Influence of Transformational Leadership Style on Job Satisfaction Among Employees in Commercial Banks in Kenya

INFLUENCE OF TRANSFORMATIONAL LEADERSHIP STYLE ON JOB SATISFACTION AMONG EMPLOYEES IN COMMERCIAL BANKS IN KENYA

NJIINU ANDREW NJIRAINI

UNITED STATES INTERNATIONAL UNIVERISTY - AFRICA

FALL 2017 INFLUENCE OF TRANSFORMATIONAL LEADERSHIP STYLE ON JOB SATISFACTION AMONG EMPLOYEES IN COMMERCIAL BANKS IN KENYA

NJIINU ANDREW NJIRAINI

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

UNITED STATES INTERNATIONAL UNIVERSITY – AFRICA

FALL 2017 STUDENT’S DECLARATION

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

Signed______Date______

Njiinu Andrew Njiraini (ID 615143)

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

Signed______Date______

Prof. George O. K’Aol

Signed______Date______

Prof. Teresia K. Linge

Signed______Date______

Dean, Chandaria School of Business

Signed______Date______

Deputy Vice Chancellor, Academic & Student Affairs

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.

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ABSTRACT

The purpose of this study was to examine the influence of transformational leadership style on job satisfaction among employees in commercial banks in Kenya. The study was guided by these research questions: To what extent does idealized influence affect job satisfaction among employees in commercial banks in Kenya? To what extent does individualized consideration influence job satisfaction among employees in commercial banks in Kenya? To what extent does inspirational motivation influence job satisfaction among employees in commercial banks in Kenya? To what extent does intellectual stimulation influence job satisfaction among employees in commercial banks in Kenya? To what extent does job security moderate the relationship between transformational leadership and job satisfaction among employees in commercial banks in Kenya?

The study adopted a positivism research philosophy and a descriptive correlation research design. The target population consisted of 10,310 managerial employees in the commercial banks in Kenya. A sample size of 424 was obtained from the population using stratified random sampling technique and data was collected using structured questionnaires. A response rate of 82% was obtained. Data analysis was conducted using both descriptive statistics and inferential statistics. Descriptive statistics used were mean and standard deviation. The inferential statistical methods used to analyze the data were: Chi-square, Pearson’s correlation, ANOVA and multiple linear regression. The Statistical Package for Social Sciences (SPSS) was used as a tool to analyze data.

In regard to the first research question, correlation analysis results revealed a positive and significant relationship between idealized influence and job satisfaction r (346) =.496, p<.05. Multiple linear regression results revealed that idealized influence significantly predicted job satisfaction (R2 = .246, F (1, 97.750) = 112.421, p < .05). Therefore, the null hypothesis that there is no significant influence of idealized influence on job satisfaction was rejected. Regarding the second research question, correlation analysis results revealed a positive and significant relationship between individualized consideration and job satisfaction r (347) =.595, p<.05. Multiple linear regression results revealed that individualized consideration significantly predicted job satisfaction (R2 = .354, F (1, 138.779) = 188.851, p < .05). Therefore, the null hypothesis that there is no significant influence of individualized consideration on job satisfaction was rejected.

In regard to the third research question, correlation analysis results revealed a positive and significant relationship between inspirational motivation and job satisfaction r (347) =.587, p<.05. Multiple linear regression results revealed that inspirational motivation significantly predicted job satisfaction of the employees (R2 = .344, F (1, 126.302) = 180.980, p < .05). Therefore, the null hypothesis that there is no significant influence of inspirational motivation on job satisfaction was rejected. Regarding the fourth research question, correlation analysis revealed a positive and significant correlation between intellectual stimulation and job satisfaction r (347) =.541, p<.05. Chi-square test revealed a significant association between intellectual stimulation and job satisfaction X2 (144, N = 347) = 426.404, p<.05). Multiple linear regression results revealed that intellectual stimulation significantly predicted job satisfaction (R2 = .292, F (1, 106.274) = 142.533, p<.05). Therefore, the null hypothesis that there is no significant influence of intellectual stimulation on job satisfaction was rejected.

In regard to the fifth research question, correlation analysis results revealed that there was a statistically significant correlation on the extent to which job security moderated the relationship between transformational leadership and job satisfaction r (347) =.697, p<.05. Multiple linear regression results revealed that job security significantly moderated the relationship between transformational leadership and job satisfaction (R2 = .446, F (5, 27.760) = 54.780, p < .05). Therefore, the null hypothesis that there is no significant moderating effect of job security between transformational leadership and job satisfaction was rejected.

The study concluded that transformational leadership significantly influenced job satisfaction among employees in commercial banks in Kenya. The study recommends that transformational leadership style should be used to enhance job satisfaction among employees in commercial banks in Kenya. Individualized consideration through support, mentoring and delegation together with job security aspects play an important role in determining job satisfaction. The study recommends that further research should be carried out on the influence of transformational leadership and job satisfaction among employees in microfinance institutions in Kenya.

ACKNOWLEDGMENT

First, I wish to thank God for enabling me to dream big and providing me with an opportunity to actualize this dream; To God be the Glory.

Secondly, I wish to acknowledge with sincere gratitude the support and guidance I got from my supervisors Prof. George O. K’Aol and Prof. Teresia K. Linge. I appreciate your invaluable time, patience and guidance through the process.

Thirdly, a special appreciation to all those who took part in my doctoral journey; my fellow doctoral students, the respondents who took part in the research and Paul who provided me with the support I required.

Lastly, I wish to acknowledge my family who cheered me on; thank you for your unwavering support and dedication to see that I completed this course successfully.

DEDICATION

I dedicate this dissertation to all leaders; both existing and aspiring with a special dedication to all commercial bank employees who seek to become effective leaders. May this research project demystify your role as a leader and help you build organizations where employees will experience job satisfaction, become engaged and transition to inspired employees.

I also dedicate this dissertation to my family: Mr. & Mrs. Njiinu Gachanja, Shiro, Gachanja & Wairimu, Kuria & Wangare, and Grace. Thank you for the investment you made in me, both financial and moral, the discipline has gotten me this far. You have all constantly been a source of wisdom, encouragement and support. Your support and prayers have been my greatest source of strength. Please accept my sincere gratitude for the significant roles that you have all played in my life and doctoral journey.

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TABLE OF CONTENTS

STUDENT’S DECLARATION ...... ii COPYRIGHT ...... iii ABSTRACT ...... iv ACKNOWLEDGMENT ...... vi DEDICATION...... vii LIST OF TABLES ...... xi LIST OF FIGURES ...... xvi ABBREVIATIONS ...... xvii

CHAPTER ONE ...... 1 1.0 INTRODUCTION...... 1 1.1 Background of the Study ...... 1 1.2 Statement of the Problem ...... 8 1.3 The Purpose of the Study ...... 8 1.4 Research Questions ...... 9 1.5 Hypotheses ...... 10 1.6 Justification of the Study ...... 10 1.7 Scope of the Study ...... 11 1.8 Definitions of Terms ...... 11 1.9 Chapter Summary ...... 13

CHAPTER TWO ...... 1 2.0 LITERATURE REVIEW ...... 14 2.1 Introduction ...... 14 2.2 Theoretical Review ...... 14 2.3 Conceptual Framework ...... 19 2.4 Empirical Review...... 20 2.5 Chapter Summary ...... 78

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CHAPTER THREE ...... 79 3.0 RESEARCH METHODOLOGY ...... 79 3.1 Introduction ...... 79 3.2 Research Philosophy ...... 79 3.3 Research Design...... 80 3.4 Target Population ...... 82 3.5 Sample Design ...... 82 3.6 Data Collection Methods ...... 85 3.7 Research Procedures ...... 86 3.8 Data Analysis Methods ...... 90 3.9 Chapter Summary ...... 97

CHAPTER FOUR ...... 98 4.0 RESULTS AND FINDINGS ...... 98 4.1 Introduction ...... 98 4.2 General Information ...... 98 4.3 Influence of Idealized Influence on Job Satisfaction ...... 101 4.4 Influence of Individualized Consideration on Job Satisfaction ...... 115 4.5 Influence of Inspirational Motivation on Job Satisfaction ...... 129 4.6 Influence of Intellectual Stimulation on Job Satisfaction ...... 143 4.7 Moderating Effect of Job Security on the Influence of Transformational Leadership on Job Satisfaction ...... 157 4.8 Chapter Summary ...... 172

CHAPTER FIVE ...... 175 5.0 SUMMARY, DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS ...... 175 5.1 Introduction ...... 175 5.2 Summary of the Study ...... 175 5.3 Discussion of Results ...... 176 5.4 Conclusions ...... 191 5.5 Recommendations ...... 193

REFERENCES ...... 195 APPENDICES ...... 224 Appendix I: Cover Letter ...... 224 Appendix II: Questionnaire ...... 225 Appendix III: USIU Research Introduction Letter ...... 231 Appendix IV: NACOSTI Research Permit ...... 232 Appendix V: Classification of Banks in tiers ...... 233

LIST OF TABLES

Table 2.1: Operationalization of Variables and Hypothesis Testing ...... 20

Table 3.1: Employment of Managerial Staff in the Banking Sector ...... 82

Table 3.2: Sample Size Distribution Based on Tiers of the Banks ...... 85

Table 3.3: Cronbach’s Alpha ...... 87

Table 3.4: Hypothesis Testing ...... 97

Table 4.1a: KMO and Bartlett's Test on Idealized Influence ...... 101

Table 4.1b: Total Variance Explained for Idealized Influence ...... 102

Table 4.1c: Component Matrix on Idealized Influence ...... 103

Table 4.2a: KMO and Bartlett’s Test on Idealized Influence on Job Satisfaction ...... 103

Table 4.2b: Total Variance Explained on Idealized Influence ...... 104

Table 4.2c: Component Matrix of Idealized Influence on Job Satisfaction ...... 105

Table 4.3: Mean and Standard Deviation of Idealized Influence ...... 106

Table 4.4: Chi-square Test of Idealized Influence and Job Satisfaction ...... 106

Table 4.5a: Correlation Analysis between Idealized Influence Variables and Job Satisfaction ……………………………………………………………………………107

Table 4.5b: Correlation Analysis between Idealized Influence and Job Satisfaction ...... 107

Table 4.6a: One-way ANOVA on Idealized Influence ...... 108

Table 4.6b: One-way ANOVA on Idealized Influence on Job Satisfaction ...... 109

Table 4.7a: One-Sample Kolmogorov-Smirnov Test on Idealized Influence ...... 110

Table 4.7b: Linearity Test on Idealized Influence ...... 111

Table 4.7c: Multicollinearity Test on Idealized Influence ...... 112

Table 4.7d: Homoscedasticity Test on Idealized Influence ...... 112

Table 4.8: Model Summary of Idealized Influence and Job Satisfaction ...... 113

Table 4.9: Regression ANOVA of Idealized Influence on Job Satisfaction ...... 113

Table 4.10: Coefficients of Idealized Influence on Job Satisfaction ...... 114

Table 4.11a: KMO and Bartlett's Test on Individualized Consideration...... 116

Table 4.11b: Total Variance Explained for Individualized Consideration ...... 116

Table 4.11c: Component Matrix on Individualized Consideration ...... 117

Table 4.12a: KMO and Bartlett's Test ...... 118

Table 4.12b: Total Variance Explained for Individualized Consideration ...... 118

Table 4.12c: Component Matrix of Individualized Consideration on Job Satisfaction ... 119

Table 4.13: Mean and Standard Deviation of Individualized Consideration...... 120

Table 4.14: Chi-square Test on Individualized Consideration and Job Satisfaction ...... 120

Table 4.15a: Correlation Analysis between Individualized Consideration Variables and Job Satisfaction ...... 121

Table 4.15b: Correlation Analysis between Individualized Consideration and Job Satisfaction ……………………………………………………………………………121

Table 4.16a: One-way ANOVA on Individualized Consideration ...... 122

Table 4.16b: One-way ANOVA of Individualized Consideration on Job Satisfaction ... 123

Table 4.17a: One-Sample Kolmogorov-Smirnov Test on Individualized Consideration 124

Table 4.17b: Linearity Test on Individualized Concentration ...... 125

Table 4.17c: Multicollinearity Test on Individualized Consideration ...... 126

Table 4.17d. Homoscedasticity Test on Individualized Consideration ...... 126

Table 4.18: Model Summary of Individualized Consideration on Job Satisfaction ...... 127

Table 4.19: Regression ANOVA of Individualized Consideration on Job Satisfaction .. 127

Table 4.20: Coefficients of Individualized Consideration on Job Satisfaction ...... 128

Table 4.21a: KMO and Bartlett's Test on Inspirational Motivation ...... 130

Table 4.21b: Total Variance Explained for Inspirational Motivation...... 130

Table 4.21c: Component Matrix on Inspirational Motivation ...... 131

Table 4.22a: KMO and Bartlett's Test Inspirational Motivation and Job Satisfaction .... 132

Table 4.22b: Total Variance Explained for Inspirational motivation on Job Satisfaction ………………………………………………………………………………132 xii

Table 4.22c: Component Matrix on Inspirational Motivation and Job Satisfaction ...... 133

Table 4.23: Mean and Standard Deviation of Inspirational Motivation ...... 134

Table 4.24: Chi-square Test on Inspirational Motivation and Job Satisfaction ...... 134

Table 4.25a: Correlation Analysis between Inspirational Motivation Variables and Job Satisfaction ...... 135

Table 4.25b: Correlation Analysis between Inspirational Motivation and Job Satisfaction ……………………………………………………………………………135

Table 4.26a: One-way ANOVA on Inspirational Motivation ...... 136

Table 4.26b: One-way ANOVA of Inspirational Motivation on Job Satisfaction ...... 137

Table 4.27a: One-Sample Kolmogorov-Smirnov Test on Inspirational Motivation ...... 138

Table 4.27b: Linearity Test on Inspirational Motivation ...... 139

Table 4.27c: Multicollinearity Test on Inspirational Motivation ...... 140

Table 4.27d: Homoscedasticity Test on Inspirational Motivation ...... 140

Table 4.28: Model Summary on Inspirational Motivation and Job Satisfaction ...... 141

Table 4.29: ANOVA for Inspirational Motivation and Job Satisfaction ...... 141

Table 4.30: Coefficients of Inspirational Motivation on Job Satisfaction ...... 142

Table 4.31a: KMO and Bartlett's Test on Intellectual Stimulation ...... 143

Table 4.31b: Total Variance Explained for Intellectual Stimulation ...... 144

Table 4.31c: Component Matrix on Intellectual Stimulation ...... 144

Table 4.32a: KMO and Bartlett's Test on Intellectual Stimulation on Job Satisfaction .. 145

Table 4.32b: Total Variance Explained for Intellectual Stimulation ...... 145

Table 4.32c: Component Matrix on Intellectual Stimulation and Job Satisfaction ...... 147

Table 4.33: Mean and Standard Deviation of Intellectual Stimulations ...... 148

Table 4.34: Chi-square Test on Intellectual Stimulation and Job Satisfaction ...... 148

Table 4.35a: Correlation Analysis between Intellectual Stimulation Variables and Job Satisfaction ...... 149

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Table 4.35b: Correlation Analysis between Intellectual Stimulation and Job Satisfaction ………………………………………………………………………………149

Table 4.36a: One-way ANOVA on Intellectual stimulation ...... 150

Table 4.36b: One-way ANOVA on Intellectual stimulation on Job Satisfaction ...... 151

Table 4.37a: One-Sample Kolmogorov-Smirnov Test on Intellectual Stimulation ...... 152

Table 4.37b: Linearity Test on Intellectual Stimulation ...... 153

Table 4.37c: Multicollinearity Test on Intellectual Stimulation...... 154

Table 4.37d: Homoscedasticity Test on Intellectual Stimulation ...... 154

Table 4.38: Model Summary on Intellectual Stimulation on Job Satisfaction ...... 155

Table 4.39: ANOVA of Intellectual Stimulation on job satisfaction ...... 155

Table 4.40: Coefficients of Intellectual Stimulation on job satisfaction ...... 156

Table 4.41a: KMO and Bartlett's Test ...... 158

Table 4.41b: Total Variance Explained for Job Security as Moderating Variable ...... 158

Table 4.41c: Component Matrix on Job Security as Moderating Effect ...... 159

Table 4.42a: KMO and Bartlett's Test ...... 159

Table 4.42b: Total Variance Explained for Job Security as Moderating Variable ...... 160

Table 4.42c: Component Matrix on Job Security as Moderating Variable on Job Satisfaction ……………………………………………………………………………161

Table 4.43: Distribution of Job Security as Moderating Variable ...... 162

Table 4.44: Chi-square Test of Job Security and Job Satisfaction ...... 162

Table 4.45a: Correlation Analysis between Job Security Variables and Job Satisfaction ………………………………………………………………………………163

Table 4.45b: Correlation Analysis between Job Security and Job Satisfaction ...... 163

Table 4.46a: One-way ANOVA on Job Security...... 164

Table 4.46b: One-way ANOVA on Job Security on Job Satisfaction ...... 165

Table 4.47a: One-Sample Kolmogorov-Smirnov Test ...... 166

Table 4.47b: Linearity Test on Job Security ...... 167 xiv

Table 4.47c: Multicollinearity Test on Job Security ...... 167

Table 4.47d: Homoscedasticity Test on Job Security ...... 168

Table 4.48: Model Summary of the Moderating Effect of Job Security between Transformational Leadership and Job Satisfaction ...... 169

Table 4.49: ANOVA Transformational Leadership and Moderating Variable on Job Satisfaction ……………………………………………………………………………170

Table 4.50: Coefficients of Independent Variables and Moderating Effect on Job Satisfaction ……………………………………………………………………………171

LIST OF FIGURES

Figure 2.1: Transformational Leadership Theory Model ...... 15

Figure 2.2: Conceptual framework ...... 20

Figure 4.1: Gender of Respondents ...... 98

Figure 4.2: Age of Respondents...... 99

Figure 4.3: Education Qualification ...... 99

Figure 4.4: Duration of Working ...... 100

Figure 4.5: Tier of the Banks ...... 100

Figure 4.6. Scree Plot for Idealized Influence ...... 102

Figure 4.7: Scree Plot for the Individualized Consideration...... 117

Figure 4.8: Scree Plot for the Inspirational Motivation ...... 131

Figure 4.9: Scree Plot for the Intellectual Stimulation ...... 146

Figure 4.10: Scree Plot for the Job Security ...... 160

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ABBREVIATIONS

CBK Central Bank of Kenya

CEO Chief Executive Officer

CFA Confirmatory Factor Analysis

CSR Corporate Social Responsibility

FMCG Fast-moving consumer goods

GLC Government Link Company

IMF International Monetary Fund

IT Information Technology

MLQ Multifactor Leadership Questionnaire

MSQ Minnesota Satisfaction Questionnaire

NACOSTI National Commission for Science, Technology & Innovation

SEM Structural Equation Modelling

SHRM Society for Human Resource Management

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CHAPTER ONE

1.0 INTRODUCTION

1.1 Background of the Study

An organization’s success largely depends on the leadership style provided by the leaders. They have the mandate of overseeing the internal context, business context and external context, which may include but not limited to the human resource and financial contexts. Leadership actions have a direct impact on cost, social and sustainable financial performance. Consequently, these actions may yield long term sustainable firm performance. Organizational success is characterized by performance, employee job satisfaction and employee affective commitment (Abouraia & Othman, 2017). This is achieved through effective leadership and a clear understanding of the organization’s vision and purpose of existence. To enhance job satisfaction, scholars have argued that leadership must provide motivation, inspiration, analytical skills and good remuneration to employees; all of these have a combined effect of low attrition rates and decreased absenteeism (Hurduzeu, 2015).

The transformational leadership style helps to create follower job satisfaction and commitment to the organization both of which lead to superior customer service and improved organizational performance (Patiar & Wang, 2016). Transformational leadership style yields inspiration which enables transformational leaders to build motivation within the followers. This enables the followers to go beyond their personal interests and focus on the collective gain of the organization. To achieve all these, transformational leaders offer intellectual challenges while paying regard to the followers needs (Belias & Koustelios, 2014).

The transformational leadership style is characterized by high interaction of the leaders with the followers and it has a significant positive effect on job satisfaction of the followers. Additionally, the transformational leadership style has a positive relationship with job satisfaction based on the impact the leader has on the followers (Muterera, Hemsworth, Baregheh & Garcia-Rivera, 2015). By demonstrating a high concern for followers, their needs, comfort, autonomy, empowerment, encouragement, reward and recognition, transformational leadership style has a positive impact on job satisfaction (Alonderiene & Majauskaite, 2016).

Job satisfaction is a pleasurable state that produces positive emotions when one evaluates his job or job experiences (Belias & Koustelios, 2014). Job satisfaction parameters can be broadly divided into two: extrinsic and intrinsic factors. Intrinsic factors are growth and advancement opportunities, recognition, responsibility, the work itself and achievement. Extrinsic factors are supervision, pay, policies, working conditions and relationships at the workplace. They help to prevent job dissatisfaction among the followers (Alonderiene & Majauskaite, 2016). Job satisfaction has also been described as happy feelings that result from how one perceives a job in light of meeting important personal values (Mahmoud & Reisel, 2014).

Job satisfaction is considered to be a sentimental response of an employee towards the job which emanates from their experience on the job. It can be seen when a job is perceived to fulfill a person’s needs and when a job possesses important job values. Additionally, job satisfaction is an acceptable measure of well being in the workplace which contributes to the psychological well being of the employees (Mencl, Wefald & Ittersum, 2016). Job satisfaction is also viewed as accepting the organization’s goals, the willingness to work hard and the intent to stay on in an organization (Jain, Sharma & Jain, 2012). Job satisfaction is also considered to be the amount of belief and emotional connection the followers have towards their respective organizations (Emmanuel & Hassan, 2015). It is also the attitude that people have regarding their jobs which emanates from their perception of the job and the fit between the individual and the organization. As put by George and Zakkariya (2015), job satisfaction is very important for the service industry employees because it is only satisfied employees who can offer good service that yields customer satisfaction. Thus, it is not only customers who should be satisfied but also the employees of the organization.

According to the full range leadership model, transformational leadership style is one of the leadership styles that make a difference in the outcomes of the leader’s associates. In this regard, when a leader pays attention to the needs of his associates, challenges them, influences them as a role model and inspires them, the associates’ needs are significantly addressed thereby yielding a level of satisfaction with their jobs (Tesfaw, 2014). This goes on to validate the fact that coupled with other factors like the organizational culture, which is also largely driven by the leadership of the organization, a style of leadership is an important antecedent of job satisfaction (Munir, Rahman, Malik & Ma’amor, 2012).

Therefore, job satisfaction or dissatisfaction is a function of the perceived relationship between the expectations from a job, what one receives from the job and the value attributed to it (George & Zakkariya, 2015; Ahmad, Adi, Noor, Rahman, & Yushuang, 2013). Job satisfaction plays a big role in reducing employee turnover and increasing the performance levels; but all these depend on the kind of leadership that is provided by the top management (Sattar & Ali, 2014). Effective leadership goes beyond the traditional managerial authority to relying on influence through social interactions between the leaders and the employees. Transformational leadership is a form of leadership where leaders are not only connected to their followers but also engaged with them (Mencl et al., 2016). Emmanuel and Hassan (2015) argued that amongst the many aspects of employee satisfaction, the most important is the leadership style.

Dissatisfied employees are less committed to their work and will more often look for other opportunities in order for them to leave an organization. When opportunities are not available, they are emotionally and mentally withdrawn from the organization. Dissatisfied employees cannot produce the same quality of work with employees who are highly satisfied with their jobs (George & Zakkariya, 2015). This makes job satisfaction a key factor that determines the employee’s intentions of leaving an organization. To improve the level of job satisfaction among employees, it calls for appropriate leadership styles which enhance job satisfaction levels. Job satisfaction is important because it helps to retain talent and ensures that employees perform their jobs as expected. Satisfied employees not only become faithful but also become deeply committed to the organization. They come up with ways of improving the business and endeavor to contribute positively to the organization. Therefore, job satisfaction becomes one of the most fundamental sources of motivation for employees surpassing pay and benefits. Job satisfaction creates a work environment where employees strive to effectively serve customers and ensure follow through of the customer value proposition (Mallikarjuna, 2014).

A leadership style is therefore an antecedent of job satisfaction and is considered to be a predictor of job satisfaction (Munir et al., 2012; Long, Yusof, Kowang & Heng, 2014). Attaining employee job satisfaction is important as it enables retention of productive and efficient employees. Productivity coupled with performance of an organization is largely based on the satisfaction the employees derive from their jobs. Additionally, employee

3 commitment to the organization also affects their output (Bushra, Usman & Naveed, 2011). The transformational leadership style enhances the performance, effectiveness, confidence and motivation of followers as a result of the effect it has on the employee’s attitude, motivation and sense of well-being. It enhances job satisfaction since it is anchored on idealized influence, inspirational motivation, intellectual stimulation and individualized consideration (Hetland et al., 2015).

Yang and Islam (2012) stated that when a leader fails in fostering the employees’ job satisfaction, it then becomes difficult to achieve the organization’s objectives. Transformational leadership helps a leader to inspire employees to achieve the organizational goals by yielding to a commitment that transcends personal achievement. It begins with the development of a vision which is aimed at exciting the employees and converting them into followers. Job satisfaction has a significant and direct impact on employee input, and it has an influence on both staff and organizational performance (Alonderiene & Majauskaite, 2016).

Transformational leadership interventions affect both small organizations with minimal turnover as well as big organizations where there is higher employee turnover (Arthur & Hardy, 2014). Transformational leadership employs four elements to create transformation and influence employees to perform beyond expectations. First, is idealized influence which refers to influencing by serving as a role model through charisma, demonstration of high performance and moral standards. Leaders earn credence and trust because of their consistency in influencing employees. Second, is inspirational motivation which refers to the ability to come up with a vision and communicating it in a convincing and attractive manner to ultimately create excitement and buy-in from the employees (Lussier & Achua, 2013).

Third, is intellectual stimulation which refers to inclusion through participation; questioning assumptions, re-evaluating problematic and challenging situations to engage their minds and create an opportunity to be heard. As a result, this encourages both creativity and innovation. Fourth, is individualized consideration which refers to leaders acting as coaches or mentors and taking into consideration the needs of their employees. Leaders in this context recognize people’s varying needs and embrace their differences in the various spectrums of personal attributes. Thus, followers are not reduced to their

4 functions and tasks but are considered as unique individuals (Yang & Islam, 2012). Employees possess higher levels of job satisfaction when a leader uses the transformational leadership style for example as compared to use of the transactional leadership style. This is because transformational leadership not only considers but also addresses the employees’ needs whereas transactional leadership looks at a model of exchange (Ramos, 2014).

Alonderiene and Majauskaite (2016) found that in the developed countries like the United States and Germany, jobs have been characterized with insecurity, high work intensity, increased stress levels and long working hours which help to explain the declining levels of job satisfaction. Some low paying jobs report higher job satisfaction levels thus validating the fact that increasing pay levels helps to improve the employees’ well-being but does not influence job satisfaction. Employees may register increased job satisfaction based on increased pay, but this is only to a certain threshold beyond which pay ceases to yield satisfaction but rather helps to reduce dissatisfaction. One of the direct consequences of low job satisfaction is absenteeism and turnover of employees from the organization. Factors like working environment, performance measurement policies and employee relations together with the grievance handling mechanisms were found to affect job satisfaction levels of employees in Canara Bank. Additionally, not having effective training and development of employees, nature of work, salaries and incentives are a great cause of dissatisfaction (Shrivastava & Purang, 2009).

In Bangladesh, according to Hossain (2014), the economic development in the world has resulted in a rapid evolution of the banking industry. This development has resulted in managerial problems in banks, mainly around the low level of employee job satisfaction, which has resulted in mediocre service quality. Among the problems cited by the employees are long working hours, pressure from the job itself, poor treatment, non- conducive working environment, minimal promotion opportunities and unfairness. The job satisfaction levels affect the quality of service and ultimately satisfaction of customers. In Pakistan job satisfaction challenges in the banking sector are also evident as a result of new rules, values and working conditions as seen to be resulting from policy changes and effects of globalization (Sattar & Ali, 2014).

Banks play a critical role in economies and are crucial in ensuring economic stability and growth. They act as the lifeline of modern trade and commerce by providing finance and payments infrastructure (Akotch & Munyoki, 2016). They are essential in the financial sector of any economy because they perform critical activities like lending and providing liquidity. Banks also facilitate payments and settlements which are aimed at supporting trade which then enables the transfer of goods and services. In economic development, they also support development of new businesses hence creating the employment opportunities whilst catalyzing economic growth (Arif & Anees, 2012). In Kenya, the sector is changing at a very fast pace which calls for a lot of dynamism not only in areas of profitability but also in policies regarding the employees’ welfare and job satisfaction because they are the greatest assets the banks have (Njuguna & Owuor, 2016).

Hagendorff, Collins and Keasey (2007) stated that in the recent past the banking sector has undergone a lot of changes owing to deregulation, globalization and technology. These have led to restructuring, mergers, acquisitions and consolidation, resulting in excess work demands thereby affecting job satisfaction of employees. This has been seen predominantly in the United States of America, Italy and Germany as a result of deregulation of the industry as seen with the abolition of the geographic restrictions and demolition of demarcation lines in various financial services. These consolidations are seen to come with benefits of costs, liquidity and risk diversification. During the world financial crisis, many financial institutions were exposed and there were demands from stakeholders to improve performance by adopting new management practices to strengthen their capital, reduce non-performing loans, reduce costs, improve corporate governance and come up with customer focused products (Munir, Baird & Perera, 2013). This has resulted in new rules, regulations and guidelines from the various regulators and the world financial systems all of which affect the employees’ attitudes, behaviors and ultimately job satisfaction levels (Sattar & Ali, 2014).

In Nigeria, Osibanjo, Kehinde and Abiodun (2012) stated that the banking sector has faced shocks and stresses in the past resulting from the economic meltdown in the world. This has resulted in restructuring of the banking system which has had a noteworthy influence on employee job satisfaction. For example, there has been anxiety regarding job security with reduction of the number of banks. The pressure has brought a lot of job demands on employees and resulted in the need for training and retraining on one hand,

6 and lack of job security on the other hand, thereby affecting job satisfaction of employees to a great extent. With the constant changes, human resource policies need to be in tandem with the changes to ensure that employees are constantly satisfied with their jobs.

In Kenya, Mukururi and Ngari (2014) noted there has been an endearing shift from a twelve-hour economy to a twenty four-hour economy. This has resulted in work intensity as banks move to more working hours. This has led not only to greater workload where shifts are not managed properly but also to higher stress levels from the long working hours. Additionally, the banks lacked policies to adequately support the well being of their employees hence lack of work life balance. Every employee has a personal and professional life and employers need to ensure the employee can attend to both parts adequately otherwise lack of balance results in lack of satisfaction. Banks have been awash with scandals which have led to loss of trust from customers, employees, the public, governments and other stakeholders. Banks must complement the pursuit for profitability with social good (ProtusKiprop, Kemboi & Mutai, 2015).

According to the Central Bank of Kenya (CBK), which is the regulator of financial institutions in Kenya, there are 44 banks in Kenya made up of 43 commercial banks and one mortgage financial institution. Out of these, 31 were locally owned and 13 foreign owned. The government has a stake in 3 banks namely National Bank of Kenya, Consolidated Bank and Development Bank of Kenya. Whereas performance for some banks has grown tremendously, performance for others has declined in equal measure. In the recent past, three of the commercial banks were placed under statutory receivership by the regulator owing to compliance issues (CBK, 2017).

The Kenyan banking sector has recently undergone a lot of turmoil with three banks being closed and placed under receivership: Dubai Bank Kenya Limited, Imperial Bank Limited and recently Chase Bank Kenya Limited. Chase Bank Kenya Limited has been re-opened and is operating under a receiver manager (CBK, 2017). Additionally, with new regulation capping interest rates, the profitability of commercial banks’ has fallen significantly. This has led to investment in technology to save on costs as well as capitalize on efficiencies. As a result, majority of the banks have closed branches in rightsizing exercises, offering retirement packages and declaring redundancies. These

7 changes have resulted in a lot of anxiety and fear of job losses, thereby inevitably affecting job satisfaction significantly among the bank employees (IMF, 2017).

1.2 Statement of the Problem

Leadership plays a significant role when dealing with people. Specific variables like job satisfaction have gained prominence in the contemporary era and have become areas of focus for organizations (Malik, Javed & Hassan, 2017). Banks are very important institutions in any economy because of the significant role they play by offering financial services. Many economies have gone through financial crisis which has resulted in far reaching effects on many banks globally. These effects have significantly affected the employees’ levels of job satisfaction. As a result, organizations are in need of leadership that will strive to create job satisfaction because such periods of crisis result into negative defensive behaviors and attitudes which result from uncertainty, all of which affect job satisfaction (Belias & Koustelious, 2014). Hanaysha et al. (2012) found that transformational leadership had a significant influence on job satisfaction among nurses in Malaysia and proposed further research in other sectors.

Globalization and recent developments in the banking sector have led to sporadic changes resulting in heightened competition among the banking institutions (IMF, 2017). These changes have brought to light leadership practice gaps and the resultant effects. One of the significant effects is on job satisfaction which has resulted from long working hours and increased work load (Mwangi & Omondi, 2016). Additionally, other effects have been revealed on employee motivation, performance and productivity (Bushra et al., 2011). Bader, Hashim and Zaharim (2013) carried out a study on job satisfaction among bank employees in Eastern Libya and found that gender and age affected job satisfaction. They recommended further studies to determine other factors that affect job satisfaction.

Globally, further research has been proposed in the area of transformational leadership and job satisfaction. In Malaysia, a study conducted by Omar and Hussin (2013) revealed a significant relationship between transformational leadership and employee job satisfaction in the academic sector. They proposed further research in other sectors like banking. E.O Darko and T.O Darko (2015) in Ghana noted that as a result of high competition in the banking industry, employees are now expected to work harder notwithstanding the dependence of performance on job satisfaction. The study proposed 8 further studies on the effect of leadership styles on employee job satisfaction in other regions. Tetteh and Brenyah (2016) found out that transformational leadership enhanced employee job satisfaction among employees in the telecommunication sector in Ghana. They recommended that further studies should be carried out in other sectors like banking. Walumbwa, Orwa, Wang and Lawler (2005) found a positive correlation between transformational leadership in Kenyan and American banks in a comparative study and recommended further research where each individual element of transformational leadership is assessed against job satisfaction.

The studies presented here indicate that there is need for further research in the area of transformational leadership and job satisfaction. Therefore, the motivation of this study was to establish the influence of transformational leadership on job satisfaction among employees in commercial banks in Kenya.

1.3 The Purpose of the Study

The purpose of this study was to examine the influence of the transformational leadership style on job satisfaction among employees in commercial banks in Kenya.

1.4 Research Questions

This study was guided by the following research questions:

1.4.1 To what extent does idealized influence affect job satisfaction among employees in commercial banks in Kenya?

1.4.2 To what extent does individualized consideration influence job satisfaction among employees in commercial banks in Kenya?

1.4.3 To what extent does inspirational motivation influence job satisfaction among employees in commercial banks in Kenya?

1.4.4 To what extent does intellectual stimulation influence job satisfaction among employees in commercial banks in Kenya?

1.4.5 To what extent does job security moderate the relationship between transformational leadership and job satisfaction among employees in commercial banks in Kenya?

1.5 Hypotheses

This study was guided by the following null hypotheses:

H01: There is no significant influence of idealized influence on job satisfaction among employees in commercial banks in Kenya.

H02: There is no significant influence of individualized consideration on job satisfaction among employees in commercial banks in Kenya.

H03: There is no significant influence of inspirational motivation on job satisfaction among employees in commercial banks in Kenya.

H04: There is no significant influence of intellectual stimulation on job satisfaction among the employees in commercial banks in Kenya.

H05: There is no significant moderating effect of job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya.

1.6 Justification of the Study

1.6.1 The Banking Sector

The findings of this study will provide new knowledge in the banking sector on the influence of transformational leadership on job satisfaction. This will therefore, enlighten senior management of banks and other financial institutions on how enhance employee job satisfaction; this will help to retain employees in the organizations. Additionally, it will demonstrate how the various components of transformational leadership can be used to harness employee performance by enhancing employee job satisfaction. This will ultimately boost achievement of the set objectives and goals.

1.6.2 Policies and Policy Makers

For policy and policy makers, the findings of this study will inform policies in financial institutions and other organizations on how to enhance employee job satisfaction. Thus, organizations will be able to come up with more effective policies around job satisfaction in the financial services sector. This is very crucial given the sensitivity of this sector and the important role it plays in the economy. This study will therefore contribute immensely by informing policies around job satisfaction.

1.6.3 Researchers and Academicians

The findings of this research will add value to the existing knowledge base on transformational leadership and job satisfaction of employees. The findings of this study will also provide knowledge on transformational leadership and job satisfaction of employees in commercial banks in Kenya. This will therefore add to the existing body of knowledge in the area of transformational leadership and job satisfaction.

1.7 Scope of the Study

This study examined the extent to which transformational leadership style influences job satisfaction of employees in commercial banks in Kenya. The study focused on the transformational leadership theory and the four dimensions commonly referred to as the 4Is. The target population was 10,301 employees who fall under the management category as per the Banking Supervision Report of 2015 (CBK, 2017). The study focused on all the 43 commercial banks in Kenya and adopted stratified random sampling technique. The Central Bank of Kenya has classified commercial banks in three tiers; tier one comprising of big banks, tier two comprising of medium sized banks and tier three comprising of small banks. The study focused on all the three tiers of commercial banks in Kenya. The research was carried out in September 2017.

1.8 Definitions of Terms

1.8.1 Transformational Leadership

Transformational leadership is a leadership style where the leader aims to articulate a compelling vision and offers clear goals whilst providing support and stimulating followers to work (Chan & Mak, 2014). Transformational leadership style has the

11 capability to motivate and also to inspire followers to identify with the leader and the organization thereby enabling employees to perform beyond expectation. It comprises of four dimensions: idealized influence, individualized consideration, inspirational motivation and intellectual stimulation (Bass & Avolio, 1997).

