sphericity and Communalities. The results are outlined per objective.

KMO expresses a measure of sample adequacy (MSA) between 0 and 1. KMO values close to 0 implies that the sum of partial correlation is large relative to the sum of correlations indicating diffusions in the patterns of correlations, and hence the factor analysis is not likely to be appropriate (Cooper & Schindler, 2011). KMO is considered by researchers to be the best technique for testing the suitability of the correlation matrix for factor analysis, and thus it is recommended to be performed before every factor analysis (Cleff 2014). The KMO values for the study constructs were all above 0.5 and therefore were suited for factor analysis

The proportion of variance was carried out to assess common variance among the variance. The lower the proportion, the more suited the data is to factor analysis. Kaiser 1974, recommends that values greater than 0.5 are acceptable, and values below 0.5 are unacceptable. Factor loadings was also used in this study. The main objective was to evaluate how much a factor explains a variable in the constructs. Factor loading range from -1 to 1 and loadings close to -1 or 1 indicate that the factor strongly affects the variable. On the other hand, loadings close to zero indicate that the factor has a weak effect on the variable. For this study, factor loadings greater than 0.5 were retained for each principal component extracted (Hair et al., 2010).

The Bartlett’s Test of Sphericity examines the assumption that the correlation matrix is an identity matrix which should specify whether the variables are unrelated and thus would be deemed not suitable for structure detection. In other words, small values such as p<0.05, of the significance level indicate that a factor analysis may be useful with one’s data. Barlett’s test of Sphericity (Bartlett, 1954) for p values was done and were all significant at below 0.05.. Communalities demonstrate the amount of variance in each

121

variable accounted for by the factor solution. Small values are indication of variables that do not fit well with the factor solution, and should possibly be dropped from the analysis. The extraction communalities for this study, were all found to be well above 0.5 denoting satisfactory factorability for all items. This meant that the variables fitted well with other variables in their factor (Pallant, 2010). When principal component analysis was applied, the results indicated a clear factor structure with an acceptable level of cross loadings.

The reliability and internal consistency of all the items constituting each construct was estimated. Scale refinement was assessed using item to total correlation analysis, taking indicators with item to total threshold of 0.3 and higher being maintained for further analysis (Hair et al., 2010). Finally, Principle Component Analysis (PCA) was then performed using SmartPLS software for measurement model estimation. The purpose of PCA was to establish the extent to which the observed data validated and fit the pre- specified theoretically based model. The subsequent section provides development model measurement and summaries of scale purification for each construct

4.2.4 Test of outliers

Outlier is defined as an extreme case that distorts the true relationship between variables by either creating a correlation that should not exist or suppressing a correlation that should exist (Abbott & McKinney, 2013). Outliers are also termed as aberrant scores that lie outside the usual range of scores expected for a particular variable. In multivariate data, outliers are units representing an unusual combination of values for a number of variables (Riani, Torti & Zani, 2012). Observations portraying characteristics or values that are markedly different from the majority of cases in a data set are normally dropped (Kline, 2005; Hair et al., 2010). For this study, the outliers were tested through computing Mahalanobis distance for each sample, with outliers being identified as those samples yielding large values of Mahalanobis distance (Webb & Copsey, 2011). The Mahalanobis distance was employed to evaluate the multivariate outliers. Kline (2005) recommended that the Mahalanobosis distance is appropriate for evaluating the multivariate outliers. Mahalanobis D2 is a multidimensional version of a z-score. It measures the distance of a case from the centroid (multidimensional mean) of a distribution, given the covariance (multidimensional variance) of the distribution. A case is a multivariate outlier if the probability associated with its D2 is 0.001 or less. D2 follows a chi-square distribution with degrees of freedom equal to the number of variables included in the calculation

122

(Tabachnick & Fidell, 2007). The results for the Mahalanobis test are presented in appendix 2.

4.2.5 Development of Measurement Model

The development of a measurement model for the structural equation model involves specifying the observed measures (indicators) for individual construct such as the latent variable, which enabled the assessment of construct validity and reliability (Hair et al., 2006). This study investigated the relationship between strategy implementation and M- Commerce performance in Kenya’s commercial banks. Strategy Implementation comprised of the following dimensions: organizational leadership (OL), organizational structure (OS), information system (IS), human resources (HR) and strategy communication (SC). M-Commerce performance comprised of the dimensions; Growth of new Applications, Growth of M-Commerce Users, Growth of bank accounts and growth of savings.

4.2.5.3 Construct Validity.

According to Hair et al., (2010), construct validity reflects the degree to which the measurement items of a construct reflect the theoretical latent constructs that those items are designed for. Once the construct validity has been validated, it guarantees that each time samples drawn from the population are measured, the test scores are representative of the true scores of the population. Convergent validity and discriminant validity are the two aspects of construct validity

4.2.5.4 Convergent Validity

Convergent validity is established when, the scores obtained with two different instruments measuring the same concept are highly correlated (Sekaran & Bougie 2013). Convergent validity is measured by three measures; factor loadings, composite reliability (CR) and average variance extracted (AVE). Convergent validity is achieved if factor loadings are statistically significant and should be above 0.5, composite reliability values for the construct should be least 0.7 and the average variance extracted (AVE) are at least 0.5 (Hair et al., 2010). Thus if all the constructs items are significantly important, then the measurement model possesses adequate level of convergent validity. All the study constructs met the threshold of convergent validity as illustrated in table 4.12.

123

Table 4.12 Convergent Validity for Constructs

Construct Item Factor T Value P Values Composite Average loading reliability variance (CR) extracted (AVE). OL1 0.725 16.012 0 OL2 0.842 20.86 0 OL 0.873 0.634 OL3 0.841 23.285 0 OL4 0.771 18.052 0 OS1 0.556 4.088 0 OS2 0.385 2.174 0.03 OS 0.785 0.5 OS4 0.9 40.405 0 OS5 0.857 23.828 0 IS1 0.787 9.704 0 IS 0.754 0.606 IS5 0.769 8.479 0 HR1 0.769 2.393 0.017 HR 0.763 0.618 HR5 0.803 2.159 0.031 SC1 0.655 9.666 0 SC2 0.662 9.369 0 SC 0.825 0.543 SC4 0.774 15.779 0 SC5 0.84 24.4 0 FP1 0.806 21.384 0 FP2 0.753 14.22 0 M- FP3 0.857 37.677 0 commerce FP4 0.779 16.089 0 0.905 0.579 performance FP5 0.667 9.56 0 FP6 0.6 7.921 0 FP7 0.829 24.782 0 MT4 0.712 11.616 0 Market MT5 0.654 6.704 0 0.762 0.518 Turbulence MT6 0.787 17.832 0

Technology TT4 0.502 3.287 0 0.732 0.601 Turbulence TT6 0.975 40.416 0

124

4.2.5.5 Discriminant Validity

Discriminant validity tests whether concepts or measurements that are supposed to be unrelated are actually not related. That is the extent to which an item measuring one construct can differentiate itself from items measuring other constructs. Discriminant validity can be assessed using two criteria; the first is that the inter-construct correlation should not be higher than 0.9. The second criterion is the square root of the Average Variance Extracted (AVE) of the construct should be larger than its correlation with the other constructs.

4.2.3.5 Cross Loading

Cross loading can also be used to test discriminant validity which shows that an indicator of any individual construct has a higher loading on its own construct than on any other constructs’ (horizontal loading). These results show that the manifest variables (indicators) presented in the model are reliable and valid as illustrated in the shaded section of table 4.13.

125

Table 4.13 Cross Loading

HR IS FP MT OS TT OL SC OL1 0.19 0.382 0.389 0.362 0.398 0.181 0.725 0.422 OL2 0.329 0.391 0.488 0.583 0.471 0.453 0.842 0.446 OL3 0.253 0.377 0.483 0.364 0.375 0.404 0.841 0.47 OL4 0.189 0.398 0.466 0.4 0.332 0.405 0.771 0.428 OS1 0.208 0.132 0.207 0.181 0.556 -0.093 0.192 0.169 OS2 0.222 0.056 0.025 0.076 0.384 0.062 0.144 0.001 OS4 0.234 0.334 0.521 0.54 0.901 0.299 0.57 0.456 OS5 0.032 0.155 0.413 0.34 0.856 0.141 0.31 0.331 IS1 0.1 0.788 0.358 0.336 0.165 0.248 0.413 0.291 IS5 0.113 0.769 0.345 0.334 0.274 0.326 0.341 0.32 HR1 0.772 0.017 0.125 0.136 0.065 0.086 0.183 0.161 HR5 0.8 0.194 0.132 0.223 0.235 0.049 0.293 0.185 SC1 0.109 0.197 0.388 0.282 0.337 0.12 0.384 0.656 SC2 0.232 0.253 0.37 0.365 0.271 0.336 0.3 0.662 SC4 0.073 0.349 0.432 0.359 0.347 0.263 0.46 0.774 SC5 0.233 0.341 0.494 0.408 0.32 0.427 0.472 0.84 FP1 0.275 0.39 0.81 0.507 0.378 0.392 0.505 0.527 FP2 0.124 0.426 0.757 0.437 0.358 0.248 0.357 0.463 FP3 0.126 0.436 0.86 0.565 0.439 0.422 0.527 0.51 FP4 0.049 0.344 0.776 0.379 0.422 0.41 0.484 0.461 FP5 0.011 0.175 0.659 0.202 0.39 0.203 0.351 0.241 FP6 -0.028 0.196 0.592 0.189 0.371 0.271 0.332 0.268 FP7 0.226 0.353 0.832 0.482 0.393 0.317 0.463 0.493 MT4 0.076 0.34 0.406 0.712 0.439 0.249 0.354 0.326 MT5 0.209 0.202 0.313 0.654 0.273 0.342 0.403 0.338 MT6 0.22 0.364 0.441 0.787 0.329 0.377 0.418 0.377 TT4 -0.068 0.054 0.116 0.243 0.021 0.502 0.15 0.096 TT6 0.112 0.393 0.452 0.429 0.223 0.975 0.473 0.413

4.2.3.6 Multicollinearity Test

Multi-collinearity validates the ability of variables to distinguish themselves from other variables. The standard issue in multicollinearity is that, the standard errors and thus the variances of the estimated coefficients are inflated when multicollinearity exists (Simon, 2004). Variance Inflation Factor (VIF) was used to test for mutillinearity among study variables. Porter and Gujarat (2010), view that as a rule of the thumb is if VIF of independent variables exceeds 10, that variable is collinear. Based on this rule of thumb, there was no collinearity among the independent variables. From the results, inspection of

126

the Variance Inflation Factors (VIFs) showed that multi-collinearity was not a concern. No variable was observed to have VIF value above 10 as suggested by Hamilton (2006).

4.2.3.7 Variance Inflation Factors

The VIF study results confirmed the absence of Multicollinearity among the independent variables of the study. The assessment also included the assessment of the indicators relative contribution to the construct, significance of weights which were all significant at 0.5 level of significance. The VIF values were calculated in Smart PLS 30 and the data was found to be free from the problems of Multicollinearity (Hair et al, 2013) as illustrated in table 4.14.

Table 4.14 VIF values for Constructs

VIF (variance Construct Weight T-Statistic Inflated Factor)

Leadership 1.898 0.233 2.759 Structure 1.391 0.237 3.553 Information system 1.347 0.164 2.031

Human resource 1.11 -0.042 0.478 strategic communication 1.561 0.287 3.307

4.3 Influence of Organizational Leadership on M-Commerce Performance

The study sought to investigate the influence of leadership on m-commerce performance in Kenya’s commercial banks. Organizational leadership was measured using four items that were factor analyzed so as to generate factor scores that were used in the Structural Equation Model (SEM). The use of questionnaire statements where the respondent indicated the extent of agreement with the statements were applied. The study presented the descriptive statistics on influence of organizational leadership and results for factor analysis, correlation analysis, Anova and SEM as follows:

127

4.3.1 Frequency Distribution on Organizational Leadership Influence

The study found that a majority of the respondents at 92 percent, agreed to a great extent that the relationship between the leadership and other staff in the bank contributed positively to m-commerce performance. This was followed closely with 90 percent of respondents also agreeing to a great extent that leadership style support m-commerce growth and influence the overall m-commerce performance. In response to the question of leadership, 87 percent respondents agreed that the bank leadership was flexible to facilitate staff contribution to m-commerce. Frequency distribution for organizational leadership is presented in table 4.15.

Table 4.15 Frequency Distribution for Organizational Leadership Influence

SE ME GE VGE No Statements (%) (%) (%) (%) To what extent do the relationship between the OL1 leadership and other staff in your bank contribute 1 7 58 34 positively to m-commerce To what extent is the bank leadership flexible to OL2 facilitate staff contribution to M-Commerce 2 11 82 5 performance? To what extent do the leadership style in your OL3 3 7 80 10 bank support M-commerce growth? To what extent do the leadership in your bank OL4 influence the overall M-Commerce performance 0 10 80 10 of the bank? SE- Small Extent, ME- Moderate Extent, GE- Great Extent, VE- Very Great Extent 4.3.2 Means and Standard Deviations for Organizational Leadership

Analysis of respondent views on organizational leadership was also carried out using means and standard deviations as presented in table 4.19. A Likert scale data was collected rating the views in a scale starting from 1 denoting “not at all” to 5 denoting to a “very great extent”. The means and standard deviations results from analysis of respondents show the variability of the individual responses from the overall mean of the responses per individual item of organizational leadership. The mean results were rated on a five point Likert scale ranging from 1 denoting “not at all”, 1.1-2.0 denoting “small extent”, 2.1 to 3.0 denoting “moderate extent”, 3.1 to 4.0 denoting “great extent” and a mean value of 4.1 and above is an indication of very great extent. The results are presented in table 4.16.

128

Table 4.16 Mean and Standard Deviation for Organizational Leadership Influence

No Statements Mean Std To what extent do the relationship between the leadership OL1 and other staff in your bank contribute positively to m- 4.25 0.63 commerce To what extent is the bank leadership flexible to facilitate OL2 3.91 0.47 staff contribution to M-Commerce performance? To what extent do the leadership style in your bank OL3 3.96 0.55 support M-commerce growth? To what extent do the leadership in your bank influence OL4 4 0.45 the overall M-Commerce performance of the bank?

The findings of the study indicate that the responses for OL1 and OL4 on the extent to which the relationship between the leadership and other staff in the bank contribute positively to m-commerce and the extent to which the leadership in the bank influenced the overall m-commerce performance of the bank with a mean of 4 and above agreed to a great extent. On the other hand, the two statements OL2 and OL3 the extent to which the respondents agree on that the bank leadership is flexible to facilitate staff contribution to m-commerce performance and the extent to which the leadership in the bank influence the overall m-commerce performance of the bank, with a mean of 3.91 and 3.96 was agreed on by the respondents to a great extent.

4.3.3 Organizational Leadership Factor Analysis

Factor analysis was carried out to reduce items of organizational leadership influence. To arrive at an appropriate measure, the organizational leadership construct was evaluated using four items. The study found that organizational leadership had a KMO sample adequacy of 0.772 which was above the threshold of 0.6 and Bartlett’s Test of Sphericity, with p<0.05, indicating suitability of data for structure detection. The results are shown in table 4.17.

129

Table 4.17 KMO and Bartlett’s Test for Organizational Leadership Influence

Kaiser-Meyer-Olkin Measure of Sampling 0.772 Adequacy Approx. Chi-Square 228.298 Bartlett’s Test of Sphericity Df 6 Sig. 0.000

Exploratory factor analysis using PCA with promax rotation revealed that item total correlations OL1 = 0.725, OL2= 0.842, OL3= 0.841, OL4 = 0.771. Total variance explained results for organizational leadership showed that the components explained 61.95% of the total variability in the four items. The details are presented in table 4.18.

Table 4.18 Factor Analysis for Influence of Organizational Leadership

PCA Variance No Statement Component Extracted Loading (%) To what extent do the relationship between the OL1 leadership and other staff in your bank contribute 0.725 positively to m-commerce To what extent is the bank leadership flexible to 61.953 OL2 facilitate staff contribution to M-Commerce 0.842

performance?

To what extent do the leadership style in your bank OL3 0.841 support M-commerce growth? To what extent do the leadership in your bank OL4 influence the overall M-Commerce performance of the 0.771 bank?

4.3.4 Reliability of Organizational Leadership Influence on M-Commerce Performance Organizational leadership construct was assessed for reliability before the SEM analysis. A coefficient alpha of 0.806 for organizational leadership indicated that the measuring scale was reliable. The results are shown in table 4.19.

130

Table 4.19 Organizational Leadership –Coefficient Alpha

Cronbach’s No Statements Alpha

To what extent do the relationship between the leadership OL1 and other staff in your bank contribute positively to m- commerce 0.806 To what extent is the bank leadership flexible to facilitate OL2 staff contribution to M-Commerce performance?

To what extent do the leadership style in your bank OL3 support M-commerce growth? To what extent do the leadership in your bank influence OL4 the overall M-Commerce performance of the bank?

Item to total correlations of above 0.3 was achieved for all items in the scale. The factors with low standardized regression weights (OL5) was deleted. The results meant that the items of measure, measured what they were initially set out to measure, hence the data was retained for further analysis. The results are provided in table 4.20

Table 4.20 Reliability Test for Organizational Leadership

Item total No Statements correlation To what extent do the relationship between the leadership OL1 and other staff in your bank contribute positively to m- 0.553 commerce To what extent is the bank leadership flexible to facilitate OL2 0.685 staff contribution to M-Commerce performance? To what extent do the leadership style in your bank support OL3 0.665 M-commerce growth? To what extent do the leadership in your bank influence the OL4 0.576 overall M-Commerce performance of the bank?

4.3.4 Correlation Analysis between Organizational Leadership and M-Commerce Performance The study conducted correlation analysis between organizational leadership and m- ommerce performance and found that organizational leadership was positively and significantly related with m-commerce performance. The results are presented in table 4.21.

131

Table 4.21 Correlation between Organizational Leadership and M-Commerce

M-Commerce Performance Leadership M-Commerce Pearson Correlation Performance 1 .576**

Sig. (2-tailed) .000

N 178 178 Leadership Pearson Correlation .576** 1

Sig. (2-tailed) .000 N 178 178 Performance

**. Correlation is significant at the 0.01 level (2-tailed).

4.3.5 Chi Square Test on Organizational Leadership Influence on M-Commerce Performance

The strength of association between organizational leadership and m-commerce performance was tested using Chi Square test. The results found that there was a strong association between organizational leadership and m-commerce performance. The results are provided in table 4.22.

Table 4.22 Chi Square Test on Organizational Leadership Influence

Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 2967.417a 2291 .000 Likelihood Ratio 506.191 2291 1.000 Linear-by-Linear Association 58.775 1 .000 N of Valid Cases 178 a. 2398 cells (99.9%) have expected count less than 5. The minimum expected count is .01.

132

4.3.6 ANOVA of Organizational Leadership Influence on M-Commerce Performance

The study conducted ANOVA to test the mean difference between organizational leadership and m-commerce performance. The results of the study indicated there was significant differences in means of leadership and m-commerce performance. The results are presented in table 4.23.

Table 4.23 ANOVA Between Organizational Leadership and M-Commerce Performance

Model Sum of Squares df Mean Square F Sig. 1 Regression 58.910 1 58.910 87.077 .000 Residual 119.068 176 .677 Total 177.978 177

4.3.7 SEM Results for Influence of Organizational Leadership on M-Commerce Performance

This section presents the influence of organization leadership on m-commerce performance.

4.3.7.1 SEM Results for Influence of Organizational Leadership on M-Commerce Performance – Unmoderated

The study sought to establish the influence of organizational leadership on m-commerce performance and tested the following hypothesis.

H1: Organizational leadership does not significantly influence m-commerce performance in Kenya’s commercial banks.

In order to ascertain the relationship of the construct under study, the path coefficients that were generated from the SEM were used to determine the direction and strength of the relationship. T-statistics provided information on the significance to the relationship between organizational leadership and m-commerce performance. The path coefficient value was 0.233, thus the relationship between organizational leadership and m- commerce performance was positive (regression weight = 0.233) and significant (t =

133

2.759, p=0.06). In this respect the study rejected H1. The findings are presented in Table 4.24, figures 4.1 and 4.2.

Table 4.24 Relationship between Organizational Leadership and M-Commerce

Path Standar T- P - Path Hypothesis coefficient d Error Value Value OL -> M-commerce performance 0.233 0.084 2.759 0.006 Supported

Figure 4.2: Path Coefficients.

134

Figure 4.3: T values

4.3.7.2 SEM Results for Influence of Organizational Leadership and M-Commerce Performance - Moderated

The introduction of a moderator, resulted in insignificant relationship. The path coefficient value of OL to MC Performance was positive at 0.144 but insignificant with p-value more than 0.05 and t-value less than 1.96. The path coefficient for OL and MC when moderated by MT, the path coefficient is negative -0.022 and insignificant (t-value 0.183 and p-value 0.855). Moderation of OL and MC by TT – the path coefficient is 0.016 and (t= 0.142, p= 0.887). In this respect the hypothesis was supported. Results are presented in table 4.25, figures 4.3 and 4.4.

135

Table 4.25 Moderated Relationship between Organizational Leadership and M- Commerce Performance

Path T- P- Standar Path Coefficie Valu Valu Hypothesis d Error nt e e Not OL -> MC Performance 0.144 0.086 1.671 0.095 supported Not OL*Market -> MC Performance -0.022 0.122 0.183 0.855 supported OL*Technology -> MC Not 0.016 0.113 0.142 0.887 Performance supported

Figure 4.4: Path Coefficients for the Moderated Model. (MT and TT)

136

Figure 4.5: T Values for the Moderated Model. (MT and TT)

4.4 Influence of Organizational Structure on M-Commerce Performance

The study sought to investigate the influence of organizational structure on m-commerce performance in Kenya’s commercial banks. The study presented descriptive statistics of organizational structure and results for factor analysis, correlation analysis, ANOVA and SEM in the subsequent sections.

137

4.4.1 Frequency Distribution on Influence of Organizational Structure on M- Commerce Performance

The study sought to analyze the respondents’ views on the influence of organizational structure on m-commerce performance. The statement on whether the banks structure allows easy decision making and contributes to the growth of new m-commerce applications was highly rated at 91 percent being agreed, 1 percent disagreed, 8 percent were non-committal. The statement regarding the bank’s structure being hierarchical, and influences positively the growth of new m-commerce applications, had 88 percent of the respondents agreeing, while 12 percent were neutral and 1 percent disagreed. 87 percent of the respondents agreed that the banks structure was flat and influenced positively the return of new m-commerce applications and the overall m-commerce performance in the bank. However, 8 percent of the respondents disagreed that reporting structure influenced positively m-commerce growth. Results are presented in table 4.26

Table 4.26 Frequency Distribution for Organizational Structure

No. Statement SD D N A SA (%) (%) (%) (%) %) The bank’s structure allows easy decision making and contributes to the growth of 0 1 8 65 26 OS1 new m-commerce applications The bank’s structure is hierarchical, and influences positively the growth of new m- 0 1 12 75 13 OS2 commerce applications The bank’s structure is flat, and influences positively return of new m-commerce 2 4 7 76 11 OS3 application The reporting structure influences 0 8 8 61 24 OS4 positively m-commerce growth The organizational structure influences positively the overall m-commerce 2 3 9 76 11 OS5 performance in the bank Key: SD- Strongly Disagree, D-Disagree, N-Neutral, A-Agree and SA- Strongly Agree

4.4.2 Mean and Standard Deviation for Organizational Structure

The highest mean rating of 4.17, 4.01 and 4.0 indicating strongly agree was for three statements OS1, OS4 and OS2 “the bank’s structure allows easy decision making and contributes to the growth of new m-commerce applications” (SD = 0.58), “the reporting

138

structure influences positively m–commerce growth” (SD = 0.79) and “the bank’s structure is hierarchical, and influences positively the growth of new m-commerce applications” (SD = 0.52). The statements with the lowest rating of 3.9 and 3.91 were OS3 “The bank’s structure is flat and influences positively return of new m-commerce application”, (SD = 0.71) and OS 5 “The organizational structure influences positively the overall m-commerce performance in the bank”, (SD=0.67). The organizational structure had a high scale rating (mean rating of above 3), an indication that it influences performance of m-commerce. Results are presented in table 4.27.

Table 4.27 Organizational Structure Mean and Standard Deviation Statistics

Mea No. Statement Std n The bank’s structure allows easy decision making and contributes to the growth of new m-commerce 4.17 0.58 OS1 applications The bank’s structure is hierarchical, and influences 4 0.52 OS2 positively the growth of new m-commerce applications The bank’s structure is flat, and influences positively 3.9 0.71 OS3 return of new m-commerce application The reporting structure influences positively m-commerce 4.01 0.79 OS4 growth The organizational structure influences positively the 3.91 0.67 OS5 overall m-commerce performance in the bank

4.4.3 Results for Reliability Analysis on Organizational Structure Influence

Organizational structure constructs were evaluated for reliability and convergent validity before the SEM analysis. A coefficient alpha of 0.702 for organizational structure indicated that the measuring scale was reliable. Results are presented in table 4.28.

139

Table 4.28 Reliability Test for Organizational Structure

Cronbach’s No Statement Alpha The bank’s structure allows easy decision making and OS1 contributes to the growth of new m-commerce applications The bank’s structure is hierarchical, and influences positively 0.702 OS2 the growth of new m-commerce applications The reporting structure influences positively m-commerce OS4 growth The organizational structure influences positively the overall OS5 m-commerce performance in the bank

4.4.4 Factor Analysis Results of Organizational Structure

Factor analysis was used to reduce items of organizational structure. The study found that Organizational structure had a KMO of sample adequacy of 0.657, which was above the threshold of 0.6 and Bartlett’s Test of Sphericity, with p<0.05, this is an indication of suitability of data for structure detection. The results are presented in table 4.29.

Table 4.29 KMO and Bartlett’s Test for Organizational Structure Influence

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.657 Approx. Chi-Square 144.884 Bartlett’s Test of Sphericity df 6 Sig. 0.000

Exploratory factor analysis using PCA with promax rotation revealed that the following factor loadings (OS1 = 0.556, OS2= 0.385, OS4= 0.9 OS5 = 0.857), were above the acceptable threshold of 0.5. The factors with low standardized regression weights were deleted. Results presented in table 4.30

140

Table 4.30 Factor Loadings for Organizational Structure

No. Statement Factor Loading The bank’s structure allows easy decision making OS1 and contributes to the growth of new m-commerce 0.556 applications The bank’s structure is hierarchical, and influences OS2 positively the growth of new m-commerce 0.385 applications The reporting structure influences positively m- OS4 0.9 commerce growth The organizational structure influences positively OS5 0.857 the overall m-commerce performance in the bank

The following items OS1, OS2, OS4 and OS5 were therefore retained for measurement model estimation as they achieved the required thresholds for reliability and convergent validity. Factor loadings results applied were above the accepted threshold of 0.5.

4.4.5 Correlation Analysis between Organization Structure and M-Commerce Performance

The study correlated organizational leadership and m-commerce performance and found that organizational leadership and m-commerce were positively and significantly related. The results are presented in table 4.31.

Table 4.31 Correlation between Organizational Structure and M-Commerce Performance

M-Commerce Performance Structure M-Commerce Pearson Correlation 1 .513** Performance Sig. (2-tailed) .000 N 178 178 Structure Pearson Correlation .513** 1 Sig. (2-tailed) .000 N 178 178

141

4.4.6 Chi Square Test of Organizational Structure Influence on M-Commerce Performance

Chi Square test was used to test the strength of association between organizational structure and m-commerce performance. The results indicated a strong association between organizational structure and m-commerce performance. The results are presented in table 4.32.

Table 4.32 Chi Square Test of Organizational Structure Influence on M-Commerce Performance

Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 4931.447a 3476 .000 Likelihood Ratio 669.291 3476 1.000 Linear-by-Linear Association 46.522 1 .000 N of Valid Cases 178 a. 3599 cells (100.0%) have expected count less than 5. The minimum expected count is .01.

4.4.7 ANOVA on Organizational Structure Influence on M-Commerce Performance

ANOVA test was done to test the mean difference between organizational structure and m-commerce performance. The results of the study indicated that there was a relationship between organizational structure and m-commerce performance. The results are presented in table 4.33

Table 4.33 ANOVA between Organization Structure and M-Commerce Performance

Model Sum of Squares df Mean Square F Sig. 1 Regression 46.905 1 46.905 62.982 .000b Residual 131.073 176 .745 Total 177.978 177 a. Dependent Variable: M-Commerce Performance b. Predictors: (Constant), Structure

142

4.4.8 SEM Results for Influence of Organizational Structure and M-Commerce Performance

This section presents results of structural Equation Modeling for organizational structure and m-commerce performance. The first model is without the moderators and the second model is inclusive of market turbulence and technological turbulence.

4.4.8.1 SEM Results for Influence of Organizational Structure on M-Commerce Performance – Unmoderated Moderated

The study sought to establish the influence of organizational structure on m-commerce performance and tested the following hypothesis.

H2: There is a relationship between organizational structure and m-commerce performance in Kenya’s commercial banks.

The results reveal that the path coefficient value was 0.237, thus the relationship between organizational structure and m-commerce performance was positive and significant (t = 3.553, p =0.000). The findings are shown in Table 4.34, figures 4.1 and 4.2.

Table 4.34 Relationship between Organizational Structure and M-Commerce

Standa Path T P Path rd Hypothesis coefficient Value Value Error OS -> M-commerce performance 0.237 0.067 3.553 0.000 Supported

4.4.8.2 SEM Results for Influence of Organizational Structure on M-Commerce Performance - Moderated

The path coefficient value of OS and MC Performance was positive at 0.166 but insignificant (t=1.923, p=0.055) in this respect the study failed to support H2 after moderation. The study also found that the path coefficient for OS and MC performance when moderated by market turbulence was negative -0.058 and insignificant (t=0.701, p= 0.484) as illustrated in table 4.35 and figures 4.3 and 4.4.

143

Table 4.35 Relationship between Organizational Structure and M-Commerce Performance

Path Standard T- P- Path Hypothesis Coefficient Error Value Value OS -> MC Performance 0.166 0.086 1.923 0.055 Not supported OS*Market -> MC -0.058 0.083 0.701 0.484 Not supported Performance OS*Technology -> MC 0.036 0.083 0.433 0.666 Not supported Performance

Similarly, moderation of organizational structure and MC-Performance by technological turbulence results with a positive path coefficient of 0.036 but insignificant relationship (t =0.433, p=0.666) as illustrated in table 4.35, figures 4.3 and 4.4

4.5 Influence of Information System on M-Commerce Performance

The study sought to investigate the influence of information system on m-commerce performance in Kenya’s commercial banks. The study presented descriptive statistics for information systems and results for factor analysis, correlation, ANOVA and SEM.

4.5.1 Frequency Distribution of Information System Statistics

Table 4.36 shows responses related to the influence of information system on bank’s growth of m-commerce. 42 percent of the respondents disagreed that bank’s information system does not support growth of new applications. In other words, they supported the fact that IS, support the growth of new applications. Notably, a majority of the respondents at 97, agree that information systems drives the bank’s growth of new m- commerce applications. 92 percent of the respondents agree that staff are empowered to support m-commerce development. In addition, 82 percent of the respondents agree that IS staff in the bank are empowered to make decisions that support excellent performance of m-commerce. On the other hand, 89 percent of respondents agree that bank’s information system supports the growth of customers. 81 and 77 percent of respondents agree that information introduced by IS was created to support implementation of m- commerce and that they can easily use IS to make decisions supporting m-commerce implementation. However, there were pockets of 10, 17, 21 and 18 percent who were neutral regarding the fact that IS supported the growth of m-commerce, staff were empowered or could use IS or that information introduced by IS could support implementation of m-commerce respectively.

144

Table 4.36 Frequency Distribution for Information System

SD D N A SA No. Statement (%) (%) (%) (%) (%) Information systems drives the bank’s growth IS1 of new M-Commerce applications 1 0 3 44 53 Bank’s information system does not support IS2 growth of new applications 42 27 4 21 6 Bank’s information system supports the IS3 growth of customers 0 1 10 69 20 Bank’s information system staff are empowered to support m-commerce IS4 development 0 3 6 83 9 Bank’s information system contributes positively to the overall m-commerce IS5 performance of the bank 0 1 9 72 18 Bank’s information system staff are empowered to make decisions that support IS6 excellent performance of m-commerce 0 1 17 64 18 I can easily use information system to make decisions supporting m-commerce IS7 implementation 0 2 21 65 12 The information introduced by information system is created to support implementation of IS8 m-commerce 0 2 18 66 15

4.5.2 Mean and Standard Deviation for Information System Influence on M- Commerce Performance The information system (IS) scale consisted of 8 items. The highest mean rating for IS was 4.49 for the item IS1 “Information systems drives the bank’s growth of new m- commerce applications”, (SD= 0.61). This indicated that the respondents believed that IS influenced performance of m-commerce. The results are presented in table 4.37.

145

Table 4.37 Frequency Distribution for Information System

No Statements Mean Std Information systems drives the bank’s growth of new M- IS1 Commerce applications 4.49 0.61 Bank’s information system does not support growth of IS2 new applications 2.22 1.34 Bank’s information system supports the growth of IS3 customers 4.08 0.56 Bank’s information system staff are empowered to IS4 support m-commerce development 3.98 0.50 Bank’s information system contributes positively to the IS5 overall m-commerce performance of the bank 4.06 0.56 Bank’s information system staff are empowered to make decisions that support excellent performance of m- IS6 commerce 4.00 0.61 I can easily use information system to make decisions IS7 supporting m-commerce implementation 3.87 0.62 The information introduced by information system is IS8 created to support implementation of m-commerce 3.93 0.63

4.5.3 Factor Analysis Results on Information Systems Factor analysis was used to reduce items of Information Systems. The study conducted KMO and Bartlett’s test for information systems and found KMO value of 0.5 and Bartlett’s test p=0.005. The results are presented in table 4.38.

Table 4.38 KMO and Bartlett’s Test for Information System

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.5 Bartlett’s Test of Sphericity Approx. Chi-Square 8.019 df 1 Sig. .005

Exploratory factor analysis using PCA with promax rotation revealed that out of the eight items, only two items (IS1 = 0.787, IS5= 0.769) returned factor loadings that were above the acceptable threshold of 0.5. These two explained 60.281 percent of variability in information systems. The two items IS1, and IS5 were retained for measurement model estimation as detailed in tables 4.39.

146

Table 4.39 Component Matrix for Influence of Information Systems

PCA variance

NO component extracted Statements loading (%) Information systems drives the bank’s growth of new IS1 0.787 60.281 M-Commerce applications Bank’s information system contributes positively to IS5 0.769 the overall m-commerce performance of the bank

4.5.3.1 Reliability of Information System Influence Information System construct was analyzed for reliability and convergent validity before the SEM analysis. A coefficient alpha of 0.749 for information system indicated that the measuring scale was reliable as indicated in table 4.40.

Table 4.40 Reliability Test for Information Systems

Cronbach’s No. Statements Alpha

Information systems drives the bank’s growth of new M- IS1 0.749 Commerce applications

Bank’s information system contributes positively to the overall IS5 m-commerce performance of the bank

4.5.4 Correlation Analysis between Information Systems and M-Commerce Performance The relationship strength between information systems and m-commerce performance was tested using correlation. The findings indicated that the correlation were positively and significantly related with m-commerce performance. The results are provided in table 4.41.

147

Table 4.41 Correlation between Information Systems and M-Commerce

M-Commerce Information Performance System M-Commerce Pearson Correlation 1 .451** Performance Sig. (2-tailed) .000 N 178 178 Information System Pearson Correlation .451** 1 Sig. (2-tailed) .000 N 178 178 **. Correlation is significant at the 0.01 level (2-tailed).

4.5.5 Chi Square Test on Information System Influence Chi Square test was used to test the strength of association between information System and m-commerce performance. The results indicated between information system and m- commerce performance was a strong association. The results are presented in table 4.42.

Table 4.42 Chi Square Test on Information System Influence

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 941.573a 711 .000 Likelihood Ratio 354.330 711 1.000 Linear-by-Linear Association 36.053 1 .000

N of Valid Cases 178 a. 798 cells (99.8%) have expected count less than 5. The minimum expected count is .01.

4.5.6 ANOVA on Information System Influence ANOVA test was done to test relationship between information systems and m-commerce performance. The results of the study indicated that there was a significant relationship between the two variables. The results are presented in table 4.43.

148

Table 4.43 ANOVA Between Information System and M-Commerce Performance

Model Sum of Squares df Mean Square F Sig. 1 Regression 36.044 1 36.044 44.695 .000b Residual 141.934 176 .806 Total 177.978 177 a. Dependent Variable: M-Commerce Performance b. Predictors: (Constant), Information System

4.5.7 SEM Results for Influence of Information Systems and M-Commerce Performance

This section presents SEM analysis of the relationship between Information System and M-Commerce Performance. The section provides the results for both unmoderated and moderated models.

4.5.7.1 SEM Results for Influence of Information Systems on M-Commerce Performance –Unmoderated

The study sought to establish the influence of information system on m-commerce performance and tested the following hypothesis.

H1: There is a relationship between information system and m-commerce performance in Kenya’s commercial banks.

In order to ascertain the relationship of the construct under study, the path coefficients generated from the SEM was used to determine the direction and strength of the relationship, while t-statistics provided information on the significance to the relationship between information system and m-commerce performance. The path coefficient value was 0.164 thus the relationship between information system and m-commerce performance was positive and significant (t = 2.031, p=0.043). In this respect the study supported H3. Results are presented in table 4.44 and figures 1 and 2.

149

Table 4.44 Relationship between Information System and M-Commerce Performance

Path Standard T - P - Path Hypothesis coefficient Error Value Value Un- IS -> MC-P 0.164 0.081 2.031 0.043 Supported moderated

IS -> MC-P 0.117 0.084 1.403 0.161 not supported

IS*Market-> not supported Moderated 0.048 0.119 0.4 0.69 MC-P

IS*Technology 0.045 0.126 0.355 0.723 not supported

4.5.8.1 SEM Results for Influence of Information Systems and M-Commerce Performance – Moderated Path

This section introduces market turbulence and technological turbulence. Details are in the subsequent section.

4.5.9 Hypothesized Relationship between Information System and M-Commerce Performance

The direct relationship of IS and M-Commerce performance had a positive regression weights of 0.117, but insignificant (t=0.084 and p=0.161). The relationship between IS and M-Commerce moderated by MT was found to be positive regression weights=0.048 and statistically insignificant (t=0.4, p=0.69). Moderating IS and MC with TT revealed regression weights=0.045, and statistically insignificant (t=0.355, p=0.723). The moderators failed to moderate the variables. The study failed to support the hypothesis. The results are presented in table 4.44, figures 4.3 and 4.4.

4.6 Influence of Human Resources on M-Commerce Performance

The study sought to investigate the influence of human resources on m-commerce performance in Kenya’s Commercial Banks. The study conducted descriptive analysis for human resources and presented results for factor analysis, correlation, regression ANOVA and SEM in the subsequent section.

150

4.6.1 Frequency Distribution on Influence of Human Resources on M-Commerce Performance

The results in table 4.48 summarizes the study findings on respondents’ views on how human resources affect performance of m-commerce in Kenya’s commercial banks. The statements on recruitment of skilled staff, training and rewards in relation to m-commerce management of strategy, growth of new applications and excellence in m-commerce development, were agreed with a rating of 95 percent. On HR4 that banks have unique staff that are responsible for the performance of m-commerce was agreed on by 88 percent of the respondents. HR 5 on banks talented human resources was agreed on at 87 percent, with 12 percent being neutral and 1 percent disagreeing. HR 6 and HR 7, on staff not being highly mobile across banks and being risk averse to new untested innovations was agreed on at 67 percent. However, there were respondents who disagreed at 28 and 26 percent for HR6 and HR 7 respectively. Details of results are presented in table 4.45.

151

Table 4.45 Frequency Distribution for Human Resources Influence on M-Commerce Performance. SD D N A SA No. Statement (%) (%) (%) (%) (%) HR1 Human resource recruits specialized skilled staff to manage 1 0 4 38 57 the m-commerce strategy HR2 Human resource train staff to support the growth of m-commerce 1 1 3 67 28 new applications HR3 The bank’s human resources rewards excellence in m-commerce 0 2 3 70 25 development HR4 Bank has unique staff that are responsible for the performance of 0 2 10 63 25 m-commerce rather than the industry’s structural characteristics HR5 The banks talented human resources drive the overall m- 0 1 12 68 19 commerce performance of the bank HR6 Bank’s human resource are not highly mobile across banks hence 1 4 28 53 14 the cause for high m-commerce performance HR7 Bank’s human resources are risk averse to new untested innovations 1 6 26 55 12 for fear of punishment

4.6.3 Mean and Standard Deviation for Human Resource Influence The Human Resource (HR) scale consisted of 7 items. The scale was intended to describe the extent to which the respondents believed HR would influence performance of m- commerce. The highest mean rating was 4.5 for the HR1 statement “human resource recruits specialized skilled staff to manage the m-commerce strategy” (SD=0.68). overall, 5 items had mean rating scores above 4 while only two items had mean rating of 3.72 and 3.76. This indicated that the respondents believed that HR influenced the performance of m-commerce. The results are presented in table 4.46.

152

Table 4.46 Mean and Standard Deviation for Human Resources Influence

No Statement Mean Std HR1 Human resource recruits specialized skilled staff to manage the m-commerce strategy 4.5 0.68 HR2 Human resource train staff to support the growth of m-commerce new applications 4.21 0.61 HR3 The bank’s human resources rewards excellence in m-commerce development 4.19 0.56 HR4 Bank has unique staff that are responsible for the performance of m-commerce rather than the industry’s structural characteristics 4.12 0.64 HR5 The banks talented human resources drive the overall m- commerce performance of the bank 4.05 0.60 HR6 Bank’s human resource are not highly mobile across banks hence the cause for high m-commerce performance 3.76 0.77 HR7 Bank’s human resources are risk averse to new untested innovations for fear of punishment 3.72 0.79

4.6.4 Factor Analysis Results for Human Resources Influence To reduce the items of Human Resources influence, factor analysis was used. The results for human resources influence showed that KMO had a value of 0.5 and Bartlett’s test, p=0.002. The results are presented in table 4.47.

Table 4.47 KMO and Bartlett’s Test for Human Resources Influence

Kaiser-Meyer-Olkin Measure of 0.5 Sampling Adequacy Bartlett’s Test of Sphericity Approx. Chi-Square 10.008 df 1 Sig. .002

Exploratory factor analysis using PCA with promax rotation revealed that out of the five items, only two items (HR1 = 0.769, HR5= 0.803), returned factor loadings that were above the acceptable threshold of 0.5. Items HR 1, and HR 5 were retained for measurement estimation model. Results are presented in table 4.48.

153

Table 4.48 Human Resource Component Matrix

PCA variance No Statement component extracted loading (%) Human resource recruits specialized skilled HR1 0.769 61.917 staff to manage the m-commerce strategy The banks talented human resources drive the HR5 0.803 overall m-commerce performance of the bank

Total variance explained results for Human Resources showed that two component items explained 61.917 percent of the total variability

4.6.4 Reliability Analysis on Human Resource Influence on M-Commerce Performance HR construct was evaluated for reliability before SEM analysis. A coefficient alpha of 0.781 was achieved denoting that the measuring scale was reliable. Results are presented in table 4.49. Table 4.49 Reliability Test for Human Resources Items

Cronbach’s No Statements Alpha Human resource recruits specialized skilled staff to HR1 manage the m-commerce strategy 0.781 The banks talented human resources drive the overall m- HR5 commerce performance of the bank

4.6.5 Correlation Analysis between Human Resources and M-Commerce Performance The study evaluated the relationship between human resources and m-commerce performance to determine if there was a correlation. The findings suggested that human resources and m-commerce performance were positively and significantly related. Results are presented in table 4.50.

154

Table 4.50 Correlation between Human Resources and M-Commerce Performance

M –Commerce Human Performance Resource M –Commerce -Performance Pearson Correlation 1 .161* Sig. (2-tailed) .032 N 178 178 Human – Resource Pearson Correlation .161* 1 Sig. (2-tailed) .032 N 178 178 *. Correlation is significant at the 0.05 level (2-tailed).

4.6.6 Chi Square Test on Human Resources Influence on M-Commerce Performance Chi Square test was used to test the strength of association between Human Resources and m-commerce performance. The results indicated that there was a strong association between human resources and m-commerce performance. The results are presented in table 4.51.

Table 4.51 Chi Square Test on Human Resource Influence

Value df Asymp. Sig. (2-sided) Pearson Chi-Square 1116.286a 948 .000 Likelihood Ratio 375.855 948 1.000 Linear-by-Linear Association 4.727 1 .030 N of Valid Cases 178 a. 1038 cells (99.8%) have expected count less than 5. The minimum expected count is .01.

4.6.7 ANOVA on Human Resource Influence on M-Commerce Performance ANOVA test was done to test the relationship between human resource influence and m- commerce performance. The results of the study indicated that there is a positive and significant relationship between human resources influence and m-commerce performance. The results are presented in table 4.52.

155

Table 4.52 ANOVA between Human Resources and M-Commerce Performance

Model Sum of Squares df Mean Square F Sig. 1 Regression 4.621 1 4.621 4.692 .032b Residual 173.357 176 .985 Total 177.978 177 a. Dependent Variable: M-commerce performance b. Predictors: (Constant), Human resource

4.6.8 SEM Results for the Influence of Human Resources on M-Commerce Performance.

This section presents findings on SEM analysis of the relationship between HR and M- Commerce Performance. The first part addressed the model without the moderators. The second part is inclusive of market turbulence and technological turbulence.