1.8.2 Idealized Influence

Idealized influence is also termed as charisma which is a behavior which brings out positive emotions from the followers and leads them to emulate the leader who in this case acts as a role model. A leader seeks to be a personal example and also maintains high ethical standards to ensure he is a role model. Idealized influence behavior constructs are charisma, trust and ethics (Bass & Avolio, 1994).

1.8.3 Individualized Consideration

Individualized consideration involves the leaders displaying attention to the developmental needs of the followers. They offer support through coaching and mentorship programs and also provide feedback on the performance of tasks. This is displayed when the leaders delegate tasks which in turn present the followers with growth opportunities. Individualized consideration behavior constructs are delegation, mentoring and support (Bass & Avolio, 1997).

1.8.4 Inspirational Motivation

Inspirational motivation involves the development and communication of an appealing vision which results in helping to focus the efforts of the followers to the organization’s vision and mission. Basically, the leader creates and communicates the vision with passion, enthusiasm and optimism to the followers. Inspirational motivation behavior constructs are communication, teamwork and motivation (Bass, 1985).

1.8.5 Intellectual Stimulation

This behavior seeks to grow the awareness of problems and leads followers to a different perception of the problems. They are influenced to creativity with a view of challenging the existing beliefs and values. Intellectual stimulation also helps leaders to challenge their follower’s ideas, values and problem-solving capabilities. It encourages followers to challenge and question status quo. Intellectual stimulation behavior constructs are knowledge sharing, creativity and risk taking (Bass & Avolio, 1994). 12

1.8.6 Job Satisfaction

Job satisfaction refers to how content a person is with his or her job, or how a person feels about their job or the various aspects of their job (Pravin & Kabir, 2011). It is how an employee perceives their job and the resultant emotions. Job satisfaction was measured by the level of commitment an employee has to the organization, absenteeism and employee’s intentions to leave the job (Emmanuel & Hassan, 2015; Ramos, 2014).

1.8.7 Job Security

Job security can be defined as the perceived stability and continuation of a job together with its features in future. Accordingly, it can be attributed to Maslow’s second hierarchy of needs which is security and safety. Job security constructs are anxiety, fairness and stress (Mahmoud & Reisel, 2014; Akpan, 2013).

1.9 Chapter Summary

This chapter presented an overview of the background of the study, the problem statement, research questions, hypotheses, scope of the study and definition of terms. Chapter two presents the theoretical framework, conceptual framework and the empirical review. Chapter three presents the research methodology. Chapter four presents the results and findings of the study. Chapter five presents the summary, discussion, conclusions and recommendations of the study.

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Introduction

This chapter presents the theoretical review and the conceptual framework of transformational leadership. The chapter also presents an empirical review of existing literature on transformational leadership and job satisfaction based on the research questions.

2.2 Theoretical Review

A theoretical review refers to a study of theories that have been formulated to explain, predict and understand various phenomena. A theory is an organized and systematic set of interrelated statements which specify the nature of relationships between variables (Green, 2014). This study was underpinned by the transformational leadership theory which was developed by James MacGregor Burns in 1978. The transformational leadership theory consists of four constructs namely idealized influence, individualized consideration, inspirational motivation and intellectual stimulation which lead to performance beyond expectations (Burns, 1978).

2.2.1 Transformational Leadership Theory

Transformational leadership theory was first articulated by Burns in the year 1978 (Burns, 1978). It was then advanced by Bernard Bass almost ten years after Burns had brought up the theory of leadership. According to Bass (1985) transformational leadership theory consists of four dimensions which are idealized influence, individual consideration, inspirational motivation and intellectual simulation which lead to performance beyond expectation as shown in Figure 2 which represents the transformational leadership theory model. Empirical studies have documented significant influence of transformational leadership on job satisfaction. Therefore, leaders should strive to integrate these attributes in their day to day leadership so as to simulate employees to work hard and perform beyond their expectations (Liao & Chuang, 2007; Liang, Chang & Chih-Wei Lin, 2017).

Transformational Leadership Theory Model

Figure 2.1: Transformational Leadership Theory Model

Source: (Bass & Avolio, 1985).

The leader is able to attain a high level of output from the followers because they do not work for self-gain. The leader provides the followers with an inspiring mission which enables them to pursue organizational objectives beyond their self-interests (Bass & Avolio, 1997). Through transformational leadership, a leader is able to drive change both in people and organizations. These four elements provide a compelling vision and leadership, encourage the followers by considering their needs, encourage followers to be innovative and to challenge the status quo, and finally provide a source of motivation. Consequently, they also help to ensure that the employees feel valued and remain inspired to perform beyond expectations (Bass, 1985; Northouse, 2013; Ramos, 2014).

Transformational leadership is grounded on the idea of transformational leaders motivating their followers to commit to the organizational objectives and to perform beyond expectations. According to Bass (1985), four leadership processes are involved in achieving these outcomes. First, leaders raise the followers’ consciousness levels about what is important and the value attached to the desired outcomes and the means within which the outcomes will be achieved. Secondly, leaders induce followers to go above and outside their self-interests for the sake of the organization by demonstrating care for their individual needs and treating them with a human touch. Thirdly, leaders foster the need to achieve higher level needs by stimulating the followers’ intellect by presenting challenges and giving them the opportunity to solve problems, offer solutions and challenge status quo. Fourthly, leaders seek to motivate and inspire the followers so that they can keep their eyes on the goal which is the organizational objectives and the means within which to attain the desired outcomes and results. When all these are fulfilled, followers perform beyond expectations, realizing the desired results and surpassing set expectations. 15

Performance beyond expectations is in relation to the collaborative, collective will and action yielded by transformational leadership which results in empowering the followers who participate in the process (Pamela, 2010).

2.2.1.1 Idealized Influence

Idealized influence is the capability to exert influence by serving as a role model through demonstration of high performance and moral standards. By this, the leaders persuade the followers to share in the organizations vision and objectives. The leaders possess a strong personal appeal and a power to influence the followers by providing direction, sense of purpose and fostering perseverance in pursuit of the goals (Muenjohn, 2010). The leaders earn legitimacy based on personal integrity and competence. From this, the followers admire and respect the leaders and further desire to emulate them (Liang, Chang & Chih- Wei Lin, 2017). Some attributes of idealized influence are vision, trust, respect, integrity and modeling. Idealized influence enables a leader to become a role model for high ethical behavior and to gain respect from the followers (Stone, Russel & Patterson, 2004).

Through charisma, a leader is able to instill pride, attract faith and respect from the followers and to make them see the bigger picture thereby communicating a sense of mission. This produces energy for achieving high work objectives (Brandt, Laitinen & Laitinen, 2016). The leaders display conviction, place emphasis on important personal values and connect those values with the organizational objectives. Transformational leaders rely on their charismatic attributes; charisma here being a form of personal power. Thus, they focus on their charismatic and enthusiastic attributes to gain influence over their followers and to motivate the followers.

2.2.1.2 Individualized Consideration

Individualized consideration refers to the degree to which leaders attend to the needs of the followers and act as coaches and mentors. They are able to recognize the employees’ unique needs for achievement, growth and desires by keenly listening to their needs and concerns. This takes care of the varying needs of autonomy, encouragement, responsibility, structure and instructions which fosters individual attention of followers as unique persons and doesn’t reduce them to their function and roles (Brandt et al., 2016). Individual consideration enables a leader to pay attention to the followers’ developmental

16 needs and therefore delegates work projects in a way that stimulates the learning experiences of the followers. A transformational leader allows the followers more discretion and opportunities in their work which satisfies their developmental needs resulting in enhanced commitment to the organization and the work. This stimulates them to achieve high levels of creativity (Cheung & Wong, 2011).

Bass and Avolio (1997) stated that leaders should share in the concerns of their followers together with their developmental needs and this paves way for each person’s individual consideration. Encouragement from the leaders allows the followers to express themselves freely and also to implement their ideas (Muenjohn, 2010). Leaders provide a supportive environment and carefully consider the needs of their followers. They also advice, teach, coach with an intent of helping their followers to develop themselves. Some attributes to individualized consideration are personal attention, mentoring, listening and empowerment. Individualized consideration enables a leader to keep communication open and also the celebration of individual employees and their contribution to the team. They provide the followers with sources of motivation within their jobs and through other avenues.

2.2.1.3 Inspirational Motivation

Inspirational motivation is the ability of a leader to behave in a way that motivates followers, generates enthusiasm and challenges people (Stewart, 2006). Leaders do this when they develop and communicate a convincing attractive future vision and also when they clearly communicate the expectations. It enables leaders to display optimism, power and encouragement to their followers (Felfe & Schyns, 2004). A leader is able to articulate an appealing vision of the future and challenge the followers’ expectations hence providing encouragement, optimism and a collective sense of purpose. Further, it allows a leader the opportunity to use symbols and emotional appeal to unite group efforts on a central purpose. This then encourages followers to achieve more than they would achieve in pursuit of their own self-interests (Cheung & Wong, 2011).

Leaders are able to motivate the followers through a compelling vision for the future and by expressing confidence in the followers which in turn nudges the followers to willingly increase their efforts to attaining the vision. It could also be a function of leaders expressing high expectations of the followers to achieve extra ordinary achievements and 17 at the same time displaying confidence in their potential. Inspirational motivation could easily overlap with charisma. However, it is worth noting that inspirational motivation could occur without a need for identifying with the leader which is the case for charismatic leaders (Muenjohn, 2010). A leader is able to achieve this by constantly encouraging the team and verbalizing confidence in their abilities, reviewing achievements progressively and also giving recognizing the follower’s efforts to achieve the set vision (Antonakis, 2006).

2.2.1.4 Intellectual Stimulation

Intellectual stimulation refers to the leader’s actions which persuade the followers to use their sense of logic and analyze situations using their creative thinking in a bid to find solutions. This tenet of transformational leadership goes on to challenge followers to come up with new ways of doing things and not to accept status quo if there is an option (Antonakis, Avolio & Sivasubramaniam, 2003). Therefore, intellectual stimulation also refers to the degree to which the leader challenges assumptions, status quo, takes risks and seeks for contributions of ideas from the followers. Transformational leaders are able to influence the followers’ creativity by ensuring the followers feel challenged and energized to seek new and novel approaches in their jobs which will translate into effectiveness (Cheung & Wong, 2011). A leader is able to arouse the followers to think in new creative ways which focus on problem solving and use of reason in judgment. Creative and innovative solutions are required and encouraged to stimulate followers (Felfe & Schyns, 2004).

Intellectual stimulation allows a leader to encourage and back up the followers to challenge the status quo, question existing or old assumptions, redefine problems, explore their intellectual curiosity and also use imagination. Leaders cheer their followers to think differently and in creative ways in order to address existing and future challenges. A few accompanying attributes are rationality and problem solving (Stone et al., 2004). This yields new ideas for the organizations on product lines or processes which have the possibility of yielding better returns or helping in the achievement of the organization’s objectives. To sustainably stimulate the followers, leaders avoid correcting the followers in public or criticizing the followers so that they are not limited in their creativity (Stewart, 2006).

2.2.1.5 Performance beyond Expectation

According to Bass (1985), when leaders practice transformational leadership, they are able to act as exemplary role models, motivate their employees to commit to the organizational vision, encourage innovation and creativity, and act as coaches and advisors to the employees; these actions result in performance that exceeds organizational expectations. Cheung and Wong (2011) note that intensive research has been carried out and findings indicate that transformational leadership is effective in enabling followers to perform beyond expectations and also transforming their personal values into higher level needs and aspirations. Additionally, they note that transformational leadership is linked to creativity of followers, performance, organizational commitment, absenteeism and satisfaction. All the above enhance innovation and a competitive advantage for the organization.

The attribute of idealized influence and inspirational motivation which enable a leader to instill pride in the followers also contributes towards inducing the follower’s interests beyond personal interests for the good of the organization. The attributes do this by reassuring the followers that together they will overcome the obstacles ahead and also by building confidence in the achievement of the set goals. Additionally, the leader’s optimistic talk about the future also helps to build hope in the followers because the leader provides an exciting image of organizational change (Guay, 2013). By constantly aligning the values of the followers to the organizational values, followers put in more effort which leads to performance beyond expectations since they can chose to operate below their thresholds. By supporting teams, transformational leadership builds a psychological attachment of the followers to the organization hence leading to a collective identity which motivates the followers (Rao & Abdul, 2015; Bass, 1985).

2.3 Conceptual Framework

A conceptual framework is a diagrammatic representation of variables or constructs that is used to map and guide the research process (Green, 2014). The conceptual framework was derived from the transformational leadership theory and consists of the four dimensions of transformational leadership which include idealized influence, individualized consideration, inspirational motivation and intellectual stimulation (Burns, 1978). Figure 2.2 presents the conceptual framework.

Independent Variables Dependent Variable

Idealized Influence  Charisma H01  Trust  Ethics

 Trust Individualized Consideration  Ethics H02 Job Satisfaction  Delegation  Mentoring  Organizational  Support Commitment  Absenteeism

Inspirational Motivation  Employee Turnover H03  Communication Intentions  Team Work  Motivation

Intellectual Stimulation H05 H04  Knowledge Sharing  Creativity  Risk Taking

Job Security  Anxiety  Fairness  Stress

Figure 2.2: Conceptual Framework

Source: Author (2017)

2.3.1 Independent Variables

A variable is a characteristic or attribute that can be tested or observed and may vary from context to context depending on subject of study. Independent variables are variables that cause, influence or affect outcomes and can also be referred to as treatment or predictor variables (Creswell, 2014). The independent variables for this study included idealized influence, individualized consideration, inspirational motivation and intellectual stimulation.

2.3.1.1 Idealized Influence (X1)

Idealized influence is the first tenet of transformational leadership that determines the effectiveness of transformational leadership. Leaders use idealized influence to wield influence through charisma, trust and ethics. Leaders earn credence and trust because of their consistency in behavior. It is a characteristic that enables the leader to create a perception of power, charisma, confidence and trust among the followers which results in admiration and a desire to imitate, respect and a need to be associated with the leader (Omar & Hussin, 2013). Idealized influence results from the leader’s behavior, beliefs, moral standards and conformity of values (Avolio & Bass, 2002; Ibraheem, Hussein & Ayat, 2011). Idealized influence is an emotional component of leadership. It describes the leaders who act as good role models and in turn arouses a desire in the followers to emulate them. Their behavior is comprised of high ethical and moral standards and this earns them a deep level of trust from their followers. Charisma tends to make people special and also make others to want to follow the vision they offer (Voon, Lo, Ngui, & Ayob, 2011).

Idealized influence helps the followers to acknowledge the unique capabilities of the leader, such as the leader’s persistence and desire to take risks in a bid to achieve the set objectives. It can also be explained as setting of knowledge creation as a means of influencing over ideals (Ngaithe, K’Aol, Lewa & Ndwiga, 2016). Idealized influence also helps the leader to provide a vision for the followers which doubles up as a driver to achieving the set objectives. It creates a sense of pride in the followers from the aspect of association with a leader who is viewed as a role model because of their boldness, dynamic capabilities, ethical nature, consistency and zeal to offer solutions. All these aspects yield motivation for the followers and result in achievement of the objectives and also performance beyond expectation (Ahmad, Abbas, Latif & Rasheed, 2014). Leaders demonstrate their willingness to sacrifice personal gain for the good of the team (Ogola, Sikalieh & Linge, 2017).

Ngaithe et al. (2016) refer to idealized influence as a way in which leaders behave that makes them role models for their followers. As a result, the leader is not only admired but also respected. This also results in trust and a desire by the followers to emulate the leader. The followers notice and give credence to the extra ordinary capabilities possessed by the leader which result in admiration. Followers have a sense of trust and respect for

21 leaders who possess idealized influence. As a result, they are readily willing to take on instructions from the leader notwithstanding the complexity of the tasks that may be involved. Idealized influence is similar to the highest level of moral reasoning; meaning such leaders are willing to forgo their interests for the benefit of their group or organization. Such leaders set high behavior standards and are role models because they walk the talk (Ogola et al., 2017).

Huang and Lin (2014) defined the most important characteristics of a charismatic leader as knowledgeable, friendliness, approachable, patient and enthusiastic. Max Weber referred to charisma as a quality of a person that sets them apart from ordinary people (Nikoloski, 2015). Charisma is also viewed as an attribute based on the followers’ perception of their leader’s behavior which goes on to suggest that charisma exists in the eyes of the beholder. Another definition is that charisma refers to attributes of personal characteristics that enable an individual to influence other people thereby impacting their feelings, opinions and behavior. Research conducted in organizations has shown that charisma is positively related to individual, group and firm level outcomes. This is because leaders are able to inspire followers to higher levels of performance and to impart in them behavioral attributes of commitment. As a result, there is a very positive perception of leaders possessing charisma because they are perceived as effective by their subordinates in comparison with less charismatic leaders. However, there are emerging theoretical advances challenging the correlation between charisma and leader effectiveness (Vergauwe, Wille, Hofmans, Kaiser & De Fruyt, 2017).

Bell (2013) referred to charisma as an untraditional form of influence where the leader has exceptional qualities which are perceived by his followers. It is a trait one perceives and is hard to describe without making reference to some known characteristics or behaviors. In his research, Bell examined behaviors like communication skills, visionary attributes, integrity, humor and expertise which were attributed to the charisma of Ronald Reagan. According to Nikoloski (2015), the ethics of charismatic leaders refers to how they use their power and in what. Charismatic leaders who are ethical have better workplace environments with less interpersonal and workplace deviance. These leaders act as role models and their behavior more often than not cascades through the organization. Human resource has become a source of competitive advantage especially

22 for the leaders with charisma because they have ability to inspire followers to own the vision and achieve the set objectives.

Ethics is an important element of an organization because it plays a big role in determining the performance of employees in the organization. The term was derived from Greek and it means moral character, custom and habit (Athar, Shahzad, Ahmad & Ijaz, 2016). Business ethics can be referred to as a criteria that is used to determine between wrong and right, good or bad. Ethical forces across the world are making businesses and business leaders to transcend their personal interests to ensure ethical issues are addressed effectively. An ethical climate refers to individual beliefs about the organizational practices, procedures, standards and ethical values. An ethical climate in organizations has been associated with enhanced levels of satisfaction (Ahmed, Shad, Mumtaz & Tanveer, 2012). Organizational ethics goes beyond the climate and involves top executive support for the ethical behavior and the association of ethical behavior with career success which are all associated with job satisfaction. Additionally, research states that organizational outcomes can be influenced by the leaders’ support and reward of ethical behavior (Awasthi, 2015).

Interpersonal trust has been described as a social lubricant which helps to facilitate collective efforts and perceptions within an organization. It is a psychological state which involves the willingness to accept helplessness based on positive expectations on the intent of another person (Kelly, Lercel & Patankar, 2015). Organizational trust is a general organizing principle; thus, the foundation of a general governing system of the contractual relations which can be invoked to control opportunism, costs and monitor problems in organizations. It has been argued that organizational trust enhances job satisfaction (Mincu, 2015). It was also found that the length of service in an organization influenced job satisfaction in some instances (Sarker, Crossman & Chinmeteepituck, 2003).

Research indicates that trust leads to participation in organizations and collaboration between people, groups and other organizations. Employees look for trust between them and their managers which affects their level of confidence in the organization leading to motivation and greater effectiveness. Mistrust leads to rumors, conflict and politics in the organization which are not desired. However, mutual trust can be a source of success for all (Pourkeiani & Tanabandeh, 2016). Organizations with higher levels of mutual trust

23 existing between management and employees could more ably maintain and sustain human talent which is a great source of competitiveness in the corporate world. Additionally, trust has been associated with employee job satisfaction and perceived organizational effectiveness. It is important but has sometimes been taken for granted by organizational leaders yet it remains a critical element in achieving the organizational objectives (Usikalu, Ogunleye & Effiong, 2015).

2.3.1.2 Individualized Consideration (X2)

Individualized consideration is the second tenet of transformational leadership where leaders show concern for their employees. Some behavioral attributes of individualized consideration consist of delegation, mentoring and support. Leaders build their people by delegating tasks to them, mentoring them and supporting them as they pursue the set objectives. Leaders in this context recognize people’s needs for achievement, growth, desires and demonstrate personal interest in helping them to satisfy their needs (Avolio & Bass, 2002). They also embrace people’s differences in the various spectrums of personal attributes; thus, followers are not reduced to their function and tasks but are considered as unique individuals (Felfe & Schyns, 2004). Leaders also use this to help them develop the abilities of their followers and empower them to accomplish higher tasks which can be achieved through delegation, support, training, guidance and effective supervision. To achieve this, a leader acts as both a referee and as a coach (Ibraheem et al., 2011). Leaders achieve this by giving personal attention to the followers and recognizing their uniqueness thus being able to help them through specific structured directions (Northouse, 2013).

This quality in the leaders helps them to pay more attention to the followers’ individual needs which yields happiness and comfort in the followers since they feel their needs are addressed from a personal and not a group point of view. The leaders train the followers on how to achieve the set goals and objectives and upon accomplishment, this leads to aspects of recognition which is a key driver of job satisfaction (Ahmad et al., 2014). The leader’s ability to create a supportive environment by listening, coaching and mentoring the followers speaks volumes to the followers because the leaders consider their needs by ensuring that as the organization grows, the employees also grow in their areas of interest. The leaders also help the employees to get through their personal challenges because they

24 are concerned not only about the work but also their followers personal matters (Alkahtani, 2016).

Delegation is one of the fundamental roles that leaders perform and it is widely credited as a result of effective management. Delegation involves giving subordinates the responsibility for decisions which are usually handled by the leader thereby enhancing their latitude and discretion (Drescher, 2017). Delegation is also referred to as the process of assigning responsibilities to subordinates by the leaders and it involves transferring authority from the leader to the employee. It results in empowering the employees to make commitments on behalf of the leader to use the resources available and also to make decisions relating to the roles assigned. Delegation stems from the fact that one individual cannot discharge all the responsibilities in an organization successfully. It can only work effectively if the person to whom responsibility is delegated is given commensurate authority to discharge the responsibilities. When it is implemented well it becomes a source of motivation and satisfaction for the employees (Agada, 2014).

Research in the area of delegation indicates that delegation positively affects the employee’s performance and satisfaction. Research goes further to differentiate the relative degrees of delegation because it is not a dichotomous element but it depends on how much authority is delegated. Delegation is similar to other forms of empowerment like consultation and participative leadership because decision making is shared and only the amount of involvement of the other parties differ (Drescher, 2017). Delegation is an inescapable practice in organizations and it helps to legitimize lower level managers by way of boosting their esteem and positive perception by the subordinates. In the modern world, where a lot is expected from the leaders, failure to delegate is almost a guarantee to failure on the job. However, it is important to note that as much as leaders should delegate authority and responsibility, they still remain accountable. Leaders should boost the confidence levels of their juniors to ensure they are confident enough to take on the tasks that are delegated to them (Badder, Salem & Hakami, 2016).

Mentoring has benefits that accrue to both the mentee and the mentor. Some of the outputs of mentoring include job performance, motivation and attitudinal benefits regarding the work (Xu & Payne, 2014). It is a subject that is attracting more and more attention because of the benefits associated with it which also include job satisfaction, organization commitment, reduced turnover intentions, career development and better

25 remuneration. It is also a key factor in the learning process in organizations and it plays a big role when it comes to work identity, improvement of outcomes and boosting the self esteem of the employees. It serves as a bridge which facilitates information exchange and knowledge acquisition in the organization (Cetin, Kizil & Zengin, 2013; Salami, 2010). Research suggests that organizations should formally support mentoring by incorporating mentoring tasks in senior employee’s development plans and performance requirements (Hartman, Rutherford, Friend & Hamwi, 2016).

Research has shown that mentoring is one of the successful ways of facilitating organizational learning and has demonstrated positive results coupled with enhanced job satisfaction. It has also shown that protégés who received mentoring support performed better in their jobs and had reduced intentions to leave the organization (Lo, Ramayah & Kui, 2013). Additionally, organizations that have effective mentoring programs in place are able to attract professional job seekers and retain good employees. Another positive result of mentoring is strengthening the relationships between the supervisors and the subordinates through the mentoring interactions (Lo et al., 2013). Mentoring is an important practice for organizations which helps to boost interpersonal relationships, learning, development and job satisfaction. It should be established with clear objectives to enable measurement of the impact and evaluation vis-à-vis the desired outcomes and objectives (Horner, 2017).

Perceived organizational support reflects employee’s perception that their supervisor values their contribution and cares for their well-being (Nicklin & McNall, 2013). It also refers to the extent in which the organization is perceived to value employees, contributes and cares for their wellness. An organization that supports its employees is committed to its workers and their needs. Research has shown that perceived organizational support is positively correlated with work attitude and effective work performance. Employees go to the extent of determining their action or inaction based on the nature of support they perceive the organization accords them through their leaders. Similarly, when there is a positive perception of organization support, then employees become more committed and work harder in their jobs. Perceived organizational support determines the organizational citizen behavior, performance, commitment, satisfaction and turnover intentions among the employees (Miao & Kim, 2010). Research has found that age had an influence on the effect of job satisfaction due to the different expectations employees have at different stages of their lives (Olorunsola, 2012).

2.3.1.3 Inspirational Motivation (X3)

Inspirational motivation is the third tenet of transformational leadership. It consists of attributes such as communication, teamwork and motivation. This is where leaders communicate and express themselves, encourage their followers to embrace team work and motivate them in the quest to achieve the set objectives (Lussier & Achua, 2013). It helps to express clearly and coherently the expectations of the employees through a shared vision which ultimately motivates the employees (Ngaithe et al., 2016). The leader convincingly emphasizes the need to perform and meet the set objectives to the followers which gives them a drive to achieve. Leaders who practice this possess the ability to influence their follower’s attitudes towards them and the objectives at hand. Such leaders also possess great communication skills which help them to communicate effectively to the followers (Bass & Avolio, 1994). The leader continues to express enthusiasm in the objectives, eagerness to achieve them and the confidence to deliver what is required of him and his followers (Trmal, Bustamam & Mohamed, 2015).

Inspirational motivation also enables leaders to communicate high expectations to the teams and inspire them to own and become part of the organization’s vision. The leaders rely on emotional appeals to focus group member’s efforts to achieve more as a group, as opposed to pursuing self-interests. It advocates for teamwork and the encouragement is achieved by ensuring everyone knows the critical role they play in the organization through their work (Tetteh & Brenyah, 2016). Additionally, inspirational motivation enables the leader to effectively communicate to the followers about the future goals of the organization and helps them to find ways of fitting into the organizational objectives by clearly identifying their roles and responsibilities. In line with this, leaders also encourage their followers to communicate and voice their ideas which create a feeling of satisfaction since their opinions are heard and valued (Ahmad et al., 2014).

Ngaithe et al. (2016) from their study noted that communication is one of the key elements of inspirational motivation. Inspirational motivation stems from the use of effective and communicative influence styles. The leaders effectively communicate the expectations from the employees and this inspires and motivates them. This also helps the leader to come up with a vision that the employees easily own. Managers who inspire employees align the individual objectives to the organizational objectives which makes the achievement of the organizational objectives an attractive way of achieving the

27 personal objectives. Inspirational motivation helps the leader to confidently and positively communicate the vision and stir energy and enthusiasm in the followers. Research indicates that communication between co-workers and between employees and supervisors can have a significant influence on the employee’s psychological outcomes including and not limited to job satisfaction. Additionally, employee perception of top management openness in communication and inclusivity in the decision-making process influences the overall job satisfaction of the employees (Winska, 2010).

Effective communication is one of the key attributes a leader should have because communication is critical for achievement of the organizational objectives; it is also a key success factor. It has been referred to as the social glue that holds the organization together. Research indicates that better communication skills lead to enhanced job satisfaction and job commitment (Paksoy, Soyer & Calik, 2017). An effective organization communication culture encourages feedback which raises the esteem of the employees. Consequently, effective communication yields increased job satisfaction, safety, productivity and performance of the organization. Additionally, communication is very important for the functioning of the organization because it reduces grievances and turnover intentions. Poor communication leads to poor performance because there is ineffective flow of important information upwards especially where employees do not trust their managers. Communication provides a platform for the provision of intrinsic needs such as recognition, appreciation and feedback which are a source of motivation for the employees (Shonubi, Abdullah, Hashim & Hamid, 2016).

Motivation refers to the psychological processes that determine the onset, direct and maintain voluntary actions towards organizational goals. It can also be referred to as a set of energetic forces that emanate from both inside and outside of the individual which helps to determine the form, direction, intensity and duration of behavior. It is a critical factor in organizations because it significantly influences job satisfaction and job performance (Rajan, 2015). Motivation plays a pivotal role in ensuring that employees achieve the set objectives because it enhances cooperation, morale, commitment and enthusiasm of employees. It is a psychological process which gives the employees behavior purpose and direction. Motivation can stem from fairness and equity, effective communication and providing performance-based rewards and incentives. Research has concluded that motivation is a set of both internal and external factors which stimulate the

28 desire and energy in people to achieve, be committed to a job and pull their weight in achieving the set objectives (Bwire, Ssekakubo, Lwanga & Ndiwalana, 2014).

One of the factors that really helps employees in achieving the set organizational goals is motivation and its absence results in lethargy and increased turnover intentions. Motivation is important as it determines the kind of excitement one pursues the set goals with. Motivation enables employees to do their work efficiently and effectively and with enjoyment. Research points to a strong positive correlation between motivation and job satisfaction (Singh & Tiwari, 2011). A motivated employee feels satisfied with their job and is empowered to strive for excellence and growth. Motivation boosts commitment and satisfaction with the job and yields greater productivity. Research notes that there is a significant difference between the productivity of motivated employees and non- motivated employees (Osakwe, 2014).

A team refers to a unit of two or more people who come together, interact and coordinate themselves in pursuit of a common goal. For there to be teamwork, the people need to interact, share a common goal and remain committed in pursuit of the goal. In a team, people are able to use their individual skills, have mutual accountability and complement each other. In order to create effective teamwork, there must be clear goals, relevant skills, mutual trust, employee commitment, effective communication, negotiation skills, good leadership coupled with both internal and external support. Teamwork has a positive impact on team performance (Musriha, 2013). Teamwork has a positive impact on achievement of job efficiency and enhancing productivity in organizations. Additionally, research has revealed that teamwork reduces human errors due to complimentary skills, enhanced performance and enhances job satisfaction of employees (Benrazavi & Silong, 2013).

A few factors like the willingness to work in team which refers to the attitude of an employee to cooperate and collaborate with others determine the effectiveness of teamwork (Benrazavi & Silong, 2013). Motivation in a team helps to overcome challenges and to create energy for achieving the objectives of the team and is therefore a catalyst for team performance (Irfan & Lodhi, 2015). Teamwork has become the standard unit of working in organizations. Through teamwork, employees are able to achieve more through collaboration which also helps them to enhance their knowledge and skills. Organizations seeking to boost productivity and satisfaction should support a teamwork

29 culture because not only does it increase results but also gives employees opportunities to participate in challenges that provide learning and feelings of accomplishment. Research goes on to indicate that organizations that encourage teamwork are more likely to attract and retain good talent (Manzoor, Ullah, Hussain & Ahmad, 2011).

2.3.1.4 Intellectual Stimulation (X4)

The fourth tenet of transformational leadership is intellectual stimulation. Examples of attributes associated with intellectual stimulation are knowledge sharing, creativity and risk taking. It refers to a leader’s ability to stimulate the followers by allowing them to be creative and innovative; thus, encouraging and allowing them to question assumptions, re-evaluate problematic and challenging situations thereby engaging their minds. The leader not only encourages but also supports creativity and innovation. Intellectual stimulation helps the leader to keep the followers constantly engaged in the tasks at hand by allowing them the free will to ask questions and provide any solutions they may have. Followers are involved in tackling setbacks and discovering new solutions (Long et al., 2014).

It allows followers to propose new ideas and also offer solutions based on their personal understanding of issues. Through this, leaders do not criticize their followers in public but encourage the followers to use the best suited approaches and to provide solutions (Omar & Hussin, 2013). Intellectual stimulation encourages the followers to challenge their own beliefs, the existing organizational beliefs and even those of the leaders. This component supports new approaches of doing things and encourages the followers to come up with new innovative ways of doing things or solving problems (Voon et al., 2011).

By encouraging followers to be creative, critical and logical, leaders help to create a feeling of satisfaction among the followers. Leaders encourage the followers to think creatively and offer new problem-solving skills. These same aspects lead to new innovations which in turn lead to recognition and career advancement thus driving job satisfaction (Ahmad, 2014). Leaders support followers because they do not necessarily see problems as a bad thing because the problems lead to creativity, critical thinking and innovation all of which could yield new profitable business ventures; for example, new products or services, efficient processes or procedures which contribute to the organization goals and objectives (Alkahtani, 2016). This quality of leaders values the ingenuity of followers and also leads to incorporation of the followers in decision making

30 and problem resolution forums so that they can give their ideas which help in sustainability and growth of the organization (Trmal et al., 2015).

Creativity is defined as the ability to bring something into being; it is differentiated by novelty and originality and is inventive in nature. In the ancient times, it was seen as a human gift to those born with creative talents. The ability to produce novel ideas and express oneself fluently are traits associated with creativity (Raju, 2017). Creativity is very important as a measure of contributing to an organization’s innovation. It has also been defined as the generation of new ideas whereas innovation is the implementation of those ideas. Organizations need to provide a supportive process and environment for employees to be creative. Additionally, the organization should provide challenges, involvement of staff and trust because these motivate employees to make contributions. An environment that allows creativity is catalyzed by some room for ambiguity, freedom and some room for risk taking (Chen, Hou & Fan, 2009).

Today, organizations have placed emphasis on productivity which involves maximum output at minimum cost. Companies are now competing on the basis of new products or business ideas which have proven to be significant sources of competitive advantage. Therefore, organizations need to encourage their employees to bring forth their novel ideas and solutions to problems be it in products, services, processes and systems all of which will allow them to be creative (Carine, Oduor & Shukla, 2015). An ethical organizational climate has been cited as an enabler of creativity for employees in organizations. Additionally, employees are more associated with organizations which encourage creativity and provide a platform with freedom of expression (Iqbal, Bhatti & Zaheer, 2013). Research has revealed that there is a positive and significant relationship between creativity and job satisfaction (Raju, 2017).

Innovation is basically the introduction of new processes or practices by creating new goods or services or for example by adopting new patterns. It is considered a strategic means through which organizations can advance their performance, growth and efficiency. In some organizations, innovation conflicts the existing status quo which is something leadership should encourage if they want to move ahead (Park, Tseng & Kim, 2016). Organizations are today considering innovation as a key source of competitive advantage especially in entrepreneurship. Organizational leadership needs to provide employees with a conducive and supportive environment to enable them to be creative

31 and innovative while also allowing room for implementation of their innovations. Research indicates that organizations that encourage innovation and provide a supportive climate are likely to experience growth and retention of talent (Farrukh, Iqbal & Khan, 2014).

It is impossible for organizations to escape innovation; whether new or existing, organizations need to be innovative in order to survive. Market leaders in the various segments emerge from the innovations they provide to the market. Innovation is a process that leaders can influence directly and positively with the aim of improving products, processes and profitability. For organizations to achieve innovation there needs to be coordination of the various efforts from different employees and integration across the specialist functions (Ghoochkanloo & Eshlaghi, 2016). Transformational leadership aims to create a good environment which is conducive for both innovation and creativity (Khalili, 2016). Such an environment encourages employees to be involved in problem resolution because all the different opinions are considered (Thamrin, 2012). Research indicates that creativity and innovation have a positive impact on job satisfaction (Park et al., 2016). Consequently, research also indicates that job satisfaction reflects on several variables like innovation and risk taking in the job (Al-Mahayreh & Abdel-Qader, 2015).

2.3.2 Dependent Variable (Y)

Dependent variables are outcomes of the independent variables and could also be termed as the response variables (Creswell, 2014). This study focused on job satisfaction as the dependent variable. Job satisfaction was studied through three elements; employee turnover intentions, employee commitment and absenteeism. The three elements were the key measures of job satisfaction in this study.

Job satisfaction according to Spector (1985) is defined as the attitudes of employees, compensation, promotion, rewards, fringe benefits, operating procedures, coworkers, nature of work and communication. Job satisfaction can be considered in terms of intrinsic and extrinsic factors with intrinsic factors being opportunities for advancement, growth, recognition, responsibility and achievement (Alonderiene & Majauskaite, 2016). Job satisfaction can be viewed from the perspective of employees’ cognitive, affective and evaluative reactions towards their jobs. Thus, it is the general attitude towards one’s job or the difference between the amount of rewards received and the amount the

32 employees believe they should receive (Akpan, 2013). It plays a big role in understanding employees’ behavior (Islam & Zaman2, 2013).

Job satisfaction results in higher employee retention rates and higher productivity from the employees (Emmanuel & Hassan, 2015). Job satisfaction cannot be overemphasized in the contemporary world especially because of the high dynamic and complex business environments that people are operating in. It is also fundamental in the creation of a well- developed leadership style (Ramos, 2014). Additionally, human resource is regarded as the organization’s most valued asset and a major source of competitive advantage. This emanates from the fact that organizations depend on people to achieve their objectives and when there is no job satisfaction then the employees are faced with choices of whether to quit or to continue staying. This negatively affects the organizational effectiveness (Tetteh & Brenyah, 2016).

Job satisfaction is mainly driven by intrinsic factors among them the work itself, recognition, autonomy, advancement and ability utilization. Work itself refers to the employees’ likes or dislikes with their job and goes on to determine whether the employee’s job is enjoyable or not. A study done on employees of various types of organizations in Pakistan indicates that there is a significant correlation between work itself with work motivation and satisfaction (Danish, 2010). Consideration of this topic in the past was by Gilbreth and Taylor in 1911 whose focus was more on specialization and simplification of tasks in order to maximize the efficiency of the workers. However, with time, this could not hold as it resulted in decreased employee satisfaction, turnover, absenteeism and difficulties in managing employees in simplified jobs (Humphrey, Nahrgang & Morgeson, 2007).

In this study, job satisfaction was measured through three constructs namely organizational commitment, absenteeism and turnover intentions which are discussed in the next section. The study formulates questions related to all the constructs intended to measure organizational commitment, absenteeism and turnover intentions.