4.6.8.1 SEM Results for Influence of Human Resources on M-Commerce Performance Unmoderated

The study sought to establish the influence of human resources on m-commerce performance and tested the following hypothesis.

H4: There is a relationship between human resources and m-commerce performance in Kenya’s commercial banks.

In order to ascertain the relationship of the construct under study, the path coefficients generated from the SEM was used to determine the direction and strength of the relationship, while t-statistics provided information on the significance to the relationship between human resources and m-commerce performance. Path coefficient was negative (β=-0.042) and insignificant (t =0.478, p=0.633). The relationship was insignificant because it did not meet the threshold of t-value being >1.96 and p-value=<0.05The hypothesis was not supported. The results are presented in table 4.53, figures 3 and 4.

156

Table 4.53 Relationship between Human Resources and M-Commerce Performance

Path Standar T P Decision for Path coefficient d Error Value Value Hypothesis Un- HR -> MC-P -0.042 0.087 0.478 0.633 Not Supported moderated

IS -> MC-P -0.041 0.083 0.491 0.624 Not supported

IS*Market-> Not supported -0.049 0.112 0.443 0.658 Moderated MC-P IS*Technolo Not supported 0.06 0.106 0.564 0.573 gy

4.6.8.2 Hypothesis Testing for the Relationship between Human Resources and M- Commerce Performance - Moderated

Model moderation with market turbulence reflected an insignificant relationship (t=0.443, p= 0.658). Similarly, moderation with technological turbulence also reflected insignificance with (t = 0.564 and p-= 0.573). Thus the hypothesis was not supported and conclude that the moderators failed to moderate the variables. In this regard therefore, the influence of HR on m-commerce performance was negative and statistically insignificant. The results are presented in table 4.53, figures 4.3 and 4.5.

4.7 Influence of Strategic Communication on M-Commerce-Performance

The study sought to investigate the influence of strategic communication on m-commerce performance. The study conducted descriptive analysis, factor analysis, correlation, ANOVA and SEM analysis discussed as follows:

4.7.1 Frequency Distribution on Strategic Communication

The study sought to evaluate respondents’ views regarding the influence of strategy communication on performance of m-commerce in Kenya’s commercial banks. Respondents agreed at 96 percent and 91 percent (SC1 and SC4) respectively, that the bank’s vision and the communication of bank strategy and related activities leads to an overall positive m-commerce performance. 89 percent of the respondents for SC3 and SC5, agreed that strategy communication to all staff and communication of overall goals to all employees leads to bank’s positive m-commerce performance. The lowest rating on

157

strategy communication was SC2 where the staff at 87 percent agreed that strategy communication of bank’s mission is directly linked to m-commerce. The results are presented in table 4.54.

Table 4.54 Frequency Distribution of Strategy Communication

SD D N A SA No Statement (%) (%) (%) (%) (%) Strategy communication of the bank’s vision supports the growth of m- 0 1 3 46 50 SC1 commerce Strategy communication of the bank’s 0 3 9 75 12 SC2 mission is directly linked to m-commerce Strategy communication to all staff contributes to the bank’s growth of new 0 1 10 67 22 SC3 accounts Communication of bank strategy and related activities leads to an overall 0 3 6 69 22 SC4 positive m-commerce performance Communication of overall goals to all employees leads to bank’s positive m- 0 4 7 70 19 SC5 commerce performance

4.7.2 Mean and Standard Deviation for Strategic Communication

The strategy communication (SC) scale consisted of 5 items. The scale was intended to describe the extent to which the respondents believed SC influenced performance of m- commerce. Overall, out of 5 items, 4 had a mean rating of 4 and above. The lowest had a mean rating of 3.96, (SD-0.59). The highest mean rating was 4.46 for the SC1 statement “Strategy communication of the bank’s vision supports the growth of m-commerce” (SD=0.59). overall, 5 items had mean rating scores above 4 while only two items had mean rating of 3.72 and 3.76. This indicated that the respondents believed that strategy communication influenced the performance of m-commerce.

158

4.7.3 Factor Analysis Results for Influence of Strategic Communication on M- Commerce Performance

Factor analysis was used to reduce the items of strategic communication influence. Results indicated that KMO had a value of 0.712 and Bartlett’s test, p=0.00. The results are presented in table 4.55.

Table 4.55 KMO and Bartlett’s Test for Strategic Communication

Kaiser-Meyer-Olkin Measure of 0.712 Sampling Adequacy Bartlett’s Test of Sphericity Approx. Chi-Square 82.351 df 6 Sig. .000

Exploratory factor analysis using PCA with promax rotation revealed that out of the five items, only one item (SC3), failed to return factor loadings that were above the acceptable threshold of 0.5. Items SC1, SC2, SC4 and SC5 were retained for measurement estimation model. Results are presented in table 4.56.

Table 4.56 Strategic Communication Component Matrix for Strategic Communication

PCA variance No Statement component extracted loading (%) Strategy communication of the bank’s vision SC1 0.655 54.547 supports the growth of m-commerce Strategy communication of the bank’s mission is SC2 0.662 directly linked to m-commerce Strategy communication to all staff contributes to SC4 0.774 the bank’s growth of new accounts Communication of bank strategy and related SC5 activities leads to an overall positive m- 0.84 commerce performance

Total variance explained results for Strategic Communication showed that two component items explained 54.547% of the total variability.

159

4.7.3 .1 Reliability Analysis on Strategic Communication Influence SC construct was evaluated for reliability and convergent validity before SEM analysis. A coefficient alpha of 0.715 was achieved denoting that the measuring scale was reliable. The results are presented in table 4.57.

Table 4.57 Reliability Test for Strategic Communication Items

Cronbach’s No. Statement Alpha

Strategy communication of the bank’s vision supports the SC1 growth of m-commerce Strategy communication of the bank’s mission is directly SC2 linked to m-commerce 0.715 Strategy communication to all staff contributes to the bank’s SC3 growth of new accounts Communication of bank strategy and related activities leads SC4 to an overall positive m-commerce performance

4.7.4 Correlation Analysis between Strategic Communication and M-Commerce Performance The study intended to measure the correlation between strategic communication and m- commerce performance. The findings suggested that strategic communication and m- commerce performance were positively and significantly related. Results are presented in table 4.58.

160

Table 4.58 Correlation Between Strategic Communication and M-Commerce Performance

M –Commerce Strategic- Performance Communication M –Commerce -Performance Pearson Correlation 1 .161* Sig. (2-tailed) .032 N 178 178 Strategic Communication Pearson Correlation .575** 1 Sig. (2-tailed) .000 N 178 178 **. Correlation is significant at the 0.01 level (2-tailed).

4.7.5 Chi Square Test on Strategic Communication Influence

Chi Square test was used to test the strength of association between strategic communication and m-commerce performance. The results indicated that there was a strong association between strategic communication and m-commerce performance. The results are presented in table 4.59.

Table 4.59 Chi Square Test on Strategic Communication Influence

Asymp. Sig. (2- Value df sided) Pearson Chi-Square 4107.000a 3160 .000 Likelihood Ratio 654.572 3160 1.000 Linear-by-Linear Association 58.484 1 .030 N of Valid Cases 178 a. 3278 cells (99.9%) have expected count less than 5. The minimum expected count is .01.

4.7.6 ANOVA on Influence of Strategic Communication on M-Commerce

ANOVA test was done to test the relationship between strategic communication and m- commerce performance. The results of the study indicated that there is a relationship between strategic communication and m-commerce performance. The results are presented in table 4.60.

161

Table 4.60 ANOVA Between Strategic Communication and M-Commerce

Model Sum of Squares df Mean Square F Sig. 1 Regression 58.458 1 58.458 86.084 .000b Residual 119.519 176 .679 Total 177.978 177 a. Dependent Variable: M Commerce Performance b. Predictors: (Constant), Strategic Communication

4.7.7 SEM Results for the Influence of Strategic Communication and M-Commerce

Performance. Unmoderated and Moderated

This section presents hypothesis tests for the SEM model; without a moderator and with moderators: Market and Technological Turbulence.

4.7.7.1 SEM Results for Influence of Strategic Communication and M-Commerce

Performance Unmoderated

The study sought to establish the influence of strategic communication on m-commerce performance and tested the following hypothesis.

H5: There is a relationship between strategic communication and m-commerce performance in Kenya’s commercial banks.

In order to ascertain the relationship of the construct under study, the path coefficients generated from the SEM was used to determine the direction and strength of the relationship, while t-statistics provided information on the significance to the relationship between strategic communication and m-commerce performance. The results indicate the relationship between strategic communication and m-commerce performance was statistically significant; positive regression weight 0.287, (t=3.307, p =0.001). Results presented in table 4.61, figures 4.1 and, 4.2.

162

Table 4.61 Relationship between Strategic Communication and M-Commerce

Model Path Standard T - P - Decision for Path coefficient Error Value Value Hypothesis un- SC -> MC-P 0.287 0.087 3.307 0.001 Supported moderated

SC -> MC-P 0.261 0.115 2.27 0.024 Supported

SC*Market-> Not supported Moderated 0.011 0.122 0.094 0.925 MC-P

SC*Technology -0.111 0.119 0.932 0.352 not supported

4.7.7.2 SEM Results for Influence of Strategic Communication on M-Commerce Performance - Moderated

The moderated model, reveals details of the inclusion of the moderating variables MT and TT. The direct relationship between strategic communication and m-commerce was found to be significant with path coefficient being positive Beta value = 0.261, and statistically significant (t=2.27, p=0.024). When the relationship between SC and M-commerce performance is moderated using MT, the relationship was found to be insignificant (t=0.094, p=0.925). Similarly, moderation of SC and M-Commerce performance using TT was found to be insignificant, a negative path coefficient -0.111, and statistically insignificant (t=0.932, p=0.352).

4.7.8 Hypothesis Testing for the Relationship between Strategic Communication and M-Commerce Performance

The hypothesis 5 postulated that there is a relationship between strategic communication and m-commerce performance. The relationship between strategy communication and m- commerce performance was positive and statistically significant. The path coefficient was positive and significant at 0.05 level of significance (β=0.287, t=3.307, p=0.001). At this point, the hypothesis H5 was supported and consequently confirm that there is a positive and statistically significant relationship between strategy communication and m- commerce. Similarly, on moderation, the model reflected a significant relationship. Moderation with market turbulence reflected a significant relationship (t= 2.27, p=0.024), which met the threshold of t-value being >1.96 and p-value =<0.05. The market

163

turbulence therefore successfully moderated the relationship between strategic communication and m-commerce performance. The hypothesis was supported.

However, the moderation with technological turbulence reflected an insignificant relationship with a negative path coefficient of -0.111, (t=0.932, p= 0.352). Thus based on this moderation, the hypothesis was not supported and conclude that the technological turbulence failed to moderate the relationship between strategic communication and m- commerce performance. In this regard therefore, the influence of SC on m-commerce performance was negative and statistically insignificant hence the study failed to support the null hypothesis. Accordingly, therefore there is no significant relationship between SC and M-Commerce performance in commercial banks in Kenya. In this respect the moderator failed to moderate the construct and hence H5 was not supported.

4.8 Influence of Market Turbulence on M-Commerce Performance

The study sought to investigate the influence of market turbulence on m-commerce performance. The study conducted descriptive analysis, factor analysis, correlation analysis, ANOVA and Simultaneous Equation Modeling discussed as follows:

4.8.1 Frequency Distribution of Market Turbulence

The study sought to evaluate respondents’ views regarding the influence of moderator; market turbulence on m-commerce performance. The findings on market turbulence in relation to m-commerce performance are presented in table 4.67 The respondents’ agreement on competition being high in MT1, was agreed on by all respondents. 96 percent agreed that customer demand in the banking industry is stable, while 94 and 93 percent of the respondents agreed that new products/services were being introduced frequently and that the business environment in the banking sector is continuously changing. In addition, respondents also agreed at 91 and 86 percent that bank customers tended to look for new products/services all the time and customers’ preferences change quite a lot over time. The results are presented in table 4.74.

164

Table 4.62 Frequency Distribution of Market Turbulence

SA D N A SA No Statements (%) (%) (%) (%) (%) There is a lot of competition in the 0 0 0 20 80 MT1 banking sector Customer demand in our the banking 0 0 4 46 50 MT2 sector is very stable New product/service introductions are 0 1 6 62 32 MT3 very frequent in the banking sector. The business environment in the banking sector is continuously 0 0 7 59 34 MT4 changing. In the banking business, customers’ preferences change quite a lot over 0 2 12 60 26 MT5 time. Bank customers tend to look for new 1 4 4 49 42 MT6 products/services all the time.

4.8.2 Mean and Standard Deviation for Market Turbulence

The scale for market turbulence (MT) was intended to describe the extent to which the respondents believed MT influenced performance of m-commerce. MT had a total of 6 items and each scale rated on a five point Likert scale ranging from 1 denoting “strongly disagree” to 5 denoting “strongly agree”. 1 denoting “strongly disagree” 1.1-2.0 denoting, “disagree”, 2.1 to 3.0 denoting “neutral” 3.1 to 4.0 denoting “strongly agree” and a mean value of 4.1 and above denotes “Strongly agree”. Overall, 100 percent of all 6 items of MT, had a mean rating score of 4 and above. The highest mean rating was 4.8 for the statement MT1 “there is a lot of competition in the banking sector” (SD=0.4). This indicated that the respondents believed that MT, influenced the performance of m- commerce. The results are presented in table 4.63.

165

Table 4.63 Mean and Standard Deviation for Market Turbulence

No Statements Mean SD MT1 There is a lot of competition in the banking sector 4.8 0.40 Customer demand in our the banking sector is 4.46 0.57 MT2 very stable New product/service introductions are very 4.25 0.58 MT3 frequent in the banking sector. The business environment in the banking sector is 4.27 0.59 MT4 continuously changing. In the banking business, customers’ preferences 4.1 0.68 MT5 change quite a lot over time. Bank customers tend to look for new 4.28 0.78 MT6 products/services all the time.

4.8.3 Factor Analysis Results on Market Turbulence

Factor analysis was conducted to reduce items of market turbulence. Market turbulence construct was measured using three items and the construct was factor analyzed so as to arrive at an appropriate measure. The study found that KMO had a value of 0.603 and Bartlett’s test. The results are presented in table 4.64

Table 4.64 KMO and Bartlett’s Test for Market Turbulence

Kaiser-Meyer-Olkin Measure of 0.603 Sampling Adequacy Bartlett’s Test of Sphericity Approx. Chi-Square 38.899 Df 3 Sig. .000

Exploratory factor analysis using PCA with promax rotation revealed that out of 6 items, three items (MT4, MT5 and MT6), achieved the acceptable threshold of 0.5. Thus the three items MT4, MT5 and MT6, were retained for measurement estimation model. Results are presented in table 4.65.

166

Table 4.65 Market Turbulence Component Matrix

PCA variance No Statement Component extracte Loading d (%) The business environment in the banking sector MT4 0.693 is continuously changing. In the banking business, customers’ preferences MT5 0.685 49.465 change quite a lot over time. Bank customers tend to look for new MT6 0.73 products/services all the time.

Total variance explained results for market turbulence showed that the three component items accounted for 49.465 percent of the total variability.

4.8.3.1 Reliability Analysis on Market Turbulence Influence

Market turbulence construct was evaluated for reliability and convergent validity before SEM analysis. Coefficient alpha of 0.737 was achieved denoting that the measured scale was reliable. The results are presented in table 4.66

Table 4.66 Reliability Test for Market Turbulence Items

Cronbach’s No Statements Alpha

The business environment in the banking sector is continuously MT4 changing. In the banking business, customers’ preferences change quite a MT5 0.737 lot over time. Bank customers tend to look for new products/services all the MT6 time.

4.8.4 Correlation Analysis between Market Turbulence and M-Commerce Performance

The study intended to measure the correlation between market turbulence and m- commerce performance. The measure was to determine if there was a correlation between market turbulence and m-commerce performance. The findings in table 4.72 suggests that market turbulence and m-commerce performance were positively and significantly related. The results are presented in table 4.67.

167

Table 4.67 Correlation Between Market Turbulence and M-Commerce Performance

M –Commerce Market Performance Turbulence M –Commerce -Performance Pearson Correlation 1 .436** Sig. (2-tailed) .000 N 178 178 Market Turbulence Pearson Correlation .436** 1 Sig. (2-tailed) .000 N 178 178 **. Correlation is significant at the 0.01 level (2-tailed).

4.8.5 Chi Square Test on Market Turbulence and M-Commerce Chi Square test was used to test the strength of association between market turbulence and m-commerce performance. The results indicated that there was a strong association between market turbulence and m-commerce performance. The results are presented in table 4.68.

Table 4.68 Chi Square Test on Market Turbulence Influence

Asymp. Sig. (2- Value df sided) Pearson Chi-Square 2343.650a 1659 .000 Likelihood Ratio 568.659 1659 1.000 Linear-by-Linear Association 52.278 1 .000 N of Valid Cases 178 a. 1759 cells (99.9%) have expected count less than 5. The minimum expected count is .01.

4.8.6 ANOVA on Market Turbulence Influence

ANOVA test was done to determine the influence of MT on MC-Performance. The results reveal that there is a positive relationship between MT and MC-Performance. The results reveal that MT significantly predicts MC-Performance, F (1,176) =73.771, p<0.05. The results are presented in table 4.69.

168

Table 4.69 ANOVA between Market Turbulence and Strategic Management

Sum of Model Squares df Mean Square F Sig. 1 Regression 52.574 1 52.574 73.771 .000b Residual 125.429 176 .713 Total 178.003 177 a. Dependent Variable: M-commerce-Performance b. Predictors: (Constant), Market-Turbulence

4.8.7 SEM Results for the influence of Market Turbulence on M-Commerce Performance

The study sought to establish the influence of market turbulence on m-commerce performance and tested the following hypothesis:

H6: There is a relationship between market turbulence and m-commerce performance. In order to ascertain the relationship of the construct under study, the path coefficients generated from the SEM was used to determine the direction and strength of the relationship.

4.8.8 Hypothesis Testing for the Relationship between Market Turbulence and M- Commerce

The study findings showed that the moderated path between market turbulence and m- commerce performance was not significant. Regression weights = 0.132, (t=0.08, p=0.4). in all the measurements, market turbulence did not meet the threshold for significant relationship. The hypothesis was therefore not supported. The results are presented in table 4.70, figures 4.3 and 4.4.

Table 4.70 Relationship between Market Turbulence and M-Commerce Performance

Standard T- P- Path Coefficient Decision Error Value Value MT -> MC 0.132 0.08 1.659 0.098 Not Supported Performance

4.9 Influence of Technological Turbulence on M-Commerce Performance

The study sought to investigate the influence of technological turbulence on m-commerce performance. Technological performance was measured using two items that were factor

169

analyzed in order to generate factor scores that were used in correlation and SEM analysis. The study presented the descriptive statistics on technological turbulence and results for factor analysis correlation, ANOVA and SEM analysis as follows:

4.9.1 Frequency Distribution for Technological Turbulence

The study also sought to find out from the respondents the influence of technological turbulence on m-commerce performance. The findings in Table 4.58, indicate that TT3 on the statement that a large number of new bank products/services have been made possible by m-commerce was agreed on by 100 percent of the respondents. 99 and 98 percent of the respondents, agreed that statements TT1 and TT2 respectively supported the fact that m-commerce technology is changing fast and that m-commerce technological changes provide banks with big opportunities for growth in their industry. Statements TT4 on m- commerce technological developments in the banking sector being major was agreed on by 89% of respondents, while for TT6 statement, on the fact that customer demand for m- commerce was driving the product development was agreed on by 88 percent of respondents and for statement TT5, which was the lowest at 84 percent, the respondents agreed that m-commerce technological developments in the banking sector is expected to revolutionize banking. The results are presented in table 4.71.

170

Table 4.71 Frequency Distribution for Technological Turbulence

No Statement SA D N A SA (%) (%) (%) (%) (%) The m-commerce technology in the banking 0 1 23 76 TT1 sector is changing rapidly. M-commerce technological changes provide banks with big opportunities for growth in our 0 1 1 49 49 TT2 industry. A large number of new bank products/services have been made possible through m-commerce 0 0 1 68 32 TT3 technological breakthroughs in our industry. M-commerce technological developments in 0 0 11 64 25 TT4 the bank sector are rather major. M-Commerce technological developments in the banking sector is expected to revolutionize 0 0 16 60 24 TT5 banking Customer demand for m-commerce is driving 1 3 8 61 27 TT6 product developments

4.9.2: Mean and Standard Deviation for Influence of Technological Turbulence

The scale for technological turbulence (TT) was intended to describe the extent to which the respondents believed TT influenced performance of m-commerce. TT had a total of 6 items and each scale rated on a five point Likert scale ranging from 1 denoting “strongly disagree” to 5 denoting “strongly agree”. 1 denoting “strongly disagree” 1.1-2.0 denoting, “disagree”, 2.1 to 3.0 denoting “neutral” 3.1 to 4.0 denoting “strongly agree” and a mean value of 4.1 and above denotes “Strongly agree”. Overall, 100 percent of all 6 items of TT, had a mean rating score of 4 and above. This indicated that the respondents believed that TT, influenced the performance of m-commerce. The results are presented in table 4.72.

171

Table 4.72 Mean and Standard Deviation for Technological Turbulence

Standard No Statement Mean Deviation The m-commerce technology in the banking 4.75 0.47 TT1 sector is changing rapidly. M-commerce technological changes provide banks with big opportunities for growth in 4.48 0.54 TT2 our industry. A large number of new bank products/services have been made possible 4.31 0.48 through m-commerce technological TT3 breakthroughs in our industry. M-commerce technological developments in 4.13 0.58 TT4 the bank sector are rather major. M-Commerce technological developments in the banking sector is expected to 4.08 0.63 TT5 revolutionize banking Customer demand for m-commerce is driving 4.11 0.73 TT6 product developments

4.9.3 Factor Analysis Results on Technological Turbulence Factor analysis was conducted to reduce items of technological turbulence. Technological turbulence construct was measured using 2 items and the construct was factor analyzed in order to come up with an appropriate measure. The study found that KMO had a value of 0.5 and Bartlett’s Test of Sphericity p=.000. The results are presented in table 4.73

Table 4.73 KMO and Bartlett’s Test for Technological Turbulence

Kaiser-Meyer-Olkin Measure of 0.5 Sampling Adequacy Bartlett’s Test of Sphericity Approx. Chi-Square 16.083 Df 1 Sig. .000

Exploratory factor analysis using PCA with promax rotation revealed that out of six items, only two items (TT4 and TT6) with component loadings above the accepted threshold were retained. Results are in table 4.74.

172

Table 4.74 Technological Turbulence Component Matrix

PCA variance No. Statement component extracted loading (%) M-commerce technological developments in TT4 0.811 65.758 the bank sector are rather major. Customer demand for m-commerce is driving TT6 0.811 product developments.

Total variance explained results for technological technology showed that two component items explained 65.758% of the total variability.