2.3.2.1 Organizational Commitment

Organizational commitment can be defined as a bond or a link of the employee to the organization. Additionally, it can be referred to as the strength of an employee’s identification and involvement in and with the organization and can be characterized by; a

33 strong belief and acceptance of the objectives of the organization, the willingness to go out of your way on behalf of the organization and a relatively strong need to continue being a member of the organization (Suma & Lesha, 2013). There has been an increased focus on organizational commitment because committed employees are known to be engaged in more organizational citizenship behaviors and go out of their way to perform as required and even above expectations. Organizational commitment could vary in relation to the emotional attachment to an organization, the costs associated with leaving the organization and feelings of obligation to remain in the organization (Park, Christie & Sype, 2014).

According to Islam and Rahman (2016), the banking industry has been curbed with problems like extended working hours, pressure, non-conducive working environments, lack of fairness, reducing career growth opportunities and poor treatment all which have a significant impact on the level of organizational commitment and job satisfaction. Noting the level of commitment can greatly influence the quality of service rendered to customers, then organizational leaders need to ensure that they promote job satisfaction and commitment yielding policies and activities. Employee commitment is beyond being passively loyal to being actively involved, ready to transcend beyond personal gain for the organizational gain (Yucel & Bektas, 2012).

2.3.2.2 Absenteeism

Absenteeism refers to a habitual pattern of absence from duty or an obligation. This has in the past been viewed as an indicator of poor performance which could result from managerial problems. High rates of absenteeism could be as a result of poor or low morale or the work environment. Additionally, people who are dissatisfied with their jobs, and more so the work itself, have a higher frequency of absence compared to people who have job satisfaction (Thirulogasundaram & Sahu, 2014). Job satisfaction is an important area for organizations to address especially due to its impact on employee absenteeism, turnover intentions and behavior. This is because a satisfied person is more often present at work making positive contributions whereas a dissatisfied person will be absent more often and is likely to experience stress and ultimately leave the organization. Studies in this area reveal that satisfied employees are usually present at work and consequently indicate that dissatisfied employees are likely to leave the organization (Islam, Mohajan & Datta, 2012).

According to Ram (2013), absenteeism is costly to the organization because human resources are the engine in most organizations. It has been attributed to employees avoiding a painful or a dissatisfying work environment which could also result from lack of motivation. Therefore, management and the organization leaders must be able to understand the relationship between job satisfaction and other factors with employee absenteeism, so that they can be able to provide the right work environment and other factors to obviate absenteeism. Understanding the factors resulting in absenteeism will help the leaders to come up with policies to address the problem. For example, motivation and communication are considered to affect how often an employee is absent (Gangai, Agrawal & Gupta, 2015).

2.3.2.3 Employee Turnover Intentions

Employee turnover refers to the number of employees moving in and leaving an organization; it is usually presented as employee turnover ratio or simply referred to as the number of employees leaving an organization. Employee turnover is a ratio that compares the number of employees leaving an organization to the average number of total employees in a given time period. It is a big concern for organizations because it is a costly expense with a direct impact on the organizations performance (Shukla & Sinha, 2013). Turnover depends on an employee’s level of satisfaction (Awasthi, 2015). Total turnover is the total number of employees leaving the organization during a given period divided by average number of employees during that period. Turnover could be as a result of many factors like right sizing, hiring freezes, layoffs, lack of career growth, leadership among others, which ultimately affect the level of satisfaction an employee derives from the job (Shukla & Sinha, 2013).

Studies have revealed that the lack of satisfaction has consequences among them turnover intentions which consequently affect the quality of service rendered, productivity and ultimately the overall organizational success. Knowledge of this and a vision of the bigger picture of the consequences of turnover and job satisfaction should spur leaders into providing the best environments to prevent this (Joarder & Ashraf, 2012). Employee turnover may be voluntary or involuntary; the involuntary turnover is initiated by the organization while voluntary turnover is initiated by employees. Job satisfaction is the attitudes and feelings people have about their work. Positive and favorable attitudes towards the job indicate job satisfaction while negative and unfavorable attitudes towards

35 the job indicate job dissatisfaction (Armstrong, Riemenschneider, Allen & Reid, 2006). Employee job satisfaction is the fulfillment, gratification, and enjoyment that come from work. It is not the money or the fringe benefits, but the feelings employees receive from the work itself (Asegid, Belachew & Yimam, 2014). Job satisfaction has been found to be a consistent predictor of turnover intentions in many organizations.

Kanwal and Majid (2013) investigated the factors which are the major contributors towards employee’s job satisfaction. It was found that low pay, long working hours, bonuses, rewards and effective communication were the contributors towards job satisfaction or dissatisfaction and have impact to the employee’s intention to leave or stay in the organization. Nyamekye (2012) argued that non-monetary benefits had a direct bearing on employees’ job satisfaction. The employees were dissatisfied with supervision and non-participation in the decision-making process which may influence their intention to leave the job. Lee and Jimenez (2011) stated that performance-based rewards and supervision support reduce the possibility that employees will opt to leave their current jobs, and that job satisfaction is the most important predictor of turnover intention. In a recent study, it was observed that it is bad working conditions, lack of career growth, unfair compensation, negative supervisory support, the lack of employee development and job stress that caused the employees to leave organizations (Sattar & Ahmed, 2014).

2.3.3 Moderating Variable (Z)

Moderating variables are variables that affect the direction or strength of a relationship between independent variables and dependent variables (Creswell, 2014). This study had job security as the moderating variable between transformational leadership and employee job satisfaction. The elements considered under job security were anxiety, fairness and stress.

Job security refers to one’s expectations regarding the continuity in a job situation. Job security goes over and above the loss or retention of a job to the continuation or loss of certain desirable job features such as promotion opportunities, favorable working conditions and long-term growth opportunities with the organization (Akpan, 2013). The importance of job security comes from its influence on work related outcomes for example employee health, turnover and job satisfaction (Yousef, 1998). This has become a key variable in management with growing emphasis on understanding employee

36 reactions to changes in the organizations, for example mergers and downsizing which result in uncertainty and major concerns on job security (Davy, Kinicki & Scheck, 1997).

Job security is a key factor that influences the employee’s perception of job satisfaction and employers should therefore strive to constantly provide it so as to ensure that employees have a positive perception of satisfaction, which in turn yields greater organizational commitment (Alonderiene & Majauskaite, 2016). Job security provides employees with job satisfaction and has an impact on their motivational levels. It has an important role in maintaining peace and contributing to the productivity of the organizations (Islam & Zaman2, 2013). Job security is also the feeling of having a proper job with the assurance of its continuation in the foreseeable future and also the absence of factors that could threaten the continuation of that job. Industrial psychologists refereed to job security as one of the key elements that yield job satisfaction and the lack of it reduces job satisfaction levels of the employees (Jandaghi, Mokhles & Bahrami, 2011).

Job security transcends from the aspect of job loss to the aspect of availability of other jobs in case the loss does happen. It has been inferred that job insecurity threatens employees given the risk of loss of material, social or psychological benefits associated with the job. Research reveals that job insecurity yields negative employee attitudes, health and behavior as well as having direct consequences on satisfaction and performance. One common source of insecurity is competition among institutions which yields pressure on profitability and results in cost-cutting initiatives which come by way of lay-offs, redundancies and reduced benefits (Reisel, Chia, Maloles & Slocum, 2007). The Kenyan banking sector is currently going through a lot of changes causing the banks to downsize and is currently characterized with offers of voluntary early retirement, redundancies and lay-offs in a bid to cut on costs. Job insecurity results in anxiety, anger and stress resulting in distraction from the organization (Reisel, Probst, Chia, Maloles & Konig, 2010).

2.3.4 Operationalization of Variables and Hypothesis Testing

Operationalization of variables refers to the translation of the variables into parameters that can be measured quantitatively (Saunders, Lewis & Thornhill, 2016). Based on the conceptual framework, the study had four independent variables, one moderating variable and one dependent variable. They were all measured using three parameters. The independent variables were idealized influence, individualized consideration,

37 inspirational motivation and intellectual stimulation. Idealized influence was measured using charisma, trust and ethics. Individualized consideration was measured using delegation, mentoring and support. Inspirational motivation was measured using communication, teamwork and motivation. Intellectual stimulation was measured using knowledge sharing, creativity and risk taking. The moderating variable was job security which was measured using anxiety, fairness and stress. The dependent variable job satisfaction was measured using organizational commitment, absenteeism and employee turnover intentions. Table 2.1 indicates the operalization of the variables and hypothesis testing.

Table 2.1: Operationalization of Variables and Hypothesis Testing

Variables and Measurement Hypothesis Test Independent Parameters Variables Idealized  Charisma H01: There is no significant Multiple Linear Influence (X1)  Trust influence of idealized influence on Regression, p ≤  Ethics job satisfaction among employees in .05 commercial banks in Kenya Individualized  Delegation H02: There is no significant Multiple Linear Consideration  Mentoring influence of individualized Regression, p ≤ (X2)  Support consideration on job satisfaction .05 among employees in commercial banks in Kenya Inspirational  Communication H03: There is no significant Multiple Linear Motivation  Teamwork influence of inspirational motivation Regression, p ≤ (X3)  Motivation on job satisfaction among .05 employees in commercial banks in Kenya Intellectual  Knowledge H04: There is no significant Multiple Linear Stimulation Sharing influence of intellectual stimulation Regression, p ≤ (X4)  Creativity on job satisfaction among the .05  Risk Taking employees in commercial banks in Kenya Moderating Variable (Z)

Job Security  Anxiety H05: There is no significant Multiple Linear  Fairness moderating effect of job security Regression, p ≤  Stress between transformational leadership .05 and job satisfaction among employees in commercial banks in Kenya Dependent Variable (Y) Job  Organizational Commitment Satisfaction  Absenteeism  Employee Turnover Intentions

2.4 Empirical Review

Empirical review is an analysis of studies that have been done by other scholars in the area under study. Empirical review for this study focused on studies done in the area of transformational leadership and job satisfaction. An empirical review is important because the review of past studies helps to bring out the methodologies used and findings of other authors. It sheds more insight on how to conduct the research and helps to inform the research methodology, data collection, analysis and presentation. The empirical review for the study is based on the research questions.

2.4.1 Influence of Idealized influence on Job Satisfaction

This section discusses the influence of idealized influence on job satisfaction. Idealized influence is broken down into three constructs which are charisma, trust and ethics.

2.4.1.1 Charisma

Ansar, Aziz, Majeed and Rassol (2016) define charisma as a certain quality of a person which sets him apart from ordinary people allowing him to be treated in a unique way since he is perceived to have supernatural powers. Such an individual enjoys loyalty and authority by virtue of a unique mission which he appears to carry. Research has shown that charismatic leaders are masters of social skills who are mindful of the social environment around them. They are able to attract a following based on the things they say which appeal to the follower’s innermost desires. They have a unique ability to put across a message in a convincing way which also charms the followers. Followers are able to identify with charismatic leaders because the leaders portray conformity of needs, desires and aspirations. A leader who possesses charisma is able to influence his followers because they already identify with him; this in turn boosts the employee’s satisfaction (Khuong & Hoang, 2015).

Charisma is an important element of transformational leadership style which has a big influence on team outcomes and leaders who adopt it become an inspiration to others through their dedication (Yang & Islam, 2012). Charisma enables a leader to inspire the followers as a result of their self confidence, boldness and communication skills (Avolio & Bass, 2002). A research study sought to examine the extent leadership, charisma and vision could be discriminated by followers and how they influenced follower commitment and performance across three countries; Singapore, New Zealand and India.

The results of the study revealed that charisma was positively related to the follower’s commitment to the performing unit. There was consistency across the three countries studied. Additionally, there was also a correlation between commitment and performance which are a function of satisfaction (Hwang, Khatri & Srinivas, 2005).

Bacha (2010) conducted a study on the relationships among organizational performance, environmental uncertainty and employee’s perceptions of CEO charisma, and found that CEOs who are found to be increasingly energetic have an impact on organizational performance, as opposed to model CEOs who have no significant impact on organizational performance. According to Khuong and Hoang (2015), as much as compensation and fringe benefits matter, the leader’s personality and characteristics are more important as they affect the motivational work environment for the staff which in turn yields positive job attitudes. Huang, Cheng and Chou (2005) in their study dubbed fitting in organizational values sought to investigate whether CEO charismatic leadership had a positive effect on the following employee outcomes: extra effort to work, satisfaction with the CEO and organizational commitment. Their findings demonstrated that charisma did indeed have significant effects on employee outcomes of extra effort, satisfaction with the CEO and organizational commitment.

According to Belias and Koustelios (2014), idealized influence or charisma produces positive emotions from followers which lead to emulation of the leader making them a role model. This leads to loyalty and high moral standards among the employees. Leadership is a process where a person influences another to perform certain tasks in a certain way for the achievement of an objective. It is anchored mainly on behavior which is aimed at obtaining respect, being trusted and gaining confidence which leads to a strong buy-in of the vision. Transformational leaders are viewed as role models who provide the guidance that followers require by letting them know of their values by what they exemplify in their daily behavior (Rowold & Vogel, 2014; Beerel 2009). By virtue of their positions in the organizations, leaders are perceived as the representatives of the organization and from this, followers who perceive their leaders as transformational role models are more probable to trust the organizations top management.

Yang and Islam (2012) conducted a study that sought to demonstrate the influence of transformational leadership on job satisfaction using the balanced scorecard perspective. The study was done on the sales employees of the top four insurance firms in Taiwan that

40 had the greatest market share. The findings showed that charisma played a role in fostering job satisfaction among the employees and was a significant predictor of job satisfaction. Hanaysha et al. (2012) conducted a study in Malaysia among administrative and clerical staff involved in graduate and postgraduate affairs in three universities. They sought to establish if there was a positive relationship between charisma and job satisfaction. The research was conducted among 320 employees through an 18-item scale questionnaire from the MLQ and a response rate of 31.5% was obtained. The findings revealed that there was a positive relationship between charisma and job satisfaction which was statistically insignificant. This finding necessitates more research in the area since majority of the studies showed a positive correlation between charisma and job satisfaction with statistical significance.

Charisma has been studied widely and according to a study in Vietnam it was found to catalyze motivation. The research was conducted in the auditing field and it sought to establish the effect of leadership styles on employee motivation. A sample size of 320 respondents was chosen in a city in Vietnam. The researcher sought to establish if charismatic leadership positively affects the employee motivation. A structured questionnaire that employed the Likert scale was used to collect data. The findings of the study revealed a positive correlation between charisma and employee motivation; thus, charisma positively affects motivation (Khuong & Hoang, 2015).

Emmanuel and Hassan (2015) carried out a study to establish the effect of transformational leadership on job satisfaction in four and five-star hotels in Kuala Lumpur. They sought to examine through one of their research objective the effect charisma has on employee job satisfaction. A total of 130 questionnaires were distributed and 123 questionnaires were returned. The findings of the study revealed that charisma had a positive and significant relationship with job satisfaction. Arzi and Farahbud (2014) who studied the impact of leadership style on job satisfaction in Iranian Hotels and found that a vision is very critical in sustaining and growing employee job satisfaction. Their study found that the leadership style significantly impacts job satisfaction.

Ngaithe et al. (2016) examined the influence of idealized influence on staff performance in state owned enterprises performance in the Kenyan perspective. The study mainly sought to examine the influence of idealized influence and inspirational motivation on employees’ performance. The study was anchored on the positivism research philosophy

41 and stratified random sampling technique was used to obtain the sample size of 163 respondents. Primary data was analyzed using both exploratory factor analysis and regression analysis. Findings of the study revealed a positive relationship between idealized influence and performance. The choice of positivism research philosophy and stratified sampling technique but the choice of descriptive research design was contrasted by analysis of data using regression analysis it would have been appropriate to adopt descriptive-correlation research design. Moreover, since the data was in ordinal scale structural equation modeling (SEM) would have been the most appropriate.

Gitoho, Muchara and Ngugi (2016) examined the influence of idealized influence on employee satisfaction amongst listed companies in Nairobi securities exchange. Stratified sampling technique was used to draw 400 employees working in heterogeneous managerial positions within the listed companies. Data was analyzed using exploratory factor analysis. Further, regression analysis was used to examine the nature of the relationship between idealized influence and employee satisfaction. The findings revealed a positive and significant relationship between idealized influence and employee job satisfaction. From the study it was deduced that management have a great role in determining employee motivation.

Metwally, Eli-bishsishy and Nawar (2014) examined the link between transformational leadership and employee job satisfaction in multinational FMCG firms in Egypt. Simple random sampling was used to draw 200 respondents who were stratified according to three departments within the company. Primary data was collected using MLQ developed by Bass in 1978 to measure leadership and job satisfaction was measured using Minnesota Satisfaction Questionnaire (MSQ), the duo adopted a 5-point Likert scale. Data was analyzed using regression and correlation analysis whose results revealed a positive and significant relationship between idealized influence, individualized consideration, inspirational motivation, intellectual simulation and job satisfaction. The study recommended that organizations ought to adopt transformational leadership to motivate their employees and consequently achieve superior performance.

Emu and Umeh (2014) empirically examined the relationship between leadership style and job satisfaction among customer relationship officers in Nigerian banks using a quantitative correlation research design. Simple random sampling was used to draw 85 customer relationship officers. The study adopted MLQ nine attributes as the main tool of

42 primary data collection. A correlation analysis revealed a positive and significant relationship between idealized influence and employee job satisfaction. The study findings acted as a benchmark which Nigerian banks can adopt to manage their employees effectively. From the study transformational leadership was perceived to be an ideal leadership style to drive job satisfaction.

Bayram and Dincs (2015) examined the role of transformational leadership on employee satisfaction in private universities in Bosnia. Simple random sampling was used to draw 150 respondents from the private universities. Data was analyzed using exploratory factor analysis, mean, standard deviation, correlation and regression analysis. The results of the study revealed a positive and significant relationship between idealized influence and employee job satisfaction. Although, the results revealed that employees were highly satisfied with the nature of work they were not satisfied with work assignment and operating conditions which needed further examination to boost the employee’s morale. Thus, private universities needed to check on the issues triggering employee motivation which should drive their primary goal of promoting firm performance.

Ahmad et al. (2014) examined the impact of transformational leadership on employee motivation in the telecommunication sector in Punjab. Simple random sampling technique was used to draw a sample of 400 respondents. Data was collected using questionnaires which were anchored on the MLQ. Descriptive analysis, correlation analysis and regression analysis were used to analyze the data. The results of the study revealed a positive and significant relationship between transformational leadership and employee motivation. A close scrutiny of transformational leadership attributes revealed that they had a positive and significant relationship with each other. The study concluded that transformational leadership had a significant influence on employee motivation.

Long et al. (2014) examined the impact of transformational leadership on job satisfaction in Malaysia. Under a descriptive research design, stratified sampling was used to draw a sample of 378 respondents from 6 departments of Government Link Company (GLC) in Malaysia. Data was collected using questionnaires anchored on the MLQ and MSQ questionnaire. Correlation and regression analysis tests revealed a positive but non- significant relationship between idealized influence and job satisfaction, though there was a positive and significant relationship between individualized consideration and job satisfaction. The study concluded that there is need for leadership to be effective, to

43 continuously replenish their knowledge and offer positive attributes which will trigger superior employee performance within their organization.

2.4.1.2 Trust

Trust occurs in a framework of interaction which is influenced by both the personality systems and the social systems. Personal trust involves a bond between individuals, one that is preserved by the emotional pain one is bound to experience in case of betrayal. Trust is a very important component in the organization’s long term stability and well being of the employees and is described as a social lubricant to relationships. Additionally, the higher the organizational trust, the more satisfied and productive employees tend to be (Salleh, Zahari, Ahmad, Aziz & Majid, 2015). It has further been described as a psychological state comprising the intention to accept vulnerability based on positive expectations of the intentions or behavior of another (Kelly et al., 2015).

Fard and Karimi (2015) conducted a study with the aim of establishing the relationship between organizational trust and job satisfaction. The study was conducted among employees of a university. The study employed a descriptive correlation research design and out of 340 employees, 180 were selected using simple random method and sampling table for the research which employed research questionnaires. A Pearson correlation coefficient test and structural equations modeling were used to analyze the data. The results of the study revealed a positive and significant relationship between trust and job satisfaction. Additionally, trust binds people together and enables the people to focus on long-term results which are necessary for organizational success.

Meral, Yashoglu and Semercioz (2016) carried out a study that sought to establish the effects of trust on job satisfaction and the mediatory role of new identification between trust and job satisfaction in mergers. They studied 143 employees of a newly merged bank called TEB and Fortis banks which consisted of 335 branches and 5646 employees before the merger and 603 branches 9945 employees after the merger. A correlation analysis was conducted to measure the strength of the relationship between trust and job satisfaction. The results of the study revealed a positive and statistically significant correlation between trust and job satisfaction. Trust enables coordination and effective performance of work, yields less anxiety amongst employees and fosters an acceptance of

44 changes that occur in the bank. The study concluded that trust to management significantly affects the employee’s job satisfaction.

Srivastava (2013) conducted a study titled job satisfaction and organizational commitment relationship with the effect of personality variables, and had trust and locus of control as the moderating variables. The research instruments were administered to 247 middle level managers in the private sector. Data was analyzed and the findings revealed that job satisfaction was positively related to organizational commitment, and trust moderated the job satisfaction and organizational commitment relationship. These findings were consistent with findings of a study dubbed the relationship between organizational trust and job satisfaction, whose context was the Federal organization of US which found that trust in an organization led to employee job satisfaction and that the two variables had a direct correlation (Callaway, 2007).

Kelly et al. (2015) studied the influence of trust and job satisfaction on safety climate among managers in a large U.S. air carrier. They developed a conceptual model to establish the influence of trust and job satisfaction on the safety climate. The study used questionnaires as the research instruments and the responses were ranked using a five- point Likert scale. The questionnaires were administered to 1299 management employees and a total of 729 usable questionnaires were returned indicating a response rate of 57.7%. Data was analyzed and the findings revealed that coworker trust and supervisor trust were significantly and directly associated with both job satisfaction and safety climate. However, the results cannot be generalized to entire organization or to other air carriers or other types of organizations because the research did not include other employees.

In the age of globalization and technological advancement, trust enables effective communication in organizations which is a key determinant of job satisfaction. Research revealed that trust lubricates organizational processes by fostering increased cooperation, acceptance and buy-in of objectives, boosting discretional performance, job and team satisfaction, organizational citizenship behavior, enhanced loyalty and reducing employee’s intentions to leave the organization (Nair & Salleh, 2015). Research indicates that people who are in high trust environments live longer, enjoy greater wellness and job satisfaction. In contrast, a low trust environment sucks energy, results into stress and

45 reduced wellness which has the possibility of destroying performance. Lack of trust also suppresses expressions which may lead to a lot of dysfunctions in the organization hence the need to cultivate a trust culture which is a precursor to job satisfaction and performance (Jameson, 2010).

Sharkie (2009) in the study titled trust in leadership is vital for employee performance discusses the importance of trust in boosting performance. The study states that trust is a very important component in the leader-employee relationship because it is one of the characteristics that motivate employees to perform beyond expectations. Trust is referred to as the key to cooperation in organizations and it derives discretional input from the employees. In other words, trust influences employee’s attitudes, cooperation and performance. The findings of the study revealed that employee reciprocity which comes in the form of commitment to the organization, the personal will to engage in extra roles depends strongly on the employee’s assessment of the level of support management accords them. Such assessment is based on the beliefs, integrity and trustworthiness of management. Hence it is critical for management to build trust in order to earn discretionary support from the employees since it plays a major role in the leader- employee psychological contract (Abdullah, Hamzah, Arshad & Isa, 2011). A study on the impact of CSR on casino employees’ organizational trust, job satisfaction and customer orientation found that CSR has a positive effect on organization trust and additionally, organization trust positively influences job satisfaction (Lee, Song & Lee, 2013).

2.4.1.3 Ethics

According to Yates (2014), ethics in leadership refers to a leader’s ability to demonstrate appropriate conduct through their actions and relationships with others. Additionally, ethics influences the impact of a leader, his relationship with the followers and also determines the organizational values (Northouse, 2013). In a study among United Kingdom companies and some continental Europe companies, it emerged that the key issues around ethics are bribery, corruption, facilitation of payments, whistle blowing, discrimination and harassment. Globally, organizations are facing many problems resulting from unethical practices. Ethical behaviors basically involve principles like honesty, integrity, fairness and concern for people. Unethical leadership leads to

46 increased costs that arise with employee turnover, the need for increased supervision, decreased employee job satisfaction and productivity (Bello, 2012).

According to Anaza, Rutherford, Rollins and Nickell (2015), job satisfaction can be triggered by an employee’s perception of the organization’s ethical climate. A good ethical environment has the potential to boost employee’s job satisfaction levels while the consequences of ethical misgivings are detrimental to the organization. Globalization has resulted in a myriad of interactions within and outside the country borders and this has resulted in conflicting expectations and ethical dilemmas which need a good ethical background to enable the employees handle them appropriately. Their study confirms that ethical climate influences job satisfaction and helps to develop affective commitment from organizational buyers.

Ren and Chadee (2017) conducted a study titled ethical leadership, self-efficacy and job satisfaction in China with the moderating role of guanxi. The purpose was to find out how employee perceptions of the ethical conduct of the leaders affect their job satisfaction. They developed a model to conduct the research and had a sample size of 388 professional employees. The findings revealed a positive correlation between ethical leadership and job satisfaction which is negative if moderated by guanxi. Guanxi is a complex relational phenomenon in Chinese tradition which may act as a substitute for ethical leadership in the Chinese workplace. The research instrument was a questionnaire that utilized the Likert scale. This research revealed the fact that different conceptualizations of ethical leadership cannot be applicable across all cultural contexts because for example in China guanxi played a substituting role and reduced the impact of ethical leadership on job satisfaction.

Ahmed et al. (2012) carried out a study that sought to establish the relationship between organizational ethics and job satisfaction among bank employees in Pakistan. The study collected data from 230 employees from a convenience sample. The study used a questionnaire to collect data from the employees and also used a 20-item job satisfaction scale and the responses were ranked in a Likert scale. A Cronbach’s alpha test was carried out for reliability and other tests, tests for correlation and structural model were run in the data analysis. Results revealed that three ethical climates existed; thus, egoistic ethical climate, benevolent ethical climate and principled ethical climate. Egoistic ethical climate focused on organizational self-interest, benevolent ethical climate focused on employee’s

47 interests and principled ethical climate focused on obedience due to cultural differences. Egoistic ethical climate was negatively related to job satisfaction, principled ethical climate had no relationship with job satisfaction while benevolent ethical climate and top management support for ethical behavior were positively related to job satisfaction.

Koh and Boo (2004) in their study on organizational ethics and employee satisfaction and commitment sought to establish the relationship between organizational ethics and organizational outcomes. They note that ethical values influence not only employee attitudes but also employee behavior. They interviewed 237 managers in Singapore and their results revealed a positive and significant relationship between ethics and job satisfaction. The ethics herein referred to top management support for ethical behavior and the association of ethical behavior with career success. They concluded that high ethical levels were associated with higher job satisfaction levels and that favorable organizational ethics produced favorable organizational outcomes like satisfaction. According to Kim and Brymer (2011), their study on the effects of ethical leadership on manager job satisfaction, commitment, behavioral outcomes and firm performance found that executive’s ethical leadership was positively related to the organizational manager’s job satisfaction and also to the organizational commitment.

Dinc and Aydemir (2014) conducted a study titled the effects of ethical climate and ethical leadership on employee attitudes in Bosnia. They studied employees from private universities in Bosnia and Herzegovina (BIH) and used a sample size of 213 employees. The sample size for the study was 260 but only 220 respondents undertook the survey with only 213 being usable. The questionnaire used a five-point Likert scale as the points of measure. Tests for correlation and regression analysis were conducted on the data that had been collected. The findings of their study revealed a positive correlation between an ethical climate and job satisfaction. A further analysis showed that there was a positive correlation between the employee’s perception of ethics in the organization and job satisfaction, which results in reduced turnover intentions from the employees (C. Pettijohn, L. Pettijohn & Taylor, 2008).

Yates (2014) conducted a study to establish whether ethical leadership contributed to job satisfaction, organizational commitment and organizational citizenship behavior. The results of the study revealed that indeed followers led by highly ethical leaders reported

48 higher levels of job satisfaction and organizational commitment than did followers who perceived their leaders as less ethical. In a study conducted by Tsai and Huang (2008), they found that employees who worked in professional environments which had concern for others derived greater satisfaction from their jobs. The study was conducted among 352 nurses who worked in Taiwan and their study sought to establish the relationship among ethical climate types, facets of job satisfaction and components of organizational commitment. Their study sought to establish the relationship between variables using factor analysis, reliability, descriptive statistics, correlation and regression. Research also indicates that good ethics is good business which points to a correlation between ethical values and performance.

In the study of ethics and job satisfaction, there is an opinion that job satisfaction leads to integrity. Research was conducted among police officers and one of the hypotheses being tested was whether job satisfaction led to integrity among the officers. Results revealed that there was indeed a positive and significant relationship between job satisfaction and the integrity of the police officers. Additionally, a code of ethics was also found to have a positive and significant relationship with integrity among the officers (Othman, 2014). Further research in the area of team virtues and performance was conducted with one of the dependent variables being satisfaction with the leader. Results revealed that there is a relationship between the leader’s behavioral integrity and follower satisfaction with the leader which is a facet of job satisfaction (Palanski & Yammarino, 2011).

2.4.2 Influence of Individualized Consideration on Job Satisfaction This section discusses the influence of individualized consideration on job satisfaction. Individualized consideration is broken down into three constructs which are delegation, support and mentoring.

2.4.2.1 Delegation

Delegation refers to a conceptualized process that involves assigning crucial tasks to subordinates and giving them the responsibility for decisions which are usually made by the manager. It leads to an enhanced amount of discretion being given to the followers which is anchored on the authority to make decisions without the prior consent from the manager. It is basically where the leader allows the followers to make decisions without necessarily running their ideas with the manager before deciding. It also comes with the

49 aspects of authority, responsibility and accountability for the decisions (Musenze, Thomas & Lubega, 2014). Additionally, the concept of delegation is highly anchored on trust between the leader and the follower. Thus, leaders usually assign authority and power to followers whom they trust will not misuse the power and authority given to them. Delegation helps to overcome obstacles of corporate decision making and results in perceived empowerment and yields job satisfaction (Noblet, Rodwell & Allisey, 2009).

Delegation has been found to have a positive relationship with job satisfaction, task performance and organization commitment. It provides an avenue for leaders to empower their followers by affording them new opportunities to gain new experience (Banford, Buckley & Roberts, 2014). Joiner and Leveson (2015) carried out a study on effective delegation among Hon Kong Chinese male managers with the mediating effects of Leader Member Exchange (LMX). They found a direct association between delegation and job satisfaction. In their research, they interviewed 186 Chinese subordinate managers in a transport company. Data was analyzed and the results revealed that employees who are entrusted with decision making and receive support from their supervisors and colleagues are more satisfied with their jobs.

Musenze et al. (2014) conducted a research on delegation and job satisfaction and evaluated the relationship within Uganda’s primary education sector. They sought to establish the effect of delegation on primary school teachers’ job satisfaction. They employed a cross sectional research design and a total of 247 survey questionnaires were distributed. Data was analyzed using Structural Equation Modelling (SEM) and the results indicated that save for decision making, the other dimensions of delegation like autonomy, authority and responsibility predicted job satisfaction. Riisgaard, Nexoe, Le, Sondergaard and Ledderer (2016) did a review paper with the aim of establishing the relationship between task delegation and job satisfaction in general practice. The review found that a few nurses had negative attitudes and experiences towards task delegation especially due to an increased workload. However, majority were generally satisfied with their jobs and the various tasks they performed which were delegated to them by the general physicians. Additionally, they attributed this satisfaction to the autonomy which they enjoyed.

Ukil (2016) studied the impact of employee empowerment on employee satisfaction and service quality in financial enterprises. They noted that one of the ways in which delegation occurred was through empowerment. The sample for this study was 240 employees drawn from 20 different financial institutions in Bangladesh. Questionnaires were used to collect data. Results of data analysis revealed that employee satisfaction and service quality largely depended on employee empowerment. Additionally, satisfied employees were found to offer better quality service. The study concluded that by empowering employees, organizations can increase the level of satisfaction their employees have with their jobs which consequently raises the quality of service they provide to their stakeholders.

Ameer, Bhatti and Baig (2014) carried out a study on the impact of employee empowerment on job satisfaction. The study adopted a descriptive research design and conducted a survey among the respondents. Data was collected using questionnaires with nineteen closed ended questions and the responses were ranked using the five-point Likert scale. The data was analyzed using correlation and regression analysis. The results revealed that empowerment was based on the notion of giving employees skills, resources, authority, opportunity, motivation, responsibility and accountability for their actions which not only contributed to job satisfaction but also their competence levels. Kombo, Obonyo and Oloko (2014) also found that delegation had a strong relationship with satisfaction and performance thorough raised enthusiasm among the employees. Additionally, delegation is not only rewarding for the employees but it also raises the employee’s sense of accomplishments and self-esteem.

Drescher (2017) conducted a study to examine the relationships between delegation, employees’ perception of leader performance and likeability, and the followers’ job satisfaction. A convenience sample of 304 participants was selected from social networks and invited to participate in an online survey. The results of regression analysis revealed that delegation leads to a positive evaluation of the leader and the mediation analysis of likeability influences the relationship between delegation and employee’s job satisfaction. Delegation affected how employees rated the leader’s performance related and affective qualities which in turn influenced the level of satisfaction.

Farmer (2011) sought to establish the effects of empowerment on supervisory relations, burnout, and job satisfaction in two American prisons. The study was a comparative case study and one of the objectives sought to assess the effect of an empowered staff management model on staff perceptions of delegation of authority on job satisfaction. The research process obtained 149 responses and data was analyzed using factor analysis and a bivariate analysis. Results revealed that contrary to most of the studies in this thematic area, the effect of delegation of authority and responsibility was not significant on job satisfaction but there were significant effects of empowerment on job satisfaction.

2.4.2.2 Mentoring

Mentoring is a reciprocal relationship bound with an emotional commitment between an apprentice worker and an experienced worker. It refers to the teaching and learning process of knowledge and competence, which involves sharing of advice and role development with both formal and informal support from the experienced worker to the apprentice. Mentoring provides people with opportunities for professional growth and job satisfaction and lack of satisfaction results in turnover (Mariani, 2012). Mentoring helps to facilitate continuous interactions between a more experienced person and a less experienced person. This results in the less experienced person becoming more skilled. Among the advantages associated with mentoring are the outcomes of positive attitudes and behavior among the less skilled workers, rejuvenation, enhanced job performance, job satisfaction, satisfaction with colleagues and the organization (Hartman et al., 2016).

Lo and Ramayah (2011) studied the thematic area of mentoring and job satisfaction in Malaysian SMEs. They sought to establish the impact of mentoring on employee job satisfaction. They conducted a survey among employees from small and medium enterprises in Malaysia. They sent out a total of 200 questionnaires and 158 Malaysian executives participated in the survey. Data was analyzed and the results revealed that there was a positive relationship between career mentoring and all dimensions of job satisfaction; for example, co-workers, the job itself, promotion opportunities and supervisors. Conversely, no significant relationship was found between psychosocial mentoring and three aspects of employee job satisfaction which were co-workers, job itself and promotion. Other scholars agree that managers should improve their career development plans and the mentoring process in order to increase job satisfaction and organizational commitment (Weng, Huang, Tsai, Chang, & Lin, 2010).

Cetin et al. (2013) sought to investigate the impact of mentoring on job satisfaction and organizational commitment among accounting-finance academics employed in Turkey. The researchers conducted a survey among the scholars and considered mentoring to cover aspects of career development, role modeling and social support. Questionnaires were distributed to the faculty and a response of 90 questionnaires was obtained and analyzed using SPSS version 13. Other tests like factor analysis and regression analysis were also conducted. The results of data analysis revealed that social support and professional commitment were positively related to job satisfaction and that they were both aspects of mentoring. However, career development and role modeling were found not to have a relationship with job satisfaction. This was attributed to the fact that career development and role modeling were a factor of age and had an impact on affective commitment.

Shujaat, Sana, Aftab and Ahmed (2013) in the study on the impact of career development on employee satisfaction in the private banking sector in India sought to determine the impact of aspects of career development on job satisfaction. A survey was conducted using structured questionnaires that were administered through both soft copy and hard copies to 500 respondents in India. The sampling procedure used was the convenience sampling method. There were 395 responses received and data was analyzed using SPSS and analytical tests like the Chi-square were conducted. The findings of the study revealed that mentoring and counseling programs have a positive impact on employee job satisfaction and that this was one important driver of job satisfaction among the employees in the private banking sector in India.

Kim (2011) sought to establish the effect of mentoring in the public sector in Georgia and Illinois. Data from 1220 public and non-profit sector managers was collected and a few tests like regression were conducted in the analysis of the data. The results of the study found a significant and positive relationship between intrinsic motivation and job satisfaction. Whereas trust was positively related to job satisfaction, economic benefits were not. Mentoring was found to have a significant mediating effect on the effect of intrinsic and extrinsic motivation factors on job satisfaction. Thus, mentoring was not only found to help employees develop their careers and to build better coworker relationships but also to have a significant effect on job motivation and job satisfaction.

Gosh and Reio (2013) conducted a research on the career benefits associated with mentoring for mentors. Mentoring is an extensively studied area and is linked with mentee career development and growth; additionally, mentors can also draw significant benefits from mentoring. An analysis was conducted on the provision of career, psychosocial and role modeling mentoring support and how it was linked with outcomes like job satisfaction, commitment, turnover intentions, performance and success on the job. Results of the study revealed that mentors vis-à-vis mentees were more satisfied with their jobs and committed to the organization. These results are in support of the mentoring theory where mentoring is a reciprocal and collaborative agenda and not only beneficial to the mentees but also to the mentors who are noted to experience increased job satisfaction.

Horner (2017) carried out a study to establish if mentoring based on Watson’s caring model positively influences nurses’ job satisfaction. The study used a mixed methods design and data was collected using an online survey which was composed of closed and open-ended questions. There was a response rate of 54% which represented 37 of 69 respondents. All the participants reported that mentor experience or relationship positively influenced job satisfaction. Additionally, job satisfaction was associated with reduced turnover of staff and improved patient retention. Salami (2010) studied the relationships of mentoring and satisfaction with mentoring and work attitudes. The study was conducted among nurses in Nigeria and data was collected using questionnaires from a sample size of 470 nurses. One of the findings of the study was that employee satisfaction with the mentoring experience significantly predicted the work attitudes which in turn determined job satisfaction, organizational commitment and job involvement.