4.9.3.1 Reliability Analysis of Technological Turbulence

TT construct was evaluated for reliability and convergent validity before SEM analysis was carried out. A Coefficient alpha of (0.738) was achieved indicating that the measuring scale was reliable. The results are presented in table 4.75.

Table 4.75 Reliability Test for Technological Turbulence

Cronbach’s No Statements Alpha M-commerce technological developments in the bank TT4 sector are rather major. 0.738 Customer demand for m-commerce is driving TT6 product developments. 4.8.4 Correlation Analysis between Technological Turbulence and M-Commerce Performance

The study intended to measure the correlation between two variables. This was measured by determining if there was a correlation between technological turbulence and m- commerce performance. The findings suggested that technological turbulence and m- commerce were positively and significantly related. Results are presented in table 4.76.

173

Table 4.76 Correlation Between Technological Turbulence and M-Commerce Performance

M –Commerce Technological Performance Turbulence M –Commerce -Performance Pearson Correlation 1 .436** Sig. (2-tailed) .000 N 178 178 Technological Turbulence Pearson Correlation .436** 1 Sig. (2-tailed) .000 N 1 178 **. Correlation is significant at the 0.01 level (2-tailed).

4.9.5 Chi Square Test on Technological Influence

Chi Square test was used to test the strength of association between Technological Turbulence and m-commerce performance. The results indicated that there was a strong association between technological turbulence and m-commerce performance. The results are presented in table 4.77.

Table 4.77 Chi Square Test on Technological Turbulence Influence

Asymp. Sig. (2- Value df sided) Pearson Chi-Square 1098.988a 869 .000 Likelihood Ratio 392.031 869 1.000 Linear-by-Linear Association 33.688 1 .000 N of Valid Cases 178 a. 958 cells (99.8%) have expected count less than 5. The minimum expected count is .01.

4.9.6 ANOVA on Technological Influence ANOVA test was done to test relationship between technological turbulence and m- commerce performance. The results of the study indicated the two variables had positive and significant relationship. The results are presented in table 4.78.

174

Table 4.78 ANOVA Between Technological Turbulence and M-Commerce

Model Sum of Squares df Mean Square F Sig. 1 Regression 33.879 1 33.879 86.084 .000b

Residual 144.125 176 .819 Total 178.003 177 a. Dependent Variable: M-Commerce Performance b. Predictors: (Constant), Technology Turbulence

4.9.7 SEM Results for the Influence of Technological Turbulence on M-Commerce Performance

This section presents the SEM results for influence of technological performance on m- commerce performance. This was illustrated with market turbulence and technological turbulence.

4.9.7.1 SEM Results for Influence of Technological Turbulence on M-Commerce Performance

The study sought to establish the influence of technological turbulence on m-commerce performance and tested the following hypothesis:

H6: There is a relationship between technological turbulence and m-commerce performance in Kenya’s commercial banks.

The path coefficient value was 0.061 (β=0.061) and (t=0.841, p=0.4). The results are presented in table 4.79, figures 1 and 2.

Table 4.79 Relationship between Technological Turbulence and M-Commerce Performance

Standard P Path Coefficient T Value Decision Error Value

TT -> MC 0.061 0.072 0.841 0.4 Not Supported Performance

175

4.9.8 Hypothesis Testing for the Relationship between Technological Turbulence and

M-Commerce Performance

Hypothesis 7 postulated that there was a relationship between technological turbulence and m-commerce performance. The results demonstrate that the relationship between technological turbulence and m-commerce performance was not significant. Positive regression weights = 0.061, insignificant (t=0.841, p=0.4). The hypothesis therefore, was not supported. The results are presented in table 4.79, figures 4.3 and 4.4.

4.10 Summary of Hypothesis Testing Results The results of hypothesis testing showed that out of the seven hypothesized relationships, one was not significant before moderation. This was the relationship between HR and M- Commerce Performance, meaning, that HR independent variable did not contribute to m- commerce performance. However, when the hypothesized relationship was moderated with market turbulence and technological turbulence, the variables; Organizational Leadership, Organizational Structure, Information Systems returned insignificant relationships with m-commerce performance in commercial banks. This is an indication that the moderation provided changed results in comparison with the initial hypothesized relationships.

Hypothesis Statement Results H1 Organizational leadership does not significantly Rejected influence m-commerce performance in Kenya’s commercial banks. H2 Organizational structure does not significantly influence Rejected m-commerce performance in Kenya’s commercial banks. H3 There is no significant relationship between information Rejected systems and m-commerce performance in Kenya’s commercial banks H4 There is no significant relationship between human Supported resources and m-commerce performance in Kenya’s commercial banks. H5 Strategic communication does not significantly affect m- Rejected commerce performance in Kenya’s commercial banks H6 Market turbulence does not significantly moderate the Rejected relationship between strategy implementation and m- commerce performance in Kenya’s commercial banks. H7 Technological turbulence does not significantly moderate Rejected the relationship between strategy implementation and m- commerce performance in Kenya’s commercial banks

176

4.11 Chapter Summary

Chapter four provided the findings from data analysis. The results presented were on demographic characteristics, factor analysis, correlation analysis, Chi Square, ANOVA and Structural Equation Modeling. The study findings are summarized per research objective in the subsequent section.

H1- The study found that organizational leadership influence was positively and significantly correlated with m-commerce performance, r (178), =576, p=0.01. Organizational leadership influence had a strong association with m-commerce performance, X2 (2967.417, N=178) = 2291, p=.000. The study conducted ANOVA test to test the relationship between leadership and m-commerce performance and found that there was a strong relationship between OL and m-commerce performance F (1,176)- 87.077, p=0.000, The results of SEM revealed that the relationship between organizational leadership and m-commerce performance was positive (regression weight = 0.233) and significant (t -value = 2.759, p-value=0.06). In this respect the study rejected

H1. However, the moderated path coefficient value of OL to MC Performance was positive at 0.144 but insignificant with p-value more than 0.05 and t-value less than 1.96.

H2- The study found that organizational structure influence was positively and significantly correlated with m-commerce performance, r (178) = 513, p<0.05. The results of the Chi square revealed that there was a positive and significant association, X2(4931.447,178) = 3476., p<0.05. The study conducted ANOVA test to test the relationship between organizational structure and m-commerce performance and found that there was a positive relationship between the two, F (1,176) = 62.982, p<0.05. Organizational structure was found to have a positive and statistically significant relationship with m-commerce performance. The path coefficient was positive and significant at 0.05 level of significance (β=0.237, t-value=3.553, p-value=0.000). In this regard, the null hypothesis H2 was rejected. Moderated relationship between organizational structure and m-commerce performance when moderated by market turbulence was negative and insignificant. Similarly, when OS and MC-Performance when moderated by technology turbulence, the relationship was insignificant. Hence hypothesis was supported.

H3 The results for correlation analysis between information systems and m-commerce performance revealed that the two variables were strongly correlated r(178)= .451,

177

p<.005. Chi square test indicated that there was a strong association between information systems and m-commerce performance, X2(941.573, N=178) = 711, p<0.05. From the ANOVA analysis, the study found that there was a relationship between information system and m-commerce performance, F (1,176) =44.695, p<0.05. SEM results showed that information systems was found to have a (positive and statistically significant relationship with m-commerce performance). The path coefficient was positive and significant at 0.05 level of significance (β=0.164, t-value=2.031, p-value 0.043). The null hypothesis H3 was rejected as there is a positive and significant relationship between information systems and m-commerce performance. However, moderation of IS and MC- P using market turbulence failed to moderate the relationship. The relationship was insignificant with t-values 0.4 and p-value of 0.69. The effect of moderating the relationship of IS and MC-P using technological turbulence similarly was insignificant with t-values of 0.355 and p-value of 0.723. Based on these findings, the study concludes that the moderators are not moderating the relationship and therefore the null hypothesis was supported.

H4 The results from correlating human resources and m-commerce performance showed that the two variables were positively and significantly related, r (178)=0.161, p<0.05. In addition, the association between human resources and m-commerce performance was strong, denoted by X2(948, N=178) =1116.286, p<0.05. The ANOVA tested the relationship between human resources and m-commerce performance and the findings were that F= (1,176) = 4.692, p<0.05. Further analysis with SEM showed that Path coefficient beta value was -0.042 (β=-0.042, t-value=0.478, p-value=0.633). The t-value of 0.478 was less than 1.96, while the p-value of 0.633 was greater than 0.05. In this regard therefore, the influence of HR on m-commerce performance was negative and statistically insignificant hence the study supported null hypothesis 4. Accordingly, therefore, there is no significant relationship between HR and M-Commerce performance in commercial banks in Kenya.

H5 The study found that there was a strong relationship between strategic communication and m-commerce performance, denoted by r (178) = 0.575, p<0.05. In addition, the study found that there was significant relationship between strategic communication and m- commerce performance, X2(3160,178) = 4107.000, p<0.05. ANOVA results also showed a relationship between strategic communication and m-commerce performance indicated by; F= (1,176) = 86.084, p<0.05. Further, the study found that strategic communication

178

and m-commerce performance was positive and statistically significant. The path coefficient was positive and significant at 0.05 level of significance (β=0.287, t- value=3.307, p-value=0.001). The null hypothesis H5 was therefore rejected because there is a positive and statistically significant relationship between strategy communication and m-commerce. This therefore indicates that there is significant relationship between strategic communication and m-commerce performance in Kenya’s commercial banks.

H6 The findings of the correlation between market turbulence and m-commerce performance found that market turbulence and m-commerce performance were positively and significantly related, r (178) = 0.436, p<0.05. The analysis for association of the two variables also indicated that there was a strong association between market turbulence and m-commerce performance, X2(1659, N=178) =2343.650, p<0.05. A relationship was also revealed using ANOVA, F= (1,176), =73.771. Finally using SEM analysis, the findings were that the relationship between market turbulence and m-commerce performance was not significant. Regression weights = 0.132, t-value=1.659 and p-value=0.098. The null hypothesis 6 was supported.

H7 The results for correlation analysis between technological turbulence and m- commerce performance revealed that the two variables were strongly correlated r (178) = 436, p<0.05. Further analysis with Chi square test showed that there was a strong association between technological turbulence and m-commerce performance, X2(869, N=178,) =1098.988, p<0.05. In addition, the ANOVA analysis showed that a relationship between technological turbulence and m-commerce performance F (1,176) = 86.084, p<0.05. The study found that the relationship between technological turbulence and m- commerce performance was not significant. Regression weights = 0.061, t-value=0.841 and p-value=0.4. The hypothesis was supported.

179

CHAPTER FIVE

5.0 SUMMARY, DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

The summary of the study is presented in this chapter as guided by the specific objectives. The conclusions observed, and recommendations based on the findings of the study are provided. The chapter finally gives direction on areas of further research.

5.2 Summary

The study’s main purpose was to investigate the influence of strategy implementation variables on performance of m-commerce in Kenya’s commercial banks. The study objectives were to establish the relationship between the strategy implementation variables; organizational leadership, organizational structure, information systems, human resources and strategic communication on m-commerce performance measured using the proxies; growth of new applications, growth of m-commerce, growth of bank accounts and growth of savings. In addition, the study also incorporated two moderating variables; Technology Turbulence and Market Turbulence. The underpinning theory of the study was agency theory. The theory is based on the contractual view of the firm, and focused on the relationship between the principals - the top management and the agents who are the managers responsible for strategy implementation. In the banking sector, the board members and executives and to some extent (the shareholders), are responsible for the vision and future growth of the banks, however, they all cannot participate in the daily management of the banks. Therefore, the agency theory allows shareholders to appoint principal who in turn appoints agents to run the bank in order to maximize the returns for the shareholders. Bank governance policies enhances the aligning of the agent and principal interests.

In addition to the underpinning agency theory on this study, other theories; resource based view theory, expectancy theory and activity theory were also discussed in detail. Resource based view theory holds that resources are the strengths that enable firms to implement their strategy effectively. RBV posits that certain assets with certain characteristics would lead to competitive advantage and high strategic returns would be achieved and therefore, the heterogeneity in firm resources and their degree of immobility determines firm trajectories. The expectancy theory is premised on the expectations of

180

individuals and hypothesized that employees are motivated by performance and the expected outcomes of their own behaviours. The last theory, was activity theory which illustrates the relationship between bank employees, strategy implementation and the outcomes which in this study is the m-commerce performance. Activity theory illustrates how to harness teamwork in the strategy implementation process.

These four theories are expected to influence the running of Kenya’s commercial banks. The empirical literature review established mixed findings on the influence of various variables of strategy implementation on performance both locally and outside the country. The positivism philosophy was the main basis of this study. The study used a cross- sectional survey design to investigate the relationship between strategy implementation and performance. All the commercial banks licensed by CBK as at the end of December 2015, were the respondents to the study questionnaires. The target population for the study was 200 was drawn from the sample frame. Yamane’s (1967) formula was used to compute a sample size of 133 respondents. The methodology adopted was stratified random sampling procedure to select a representative sample. A stratified random sample was used because the population was homogeneous. The sampling stratum was based on the different levels of management which included: top level managers, middle level managers and lower level managers. The data obtained was analyzed using the Statistical Packages for Social Sciences (SPSS version 21) software for descriptive and inferential statistics measures. The study used descriptive and inferential data analysis, where descriptive statistical analysis included the mean, standard deviation and frequency distributions. Inferential analysis consisted of correlation analysis and regression weights.

The results of the research show that there was statistically significant relationship between strategy implementation and m-commerce performance.

5.3 Discussion of Results

The purpose of the study was to investigate the influence of strategy implementation on m-commerce performance. To achieve this, the study investigated the influence of organizational leadership, organizational structure, information systems, human resources and strategic communication on m-commerce performance using, factor analysis, correlation analysis and structural equation modeling. The results for each research objective are discussed in the subsequent sections.

181

5.3.1 The Influence of Organizational Leadership on M-Commerce Performance

Strategic leadership is viewed as a key driver to effective strategy implementation and a lack of leadership especially at the top of the organization was identified as one of the major barriers to effective strategy implementation (Joose & Fourie, 2009). The leadership management in an organization determines the strategy implementation performance. In addition, it was established that the extent to which a leader carries out their strategic implementation activities is related to the level of their organizational performance (Ogunmokun et al., 2005). The study by Ogunmokun et al., (2005), which sought to establish whether there were significant differences in the extent to which marketing strategies were implemented in private hospitals that have high level of organizational performance in comparison to private hospitals that have low level of organizational performance, found no significant differences between low level performers and high level performers. However, the study findings were that the extent to which the private hospitals carry out their strategic implementation activities are related to the level of their organizational performance. The respondent’s claims were that their organizations carried out to a large extent changes in organization’s structure, communicated to staff how and when the strategies would be carried out, provided incentives for staff to carry out effectively the strategies, and assigned the appropriate staff who are able to implement the strategies. This was more common from organizations with high level of performance when compared to organizations with low level. Brenes et al., (2007), point out that CEO’s leadership was among the key dimensions of successful implementation of business strategy. The key dimensions are; the strategy formulation process, implementation control and follow-up, CEO’s leadership and suitable, motivated management and employees, and corporate governance.

The study findings were that there was a significant relationship between organizational leadership and m-commerce performance. The path coefficient value was positive. (Regression weight = 0.233) and significant (t -value = 2.759, p-value=0.06). For this study, the p-value was ≤ 0.05 and t-value > 1.96, the study supported the null hypothesis and stated that organizational leadership significantly affects the m-commerce performance as illustrated in table 5.1.

182

Table 5.1 Organizational Leadership and M-Commerce Performance

Path Standard T P Path coeffic Result Error Value Value ient

OL -> M-commerce performance 0.233 0.084 2.759 0.006 Supported

This study is similar to a study presented by Karamat (2013), which demonstrated that there was a strong influence of leadership behaviour on organizational performance. From the study, the success of the company that was studied, (D&R Cambric Communication), was attributed to the leadership of the CEO and the employees of the company. Karamat concluded that leadership behaviours were key factors for the growth of the companies in the service sectors. Another study by Bruno (2008), pointed out that there was a high positive relationship between personal values balance and leadership effectiveness, and that both variables presented a high positive relationship with the overall success of organizational indexes. He however, recommended that Brazilian executives of several organizations needed to be trained in terms of leadership skills, to enable them be more flexible and be able to make use of the appropriate leadership style depending on the situation.

A contradictory study was by Jing and Avery (2008), on the effectiveness of leadership and behaviours. They claim that the existing research on leadership-performance have unresolved problems related to research methodology. From the research studies reviewed by Jing and Avery (2008), the extent to which leadership behaviour influence improvement of organizational performance cannot be drawn from the study conclusion. They raise issues with the quality of performance measurement utilized by researchers. They point out that some researchers use financial measurements or non-financial measurements, instead of employing both kinds of measures in order to enhance the validity of the research. According to Jing and Avery, (2008), in using only one parameter such as the financial measure only, they ignore the interrelationship between financial performance, customer satisfaction and employee satisfaction. Both financial measurements and non-financial measurements of performance are essential in order to enhance research validity.

183

A different study by Flaninga, et al., (2013), establishing the impact of leadership on organizational outcomes, examined transformational and transactional leadership behaviours on financial measures of success at a branch office of an industrial distribution branch office. Using Multifactor Leadership Questionnaire survey, demonstrated that when managers believed they practiced transformational leadership, their performance improved and on the other hand, when the subordinate’s perception was that their leader employed transactional style, their performance would go down, illustrating that transformational leadership is more effective than transactional leadership and that the perception of subordinate staff has both positive and negative implication on performance. Following the regression analysis of the study, the conclusion was that there was positive relationship between the independent variables (leadership style) and sales (Profit margin performance). The regression also revealed a negative relationship between sales and leadership when the followers perceived their leader to be more transactional in nature.

Several studies by DuBrin, (2013), one a survey based on 205 executives from public and private companies, and another analysed 200 management techniques employed by 150 companies over a period of 10 years, resulted in a confirmation that CEOs influence performance with company profitability or total return to shareholders. With different methodologies, the results were the same that changes in leadership has an impact on organizational performance. With the mixed study results on leadership, this study holds that leadership influence on strategy implementation, influences m-commerce performance in Kenya’s commercial banks.

5.3.2 The Influence of Organizational Structure on M-Commerce Performance

Organization’s structure expresses how people are ordered and how jobs are distributed and coordinated (Mintzberg, 2009). Organizational structure defines productivity and operational process of an organization (Anderson, 2014). In this study, organization structure was examined to assess the influence of organizational structure on m- commerce performance. The findings of this study was that there was a positive and statistically significant relationship between structure and m-commerce performance. The path coefficient was positive and significant at 0.05 level of significance (β=0.237, t- value=3.553, p-value=0.000). The researcher therefore supported the null hypothesis H2. The supporting data are presented in table 5.2.

184

Table 5.2 Organization Structure and m-commerce performance

Path Standard T P Path coeffic Result Error Value Value ient OS -> M-commerce performance 0.237 0.067 3.553 0.000 Supported

A study by Madueny et. al., (2015), confirmed that organizational structure impacts organization’s performance and affects the behaviour of employees. They aver that the organizational performance depends on the structure. Researchers argue that organizational structure is a combination of job positions and their relationships to each other and their responsibilities for the processes and sub-process deliverables (Gerwin & Kolodny, 1992; Greenberg, 2011; Long, Ajagbe, Nor & Suleiman, 2012). A key factor therefore to the efficiency of a structure is the relationship between the top executives, senior management, middle managers and the rest of the staff in Kenya’s commercial banks. The efficiency depended on how the senior management maintained their relationships and workflow instruction. This is supported by the activity theory which holds that structure sets out who is doing what, why and how. Activity theory also supports the relationships of staff across the different management levels as it holds that activity theory harmonizes staff relationships.

5.3.3 The Influence of Information Systems on M-Commerce Performance

Information systems in the last decade have assumed an increasingly strategic role in organizations, helping organizations to conduct their daily activities, enhancing functionality and support decision making (Altameem, Aldrees & Alsaeed, 2014). Altameem et al., (2014), suggest that in the last 10 years, there has been an increase in recognition of information system and its role in the strategy of organizations. In their study they aver that, information systems can be regarded as a strategic resource in an organization as it provides the following opportunities; competitive advantage, improvement of productivity and performance, enables new ways of managing and organizing and developing new businesses.

This study sought to find out the influence of information system on m-commerce performance in Kenya’s commercial banks. The findings showed that there was positive and statistically significant relationship with m-commerce performance. The path

185

coefficient was positive and significant at 0.05 level t-value of 2.031, p-value =0.043. The results are presented in table 5.3.

Table 5.3 Relationship between Information System and M-Commerce Performance

Path Standard T- P- Path Hypothesis coefficient Error Value Value

IS -> MC-P 0.164 0.081 2.031 0.043 Supported

This means that the predicted hypothesis stating that there is a relationship between information systems and m-commerce is supported. These results are consistent with the findings from other studies that have demonstrated that there are significant effects of IS investments on the productivity and profitability of a firm (Brynjolfsson & Hitt, 1996).

Chou, Chuang, and Shao (2014), carried out a study using a panel data from 30 Organizations of Economic Cooperation and Development (OECD) countries over a period of 10 years to empirically test the relationship between information systems and Total Factor Productivity (TFP) link. The impact of IS on TFP was assessed through an integrative framework of IS-induced externalities and IS-leveraged innovation. From the study their findings were that computerization had reshaped the competitive landscape into a network economy with IS-induced externalities that benefited all stakeholders. The results indicated that there was a significant positive association between IS and TFP.

Collaborating the above is a study by Eruemegbe (2015), on the effect of information and communication technology (ICT) on Organization Performance in Banking industry, found that ICT in the banking services had a positive effect in the development and growth of the organization. From the results, she concluded that ICT leads to efficient and effective performance of banks and leads to competitive advantage over others and thus increasing banks profitability. Another study by Aliyu and Tasmin (2012), on the impact of information and communication technology on banks’ performance, they were of the opinion that ICT was not significantly accepted by consumers.

A study by Binuyo and Aregbeshola, (2014) on the impact of information and communication technology (ICT) and Information and Communication Technology Cost Efficiency (ICTE) on commercial bank performance was carried out in South Africa. This

186

was done using a review of existing data (of 22 years) sourced of from Bankscope-world banking. The study conclusion was that it increased return on capital employed and return on assets. They indicate that the most contribution to performance came from cost efficiency. Their recommendation was that banks should emphasize policies that enhance proper utilization of existing ICT equipment rather than additional investments. Contrary to this study is one by Monyoncho, (2015), who avers that adoption of technologies had a positive influence on the performance of commercial banks in Kenya. The study recommended that Kenya’s commercial banks should continue investing in information systems.

Inconsistent with the above studies was a study by Makadok (2001). His study found that there was no statistically significant relationship between information technology competence and firm performance. Another inconsistent finding was also a study by Ray, Muhanna and Barney (2005). In their study, they found that there was no effect from information technology resources on the performance of the customer service process, that led to the overall firm performance.