Lo et al. (2013) conducted a study on mentoring and job satisfaction in Malaysia in small medium enterprises. One of the aims of the study was to test whether mentoring positively influences employees’ job satisfaction. The study was conducted among 21 small and medium enterprises with 200 questionnaires being sent out to middle and lower level management. The study used a 7-point Likert scale to gauge each of the key variables in the study. In measuring job satisfaction, the variables that were used were promotion, supervisor and co-worker relationship and the dimensions of the job itself. The study found that career mentoring had a significant and a positive relationship with

54 the job satisfaction dimensions of the job itself, co-workers, supervisors and promotions. Additionally, employees who are mentored learn better than those who are not mentored in their jobs. Xu and Payne (2014) contribute to the field of mentoring and job satisfaction stating that the value of mentors, mentorship quality and satisfaction with mentoring all predict job satisfaction, affective commitment and turnover intentions.

2.4.2.3 Support

Support refers to a leader’s sensitivity to the needs of the followers needs which may be organizational or personal. Sensitivity to the needs of the employees has a direct influence on the employees’ commitment and performance. A leader demonstrates individualized consideration when providing a wide support for the efforts of the followers (Anderson & Sun, 2015). They went on to note that the improvement of individualized consideration around supportive and developmental leadership is likely to have a transformational impact (Long et al., 2014). Social support also predicts job involvement and job satisfaction because it acts as a buffer to stressors that arise from the work or interaction with colleagues (Salami, 2010).

Belias and Koustelios (2014) stated that individualized consideration fosters the provision of support, encouragement, coaching, feedback mechanisms and delegation, which play a big role in the follower’s personal development. It is viewed as personal attention of the leaders to the needs of the followers which makes the followers all feel valued. It also helps to ensure equitable treatment to the followers avoiding favoritism and enhancing individuality as opposed to a group treatment. Miao and Kim (2010) investigated the influence of perceived organizational support and job satisfaction as positive correlations of employee performance in China among 130 employees and their 34 immediate supervisors. The study determined four dimensions of organizational citizenship behavior. Data was analyzed and the results revealed that organizational citizenship behavior increases with more favorable perception of organizational support and job satisfaction.

Long et al. (2014) conducted a study on the impact of transformational leadership style on job satisfaction and found that only the aspect of individualized consideration and more so the support a leader offers to his employees had a significant impact on job satisfaction. A sample size of 378 employees was obtained for the study. The research

55 adopted a descriptive research design and there was a response rate of about 67%. The research instrument used the Multifactor Leadership Questionnaire (MLQ) to measure the transformational leadership style. The results of the study found that leaders should coach followers, pay attention to their follower’s needs and provide a supportive environment for the followers to develop their talents and this will boost their job satisfaction.

Cheung and Wong (2011) carried out a study on transformational leadership, leader support and employee creativity seeking to examine the moderating role played by leaders’ tasks and relations support in the relationship between transformational leadership and followers level of creativity. They conducted their research among 182 supervisor-subordinate dyads. Their findings revealed that leader relations support had a direct impact on an employee’s creativity. Thus, continuous concern for employees’ socio-emotional needs catalyze the generation of more creative ideas which impact performance and satisfaction. A supportive management style which is evidenced by open communication, respect and recognition enhances employees’ job satisfaction. The study also revealed that job satisfaction has a direct correlation with management support among other factors like recognition and job security (Mosadeghrad & Ferdosi, 2013).

Emmanuel and Hassan (2015) carried out a study to establish the effect of transformational leadership on job satisfaction in four and five-star hotels in Kuala Lumpur. They sought to examine through one of their research objective; how individualized consideration affects employee job satisfaction. A total of 130 questionnaires were distributed and a response rate of 95% being 123 questionnaires was obtained. The results of the study revealed a positive correlation between individualized consideration, support and job satisfaction backed by statistical significance of the relationship. Arzi and Farahbod (2014) in their study on the impact of leadership on job satisfaction in Iranian hotels found that supportive leadership had a significant impact on job satisfaction but recognition did not affect job satisfaction which is contrary to the findings of many studies.

Kula and Guler (2014) sought to establish the influence of supervisor support on job satisfaction levels in the Turkish National Police Officers. The theory underpinning the study was Herzberg’s Two-Factor theory. The respondents were 216 employees of the police service. Data was analyzed using descriptive statistics, confirmatory factor analysis

(CFA) and structural equation modeling (SEM). The results of the study revealed that supervisor support had a statistically significant effect on job satisfaction; the more the employees perceived their supervisors as supportive, the higher their job satisfaction levels were. This study underscores the fact that job satisfaction emanates from what the employee receives from the job and discredits remarks linking demographic factors to job satisfaction.

Baloyi, Waveren and Chan (2014) carried out a study on the role of supervisor support in predicting employee job satisfaction from their perception of the performance management system. One of the hypotheses of the study was supervisor support increases employee’s job satisfaction. The study stated that the support from supervisors may be important in helping employees to understand the performance management system and that support could mediate the relationship between the performance management system and job satisfaction. The study revealed that when employees perceive a good performance management system, they attribute the good experience to the support from the supervisor and this leads them to feeling satisfied with their jobs. In support, supervisors not only give feedback about performance of their employees but also provide encouragement, more information about the expectations and how to achieve what is required. In the process, they also recognize, praise and celebrate successes.

Hwang et al. (2005) carried out a study that sought to find out the extent to which leadership charisma and vision could be discriminated by followers and how they influenced follower commitment and performance across three countries Singapore, New Zealand and India. They discovered a relationship across the three countries of their study that revealed the need for sensitivity to followers needs in influencing their commitment, satisfaction and performance. According to Mustafa and Lines (2014), supportive leadership has a positive impact on job satisfaction which reaffirms that a leader’s characteristics and behaviors play an important role in boosting job satisfaction which ultimately leads to positive outcomes in the workplace.

2.4.3 Influence of Inspirational Motivation on Job Satisfaction

This section discusses the influence of inspirational motivation on job satisfaction. Inspirational motivation is broken down into three constructs which are communication, team work and motivation.

2.4.3.1 Communication

Communication refers to the transfer of information from one person to another. In an organization, this refers to the signs, signals, and interactions between the employees. Many organizations spend considerable resources in the deployment of an effective communication system because it is a must have for the organization to achieve its objectives (Farahbod, Salimi & Dorostkar, 2013). However, important as it is, communication is an elusive organizational characteristic despite its importance in the organization. A good communication climate means that communication must be effective both from management to employees and from employees to management, and there must be an element of trustworthiness for it to be effective. Studies have linked a good communication climate to organizational identification, commitment and job satisfaction; thus, an employee’s perception of the supervisor’s communication style, credibility and the overall organizational communication climate influences the amount of satisfaction the employee receives from the job (Paksoy et al., 2017).

D. Ilozor, B. Ilozor and Carr (2011) carried out a study in the field of telecommuting which sought to examine the relationship between several management communication strategies and the job satisfaction of telecommuters. A sample of 43 telecommuters mostly from IBM Australia was surveyed. Results were analyzed using Pearson’s correlation. A number of aspects of the strategies were found to have a significant influence on the job satisfaction of the telecommuters. Examples of the strategies were communicating on job responsibilities, goals, deadlines and expectations, communicating freely and regularly, providing appropriate equipment, training, review of work and salary. The results revealed that there is need for effective communication as communication ultimately affects job satisfaction. Another study revealed that communication whether horizontal or vertical, formal or informal is an important factor that influences the organization’s success. The study concluded that job satisfaction is strongly impacted by communication (Epure, Ionescu & Nancu, 2013).

According to SHRM (2012), in their study on job satisfaction and engagement, 57% of the employees ranked communication as one of the top five contributors of job satisfaction. Communication between both employees and management is therefore very important. Among the older employees of between 11 to15 years, communication was the most important contributor to job satisfaction and engagement. It is very important that

58 senior management and managers communicate effectively on the organizations goals, policies and the vision. Effective communication helps to engage employees, provide direction and foster trust and respect. In effect, communication should be encouraged both from senior management to employees and vise versa. This factor was considered very important by employees in middle management and non-management employees than did the professional non-management employees. Ramos (2014) underscores the importance of communication and notes that it is one of the greatest factors influencing satisfaction.

A. Monga, Verma and O. Monga (2015) conducted a study on job satisfaction among employees of ICICI bank in India. The primary focus of the study was to examine the level of job satisfaction of the employees. The research was conducted in six branches of the bank using questionnaires that had responses rated using the five-point Likert scale. The results obtained revealed the importance of communication as one of the key factors determining job satisfaction at the branches of ICICI bank that were studied. This study focused on the hygiene factors of the Herzberg Two Factor theory and had many aspects like pay that also contributed to the job satisfaction of employees at ICICI bank branches that were studies.

Kakakhel, Khan, Gul and Jehangir (2015) carried out a study on the impact of organizational communication on organizational commitment and job satisfaction in Pakistan. Data was collected from a sample of 300 employees working in different organizations using a questionnaire that had closed ended questions. Data was analyzed and the findings of the study revealed that organizational communication had a positive effect on job satisfaction. The study goes on to state that job satisfaction increases when employees receive proper communication about their roles, responsibilities and performance expectations. Thus, the supervisor’s role of communication cannot be overemphasized because of the significant impact it has on job satisfaction.

Darijani, Soltani and Pourroostaei (2014) carried out a study on the impact of the effectiveness of organizational communication on job satisfaction of employees of a telecommunication company. Simple random sampling technique was used to select a sample of 248 respondents and data collection was done using questionnaires. After data analysis, the results revealed that the effectiveness of organizational communication had a significant impact on job satisfaction. The study recommended that the directors of the

59 company should focus more on communication in order to improve on job satisfaction of the workers. Paksoy et al. (2017) found that the impact of managerial communication skills on job satisfaction was significant thereby raising the need for management to communicate effectively since this is one of the factors that affect job satisfaction levels. The study stated that effective communication is important and any gaps should be addressed through training and re-training of the managerial staff in order to build and sustain communication competence.

Shonubi et al. (2016) conducted a study to establish the psychological influence of organizational communication on employee job satisfaction. The study found that communication plays a vital role in organizations and contributes to creating a motivational organizational climate. Winska (2010) found that job satisfaction was to a significant extent predicted and moderated by the communication of the superior. The study went on to note that aspects like appreciation from the boss, feedback from the supervisors and downward communication were elements that predicted and moderated job satisfaction. The key elements they stated as predictors of job satisfaction were supervisor skills and behavior, leader’s oral communication, perception of the supervisory communication competence, leader effectiveness and the communication climate.

A. Akpinar, Torun, Okur and O. Akpinar (2013) in their study found that job satisfaction is a result of organizational commitment and not organizational communication. They conducted a study to establish the effect of organizational communication and job satisfaction on organizational commitment in small businesses. The study was conducted among 118 small businesses in Turkey and data was analyzed using Pearson correlation and multiple regression analysis. The results revealed a positive relationship between employee’s perception of organizational communication and organizational commitment. However, unlike many studies, the results indicate that communication to a greater extent predicts organizational commitment and not job satisfaction. Effective communication leads to increased trust levels and attachment to the organization. The study was however limited to only small businesses and thus the results cannot be generalized which necessitates a more comprehensive study.

2.4.3.2 Teamwork

A team comprises of two or more people who interact and coordinate their work to achieve a common goal. It then follows that work teams are people who come together to work on a common goal and leverage on their positive synergies and complementary skills. A team is one of the most ideal approaches of ensuring information sharing, effective coordination and exchange of material necessary for successful task accomplishment. It has been found that good team work results in good attitudes for example self-management skills, increased commitment, responsibility and turns the work environment into a fun place (Musriha, 2013). Teams have become the primary unit in organizations in the contemporary business world because they are more effective. Monga et al. (2015) who studied job satisfaction of employees of ICICI bank found that among other factors like communication, attitudes of supervisors and job security, team work had a significant role in determining employee job satisfaction.

Halepota and Shah (2010) carried out an empirical investigation of organizational antecedents on employee job satisfaction in a developing country. They sought to establish in one of their hypothesis whether team work affects job satisfaction. The study adopted a positivist research philosophy and research was qualitative. The sample comprised of 200 full time medical practitioners who were randomly selected. The findings of the study revealed that team spirit among employees had a positive and significant impact on employee’s job satisfaction. Polychroniou (2009) carried out a research to establish the relationship between emotional intelligence and transformational leadership of supervisors in Greek organizations. They interviewed 267 managers working at various units and in different levels. Data was analyzed and the results revealed that organizations which adopt their prescriptions are likely to empower teamwork and boost employee satisfaction which will ultimately lead to superior performance.

Hanaysha and Tahir (2015) examined among other factors the effect of team work on job satisfaction in Malaysia public universities. They collected data from 22 employees and the data was then analyzed using structural equation modeling. The findings revealed that teamwork had a positive and significant effect on job satisfaction. Rizwan et al. (2012) carried out an empirical study on employee job satisfaction aimed at establishing the crucial problems faced by employees and finding ways to enhance employee loyalty. A

61 survey was conducted among 200 employees located in Punjab Pakistan. A model incorporating theoretical considerations and employee job satisfaction constructs was used for the study. Findings revealed that there was a strong and positive relationship between team work and job satisfaction.

Shujaat, Manzoor and Syed (2014) conducted a research to establish the impact of team work on employee job satisfaction. Their study was informed by the importance of team work in achieving organizational goals. They conducted a survey among 384 employees from various organizations using questionnaires and applied regression analysis to establish the significance of the linear relationship between team work and job satisfaction. Results of the study revealed that team work had a significant impact on job satisfaction. This shows that it is important for organizational leaders to build a team work culture, build team skills and hold it in high regard because of its significant effect on job satisfaction and achievement of organizational goals.

Rana (2015) sought to determine the job satisfaction factors affecting employees in the Bangladesh banking sector. A point of focus was to determine the impact of the human resource management practices like team work, job autonomy and leadership behavior on job satisfaction. A sample size of 450 employees working in different bank branches in Bangladesh were selected for the study and data was collected through questionnaires which employed the 5-point Likert scale. There was a response of 65% was received representing 295 questionnaires which were processed and data analyzed through SPSS. The results of the study revealed that there was a significant and positive relationship between human resource management practices like team work, job autonomy and leadership behavior on job satisfaction; however, team work was the most important factor affecting job satisfaction.

Irfan and Lodhi (2015) conducted a study on the impact of teamwork on employee motivation in the banking sector of Pakistan. Among the hypotheses tested was whether teamwork at the banking sector had an impact on job security and job satisfaction of the employees. The findings of correlation analysis tests revealed that there was a positive correlation between teamwork and motivation. Additionally, the individuals working in a team were more satisfied with their jobs and considered themselves as an asset to the organization. Teamwork is an important attribute through which people are able to

62 achieve more and is an important tool for organizational success especially in today’s competitive world.

2.4.3.3 Motivation

The word motivation refers to the force that constantly induces someone to move or to perform in a certain desired way. It is a psychological process that yields stimulation, direction and persistence in behavior towards a desired cause. A number of factors could be considered as motivational but this varies depending on how they are perceived by the employees. A good example is rewards, incentives and recognition which could have varying effects on the receiver. Research states that in some instances, employees’ job satisfaction is influenced by the rewards or motivation they receive from their job. Workers who are motivated tend to give their best efforts and work hard at their job because they feel fulfilled (Jehanzeb, M. Rasheed, A. Rasheed & Aamir, 2012). Additionally, there are the intrinsic motivators and extrinsic motivators and it was found that there is a significant and positive correlation between intrinsic motivators and job satisfaction in banks in India (Chatterjee & Chattopadhyay, 2015).

Panagiotakopoulos (2014) conducted a study which explored the motivational techniques used by 30 CEOs in the context of an advancing economy and evaluated the impact of the motivational tools on staff performance. The study collected data from 113 workers and 30 CEOs. The study revealed that 87% of the CEOs argued that employee motivation needs to be around the threat of punishment; thus, they direct behavior with threats of punishment and replacement. Four leaders opined that involvement in decision making, recognition of employee contribution, team work and learning were appropriate tools to boost motivation. It was evident that the benefits that accrue to the organization from use of inspirational motivation were outnumbered by benefits of using fear motivation. The four leaders emphasized that most employees work with enthusiasm, had job satisfaction and increased productivity. The employees confirmed that their relations with the firm were harmonious, their jobs were interesting and that trust existed. This raised their morale, job satisfaction and led to a decline in mistakes.

Wambui, Maru and Cheruiyot (2017) examined the link between leadership personality traits and job satisfaction among employees in the media industry in Kenya through exploratory research. Regression analysis revealed that there was a positive and

63 significant relationship between leader extraversion, leader agreeableness, leader conscientiousness, leader openness, leader emotional stability and employee job satisfaction. The findings are timely since organizational survival in the current dynamic environment calls for leadership qualities which will enhance employee motivation.

Hanaysha et al. (2012) conducted a study in Malaysia among administrative and clerical staff involved in graduate and postgraduate affairs in three universities. They sought to establish if there was a positive relationship between individualized consideration and job satisfaction. The research was conducted among 320 employees through an 18-item scale questionnaire from the MLQ and a response rate of 31.5% was obtained. The findings revealed that individualized consideration was negatively related to job satisfaction which goes against most research findings and thus necessitates further research to validate the findings. It is however attributed to the fact that perhaps employees could not meet their leaders due to their busy schedules. However, the study found that intellectual stimulation was positively correlated to job satisfaction because leaders foster inspiration and which in turn creates excitement and yields renewed efforts from the employees in pursuit of the organizational goals.

According to Bass (1985), transformational leaders motivate the followers and raise their performance to higher levels through inspiration. This is done by use of an appealing vision, symbols and images aimed at enhancing appropriate behaviors among the followers (Belias & Koustelios, 2014). It is also achieved when a leader communicates the vision with confidence which in turn raises optimism. Transformational leadership goes beyond transactions and aims to improve the follower’s achievements through an influence of their needs and values which yield higher levels of performance, effort and satisfaction. Transformational leadership can thus be viewed as an extension of transactional behavior because transformational leaders motivate their followers to achieve more than they ought to achieve by addressing and modifying their values and self-esteem. This yields an inspiration to go beyond the basic call of duty to voluntarily and willingly doing more (Felfe & Schyns, 2004). According to Groves (2006), a leader’s ability to articulate a powerful and a compelling vision is very important because it acts as a source of motivation to the followers which ultimately leads to satisfaction in some employees.

Transformational leadership is a leadership style that employers can use to motivate employees by stirring their interests in the organization thus enabling them to look beyond their personal interests and placing focus on the organizations interest (Stone et al., 2004). Additionally, according to Lussier and Achua (2013), transformational leadership helps in a shift from personal to collective interests and through inspiration and motivation, the followers are able to follow the leader as a result of enhanced trust and confidence. According to Indermun and Bayat (2013), rewards and benefits are extrinsic needs to the employee and they are important in job satisfaction. Good rewards are intended to attract and retain suitable employees in the organization. The promise of rewards and benefits encourages and motivates employees to perform in order to ensure they reap the rewards and the benefits; hence they are a source of motivation. In modern society, there is a shift to performance related pay which unfortunately assumes that pay alone satisfies the workers yet this notion has been discredited. This is because a worker with a good pay but no intrinsic rewards will probably not be satisfied and will look for satisfaction even if it means leaving the organization.

The SHRM (2012) survey among 600 employees in the US on job satisfaction and engagement revealed that 6 out of every 10 employees ranked compensation as the first factor affecting their job satisfaction. Compensation has remained on the list of the top five job satisfaction factors consistently for many years. It is an important strategy for attracting and retaining talent in the organizations. However, age affects the perception because it was ranked as the most important by employees of three to five years working tenure and showing that as one grows in age, other factors affecting job satisfaction come into play. Benefits also ranked highly and came in as the sixth factor affecting job satisfaction. It was found to be a critical factor in large organizations. Benefits and pay are to some people very important and greatest source of their motivation and job satisfaction.

2.4.4 Influence of Intellectual Stimulation on Job Satisfaction This section discusses the influence of intellectual stimulation on job satisfaction. Intellectual stimulation is broken down into three constructs which are knowledge sharing, creativity and risk taking.

2.4.4.1 Knowledge Sharing Knowledge sharing falls under the umbrella of knowledge management in an organization. Knowledge management consists of processes such as knowledge creation, sharing and transfer together with the processes and capabilities which help to support the advancement of knowledge. Knowledge sharing is fundamental for managing tacit knowledge hence organizations should encourage regular communication and the creation of shared learning experiences by encouraging a knowledge sharing culture (Kianto, Vanhala & Heilmann, 2016). A knowledge sharing culture paves way for communication and information exchange, problem resolution, team work and decision making (Trivellas, Akrivouli, Tsifora & Tsoutsa, 2015).

Raisi and Forutan (2017) conducted a study of the relationship between a knowledge sharing culture and job satisfaction in the context of Bank Sepah Branches in shriraz, Iran. They interviewed 392 employees from 53 Bank Sepah branches in Shiraz. The study obtained a response rate of 159 employees and the data was collected using questionnaires based on a job satisfaction. Ratings were based on a 5-point Likert scale. Data was analyzed using SPSS and the critical test conducted was the Pearson Correlation coefficient. The results revealed that there was a positive and significant relationship between a knowledge sharing culture and components of job satisfaction.

Kianto et al. (2016) sought to establish whether knowledge management could be used to nurture job satisfaction. They also examined how knowledge management could be used to increase individual employee job satisfaction. One of the hypotheses of the study was knowledge sharing will be positively associated with job satisfaction. Research data was sought from 824 respondents who worked in a municipal organization in Finland. Data was analyzed using Structural Equation Modeling and the results revealed that knowledge sharing was a key component of the knowledge management process which was found to have a correlation with job satisfaction. The overall study concluded that having knowledge management processes in place was linked to high job satisfaction.

Trivellas et al. (2015) noted that globalization, competition and financial crisis have brought about the need to have a knowledge driven economy. In a study on the impact of knowledge sharing on job satisfaction in accounting firms, the researchers sought to establish if knowledge sharing exerted a significant positive impact on individual job satisfaction. In the research, 84 accounting officers were interviewed using questionnaires

66 and data was analyzed using principal component analysis and regression analysis. The results revealed that there was a positive and significant relationship between a knowledge sharing and job satisfaction. Tong, Tak and Wong (2014) sought to establish the impact of knowledge sharing on the relationship between organizational culture and job satisfaction among ICT practitioners in Hong Kong. They found that knowledge sharing had a significant mediating effect between organization culture and job satisfaction.

Masa’deh (2016) carried out a study to establish the role of knowledge management infrastructure in enhancing job satisfaction in five-star hotels in Jordan. A total of 216 respondents were sampled for this study and data was collected using questionnaires. The hypotheses were tested using regression analysis. The results revealed that there was a positive and significant impact of the knowledge management infrastructure on job satisfaction. Saleh and Khoualdi (2015) in a similar study in Saudi public universities also found that there was a positive and significant relationship between the knowledge management structures and job satisfaction.

Malik and Kanwal (2018) sought to establish the impact of organizational knowledge sharing practices on employee job satisfaction. The study had learning commitment and adaptability as the mediating roles. The study was conducted among the service sector organizations in Pakistan. The sample size consisted of 435 employees from banks, insurance and telecommunications companies. The findings of regression analysis revealed that the organization’s support for knowledge sharing promotes learning, commitment and adaptability which ground job satisfaction. Mogotsi, Boon and Fletcher (2011) modeled the relationships between knowledge sharing, organizational citizenship behaviour, job satisfaction and organizational commitment among school teachers in Botswana. However, the results revealed that both job satisfaction and organizational commitment were not related to knowledge sharing.

Jadidi, Ehsanifar and Moshtaghi (2013) carried out a study which sought to establish the effect of knowledge management on job satisfaction in the Iranian texture industry. The study used questionnaires to collect data from 230 employees. Data was analyzed using structural equation modeling to test the hypotheses. The results of the study revealed that organizational learning and organizational improvement positively influenced job satisfaction of the Iranian texture workers.

2.4.4.2 Creativity

Cheung and Wong (2011) refer to creativity as the generation of new and novel ideas or new useful ideas in the lines of products, services, processes or procedures. It enables employees to utilize their diverse skills, abilities, knowledge and experience to come up with the new ideas that aid decision making, problem solving and effective task management (Mohammadi & Azizmalayeri, 2016). It can be a task in a job or it may happen outside the scope of work. In their study on transformational leadership, leader support and employee creativity, the study sought to examine the moderating role played by leader’s tasks and relations support in the relationship between transformational leadership and follower’s level of creativity. They sampled 182 supervisor-subordinate relations from a hotel, restaurant, bookstore and a bank. Their findings of the study revealed that there was a positive relationship between transformational leadership and the follower’s creativity and it is stronger when there is a high degree of leader’s tasks and relations support (Cheung & Wong, 2011).

Mittal and Dhar (2015) in their study on transformational leadership and employee creativity sought to establish the effect of transformational leadership on employee creativity. The study was conducted among 348 manager-employee dyads from the Indian IT small medium enterprises professionals. The findings revealed that transformational leadership has a positive significant effect on employee creativity and went on to suggest that transformational leadership does indeed foster employee creativity which leads to a creative work environment. Creativity is the key to competitive advantage in the growing area of IT. It leads, provokes and allows employees to think and front new ideas or solutions thereby making them feel valued and ultimately yielding job satisfaction.

Carine et al. (2015) carried out a study on the determinants of employee creativity and project performance in Rwanda. Data was collected from a sample of 90 project members and analyzed. The findings of the study revealed that creativity was a fundamental source of competitive advantage and should be a focus for organizations that wish to differentiate themselves. Additionally, job satisfaction positively and significantly influenced the creativity of employees.

Cheung and Wong (2011) examined the link between transformational leadership and employee creativity in Hong Kong. Primary data was collected using questionnaires among respondents who were drawn from strata that included; restaurants, banks, hotel,

68 retail store and travel agents. Data was analyzed using regression analyses. The results of the study revealed a positive and significant relationship between transformational leadership and employee creativity which in turn boosts employee job satisfaction. Although the study contributed to the empirical link between transformational leadership and employee creativity, a sample size of 182 was too small considering it was stratified amongst the different groups. However, there was need for leadership to create an environment for creativity amongst employees.

Yee, Pink and Sern (2014) conducted a study on the effect of a psychological climate for creativity on job satisfaction and work performance. They did a cross sectional study and sampled 118 electrical engineers working in Malaysia. Data was collected using questionnaires which were distributed by means of the snowball technique via electronic mail. The results of the study revealed that a good working environment is a key factor in creating job satisfaction. Additionally, a creative climate is a key predictor of job satisfaction and work performance among electrical engineers. It is an antecedent for innovation and change which also affects the outcomes of the employees. In this regard, leaders need to create a culture and an environment that promotes creativity in their organizations and eliminate organizational factors that bar creativity since it is a predictor of job satisfaction in organizations.

Abraiz, Tabassum, Raja and Jawad (2012) carried out a study on the empowerment effects and employee job satisfaction in Pakistan. The dimensions of empowerment in the study were autonomy, responsibility, information and creativity. The context of the study was the service sector which involved hotels, hospitals and the education sector with 600 respondents. Data was analyzed and the results revealed a positive relationship between creativity and job satisfaction. The study stated that there was a strong relationship between creativity and job satisfaction than the other dimensions. Valentine, Godkin, Fleischman & Kidwell (2011) conducted a study to establish the degree to which perceived corporate values work with group creativity to influence job satisfaction and turnover intention. Information was collected from 781 healthcare and administrative employees and 127 sales and marketing employees. The results revealed that group creativity and ethical values were positively related and that both were associated with increased job satisfaction.

Raju (2017) conducted a study on the relationship between teacher creativity and job satisfaction among degree holders in India. One of the objectives of the study was to establish the impact of creativity on job satisfaction. Data was collected from 146 lecturers using questionnaires as the data collection instruments. The hypotheses of the study were tested and the results revealed a positive and significant relationship between the teacher’s creativity and teacher job satisfaction. Dallal, Ahmadi and Barzegar (2013) conducted a study on the relationship between creativity and job satisfaction in Shiraz hand craft employees. A sample size of 89 employees was selected for the study and questionnaires used to collect data. Results of the study revealed that the higher the creativity and innovation rates, the higher the job satisfaction was. Therefore, the study concluded that creativity significantly predicted job satisfaction.

2.4.4.3 Risk Taking

Hosseini and Shahmandi (2014) examined the effects of creativity and innovation, self- control and risk taking on job satisfaction of staff in a social security organization in Iran through a descriptive research. They stated that risk tolerance in the organization was important as opposed to stressing rules with no risk taking. The sample size was comprised of 150 respondents. The findings of the research revealed that the three elements of creativity and innovation, self-control and risk taking were related to job satisfaction. Specific to risk tolerance, there was a strong and significant relationship with a correlation coefficient of 53 percent to job satisfaction.

Habib, Aslam, Hussain, Yasmeen and Ibrahim (2014) carried out a study dubbed the impact of organizational culture on job satisfaction, employee commitment and turnover intention. They broke down organization culture into a few constructs one among them innovation and risk taking. The study was carried out among 235 bank employees in Pakistan and data was collected through questionnaires. Data was analyzed and the results revealed that organization culture, specifically, innovation and risk taking highly influenced employee commitment, job satisfaction and retention.

Abbaspour and Noghreh (2015) examined the relationship between organizational culture and job satisfaction of Tourism bank employees in Iran. Among the components of organizational culture was risk taking and its relationship with job satisfaction was measured. The study had a sample size of 196 employees which was arrived at through stratified random sampling technique. Data was collected using questionnaires. The

70 results revealed that there was a relationship between organizational culture factors like risk taking and job satisfaction. Specifically, there was a relationship between risk-taking which was not significant statistically in the level of job satisfaction.

Shah, Memon and Laghari (2011) studied risk as a variable of organizational culture in their study on the impact of organizational culture on employee job satisfaction among faculty of public universities of Pakistan. The research obtained a response rate of 72% from a sample size of 300 respondents. The Pearson correlation coefficient was used to analyze the data. The results of the study could not find a relationship of innovation and risk taking with employee job satisfaction; thus, the increase or decrease of innovation or risk taking was deemed not to affect the level of employee job satisfaction among the faculty of public universities of Pakistan.

Lee (2016) in a study titled comparison of job satisfaction between nonprofit and public employees had risk perception as one of the hypotheses. The results of the study found that perception of top management’s risk taking is negatively associated with public manager’s job satisfaction. However, it also did not explain nonprofit manager’s likelihood of higher job satisfaction. Thus, for public employees, the perception of organizational stability through minimal risks positively influenced their job satisfaction.

Qazi and Kaur (2017) conducted a study on the impact of culture on job satisfaction among university faculty members. They sought to understand the correlation between organizational culture and job satisfaction and come up with ways of improving the culture and ultimately job satisfaction. The study was conducted among 368 faculty members from both private and public universities. The results of the study revealed that organizational culture was positively and significantly correlated with job satisfaction with culture components like openness and risk-taking leading among the components that yielded job satisfaction among the university faculty members.

2.4.5 Moderating Effect of Job Security on the Influence of Transformational Leadership on Job Satisfaction This section discusses the moderating effect of job security on the influence of transformational leadership on job satisfaction. Job security is broken down into three constructs which are anxiety, fairness and stress.

2.4.5.1 Anxiety The fear of the unknown or unpredictability of things in the work place causes anxiety which is a factor that influences job satisfaction. It leads to uncertainty of the future and this makes employees anxious and when in this situation they are unable to react to issues appropriately. Research notes that anxiety is accompanied with negative attitudes in the work place such as feelings of dissatisfaction, strained coworker relationships, reduced organization commitment and heightened turnover intentions as the employees seek a safer haven (Silla, Gracia, Manas & Peiro, 2010). Anxiety manifests itself through future concerns and the inability to predict the future employment and career concerns, all which have the possibility of affecting the employee’s judgments, perceptions, satisfaction and productivity. A leader should endeavor to reassure his employees through effective and accurate communication to dismay any anxieties for there to be job satisfaction (Kler, Leeves & Shankar, 2015).

Ferguson, Frost and Hall (2012) sought to investigate predictors of anxiety, depression and job satisfaction among teachers in North Ontario, Canada. They used data obtained through self-report questionnaires and conducted factor analysis and multiple linear regression to determine the sources of stress, stress symptoms and also to explore the effects of stress, depression and anxiety on job satisfaction. The results revealed that workload and student behavior were significant predictors of depression among the teachers. However, anxiety, gender, grade level and position were not found to have significant statistical influence on teacher job satisfaction. Thorsteinsson, Brown and Richards (2014) examined the association between stress, organizational support and staff health which incorporated anxiety, depression and fatigue together with work outcomes like turnover intentions, organizational commitment and job satisfaction. They collected data from 201 staff who were recruited through email and snowball sampling. The findings of the study revealed that high work stress was associated with worse staff health like anxiety, depression and fatigue all of which lead to negative work outcomes like low job satisfaction, high turnover intentions and less organizational commitment.

Poursadeghiyan et al. (2016) carried out a study to establish the relationship between job stress and anxiety, depression and anxiety among nurses in Iran. They obtained 250 nurses to participate in the research and data was collected using questionnaires. The results showed that stress is a big contributor to anxiety but this was negatively associated

72 with depression and job satisfaction. Zalewska (2011) agrees with the findings that job anxiety is negatively correlated with the level of job satisfaction from their study carried out among 240 employees. Nadinloyi, Sadeghi and Hajloo (2013) sought to examine the relationship between job satisfaction and mental health. Their study was composed of 90 respondents and data was analyzed using multiple regression analysis. One of the findings of the study based on their hypotheses revealed that there was a relatively weak but significant correlation between job anxiety and job satisfaction meaning leaders need to ensure there is no anxiety for job satisfaction to grow.

Allan, Dexter, Kinsey and Parker (2016) carried out a study on meaningful work and mental health with job satisfaction as a moderator. They looked at depression, anxiety and stress as variables of mental health which were some of the common problems modern workers face. They noted that although having meaningful work is important and facilitates personal growth, it is also important that work is satisfying and enjoyable in order to improve outcomes. The sample was comprised of 212 working adults of various age groups and data was analyzed using correlation and regression analysis. The study found that having meaningful work was associated with better mental health which translated to lower rates of depression, anxiety and stress. This also predicted lower depression but did not significantly predict anxiety or stress. Thus, meaningful work contributes to the level of job satisfaction.

According to Yaacob and Long (2015), anxiety that is not addressed ultimately leads to stress. They sought to investigate the relationship between occupational stress and job satisfaction among teachers through a cross sectional study. The study hypothesized occupational stress as role ambiguity, role overload and work-family conflict. The sample consisted of 386 teachers and data was collected using questionnaires. Thereafter, data was analyzed and the results revealed that there was a significant relationship between occupational stress and job satisfaction; therefore, organizational leaders should ensure their employees have no anxiety and no work-related stress to ensure they feel satisfied with their work. Higher job satisfaction levels yield better quality service which culminates in better quality service.

2.4.5.2 Fairness Fairness refers to the appropriateness or rightfulness in the way outcomes should be divided in the organization, the procedure used to determine the distribution and basically how people are treated. It is an important element in human relations. Perceived fair treatment influences employees to reciprocate by actively pursuing the organizational objectives. It also elicits and sustains positive attitudes towards the organization. It acts as an inducement and nudges employees to perform beyond expectations. Additionally, the positive attitudes and behaviors that are elicited also include job satisfaction, enhanced commitment and fewer turnover intentions (Silla et al., 2010). According to a study done on why employees worry about their jobs, fairness was a construct presented in the study because its absence led to insecurity (Keim, Pierce, Landis & Earnest, 2014).

Silla et al. (2010) studied job insecurity and employees’ attitudes with the moderating role of fairness. Their study sought to examine the relationships between both job insecurity and fairness and employees’ attitudes. They sampled 697 employees from a Spanish public organization and conducted a cross sectional study based on self reported data. The results of the study revealed that job insecurity was significantly and negatively associated with organizational commitment and positively associated with turnover intentions. However, the relationship between job insecurity and job satisfaction was not significant.

Imran, Majeed and Ayub (2015) carried out a study on the relationship between organizational justices, job security, and job satisfaction on organizational productivity in Pakistan. Data was collected using questionnaires and was later analyzed using inferential statistics. Both correlation and regression analysis revealed a positive and significant relationship between job security and job satisfaction therefore making job security one of the most important considerations leaders should offer their employees. Susanj and Jakopec (2012) in their study exploring the role of justice perceptions and job satisfaction concluded that employee job satisfaction depends on the perceived justice levels in the organization.

Rai (2013) examined the impact of organizational justice on satisfaction, commitment and turnover intentions with a view of establishing if fair treatment by organizations could make a difference in the attitudes and behaviors of workers. He collected data from 511 employees from 10 health and rehabilitation centers and analyzed the data using Pearson

74 correlation and hierarchical regression. The results of the study revealed that perceived justice influenced job satisfaction, organizational commitment and the intention to leave. If organizations want to effectively enhance job satisfaction while reducing turnover intentions, they must pay attention to developing policies and procedures that encourage fairness whilst adopting leadership styles that promote fairness.

Baah (2014) carried out a study on the organizational antecedents and perceptions of fairness in policy implementation among employees in the banking sector of Ghana. The study adopted a correlational research design and selected 100 participants randomly. Data was collected using questionnaires and analyzed using the Pearson correlation and ANOVA. The findings of the study revealed that job satisfaction and commitment were positively and significantly related to the employees’ perception of fairness in the implementation of policies. The study recommends implementation of policies that enhance employee job satisfaction in the organization.

Kaur (2016) in a study on the psychological effect of organizational justice perceptions on job satisfaction stated the importance of the perception of fairness in the organization. The study noted that fairness results in favorable work outcomes like job satisfaction and the lack of it thereof results in unfavorable work outcomes. A study of 218 employees from the Indian Public Sector was carried out. Results from the Pearson correlation and multiple regression revealed that distributive justice perceptions are likely to yield positive organizational justice perceptions; therefore, organizational justice is a predictor of job satisfaction. Paposa and Kumar (2015) also noted that there was a positive and significant impact of performance measurement systems on job satisfaction.