This study is consistent with RBV which states that the achievement of performance is attributed to resources having intrinsically different levels of efficiency, in the sense that they enable firms to deliver greater benefits to the organization (Barney, 1991). In this case the efficiency is obtained from the information system. However, Gizawi (2014), is opposed to the use of RBV for information systems, arguing that that RBV theory is has limited focus on the mechanisms by which resources contribute to competitive advantage. The researcher agrees partially to his argument that competitive advantage would be attributed to those companies that were able to react rapidly and flexibly to product innovation, while simultaneously possessing the capacity to manage firm specific capabilities in such a way as to effectively coordinate and redeploy internal and external competencies.

5.3.4 The Influence of Human Resources on M-Commerce Performance

This study sought to investigate if there was a relationship between HR and m-commerce performance. This study found that there was no relationship between HR and m- commerce performance. The influence of HR on m-commerce performance was negative and statistically insignificant as illustrated in table 5.4.

187

Table 5.4 Relationship between HR and M-Commerce Performance

Path Standard T - P - Decision for Path coefficient Error Value Value Hypothesis

HR -> MC-P -0.042 0.087 0.478 0.633 Not Supported

The study findings are inconsistent with Riaz (2015) study, where HR was found to significantly predict organizational performance as a result of High Performance Work Systems (HPWS), in addition, the exchange relationships between firms and staff were noted to have a positive impact on organizational performance deriving support from social exchange theory. Consistent however with the researcher’s study is the study by Husselid (1995), whose study showed that HR practices have an economically and statistically insignificant impact on both intermediate employee outcomes and corporate financial performance.

Consistent with the researcher’s study, is a research undertaken by Cetin in (2010). using correlation and regression analyses, he established that human resource management does not have a significant effect on a firm’s financial performance. The study concluded that the effect of human resource on financial performance was not positive as compared to influence from both marketing and manufacturing performances. However, a study by Khalumba (2012), in Kenya’s commercial banks, demonstrated that HR had a significant influence on the financial performance of commercial banks in Kenya. This study contradicts RBV’s theory that the achievement of performance is attributed to resources having intrinsically different levels of efficiency, enabling firms to deliver greater benefits to the organization. Another pertinent point in RBV that is contradicted by this study is premised on RBV’s assumption that an organization would have competitive advantage over its competitors through the acquisition of unique resources and capabilities, which most banks do, but the results of the study does not show the relationship (Hitt, 2013). The point of view of the researcher is in support of RVB that an organization’s resource is a source of sustained competitive advantage, if it is rare, imperfectly imitable, valuable, and substitutable (Barney, 1991). The study findings indicated insignificant relationship between HR and m-commerce performance and this

188

would mean that the resources in the banking industry are no longer rare, inimitable or not able to be substituted. Most of the resources are common to all banks, and therefore the study concludes HR on its own does not support m-commerce performance.

5.3.5 The Influence of Strategy Communication on M-Commerce Performance

Strategic communication is defined as a model consisting of purposeful activities of organizations with missions and targets (Hallahan, et al., 2007). The scholars Hallahan et al., (2007) reckon that the main purpose of strategic communication is to maintain favourable reputations with all stakeholders in order to harness the strategic interests of the organization. Communication is a critical determinant in directing, mobilizing and encouraging the workforce towards the achievement of the organizations goals and objectives (Stephen, 2011). To determine the influence of strategic communication, this study investigated the relationship between strategic communication and m-commerce performance. The study findings were that there was a relationship between strategic communication and m-commerce performance which was positive and statistically significant; regression weight 0.287, (t=3.307, p =0.001). Results presented in table 5.5

Table 5.5 Relationship between Strategic Communication and M-Commerce

Path Standard T- P- Decision for Path coefficient Error Value Value Hypothesis

SC -> MC-P 0.287 0.087 3.307 0.001 Supported

The study findings are consistent with a study undertaken by (Invernizzi, et al., (2012). Their study’s purpose was to discuss the strategic role of internal communication in the development and management of organizations based on previous studies on entrepreneurial organization. The results of the study indicated that internal communication supports the organization in three critical ways; in developing stronger links with the organizational context, by aligning the role of internal communication adequately and by sustains the organization by helping the organization to stay in touch with its organizational base. In so doing, the internal communication becomes the

189

gateway and to get closer to employees’ expectations and opinions. Secondly, the internal communication, support the organization to build a network among staff who are strongly committed to the organizational goals and vision. Thirdly, that internal communication supports managers in helping them develop and carry out their roles more effectively.

Inconsistent with this study is a study by Gaither (2012), who sought to evaluate the effect of internal communication practices on employee engagement for Kosair Children Hospital (KCH). The study findings were that there was no significant increase in employee engagement for KCH as a result of the interventions. Secondly, KCH did not have a significant increase in mean engagement scores, although KHC was the only hospital that had a 3 percent increase in their engagement score.

The study findings collaborate agency theory which states that synergy between the management and its stakeholders enables the achievement of common goals. The cooperation of the management and strategy implementers is key to the success of the strategy implementation, this is because agency theory is seen as the central approach to managerial behaviour (Rugman & Verbeke, 2008). This is because when handled well through strategic communication, strategy implementation, becomes a success. This study is also consistent with the study by Brinkschröder (2014), whose study on strategy implementation and firm performance concluded that most challenges would be resolved through communication in achieving effective strategy implementation. The study findings are also consistent with the activity theory, where human activity is termed as purposeful and is carried out by sets of actions and by use of tools which can either be physical or psychological. The psychological tools include language which is the collaborative human activity, and is experienced through communication (Engeström, 1999).

5.3.6 The Moderating Effect of Market Turbulence and Technology Turbulence on M-Commerce Performance

To determine the moderating effect of market turbulence on the relationship between OL, OS, IS, HR and SC and MC-Performance in Kenya’s commercial banks, the study used Partial Least Squares method to test for the significance of interaction between the predictors in each of the hypothesized relationship. The results of the structural equation modeling are illustrated in table 5.6.

190

Table 5.6 Moderating Influence of Market Turbulence on all Variables

Standard T- P- Path Coefficient Decision Error Value Value Not OL*Market -> MC Performance -0.022 0.122 0.183 0.855 Supported Not OS*Market -> MC Performance -0.058 0.083 0.701 0.484 Supported Not IS*Market -> MC Performance 0.048 0.119 0.4 0.69 Supported HR*_Market -> MC Not -0.049 0.112 0.443 0.658 Performance Supported Not SC*Market -> MC Performance 0.011 0.122 0.094 0.925 Supported Not MT -> MC Performance 0.132 0.08 1.659 0.098 Supported The study findings show that moderating influence was insignificant between all the independent variables and m-commerce performance in commercial banks in Kenya. Similarly, negative coefficient effect was reported in the relationship between the variables OL and OS when moderated by market turbulence. This therefore means that the market turbulence does not moderate the independent variables OL, OS, IS, HR. SC and MC-performance in commercial banks in Kenya

This study is in consistent with studies by Kumar, et al., (1998), which showed that market turbulence did not moderate the relationship between market orientation and growth in revenue. This study’s results also are similar to those of Slater and Narver (1994), whose findings were that there was no moderation by market turbulence on MO performance. Another research which supported this study is by Morah, et al., (2014) on the moderating effect of MT on profitability (performance) and market share (performance). Their findings were that the moderation effect of market turbulence does not hold true for profitability and market share.

5.3.6 The Moderating Effect of Technology Turbulence on M-Commerce Performance

The study sought to determine the moderating effect of technological turbulence orientation on the relationship between OL, OS, IS, HR, SC and m-commerce performance in Kenya’s commercial banks. The study used Partial Least Squares method to test for the significance of interaction between the predictors in each of the hypothesized relationships. Insignificant moderating effect was reported in the relationship between all the independent variables and m-commerce performance in commercial banks in Kenya as illustrated in table 5.7.

191

Table 5.7 Moderating Influence of Technological Turbulence on all Variables

Standard T- P- Path Coefficient Decision Error Value Value OL*Technology -> MC 0.016 0.113 0.142 0.887 Performance Supported OS*Technology -> MC 0.036 0.083 0.433 0.666 Supported Performance IS*Technology -> MC 0.045 0.126 0.355 0.723 Performance Supported HR*Technology -> MC 0.06 0.106 0.564 0.573 Performance Supported SC*Technology -> MC -0.111 0.119 0.932 0.352 Performance Supported TT -> MC Performance 0.061 0.072 0.841 0.4 Supported

While the predictor variables OL, OS, IS and SC as single constructs were significantly and positively correlated with M-Commerce performance factors, in that they showed as predictors they had significant and positive impact on predicting m-commerce with p<0.05, the relationship changed with the introduction of moderating variables. The technology turbulence did not moderate the relationship between strategy implementation variables and M-Commerce performance. Based on the results in table 5.7, technological turbulence does not moderate the independent variables (OL, OS, IS, HR and SC) in commercial banks in Kenya. The correlation of SC with m-commerce performance became negative when moderated by technological turbulence. The findings of this study are inconsistent with the findings of Perez- Nordtveldt et al (2015), who postulated that there was a direct and positive effect of learning gained from firm performance which was moderated by technological turbulence. However, this study’s findings are supported by Morah et al., (2014) study which indicated that TT negatively moderated the effect of MO on profitability and TT’s moderation on market share, was insignificant and therefore. Other similar studies are by Narver, (1994); Kirca, Jayachndran and Bearden, (2005) who found no supporting evidence of moderation by technological turbulence.

5.4 Conclusions

In today’s competitive environment, the banks that would have sustainable competitive advantage over others would be the banks that focus on effective strategy implementation. Strategy implementation in organizations including commercial banks is

192

becoming a complex, challenging task in today’s global and technologically advancing business environment. This calls for stronger approaches to effective strategy implementation in organizations. Organization strategies fail because of inadequate strategy implementation and not because of insufficient formulation. A well-formulated strategy may fail to provide superior performance for the firm if it is not successfully implemented. Without effective Organizational Leadership, Organizational Strategy, Information Systems, Human resources and Strategic Communication, the probability that an organization can achieve superior performance in the global economy is significantly reduced. In this study, the strategy implementation in Kenyan banks has focused mainly on the teams involved in strategy implementation across different functions in the banks.

The factors investigated as strategy implementation enablers/barriers were Organizational Leadership, Organizational Structure, Information Systems, Human Resources and Strategic Communication. Strategy implementation factors are seen as the drivers of excellent performance of m-commerce in commercial banks in Kenya, and as such this calls for better understanding of strategy implementation. Most of the work done in strategy and m-commerce has been in the area of customer adoption of m-commerce both locally and globally. This study therefore is adding knowledge into the growing need to understand strategy implementation and m-commerce performance in Kenya.

Although many models have been developed for strategy implementation, strategic management theory is still searching for their usability and associated implications. This study therefore investigated the factors that support an effective strategy implementation in relation to m-commerce performance. The aim of the study was to investigate the influence of implementation factors on m-commerce performance in Kenya’s Commercial Banks. The m-commerce performance was described as an increase of a bank’s growth of new applications, growth of m-commerce users, growth of bank accounts and growth of savings, which becomes the measures of the actions taken.

The main contribution of this study is the development of a measurement parameters of m-commerce performance in the banking industry. In addition, the instrument developed in this study can be useful for further studies of strategy implementation and other outcomes such as m-commerce performance.

193

5.4.1 Organizational Leadership and M-Commerce Performance

The organizational leadership in commercial banking sector in Kenya was found to have a significant and positive relationship with m-commerce performance. This means that organizational leadership contributes positively to m-commerce growth by providing guidelines and rules that determine the direction of the strategy. This is achieved through the established relationships with bank staff, leader flexibility towards the support of the strategy implementation and the leadership style plays a big role in the overall strategy implementation. When senior managers in banks work closely with their juniors and act as role models, the productivity increases and thus the implementation of m-commerce. A clear perception of involvement and leadership styles would contribute to successful m- commerce strategy implementation. The current changes in technology demand for big investments to meet customers’ needs. Therefore, the leadership in banks are in a good position to identify business opportunities and to avail sufficient resources for the implementation of these new technologies in commercial banks.

5.4.2 Organizational Structure and M-Commerce Performance

The study found that the regression coefficient for the unmoderated model was positive and statistically significant. This finding mean that organizational structure was positively related with m-commerce performance. The null hypothesis was therefore supported because the findings implied that the influence of organizational structure, significantly affected the implementation of m-commerce performance. Therefore, banks must adjust their structures to support strategy implementation. This result showed that m-commerce performance or growth increased with a supportive structure. It therefore means that organizational structure is very important in enabling improved performance of m- commerce. This is because structure supports strategy as it is the frame that holds all elements of an organization together.

5.4.3 Information Systems and M-Commerce Performance

The study found that the correlation between information systems and m-commerce performance for the unmoderated mode to be positive and statistically significant. The findings mean that information systems drive the bank’s growth in m-commerce. It means that the information systems influence in strategy implementation is significant because it is a single factor that links all elements in banks together from the people to operational

194

activities. Information systems facilitates and aids in attaining efficient decision making in banks.

5.4.4 Human Resource and M-Commerce Performance

The study sough to investigate the relationship between human resources and m- commerce performance. The coefficient for between human resources and m-commerce performance reflected a negative and statistically insignificant relationship; path coefficient value was -0.042, t-value=0.478, p-value=0.633. The p-value was greater than the threshold of 0.05, while t-value was less than 1.96. This means that HR does not influence strategy implementation because bank staff are mobile, can move from bank to bank, skill sets that are crucial in strategy implementation may be lacking, staff are risk averse to new un-tested innovations for fear of negative consequences in case of failure and banks reward for performance may not be linked to strategy implementation. The study therefore concluded that an improvement in specialized skilled staff to manage strategy implementation, motivation and staff retention strategies would be key in ensuring successful strategy implementation.

5.4.5 Strategic Communication and M-Commerce

The study sought to examine the relationship between strategic communication and m- commerce performance. The relationship between strategy communication and m- commerce performance for both the moderated and un-moderated path was positive and statistically significant. The study rejected the null hypothesis. The findings mean that strategic communication is very important in a bank. This is because the workplace requires constant communication between human resources, management staff, all staff and also with external stakeholders. Strategic communication is important because how the mission, vision and company objectives are shared out determines the success of strategy implementation. When staff perceive that there is very clear communication of bank’s overall goals they tend to have positive attitudes towards their role in strategy implementation.

5.4.6 Market Turbulence

The study investigated the relationship between market turbulence and m-commerce performance and the moderated path coefficient demonstrated that the relationship between market turbulence and m-commerce performance was not statistically

195

significant. The null hypothesis was therefore not supported because it does not moderate the relationship between strategy implementation and M-Commerce Performance. The results mean that market turbulence that is the consumer tastes and preferences demonstrate stable preferences. The influence of consumers on m-commerce performance is negligible. This is because the Kenyan market has had the influence of several other financial services from the mobile operators, banks and vendors such as; Mpesa, Airtel Money, Equitel, Orange Money, Tangaza Pesa and MobiKash. In addition, other services include internet banking, banking using short message services USSD, (Unstructured Supplementary Service Data) from all the banks. This therefore means that there is stability in the current market, hence the consumers in the market do not influence the performance of m-commerce much.

5.4.6 Technological Turbulence

The study sought to investigate the relationship between technological turbulence and m- commerce performance. The coefficient demonstrated that the relationship between technological turbulence and m-commerce performance was not significant. Regression weights = 0.061, t-value=0.841 and p-value=0.4. The hypothesis was not supported. The findings mean that there is no meaningful relationship between technological turbulence and m-commerce performance. That an increase in technological turbulence in the market, would not affect the performance of m-commerce performance in commercial banks in Kenya. This also means that the performance of m-commerce is not linked to the technological turbulence because m-commerce already has a defined clear service offering that cannot be changed by the technological turbulence. Technological changes are also expected to have high cost implications to banks, hence the insignificant relationship, because as the technological changes increases externally, this does not change the performance of m-commerce.

5.5. Recommendations

Based on the study results, the study provided recommendations for improvement and for further research as follows:

196

5.5.1 Suggestions for Further Improvement

5.5.1.1 Influence of Organizational Leadership on M-Commerce Performance

The study found that organizational leadership had a significant influence on m- commerce performance. Kenyan commercial banks are changing at a faster rate than ever before and technology, globalization, are reshaping the banking landscape, therefore, banks need experienced people with well-developed leadership capabilities and acumen. Bank leaders should build a strong relationship with their teams, be flexible in the current environment to support new innovations and to be seen to be the visible vision bearer of innovations in the bank.

5.5.1.2 Influence of Organizational Structure on M-Commerce Performance

The study findings show that organizational structure has a positive and significant influence on m-commerce. For successful implementation of strategy, banks should have the structure totally aligned with strategy for the bank to achieve its mission and goals because structure supports strategy. A change in a banks’ strategy, necessitates a change in its structure to support the new strategy. The banks’ business defines its strategy, therefore changing strategy would mean changing structure as it ends up changing what everyone in the bank does. Banks can use the findings of the study to review the current structure against their strategy.

5.5.1.3 Influence of information Systems on M-Commerce Performance

There was a positive influence on m-commerce performance from information systems. This result suggested that banks are attempting to advance their agility level by improving on information systems that would make bank operations more efficient and highly effective to meet the constant dynamic changes in the market. To achieve this, banks need to enhance current systems or adopt new management information systems that would facilitate the provision of services and the speed of the adoption should be expected to grow faster because of the current technological expansion. This is linked to the fact that to remain competitive, banks need to have systems that help them understand market dynamics and anticipate customer needs and develop or modify existing products in a timely manner.

197

5.5.14 Influence of Human Resources on M-Commerce Performance

There was a negative relationship between human resource and m-commerce performance. This therefore meant that the strategies used in HR were not efficient in supporting the implementation of strategy. An effective Strategic HR would have a significant influence in ensuring strategy is appropriately implemented. This would mean that for banks to succeed, they need to recruit specialized skilled staff to manage strategy implementation, that there is need to align human resources activities with the bank strategy in order to achieve successful strategy implementation.

5.5.1.5 Influence of Strategic Communication on M-Commerce Performance

Of all the independent variables investigated in the study, strategic communication was the only one with positive influence on m-commerce in the un-moderated and moderated paths. The findings suggest that strategic communication is a crucial factor in strategy implementation. This means that banks targeting to remain competitive must work on a communication strategy. The communication strategy should be part of the strategy development which would ensure that the banks vision, mission, overall goals and related bank activities are linked to the overall bank strategy and communicated to staff in an organized and properly scheduled process.

5.5.2 Suggestions for Further Research

This study has been able to show the relationship between strategy implementation and m-commerce performance in Kenya’s commercial banks. In this study the sample comprised of only commercial banks hence a single meaning that the results may not be generalizable to other industries. This study therefore recommends that future researchers should explore a mix of industry respondents such from other sectors, but within the financial industry such as insurance companies, investment funds and real estate. There is need to undertake the same study and use different moderating variables. The focus of this study was on m-commerce performance, however there is need to investigate why in a developing country like Kenya, with a high penetration rate of mobile phones, the performance of m-commerce is still dismal.

198

REFERENCES Abbott, M. L., & Mckinney, J. (2013). Understanding and Aplying Research Design. (1st ed.). Somerset, NJ: John Wiley & Sons.

Ackermann, F. & Collin E. (2004). Making Strategy, The Journey of Strategic Management. New Delhi Sage publication.

Aduda, J., & Kingoo, N. (2012). The relationship between Electronic Banking and Financial Performance among Commercial Banks in Kenya. Journal of Finance and investmentAnalysis, 1(3), 99-118.

Ahmed, S. U., & Uchida, S. (2005). Impact of relationship banking on the performance of Corporate Firms a Comparison between Japan and Bangladesh. Journal of Business and Economics, 83(3), 237-245.

Al-Ghamdi, S. (1998). Obstacles to Successful Implementation of Strategic Decisions: The British Experience, European Business Review. 98(6), 322-237.

Aker, C. J., & Mbiti, M. I. (2010). Mobile Phones and Economic Development in Africa. Journal of Economic Perspectives, 24(3), 207-232.

Alavi, S., Wahab, A. D. Muhamad N., & Shirai, A. B. (2014). Organic Structure and Organizational Learning as the Main Antecedents of Workforce Agility. International Journal of Production Research. 52(21), 6273-6295.

Aliyu, A. A., & Tasmin H. J. B. R. (2012). The Impact of Information and Communication Technology on Banks’ Performance and Customer Service Delivery in the Banking Industry. International Journal Latest Trends Finance, Economics and Science. 2(1), 80-90.

Allio, M. K. (2005). A short, practical guide to implementing strategy. Journal of Business Strategy, 26 (4), 12-21.

Andersén, J. (2010). A critical examination of the EO-performance relationship. International Journal of Entrepreneurial Behaviour & Research, 16(4), 309–328.

Altaher A. (2012). M-Commerce Service Systems Implementation. International Journal of Advanced Computer Science and Applications, 3(8),131-136.

199

Altameem, A. A., Aldrees I. A., & Alsaeed, A. N. (2014). Strategic Information Systems Planning (SISP). Proceedings of the World Congress on Engineering and Computer Science,1 (WCECS), 22-24. Sn Francisco, USA.

Amis, A. (2003). Good things come to those who wait: The strategic management of image and reputation at Guinness. European Sport Management Quarterly, 3(3),189-214.

Amit, R., & Schoemaker P, J. H. (1993). Strategic assets and organizational rent. Strategic Management Journal, 14(1), 33-46.

Aosa, E. (2011). Strategic Management within Kenya Firms. DBA Africa Management Review. 1(1), 25-30.

Argawal, A., & Mandelker, G. (1987). Managerial incentives and corporate investment and financing decisions. Journal of Finance, 42, 823-837.

Armstrong, M. (2006). A Handbook of Human Resource Management Practice. (10th. Ed.), London, UK, Kogan Page.

Arnold, A. K. (2011, August 09). Strategic Communication versus Communication. Retrieved from http://blogs.worldbank.org/publicsphere/strategic-communication- vs-communication.

Arooj Azhar, S. I. (2012). The Role of Leadership in Strategy Formulation and Implementation.International Journal of Management and Organizational Studies, 1(2),32-38.

Azhar, A., Ikram, S. Rashid, S. & Saqib, S. (2013). The Role of Leadership in Strategy Formulation and Implementation. International Journal of Management & Organizational Studies. (1)2,1-7

Banihashemi, S. A. (2011). The Role of Communication to Improve Organizational Process. European Journal of Humanities and Soial Sciences 1(1),13-24.

Ballocco, R., Mogre, R. & Toletti, G. (2009), “Mobile internet and SMEs: a focus on the adoption”, Industrial Management & Data Systems, 109 (2), 245-61.

Balogun, J. & Johnson, G. (2004). Organizational Restructuring and Middle Manager Sense Making, Academy of Management Journal, 47(4), 523-549.

200

Balogun, J. & Hailey, H. V. (2008). Exploring Strategic Change (3rd Edition). London, UK, Prentice Hall.

Balogun, J. (2006). Managing Change: Steering a Course between Intended Strategies and Unaticipated Outcomes. Long Range Planning, 39 (1), 29-49.

Banking Act of Kenya (2010). The Central Bank of Kenya Act. Nairobi, KE Government Printer.