Umair, Javaid, Amir and Luqman (2016) investigated the employee’s perception of fairness in the performance appraisal system and the effect this had on job satisfaction of the employees. They noted that the perception of fairness consisted of distributive justice, procedural justice and interactional justice all of which were used as the independent variables. Data was analyzed and the results of the study revealed that perceived fairness in the appraisal system had an impact on job satisfaction among employees. Al-Ansi, Rahardjo and Prasetya (2015) analyzed the impact of leadership style and pay fairness on job satisfaction and organizational commitment. One of their research objectives was to examine the relationship between pay fairness and job satisfaction. They had a

75 convenience sample size of 120 employees. They concluded that pay fairness has a positive and direct effect on job satisfaction but no effect on organizational commitment.

Yamazaki and Yoon (2012) studied fairness and job satisfaction of Japanese multinationals in Asia with a focus on procedural justice and fairness. They focused on HR practices of fairness where they sought to describe the processes that are used in evaluating employee performance and promotion, by which employees can generally judge how they are treated through how fair they perceive the process to be. Data was collected and analyzed using various statistical tests. The results of the study revealed that the perception of procedural and transparent justice had a significant impact on job satisfaction in Asian multinational contexts of Japan, China, Hong Kong and Thailand. Management therefore needs to review the processes to ensure there is a fair perception of justice which will in turn enhance the satisfaction levels of the employees. However, Khalifa and Truong (2010) conducted a study to establish the relationship between employee perceptions of equity and job satisfaction in Egyptian private universities and found a relationship where a motivator was involved and no relationship where a hygiene factor was in consideration.

2.4.5.2 Stress

Riaz et al. (2016) sought to establish the impact of job stress on employee job satisfaction in the nursing sector of DHQ Hospital of Okara. The study measured job stress under workload, role conflict and physical environment and their impact on employee job satisfaction. The sample size was composed on 100 nurses from the hospital and data was collected using questionnaires. Data was analyzed using SPSS and different tests including reliability, regression and correlation analysis were carried out. The results of the study showed that there was a positive and very strong correlation between job stress and employee job satisfaction. However, according to Agarwal (2015) who measured the relationship of job stress and job satisfaction in the Indian IT Sector, there is no relationship between job stress and job satisfaction.

Mansoor, Fida, Nasir and Ahmad (2011) carried out a study to determine the impact of job stress on employee job satisfaction in the telecommunication sector of Pakistan. The study had a sample size of 134 employees and job stress was measured under the variables of conflict at work, workload and physical environment. Data was collected using an instrument adapted from the Minnesota Satisfaction Questionnaire (MSQ). The

76 results revealed that the people who had stressful jobs found their jobs less satisfying. The findings are contradicted by a similar study in private colleges in Pakistan where the results of the study reveal that stress is positively related to employee job satisfaction but depending on the context (Ur Rehman et al., 2012).

Lin, Bahron and Boroh (2014) conducted a study on role stress and job satisfaction among bank employees in Sabah, Malaysia. The study was inspired by many changes in the industry’s competitive environment. One of the key variables under study was the relationship of roles stress and job satisfaction. Role stress was measured using role ambiguity and role conflict. The study had 163 respondents from 14 commercial banks and data was collected using questionnaires. The hypotheses of the study were tested and the results of the study showed that there was a significant relationship between role stress and job satisfaction.

Khan, Ramzan and Butt (2013) sought to determine whether job satisfaction of Islamic banks operational staff was determined through organizational climate, occupational stress, age and gender. To this end, they interviewed 40 bank managers and officers from five Islamic bank branches and had a response rate of 85%. The study was grounded on exploratory research in which meetings, observations and interviews were conducted. The results of the study revealed that organizational climate and occupational stress had a significant impact on the level of job satisfaction. The study goes on to note that occupational stress may not be avoided as it stems from both internal and external factors such as political, economic and technological sources.

AbuRuz (2014) contends that there is a negative relationship between job stress and job satisfaction among nurses in Jordan and Saudi. The cross-sectional study was carried out among 150 nurses from 250 nurses from hospitals in both jurisdictions and the results from both contexts were similar. Khamisa, Oldenburg, Peltzer and Ilic (2015) in a study on nurses and job satisfaction found that the highest amount of variance in job satisfaction was explained by job stress. Iqbal and Waseem (2012) sought to establish the impact of job stress on job satisfaction among air traffic controllers from Pakistan. They found that there was a negative relationship between job stress and job satisfaction and those who had high job stress had low job satisfaction.

Ramos, Ales and Sierra (2014) carried out a study on role stress and work engagement as antecedents of job satisfaction among Spanish workers and hypothesized that the role

77 stress would negatively predict job satisfaction. The study was carried out among 435 Spanish workers from both public and private companies. Collection of data was done through questionnaires which were administered by the researcher. The hypothesis was tested using regression analysis having passed the preliminary tests to ensure that there was no violation of the assumptions of regression. The results of the study revealed that there was a negative relationship between role stress and job satisfaction. Hoboubi, Choobineh, Ghanavati, Keshavarzi and Hosseini (2017) also found that job stress influenced job satisfaction and workforce productivity in the Iranian petrochemical industry.

2.5 Chapter Summary

This chapter has presented the theoretical review of the transformational leadership theory, the conceptual framework for the study and an empirical review. Chapter three presents the research methodology.

CHAPTER THREE

3.0 RESEARCH METHODOLOGY

3.1 Introduction

This chapter presents the research methodology used in this study. The chapter discusses the research philosophy of the study, the research design used, the target population and sample population for the study. The data collection method, the research procedures and the data analysis methods are also discussed in this chapter.

3.2 Research Philosophy

According to Saunders et al. (2016), a research philosophy is a belief in which data about a phenomenon should be gathered, analyzed and used. The philosophy of a study serves as a base for the research strategy. Examples of research philosophies are pragmatism, positivism and constructivism. Pragmatism as a world view comes from actions, situations and consequences as opposed to antecedent conditions and is not committed to any one philosophy. It encourages the use of the approaches available to understand a problem. It mainly underpins the use of mixed methods in research. It also suggests that the most critical determinants of the research philosophy which a research adopts are the research questions and objectives (Saunders & Lewis, 2018).

The positivism philosophical approach contends that reality is stable, can be observed and described objectively. Positivism also holds a deterministic ideal where causes determine effects and outcomes. It is mainly related to the observations and experiments which guide the research process and help to identify and assess the causes that influence outcomes. The main concern for a positivist research is to study observable and measurable variables in controllable conditions and to also illustrate the reactions of the variables to the treatment applied by the researcher. Therefore, the emphasis is on predicting the outcomes of the research so that the variables can be controlled in future (Saunders et al., 2016; Bryman & Bell, 2015).

Constructivism contends that individuals seek to understand the world and the environment in which they live and work in. They go on to develop subjective meanings of their encounters directed towards understanding certain phenomenon. It relies on the individual’s or participant’s view of what is being studied (Creswell, 2014).

Constructivism challenges the thought that items like organization and culture are predetermined and confronts social issues as external realities (Saunders & Lewis, 2018).

Positivism research philosophy was adopted in this study. This is because the positivism research philosophy relates to the philosophical standpoint of natural science and is concerned with an observable social reality to produce law-like generalizations (Bryman & Bell, 2015). Additionally, positivism yields unambiguous accurate knowledge and allows for a causal explanation and prediction of the relationship between variables. The researcher developed hypotheses on the basis of the existing theory of transformational leadership. To test the hypotheses the study translated idealized influence, individualized consideration, intellectual simulation and intellectual simulation into testable hypotheses in measurable forms (Saunders et al., 2016).

3.3 Research Design

A research design is a plan or a structure of investigation which is conceived with the aim of helping a researcher to obtain answers to research questions. It can also be referred to as a plan to be followed for data collection, measurement and analysis (Cooper & Schindler, 2014). A research design helps to integrate the different components of a study in a coherent, logical and acceptable way which helps in ensuring the study effectively addresses the research problem under review. Examples of research designs are exploratory, descriptive and causal (Creswell, 2014). Exploratory research design tends to have loose structures and is mostly useful when researchers lack a clear idea of the problem. It also tends to be qualitative as opposed to quantitative and in many cases does not provide conclusive evidence. It is ideal when the researcher has little information, where the researcher knows little about the problem and is designed to discover new relationships, patterns, themes and ideas. It is therefore useful where a researcher wishes to clarify an understanding of a phenomenon (Hair, Money, Samuel & Page, 2007).

Causal research seeks to identify the cause and effect relationships among variables and its main aim is to determine cause and effect. Causality is difficult to prove without examination and therefore the studies are done through experiments and simulations. The main aim is to try to explain relationships among interacting variables. For example, a causal predictive study aims to determine an effect of one variable on another by manipulating the former and holding the latter constant (Cooper & Schindler, 2014). It helps to bring out the causes of the variable being predicted and answers questions like

80 why. Causal research is mostly recommended in experiments and is also known as experimental designs (Shajahan, 2009).

Descriptive research design aims to generate data that describes the characteristics of the research item. The main aim of descriptive research design is to reveal an accurate profile of the phenomenon under study (Shajahan, 2009). With the descriptive research design, data collection is usually structured. Studies under descriptive are either cross sectional or longitudinal studies (Hair et al., 2007). It also illustrates characteristics of a phenomenon by answering questions like, when, who, where what and how. It seeks to determine the frequency of occurrence and whether a relationship exists between two variables. Descriptive research design consists of case study, survey, meta-analysis and correlation (Sekaran & Bougie, 2016).

Case studies focus on gathering information regarding a specific object, event or activity, for example a specific organization or business unit. It is an in-depth study of a bounded entity. Survey is a way of collecting information from or about people to describe, compare and explain their behavior. It involves setting objectives for data collection, designing the study, preparing a survey instrument, administering it, managing the data collection process, analyzing the data and reporting the results (Bryman & Bell, 2015; Hair et al., 2007). Meta-analysis involves a quantitative analysis of data sets from previous research projects. It is a process of amalgamating existing data sets, combining them and analyzing them. The researcher does not guarantee validity of the data since they were not involved in the research process of the existing data sets (Quinlan, 2011). Correlation helps to predict relationships; for example, the relationship between an independent and dependent variable can be examined using correlation. Correlation examines the relationship between two or more variables and tries to determine whether and to what degree a relationship exists between them. It is highly recommended where a study is seeking to test the relationship of two variables because it provides a platform for description of the relationships between variables (Cooper & Schindler, 2014).

This study adopted a descriptive correlational research design because it was relevant to the study at hand whose aim was to determine the influence of transformational leadership on job satisfaction among employees in commercial banks in Kenya. The descriptive correlational research design was chosen because it is ideal in revealing accurate information about a phenomenon. The information derived is useful in making

81 inferences on the extent of association and the relationship between variables; in this case the relationship between transformational leadership and job satisfaction (Harrison & Reilly, 2011).

3.4 Target Population

Cooper and Schindler (2014) describe the target population as a complete enumeration of all the elements in consideration. Additionally, the target population is the total number of individual elements with common observable characteristics. The target population in this study was composed of all the managerial employees working in the 43 commercial banks in Kenya (Appendix V). The target population was comprised of 10310 managerial employees from the three bank tiers as indicated in Table 3.1 (CBK, 2017).

Table 3.1: Employment of Managerial Staff in the Banking Sector

Tier Population One 4,495 Two 3,629 Three 2,186 Total 10,310

3.5 Sample Design According to Cooper and Schindler (2014), a sample design is a plan for obtaining a sample from a given population. Sampling helps to boost the accuracy of results because it enables the researcher to focus on a specific group of people as opposed to focusing on the entire target population. Under the sample design, the sampling frame, sampling technique and the sample size are discussed.

3.5.1 Sampling Frame

A sampling frame refers to the collection of source information from which the sample is drawn (Cooper & Schindler, 2014). The sampling frame consists of all items from which the sample is to be drawn from. The sampling frame was obtained from the Banking Supervision Report by the Central Bank of Kenya which outlined the number of employees in the banking sector in four categories; management, supervisory, clerical and secretarial, and support staff. In this study, the sampling frame was constituted of 10,310 employees serving in the commercial banks at the management level who were likely to

82 experience leadership decisions firsthand and also be charged with implementation (CBK, 2017). Management employees were selected since they constitute the team which is mostly impacted on by the nature of leadership; they are also the key persons running with the day to day strategy implementation meaning there is substantial interaction between them and the executive leadership.

3.5.2 Sampling Technique

A sampling technique refers to the identification of the process through which a sample will be selected. There are various ways of sampling which can be divided into probability and non-probability sampling. Non-probability sampling refers to sampling techniques which are used to select a sample when there is no complete list of the population; thus no sampling frame which introduces an element of subjective judgment. Examples of sampling techniques are convenience and snowball. Convenience refers to selecting elements that are easily available to obtain from the sample whereas snowball refers to making contact with one or two elements in the population then asking them to identify more elements and so on and stopping when the required sample is attained or there are no more elements (Sekaran & Bougie, 2016).

Probability sampling presents an equal chance of selection or when the elements have a known and nonzero chance of being chosen as samples in a population. It can be simple random sampling where all elements have a known and equal chance of being selected as a subject which could be cumbersome and expensive not withstanding an updated listing of the population may not be available. Probability sampling is guided by restricted sampling which offers viability and more efficiency in the sample selection. It takes the form of simple random, systematic random, stratified random, cluster or multi-stage sampling (Saunders et al., 2016).

Simple random sampling involves the selection of a sample at random from the sampling frame using a computer or random number tables. Systematic random sampling involves selecting a sample at regular intervals from a sampling frame (Creswell, 2014). Stratified random sampling involves dividing the target population in strata based on one or more attributes thus dividing the sampling frame in sub-sets from which a random sample which could be simple or systematic is drawn from each stratum (Cooper & Schindler, 2014). In cluster sampling, the target population is divided into distinct groups before sampling and the groups are based on any naturally occurring grouping. The sampling

83 frame then becomes the complete set of clusters from which a few clusters are selected using simple random sampling and data collected from every case in the selected clusters. Multi-stage sampling is a development of cluster sampling which occurs where sampling is done in several stages by modifying a cluster sample by adding another stage of sampling which may take the form of random sampling (Saunders et al., 2016).

This study adopted stratified random sampling technique where the banks were grouped into three tiers as per the Central Bank of Kenya classification which is based on capitalization, market share and profitability (CBK, 2017). Tier one comprised of 7 banks, tier two 14 banks and tier three 22 banks. Tier one was comprised of the big banks, tier two the medium sized banks and tier three the small banks. The target population was distributed proportionately across the three tiers based on the sample size and the number bank branches in each tier. Thereafter, the sample size for each tier was determined using simple random sampling technique. Specifically, simple random sampling was carried out by using computer generated random numbers for each tier.

3.5.3 Sample Size

A sample is a subset of the target population which is used in order to answer the research questions. Additionally, the use of the entire target population as study respondents is faced with several difficulties such as inadequate research budget, limited time factor and large geographical coverage of the respondents which can be mitigated by drawing of a subset of the target population as a true representative of the study population (Sekaran & Bougie, 2016). This study used Yamane (1967) formula to obtain the sample size.

Yamane (1967), Formula:

Where:

N = target (total) population (10, 310) n = desired sample size d= confidence interval (0.05 testing at 5% significant level)

The total sample size was 424 which included an additional 10% which was 39 employees to cater for non respondents as indicated in table 3.2.

The formula is recommended for determining a sample size where the population is known and finite. A finite population is a population whose total number of elements is known or can be counted. It is also suitable because it assumes a normal distribution, a 95% confidence level and a p< 0.05 (Yamane, 1967).

Table 3.2: Sample Size Distribution Based on Tiers of the Banks

Tier Target Employees Sample Employees

One 4495 (424/10301)4495 = 185

Two 3629 (424/10301)3629 = 149

Three 2186 (424/10301)2186 = 90

Total 10, 310 424

The study obtained the respondents from all the commercial banks in Kenya in the three tiers as grouped by the Central Bank of Kenya; this was achieved by distributing the sample employees per tier between all banks in a particular tier. Additionally, the study focused on employees from the head offices of the various commercial banks all of which were located in Nairobi. The focus on head office was informed by the fact that most of the employees at management level were located in the head office.

3.6 Data Collection Methods

According to Sekaran and Bougie (2016), data collection methods include interviews, observations and questionnaires. Interviews involve the researcher asking or interrogating the respondents to obtain information on the issues of interest and it is suitable for exploratory studies. They may be structured or unstructured, conducted face to face, by telephone or online. Observation involves going into the field and watching what the subjects of the study do, describing, analyzing and interpreting what one has seen. It is most suited for research that requires behavior to be examined without directly asking the respondents. A questionnaire is a written set of questions formulated by the researcher to which respondents record their responses (Cooper & Schindler, 2014).

This study sought to collect primary data by use of questionnaires. The questionnaires were informed by the Multifactor Leadership Questionnaire (MLQ) which contains the most commonly used and validated measures of transformational leadership which are idealized influence, individualized consideration, inspirational motivation and intellectual stimulation developed by Bass (1985). The questionnaire had six sections which addressed the demographic and general information, idealized influence and job satisfaction, individualized consideration and job satisfaction, inspirational motivation and job satisfaction, intellectual stimulation and job satisfaction, and the moderating effect of job security between transformational leadership and job satisfaction. The questions were all closed ended and adopted a five-point Likert scale which was used to rate the answers from the respondents; the ratings were: 1= Strongly Disagree (SD), 2 = Disagree (D), 3 = Neutral (N), 4 = Agree (A), 5 = Strongly Agree (SA). Under each research question, there were two sub-sections; the first was to determine if the leader was transformational and the second one was to determine the influence of the constructs of transformational leadership on job satisfaction.

3.7 Research Procedures

The research procedures outline how the pilot study was carried out, the results of the reliability and validity tests from the pilot study, how the research instruments were administered and the ethical considerations made by the researcher. The pilot study was conducted after approval of the research proposal and the research instrument by the researcher’s supervisors and the business school at USIU-Africa. The researcher also obtained a permit to conduct the research from the National Commission for Science, Technology and Innovation (NACOSTI). The questionnaires were administered to the respondents by the researcher. The researcher visited banks in every tier and approached the respondents to participate in the research. The respondents were briefed about the research and the ethical considerations of anonymity and confidentiality of their responses. Thereafter, they were requested to complete the questionnaires.

3.7.1 Pilot Study

A pilot study refers to the process of testing a questionnaire, interview schedule or whichever data collection method with a small group of respondents who are similar to those who will be used in the actual research to see if it works. Any issues that arise from the pilot study can be corrected before the undertaking the actual research (Saunders et

86 al., 2016). The primary aim of the pilot study was to test the reliability and validity of the research instrument and also to ensure there were no problems in recording the data. Cooper and Schindler (2014) explain reliability as the true measure of whether the research instrument meets the intended purpose. With a pilot study, the researcher is able to detect weaknesses in design of the questionnaire used and to adjust it accordingly (Bryman, 2012).

This study focused on the influence of transformational leadership on job satisfaction among employees in commercial banks in Kenyan. The total number of respondents involved in the pilot study was 42 representing 10% of the entire sample size for the study. The pilot response rate was 100% from 9 different banks; 17 respondents from tier one, 14 respondents from tier two and 9 respondents from tier three. Collected data was coded and entered in SPSS Version 22, cleaned and analyzed. The statistical test conducted was Cronbach Alpha to test the reliability of the questionnaires.

3.7.2 Reliability of the Instruments

Reliability refers to the consistency of a measure of a concept; thus, the characteristic of consistency of a measure which gives the same results when conducted on several occasions (Bryman, 2012; MCBurney & White, 2007). Reliability is also known as internal consistency and provides an estimate of the equivalence of sets of items from the same tests. In this research, reliability was measured using Cronbach’s alpha which has been used widely to measure reliability of research instruments. Sekaran and Bougie (2016) highlighted that Cronbach’s alpha coefficient is a good measure of reliability; it ranges between 0 and 1 with values of 0.7 or above indicating that the questions combined in the scale are measuring the same thing and values 0.5 and less are considered unacceptable and not measuring the same thing. The summary of the combined variables in the study showed the item total Cronbach’s Alpha was .978. This indicates that the questionnaire was highly reliable to be used in the study as indicated in Table 3.3.

Table 3.3: Cronbach’s Alpha

Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items

.966 .978 30

3.7.3 Validity of the Instruments

The ability of a research instrument to be a true measure of what it claims to measure or the ability to gain meaning out of what the tool was supposed to measure is referred to as validity (Bryman, 2012). In this research, validity was measured in three forms; content, criterion, and construct validity. Face validity was performed to evaluate the outlay of the tool while factor analysis was performed to evaluate construct validity.

The face validity of the instrument was used to determine criterion, and construct validity. This was performed by observing the number of questionnaires filled (43 questionnaires). Further the researcher collected feedback from the respondents on the flow of the questions, understanding and comprehension. The questions raised as not clear were as follows. My leader delegates work and authority to me: these were found to be two questions in one. This was rephrased to ‘My leader delegates work to me’. My leader persuades me to be creative and innovative in my job, ‘creative and innovation’ were different and this was rephrased to ‘My leader permits me to be creative in my job’. I am committed to the organization because my leader treats everyone fairly, the term ‘treats everyone fairly’ was vague and was replaced to ‘My leader encourages fair treatment to everyone’.

Content validity involves testing the items to ensure they give appropriate measures for the concepts under study (Cooper & Schindler, 2014). Factor analysis was used to determine the content validity of the questions. These questions were grouped based on the number of items in each objective. The items with nearly zero difference had strong content validity while those with bigger margins were not valid hence needed to be rephrased.

3.7.4 Administration of the Instruments

Before data collection, the researcher obtained a clearance letter from USIU-Africa (Appendix III) and a research permit from National Commission for Science, Technology and Innovation (NACOSTI) (Appendix IV). All the approvals, NACOSTI and USIU were presented to the respective bank managers by the researcher when collecting data. This helped to authenticate the research and helped to obtain feedback from the respondents with more ease.

The corrected questionnaire based on the pilot study report was administered to the respondents by the researcher. The research instrument had a cover letter which introduced the research and gave details of the researcher, the USIU-Africa permit and NACOSTI permit authorizing the researcher to carry out the study. The researcher recruited and trained one research assistant on the study objectives and the content of the questionnaires to be administered. Further, the research assistant was trained on the nature of ethics and confidentiality that is required during the data collection exercise since banks are very sensitive institutions as far as information concerned. Data was collected by administering the questionnaires to the respondents through drop and pick method where questionnaires were dropped at the respondent’s desk and collected upon completion as requested by the researcher. The respondents were given ten days to complete the questionnaires but as observed by the researcher it took two to three days. Reminders were sent after four days to the respondents who had not returned the questionnaires. All questionnaires, filled and unfilled were collected by the researcher and prepared for data entry. A total of 347 questionnaires were returned representing an 82% response rate; they were all coded to ensure they could all be accounted for.

3.7.5 Ethical Considerations

Research ethics refers to the appropriateness of the researcher’s behavior in regard to the rights of those who become the subject of the research project or those who are affected by it (Saunders et al., 2016). The researcher addressed the following ethical issues: participant’s consent, confidentiality and anonymity through the research process and the reporting. The researcher obtained a clearance letter from USIU-Africa and a research permit from National Commission for Science, Technology and Innovation (NACOSTI). All the approvals, NACOSTI and USIU-Africa clearance letters were presented to the respective bank managers by the researcher and research assistant to obtain the final authority to reach out to the staff in their organizations. Upon obtaining authority from the bank management, the respondents were requested to participate in the study voluntarily. The researcher notified the participants of their rights for information, asking questions, and that they can withdraw from the research at will. The respondents were also assured of their anonymity and that the information they provide towards the study would be treated with confidentiality and for the sole purpose of this study. Information received was also to be treated objectively and not to be altered for a desired objective.

3.8 Data Analysis Methods

Descriptive and inferential statistical analyses used in the study are discussed. The descriptive statistics adopted were mean, standard deviation, percentages and frequency of response while inferential statistics adopted included test of correlations, Chi-square, the ANOVA tests and regression analysis to obtain the relationship between the variables of the study. The quantitative data analysis was done using Statistical Package for the Social Sciences (SPSS). Presentations were done using tables and figures with detailed interpretation of the findings.

3.8.1 Data Preparation

After data collection, the following iterative steps were taken. First, data was coded, secondly, data was cleaned which meant detecting and correcting illogical, inconsistent, illegal data and omission of information received from the respondents. Lastly, data was transformed; this meant changing the ordinal numerical representation of a quantitative value to another value to avoid problems in successive stages of the data analysis process. This was done using the most suitable methods (Sekaran & Bougie, 2016).

3.8.2 Descriptive Analysis

According to Christensen, Johnson and Turner (2014), descriptive statistics are statistical analysis focused on describing, summarizing and explaining a set of data. Descriptive statistics are broken down into measures of central tendency and measures of variability. A measure of central tendency refers to a single numerical value that is considered most typical of the values of a quantitative variable and the measures include the mean, median and mode. Cooper and Schindler (2014) state that a measure of variability is a numerical index that provides information about how much variation there is in a variable and the measures include standard deviation, range and variance. According to Sekaran and Bougie (2016), descriptive statistics enable meaningful description of a distribution of scores or measurements using a few indices or statistics. Descriptive statistics used included mean, standard deviation, frequencies and percentages.

3.8.3 Inferential Analysis

According to Bandyopadhyay and Forster (2011), inferential statistics are concerned with making inferences based on relations found in the sample to relations in the population. The goal of inferential statistics is to go beyond the immediate available set of data and to

90 infer characteristics of populations based on the sample data; thus, researchers use sample data to make generalizations about populations. The inferential statistics that were used in this study were correlation analysis, ANOVA, factorial analysis and multiple linear regression analysis. Inferential analysis was majorly for testing the hypotheses (Sekaran & Bougie, 2016). In this study, regression analysis aimed to predict the dependent variable - job satisfaction - based on the independent variables – the four dimensions of transformational leadership. Details of each of the inferential statistics are explained in the successive sections.

3.8.3.1 Factor Analysis

Factor analysis is a statistical analysis used to determine the number of dimensions in a set of items and informs the researcher if a test is unidimensional or multidimensional (Cooper & Schindler, 2014). The tests conducted were Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s sphericity tests. From the test, values greater than 0.6 qualified the use of factor analysis. Factor analysis was used to reduce the items of analysis into few related items. In this study, questions with 5 scale measures under each variable were tested. Factor analysis was based on component matrix where any value of the component matrix above 0.6 indicated that there was no redundancy in the questions and therefore the particular questions should not be dropped.

3.8.3.2 Correlation Analysis

According to Sekaran and Bougie (2016), correlation analysis is a method of statistical evaluation used to study the strength of a relationship between two, numerically measured, continuous variables. Any correlation value of the variable between 0.0-0.3 would indicate ‘no correlation’, correlation value between 0.31-0.69 would indicate ‘weak correlation’ and any correlation value above 0.7 would indicate strong correlation between the variables. However, the statistical significance of the strength of the correlation was based on a 5% significance level (P<.05). Correlation analysis was done to test the correlation between the transformational leadership variables and job satisfaction.

3.8.3.3 Chi-square

Chi-squared test is a statistical test applied to sets of categorical data to evaluate if two variables are independent or whether the observed pattern occurred by chance (Sekaran & Bougie, 2016). It is suitable for unpaired data from large samples (Bandyopadhyay & 91

Forster 2011). It was used to test whether job satisfaction was related to transformational leadership. The Chi-square test was at a 5% significance level (P<=.05). Chi-square value above 5% significance level (P<.05) indicated job satisfaction was not related to transformational leadership while any value below 5% significance level (P<=.05) indicated job satisfaction was related to transformational leadership not by chance but by factors alone.

3.8.3.4 One-way ANOVA

According to Bailey (2008), analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences among group means. The One-way ANOVA test was performed to test the mean differences between idealized influence and the demographic information of respondents; gender, age, education level, duration of working at the bank and the tier of the bank. The significance measure was set at 5% significance level (P<.05). Where the results were not statistically significant, it indicated there was no significant difference between the mean values of all the respondents’ demographic information – gender, age, education level, duration of working, tier of the bank - and the independent variables – idealized influence, individualized consideration, inspirational motivation and intellectual stimulation.

3.8.3.5 Regression Analysis

Regression analysis is a model used to determine the relationships among variables; direction, strength and projection. It includes many techniques for modeling and analyzing several variables and it helps one to understand how the typical value of the dependent variable changes when any one of the independent variables is varied (Creswel, 2014). In this study, regression analysis was used to determine the relationship, magnitude of the influence and the extent to which transformational leadership predicted job satisfaction among employees in commercial banks in Kenya.

3.8.3.5.1 Assumptions of Regression Analysis

This section discusses the assumptions of the regression analysis that were tested. The assumptions of regression have great impact in regression analysis (Ghasemi & Zahediasl, 2012). The assumptions tested included normality, linearity, homoscedasticity and multicollinearity.

3.8.3.5.1.1 Normality

Normality test was conducted using Skewness test and Kurtosis test where Kurtosis was demonstrated based on three distributions; smallest or flattest peak, medium peak and peak (leptokurtic) (Cooper & Schindler, 2014). Any skewness value between -2 to 0 indicated that there was no excessive skewness in that data. Kurtosis was used to measure the level of data pickiness based on the normal distribution of data. Any kurtosis value between -1 to +2 would indicate that there was no excessive skewness in the data. Lack of excessive skewness and kurtosis in the data indicated that the normality assumption was not strictly violated and the research data (job satisfaction and transformational leadership data) was fit for regression analysis.

3.8.3.5.1.2 Linearity

Linearity is the property of a mathematical relationship or function which means that it can be graphically represented as a straight line. For the linear regression model to be used, the expected value of the dependent variable is a straight-line function of each independent variable holding others constant. Additionally, effects of different independent variables on the expected value of the dependent variable are additive (Bewick, Chuck & Ball, 2003). The linearity test was conducted to determine whether the nature of the relationship between transformational leadership and job satisfaction was linear or not. This was done on the basis of 5% significance level, one tail test where any significant deviation from linearity (deviation > 0.05) greater than 0.05 would indicate that there was linear relationship between the between transformational leadership and job satisfaction variables.

3.8.3.5.1.3 Homoscedasticity

In statistics, Homoscedasticity test establishes if the dependent and independent variables have similar variance on their distribution. Homoscedasticity test was done using the Leven statistics at 5% significance level. One tail test would indicate that the variance was homogenous and hence fitted for regression analysis (Hair et al., 2007). In this research Homoscedasticity test was carried out to determine if transformational leadership had similar variance to job satisfaction of the bank employees on the regression values.

3.8.3.5.1.4 Multicollinearity

Multicollinearity was tested using variance inflation factors (VIF). According to Lather (2004), VIF assesses how much the variance of an estimated regression coefficient increases if the predictors are correlated. If the VIF measure is equal to or less than one (1) or above ten (10) there is multicollinearity among factors. This means the correct measure of VIF should be above 1 and less than 10 (Sekaran & Bougie, 2016; Oakshott, 2014). The test was done on the transformational leadership and job satisfaction variable and the overall interpretation of multicollinearity test was based on the VIF values where any VIF value between 1 to 10 indicated that the level of multicollinearity that exists in the study was fit for regression analysis.

3.8.3.5.2 Regression Model and Hypotheses Testing

This section presents the regression model used for the regression analysis and hypotheses testing.

3.8.3.5.2.1 Regression Model

Linear regression was used to determine the influence of transformational leadership on job satisfaction among employees in commercial banks in Kenya. Multiple linear regression analysis was used where there was more than one independent variable to explain the variance on the dependent variable (Cooper & Schindler, 2014; Sekaran & Bougie, 2016; Saunders et al., 2016).

Fischer distribution test called F-test was applied to test the joint significant contribution of attributes of transformation leadership on job satisfaction. The p-value for the F- statistic was applied in determining the robustness of the model. The conclusion was, if the value was significant (p<.05) the model was significant and had good predictors of the dependent variable hence null hypothesis rejected. Alternatively, if the p-value was greater than 0.05, then the model would not be significant and cannot be used to explain the variations in the dependent variable hence the null hypothesis would not be rejected.

Y =β0 + β1X1 + β2X2 + β3X3 + β4X4+ β5Z + ∑

Where;

Y = Job Satisfaction

β0 = Constant

X1 = Idealized influence

X2 = Individualized consideration

X3 = Inspirational motivation

X4 = Intellectual Stimulation

Z = Moderating Variable (Job security)

∑ = Standard Error

In the model, β0 = the constant term while the coefficient βi = 1…4 was used to measure the sensitivity of the dependent variable (Y) to unit change in the predictor variables. ∑ was the error term which captured the unexplained variations in the model. In each test, the null hypothesis was rejected or not rejected; if the p-value was less than 0.05, the model was significant hence null hypothesis rejected. Alternatively, if the p-value was greater than 0.05, then the model was not significant hence the null hypothesis not rejected.

3.8.3.5.2.2 Hypotheses Testing

The hypotheses were tested using multiple linear regression analysis which is recommended where there is more than one independent variable to explain the variance on the dependent variable (Sekaran & Bougie, 2016). The five hypotheses of the study were all tested through the regression models outlined hereunder.

To test for H01: There is no significant influence of idealized influence on job satisfaction among employees in commercial banks in Kenya.

Regression model 1:

Job Satisfaction = β0 + βi x idealized influence + ∑……… Equation 1

To test for H02: There is no significant influence of individualized consideration on job satisfaction among employees in commercial banks in Kenya.

Regression model 2:

Job Satisfaction = β0 + βi x individualized consideration + ∑……… Equation 2

To test for H03: There is no significant influence of inspirational motivation on job satisfaction among employees in commercial banks in Kenya.

Regression model 3:

Job Satisfaction = β0 + βi x inspirational motivation + ∑……… Equation 3

To test for H04: There is no significant influence of intellectual stimulation on job satisfaction among the employees in commercial banks in Kenya.

Regression model 4:

Job Satisfaction = β0 + βi x intellectual stimulation + ∑……… Equation 4

To test for H05: There is no significant moderating effect of job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya.

Regression model 5:

Job Satisfaction = β0 + β1 x Idealized influence + β2 x Individualized consideration + β3 x

Inspirational motivation + β4 x Intellectual Stimulation + β5 x Job security + ∑…...... Equation 5

Table 3.4 presents a summary of the hypotheses testing.

Table 3.4: Hypothesis Testing

Variable Hypothesis Test

Idealized To test for H01: There is no Linear regression: influence significant influence of idealized Y =β + β X + ∑ influence on job satisfaction among 0 1 1 employees in commercial banks in Accept if p<.05 or otherwise Kenya reject

Individualized To test for H02: There is no Linear regression: consideration significant influence of Y =β + β X + ∑ individualized consideration on job 0 2 2 satisfaction among employees in Accept if p<.05 or otherwise commercial banks in Kenya reject

Inspirational To test for H03: There is no Linear Equation: motivation significant influence of inspirational Y =β + β X + ∑ motivation on job satisfaction 0 3 3 among employees in commercial Accept if p<.05 or otherwise banks in Kenya reject

Intellectual To test for H04: There is no Linear regression: Stimulation significant influence of intellectual Y =β + β X + ∑ stimulation on job satisfaction 0 4 4 among the employees in Accept if p<.05 or otherwise commercial banks in Kenya reject

Moderating To test for H05: There is no Multiple linear regression: effect of Job significant moderating effect of job Y =β0 + β1X1 + β2X2 + β3X3 + satisfaction security between transformational β X + β5Z + ∑ leadership and job satisfaction 4 4 among employees in commercial Accept if p<.05 or otherwise banks in Kenya reject

3.9 Chapter Summary

This chapter has discussed the research methodology, the research philosophy, the research design, the sampling design, the data collection methods, research procedures and data analysis methods used in the study. It has also outlined the descriptive and inferential statistics that the study adopted for the analysis and presentation of results and findings. Chapter four presents the results and findings of data analysis.

CHAPTER FOUR

4.0 RESULTS AND FINDINGS

4.1 Introduction This chapter presents the results and findings of the study. The presentation of the results is done sequentially according to the research questions. Demographic and general information is presented first followed by the descriptive and inferential statistics of idealized influence, individualized consideration, inspirational motivation, intellectual stimulation and job satisfaction, and lastly the moderating effect of job security between transformational leadership and job satisfaction.

A total of 424 questionnaires were distributed, 347 questionnaires were returned and analyzed representing an 82% response rate.

4.2 General Information This section presents the demographic and general information. Descriptive statistics were used to analyze the demographic information in form of percentage, mean and standard deviation. The information analyzed included gender, age, education, duration of working in the bank and tiers of the banks and the results are presented below.

4.2.1 Gender Out of the 347 respondents, 52% were male and 48% who were female as indicated in the figure 4.1.

Female 48% Male 52%

Figure 4.1: Gender of Respondents

4.2.2 Age of Respondents

Figure 4.2 shows the age distribution of the respondents. The results revealed that 49% were aged 30-39; 35% were aged 21-29 years, 13% were aged 40-49 years, 3% were aged 50-59 years while those who were aged over 60 years were less than 1%.

60.00% 48.50% 50.00%

40.00% 35.20%

30.00%

20.00% 13.10% 10.00% 2.90% 0.30% 0.00% 21-29 30-39 40-49 50-59 Over 60

Figure 4.2: Age of Respondents

4.2.3 Education of Respondents

Figure 4.3 shows the distribution of respondents based on education qualification. The results revealed that majority of the respondents (about 59%) had a Bachelor’s degree. This was followed by approximately 33% who were Master’s degree holders. A few of the respondents (about 1%) were certificate holders.

70.00% 58.50% 60.00%

50.00%

40.00% 32.90% 30.00%

20.00%

10.00% 5.80% 1.20% 1.70% 0.00% Certificate Diploma Bachelor's Master's PhD

Figure 4.3: Education Qualification

4.2.4 Duration of Working

Figure 4.4 shows results on the duration of work in the bank. There was clear pattern on the duration of working; majority of respondents had worked for fewer years and fewer of the respondents had worked for more years in the banking sector. Those who had worked for between 0-5 years were about 37% followed by those who had worked for 6-10 years who were about 34%. Only about 6% of the respondents had worked for more than 20 years. This showed that fewer people opted to remain in the banking sector as they grew older.

over 20 5.50%

16-20 4.00%

11-15 19.10%

6-10 34.10%

0-5 37.30%

Figure 4.4: Duration of Working

4.2.3 Tiers of the Banks

Figure 4.5 presents results on the tiers of the banks. In Kenya, banks are classified into three different tiers; thus, tiers; 1, 2 and 3. The respondents were asked to indicate the tier of their bank; 49% indicated they worked for banks classified as tier 1, 34% worked for banks classified as tier 2 and lastly 17% worked for banks classified as tier 3.

Tier 3 17% Tier 1 49% Tier 2 34%

Figure 4.5: Tier of the Banks

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4.3 Influence of Idealized Influence on Job Satisfaction

The first objective was to determine the influence of idealized influence on job satisfaction. This was guided by the independent variable questions on idealized influence and dependent variable questions on job satisfaction. The independent variable questions were: my leader has charismatic attributes; my leader demonstrates trust in my abilities; and my leader is ethical in the workplace. The dependent variable questions on job satisfaction were: I am committed to the organization because of my leader’s charismatic attributes; I am present at work because my leader demonstrates trust in my abilities; and I have no intentions of leaving my job because my leader is ethical in the workplace. The results and findings of both descriptive and inferential statistics are presented below.

4.3.1 Factor Analysis

Factor analysis was used to evaluate the variability among the observed correlated variables to ensure the questions in the research instrument relate to the construct of measure. Questions that did not relate to the construct were extracted from the analysis. Factor analysis was conducted on three questions for the dependent variable ‘job satisfaction’ and the three questions for independent variable ‘idealized influence’ as presented below.

4.3.1.1 Factor Analysis on Idealized Influence

The independent variable in the study was idealized influence. As indicated in table 4.1a, only one factor was derived with Kaiser-Meyer result of 0.721. The Bartlett’s test of Sphericity was significant at X2 (3, N=347) = 462.905, p<.05. The factor was adequate for extraction of component since Kaiser-Meyer-Olkin Measure was greater than 0.6 and the Bartlett’s test was significant (p<.05). Table 4.1a shows the results.

Table 4.1a: KMO and Bartlett's Test on Idealized Influence

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .721 Bartlett's Test of Sphericity Approx. Chi-square 462.905 Df 3 Sig. .000 * Significant at p<0.05 level

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Using the principal component analysis, the total variance explained on the extraction showed that the extracted values presented 78% of the first component. Only one component was extracted ‘idealized influence’. Further, average value principle was used to obtain the measure of the extracted independent variable by transformation. Table 4.1b shows the results of the variance explained.

Table 4.1b: Total Variance Explained for Idealized Influence

Initial Eigenvalues Extraction Sums of Squared Loadings % of Cumulative % of Component Total Variance % Total Variance Cumulative % 1 2.327 77.554 77.554 2.327 77.554 77.554 2 .396 13.216 90.770 3 .277 9.230 100.000 Extraction Method: Principal Component Analysis.

One component for idealized influence had an Eigen value that was greater than one which was in line with the results for total variance explained for idealized influence as shown in Figure 4.6.

Figure 4.6: Scree Plot for Idealized Influence

The variables of the extracted components were indicated on the component matrix Table 4.1c. Only one factor was extracted representing ‘idealized influence’. The variables

102 extracted and values were; my leader has charismatic attributes (.875), my leader demonstrates trust in my abilities (.905) and my leader is ethical in the workplace (.861). All the variables and components measured under the factor loading were greater than .60. Further, using the average of the components, the transformed data had a stronger component of .880 which was greater than .60. All the components were stronger and were included as variables of analysis in the model ‘idealized influence’ since the values were greater than .60.

Table 4.1c: Component Matrix on Idealized Influence

Component

Idealized Influence 1

My leader has charismatic attributes .875

My leader demonstrates trust in my abilities .905

My leader is ethical in the workplace .861

Extraction Method: Principal Component Analysis. a. 1 component extracted.

4.3.1.2 Factor Analysis of Idealized Influence on Job Satisfaction

The dependent variable in the study was idealized influence on job satisfaction. As indicated in Table 4.2a, only one factor was derived with Kaiser-Meyer Olkin result of 0.739. The Bartlett’s test of Sphericity was significant at X2 (3, N=347) = 497.434, p<.05. The factor was adequate for extraction of the component since Kaiser-Meyer Olkin Measure was greater than 0.6 and the Bartlett’s test was significant (p<.05).

Table 4.2a: KMO and Bartlett’s Test on Idealized Influence on Job Satisfaction

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .739

Bartlett's Test of Sphericity Approx. Chi-square 497.434

Df 3

Sig. .000

* Significant at p<0.05 level

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Using the principal component analysis, the total variance explained on the extraction shows the extracted values presented 79% of the component. Only one component was extracted ‘idealized influence on job satisfaction’. Further, average value principle was used to obtain the measure of the extracted independent variable named ‘idealized influence on job satisfaction’ by transformation. Table 4.2b shows the results of the total variance explained on idealized influence.

Table 4.2b: Total Variance Explained on Idealized Influence

Extraction Sums of Squared Initial Eigenvalues Loadings

% of Cumulative % of Cumulative Component Total Variance % Total Variance %

1 2.370 79.010 79.010 2.370 79.010 79.010

2 .326 10.871 89.880

3 .304 10.120 100.000

Extraction Method: Principal Component Analysis

The variables of the extracted components are indicated on the component matrix Table 4.2c. Only one factor was extracted representing ‘idealized influence on Job satisfaction’. The variables and values extracted were: ‘I am committed to the organization because of my leader’s charismatic attributes’ and ‘I have no intentions of leaving my job because my leader is ethical in the workplace’ had similar component matrix value of .891 while ‘I am present at work because my leader demonstrates trust in my abilities’ had component matrix value of .884. This shows the variables and components measured under the factor loading were greater than .60. Further, using the average of the components, the transformed data had a stronger component of .889 which was greater than .60. The components of the dependent variables were included as variables of analysis in the model as ‘idealized influence on job satisfaction’ since the values were greater than .60. Table 4.2c shows the component matrix of idealized influence on job satisfaction.

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Table 4.2c: Component Matrix of Idealized Influence on Job Satisfaction

Component Idealized Influence on Job Satisfaction 1

I am committed to the organization because of my leader has .891 charismatic attributes

I am hardly absent from work because my leader demonstrates .884 trust in my abilities

I have no intentions of leaving my job because my leader is .891 ethical in the workplace

Extraction Method: Principal Component Analysis. a. 1 component extracted.

4.3.2 Descriptive Statistics for Idealized Influence

On idealized influence, majority of the respondents agreed on the attribute ‘my leader is ethical in the workplace’ (M= 4.23, SD = 0.84) and also ‘my leader demonstrates trust in my abilities’ (M= 4.08, SD = 0.88). This clearly shows the difference; with the decrease in mean, the standard deviation increased indicating responses were varied. Table 4.3 shows the results of the descriptive statistics of idealized influence. On job satisfaction, majority of the respondents agreed on the attribute ‘I am hardly absent from work because my leader demonstrates trust in my abilities’ (M= 3.42, SD = 1.21) followed by ‘I am committed to the organization because of my leader’s charismatic attributes’ (M= 3.25, SD = 1.14). The trend of the mean and standard deviation varied depicting varied responses as indicated in Table 4.3.

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Table 4.3: Mean and Standard Deviation of Idealized Influence

Idealized Influence M SD Skewness Std Error

My leader has charismatic attribute 3.89 0.97 -1.03 0.13

My leader demonstrates trust in my abilities 4.08 0.88 -1.05 0.13

My leader is ethical in the workplace 4.23 0.84 -1.28 0.13

Effect of Idealized Influence on Job Satisfaction

I am committed to the organization because of 3.25 1.14 -0.23 0.13 my leader has charismatic attributes

I am hardly absent from work because my 3.42 1.21 -0.44 0.13 leader demonstrates trust in my abilities

I have no intentions of leaving my job 2.91 1.27 0.04 0.13 because my leader is ethical in the workplace

4.3.3 Chi-square Test of Idealized Influence and Job Satisfaction

The Chi-square test was used to determine whether there was a significant association between idealized influence and job satisfaction. The chi-square test results showed that there was a significant association between idealized influence and job satisfaction X2 (132, N = 346) = 302.886, p<.05). The results are presented in Table 4.4.

Table 4.4: Chi-square Test of Idealized Influence and Job Satisfaction

Idealized Influence Value df Asymp. Sig. (2-sided) Pearson Chi-square 302.886a 132 .000

Likelihood Ratio 260.451 132 .000

Linear-by-Linear Association 84.977 1 .000

N of Valid Cases 346 a. 134 cells (85.9%) have expected count less than 5. The minimum expected count is .01. * Significant at p<0.05 level

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4.3.4 Correlation Analysis between Idealized Influence and Job Satisfaction

Correlation analysis was used to test the relationship between idealized influence variables and job satisfaction. As shown in Table 4.5a, all the variables were highly correlated. The first variable under idealized influence ‘my leader has charismatic attributes’ was positively correlated with job satisfaction r (345) =.563, p<.05; ‘my leader demonstrates trust in my abilities’ was positively correlated with job satisfaction r (346) =.596, p<.05; and ‘my leader is ethical in the workplace’ was positively correlated with job satisfaction r (343) =.564, p<.05.

Table 4.5a: Correlation Analysis between Idealized Influence Variables and Job Satisfaction

Idealized Influence Pearson Correlation Job Satisfaction My leader has charismatic attribute. Pearson Correlation .563** Sig. (2-tailed) .000 N 345 My leader demonstrates trust in my Pearson Correlation .596** abilities. Sig. (2-tailed) .000 N 346 My leader is ethical in the workplace. Pearson Correlation .564** Sig. (2-tailed) .000 N 343 * Significant at p<0.05 level

Further, correlation analysis was used to test the relationship between idealized influence and job satisfaction. The results showed that there was a strong and positive correlation between idealized influence and job satisfaction r (346) =.496, p<.05. The results are presented in Table 4.5b.

Table 4.5b: Correlation Analysis between Idealized Influence and Job Satisfaction

Job Satisfaction Idealized influence Pearson Correlation .496** Sig. (2-tailed) .000 N 346 * Significant at p<0.05 level 107

4.3.5 One-way ANOVA on Idealized Influence

The One-way ANOVA test was performed to test the mean differences between idealized influence and the demographic information of respondents; gender, age, education level, duration of working at the bank and the tier of the bank. Table 4.6a shows the results which indicate that there was no significant difference between the mean values of all the respondents’ demographic information of gender, age, education level, duration of working, tier of the bank and idealized influence.

Table 4.6a: One-way ANOVA on Idealized Influence

Sum of Mean Squares df Square F Sig. Gender Between Groups 3.773 11 .343 1.388 .177 Within Groups 82.041 332 .247 Total 85.814 343

Age Between Groups 9.847 11 .895 1.514 .124 Within Groups 195.652 331 .591 Total 205.499 342

Education Between Groups 4.900 11 .445 1.144 .326 Within Groups 129.259 332 .389 Total 134.160 343

How long Between Groups 13.850 11 1.259 1.031 .418 have you Within Groups 406.747 333 1.221 worked Total 420.597 344

Tier of your Between Groups 10.262 11 .933 1.686 .075 bank Within Groups 184.767 334 .553 Total 195.029 345 * Significant at p<0.05 level

108 no significant difference between the mean values of majority of the demographic variables and job satisfaction. However, the means for job satisfaction were significantly different across the demographic variable of the number of years worked in the organization.

Table 4.6b: One-way ANOVA on Idealized Influence on Job Satisfaction

Sum of df Mean F Sig. Squares Square Between Groups 2.103 12 .175 .693 .758 Gender Within Groups 83.711 331 .253 Total 85.814 343 Between Groups 7.701 12 .642 1.071 .384 Age Within Groups 197.798 330 .599 Total 205.499 342 Between Groups 4.325 12 .360 .919 .528 Education Within Groups 129.835 331 .392 Total 134.160 343 Between Groups 30.497 12 2.541 2.163 .013 How long have Within Groups 390.100 332 1.175 you worked Total 420.597 344 Between Groups 4.647 12 .387 .677 .773 Tier of your Within Groups 190.382 333 .572 bank Total 195.029 345 * Significant at p<0.05 level

4.3.6 Regression Analysis and Hypothesis Testing

This section presents the regression analysis, the model used for hypothesis testing in the study and the assumptions of regression. Regression analysis was done to determine the relationship, magnitude of the effect and projection of the influence of idealized influence on job satisfaction among employees in commercial banks in Kenya.

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4.3.6.1 Assumptions for Regression Analysis on Idealized Influence

Before running the regression analysis, assumptions for regression were tested. The following tests were conducted: normality test, linearity test, homoscedasticity test and multicollinearity tests as presented hereunder.

4.3.6.1.1 Normality Test on Idealized Influence

Using one sample Kolmogorov-Smirnov Test, the test of normality was conducted to determine the distribution of data depicting either a normal or skewed curve. This was determined by the statistical significance of the dependent and the independent variable (p<.05). The normal parameters test indicated difference on mean: idealized influence had (M= 4.07, SD = .788) compared to Job satisfaction (M= 3.20, SD = 1.07). The variance on the mean was low compared to the standard deviation variance which was high. Further, the results showed the variance on the most extreme differences was minimal and the variables were significant to each other (p<.05) indicating a high level of relationship hence the data was not normally distributed (p<.05). Table 4.7a indicates the results of the normality test.

Table 4.7a: One-Sample Kolmogorov-Smirnov Test on Idealized Influence

Idealized_ influence Job_ satisfaction N 346 346 Normal Parametersa,b Mean 4.0655 3.1965 Std. Deviation .78771 1.07253 Most Extreme Differences Absolute .184 .103 Positive .118 .084 Negative -.184 -.103 Test Statistic .184 .103 Asymp. Sig. (2-tailed) .000c .000c a. Test distribution is Normal. b. Calculated from data. c. Lilliefors Significance Correction. * Significant at p<0.05 level

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4.3.6.1.2 Linearity Test on Idealized Influence

The analysis of variance (ANOVA) was used to determine linearity. The linearity test was conducted to determine whether the nature of the relationship between idealized influence and job satisfaction was linear or not. As indicated in Table 4.7b, there was a significant relationship between idealized influence and job satisfaction on the combined and linearity tests (p<.05). However, the deviation from linearity was not significant. Hence the relationship between idealized influence and job satisfaction was linear and passed the test of linearity.

Table 4.7b: Linearity Test on Idealized Influence

Sum of Mean Squares df Square F Sig. Idealized Between (Combined) 62.420 12 5.202 11.422 .000 influence * Job Groups Linearity 52.728 1 52.728 115.782 .000 satisfaction Deviation from 9.692 11 .820 1.988 .071 Linearity Within Groups 151.651 333 .455 Total 214.071 345 * Significant at p<0.05 level

4.3.6.1.3 Multicollinearity Test on Idealized Influence

Multicollinearity test was performed to determine if the values of idealized influence and job satisfaction had high similarity. The test of multicollinearity was using the Variance Inflation Factor (VIF). Statistically, there is no multicollinearity when the value of VIF between 1 and 10. As indicated in Table 4.7c, the VIF value was 1.860 which shows there was no multicollinearity between idealized influence and job satisfaction.

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Table 4.7c: Multicollinearity Test on Idealized Influence

Unstandardized Standardized Collinearity Coefficients Coefficients Statistics Std. Model B Error Beta T Sig. Tolerance VIF 1 (Constant) .449 .264 1.702 .090 Idealized .676 .064 .496 10.603 .003 1.860 1.860 Influence

4.3.6.1.4 Homoscedasticity Test on Idealized influence

Homoscedasticity test was carried out to determine if idealized influence of the bank employees had similar variance to job satisfaction on the regression values. As indicated on Table 4.7d, the results indicate that the value of the Levene Statistic, F (10, 334) = 1.69, p = .08 was above the study’s level of significance (p <.05) indicating the data was homogenous.

Table 4.7d: Homoscedasticity Test on Idealized Influence

Levene Statistic df1 df2 Sig. 1.688 10 334 .082 * Significant at p<0.05 level

4.3.6.2 Regression and Hypothesis Testing

Regression analysis was carried out to determine the extent to which idealized influence influenced job satisfaction among employees in commercial banks in Kenya. Multiple linear regression was used to predict job satisfaction of employees in commercial banks in Kenya from idealized influence. The hypothesis tested was:

H01: There is no significant influence of idealized influence on job satisfaction among employees in commercial banks in Kenyan.

The regression results for the hypothesis testing were presented in the form of the model summary, regression ANOVA and regression coefficient.

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4.3.6.2.1 Regression Model Summary

The model summary results presented in Table 4.8 indicate that idealized influence explained 25% of job satisfaction among employees in commercial banks in Kenya (R2) = .246.

Table 4.8: Model Summary of Idealized Influence and Job Satisfaction

Change Statistics Std. Adjusted Error of R R R the Square F Sig. F Model R Square Square Estimate Change Change df1 df2 Change 1 .496a .246 .244 .93247 .246 112.421 1 344 .000 a. Predictors: (Constant), Idealized Influence * Significant at p<0.05 level

4.3.6.2.1 Regression ANOVA

The regression ANOVA showed that idealized influence had a significant influence on job satisfaction F(1, 97.750) = 112.421, p<.05) as indicated Table 4.9. This means that the regression model was suitable for predicting the outcome variable on how idealized influence influenced job satisfaction among employees in commercial banks in Kenya.

Table 4.9: Regression ANOVA of Idealized Influence on Job Satisfaction

Sum of Model Squares Df Mean Square F Sig. 1 Regression 97.750 1 97.750 112.421 .000b Residual 299.108 344 .869 Total 396.858 345 a. Dependent Variable: Job satisfaction b. Predictors: (Constant), idealized influence * Significant at p<0.05 level

4.3.6.2.3 Regression Coefficient on Idealized Influence

Table 4.10 shows the results of the regression coefficient. In the regression coefficients model, the analysis showed that idealized influence statistically predicted job satisfaction

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(β = .676, (.449) t = 10.603, p<.05). The beta weight gauges the importance of explanatory variable across the model and was positive on idealized influence, Beta of .449 and statistically significant at p<.05. This means, one unit of increase in idealized influence increased the unit of job satisfaction by .449.

Table 4.10: Coefficients of Idealized Influence on Job Satisfaction

Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta T Sig. 1 (Constant) .449 .264 1.702 .090 Idealized Influence .676 .064 .496 10.603 .003 a. Dependent Variable: Job satisfaction * Significant at p<0.05 level

From the coefficient table, the values of the regression model were derived:

The general form of the regression model used was:

= Constant; = idealized influence and = Error term.

From the coefficient table, idealized influence influenced job satisfaction among employees in commercial banks in Kenya.

Y= 0.449 + .676X + .064

Multiple linear regression analysis was used to test if idealized influence significantly predicted job satisfaction among employees in commercial banks in Kenya. The results revealed that idealized influence explained 25% of job satisfaction (R2 = .246, F (1, 97.750) = 112.421, p<.05) while the remaining 75% of job satisfaction were explained by other factors. Further, idealized influence significantly predicted job satisfaction (β =

.676, (.449) t = 10.603, p<.05). Therefore, the study rejected the null hypothesis H01: There is no significant influence of idealized influence on job satisfaction among employees in commercial banks in Kenya and accepted the alternate hypothesis, H11:

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There is a significant influence of idealized influence on job satisfaction among employees in commercial banks in Kenya.

4.4 Influence of Individualized Consideration on Job Satisfaction

The second objective was to determine the influence of individualized consideration on job satisfaction. This was guided by the independent variable questions on individualized consideration and dependent variable questions on job satisfaction. The independent variable questions were: my leader delegates work to me; my leader mentors me in the workplace; and my leader supports me in my work. The dependent variable questions on job satisfactions were: I have no intentions of leaving my job because my leader delegates work to me; I am committed to the organization because my leader mentors me in the workplace; and I am hardly absent from work because my leader supports me in my work. The results and findings of both the descriptive and inferential statistics are presented below.

4.4.1 Factor Analysis

Factor analysis was used to evaluate the variability among the observed correlated variables to ensure the questions in the research instrument relate to the construct of measure. Questions that did not relate to construct were extracted from the analysis. Factor analysis was conducted on three questions for dependent variable ‘job satisfaction’ and three questions for independent variable ‘individualized consideration’ as presented below.

4.4.1.1 Factor Analysis on Individualized Consideration

The independent variable in the study was individualized consideration. As indicated in Table 4.11a, only one factor was derived with Kaiser-Meyer Olkin result of .654. The Bartlett’s test of sphericity was significant at X2 (3, N=347) = 309.573, p<.05. The factor was adequate for extraction of component since Kaiser-Meyer-Olkin Measure was greater than .60 and the Bartlett’s test was significant (p<.05).

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Table 4.11a: KMO and Bartlett's Test on Individualized Consideration.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .654 Bartlett's Test of Sphericity Approx. Chi-square 309.573 df 3 Sig. .000 * Significant at p<0.05 level Using the Principal component analysis, the total variance explained on the extraction showed the extracted values presented 69% of the first component. Only one component was extracted ‘individualized consideration’. The average value principle was used to obtain the measure of the extracted independent variable by transformation. Table 4.11b shows the results of the variance explained.

Table 4.11b: Total Variance Explained for Individualized Consideration

Extraction Sums of Squared Initial Eigenvalues Loadings % of Cumulative % of Cumulative Component Total Variance % Total Variance % 1 2.069 68.980 68.980 2.069 68.980 68.980 2 .606 20.217 89.196 3 .324 10.804 100.000

Extraction Method: Principal Component Analysis.

One component for individualized consideration had an Eigen value that was greater than one which was in line with the results for total variance explained for individualized consideration as shown in Figure 4.7.

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Figure 4.7: Scree Plot for Individualized Consideration

The variables of the extracted components were indicated on the component matrix Table 4.11c. Only one factor was extracted representing ‘individualized consideration’. The variables extracted and values were; my leader mentors me in the workplace (.847), my leader supports me in my work (.885) and my leader delegates work to me (.754). All the variables and components analyzed under the factor loading were greater than .60. Further, using the average of the components, the transformed data had a stronger component of .829 which was greater than .60. All the components were included as variables of analysis in the model ‘individualized consideration’ since the values were greater than .60. Table 4.11c shows the component matrix on individualized consideration.

Table 4.11c: Component Matrix on Individualized Consideration

Component Individualized consideration 1 My leader mentors me in the workplace .847 My leader supports me in my work .885 My leader delegates work to me .754 Extraction Method: Principal Component Analysis. a. 1 component extracted.

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4.4.1.2 Factor Analysis of Individualized Consideration on Job Satisfaction

The dependent variable in the study was individualized consideration on job satisfaction. As indicated in Table 4.12a, only one factor was derived with Kaiser-Meyer Olkin result of .730. The Bartlett’s test of sphericity was significant at X2 (3, N=347) = 590.668, p<.05. The factor was adequate for extraction of the component since Kaiser-Meyer- Olkin Measure was greater than .60 and the Bartlett’s test was significant (p<.05).

Table 4.12a: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .730 Bartlett's Test of Sphericity Approx. Chi-square 590.668 df 3 Sig. .000 * Significant at p<0.05 level

Using the Principal component analysis, the total variance explained on the extraction showed the extracted values presented 81% of the component. Only one component was extracted ‘individualized consideration on job satisfaction’. Further, average value principle was used to obtain the measure of the extracted independent variable named ‘individualized consideration on job satisfaction’ by transformation. Table 4.12b shows the results of the variance explained.

Table 4.12b: Total Variance Explained for Individualized Consideration

Extraction Sums of Squared Initial Eigenvalues Loadings % of Cumulative % of Component Total Variance % Total Variance Cumulative % 1 2.439 81.315 81.315 2.439 81.315 81.315 2 .350 11.669 92.984 3 .210 7.016 100.000 Extraction Method: Principal Component Analysis.

The variables of the extracted components are indicated on the component matrix table. Only one factor was extracted representing ‘individualized consideration on job

118 satisfaction’. The variables extracted and values were: ‘I am committed to the organization because my leader mentors me in the workplace’ had a component matrix value of .909; ‘I am hardly absent from work because my leader supports me in my work’ had a component matrix value of .923 and lastly, ‘I have no intentions of leaving my job because my leader delegates work to me’ had a component matrix value of .873. All the variables and component measure under the factor loading were greater than .60. Further, using the average of the components, the transformed data had a stronger component of .902 which was greater than .60. All the components of dependent variables were included as variable of analysis in the model as ‘individualized consideration on job satisfaction’ since the values were greater than .60. Table 4.12c shows the component matrix for individualized consideration.

Table 4.12c: Component Matrix of Individualized Consideration on Job Satisfaction

Component Individualized Consideration 1 I am committed to the organization because my leader mentors me in the .909 workplace I am hardly absent from work because my leader supports me in my work .923 I have no intentions of leaving my job because my leader delegates work to .873 me Extraction Method: Principal Component Analysis. a. 1 component extracted.

4.4.2 Descriptive Statistics for Individualized Consideration

On individualized consideration, majority of the respondents agreed on attribute ‘my leader delegates work to me’ (M= 4.2, SD = .774) followed by ‘my leader supports me in my work’ (M= 4.0, SD = .900). This clearly showed the difference, with the decrease in mean, the standard deviation increased which means there were varied responses. Table 4.13 shows the results of the descriptive statistics of individualized consideration. On job satisfaction, majority of the respondents agreed on the attribute, ‘I am committed to the organization because my leader mentors me in the workplace’ (M= 3.29, SD = 1.19). This was followed by ‘I am hardly absent from work because my leader supports me in my work’ (M= 3.27, SD = 1.14). The trend of the mean and standard deviation was on the opposite direction indicating varied responses as indicated in Table 4.13.

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Table 4.13: Mean and Standard Deviation of Individualized Consideration

Individualized Consideration M SD Skewness Std Error My leader mentors me in the workplace 3.7637 1.07873 -.824 .131 My leader supports me in my work 4.0202 .90096 -1.137 .131 My leader delegates work to me 4.1902 .77431 -1.282 .131 Influence of Individualized Consideration on Job Satisfaction I am committed to the organization because my 3.2911 1.18712 -.287 .131 leader mentors me in the workplace I am hardly absent from work because my leader 3.2795 1.14786 -.344 .131 supports me in my work I have no intentions of leaving my job because my 2.9308 1.20972 -.005 .131 leader delegates work to me

4.4.3 Chi-square Test on Individualized Consideration and Job Satisfaction

The Chi-square test was used to determine whether there was a significant association between individualized consideration and job satisfaction. The chi-square test results showed that there was a significant association between individualized consideration and job satisfaction X2 (132, N = 347) = 385.123, p<.05) as shown in Table 4.14.

Table 4.14: Chi-square Test on Individualized Consideration and Job Satisfaction

Individualized consideration Value df Asymp. Sig. (2-sided)

Pearson Chi-square 385.123a 132 .000 Likelihood Ratio 296.020 132 .000 Linear-by-Linear Association 122.398 1 .000 N of Valid Cases 347 a. 136 cells (87.2%) have expected count less than 5. The minimum expected count is .01. * Significant at p<0.05 level

4.4.4 Correlation Analysis between Individualized Consideration and Job Satisfaction

Correlation analysis was used to test the relationship between the individualized consideration variables and job satisfaction. As shown in Table 4.15a, all the variables

120 were highly correlated. The first variable under idealized influence ‘my leader mentors me in the workplace’ was positively correlated with job satisfaction r (347) =.872, p<.05; ‘my leader supports me in my work’ was positively correlated with job satisfaction r (347) =.876, p<.05; and ‘my leader delegates work to me’ was positively correlated with job satisfaction r (347) =.734, p<.05.

Table 4.15a: Correlation Analysis between Individualized Consideration Variables and Job Satisfaction

Individualized Consideration Pearson Correlation Job Satisfaction My leader mentors me in the Pearson Correlation .872** workplace Sig. (2-tailed) .000 N 347 My leader supports me in my work Pearson Correlation .876** Sig. (2-tailed) .000 N 347 My leader delegates work to me Pearson Correlation .734** Sig. (2-tailed) .000 N 347 * Significant at p<0.05 level

Further, correlation analysis was used to test the relationship between individualized consideration and job satisfaction. The results showed that there was strong and positive correlation between individualized consideration and job satisfaction r (347) =.595, p<.05. The results are shown in Table 4.15b.

Table 4.15b: Correlation Analysis between Individualized Consideration and Job Satisfaction

Job Satisfaction Individualized Pearson Correlation .595** Consideration Sig. (2-tailed) .000 N 347 * Significant at p<0.05 level

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4.4.5 One-Way ANOVA on Individualized Consideration

The One-way ANOVA test was performed to test the mean difference between individualized consideration and the demographic information of respondents; gender, age, education level, duration of working at the bank and the tier of the bank. Table 4.16a shows the results which indicate there was no significant difference between the mean values of all the respondents’ demographic information and individualized consideration.

Table 4.16a: One-way ANOVA on Individualized Consideration

Sum of Mean Squares df Square F Sig. Gender Between Groups 4.020 11 .365 1.484 .136 Within Groups 82.021 333 .246 Total 86.041 344

Age Between Groups 3.713 11 .338 .552 .867 Within Groups 203.122 332 .612 Total 206.834 343

Education Between Groups 6.285 11 .571 1.469 .142 Within Groups 129.558 333 .389 Total 135.843 344

How long Between Groups 17.074 11 1.552 1.285 .232 have you Within Groups 403.528 334 1.208 worked Total 420.601 345

Tier of your Between Groups 5.789 11 .526 .929 .512 bank Within Groups 189.704 335 .566 Total 195.493 346 * Significant at p<0.05 level

122 no significant difference between the mean values of the demographic variables of gender and tier of the bank with job satisfaction. However, the means for job satisfaction were significantly different across the age, education and number of years worked.

Table 4.16b: One-way ANOVA of Individualized Consideration on Job Satisfaction

Sum of df Mean F Sig. Squares Square Between Groups 2.796 12 .233 .929 .518 Gender Within Groups 83.245 332 .251 Total 86.041 344 Between Groups 13.382 12 1.115 1.908 .033 Age Within Groups 193.452 331 .584 Total 206.834 343 Between Groups 8.682 12 .723 1.889 .035 Education Within Groups 127.162 332 .383 Total 135.843 344 Between Groups 28.751 12 2.396 2.036 .021 How long have you Within Groups 391.850 333 1.177 worked Total 420.601 345 Between Groups 5.702 12 .475 .836 .613 Tier of Within Groups 189.791 334 .568 your bank Total 195.493 346 * Significant at p<0.05 level

4.4.6 Regression Analysis and Hypothesis Testing

This section presents the regression analysis, the model used for hypothesis testing in the study and the assumptions for the regression. The regression analysis was done to determine the relationship, magnitude of the influence and projection of the effect of individualized consideration on job satisfaction among employees in commercial banks in Kenya.

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4.4.6.1 Assumptions for Regression Analysis on Individualized Consideration

4.4.6.1.1 Normality Test on Individualized Consideration

Using one sample Kolmogorov-Smirnov Test, the test of normality was conducted to determine the distribution of data depicting either a normal or skewed curve. This was determined by the statistical significance of the dependent and the independent variable (p<.05). The normal parameters test indicated difference on mean: individualized consideration had (M= 3.99, SD = .766) compared to Job satisfaction (M= 3.18, SD = 1.06). The variance on the mean was low compared to the standard deviation variance which was high. Further, the output showed the variance on the most extreme differences was minimal and the variables were significant to each other (p<.05) indicating high level of relationship hence the data was not normally distributed (p<.05). Table 4.17a indicates the results of normality test.

Table 4.17a: One-Sample Kolmogorov-Smirnov Test on Individualized Consideration

Individualized Individualized concentration on Job concentration satisfaction N 347 347 Normal Parametersa,b Mean 3.9914 3.1671 Std. Deviation .76592 1.06481 Most Extreme Differences Absolute .173 .115 Positive .101 .083 Negative -.173 -.115 Test Statistic .173 .115 Asymp. Sig. (2-tailed) .000c .000c a. Test distribution is Normal. b. Calculated from data. c. Lilliefors Significance Correction. * Significant at p<0.05 level

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4.4.6.1.2 Linearity Test on Individualized Consideration

The analysis of variance (ANOVA) was used to determine linearity. The linearity test was conducted to determine whether the nature of the relationship between individualized consideration and job satisfaction was linear or not. As indicated in Table 4.17b, the output indicated a significant relationship between individualized concentration and job satisfaction on the combined and linearity tests (p<.05). However, the deviation from linearity was not significant. Hence individualized concentration and job satisfaction were linear and passed the test of linearity.

Table 4.17b: Linearity Test on Individualized Concentration

Sum of Mean Squares df Square F Sig. Job Between (Combined) 144.353 11 13.123 17.730 .000 Satisfaction * Groups Linearity 138.779 1 138.779 187.500 .000 Individualized Deviation concentration 5.575 10 .557 .753 .674 from Linearity Within Groups 247.952 335 .740 Total 392.305 346 * Significant at p<0.05 level

4.4.6.1.3 Multicollinearity Test on Individualized Consideration

Multicollinearity test was performed to determine if the values of individualized consideration and job satisfaction had high similarity. The test of multicollinearity was analyzed by the variance inflation factor (VIF); statistically, there was no multicollinearity when the value of VIF between 1 and 10. As indicated in Table 4.17c, the VIF value was 1.210 shows there was no multicollinearity between individualized consideration and job satisfaction.

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Table 4.17c: Multicollinearity Test on Individualized Consideration

Unstandardized Standardized Collinearity Coefficients Coefficients Statistics Std. Model B Error Beta T Sig. Tolerance VIF 1 (Constant) 2.627 .110 23.957 .000 Job Satisfaction .426 .032 .577 13.127 .000 1.210 1.210

4.4.6.1.4 Homoscedasticity Test on Individualized Consideration

Homoscedasticity test was carried out to determine if individualized consideration of the bank employees had similar variance to job satisfaction on the regression values. As indicated on Table 4.17d, the results indicate that the value of the Levene Statistic, F(10, 335) = 1.84, p = .053 was above the study’s level of significance (p ≤ .05) indicating the data was homogenous.

Table 4.17d: Homoscedasticity Test on Individualized Consideration

Levene Statistic df1 df2 Sig. 1.837 10 335 .053 * Significant at p<0.05 level

4.4.6.2 Regression and Hypothesis Testing on Individualized Consideration

Regression analysis was carried out to determine the extent to which individualized consideration influenced job satisfaction among employees in commercial banks in Kenya. Multiple linear regression was used to predict job satisfaction among employees in commercial banks in Kenya from individualized consideration. The hypothesis tested was:

H02: There is no significant influence of individualized consideration on job satisfaction among employees in commercial banks in Kenya

The regression results for the hypothesis testing were presented in form of the model summary, regression ANOVA and regression coefficient.

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4.4.6.2.1 Regression Model Summary

The model summary results presented in Table 4.18 indicate that individualized consideration explained 35% of job satisfaction among employees in commercial banks in Kenya (R2) = .354.

Table 4.18: Model Summary of Individualized Consideration on Job Satisfaction

Std. Change Statistics Error of R Adjusted the R Square F Sig. F Model R Square R Square Estimate Change Change df1 df2 Change 1 .595a .354 .352 .85724 .354 188.851 1 345 .000 a. Predictors: (Constant), Individualized Consideration * Significant at p<0.05 level

4.4.6.2.2 Regression ANOVA

The regression ANOVA showed that idealized influence had a significant influence on job satisfaction F(1, 97.750) = 112.421, p<.05) as indicated Table 4.19.

The regression ANOVA revealed that individualized consideration had a significant influence on job satisfaction F(1, 138.779) = 188.851, p<.05) as indicated in Table 4.19. This means that the regression model constructed was suitable for predicting the outcome variable on how individualized consideration influenced job satisfaction among employees in commercial banks in Kenya.

Table 4.19: Regression ANOVA of Individualized Consideration on Job Satisfaction

Model Sum of Squares df Mean Square F Sig. 1 Regression 138.779 1 138.779 188.851 .000b Residual 253.527 345 .735 Total 392.305 346 a. Dependent Variable: Job Satisfaction b. Predictors: (Constant), Individualized Consideration * Significant at p<0.05 level

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4.4.6.2.3 Regression Coefficient of Individualized Consideration

Table 4.20 shows the output of the regression coefficient. In the regression coefficient model, the analysis showed that individualized consideration statistically predicted job satisfaction (β = .827, (-.545) t = 13.742, p<.05). The beta weight gauges the importance of the explanatory variable across the model and was positive on the individualized consideration, Beta of .827 and statistically significant at p<.05. This means a unit increase in individualized consideration increases the unit of job satisfaction by .827.

Table 4.20: Coefficients of Individualized Consideration on Job Satisfaction

Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta T Sig. 1 (Constant) -.133 .245 -.545 .586 Individualized .827 .060 .595 13.742 .000 Concentration a. Dependent Variable: Job Satisfaction * Significant at p<0.05 level

From the coefficient table, the values of the regression model were derived:

The general form of the regression model used was:

= Constant; = individualized consideration and = Error term.

From the coefficient table, individualized consideration influences job satisfaction among employees in commercial banks in Kenya.

Y= -.133 + .827X + .060

The multiple linear regression analysis was used to test if individualized consideration significantly predicted job satisfaction among employees in commercial banks in Kenya. The results revealed individualized consideration explained 35% of job satisfaction (R2 = .354, F(1, 138.779) = 188.851, p<.05) while the remaining 65% of job satisfaction was explained by other factors. Further, individualized consideration significantly predicted job satisfaction (β = .827, (-.545) t = 13.742, p<.05). Therefore, the study rejected the null

128 hypothesis H02: There is no significant influence of individualized consideration on job satisfaction among employees in commercial banks in Kenya and accepted the alternate hypothesis, H12: There is a significant influence of individualized consideration on job satisfaction among employees in commercial banks in Kenya.

4.5 Influence of Inspirational Motivation on Job Satisfaction

The third objective in this research was to determine the influence of inspirational motivation on job satisfaction. This was guided by the independent variable questions on inspirational motivation and dependent variable questions on job satisfaction. The independent variable questions were: my leader encourages two-way communication; my leader promotes teamwork among employees; and my leader’s behavior motivates me at work. The dependent variable questions on job satisfaction were: I am committed to the organization because my leader encourages two-way communication, I am hardly absent from work because my leader promotes teamwork among employees and lastly, I have no intentions of leaving my job because my leader’s behavior motivates me at work, the findings are presented as shown below.