Barreiro, L. P. & Albandoz, P., J. (2001). Population and Sample : Sampling Techniques. Journal of ManagementMathematics , CPI-CP21.

Barnat, R. (2014). Strategic Management: Formulation and Implementation. The Nature of Strategy Implementation. Retrieved from http://www.strategyimplementation.24xls.com/en101.

Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120.

Barney, J. B. (2002), Gaining and Sustaining Competitive Advantage (2nd ed.). Upper Saddle River, NJ: Prentice Hall.

Bartlett, M. S. (1954). A note on Multiplying factors for Various Chi-Squared approximations. Journal of the Royal Statistical Society, Series B 16, 296-298.

Barnea, A., Haugen, R. A., & Senbet, L. W. (1985). Agency Problems and Financial Contracting, New Jersy, NJ: Prentice.

Bartel, P. A. (2004). Human Resource Management and Organizational Performance: Evidence from Retail Banking. Industrial and Labour Relations Review, 57(2), 181-204.

Binuyo, O. A., & Aregbeshola, A. R. (2014). The Impact of Information and Communication Technology (ICT) on Commercial Bank Performance: Evidence from South Africa. Problems and Perspectives in Management, 12(3), 59-68.

Blackler, F. (1993). Knowledge and Theory of Organizations: Organizations as Activity Systems and the Reframing of Management. Journal of Management Studies, 30(6), 863-884.

201

Blackler, F., & Regan, S. (2009). Intentionality, Agency, Change: Practice theory and management. A Management Learning, 40(2), 161-176.

Blahová, M. & Knápková, A. (2011). Effective Strategic Action: from Formulation to Implementation. International Conference on Economic Business and Management. (2), 61-65.

Bolton, M. (1988) Organizational Miming: When do late adopters of organizational innovations outperform pioneers? Paper presented at the meeting of the Academy of Management, Anaheim, CA.

Bonoma, T. V., & Crittenden, V. L. (2008). Managing marketing implementation. Sloan Management Review, 29 (2), 7-14.

Bosire S.E.O. (2005). Report of the Judicial Commission of Inquiry into the Goldenberg Affair. Nairobi, KE Government Printer.

Bossidy, L., Charan, R., & Burck, C. (2002). Execution: The Discipline of Getting Things Done. New York, NY. Crown Business.

Boudreau, J. W. & Ramstad, M., P. (2007). Beyond HR. Boston. Harvard Business School Press.

Bourgeois L. J. &. Brodwin, D. R. (1984). Strategic implementation: Five approaches to an elusive phenomenon, Strategic Management Journal, 5, 241-264.

Bridoux, F. (2004). A Resource-Based Approach to Performance and Competition: An Overview of the Connections between Resources and Competition.

Brenes, E.R., Mena, M. & Molina, G.E. (2007), Key success factors for strategy implementation in Latin America, Journal of Business Research. 61(2008), 590- 598.

BrinkschrÖder, N. (2014). Strategy implementation: Key Factors, Challenges and Solutions. University of Twente, The Netherlands.

Bruno, C. F. L. (2008). Leadership and Performance beyond expectations. E-Leader Bangkok, Fundação Dom Cabral, Brazil.

Brynjolfsson, E. (1993), The productivity paradox of information technology, Communicationsof the ACM, 36(12), 66-77.

202

Brynjolfsson, E. & Hitt, L. (1996), Paradox lost? Firm-level evidence on the returns to information systems spending”, Management Science, 42(4), 541-58.

Bulmer, M. (2004). Questionnaires, 1st edition, Sage Benchmarks in Social Science Research Methods, Sage Publications, London, UK.

Burns, N. & Grove, S. K. (2003). Understanding Nursing Research. 3rd Ed. Philadelpia; Saunders Company.

Bussin M. & Modau, F. M. (2015). CEO Pay-Performance Sensitivity in the South African Context. South African Journal of Economic and Management Sciences, 18(2), 232- 244.

CAK, (2014). Communication Authority of Kenya. Quarterly Sector Statistics Report.

CAK, (2016). Communication Authority of Kenya. Quarterly Sector Statistics Report.

Calantone, R., Garcia, R., & Dröge, C. (2003). The effects of environmental turbulence on new product development strategy planning. Journal of Product Innovation Management, 20(2): 90–103.

Caliskan, N. E. (2010). The Impact of Strategic Human Resource Management on Organizational Performance. Journal of Naval Science and Engineering. 6(2), 100-116.

Candido, J. F. C. & Santos, S.P. (2008). Strategy Implementation: What is the Failure Rate? Case Discussion Paper No. 18/2008, Universidade do Algarve, Faro.

Carson, S., Godor, I., Kersch, P., Kälvemark, A., Lemne, G. & Lindberg P. (2015). Ericsson Mobility Report. On the Pulse of the Networked Society.

Cano, C.R., Carrillat, F.A., & Jaramillo, F. (2004). A meta-analysis of the relationship between market orientation and business performance. International Journal of Research in Marketing, 21(2), 179-200.

Cascella, V. (2002). Effective Strategic Planning. Quality Progress, 35(11), 62-67.

Cater, T., & Pucko, D. (2010). Factors of effective strategy implementation: Empirical evidence from Slovenian business practice. Journal for East European Management Studies, 15(3), 207-236.

203

Cetin, T. A. (2010). The Effects of Human Resource, Marketing and Manufacturing Performance on Financial Performance. Journal of Global Strategic Management, 4(1), 112-128.

Chaitanya, V. (2013, March 13). E-Commerce in Mobile Computing. Retrieved from https://www.slideshare.net/gsantosh031/m-commerce-ppt.

Charles, W. L., & Gareth, R. J. (2007). Strategic Management Theory: An Integrated Approach (5th Ed). New York, NY, General Learning Press.

Chen, L., Ellis, C., S. & Suresh N. (2014). A Supplier Development Adoption Framework using Expectancy Theory. International Journal of Operations & Production Management. 36(5), 592-615.

Chen, X. (2009). The Challenges and Strategies of Commercial Bank in Developing E- Banking Business. Guangdong University of Foreign Studies. Retrieved from https://link.springer.com/chapter/10.1007/978-3-642-11618-6_10.

Cheney, G. (2006). “Democracy at Work within the Market: Reconsidering the Potential”, in Smith V. (ed.), Worker Participation: Current Research and Future Trends (Research in the Sociology of Work, 16(Emerald Group Publising Limited), 179-203.

Childress, R. J. (2009). Leadership Behaviour and Organization Performance. The “Shadow of the Leader” Concept. The Principia Project. London, UK.

Chou, C. Y., Chuang, C.H. H., & Shao M. B. B. (2014). The Impacts of Information Technology on Total Factor Productivity: A Look at Externalities and innovations.

Choi, B. S. & Lee, H. D. (2014). The moderating Effects of Technological and Market Turbulences on Market Orientation-Firm Innovativeness Relationship. The Journal of Small Business Innovation 17(2), 61-72.

Chowdhury, D. (2004). Incentives, Control and Development: Governance in Private and Public Sector with Special Reference to Bangladesh Dhaka: Viswavidyalay Prakashana Samstha.

Christensen C. M. (1997). The Innovator’s Dilemma. When New Technologies Cause Great Firms to Fail. Harvard Business School Press.

204

Conner, K. (1991), A Historical Comparison of Resource-Based Theory and Five Schools of Thought Within Industrial Organization Economics: Do We Have a New Theory of the Firm?, Journal of Management, 17(1), 121-154.

Conlon, E., & Parks, J. (1988). The effects of monitoring and tradition on compensation arrangements: An experiment on principal/agent dyads. Academy of Management, Best papers proceedings (pp. 191-195).

Contini, D., Growe, M., Merritt, M., Oliver, R. & Moth, S. (2011). Mobile Payments in the United States: Mapping out the Road Ahead. Federal Researve Bank of Atlanta and Federal Reserve Bank of Boston, White Paper, 57.

Conway, T., & Greenslade, M. (2011). DFID Cash Transfers Evidence Paper. Evidence Paper Policy Division. London, UK: Department for International Development.

Cooper, D. R., & Schindler, P. S. (2010). Business Research Methods. (4th ed.), New York, NY: McGraw Hill Irwin.

Cooper, D. R., & SChinder, P. S. (2011). Business Research Methods, 11th. Edition, McGraw-Hill Publishing, Co. Ltd. New Delhi-India.

Cooper, D. R., & Schindler, P. S. (2014). Business research methods (12th Ed). New York: McGraw-Hill/Irwin.

Corvellec, H. (2013). What is Theory? Answers from the Social and Cultural Sciences. Stockholm: Copenhagen Business School Press

Crawford, K., & Hasan, H. (2006). Demonstrations of the Activity Theory Framework for Research in Information systems. Australasian Journal of Information Systems, 13 (2), 49-68.

Crow P. R., & Lockhart C. J. (2016). How Boards Influence Business Performance: Developing an Explanation. Leadership & Organization Development Journal. Vol. 37(8), 1022-1037.

Cunningham, E. (2008). Structural Equation Modeling using AMOS 6.0. Melbourne: Swinburn University of Technology.

Cushaman P. D. & King, S. S.(1997). Continuously Improving an Organization’s Performance. Albany, Suny Press.

205

Daft, R.L., (2001) “Organization Theory and Design”, South-Western Thomson Learning, (7th ed.), Canada.

Dahlberg, T., Guo, J., & Ondrus, J. (2015). A Critical Review of Mobile Payment Research.

Dalton R. D., Cailiy, M. C., Ellstrand, E. A., & Johnson, L. J. (1998). Meta-Analytic Reviews of Board Composition, Leadership Structure and Financial Performance. Strategic Management Journal, 19, 269-290.

Dasgupta, S. Sarkis, J. & Talluri, S. (1999). Influence of Information Technology Investment on Firm Productivity: A Cross –Sectional Study. Logistigs Information Management. 12(12), 120-129.

Davis, J. H., Schoorman, F.D., & Donaldson, L. (1997). Toward a Stewardship Theory of Management, Academy of Management Review, 22(1), 20-47.

Dawes, C. (2013). Structuring for Decisions: A Better Way to execute What Matters. Melbourne, AU: Terra Firma pty Ltd.

Delery, J.E. & Doty, D.H. (1996). Modes of Theorizing in Strtegic Human Resource Management: Tests of Universalistic, Contingency and Configurational Performance Predictions. Academy of Management Journal, 39(4), 802-835.

Dion, C., Allday, D., Lafforet, C., Derain, D., & Lahiri, G. (2007). Dangerous liaisons, Mergers and Acquisitions: The integration game, Report by Hay Group, Philadelphia, USA, Retrieved from www.haygroup.com.

Deshpande, R., & Farley, J. U. (1998). Measurign Market Orientation: Generalization and Synthesis. Journal of Market-Focused Management, 2(3), pp.213-232.

Devaraj, S., & Kohli, R. (2003). Performance Impacts of Information Technology: Is Actual Usage the Missing Link?, Management Science, 49(3), 273-289.

DuBrin J. A. (2013). Leadership: Research Findings, Practice and Skills. 7th Ed. South- Western, Natorp Boulevard, Mason, USA.

Dickson, P.R. (1992), “Toward a General Theory of Competitive Rationality: Journal of Marketing, 56(1), 69-83.

206

Eckerson, E. W. (2009). Performance Management Strategies. How to Create and Deploy Effective Metrics.

Eisenhardt, K.M. (1985). Control: Organizational and Economic Approaches. Management Science, 31, 134-149.

Eisenhardt, K.M. (1989). Agency Theory: An Assessment and Review. International Journal of Management, 5, 341 – 353.

Engestrom, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki, Finland: Orienta-Konsultit.

Engestrom, Y. (1990). Learning, working and imagining: Twelve studies in activity. Helsinki, Finland: Orienta-Konsultit.

Engestrom, Y. & Escalante, V (1996). Mundane Tool or Object of Affection? The Rise and Fall of the Postal Buddy, MIT Press.

Engestrom, Y. (2001). Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Learning at Work, 14(1), 133-156.

Eric, N. (2014). Banking on Africa. Valletta, Malta: Fund Management Group.

Eriksson, P., & Kovalainen, A. (2008). Qualitative Methods in Business Research (1st Ed.), Thousand Oaks, California: SAGE Publications.

Eruemegbe, O. G. (2015). Effect of Information and Communication Technology on Organization Performance in the Banking Sector. International Journal of Research in Engineering & Technology. 3(4), 13-22.

European Central Bank, (2010). Beyond ROE – How to Measure Bank Performance. Frankfurt, Germany.

Ernst & Young Global Limited. (2014). Big Data, Changing the Way Businesses, Compete and Operate.

Evans, S. K. (2014). Defining Distinctiveness: The Connections Between Organizational Identity, Competition, and Strategy in Public Radio Organizations. International Journal of Business Communications. 52(1) 42-67.

Ferri, G., Kalmi, P. & Kerola, E. (2010). Organizational Structure and Performance in European Banks: A Reassessment.

207

Fienberg, E. S. (2003). Notes on Stratified Sampling (for Statistics 36-303: Sampling, Surveys and Society. Department of Statistics, Carnegie Mellon University. Sourced from:http://www.stat.cmu.edu/~fienberg/Stat36-303- 3/Handouts/StratificationNotes-03.pdf

FINsights. (2008). Enterprise Payments. Technology Insights for the Financial Services Industry. Retrieved from http://www.infosys.com

Flanigan, L. R., Stewardson, G., Dew, J. Fleig-Palmer M. M. & Reeve, E. (2013). Effects of Leadership on Financial Performance at the Local Level of Industrial Distributor. The Journal of Technology, Management, and Applied Engineering. 29(4), 1-10.

Fleisher, C. S. & Bensoussan, B. E. (2007). Business and Competitive Analysis: Effective Application of New and Classic Methods.

Foss, N. J. (1998). The Resource-Based Perspective: An Assessment and Diagnosis of Problems, Working Paper, Department of Industrial Economics and Strategy, Copenhagen Business School.

Foss, K., Foss J. N., Klein G. P., & Klein K. S. (2007b). The Entrepreneurial Organization of Heterogeneous Capital. Journal of Management Studies, 44:7.

Foss, J. N. (2007). Towards a Dynamic Resource-Based View: Insights from Austrian Capital and Entrepreneurship Theory. Organization Studies. 28(5), 749–772

Florea V. N. (2014). Implementing A Model of Strategic Communication to Obtain Organizational Performance. Social-Behavioural Sciences. 2 (75),749-772.

Frazier, P. A., Tix, A. P & Barron, K. E. (2004). Testing Moderator and Mediator Effects in Counseling Psychology Research. Journal of Counselling Psychology, 51 (2), 115-134.

Gabcanova, I. (2011). The Employees- The Most Important Asset in the Organizations. Human Resources Management & Ergonomics, 5(1), 1-12

Gaines, C. Hoober, D., Foxx, W., Matuszek, T. & Morrison, R. (2013). Information Systems as a Strategic Partner in Organizational Performance. Journal of Management and Marketing Research. 1-17

208

Gaither, C. (2012). The Role of Internal Communication and the Effect on Employee Engagement.

Gaur, S. S., Vasudevan, H., & Gaur, A. S. (2011). Market orientation and manufacturing performance of Indian SMEs: Moderating role of firm resources and environmental factors, European Journal of Marketing, 45(7/8), 1172 – 1193.

Gaur, A., & Ondrus, J. (2013). The Role of Banks in the Mobile Payment Ecosystem: A Strategic Asset Perspective.

Gerwin, P., & Kolodny, H. (1992). Management of Advanced Manufacturing Technology: Strategy, Organization, and Innovation., New York, NY. Wiley/Interscience

Gichungu, N. Z. (2015). Relationship between Bank Innovations and Financial Performance of Commercial Banks in Kenya. International Journal of Education and Research. 3(5), 443-456.

Gibbons, R., 2003. Team theory, Garbage Cans, and Real Organizations: Some History and Prospects of Economic Research on Decision-Making in Organizations. Industrial and Corporate Change 12, 753–787.

Gitau, L., & Nzuki, D. (2014). Analysis of Determinants of M-Commerce Adoption by Online Consumers. International Journal of Business, Humanities and Technology, (4)3, 88-94.

Garrett, K. (2016). Strategic Planning in an Age of Turbulence. Extracted from http://www.accaglobal.com/vn/en/student/.

Gizawi, E. N. (2014). The Dynamic Capabilities Theory: Assessment and Evaluation as a Contributing Theory for Supply Chain Management.

Golden, R. A. S & Regi, B. S. (2013). Mobile Commerce in Mordern Business Era. International Journal of Current Research and Academic Review. 1(4), 96-102.

Guptal, S. & Vyas, A. (2014). Benefits and Drawbacks of M-Commerce in India a Review. International Journal of Advanced Research in Computer and Communication Engineering, 3(4), 6327-6329.

209

Graen, G., & Cashman, J. (1975). A Role Making Model of Leadership in Formal Organizations: A Developmental Approach. Leadership frontiers. Kent, Ohio, Kent State University Press.

Graham, P. (2005). Applied Multivariate Statistics for the Social Sciences. (end Ed.). Hillsdale, NJ: Erlbaum.

Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17, 109.

Greenberg, J. (2011). Behaviour in Organizations (10th ed.). Upper Saddle River, NJ: Prentice Hall, 2011.

Greene, H. (2012). Econometric Analysis 7th ed. Upper Saddle River, NJ: Prentice Hall.

Hakkinen, H., & Korpela, M. (2006). A participatory assessment of IS integration needs in maternity clinics using activity theory, International Journal of Medical Informatics. 76(11-12), 843-9.

Hallahan, K., Holtzhausen, D., van Ruler, D., Verčič, D & Sriramesh, K. (2007). Defining Strategic Communication. International Journal of Strategic Communication, 1(1), 3-35.

Hallahan, K. (1999). No, Virginia, it’s not true what they say about publicity’s implied third-party endorsement effect. Public Relations Review, 25(3), 331-350.

Halowka T. A. R. (2015). A Systematic Literature Review of the Extant Body of Knowledge on How to Successfully Implement Strategy. University of Twente, The Netherlands.

Hamilton, L.C. (2006). Statistics with Stata. Cengage: Belmont, CA.

Hair, J.F., Black, W.C., Babin. B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (5th Ed.). New Jersey: Pearson Prentice Hall.

Hair, J.F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6), Upper Saddle River, NJ: Pearson Prentice Hall.

210

Hair, J.F., Ringle, C. M., & Sarstedt, M. (2013). Editorial-Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1), 1-12.

Hair Jr. J.F., Sartedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial Least Squares Structural Equitation Modeling (PLS-SEM); An emerging tool in business research. European Business Review, 26(2), 106-121.

Hasan, H. (1998). Integrating IS and HCI using Activity Theory as a Philosophical and Theoretical Basis.

Hasan, H. (2002). Relating Knowledge Management to Business Strategy by means of an Activity Theory Framework. Department of Information Systems University of Wollongong, Wollongong, Australia.

Hashim H., N., & Jones, L., M., (2007). Activity Theory: A framework for Qualitative Analysis. University of Wollongong, Research Online.

Hau, L.N., Evangelista, F. & Thuy, P.N. (2013). Does it pay for firms in Asia’s Emerging Markets to be Market Oriented? Evidence from Vietnam, Journal of Business Research, 66(12), 2412-2417.

Henderson, A., Cheney, G. & Wever, K. C (2015). The Role of Employee Identification and organizational Identity in Strategic Communication and Organizational Issue Management about Genetic Modification. International Journal of Business Communication, 52(1-2)12-41.

Herzberg, F, Mausner B. & Snyderman B. (1959). The Motivation to Work. New York: Wiley, Higgins, J. (2005), The Eight ‘S’ of Successful Strategy Execution. Journal of Change Management, 5(1), 3-13.

Hill, C. W. L., & Jones, G. R. (2009). Theory of Strategic Management with cases, (8th Ed.), Canada: South-Western Cengage Learning.

Hitt, M. A., Ireland, D. R., & Hoskisson, R. E. (2013). Strategic Management: Competitiveness & Globalization: Concepts and Cases (10th ed..). South Western, USA: Cenveo Publisher Services.

Hitt, M., Freeman, E., & Harrison, J. (2006). The Blackwell Handbook of Strategic Management. Blackwell Publishing, Oxford.

211

Hough, J., Thompson, Arthur A. Strickland, A. J. III & Gamble J.E. (2011). Crafting and Executing Strategy. Creating Sustainable High Performance in South Africa (2nd, Ed). McGraw-Hill.

Hrebiniak, L. G. (2005). Making Strategy Work. Leading Effective Execution and Change. Pearson Education, Upper Saddle River.

Hrebiniak, L. G. (2006). Obstacles to effective strategy implementation. Organizational Dynamics, 35 (1), 12-31.

Hsiao, J. Weng. C. M and Shih-Chin Su. (2008). The relationship between organizational structure supply chain management, and organization performance: S study on the Semiconductor Industry. Journal of Information and Optimization Science. 29(2),217-240.

Huber, A. (2011). Effective Strategy Implementation. Conceptualizing Firm’s Strategy Implementation Capabilities and Assessing their Impact on Firm Performance.

Hunt, S.D. & Morgan, R.M. (1995), The comparative advantage theory of competition, Journal of Marketing, 59, 1-15.

Huselid, M. A. (1995). The impact of human resource management practices on turnover, productivity, and corporate financial performance. Academy of Management Journal, 38(3), 635–672.

Invernizzi, E., Biraghi, S. & Romenti, S. (2012). Entrepreneurial Communication and the Strategic Role of Internal Communication.

Insights, M. (2014). M-Commerce: Building the Revenue Opportunity for Banks. London, UK: Monitise.

Ismail, A. I., Raduan R.C., Uli J. & Abdullah H. (2011). The Relationship between Organizational Resources and Systems: An Empirical Research. Asian Social Science, 7(5), 72-80.

Jacks, T., Palvia, P. Schilhvy, R. & Wang, L. (2011). A framework for the Impact of IT on Organizational Performance. Business Process Management Journal, 17(5),846-870.

212

Jamali, K. S. Marthandan, G. Khazaei, M., Samadi, B. & Fie, G. Y. D. (2015). Conceptualizing Model of Factors Influencing Electronic Commerce Adoption in Iranian Family SMEs. Asian Social Science, 11(10), 256-280.

Jaworski, B. J., & Kohli, A. (1993). Market Orientation: Antecedents and Conseuences. Journal of Marketing, 57(3), 53-70.

Jensen, M. & Meckling W. (1976). Theory of the Firm: Managerial Behaviour, Agency Costs and ownership Structure, Journal of Financial Economics. 3(4), 305-360.

Jing, F.F. & Avery, C. G. (2008), The relationship between Leadership and Organizational Performance. International Business and Economic Research Journal, 7(5), 67-78.

Johnson, L. K. (2004). Execute your strategy without killing it. Harvard Management Update, 9(12), 2-3.

Joose, C., & Fourie, B. (2009). The role of strategic leadership in effective strategy implementation: Perceptions of South African strategic leaders. Southern African Business Review, 13(3), 51-68.

Juholin, E. Aberg, L. & Aula P. (2014). Towards Responsible Dialogue: Searching for the missing Piece of Strategic Employee Communication. Strategic Employee Communication- What does it really mean?

Kain, D. & Wardle E., D. K. (2000). Activity theory: An introduction for the Writing Classroom. East Carolina University. U.S.

Kahm, J., Edensvard, F & Nelson, E. (2014). Banking on Africa, A Closer Look at Sub- Saharan Banking. FMG Africa Report.

Kaptelinin, V., Kuutti, K. & Bannon, L. (1995). Activity Theory: Basic Concepts and Applications. Human Computer Interaction.

Kaptelinin, V. (1997). Activity Theory: Basic Concepts and Applications. Electronic Publications: Tutorials extracted from http://www.signchi.org/chi97/ngs

Karamat, U. A. (2013). Impact of Leadership on Organizational Performance. A Case Study of D&R Cambric Communication.

213

Kenya Communications Authority. (2014). Quarterly Sector Statistics Report. Nairobi, KE: Communications Authority of Kenya.

Khalumba, M. (2012). The Influence Of Human Resource Management Practices On Financial Performance of Commercial Banks in Kenya.

Kharuddin, S., Ashhari, Z. M. & Nassir, A. M. (2010). Information System and Firms’ Performance: The Case of Malaysian Small Medium Enterprises. International Business Research, 3(4), 28-35.

Kim, M., Song, J. & Triche, J. (2015). Toward an integrated framework for innovation in service: A resource-based view and dynamic capabilities approach, 1,533–546.

Kirca, H. A., Jayachandran, S., & Bearden O. W. (2005). Market Orientation: A Meta- Analytic Review and Assessment of Its Antecedents and Impact on Performace, Journal of Marketing, 69, 24-41.

Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling (2nd ed.). New York: Guildford.

Koch, R. (2011). Strategy How to Create, Pursue and Deliver a Winning Strategy. Edinburgh Gate, UK: FT Prentice Hall.

Kombo, D. K., & Tromp, D. L. A., (2009). Proposal and Thesis Writing: An Introduction. Paulines publications Africa, Don Bosco Printing Press, Nairobi, Kenya.

Kor, Y. Y., & Leblebici, H. (2005). How Do Interdependencies Among Human –Capital Deployment, Development and Diversification Strategies Affect Firms’ Financial Performance. Strategic Management Journal, 26(10), 967-985.

Kostopoulos, K. C., Spanos, Y. E., & Prastacos, G., P. (2003). The Resource-Based View of the Firm and Innovation: Identification of Critical Linkages. Athens, G: Athens University of Economics and Business.

Kumar, K., Subramanian, R. & Yauger, C. (1998). Examining the Market Orientation – Performance Relationship: A Context- Specific Study.

Kurkovsky, S. (2007). Mobile Commerce Technologies. Central Connecticut State University. Retrieved from http://www.cs.ccsu.edu.

214

Kuutti, K. (1996). Activity theory as a potential framework for human computer interaction research.

Kohli, K. A. & Jaworski, J. B. (1990). Market Orientation: The Construct, Research Propositions, and Managerial Implications.

Kraaijenbrink, J., Spender, J. C., & Groen, A. J. (2010). The Resource-Based View: A Review and Assessment of its critiques. Journal of Management, 36(1) 349- 372.

Kraaijenbrink, J., & Wijnhoven, F. (2008). Managing Heterogeneous Knowledge: A Theory of External Knowledge Integration. Knowledge Management Research & Practice, 6(4), 274-286.

Kyereboah-Coleman, A. (2007). Corporate Governance And Firm Performance In Africa: A Dynamic Panel Data Analysis. Global Corporate Governance Forum, (GCGF). University of Ghana Business School, Legon, Ghana.

Laffont, J. & Martimort, D. (2002). The Theory of incentives: The Principal Agent Model. London, Uk; Pinceton, Princeton University Press.

Lange, T. (2005). A Theory of the Firm Only a Microeconomist Could Love? A Microeconomist’s Reply to Lubatkin’s Critique of Agency Theory. Journal ofManagement Inquiry, 14, 404-406.

Lekovic, B., & Berber, N. (2014). The Relationship Between Communication Practice and Organizational Performances in Organizations from Europe.

Long, C.S., Ajagbe, A., M., Nor Md & Suleiman, S. E. (2012). The Approaches to Increase Employees’ Loyalty: A review on Employees’ Turnover Models. Australian Journal of Basic and Applied Sciences, 6(10), 282-291.

Lee, S. (2008). Onwership Structure and Financial Performance: Evidence from Panel Data of South Korea. University of Utah, Department of Economics.

Lee, S. (2007). Vroom’s Expectancy Theory and the Public Library Customer Motivation Model.

Leont'ev, A. (1974). The problem of activity in psychology. Soviet Psychology 13(2):4– 33. Leont'ev, A. (1978). Activity, Consciousness, and Personality. Englewood Cliffs, NJ: Prentice-Hall.

215

Leontiev, A. A. (1981) Psychology and the Language Learning Process, Oxford:Pergamon.

Levine, S., & Prietula, M. (2012). How Knowledge Transfer Impacts Performance; a Multilevel Model of Benefits and Liabilities. Organization Science, 23(6), 1748- 1766.

Li, Y., Guohui, S. & Eppler, J. M. (2008). Making Strategy Work: A literature Review on the Factors Influencing Strategy Implementation. ICA Working Paper 2/2008.

Li, Y. (2010). The Case Analysis of the Scandal of Enron. International Journal of Business and Management. 5(10), 37-41.

Lieberson, S., & O’Connor, F. J. (1972), Leadership and Organizational Performance: A Study of Large Corporations. American Sociological Review, 37(2) ,117-130.

Lipaj, D., & Davidavičienė, V. (2013). Influence of Information Systems on Business Performance. Business in XXI century, 5(1), 38–45.

Lipsey, R. (1983). An Introduction to Positive Economics., London, UK. Weidenfeld and Nicolson Ltd.

Longman, A. & Mullins J. (2004). Project Management: Key Tool for Implementing Strategy. Journal of Business Strategy, 25(5), 54-60.

Lubatkin, M. (2005). A theory of the firm only a microeconomist could love. Journal of Management Inquiry, 14: 213-216.

Lunenburg, F.C. (2011). Organizational Culture-Performance Relationships: Views of Excellence and Theory Z. National Forum of Educational Administration and Supervision Journal, 29(4), 1-10.

Lussier, R., & Achua, C. (2007). Effective Leadership. Ohio : Thompson Higher Education.

MacLennan, A. (2011). Strategy Execution: Translating strategy into action in complex organizations. Abigdon: Routledge.

Maduenyi, S., Oke O. A. & Fadeyi, O. (2015). Impact of Organizational Structure on Organizational Performance. International Conference on African Development Issues. Social and Economic Models for Development Track.

216

Mallat, N., & Tuunainen, V. K. (2008). Exploring merchant adoption of mobile payment systems: An empirical study. e-Service Journal, 6(2), 24-57.

Mang’unyi, E. E. (2011). Ownership Structure and Corporate Governance and Its Effects on Performance: A Case of Selected Banks in Kenya. International Journal of Business Administration 2(3),1-17.

Makadok, R. (2001). Towards a Synthesis of the Resource-based and Dynamic- Capability Views of Rent Creation. Strategic Management Journal, 22(5)387-401.

Martin S.S. (2012). Factors Determining Firms’ Perceived Performance of Mobile Commerce. Industrial Management & Data Systems. 112(6), 946 – 963.

Maslow, A. H. (1954). Motivation and Personality. New York: Harper and Bros.

Mathers, N., Fox, N. & Hunn, A (2009). Surveys and Questionnaires. The National Institute Health Research (NHIR) Research Design Service for the East Midlands.

McGregor, D. (1960). The Human Side of Enterprise. New York: McGraw-Hill.

Mcnurlin, C.B., Sprague, H.R., & Bui X. T. (2009). Information Systems Management in Practice Upper Saddle River.

Melville, N., Kraemer, K. & Gurbaxani, V. (2004), Information technology and organizational performance: an integrative model of IT business value, MIS Quarterly, 28(2), 283-322.

Mintzberg, H., Joseph, L., James, B., Q., & Sumantra, G. (2003) The strategy Process, Concepts, Contexts and Cases Pearson Education Ltd.

Mintzberg, H. (2004). Enough Leadership. Harvard Business Review.

Mintzberg, H. (2009). Tracking strategies: Toward a general theory of strategy formation. New York, NY: Oxford University Press.

Mitncik, B. (1992). The Theory of Agency and Organizational Analysis. New York, Oxford University Press.

Monitise Report, (2014), Mobile Money: Market Statistics and Expert Views.

Montgomery, C. & Collis, D. (1995). Competing on Resources: Strategy in the 1990s. Harvard Business Review.

217

Monyoncho, N. L. (2015). The Relationship between Banking Technologies and Financial Performance of Commercial Banks in Kenya. International Journal of Economics, Commerce and Management. III (11)784-815.

Morah, M. I. E., Wilson, J., & Tzempelikos, N. (2014). Moderation Effects on the Market Orientation –Performance Connubial Relationship: A developing World Perspective.

Morf, M. E., & Weber, W. G. (2000). I/O Psychology and the Bridging Potential of Activity Theory. Canadian Psychology Issue 41(2).81-93.

Mugenda, O. M., & Mugenda, A. G. (1999). Research Methods: Quantitative and Qualitative Approaches. Nairobi, Kenya: Acts Press.

Mugenda, O. M., & Mugenda, A. G. (2008). Social Science Research Theory and Principles. Nairobi, Kenya: Acts Press.

Mulube, J. (2009). Effects of Organizational Culture and Competitive Strategy on the Relationship between Human Resource Management Strategic Orientation and Firm Performance. PhD Thesis, University of Nairobi.

Mwando, E. K., & Muturi, M. W. (2016). Strategic Management Practices in the Government of Kenya Ministries and their Role on Change Implementation. International Journal of Recent Research in Commerce Economics and Management, 3(2), 72-84.

Najjar, L., Huq, Z., Aghazadeh, M. S., & Hafeznezami, S. (2012). Impact of IT on Process Improvement. Journal of Emerging Trends in Computing and Information Sciences. 3(1), 67-80.

Naranjo-Gil, D. & Hartmann, F. (2006). How Top Management Teams Use Management Accounting Systems to Implement Strategy. Journal of Management Accounting Research,18(1), 21-54.

Nardi B., A. (1999). Studying Context: A comparison of Activity Theory, Situated Action Models, and Distributed Cognition.

NIBUSINESS INFO. CO.UK sourced from https://www.nibusinessinfo.co.uk/Neilson, G. L., Martin, K. L., Powers, E. (2008). “The Secrets to Successful Strategy Execution”, Harvard Business Review

218

Newbert, S. L. (2007). Empirical Research on the Resource-Based View of the Firm: An Assessment and Suggestions for Future Research. Strategic Management Journal, 28: 121-146.

Ndofar, E. (2006). What to do with the resource-based view. Strategic Management Journal, 15(1), 131-148.

Ngai, T. W. E. & Gunasekaran, A. (2007). Review for Mobile Commerce Research and Applications. Decision Support Systems 43, 3-15.

Ngumi, M. P. (2013). Effect of Bank Innovations on Financial Performance of Commercial Banks in Kenya.

Nkemdima, S. M. (2015). Effective Communication as A Strategic Tool for Enhancing Employee Performance. (A case Study of Skye Bank PLC, Lagos State.

Noble, C. H. (1999). The Eclectic Roots of Strategy Implementation Research‟. Journal of Business Research, 45, 119-134.

Kaplan R.S & Norton, D.P. (2001). Transforming the Balanced Scorecard from Performance Measurement to Strategic Management; Part II, American Accounting Association Accounting Horizons. 15(2), 147-160.

Kaplan, R.S. & Norton, D.P. 2004. Strategy Maps: Turning Intangible Assets into Tangible Results. Boston, MA: Harvard Business School Press.

Norton, P. D., & Kaplan R. S. (2005). Creating the office of strategy management.

Nyanjom, O. (2011). Devolution in Kenya’s New Constitution. Constitution Working Paper No. 4.

Nysveen, H. Pedersen, E. P., & Skard, R.E.S. (2015). A review of mobile services research: Research gaps and suggestions for future research on mobile apps. SNF Working Paper No. 01/15.

Ogunmokun, G. Hopper, T. & Mcclymont, H. (2005). Strategy Implementation and Organizational Performance: A Study of Private Hospitals.

Ojo, O. (2011). Impact of Strategic Human Resource Practice on Corporate Performance in Selected Nigerian Banks. Ege Academic Review.

219

O'Keeffe, M. Mavondo, F. T., & Schroder, W. (1998), The Resource-Advantage Theory of Competition: Implications for Australian Agribusiness. Agribusiness Perspective Papers.

Okumus, F. (2001). Towards a Strategy Implementation Framework. International Journal of Contemporary Hospitality Management, 13 (7), 327-338.

Ololade, S. S., Eleyowo, O. I., Abiodun M. S., & Olalekan F. O. (2015). Human resource development as a correlate of performance of the banking industry in Ogun State Nigeria. Journal of Economics and International Finance, 7(5), 112-126.

Olson, E. M., Slater, S. F., & Hult, T. M, (2005). The Importance of Structure and Process to Strategy Implementation, Business Horizons, (48), 47-54.

Olugbode, M., Elbeltagi, I., Simmons, M. & Biss, T. (2008). The Effect of Information Systems on Firm Performance and Profitability Using a Case-Study Approach.

Owino, O. J. & Kibera, F. (2015). The influence of Organizational Culture and Market Orientation on Performance of Microfinance Institutions in Kenya. International Journal of Business and Management; 10(8), 204-211.

Oyewole, S. O., Abba, El-maude & Gambo, J. (2013). E-Banking and Bank Performance: Evidence from Nigeria. International Journal of Scientific Engineering and Technology, 2(8), 766-771.

Pandy, S. (2014). Update on the U.S. Regulatory Landscape for Mobile Payments – summary of meeting between Mobile Payments Industry Workgroup (MPIW) and Federal and State Regulators. Retrieved from http://www.Bostonfed.org/bankinfor/payment-strategies/publications/2014.

Pallant, J. (2010). SPSS Survival Manual. A Step by Step Guide to Data Analysis Using SPSS (4th. Ed.). Melbourne: Open University Press.

Parijat, P. & Bagga, S., (2014). Victor Vroom’s Expectancy Theory of Motivation – An Evaluation. International Research Journal of Business and Management, 7(9).

Pearce, J. & Robinson, R. (2013). Strategy Formulation and Implementation, McGraw- Hill/Irwin Publishers, (13th Ed).

220

Perez-Nordtvedt, L., Mukherjee, D. & Kedia, B.L. (2015). Cross-Border Learning, Technological Turbulence and Firm Performance. Management International Review. 55(1), 23-51.

Peteraf, A. M. (1993). The Cornerstones of Competitive Advantage: A Resource-Based View. Strategic Management Journal, 14(3),179-191.

Peteraf, M. & Barney, J. (2003). Unraveling The Resource-Based Tangle, Managerial and Decision Economics, 24, 309-323.

Peters, T., & Waterman, R. (1982). In search of excellence. New York, Harper & Row. Pettigrew, A.M. (1985), “Contextualist Research: a Natural Way to Link Theory and Practice” Jossey-Bass, San Francisco, CA.

Pfeffer, J. (1994). Competitive Advantage Through People: Unleashing the Power of the Workforce, Boston: Harvard Business School Press.

Pisano, P. G. (2015). A Normative Theory of Dynamic Capabilities: Connecting Strategy, Know-How, and Competition.

Popa, M. B. (2012). The Relationship between Leadership Effectiveness and Organizational Performance. Journal of Defense Resources Management, 3(1), 123-127.

Prescott, E. J. (1986). Environments as Moderators of the Relationship between Strategy and Performance. Academy of Management Journal 29, 329-346.

Pratono H. A. & Mahmood R. (2015). Mediating Effect of Marketing Capability and Reward Philosophy in the Relationship between Entrepreneurial Orientation and Firm Performance.

Polit D. F., Beck, C. T. & Hungler B. P. (2003). Essentials of Nursing Research Methods, Appraisal and Utilization. 5th Ed. Lippincott-Raven, Philadelphia.

Pulendran, S., Speed, R., & Widing, R.E. (2000). The antecedents and Consequences of Market Orientation in Australia. Australian Journal of Management, 25(2): 119 – 143.

Quick, L., T. (1988). Expectancy Theory in Five Simple Steps. Training and Development Journal. (42) 7, 1-30.

221

Rajasekar J. (2014). Factors Affecting Effective Strategy Implementation in a Service Industry: A Study of Electricity Distribution Companies in the Sultanate of Oman. International Journal of Business and Social Science. 5(1), 169-183.

Ray, G., Muhanna, W. A., & Barney, J.B. (2005). Information Technology and the Performance of the Customer Service Process: A Resource-Based Analysis. MIS Quarterly, 29(4), 625-651.

Riani, M., Torti, F., & Zani, S. (2012). Modern Analysis of Customer Surveys with Applications Using R. (1st Ed.). Somerset, NJ: John Wiley & Sons.

Riaz, S. (2015). High Performance Work Systems and Organizational Performance: An Empirical Study on Manufacturing and Service Organizations in Pakistan.

Rose, G. M. & Shoham, A. (2002), Export performance and market orientation: establishing an empirical link, Journal of Business Research, 55 (3), 217-225

Rugman, A. M. & Verbeke, A. (2008). “Corporate Strategies and Environmental Regulations: An Organizing Framework,” Strategic Management Journal 19:363- 375.

Ruparelia, R.V. (2015). Relationship between Corporate Governance and Financial Performance in the Financial Services Industry: A Case of Companies Listed at Nairobi Securities Exchange in Kenya.

Saeidi, H. (2014). The Impact of Accounting Information Systems on Financial Performance – A Case Study of TCS- India. Indian Journal of Fundamental and Applied Life Sciences, 4(S4), 412-417.

Saidi, E. (2010). Towards a Faultless Mobile Commerce Implementation in Malawi. Journal of Internet Banking and Commerce. 15(1), 1-13.

Saidi, S.M.H. A., Azad, I. & Noorudin, F.M. (2010). The Prospects and User Perceptions of M-Banking in the Sultanate Of Oman. Journal of Internet Banking and Commerce. 15, (2), 1-11.

Salameh, A. A. & Hassan B. S. (2015). Measuring Service Quality in M-Commerce Context: A Conceptual Model. International Journal of Scientific and Research Publications, 5(3), 1-9.

222

Salo, J., Sinisalo, J. & Karjaluoto, H. (2008), Intentionally developed business network for mobile marketing: a case study from Finland, Journal of Business and Industrial Marketing, 23(7), 497-506.

Saunders, M., Lewis, P., & Thornhill, A. (2009). Research Methods for Business Students, (5th Ed.), Prentice Hall.

Saunders, M., Lewis, P., & Thornhill, A. (2014). Research Methods for Business Students, (4th Ed). Prentice Hall Financial Times, Harlow.

Saunders, M., Lewis P., & Thornhill A. (2012). Research Methods for business students, (6th.ed.) London, UK: Pearson.

Schreyogg, G., & Kliesch-Eberl M. (2007). How dynamic can organizational capabilities be? Towards a dual-process model of capability dynamization. Strategic Management Journal, 28: 913- 933.

Schutt, R. K. (2012). Investigating the social world: The process and practice of research. Thousand Oaks: CA: Sage.

Sels, L., Winne, D. S., Delmotte, J., Macs, J., Faems, D., & Forrier A. (2006). Linking HRM and Small Business Performance: An Examination of the Impact of HRM Intensity on the Productivity and Financial Performance of Small Business. Small Business Economics, 26, 83-101.

Setley, M. D., Dion, P., & Miller, J. (2013). Do Various Styles of Leadership Significantly Relate to a Subordinate’s Perceived Relationship with His Leader? International Journal of Humanities and Social Science, 3(21), 1-10.

Shankar, V. & Balasubramanian S. (2009). Mobile Marketing: A Synthesis and Prognosis, Journal of Interactive Marketing 23 (2009) 118–129.

Shankar, V., Venkatesh, A., Hofacker, C. & Naik, P. (2010). Mobile Marketing in the Retailing Environment: urrent Insights and Future Research Avenues. Journal of Interactive Marketing, 24, 111-120.

Sekaran, U. (2006). Research Methods for Business: A Skill Building Approach. (4th ed.), N.Y: John Willy and Sons Inc Sekaran, U., & Bougie, R. (2010), Research Methods. For Business: A Skill Building Approach (5th Ed.). West. Sussex, UK: John Wiley & Sons Ltd.

223

Sekaran, U., & Bougie, R. (2013), Research Methods. For Business: A Skill Building Approach (6th Ed.). West. Sussex, UK: John Wiley & Sons Ltd.

Shapiro, P. S. (2005). Agency Theory. American Bar Foundation, 31, 263-284.

Sharma, A. (1997), Professional as agent: Knowledge asymmetry in agency exchange. Academy of Management Review 22(3): 758–798.

Sherwin, L. (2007). Managing Change Tool Kit.

Shekhar, K. C. (2005). Banking Theory and Practice (19th ed.), New Delhi, India: Vikas Publishing House PVT Ltd.

Shockley-Zalaback, P. (2009). Fundamentals of Organizational Communication. Knowledge, Sensitivity, Skills. Values, Allyn and Bacon Publishers, Boston.

Simon, L. J., (2004). Multicollinearity. The Pennsylvania State University. Retrieved from http://online.stat.psu.ed.

Skan, J., Dickerson J., & Masood, S. (2014). The Future of Fintech and Banking Digitally Disrupted or Reimagined? London, UK: Accenture.

Slater, E. A. (2010). Worried about strategy implementation? Do not overlook marketing's role. Business Horizons, 10. SAGE Publications Ltd., London: UK.

Slater, S.F. & Narver, J. C (1994). Does Competitive Environment Moderate the Market Orientation-Performance Relationship? Journal of Marketing, 58(1) pp 46-55.

Slater, S.F. & Narver, J.C. (1998). “Customer-led and Market-Oriented: Let’s not Confuse the Two”, Journal of Marketing, (19), 1001-1006.

Smith, E. E. (2011). Perceptions Regarding Strategy Implementation Tasks in Selected Industries: A South African Perspective. International Journal of Business and Commerce, 1(4), 22-45.

Smyth, R. (2004). Exploring the usefulness of a conceptual framework as a research tool: A researcher’s reflection. Issues in Education Research, 14(2), 1-13.

Snow C.C. & Hrebiniak L. G., (1980). Strategy Distinctive Competence, and Organizational Performance. Administrative Science Quarterly, 25, 317-336.

224

Song, M., DiBenedetto, A., & Nason, R. W. (2007). Capabilities and financial performance: The moderating effect of strategic type. Journal of the Academy of Marketing Science, 35(1), 18–34.

Sorrell, S., O’Malley, E., Schleich, J & Scott, S. (2004). The Economics of Energy Efficiency. Edward Elgar Cheltenham, UK.