4.5.1 Factor Analysis

Factor analysis was used to evaluate the variability among the observed correlated variables to ensure the questions in the research instrument relate to the construct of measure. Questions that did not relate to construct were extracted from the analysis. Factor analysis was conducted on three questions for dependent variable ‘job satisfaction’ and three questions for independent variable ‘inspirational motivation’ presented separately as follow.

4.5.1.1 Factor Analysis on Inspiration Motivation

The independent variable in this study was inspirational motivation. As indicated in Table 4.21a, only one factor was derived with Kaiser-Meyer Olkin result of .747. The Bartlett’s test of sphericity was significant at X2 (3, N=347) = 608.536, p<.05. The factor was adequate for extraction of component since Kaiser-Meyer-Olkin Measure was greater than .60 and the Bartlett’s test was significant (p<.05).

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Table 4.21a: KMO and Bartlett's Test on Inspirational Motivation

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .747

Bartlett's Test of Sphericity Approx. Chi-square 608.536

df 3

Sig. .000

* Significant at p<0.05 level

Using the Principal component analysis, the total variance explained on the extraction showed the extracted values presented 83% of the first component. Only one component was extracted ‘inspiration motivation’. Further, average value principle was used to obtain the measure of the extracted independent variable by transformation. Table 4.21b shows the results of the variance explained.

Table 4.21b: Total Variance Explained for Inspirational Motivation

Extraction Sums of Squared Initial Eigenvalues Loadings

Componen % of Cumulative % of Cumulative t Total Variance % Total Variance %

1 2.477 82.559 82.559 2.477 82.559 82.559

2 .293 9.753 92.312

3 .231 7.688 100.000

Extraction Method: Principal Component Analysis.

One component for inspirational motivation had an Eigen value that was greater than one which was in line with the results for total variance explained for inspirational motivation as shown in Figure 4.8.

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Figure 4.8: Scree Plot for Inspirational Motivation

The variables of the extracted components are indicated on the component matrix Table 4.21c. Only one factor was extracted representing ‘inspirational motivation’. The variables extracted and values were: my leader encourages two-way communication (.897), my leader promotes teamwork among employees (.919), and my leader’s behavior motivates me at work (.909). All the variables and component measure under the factor loading were greater than .60. Further, using the average of the components, the transformed data had a stronger component of .908 which was greater than .60. All the components were included as variables of analysis in the model ‘inspirational motivation’ since the values were greater than .60.

Table 4.21c: Component Matrix on Inspirational Motivation

Component Inspirational Motivation 1 My leader encourages two-way communication .897 My leader promotes teamwork among employees .919 My leader’s behavior motivates me at work .909 Extraction Method: Principal Component Analysis. a. 1 component extracted.

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4.5.1.2 Factor Analysis on Inspirational Motivation and Job Satisfaction

The dependent variable in the study was influence of inspirational motivation on job satisfaction. As indicated in Table 4.22a, only one factor was derived with Kaiser-Meyer Olkin result of .75. The Bartlett’s test of sphericity was significant at X2 (3, N=347) = 685.906, p<.05. The factor was adequate for extraction of the component since Kaiser- Meyer-Olkin Measure was greater than .60 and the Bartlett’s test was significant (p<.05).

Table 4.22a: KMO and Bartlett's Test Inspirational Motivation and Job Satisfaction

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .750

Bartlett's Test of Sphericity Approx. Chi-square 685.906

df 3

Sig. .000

* Significant at p<0.05 level

Using the Principal component analysis, the total variance explained on the extraction showed the extracted values presented 84% of the component. Only one component was extracted ‘inspirational motivation on job satisfaction’. Further, the average value principle was used to obtain the measure of the extracted independent variable named ‘inspirational motivation on job satisfaction’ by transformation. Table 4.22b shows the results of the variance explained.

Table 4.22b: Total Variance Explained for Inspirational motivation on Job Satisfaction

Extraction Sums of Squared Initial Eigenvalues Loadings % of % of Cumulative Component Total Variance Cumulative % Total Variance % 1 2.529 84.313 84.313 2.529 84.313 84.313 2 .274 9.128 93.441 3 .197 6.559 100.000 Extraction Method: Principal Component Analysis.

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The variables of the extracted components were indicated on the component matrix table. Only one factor was extracted representing ‘inspirational motivation on job satisfaction’. The variables extracted and values were; I am committed to the organization because my leader encourages two-way communication had a component matrix value of .918; I am hardly absent from work because my leader promotes teamwork among employees had a component matrix value of. 932 and lastly; I have no intentions of leaving my job because my leader’s behavior motivates me at work had a component matrix value of .905. All the variables and component measure under the factor loading were greater than .60. Further, using the average of the components, the transformed data has a stronger component of .918 which is greater than .60. This means all the components of the independent variable were included as variables of analysis in the model as ‘inspirational motivation on job satisfaction’ since the values were greater than .60. Table 4.22c shows the component matrix of inspirational motivation on job satisfaction.

Table 4.22c: Component Matrix on Inspirational Motivation and Job Satisfaction

Component Inspirational Motivation on Job Satisfaction 1 I am committed to the organization because my leader encourages .918 two-way communication I am hardly absent from work because my leader promotes .932 teamwork among employees I have no intentions of leaving my job because my leader’s behavior .905 motivates me at work Extraction Method: Principal Component Analysis. a. 1 components extracted.

4.5.2 Descriptive Statistics for Inspirational Motivation

On inspirational motivation, majority of the respondents agreed on the attribute ‘my leader promotes teamwork among employees’ (M= 4.12, SD = .92), followed by ‘my leader encourages two-way communication’ (M= 4.08, SD = .92). This clearly shows the difference; with decrease in mean, the standard deviation increased indicating varied responses. Table 4.23 indicates the results of the descriptive statistics of inspirational motivation. On job satisfaction, majority of the respondents agreed that ‘I am committed to the organization because my leader encourages two-way communication’ (M= 3.41, SD = 1.10). This was followed by ‘I am hardly absent from work because my leader

133 promotes teamwork among employees’ (M= 3.23, SD = 1.09. The trend of the mean and standard deviation varied depicting varied responses as indicated in Table 4.23.

Table 4.23: Mean and Standard Deviation of Inspirational Motivation

Inspirational Motivation M SD Skewness Std Err My leader encourages two-way communication 4.0807 .91825 -1.219 .131 My leader promotes teamwork among 4.1210 .92323 -1.262 .131 employees My leader’s behavior motivates me at work 3.8542 1.05230 -.917 .132 Inspirational Motivations on Job

Satisfaction I am committed to the organization because my 3.4150 1.10992 -.467 .131 leader encourages two-way communication I am always present at work because my leader 3.2305 1.09063 -.240 .131 promotes teamwork among employees I have no intentions of leaving my job because 3.0490 1.16570 -.063 .131 my leader’s behavior motivates me at work

4.5.3 Chi-square Test: Inspirational Motivation and Job Satisfaction

The Chi-square test was used to determine whether there was a significant association between inspirational motivation and job satisfaction. The chi-square test results showed that there was as significant association between inspirational motivation and job satisfaction X2 (156, N = 347) = 445.180, p<.05). The results are presented in Table 4.24.

Table 4.24: Chi-square Test on Inspirational Motivation and Job Satisfaction

Inspirational Motivation Value df Asymp. Sig. (2-sided)

Pearson Chi-square 445.180a 156 .000 Likelihood Ratio 312.954 156 .000 Linear-by-Linear Association 119.052 1 .000 N of Valid Cases 347 a. 163 cells (89.6%) have expected count less than 5. The minimum expected count is .02.

* Significant at p<0.05 level

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4.5.4 Correlation Analysis between Inspirational Motivation and Job Satisfaction

Correlation analysis was used to test the relationship between the inspirational motivation variables and job satisfaction. As shown in Table 4.25a, all the variables were highly correlated. The first variable under inspirational motivation ‘my leader encourages two- way communication’ was positively correlated with job satisfaction r (347) =.893, p<.05; ‘my leader promotes teamwork among employees’ was positively correlated with job satisfaction r (347) =.915, p<.05; and ‘my leader’s behavior motivates me at work’ was positively correlated with job satisfaction r (347) =.917, p<.05.

Table 4.25a: Correlation Analysis between Inspirational Motivation Variables and Job Satisfaction

Inspirational Motivation Pearson Correlation Job Satisfaction My leader encourages two-way Pearson Correlation .893** communication Sig. (2-tailed) .000 N 347 My leader promotes teamwork among Pearson Correlation .915** employees Sig. (2-tailed) .000 N 347 My leader’s behavior motivates me at Pearson Correlation .917** work Sig. (2-tailed) .000 N 347 * Significant at p<0.05 level

Further, correlation analysis was used to test the relationship between inspirational motivation and job satisfaction. The results show that there was a strong and positive correlation between inspirational motivation and job satisfaction r (347) =.587, p<.05. The results are as shown in Table 4.25b.

Table 4.25b: Correlation Analysis between Inspirational Motivation and Job Satisfaction

Job Satisfaction Inspirational Motivation Pearson Correlation .587** Sig. (2-tailed) .000 N 347 * Significant at p<0.05 level

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4.5.5 One-Way ANOVA on Inspirational Motivation

The One-way ANOVA test was performed to test the mean difference between inspirational motivation and the demographic information of respondents; gender, age, education level, duration of working at the bank and the tier of the bank. Table 4.26a shows the results which indicate that there was no significant difference between the mean values of all the respondents’ demographic information and inspirational motivation.

Table 4.26a: One-way ANOVA on Inspirational Motivation

Sum of Squares df Mean Square F Sig. Gender Between Groups 4.328 13 .333 1.349 .183 Within Groups 81.712 331 .247 Total 86.041 344

Age Between Groups 4.501 13 .346 .565 .881 Within Groups 202.333 330 .613 Total 206.834 343

Education Between Groups 4.072 13 .313 .787 .674 Within Groups 131.771 331 .398 Total 135.843 344

How long Between Groups 15.776 13 1.214 .995 .455 have you Within Groups 404.825 332 1.219 worked Total 420.601 345

Tier of your Between Groups 11.005 13 .847 1.528 .105 bank Within Groups 184.488 333 .554 Total 195.493 346 * Significant at p<0.05 level

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The One-way ANOVA test was also performed to test the mean differences between job satisfaction and the demographic factors of gender, age, education, duration of working at the bank and tier of the bank. Table 4.26b shows the results which indicate that there was no significant difference between the mean values of the demographic variables on inspirational motivation and job satisfaction.

Table 4.26b: One-way ANOVA of Inspirational Motivation on Job Satisfaction

Sum of df Mean F Sig. Squares Square Between 2.985 12 .249 .994 .454 Groups Gender Within 83.055 332 .250 Groups Total 86.041 344 Between 7.091 12 .591 .979 .468 Groups Age Within 199.743 331 .603 Groups Total 206.834 343 Between 6.501 12 .542 1.391 .168 Groups Education Within 129.343 332 .390 Groups Total 135.843 344 Between 18.449 12 1.537 1.273 .233 Groups How long have you Within worked 402.152 333 1.208 Groups Total 420.601 345 Between 4.348 12 .362 .633 .814 Groups Tier of your bank Within 191.145 334 .572 Groups Total 195.493 346 * Significant at p<0.05 level

4.5.6 Regression Analysis and Hypothesis Testing

This section presents the regression analysis, the model used for hypothesis testing in the study and the assumptions for the regression. The regression analysis was done to determine the relationship, magnitude of the effect and projection of the effect of inspirational motivation on job satisfaction among employees in commercial banks in Kenya.

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4.5.6.1 Assumptions for Regression Analysis on Inspirational Motivation

4.5.6.1.1 Normality Test on Inspirational Motivation

Using one sample Kolmogorov-Smirnov Test, the test of normality was conducted to determine the distribution of data depicting either normal or skewed curve. This was determined by statistical significance of the dependent and the independent variable (p<.05). The normal parameters test indicated a difference on mean: inspirational motivation had (M= 4.02, SD = .876) compared to Job satisfaction (M= 3.23, SD = 1.03). The variance on the mean was low compared to the standard deviation variance which was high. Further, the output showed the variance on the most extreme differences was minimal and the variables were significant to each other (p<.05) indicating high level of relationship hence the data was not normally distributed (p<.05). Table 4.27a shows the output of the normality test.

Table 4.27a: One-Sample Kolmogorov-Smirnov Test on Inspirational Motivation

Inspirational Inspirational motivation on motivation Job Satisfaction N 347 347 Normal Parametersa,b Mean 4.0216 3.2315 Std. Deviation .87640 1.03000 Most Extreme Differences Absolute .185 .126 Positive .132 .075 Negative -.185 -.126 Test Statistic .185 .126 Asymp. Sig. (2-tailed) .000c .000c a. Test distribution is Normal. b. Calculated from data. c. Lilliefors Significance Correction. * Significant at p<0.05 level

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4.5.6.1.2 Linearity Test on Inspirational Motivation

The analysis of variance (ANOVA) was used to determine linearity. The linearity test was conducted to determine the nature of the relationship between inspirational motivation and job satisfaction whether linear or not. As indicated in Table 4.27b, there was a significant relationship between inspirational motivation and job satisfaction on the combined and linearity tests (p<.05). However, the deviation from linearity was not significant. Hence inspirational motivation and job satisfaction were linear and passed the test of linearity.

Table 4.27b: Linearity Test on Inspirational Motivation

Sum of Mean Squares Df Square F Sig. Inspirational Between (Combined) 134.195 13 10.323 14.761 .000 motivation on Groups Linearity 126.302 1 126.302 180.606 .000 Job Satisfaction Deviation * Inspirational 7.893 12 .658 .941 .506 motivation from Linearity Within Groups 232.874 333 .699 Total 367.069 346

* Significant at p<0.05 level

4.5.6.1.3 Multicollinearity Test on Inspirational Motivation

Multicollinearity test was performed to determine if the values of inspirational motivation and job satisfaction had higher similarity. The test of multicollinearity was tested using the variance inflation factor (VIF); statistically, there was no multicollinearity when the value of VIF between 1 and 10. As indicated in Table 4.27c, the VIF value was 2.260 which showed that there was no multicollinearity between inspirational motivation and job satisfaction.

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Table 4.27c: Multicollinearity Test on Inspirational Motivation

Unstandardized Standardized Collinearity Coefficients Coefficients Statistics Std. Model B Error Beta t Sig. Tolerance VIF 1 (Constant) .459 .211 2.177 .030 Inspirational .689 .051 .587 13.453 .000 2.260 2.260 motivation

4.5.6.1.4 Homoscedasticity Test on Inspirational Motivation

Homoscedasticity test was carried out to determine if inspirational motivation of the bank employees had similar variance to job satisfaction on the regression values. As indicated on Table 4.27d, results indicate that the value of the Levene Statistic, F(12, 333) = 3.25, p = .00 was above the study’s level of significance (p ≤ .05) indicating the data was not homogenous.

Table 4.27d: Homoscedasticity Test on Inspirational Motivation

Levene Statistic df1 df2 Sig. 3.248 12 333 .000 * Significant at p<0.05 level

4.5.6.2 Regression and Hypothesis Testing on Inspirational Motivation

Regression analysis was carried out to determine the extent to which inspirational motivation influenced job satisfaction among employees in commercial banks in Kenya. Multiple linear regression was used to predict job satisfaction among employees in commercial banks in Kenya from inspirational motivation. The hypothesis tested was:

H03: There is no significant influence of inspirational motivation on job satisfaction among employees in commercial banks in Kenya

The regression results for the hypothesis testing were presented in the form of the model summary, regression ANOVA and regression coefficient.

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4.5.6.2.1 Regression Model Summary

The model summary results presented in Table 4.28 indicate that inspirational motivation explained 34% of job satisfaction of employees in commercial banks in Kenya (R2) = .344.

Table 4.28: Model Summary on Inspirational Motivation and Job Satisfaction

Change Statistics Std. Adjusted Error of R R R the Square F Sig. F Model R Square Square Estimate Change Change df1 df2 Change 1 .587a .344 .342 .83539 .344 180.980 1 345 .000 a. Predictors: (Constant), Inspirational motivation * Significant at p<0.05 level

4.5.6.2.2 Regression ANOVA

The regression ANOVA showed that inspirational motivation had a significant influence on job satisfaction F(1, 126.302) = 180.980, p<.05) as indicated in Table 4.29. This showed the regression model constructed was suitable in predicting the outcome variable on how inspirational motivation influenced job satisfaction among employees in commercial banks in Kenya.

Table 4.29: ANOVA for Inspirational Motivation and Job Satisfaction

Sum of Model Squares df Mean Square F Sig. 1 Regression 126.302 1 126.302 180.980 .000b Residual 240.767 345 .698 Total 367.069 346 a. Dependent Variable: Inspirational motivation on Job Satisfaction b. Predictors: (Constant), Inspirational motivation * Significant at p<0.05 level

4.5.6.2.3 Regression Coefficient of Inspirational Motivation

Table 4.30 shows the results of the regression coefficient. In the regression coefficient model, the analysis showed inspirational motivation statistically predicted job satisfaction 141

(β = .689, (2.117) t = 13.453, p<.05). The beta weight gauges the importance of explanatory variable across the model and was positive on the inspirational motivation, Beta of .689 and statistically significant at p<.05. This means, one unit of increase in inspirational motivation increased the unit of satisfaction by .689.

Table 4.30: Coefficients of Inspirational Motivation on Job Satisfaction

Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) .459 .211 2.177 .030 Inspirational .689 .051 .587 13.453 .000 motivation a. Dependent Variable: Inspirational motivation on Job Satisfaction * Significant at p<0.05 level

From the coefficient table, the values of the regression model were derived:

The general form of the regression model used was:

= Constant; = Inspirational Motivation and = Error term.

From the coefficient table, inspirational motivation influenced job satisfaction among employees in commercial banks in Kenya.

Y= 0.459 + .689X +.051

Multiple linear regression analysis was used to test if inspirational motivation significantly predicted job satisfaction among employees in commercial banks in Kenya. The results revealed that inspirational motivation explained 34% of the job satisfaction (R2 = .344, F(1, 126.302) = 180.980, p<.05) while the remaining 66% of job satisfaction was explained by other factors. Further, inspirational motivation significantly predicted job satisfaction (β = .689, (2.117) t = 13.453, p<.05). Therefore, the study rejected the null hypothesis H03: There is no significant influence of inspirational motivation on job satisfaction among employees in commercial banks in Kenya and accepted the alternate hypothesis, H12: There is a significant influence of inspirational motivation on job satisfaction among employees in commercial banks in Kenya.

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4.6 Influence of Intellectual Stimulation on Job Satisfaction

The fourth objective in this study was to determine the influence of intellectual stimulation on job satisfaction. This was guided by the independent variable questions on intellectual stimulation and dependent variable questions on job satisfaction. The independent variable questions were: my leader encourages knowledge sharing among employees; my leader permits me to be creative in my job and; my leader allows me to take risks in my job. The dependent variable questions were: I am committed to the organization because my leader encourages knowledge sharing among employees; I am hardly absent from work because my leader permits me to be creative in my job; and I have no intentions of leaving my job because my leader allows me to take risks in my job. The findings are presented as follows.

4.6.1 Factor Analysis

Factor analysis was used to evaluate the variability among the observed correlated variables to ensure the questions in the research instrument relate to the construct of measure. Questions that did not relate to construct were extracted from the analysis. Factor analysis was conducted on three questions for dependent variable ‘intellectual stimulation’ and three questions on independent variable ‘intellectual stimulation’ presented separately as shown below.

4.6.1.1 Factor Analysis on Intellectual Stimulation

The independent variable in study was intellectual stimulation. As indicated in Table 4.31a, only one factor was derived with Kaiser-Meyer Olkin result of .712. The Bartlett’s test of sphericity was significant at X2 (3, N=347) = 460.533, p<.05. The factor was adequate for extraction of component since Kaiser-Meyer-Olkin Measure was greater than .60 and the Bartlett’s test was significant (p<.05).

Table 4.31a: KMO and Bartlett's Test on Intellectual Stimulation

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .712 Bartlett's Test of Sphericity Approx. Chi-square 460.533 df 3 Sig. .000 * Significant at p<0.05 level

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Using the Principal component analysis, the total variance explained on the extraction showed that the extracted values presented 77% of the first component. Only one component was extracted ‘intellectual stimulation’. Further, the average value principle was used to obtain the measure of the extracted independent variable by transformation. Table 4.31b shows the results of the variance explained.

Table 4.31b: Total Variance Explained for Intellectual Stimulation

Extraction Sums of Squared Initial Eigen values Loadings % of Cumulative % of Cumulative Component Total Variance % Total Variance % 1 2.310 77.010 77.010 2.310 77.010 77.010 2 .418 13.938 90.948 3 .272 9.052 100.000 Extraction Method: Principal Component Analysis

The variables of the extracted components are indicated on the component matrix table. Only one factor was extracted representing ‘intellectual stimulation’. The variables and values extracted were: ‘my leader encourages knowledge sharing among employees’ (.859), ‘my leader permits me to be creative in my job’ (.908); and ‘my leader allows me to take risks in my job’ (.864). All variables and component measure under the factor loading were greater than .60. Further, using the average of the components, the transformed data had a stronger component of .877 which is greater than .60. All the components were included as variables of analysis in the model ‘intellectual stimulation’ since the values were greater than .60. Table 4.31c shows the component matrix for intellectual stimulation.

Table 4.31c: Component Matrix on Intellectual Stimulation

Component Intellectual stimulation 1 My leader encourages knowledge sharing among employees .859 My leader permits me to be creative in my job .908 My leader allows me to take risks in my job .864 Extraction Method: Principal Component Analysis. a. 1 component extracted.

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4.6.1.2 Factor Analysis on Intellectual Stimulation on Job Satisfaction

The dependent variable in the study was intellectual stimulation on job satisfaction. As indicated in Table 4.32a, only one factor was derived with Kaiser-Meyer Olkin result of .733. The Bartlett’s test of sphericity was significant at X2 (3, N=347) = 600.174, p<.05. This indicated the factor was adequate for extraction of component since Kaiser-Meyer- Olkin Measure was greater than .60 and the Bartlett’s test was significant (p<.05).

Table 4.32a: KMO and Bartlett's Test on Intellectual Stimulation on Job Satisfaction

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .733 Bartlett's Test of Sphericity Approx. Chi-square 600.174 df 3 Sig. .000 * Significant at p<0.05 level

Using the Principal component analysis, the total variance explained on the extraction showed the extracted values presented 82% of the component. Only one component was extracted ‘intellectual stimulation on job satisfaction’. Further, the average value principle was used to obtain the measure of the extracted independent variable named ‘intellectual stimulation on job satisfaction’ by transformation. Table 4.32b shows the results of the variance explained.

Table 4.32b: Total Variance Explained for Intellectual Stimulation

Extraction Sums of Squared Initial Eigenvalues Loadings % of Cumulative % of Cumulative Component Total Variance % Total Variance % 1 2.454 81.786 81.786 2.454 81.786 81.786 2 .331 11.046 92.832 3 .215 7.168 100.000 Extraction Method: Principal Component Analysis

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One component for intellectual stimulation had an Eigen value that was greater than one which was in line with the results for total variance explained for intellectual stimulation as shown in Figure 4.9.

Figure 4.9: Scree Plot for Intellectual Stimulation

The variables of the extracted components are indicated on the component matrix table. Only one factor was extracted representing ‘intellectual stimulation on job satisfaction’. The variables and values were: ‘I am committed to the organization because my leader encourages knowledge sharing among employees’ had a component matrix value of .897; ‘I am hardly absent from work because my leader permits me to be creative in my job’ had a component matrix value of. 927 and lastly; ‘I have no intentions of leaving my job because my leader allows me to take risks in my job’ had a component matrix value of .889.

All the variables and component measure under the factor loading were greater than .60. Further, using the average of the components, the transformed data had a stronger component of .904 which was greater than .60. All the components were included as variables of analysis in the model as ‘intellectual stimulation’. Table 4.32c shows the component matrix for intellectual stimulation on job satisfaction.

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Table 4.32c: Component Matrix on Intellectual Stimulation and Job Satisfaction

Component Intellectual stimulation on job satisfaction 1 I am committed to the organization because my leader encourages .897 knowledge sharing among employees I am hardly absent from work because my leader permits me to be .927 creative in my job I have no intentions of leaving my job because my leader allows .889 me to take risks in my job Extraction Method: Principal Component Analysis. a. 1 component extracted.

4.6.2 Descriptive Statistics on Intellectual Stimulation

On intellectual stimulation, majority of the respondents agreed on the attribute ‘my leader encourages knowledge sharing among employees’ (M= 4.13, SD = .86) followed by ‘my leader permits me to be creative in my job’ (M= 4.0, SD = .94). This clearly showed the difference; with decrease in mean, the standard deviation increased indicating varied responses. Table 4.33 shows the results of the descriptive statistics of intellectual stimulation.

On job satisfaction, majority of the respondents agreed on the attribute ‘I am committed to the organization because my leader encourages knowledge sharing among employees’ (M= 3.48, SD = 1.11). This was followed by ‘I am hardly absent from work because my leader permits me to be creative in my job’ (M= 3.31, SD = 1.12). The trend of the mean and standard deviation indicates variance in responses as presented in Table 4.33.

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Table 4.33: Mean and Standard Deviation of Intellectual Stimulations

Intellectual Stimulation M SD Skewness Std. Err My leader encourages knowledge sharing 4.1326 .86000 -1.273 .131 among employees My leader permits me to be creative in my 4.0086 .93884 -1.050 .131 job My leader allows me to take risks in my 3.5850 1.04555 -.433 .131 job Influence of Intellectual Stimulation on Job Satisfaction I am committed to the organization because my leader encourages knowledge 3.4784 1.11297 -.445 .131 sharing among employees I am always present at work because my 3.3112 1.12305 -.267 .131 leader permits me to be creative in my job I have no intentions of leaving my job because my leader allows me to take risks 2.9193 1.16517 .070 .131 in my job

4.6.3 Chi-square Test on Intellectual Stimulation and Job Satisfaction

The Chi-square test was used to determine whether there was a significant association between intellectual stimulation and job satisfaction. The chi-square test showed that there was a significant association between intellectual stimulation and job satisfaction X2 (144, N = 347) = 426.404, p<.05). The results are presented in Table 4.34.

Table 4.34: Chi-square Test on Intellectual Stimulation and Job Satisfaction

Intellectual Stimulation Value df Asymp. Sig. (2-sided)

Pearson Chi-square 426.404a 144 .000 Likelihood Ratio 314.315 144 .000

Linear-by-Linear Association 101.155 1 .000

N of Valid Cases 347 a. 147 cells (87.0%) have expected count less than 5. The minimum expected count is .03. * Significant at p<0.05 level

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4.6.4 Correlation Analysis between Intellectual Stimulation and Job Satisfaction

Correlation analysis was used to test the relationship between the intellectual stimulation variables and job satisfaction. As shown in Table 4.35a, all the variables were highly correlated. The first variable under intellectual stimulation ‘my leader encourages knowledge sharing among employees’ was positively correlated with job satisfaction r (347) =.638, p<.05; ‘my leader permits me to be creative in my job’ was positively correlated with job satisfaction r (347) =.547, p<.05; and ‘my leader allows me to take risks in my job’ was positively correlated with job satisfaction r (347) =.550, p<.05.

Table 4.35a: Correlation Analysis between Intellectual Stimulation Variables and Job Satisfaction

Intellectual Stimulation Pearson Correlation Job Satisfaction My leader encourages knowledge Pearson Correlation .638** sharing among employees Sig. (2-tailed) .000 N 347 My leader permits me to be creative in Pearson Correlation .547** my job Sig. (2-tailed) .000 N 347 My leader allows me to take risks in my Pearson Correlation .550** job Sig. (2-tailed) .000 N 347 * Significant at p<0.05 level

Further, correlation analysis was used to test the relationship between intellectual stimulation and job satisfaction. The results show there was strong and positive correlation between intellectual stimulation and job satisfaction r (347) =.541, p<.05. The results are outlined in Table 4.35b.

Table 4.35b: Correlation Analysis between Intellectual Stimulation and Job Satisfaction

Job Satisfaction Intellectual Stimulation Pearson Correlation .541** Sig. (2-tailed) .000 N 347 * Significant at p<0.05 level 149

4.6.5 One-way ANOVA on Intellectual Stimulation

The One-way ANOVA test was performed to test the mean differences between intellectual stimulation and the demographic information of respondents; gender, age, education level, duration of working at the bank and lastly the tier of the bank. Table 4.36a shows the results which indicate there was no significant difference between the mean values of all the respondents’ demographic information and intellectual stimulation.

Table 4.36a: One-way ANOVA on Intellectual stimulation

Sum of Mean Squares Df Square F Sig. Gender Between Groups 3.659 12 .305 1.229 .261 Within Groups 82.381 332 .248 Total 86.041 344

Age Between Groups 3.507 12 .292 .476 .928 Within Groups 203.327 331 .614 Total 206.834 343

Education Between Groups 6.618 12 .551 1.417 .156 Within Groups 129.226 332 .389 Total 135.843 344

How long Between Groups 15.730 12 1.311 1.078 .378 have you Within Groups 404.871 333 1.216 worked Total 420.601 345

Tier of Between Groups 7.987 12 .666 1.186 .292 your bank Within Groups 187.506 334 .561 Total 195.493 346 * Significant at p<0.05 level

150 no significant difference between the mean values of the demographic variables on intellectual stimulation and job satisfaction.

Table 4.36b: One-way ANOVA on Intellectual stimulation on Job Satisfaction

Sum of df Mean F Sig. Squares Square Between 2.909 12 .242 .968 .479 Groups Gender Within 83.132 332 .250 Groups Total 86.041 344 Between 7.890 12 .658 1.094 .364 Groups Age Within 198.944 331 .601 Groups Total 206.834 343 Between 5.453 12 .454 1.157 .313 Groups Education Within 130.390 332 .393 Groups Total 135.843 344 Between 21.948 12 1.829 1.528 .112 Groups How long have you Within worked 398.653 333 1.197 Groups Total 420.601 345 Between 5.570 12 .464 .816 .634 Groups Tier of your bank Within 189.923 334 .569 Groups Total 195.493 346 * Significant at p<0.05 level

4.6.6 Regression Analysis and Hypothesis Testing

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intellectual stimulation on job satisfaction among employees in commercial banks in Kenya.

4.6.6.1 Assumptions for Regression Analysis on Intellectual Stimulation

4.6.6.1.1 Normality Test on Intellectual Stimulation

Using one sample Kolmogorov-Smirnov Test, the test of normality was conducted to determine the distribution of data depicting either normal or skewed curve. This was determined by statistical significance of the dependent and the independent variable (p<.05). The normal parameters test indicated a difference on mean: intellectual stimulation had (M= 3.91, SD = 0.832) compared to Job satisfaction (M= 3.24, SD = 1.02). The variance on the mean was low compared to the standard deviation variance which was high. Further, the output showed the variance on the most extreme differences was minimal and the variables were significant to each other (p<.05) indicating a high level of relationship hence the data was not normally distributed (p<.05). Table 4.37a indicates the results of the normality test.

Table 4.37a: One-Sample Kolmogorov-Smirnov Test on Intellectual Stimulation

Intellectual Stimulation Job Satisfaction N 347 347 Normal Parametersa,b Mean 3.9087 3.2363 Std. Deviation .83227 1.02499 Most Extreme Differences Absolute .146 .112 Positive .105 .085 Negative -.146 -.112 Test Statistic .146 .112 Asymp. Sig. (2-tailed) .000c .000c a. Test distribution is Normal. b. Calculated from data. c. Lilliefors Significance Correction. * Significant at p<0.05 level

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4.6.6.1.2 Linearity Test on Intellectual Stimulation The analysis of variance (ANOVA) was used to determine linearity. The linearity test was conducted to determine whether the nature of the relationship between intellectual stimulation and job satisfaction was linear or not. As indicated in table 4.37b, there was significant relationship between intellectual stimulation and job satisfaction on the combined and linearity tests (p<.05). However, the deviation from linearity was not significant. Hence intellectual stimulation and job satisfaction were linear and passed the test of linearity.

Table 4.37b: Linearity Test on Intellectual Stimulation

Sum of Mean Squares df Square F Sig. Job Between (Combined) 114.587 12 9.549 12.813 .000 Satisfaction Groups Linearity 106.274 1 106.274 142.596 .000 * Intellectual Deviation Stimulation 8.313 11 .756 1.014 .433 from Linearity Within Groups 248.924 334 .745 Total 363.511 346 * Significant at p<0.05 level

4.6.6.1.3 Multicollinearity Test on Intellectual Stimulation

Multicollinearity test was performed to determine if the values of intellectual stimulation and job satisfaction had higher similarity. The test of multicollinearity was tested by the Variance Inflation Factor (VIF); statistically, there was no multicollinearity when the value of VIF between 1 and 10. As indicated in Table 4.37c, the VIF value was 1.801 hence it indicated there was no multicollinearity between intellectual stimulation and job satisfaction.

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Table 4.37c: Multicollinearity Test on Intellectual Stimulation.

Unstandardized Standardized Collinearity Coefficients Coefficients Statistics Std. Model B Error Beta t Sig. Tolerance VIF 1 (Constant) .633 .223 2.842 .005 Intellectual .666 .056 .541 11.939 .000 1.801 1.801 Stimulation

4.6.6.1.4 Homoscedasticity Test on Intellectual Stimulation

Homoscedasticity test was carried out to determine if intellectual stimulation of the bank employees gives similar variance to job satisfaction on the regression values. As indicated on Table 4.37d, the results indicate that the value of the Levene Statistic, F(12, 334) = 2.26, p = .01 was below the study’s level of significance (p ≤ .05) indicating the data was not homogenous.

Table 4.37d: Homoscedasticity Test on Intellectual Stimulation

Levene Statistic df1 df2 Sig. 2.259 12 334 .009 * Significant at p<0.05 level

4.6.6.2 Regression and Hypothesis Testing on Intellectual Stimulation

Regression analysis was carried out to determine the extent to which intellectual stimulation influenced job satisfaction among employees in commercial banks in Kenya. Multiple linear regression was used to predict job satisfaction of employees in commercial banks in Kenya from intellectual stimulation. The hypothesis tested was:

H04: There is no significant influence of intellectual stimulation on job satisfaction among the employees in commercial banks in Kenya.

The regression results for the hypothesis testing were presented in the form of the model summary, regression ANOVA and regression coefficient.

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4.6.6.2.1 Regression Model Summary

The model summary results presented in Table 4.38 indicate that intellectual stimulation explained 29% of job satisfaction of employees in commercial banks in Kenya (R2) = .292.

Table 4.38: Model Summary on Intellectual Stimulation on Job Satisfaction

Change Statistics Adjusted Std. Error R R R of the Square F Sig. F Model R Square Square Estimate Change Change df df2 Change 1 .541a .292 .290 .86349 .292 142.533 1 345 .000 a. Predictors: (Constant), Intellectual Stimulation b. Dependent Variable: Job Satisfaction * Significant at p<0.05 level

4.6.6.2.2 Regression ANOVA

The regression ANOVA showed that intellectual stimulation had a significant influence on job satisfaction F(1, 106.274) = 142.533, p<.05) as indicated in Table 4.39. This means that the regression model was suitable for predicting the outcome variable on how intellectual stimulation influenced job satisfaction among employees in commercial banks in Kenya.

Table 4.39: ANOVA of Intellectual Stimulation on job satisfaction

Model Sum of Squares df Mean Square F Sig. 1 Regression 106.274 1 106.274 142.533 .000b Residual 257.237 345 .746 Total 363.511 346 a. Dependent Variable: Job Satisfaction b. Predictors: (Constant), Intellectual Stimulation * Significant at p<0.05 level

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4.6.6.2.3 Regression Coefficient of Intellectual Stimulation

Table 4.40 shows the results of the regression coefficient. In the regression coefficient model, the analysis showed that intellectual stimulation statistically predicted job satisfaction (β = .666, (2.842) t = 11.939, p<.05). The beta weight gauges the importance of explanatory variable across the model and was positive on intellectual stimulation, Beta of .666 and statistically significant at p<.05. This means, one unit of increase in intellectual stimulation increased the unit of job satisfaction by .666.

Table 4.40: Coefficients of Intellectual Stimulation on job satisfaction

Unstandardized Standardized Collinearity Coefficients Coefficients Statistics Model B Std. Error Beta t Sig. Tolerance VIF 1 (Constant) .633 .223 2.842 .005 Intellectual .666 .056 .541 11.939 .000 1.000 1.000 Stimulation a. Dependent Variable: Job Satisfaction * Significant at p<0.05 level

From the coefficient table, the values of the regression model were derived: The general form of the regression model used was:

= Constant; = Intellectual Stimulation and = Error term.

From the coefficient table, intellectual stimulation influences job satisfaction in the Kenyan banking sector.

Y= 0.633 + .666X +.056

The multiple linear regression analysis was used to test if intellectual stimulation significantly predicted job satisfaction among employees in commercial banks in Kenya. The results revealed that intellectual stimulation explained 29% of the job satisfaction (R2 = .292, F(1, 106.274) = 142.533, p<.05) while the remaining 71% of job satisfaction was explained by other factors. Further, intellectual stimulation significantly predicted job satisfaction (β = .666, (2.842) t = 11.939, p<.05). Therefore, the study rejected the null hypothesis H04: There is no significant influence of intellectual stimulation on job

156 satisfaction among the employees in commercial banks in Kenya and accepted the alternate hypothesis, H14: There is a significant influence of intellectual stimulation on job satisfaction among the employees in commercial banks in Kenya.

4.7 Moderating Effect of Job Security on the Influence of Transformational Leadership on Job Satisfaction

The last objective in this study was to determine the moderating effect of ‘job security’ on the influence of transformational leadership on job satisfaction. The moderating effect of job security questions were: my leader encourages fair treatment to everyone; my leader’s behavior does not cause me stress; and my leader does not leave room for anxiety. The dependent variable questions were: I am committed to the organization because my leader encourages fair treatment to everyone; I am hardly absent from work because my leader’s behavior does not cause me stresses; and I have no intentions of leaving my job because my leader does not leave room for anxiety. All the responses were measured on a five point Likert scale. The results and findings of both the descriptive and inferential statistics are presented below.

4.7.1 Factor Analysis

Factor analysis was used to evaluate the variability among the observed correlated variables to ensure the questions in the research instrument relate to the construct of measure. Questions that did not relate to construct were extracted from the analysis. Factor analysis was conducted on three questions for dependent variable ‘job satisfaction’ and three questions for moderating variable ‘job security’ presented below.