Sreenivasan, J & Noor M. N. M, (2011). A Conceptual Framework on Mobile Commerce Acceptance and Usage among Malaysian Consumers.

Steers, R. M. & Porter, L. W. (1975). Motivation and Work Behaviour, McGraw Hill Book Company, New York, NY.

Stephen, C. (2011). Research Methodology in Business and Social Science, Owerri Canon. Statistics Canada: http://www.statcan.gc.ca/pub/12-539- x/2009001/sample-plan-eng.htm

Stieglitz, N., & Heine, K. (2007). Innovations and the Role of Complementarities in a Strategic Theory of the Firm. Strategic Management Journal, 28: 1-15.

Strickland, J. A. & Thompson, A. A. (2007). Strategic Management: Concepts and Cases. McGraw-Hill Education (ISE Ed.).

Stonich, P. J. (1982). Implementing Strategy: Making Strategy Happen. Pensacola: Ballinger Publishing.

Su, Z., Peng, J., Shen, H. & Xiao, T. (2013). Technological Capability, Marketing Capability, and Firm Performance in Turbulent Conditions. Management and Organization Review 9(1), 115–137.

Subramanian, R., & Gopalakrishna, P. (2001) The market orientation – performance relationship in the context of a developing economy: An empirical analysis. Journal of 13 Business Research, 53, 1-13.

Swanson, R. A. (2013). Theory Building in Applied Discipline. San Francisco, CA, Barrette-Koehler Publishers.

Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics (5th Ed.). Boston, Massachusetts: Prentice Hall.

225

Teece, D., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18 (7), 509-533.

Teece, D. J. (2007). Explicating Dynamic Capabilities: The Nature and Micro foundations of (Sustainable) Enterprise Performance. Strategic Management Journal, 28(13), 1319-1350.

Teixeira R., Koufteros, X., & Peng D. X. (2012). Organizational Structure, Integration, and Manufacturing Performance: A Conceptual Model and Propositions. Journal of Operations and Supply Chain Management 5 (1), 69-81.

Thompson, A., Gamble E. J. Jr. & Strickland J. A. (2005). Strategy: Winning in the Marketplace: Core Concepts, Analytical Tools and Cases.

Thompson, A. & Strickland, J.A., (2003). Strategic Management: Concepts and Cases. McGraw-Hill/Irwin.

Tiwari, R. & Buse S. (2006). The Mobile Commerce Prospects: A Strategic Analysis of Opportunities in the Banking Sector. Rajnish Tiwari Stephan Buse Cornelius Herstat.

Tiwari, R., Buse, S. & Herstat C. (2007). Mobile Banking as Business Strategy: Impact of Mobile Technologies On Customer Behaviour and Its Implications for Banks.

Trochim, W. (2006). The Research Methods Knowledge Base (2nd Ed.). Atomic Dog Publishing, Cincinnati, OH.

Waterman Jr., Robert, H., Peters, T. J. & Phillips R. J. (1980). Structure is not Organization. Business Horizons vol. 23(3), 2-96

Webster, J. & Watson T.R., (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii-xxiii

Weernink, O. W. M. (2014). Strategy implementation processes of small businesses in the hospitality industry. University of Twente, The Netherlands.

Wernerfelt, B, (1984). A Resource-Based View of the Firm. Strategic Management Journal, 5(2), 171-180.

West, J., & Mace, M. (2010). Browsing as the Killer App: Explaining the Rapid Success of Apple's iPhone. Telecommunications Policy, 34(5), 270-286.

226

Wright, P., Mukherji A., & Kroll, M. (2001). A Reexamination of Agency Theory Assumptions: Extensions and Extrapolations. The journal of Socio-Economics, 30(2001) 413-429.

Wu, W., Chou, C.H., & Wu, Y. (2004). A study of strategy implementation as expressed through Sun Tzu's principles of war, Industrial Management & Data Systems, 104(5), 396-408.

Vermeeren, B., Kuippers, B. & Steijn B. (2014). Does Leadership Style Make a Difference? Linking HRM, Job Satisfaction and Organizational Performance. Review of Public Personnel Administration, 34 (2), 174-195.

Vroom, V. H. (1964). Work and motivation. San Francisco, CA: Jossey-Bass.

Vygotsky, L. (1978). Mind in Society. Cambridge, MA: Harvard University Press.

Yamane, T. (1967). Statistics: An Introductory Analysis, (2nd Ed.), New York, NY: Harper and Row.

Yermack, D. (1996), Higher Market Valuation of Companies with a Small Board of Directors. Journal of Financial Economics, 40(1996), 185-211.

Ernst & Young, E. A. (2014). Eastern Africa Banking Sector. Strong economies stimulate asset growth, but lower interest rates squeeze profits. A review of the 2013 Calendar Year. Nairobi, Kenya.

Zaribaf, M., & Bayrami, H. (2010). An Effective Factors Pattern Affecting Implementation of Strategic Plans. Academic and Business Research Institute.

Zajac E. J. & Westphal, J. D. (2004). The Social Construction of Market Value: Institutionalization and Learning Perspectives on Stock Market Reactional, 69(3), 233-257.

Zenger, T. (1988). Agency Sorting, Agent Solutions and Diseconomies of scale: An empirical Investigation of Employment contracts in high technology R&D. Paper presented at the meeting of the Academy of Management, Anaheim, CA.

Zikmund, G.W., Babin, B.J., Carr, C.J., & Griffin, M. (2010). Business Research Methods (8th Ed)., South-Western, Cengage Learning.

227

APPENDICES

APPENDIX 1: Questionnaire

Thank you for agreeing to take part in this important survey. The objective of the survey is to contribute to the understanding of strategy implementation and performance of m- commerce in Kenyan commercial banks. The results will be used to contribute to knowledge gap in literature, policy development and to support strategy implementation of m-commerce. Results should be availed to all respondents. We value your honest and detailed responses. The questionnaire should take approximately 15 minutes to complete. Your responses are completely anonymous and confidential.

SECTION A: GENERAL QUESTIONS

What best describes your current position in your bank?

Top Management [ ] Middle Management [ ] Entry Level Management [ ]

Other Specify ……………………….

Which of the following categories best describes your age? (Tick your response) ears [ ], 30-39 years [ ] 40-49 years [ ] 50 years and over [ ]

Which of the following terms best describes you? Male [ ]Female [ ]

Please indicate the highest education level attained (Please tick one).

Doctorate Degree [ ], Master’s Degree [ ] Bachelor’s Degree [ ] Diploma [ ]

Please tick one to indicate your work experience in years.

0-5 years [ ] 6-10 years [ ] 11-20 years [ ] Above 20 years [ ]

Please tick one to indicate the tier group your bank belongs to Tier 1 [ ], Tier 2 [ ], Tier 3 [ ] From the following list of products and services of mobile commerce, please tick to indicate products offered by your bank Account Transfers

228

From own bank account to own phone [ ] From own bank account to another person’s phone [ ] From own bank account to another bank [ ] Airtime Top UP [ ] Pay Bill Merchants [ ] DSTV [ ] KPLC [ ] Jumia Shopping [ ] GoTV [ ] Star Times [ ] Nairobi Water [ ] Zuku [ ] Agent Banking [ ] Information Alerts [ ] Load Card [ ] Load Card [ ] Value Add Products: Personal Financial Management [ ] Monitoring and Transfer Services [ ] Performance of the bank shares [ ] Other market stock [ ] Forex [ ] Business news [ ]

229

SECTION B: M-COMMERCE STRATEGY IMPLEMENTATION

Please indicate your responses to the following statements regarding the relationship between leaders of your bank and m-commerce performance. Tick your choice in the appropriate answer box.

Where 1 = Not at all, 2 = Small Extent, 3 = Moderate Extent 4 = Great Extent, 5 = Very great extent

Organizational Leadership OL1 To what extent do the relationship between the leadership and 1 2 3 4 5 other staff in your bank contribute positively to m-commerce OL2 To what extent is the bank leadership flexible to facilitate staff 1 2 3 4 5 contribution to M-Commerce performance? OL3 To what extent do the leadership style in your bank support M- 1 2 3 4 5 commerce growth? OL4 To what extent do the leadership in your bank influence the 1 2 3 4 5 overall M-Commerce performance of the bank?

Please indicate the extent you agree or disagree with each of the following statements regarding your banks organizational structure. (Where 1= strongly disagree; 2= disagree; 3=neutral; 4= agree; 5 strongly agree)

Organizational Structure OS1 The bank’s structure allows easy decision making and 1 2 3 4 5 contributes to the growth of new m-commerce applications OS2 The bank’s structure is hierarchical, and influences positively the 1 2 3 4 5 growth of new m-commerce applications OS3 The bank’s structure is flat, and influences positively return of 1 2 3 4 5 new m-commerce application OS4 The reporting structure influences positively m-commerce 1 2 3 4 5 growth OS5 The organizational structure influences positively the overall m- 1 2 3 4 5 commerce performance in the bank

Please circle or tick the number corresponding to your opinion to indicate the extent you agree or disagree with each of the statements regarding information system in your bank. (Where 1= strongly disagree; 2= disagree; 3=neutral; 4= agree; 5 strongly agree)

Information System IS1 Information systems drives the bank’s growth of new M- 1 2 3 4 5 Commerce applications IS2 Bank’s information system does not support growth of new 1 2 3 4 5 applications IS3 Bank’s information system supports the growth of customers 1 2 3 4 5 IS4 Bank’s information system staff are empowered to support m- commerce development IS5 Bank’s information system contributes positively to the overall 1 2 3 4 5 m-commerce performance of the bank

230

IS6 Bank’s information system staff are empowered to make decisions that support excellent performance of m-commerce IS7 I can easily use information system to make decisions supporting m-commerce implementation IS8 The information introduced by information system is created to support implementation of m-commerce

Please indicate to what extent you agree or disagree with each of the following statements regarding the human resource in your bank. (Where 1= strongly disagree; 2= disagree; 3=neutral; 4= agree; 5 strongly agree)

Human Resource HR1 Human resource recruits specialized skilled staff to manage the 1 2 3 4 5 m-commerce strategy HR2 Human resource train staff to support the growth of m- 1 2 3 4 5 commerce new applications HR3 The bank’s human resources rewards excellence in m-commerce 1 2 3 4 5 development HR4 Bank has unique staff that are responsible for the performance of 1 2 3 4 5 m-commerce rather than the industry’s structural characteristics HR5 The banks talented human resources drive the overall m- 1 2 3 4 5 commerce performance of the bank HR6 Bank’s human resource are not highly mobile across banks 1 2 3 4 5 hence the cause for high m-commerce performance HR7 Bank’s human resources are risk averse to new untested 1 2 3 4 5 innovations for fear of punishment

Please indicate to what extent you agree or disagree with each of the following statements regarding the level of strategy communication in your bank. (Where 1= strongly disagree; 2= disagree; 3=neutral; 4= agree; 5 strongly agree)

Strategy Communication SC1 Strategy communication of the bank’s vision supports the 1 2 3 4 5 growth of m-commerce SC2 Strategy communication of the bank’s mission is directly linked 1 2 3 4 5 to m-commerce SC3 Strategy communication to all staff contributes to the bank’s 1 2 3 4 5 growth of new accounts SC4 Communication of bank strategy and related activities leads to 1 2 3 4 5 an overall positive m-commerce performance SC5 Communication of overall goals to all employees leads to bank’s 1 2 3 4 5 positive m-commerce performance

231

SECTION C: TECHNOLOGICAL TURBULENCE Please indicate, by placing a tick  in the spaces provided, your level of agreement or disagreement with each of the following statements regarding the level of technological turbulence in the banking industry in Kenya: (Where 1= strongly disagree; 2= disagree; 3=neutral; 4= agree; 5 strongly agree)

Technological Turbulence

TT1 The m-commerce technology in the banking sector is changing rapidly. 1 2 3 4 5 TT2 M-commerce technological changes provide banks with big opportunities for growth in our industry. 1 2 3 4 5 TT3 A large number of new bank products/services have been made possible through m-commerce technological breakthroughs in 1 2 3 4 5 our industry. TT4 M-commerce technological developments in the bank sector are rather major. 1 2 3 4 5 TT5 M-Commerce technological developments in the banking sector is expected to revolutionize banking 1 2 3 4 5 TT6 Customer demand for m-commerce is driving product 1 2 3 4 5 developments

Kindly Tick appropriate box or correct response on the basis of the following scale Where; 1 = Strongly Disagree; 2 = Disagree; 3 = Not Sure; 4 = Sure; 5 = Strongly Agree

Market Turbulence MT1 There is a lot of competition in the banking sector 1 2 3 4 5 MT2 Customer demand in our the banking sector is very stable 1 2 3 4 5 MT3 New product/service introductions are very frequent in the 1 2 3 4 5 banking sector. MT4 The business environment in the banking sector is continuously 1 2 3 4 5 changing. MT5 In the banking business, customers’ preferences change quite a 1 2 3 4 5 lot over time. MT6 Bank customers tend to look for new products/services all the 1 2 3 4 5 time.

232

SECTION D: FIRM PERFORMANCE Please indicate the extent to which your bank has realized improved performance over the last 5 years, from 2010 to 2015. Tick your response in the appropriate answer box. 1 = Not at all, 2 = Small Extent,3 = Moderate Extent 4 = Great Extent, 5 = Very great extent M-Commerce Performance (2010-2015) FP1 Your bank’s growth in m-commerce transactions 1 2 3 4 5 FP2 Your bank’s growth in cash transfers 1 2 3 4 5 FP3 Your bank’s growth in Airtime top up 1 2 3 4 5 FP4 Your bank’s growth in pay bill merchants 1 2 3 4 5 FP5 Your bank’s growth in agent banking 1 2 3 4 5 FP6 Your bank’s growth in savings using the phone 1 2 3 4 5 FP7 Your bank’s overall growth in mobile banking functionalities 1 2 3 4 5

233

APPENDIX 2: Factor Analysis, Kurtosis and sem Modeling

Leadership. Communalities Bartlett's Test of Sphericity

Item Communalities Approx. Chi-Square df Sig.

OL1 0.547 228.298 6 .000 OL2 0.708 OL3 0.704 OL4 0.578

M-commerce performance Communalities Bartlett's Test of Sphericity Item Communalities Approx. Chi-Square df Sig. FP1 0.77 780.819 21 .000 FP2 0.83 FP3 0.73 FP4 0.67

FP5 0.82 FP6 0.85 FP7 0.79

Strategic Communication Communalities Bartlett's Test of Sphericity

Item Communalities Approx. Chi-Square df Sig.

SC1 0.503 82.351 6 .000 SC2 0.417

SC3 0.479

SC4 0.526

Structure Communalities Bartlett's Test of Sphericity Item Communalities Approx. Chi-Square df Sig.

234

OS1 0.465 144.884 6 .000 OS2 0.391 OS4 0.641 OS5 0.626

Human resource Communalities Bartlett's Test of Sphericity Item Communalities Approx. Chi-Square df Sig.

HR1 0.618 10.008 1 .002 HR5 0.618

Information system Communalities Bartlett's Test of Sphericity

Item Communalities Approx. Chi-Square df Sig.

IS1 0.606 8.019 1 .005 IS5 0.606

Market Turbulence Communalities Bartlett's Test of Sphericity

Item Communalities Approx. Chi-Square df Sig.

MT4 0.446 38.899 3 .000 MT5 0.521

MT6 0.594

235

Technology Turbulence Bartlett's Test of Sphericity Communalities

Item Communalities Approx. Chi-Square df Sig.

TT4 0.648 16.083 1 .000

TT6 0.648

Test of outliers

Observations farthest from the centroid (Mahalanobis distance) (Group number 1)

Observation Mahalanobis d- p1 p2 number squared 125 46.326 .001 .114 41 44.949 .001 .015 103 43.975 .002 .002 108 43.101 .002 .000 105 42.777 .002 .000 102 42.770 .002 .000 45 41.659 .003 .000 44 41.279 .003 .000 5 41.073 .004 .000 165 38.449 .008 .000 107 38.157 .008 .000 126 37.756 .009 .000 142 37.756 .009 .000 109 37.607 .010 .000 166 36.965 .012 .000 106 36.792 .012 .000 132 36.051 .015 .000 40 35.548 .017 .000 47 34.176 .025 .000 104 33.866 .027 .000 28 33.586 .029 .000 146 33.504 .030 .000 112 33.073 .033 .000 124 32.611 .037 .000

31 32.516 .038 .000 122 32.391 .039 .000

236

Observation Mahalanobis d- p1 p2 number squared 97 32.028 .043 .000 121 31.878 .045 .000 43 31.718 .046 .000 7 29.900 .071 .000 141 29.768 .074 .000 158 29.726 .074 .000 138 28.914 .089 .000 139 28.068 .108 .000 90 27.124 .132 .003 88 26.483 .150 .014 27 26.364 .154 .012 55 26.089 .163 .017 39 26.088 .163 .010 163 25.827 .172 .014 144 25.677 .177 .014 70 25.662 .177 .009 123 25.359 .188 .015 71 24.905 .205 .038 98 24.850 .207 .029 164 24.755 .211 .026 143 24.607 .217 .027 67 24.374 .226 .036 87 24.343 .228 .026 96 24.266 .231 .022 140 23.784 .252 .063 42 23.500 .265 .094 74 23.435 .268 .082 92 23.115 .283 .132 91 22.609 .308 .287 119 22.351 .322 .361 133 22.201 .330 .382 64 21.967 .342 .453 116 21.729 .355 .529 78 21.638 .360 .519 130 21.184 .386 .718 37 21.073 .393 .721 30 21.019 .396 .695 46 20.999 .397 .648 113 20.843 .406 .679 29 20.655 .418 .726 32 20.513 .426 .748 20 20.269 .441 .815 36 20.118 .451 .837

237

Observation Mahalanobis d- p1 p2 number squared 14 20.008 .457 .842 120 19.989 .459 .810 35 19.730 .475 .873 8 19.728 .475 .839 11 19.675 .478 .821 50 19.396 .496 .889 131 19.167 .511 .926 118 18.895 .529 .960 33 18.785 .536 .962 53 18.658 .544 .967 155 18.586 .549 .965 128 18.581 .549 .952 162 18.140 .578 .988 18 18.050 .584 .988 137 17.735 .605 .996 56 17.700 .607 .995 85 17.590 .614 .995 17 17.525 .619 .995 159 17.425 .625 .995 60 17.364 .629 .994 153 17.146 .643 .997 63 16.836 .664 .999 150 16.728 .671 .999 19 16.723 .671 .999 73 16.394 .692 1.000 89 16.343 .695 1.000 145 16.312 .697 1.000 49 16.119 .709 1.000 147 16.000 .717 1.000 69 15.710 .734 1.000 129 15.710 .734 1.000

238

Normality test

Assessment of normality (Group number 1)

Variable min max skew c.r. kurtosis c.r. IS6 3.000 5.000 -.006 -.029 -.137 -.362 IS3 3.000 5.000 .107 .565 .336 .884 MT6 2.000 5.000 -.505 -2.655 .411 1.080 MT3 2.000 5.000 -.145 -.763 .803 2.111 OS2 3.000 5.000 .116 .608 1.728 4.543 OS1 3.000 5.000 .123 .645 -.049 -.128 HR4 3.000 5.000 -.005 -.026 -.143 -.376 HR3 3.000 5.000 .669 3.521 -.264 -.694 HR2 3.000 5.000 .449 2.364 -.170 -.446 HR1 3.000 5.000 -.702 -3.691 -.497 -1.306 SC5 2.000 5.000 -.866 -4.555 2.934 7.716 SC4 2.000 5.000 -.777 -4.087 3.417 8.985 SC2 2.000 5.000 -.968 -5.091 3.692 9.709 FP6 1.000 5.000 -.362 -1.903 .819 2.153 FP5 1.000 5.000 -.469 -2.468 .724 1.903 FP4 1.000 5.000 -.970 -5.102 1.356 3.567 OL4 3.000 5.000 .173 .911 2.908 7.648 OL3 2.000 5.000 -.675 -3.549 4.761 12.522 OL2 3.000 5.000 -.977 -5.140 4.525 11.901 OL1 2.000 5.000 -.274 -1.442 .547 1.437 Multivariate 65.303 14.181

Missing data Overall model ANOVA

ANOVAa

Sum of

Model Squares df Mean Square F Sig. 1 Regression 86.670 5 17.334 32.652 .000b Residual 91.308 172 .531

Total 177.978 177 a. Dependent Variable: M_commerce_performance b. Predictors: (Constant), strategic_communication, Human_resource, Information_system, Structure, leadership

239

Moderated model

ANOVAa Sum of Model Squares df Mean Square F Sig. b 1 Regression 94.623 17 5.566 10.681 .000 Residual 83.381 160 .521 Total 178.003 177 a. Dependent Variable: M_commerce_performance b. Predictors: (Constant), structureXTechnology, Human_resource, Structure, Information_system, Human_resourceXTechnology, Technology_Turbulence, strategic_communication, InformationXMarket, Market_Turbulence, leadership, StrategicXTechnology, structureXMarket, InformatiomXTechnology, Human_resourceX_Market, LeadershipXTechnology, strategyXMarket, leadershipXMarket

240

APPENDIX 3: List of Kenya’s Commercial Banks as at December 31, 2015

1. Kenya Commercial Bank Ltd 2. Co-operative Bank of Kenya Ltd 3. Equity Bank Ltd 4. Standard Chartered Bank (K) Ltd 5. Barclays Bank of Kenya Ltd 6. Commercial Bank of Africa Ltd 7. Diamond Trust Bank (K) Ltd 8. CfC Stanbic Bank (K) Ltd 9. NIC Bank Ltd 10. I&M Bank Ltd 11. National Bank of Kenya Ltd 12. Citibank N.A. Kenya 13. Family Bank Ltd. 2.36% 14. Bank of Baroda (K) Ltd 2.04% 15. Prime Bank Ltd 1.82% 16. Bank of Africa (K) Ltd 1.81% 17. Housing Finance Ltd 1.76% 18. Ecobank Kenya Ltd 1.42% 19. Bank of India 1.16% 20. Guaranty Trust Bank Ltd 0.97% 21. Gulf African Bank Ltd 0.72% 22. Sidian Bank Ltd 0.60% 23. African Banking Corporation Ltd 24. Victoria Commercial Bank Ltd 25. Jamii Bora Bank Ltd 26. Giro Commercial Bank Ltd 27. Development Bank of Kenya Ltd 28. Guardian Bank Ltd 29. Equatorial Commercial Bank Ltd 30. First Community Bank Ltd 31. Fidelity Commercial Bank Ltd 32. Habib Bank Ltd 33. Consolidated 34. Habib Bank A.G. Zurich 35. Trans - National Bank Ltd 36. Paramount Universal Bank Ltd 37. Oriental Commercial Bank Ltd 38. Credit Bank Ltd 39. UBA Kenya Ltd 40. Middle East Bank (K) Ltd

241

APPENDIX 4: Research Authorization

242

APPENDIX 5: NACOSTI Permit Letter

243