4.7.1.1 Factor Analysis on ‘Job Security’ as Moderating Variable

The dependent and moderating variables had three questions each. As indicated in Table 4.41a, only one factor was derived with Kaiser-Meyer Olkin result of .733. The Bartlett’s test of sphericity was significant at X2 (3, N=347) = 563.351, p<.05. The factor was adequate for extraction of components since Kaiser-Meyer-Olkin Measure was greater than .60 and the Bartlett’s test was significant (p<.05).

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Table 4.41a: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .733 Bartlett's Test of Sphericity Approx. Chi-square 563.351 df 3 Sig. .000 * Significant at p<0.05 level

Using the Principal component analysis, the total variance explained on the extraction showed the extracted values presented 81% of the first component. Only one component was extracted ‘Job security as moderating effect’. Further, average value principle was used to obtain the measure of the extracted independent variable by transformation. Table 4.41b shows the results of the variance explained.

Table 4.41b: Total Variance Explained for Job Security as Moderating Variable

Extraction Sums of Squared Initial Eigenvalues Loadings % of Cumulative % of Cumulative Component Total Variance % Total Variance % 1 2.424 80.798 80.798 2.424 80.798 80.798 2 .350 11.665 92.462 3 .226 7.538 100.000 Extraction Method: Principal Component Analysis.

The variables of the extracted components are indicated on the component matrix table. Only one factor was extracted representing the ‘moderating effect’. The variables and values were: ‘my leader encourages fair treatment to everyone’ with component matrix of .872; ‘my leader’s behavior does not cause me stress’ with component matrix of .913; and ‘my leader does not leave room for anxiety’ with component matrix of .911. All variables and component measure under the factor loading were greater than .60. Further, using the average of the components, the transformed data had a stronger component of .899 which was greater than .60. All the components were included as variables of analysis in the model. Table 4.41c shows the component matrix for the moderating effect of job security.

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Table 4.41c: Component Matrix on Job Security as Moderating Effect

Component Job security as moderating effect 1 My leader encourages fair treatment to everyone .872 My leader’s behavior does not cause me stress .913 My leader does not leave room for anxiety .911 Extraction Method: Principal Component Analysis. a. 1 component extracted.

4.7.1.2 Factor Analysis on Job Security as Moderating Variable on Job Satisfaction

Job security was the moderating variable in this study. As indicated in Table 4.42a, only one factor was derived with Kaiser-Meyer Olkin result of .729. The Bartlett’s test of sphericity was significant at X2 (3, N=347) = 601.909, p<.05. This indicated the factor as adequate for extraction of component since Kaiser-Meyer-Olkin Measure is greater than .60 and the Bartlett’s test is significant (p<.05).

Table 4.42a: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .729

Bartlett's Test of Sphericity Approx. Chi-square 601.909 df 3 Sig. .000 * Significant at p<0.05 level

Using the Principal component analysis, the total variance explained on the extraction shows the extracted values present 82% of the component. Only one component was extracted ‘moderating effect on job satisfaction’. Further, average value principle was used to obtain the measure of the extracted independent variable named ‘moderating effect on job satisfaction’ by transformation. Table 4.42b shows the results of the variance explained.

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Table 4.42b: Total Variance Explained for Job Security as Moderating Variable

Initial Eigenvalues Extraction Sums of Squared Loadings % of Component Total Variance Cumulative % Total % of Variance Cumulative % 1 2.453 81.769 81.769 2.453 81.769 81.769 2 .338 11.283 93.051 3 .208 6.949 100.000 Extraction Method: Principal Component Analysis.

One component for job security had an Eigen value that was greater than one which was in line with the results for total variance explained for job security as shown in Figure 4.10.

Figure 4.10: Scree Plot for Job Security

The details on the extracted component forming the dependent variable ‘Moderating effect’ were indicated in the Table 4.42c. The component extraction of each of variable showed ‘I am committed to the organization because my leader encourages fair treatment to everyone’ had a component matrix value of .897; ‘I am hardly absent from work because my leader’s behavior does not cause me stress’ had a component matrix value of.

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929 and lastly; ‘I have no intentions of leaving my job because my leader does not leave room for anxiety’ had a component matrix value of .886. This showed the variables and component measure under the factor loading were greater than .60. Further, using the average of the components, the transformed data had a stronger component of .904 which was greater than .60. All the components of dependent variables were included as variable of analysis. Table 4.42c indicates the component matrix measure of ‘job security’ as moderating effect on job satisfaction.

Table 4.42c: Component Matrix on Job Security as Moderating Variable on Job Satisfaction

Component Job security on job satisfaction 1 I am committed to the organization because my leader encourages fair treatment to .897 everyone I am hardly absent from work because my leader’s behavior does not cause me .929 stress I have no intentions of leaving my job because my leader does not leave room for .886 anxiety Extraction Method: Principal Component Analysis. a. 1 Component extracted.

4.7.2 Descriptive Statistics for Moderating Variable

The mean and standard deviation of the moderating variable were analyzed using descriptive statistics. Majority of the respondents agreed on the attribute ‘my leader encourages fair treatment to everyone’ (M= 3.92, SD = 1.06) and also ‘my leader’s behavior does not cause me stress’ (M= 3.66, SD = 1.08). Other results are also presented in the table. This clearly showed the difference, with the decrease in mean, the standard deviation increased indicating varied responses. Table 4.43 shows the results of the descriptive statistics of the moderating effect of job security.

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Table 4.43: Distribution of Job Security as Moderating Variable

Job Security M SD Skewness Std Err My leader encourages fair treatment to 3.9164 1.06247 -1.069 .131 everyone My leader’s behavior does not cause 3.6599 1.08574 -.559 .131 me stress My leader does not leave room for 3.5116 1.05249 -.325 .131 anxiety Moderating Effect of Job Security on Job Satisfaction I am committed to the organization because my leader encourages fair 3.4162 1.16478 -.449 .131 treatment to everyone I am hardly absent from work because my leader’s behavior does not cause 3.2594 1.13873 -.250 .131 me stress I have no intentions of leaving my job because my leader does not leave room 2.9366 1.16128 .068 .131 for anxiety

4.7.3 Chi-square Test of Job Security as Moderating variable and Job Satisfaction

The Chi-square test was used to determine whether there was a significant association between job security and job satisfaction. The chi-square test showed that there was a significant association between job security as the moderating effect variable and job satisfaction X2 (144, N = 347) = 664.814, p<.05). The results are presented in Table 4.44.

Table 4.44: Chi-square Test of Job Security and Job Satisfaction

Job security as moderating Value df Asymp. Sig. (2- variable sided) Pearson Chi-square 664.814a 144 .000 Likelihood Ratio 431.895 144 .000

Linear-by-Linear Association 168.179 1 .000

N of Valid Cases 347 a. 153 cells (90.5%) have expected count less than 5. The minimum expected count is .02. * Significant at p<0.05 level

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4.7.4 Correlation Analysis between Job Security and job satisfaction

Correlation analysis was used to test the relationship between job security variables and job satisfaction. All the variables were highly correlated. The first variable under job security ‘My leader encourages fair treatment to everyone was positively correlated with job satisfaction r (347) =.593, p<.05; ‘My leader’s behavior does not cause me stress r (347) =.628, p<.05; and ‘My leader does not leave room for anxiety’ r (347) =.660, p<.05. The results of the correlation test are presented in Table 4.45.

Table 4.45a: Correlation Analysis between Job Security Variables and Job Satisfaction

Moderating effect of Job Security Pearson Correlation Job Satisfaction My leader encourages fair treatment to Pearson Correlation .593** everyone Sig. (2-tailed) .000 N 347 My leader’s behavior does not cause me Pearson Correlation .628** stress Sig. (2-tailed) .000 N 347 My leader does not leave room for anxiety Pearson Correlation .660** Sig. (2-tailed) .000 N 347 * Significant at p<0.05 level Further, correlation analysis was used to test the relationship between job security and job satisfaction. The results showed that there was strong and positive correlation between job security and job satisfaction r (347) =.697, p<.05. The results are as shown in Table 4.45b.

Table 4.45b: Correlation Analysis between Job Security and Job Satisfaction

Job security on job satisfaction Job Satisfaction Job security as Pearson Correlation .697** moderating variable Sig. (2-tailed) .000 N 347 * Significant at p<0.05 level

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4.7.5 One-Way ANOVA on Job Security

The One-way ANOVA test was conducted to test the mean difference between job security and the demographic information of respondents; gender, age, education level, duration of working at the bank and lastly the tier of the bank. Table 4.46a shows the results which indicate there was no significant difference between the mean values of all the respondents’ demographic information and job security.

Table 4.46a: One-way ANOVA on Job Security

Sum of Squares Df Mean Square F Sig. Gender Between Groups 1.803 12 .150 .592 .848 Within Groups 84.237 332 .254 Total 86.041 344 Age Between Groups 4.520 12 .377 .616 .828 Within Groups 202.314 331 .611 Total 206.834 343 Education Between Groups 5.554 12 .463 1.179 .296 Within Groups 130.289 332 .392 Total 135.843 344 How long Between Groups 13.483 12 1.124 .919 .528 have you Within Groups 407.118 333 1.223 worked Total 420.601 345 Tier of your Between Groups 4.102 12 .342 .596 .845 bank Within Groups 191.391 334 .573 Total 195.493 346 * Significant at p<0.05 level

The One-way ANOVA test was also performed to test the mean differences between job satisfaction and the demographic factors of gender, age, education, duration of working at the bank and tier of the bank. Table 4.46b shows the results which indicate that there was no significant difference between the mean values of the demographic variables on job security and job satisfaction.

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Table 4.46b: One-way ANOVA on Job Security on Job Satisfaction

Sum of Squares df Mean Square F Sig. Between Groups 3.149 12 .262 1.051 .402 Gender Within Groups 82.892 332 .250 Total 86.041 344 Between Groups 5.977 12 .498 .821 .629 Age Within Groups 200.857 331 .607 Total 206.834 343 Between Groups 5.268 12 .439 1.116 .346 Education Within Groups 130.575 332 .393 Total 135.843 344 Between Groups 13.417 12 1.118 .914 .533 How long have you Within Groups 407.184 333 1.223 worked Total 420.601 345 Between Groups 4.520 12 .377 .659 .791 Tier of Within Groups 190.973 334 .572 your bank Total 195.493 346 * Significant at p<0.05 level

4.7.6 Regression Analysis and Hypothesis Testing

This section presents the regression analysis, the model used for hypothesis testing in the study and the assumptions for the regression. Regression analysis was done to determine the relationship, magnitude of the effect and projection of the moderating effect job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya.

4.7.6.1 Assumptions for Regression Analysis on the Moderating Variable

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4.7.6.1.1 Normality Test on Job Security

Using one sample Kolmogorov-Smirnov Test, the test of normality was conducted to determine the distribution of data depicting either a normal or skewed curve. This was determined by the statistical significance of the dependent and the independent variable (p<.05). The normal parameters test indicates the difference on mean in all the dependent, independent and moderating effect variables. The results showed the mean and standard deviation were as follows: Idealized influence (M = 4.07, SD =.79) followed by Inspirational motivation (M =4.02, SD=.88), Individualized consideration (M = 4.0, SD= .77), Intellectual stimulation (M= 3.90, SD = .83), Moderating effect of job security (M = 3.7, SD= .96) and lastly, the job satisfaction (M = 3.2, SD= .95). The results of the mean showed the difference in mean and variance in responses. The variance on the most extreme differences was minimal and the variables were significant to each other (p<.05) indicating a high level of relationship hence the data was not normally distributed. Table 4.47a shows the results of the normality test.

Table 4.47a: One-Sample Kolmogorov-Smirnov Test

Job Satisfaction Inspirational motivation

Consideration

Individualized

Idealized Idealized influence Intellectual Stimulation Moderating Effect N 347 346 347 347 347 347 Normal Mean 3.2077 4.0655 3.9914 4.0216 3.9087 3.6964 Parametersa,b SD .94919 .78771 .76592 .87640 .83227 .96073 Most Extreme Absolute .055 .184 .173 .185 .146 .142 Differences Positive .040 .118 .101 .132 .105 .094 Negative -.055 -.184 -.173 -.185 -.146 -.142 Test Statistic .055 .184 .173 .185 .146 .142 Asymp. Sig. (2-tailed) .014c .000c .000c .000c .000c .000c a. Test distribution is Normal. b. Calculated from data. c. Lilliefors Significance Correction. * Significant at p<0.05 level

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4.7.6.1.2 Linearity Test on Job Security

The analysis of variance (ANOVA) was used to determine linearity. The linearity test was conducted to determine if the relationship between transformational leadership and job satisfaction was linear or not. As indicated in Table 4.47b, there was a significant relationship between transformational leadership variables (collapsed value of independent variable) and job satisfaction p<.05. Similarly, the linearity between the transformational leadership variables and job satisfaction was significant p<.05. However, the deviation from linearity was not significant. Hence the relationship between transformational leadership and job satisfaction was linear and passed the test of linearity.

Table 4.47b: Linearity Test on Job Security

Sum of Mean Squares Df Square F Sig. Job Satisfaction Between (Combined) 168.274 79 2.130 3.964 .000 Groups Linearity 129.561 1 129.561 241.139 .000 Deviation 38.713 78 .496 .924 .655 from Linearity Within Groups 143.456 267 .537 Total 311.731 346 * Significant at p<0.05 level

4.7.6.1.3 Multicollinearity Test on Job Security

Multicollinearity test was performed to determine if the values of transformational leadership, moderating effect of job security and job satisfaction had higher similarity. The test of multicollearity was tested using the Variance Inflation Factor (VIF); statistically, there is no multicollinearity when the value of the VIF is between 1 and 10. As indicated in Table 4.47c, Idealized influence had a VIF of 2.425; individualized consideration had VIF of 2.313; Inspirational motivation had VIF of 3.207; Intellectual stimulation had VIF of 2.113; and moderating effect variable had a VIF of 2.577, no multicollinearity.

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Table 4.47c: Multicollinearity Test on Job Security

Unstandardized Standardized Collinearity Coefficients Coefficients Statistics Std. Model B Error Beta t Sig. Tolerance VIF 1 (Constant) .142 .229 .622 .534 Idealized Influence -.039 .076 -.032 -.511 .610 .412 2.425 Individualized .219 .076 .177 2.880 .004 .432 2.313 Consideration Inspirational .190 .078 .175 2.427 .016 .312 3.207 Motivation Intellectual .042 .067 .036 .621 .535 .473 2.113 Stimulation Moderating Variable .384 .064 .389 6.011 .000 .388 2.577 a. Dependent Variable: Job Satisfaction

4.7.6.1.4 Homoscedasticity Test on Job Security

Homoscedasticity test was carried out to determine if the moderating effect of job security of the bank employees gave similar a variance to job satisfaction on the regression values. As indicated in Table 4.47d, the results indicate that the value of the Levene Statistic, F(12, 334) = 3.62, p = .00 was below the study’s level of significance (p ≤ .05) indicating the data was not homogenous.

Table 4.47d: Homoscedasticity Test on Job Security

Levene Statistic df1 df2 Sig. 3.620 12 334 .000 * Significant at p<0.05 level

4.7.6.2 Regression and Hypothesis Testing

Regression analysis was carried out to determine the extent to which job security moderated the relationship between transformational leadership and job satisfaction among employees in commercial banks in Kenya. Multiple linear regression analysis was used to predict the moderating effect of job security on the influence of transformational

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leadership on job satisfaction among employees in commercial banks in Kenya. The hypothesis tested was:

H05: There is no significant moderating effect of job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya.

The regression results for the hypothesis testing were presented in the form of the model summary, regression ANOVA and regression coefficient.

4.7.6.2.1 Regression Model Summary

The model summary results presented in Table 4.48 indicate that the moderating effect of job security between transformational leadership and job satisfaction explained 44% of job satisfaction of employees in commercial banks in Kenya (R2) = .446.

Table 4.48: Model Summary of the Moderating Effect of Job Security between Transformational Leadership and Job Satisfaction

Change Statistics Std. Adjusted Error of R R R the Square F Sig. F Model R Square Square Estimate Change Change df1 df2 Change 1 .668e .446 .438 .71187 .059 36.126 1 340 .000 e. Predictors: (Constant), Idealized Influence, Individualized Consideration, Inspirational Motivation, Intellectual Stimulation, Moderating variable, f. Dependent Variable: Job Satisfaction * Significant at p<0.05 level

4.7.6.2.2 Regression ANOVA

The regression ANOVA showed that job security had a significant moderating effect between transformational leadership and job satisfaction F(5, 27.760) = 54.780, p<.05) as indicated Table 4.49.

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Table 4.49: ANOVA Transformational Leadership and Moderating Variable on Job Satisfaction

Model Sum of Squares df Mean Square F Sig.

1 Regression 138.802 5 27.760 54.780 .000f

Residual 172.299 340 .507

Total 311.101 345 f. Predictors: (Constant), Idealized Influence, Individualized Consideration, Inspirational Motivation, Intellectual Stimulation, Moderating Variable Effect * Significant at p<0.05 level

4.7.6.2.3 Regression Coefficient of Job Security

Table 4.50 shows the results of the regression coefficient. In the regression coefficient model, the analysis showed that ‘idealized influence’ and ‘intellectual stimulation’ were not statistically significant hence dropped from the equation and that ‘individualized consideration’ statistically predicted job satisfaction (β = .219, t (.622) = 2.880, p<.05). The beta weight gauges the importance of the explanatory variable across the model and was positive on ‘individualized consideration’ and was statistically significant. This meant that one unit of increase in ‘individualized consideration’ increased the unit of job satisfaction by .219 with the inclusion of moderating variable. The variable ‘inspiration motivation’ also statistically predicted job satisfaction (β = .190, t (.622) = 2.427, p<.05). The beta weight gauges the importance of the explanatory variable across the model and was positive on ‘inspiration motivation’ and statistically significant indicating one unit of increase in ‘inspiration motivation’ increased the unit of job satisfaction by .190. Lastly, the moderating variable statistically predicted job satisfaction (β = .384, t (.622) = 6.011, p<.05). The beta weight gauges the importance of moderating variable across the model and was statistically significant (p<.05). This meant, one unit of increase in ‘moderating variable’ increased the unit of job satisfaction by .384 without the influence of moderating variable.

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Table 4.50: Coefficients of Independent Variables and Moderating Effect on Job Satisfaction

Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta T Sig. 1 (Constant) .142 .229 .622 .534 Idealized Influence -.039 .076 -.032 -.511 .610 Individualized .219 .076 .177 2.880 .004 Consideration Inspirational .190 .078 .175 2.427 .016 Motivation Intellectual Stimulation .042 .067 .036 .621 .535 Moderating Variable .384 .064 .389 6.011 .000 a. Dependent Variable: Job Satisfaction * Significant at p<0.05 level

The general form of the multiple linear regression model used was:

Job Satisfaction = β0 + β1 x Idealized influence + β2 x Individualized consideration + β3 x

Inspirational motivation + β4 x intellectual stimulation + β5 x Job security + ∑

X1 = Idealized influence

X2 = Individualized consideration

X3 = Inspirational motivation

X4 = Intellectual stimulation

X5 = Moderating Variable

∑ = Error term

Idealized influence and intellectual stimulation were not statistically significant hence dropped from the model equation which was comprised of individualized consideration, inspirational motivation and job security.

2 3 4 5 Job Satisfaction = β0 + β2 X + β3 X + β4 X + β5 X + ∑

Y= 0.142 + .219X2 + .190X3 + .384X5 + .229

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Multiple linear regression analysis was used to test if there was a significant moderating effect of job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya. The results revealed that job security had a significant moderating effect between transformational leadership and job satisfaction (R2 = .446, F(5, 27.760) = 54.780, p<.05) . Individualized consideration statistically predicted job satisfaction with inclusion of the moderating variables (β = .219, t (.622) = 2.880, p<.05): the beta weight decreased from .258 to .219 with the inclusion of moderating variable reducing its influence on job satisfaction. Inspirational motivation variable statistically predicted job satisfaction with the inclusion of the moderating variable, (β = .190, t (.622) = 2.427, p<.05), the beta weight also decreased from .338 to .190 reducing its effect on job satisfaction. However, both idealized influence and intellectual stimulation were not significant. Lastly, the moderating variable significantly predicted job satisfaction (β = .384, t (.622) = 6.011, p<.05).

This showed that with the moderating effect of job security, transformational leadership significantly predicted job satisfaction. Therefore, the study rejects the null hypothesis

H05: There is no significant moderating effect of job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya and accepts the alternate hypothesis, H15: There is a significant moderating effect of job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya.

4.8 Chapter Summary

This chapter has presented the findings of the study. The demographic presentation covered the characteristics of the respondents working in the banking industry; age, gender, education level, working duration and the tiers of the bank. This was followed by the presentation of the results based on each research question. The items presented were; factor analysis, descriptive statistics, Chi-square test, correlation analysis and regression analysis.

On the first research question, the results revealed that there was a significant correlation between idealized influence and job satisfaction among employees in commercial banks in Kenya r (346) =.496, p<.05. Chi-square test revealed a significant association between idealized influence and job satisfaction X2 (132, N = 346) = 302.886, p<.05). The One- 172 way ANOVA test showed there was no significant difference on the mean values of the respondents’ demographic information on idealized influence and job satisfaction except for number of years worked in the organization. The multiple linear regression analysis results revealed that idealized influence explained 25% of job satisfaction (R2 = .246, F(1, 97.750) = 112.421, p<.05) and significantly predicted job satisfaction (β = .676, (.449) t = 10.603, p<.05). Therefore, the null hypothesis that there is no significant influence of idealized influence on job satisfaction among employees in commercial banks in Kenya was rejected.

On the second research question, the results revealed that there was a significant correlation between individualized consideration and job satisfaction among employees in commercial banks in Kenya r (347) =.595, p<.05. Chi-square test revealed a significant association between individualized consideration and job satisfaction X2 (132, N = 347) = 385.123, p<.05). The One-way ANOVA test showed that there was no significant difference on the mean values of the respondents’ demographic information on individualized consideration and job satisfaction except for age, education and number of years worked in the organization. The multiple linear regression results revealed that individualized consideration explained 35% of job satisfaction (R2 = .354, F(1, 138.779) = 188.851, p<.05) and significantly predicted job satisfaction (β = .827, (-.545) t = 13.742, p<.05). Therefore, the null hypothesis that there is no significant influence of individualized consideration on job satisfaction among employees in commercial banks in Kenya was rejected.

On the third research question, the results revealed that there was a significant correlation between inspirational motivation and job satisfaction among employees in commercial banks in Kenya r (347) =.587, p<.05. The Chi-square test showed a significant association between inspirational motivation and job satisfaction X2 (156, N = 347) = 445.180, p<.05). The One-way ANOVA test revealed there was no significant difference on the mean values of the respondents’ demographic information on inspirational motivation and job satisfaction. The multiple linear regression results revealed that inspirational motivation explained 34% of job satisfaction (R2 = .344, F(1, 126.302) = 180.980, p<.05) and significantly predicted job satisfaction (β = .689, (2.117) t = 13.453, p<.05). Therefore, the null hypothesis that there is no significant influence of

173 inspirational motivation on job satisfaction among employees in commercial banks in Kenya was rejected.

On the fourth research question, the results revealed that there was a significant correlation between the intellectual stimulation and job satisfaction among employees in commercial banks in Kenya r (347) =.541, p<.05. The Chi-square test revealed a significant association between intellectual stimulation and job satisfaction X2 (144, N = 347) = 426.404, p<.05). The One-way ANOVA test showed there was no significant difference on the mean values of the respondents’ demographic information on intellectual stimulation and job satisfaction. The multiple linear regression results revealed that intellectual stimulation explained 29% of job satisfaction (R2 = .292, F(1, 106.274) = 142.533, p<.05) and significantly predicted job satisfaction (β = .666, (2.842) t = 11.939, p<.05). Therefore, the null hypothesis that there is no significant influence of intellectual stimulation on job satisfaction among employees in commercial banks in Kenya was rejected.

On the last research question, the results revealed that there was a significant correlation between the moderating effect of job security and job satisfaction among employees in commercial banks in Kenya r (347) =.697, p<.05. The Chi-square test revealed a significant association between job security as moderating effect variable and job satisfaction X2 (144, N = 347) = 664.814, p<.05). The One-way ANOVA test showed there was no significant difference on the mean values of the respondents’ demographic information on job security and job satisfaction. The multiple linear regression results revealed that transformational leadership explained 44% of job satisfaction when moderated by job security (R2 = .446, F(5, 27.760) = 54.780, p<.05). Job security had a significant moderating effect between transformational leadership and job satisfaction (β = .384, (.622) t = 6.011, p<.05). Therefore, the null hypothesis that there is no significant moderating effect of job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya was rejected.

The next chapter presents a summary of the findings, discussions of the findings, conclusions and recommendations based on the findings of the study.

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CHAPTER FIVE

5.0. SUMMARY, DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

This chapter presents the summary, discussion, conclusions and recommendations of the study. The summary, discussions and conclusions are presented based on the research questions. Recommendations are made based on the findings of the study. The study also gives suggestions for future research.

5.2 Summary of the Study

The purpose of this study was to examine the influence of transformational leadership style on job satisfaction among employees in commercial banks in Kenya. This study was guided by the following research questions: To what extent does idealized influence influence job satisfaction among employees in commercial banks in Kenya? To what extent does individualized consideration influence job satisfaction among employees in commercial banks in Kenya? To what extent does inspirational motivation influence job satisfaction among employees in commercial banks in Kenya? To what extent does intellectual stimulation influence job satisfaction among employees in commercial banks in Kenya? To what extent does job security moderate the relationship between transformational leadership and job satisfaction among employees in commercial banks in Kenya?

The study was based on the positivism research philosophy. The study adopted the descriptive correlation research design. The target population was 10,310 managerial employees in the commercial banks in Kenya. Stratified random sampling technique was used to select a sample of 424 managers in commercials banks in Kenya who participated in the study. A response rate of 82% was obtained. The study used a structured questionnaire to collect data from the managerial employees. Data analysis was done using both descriptive and inferential statistics; descriptive statistics tests performed were percentage, mean and standard deviation. The inferential statistical tests carried out were chi-square test, correlation analysis, ANOVA, and regression analysis to obtain the relationship between the variables of the study. Statistical Package for the Social Sciences (SPSS) was used to analyze the data.

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The first research question sought to examine the extent to which idealized influence influenced job satisfaction among employees in commercial banks in Kenya. The results revealed that there was a significant correlation between idealized influence and job satisfaction among employees in commercial banks in Kenya r (346) =.496, p<.05. Chi- square test revealed that there was a significant association between idealized influence and job satisfaction X2 (132, N = 346) = 302.886, p<.05). The One-way ANOVA test showed that there was no significant difference on the mean values of the respondents’ demographic information on idealized influence and job satisfaction except for number of years worked in the organization at (p<.05). The multiple linear regression analysis results revealed that idealized influence explained 25% of job satisfaction (R2 = .246, F(1, 97.750) = 112.421, p<.05) and significantly predicted job satisfaction (β = .676, (.449) t =

10.603, p<.05). Therefore, the study rejected the null hypothesis, H01: There is no significant influence of idealized influence on job satisfaction among employees in commercial banks in Kenya.

The second research question sought to examine the extent to which individualized consideration influenced job satisfaction among employees in commercial banks in Kenya. The results revealed that there was a significant correlation between individualized consideration and job satisfaction among employees in commercial banks in Kenya r (347) =.595, p<.05. Chi-square test revealed that there was a significant association between individualized consideration and job satisfaction X2 (132, N = 347) = 385.123, p<.05). The One-way ANOVA test showed that there was no significant difference on the mean values of the respondents’ demographic information on individualized consideration and job satisfaction except for age, education and number of years worked in the organization at (p<.05). The multiple linear regression results revealed that individualized consideration explained 35% of job satisfaction (R2 = .354, F(1, 138.779) = 188.851, p<.05) and significantly predicted job satisfaction (β = .827, (-

.545) t = 13.742, p<.05). Therefore, the study rejected the null hypothesis, H02: There is no significant influence of individualized consideration on job satisfaction among employees in commercial banks in Kenya.

176 motivation and job satisfaction among employees in commercial banks in Kenya r (347) =.587, p<.05. The Chi-square test revealed that there was a significant association between inspirational motivation and job satisfaction X2 (156, N = 347) = 445.180, p<.05). The One-way ANOVA test showed that there was no significant difference on the mean values based on the respondents’ demographic information on inspirational motivation and job satisfaction (p<.05). The multiple linear regression results revealed that inspirational motivation explained 34% of job satisfaction (R2 = .344, F(1, 126.302) = 180.980, p<.05) and significantly predicted job satisfaction (β = .689, (2.117) t =

13.453, p<.05). Therefore, the study rejected the null hypothesis, H03: There is no significant influence of inspirational motivation on job satisfaction among employees in commercial banks in Kenya.

The fourth research question sought to examine the extent to which intellectual stimulation influenced job satisfaction among employees in commercial banks in Kenya. The results revealed that there was a significant correlation between intellectual stimulation and job satisfaction among employees in commercial banks in Kenya r (347) =.541, p<.05. The Chi-square test revealed there was a significant association between intellectual stimulation and job satisfaction X2 (144, N = 347) = 426.404, p<.05). The One-way ANOVA test showed that there was no significant difference on the mean values based on the respondents’ demographic information on intellectual stimulation and job satisfaction (p<.05). The multiple linear regression results revealed that intellectual stimulation explained 29% of job satisfaction (R2 = .292, F(1, 106.274) = 142.533, p<.05) and significantly predicted job satisfaction (β = .666, (2.842) t = 11.939, p<.05).

Therefore, the study rejected the null hypothesis, H04: There is no significant influence of intellectual stimulation on job satisfaction among employees in commercial banks in Kenya.

177 test showed there was no significant difference on the mean values of the respondents’ demographic information on job security and job satisfaction. The multiple linear regression results revealed that transformational leadership explained 44% of job satisfaction when moderated by job security (R2 = .446, F(5, 27.760) = 54.780, p<.05). Job security had a statistically significant moderating effect between transformational leadership and job satisfaction (β = .384, t (.622) = 6.011, p<.05). This showed that job security had a significant moderating effect between transformational leadership and job satisfaction. Therefore, the study rejected the null hypothesis, H05: There is no significant moderating effect of job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya.

5.3 Discussion of Results

This section presents the discussion of results which is presented in line with the research questions. Correlation analysis, Chi-square, One-way ANOVA and Multiple linear regression results are discussed in this section.

5.3.1 Influence of Idealized Influence on Job Satisfaction

The first research question sought to examine the extent to which idealized influence influenced job satisfaction among employees in commercial banks in Kenya. The results of correlation analysis revealed a positive correlation between idealized influence variables and job satisfaction; charismatic attributes (r (345) =.563, p<.05), trust (r (346) =.596, p<.05), and ethical (r (343) =.564, p<.05). This showed idealized influence had a positive and strong correlation with job satisfaction. The findings were similar to Hwang et al. (2005) study which showed there was correlation between commitment and performance which are mainly a function of satisfaction. Huang et al. (2005) in their study dubbed ‘fitting in organizational values’ sought to investigate whether CEO charismatic leadership had a positive effect on employees. The findings demonstrated that charisma had significant effects on employee outcomes of extra effort, job satisfaction and organizational commitment. Ahmed et al. (2012) sought to establish the relationship between organizational ethics and job satisfaction in employees of banks in Pakistan and found that benevolent ethical climate and top management support for ethical behavior were positively correlated to job satisfaction.

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However, there are studies that have found no correlation between idealized influence and job satisfaction. Hanaysha et al. (2012) study in Malaysia among administrative and clerical staff involved in graduate and postgraduate affairs in three universities found a positive relationship between charisma and job satisfaction which was statistically insignificant. Ahmed et al. (2012) sought to establish the relationship between organizational ethics and job satisfaction in employees of banks in Pakistan and found out that egoistic ethical climate was negatively related to job satisfaction. Further, principled ethical climate had no relationship with job satisfaction. These findings necessitate more research in the area since majority of the studies indicate a positive correlation between charisma and job satisfaction with statistical significance. However, there are very few studies with a negative relationship hence most agree that idealized influence is positively correlated with job satisfaction.

The chi-square test was used to establish the strength of association between idealized influence and job satisfaction. The results showed that there was a significant association between idealized influence and job satisfaction X2 (132, N = 346) = 302.886, p<.05). The idealized influence variables were trust, charisma and ethics. Research indicates that people who are in high trust environments live longer, enjoy greater wellness and job satisfaction. In contrast, a low trust environment sucks energy, results into stress and reduced wellness which has the possibility of destroying performance. Lack of trust also suppresses expressions which may lead to a lot of dysfunctions in the organization hence the need to cultivate a trust culture which is a precursor to job satisfaction and performance (Jameson, 2010).

Bacha (2010) in a study on the relationships among organizational performance, environmental uncertainty and employee’s perceptions of CEOs found that CEOs who are found to be increasingly energetic have an impact on organizational performance as opposed to model CEOs who have no significant impact on organizational performance. This shows that the environment that creates ethics or trust between employees is determined by the individual characteristics. Khuong and Hoang (2015) further affirmed this when they found that as much as compensation and fringe benefits matter, the leader’s personality and characteristics are more important as they affect the motivational work environment for the staff which in turn yields positive job attitudes. This shows that ethics, trust and charisma are determined by a leader. Lastly, a good ethical environment

179 has the potential to boost an employee’s job satisfaction level while the consequences of ethical misgivings are detrimental to the organization (Yang & Islam, 2012; Avolio & Bass, 2002; Khuong & Hoang, 2015). All these studies found out that idealized influence factors; trust, charisma and ethics are determined by individual leaders which has a significant impact on the job satisfaction.

The One-way ANOVA test results showed that there was no significant difference on the mean values of all the respondents’ demographic information on idealized influence and job satisfaction except on the number of years worked in the organization (p<.05). This shows that idealized influence is not determined by an individual’s background but by other factors as outlined by researcher. According to Nikoloski (2015), the ethics of charismatic leaders refers to how they use their power, and in what. Charismatic leaders who are high on ethics have better workplace environments with less interpersonal and workplace deviance. These leaders act as role models and their behavior more often than not cascades through the organization. Khuong and Hoang (2015) also stated that a leader who possesses charisma, trust and ethics is able to influence his followers because followers identify with him; this in turn boosts the employee’s job satisfaction. This supports the findings that idealized influence on an individual is not determined by the background. Further, an ethical climate refers to individual beliefs about the organizational practices, procedures, standards and ethical values and not the individuals background (Ahmed et al., 2012). Sarker et al. (2003) found that the overall job satisfaction indicated that job satisfaction rises in the tenure of service in majority of the age groups except those below twenty five years old. Therefore, job satisfaction among hotel employees was significantly dependent on the tenure of service in the organization.

The results of multiple linear regression indicated that idealized influence significantly influenced job satisfaction (R2 = .246, F (1, 97.750) = 112.421, p<.05). The analysis revealed that idealized influence statistically predicted job satisfaction (B = .676, (.449) t = 10.603, p<.05). Different studies support this including Gitoho et al. (2016) who studied the influence of idealized influence on employee satisfaction amongst listed companies in Nairobi securities exchange and found out that idealized influence affects job satisfaction. Emu and Umeh (2014) empirically examined the relationship between leadership style and job satisfaction among customer relationship officers in Nigerian banks. The results indicated that idealized influence explained 25% of job satisfaction.

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The low degree of influence (25%) was similar to what Long et al. (2014) found in their study ‘impact of transformational leadership on job satisfaction in Malaysia’. The results of the study revealed a positive but non-significant relationship between idealized influence and job satisfaction. The research findings were in line with existing findings that idealized influence significantly influenced job satisfaction.

5.3.2 Influence of Individualized Consideration on Job Satisfaction

The second research question sought to examine the extent to which individualized consideration influenced job satisfaction among employees in commercial banks in Kenya. The results of correlation analysis showed a positive significant relationship between individualized consideration and job satisfaction: mentorship at the workplace (r (347) =.872, p<.05), support in and outside work place (r (347) =.876, p<.05) and work delegation (r (347) =.734, p<.05). Miao and Kim (2010) investigated the influence of perceived organizational support and job satisfaction as positive correlations of employee performance in China. The results indicated that organizational citizenship behavior increased with more favorable perception of organizational support and job satisfaction.

Long et al. (2014) carried out a study on the impact of transformational leadership style on job satisfaction and found that only the aspect of individualized consideration and more so the support a leader offers to his employees had a significant impact on job satisfaction. Social support also predicts job involvement and job satisfaction because it acts as a buffer to stressors that arise from the work or interaction with colleagues (Salami, 2010). Research that supports the correlation between individualized consideration and job satisfaction includes but is not limited to management support among other factors like recognition and job security (Mosadeghrad & Ferdosi, 2013).

Emmanuel and Hassan (2015) carried out a study to establish the effect of transformational leadership on job satisfaction in four and five star hotels in Kuala Lumpur. The results of this study revealed a positive and significant correlation between individualized consideration and job satisfaction. It has also been established that employees who are entrusted with decision making and receive support from their supervisors and colleagues are more satisfied with their jobs (Musenze et al., 2014). Conversely, there is research that has found a negative correlation between individualized consideration and job satisfaction. In a study by Weng at al. (2010), there was no 181 significant relationship between psychosocial mentoring and three aspects of employee job satisfaction which were; co-workers, the job itself and promotion.

Further, Arzi and Farahbod (2014) in their study on the impact of leadership on job satisfaction in Iranian hotels found that supportive leadership had a significant impact on job satisfaction but recognition did not affect job satisfaction. Additionally, Riisgaard et al. (2016) in a review paper with the aim of establishing the relations between task delegation and job satisfaction in general practice found that nurses had negative attitudes and experiences towards task delegation especially due to an increased workload. However, majority were generally satisfied with their jobs and the various tasks they performed which were delegated to them by the general physicians. They attributed this satisfaction to the autonomy which they enjoyed. The results revealed that contrary to most of the studies in this thematic area, the effect of delegation of authority and responsibility was not significant on job satisfaction but empowerment had a significant impact on job satisfaction.

The chi-square test was used to determine the strength of association between individualized consideration and job satisfaction. The results revealed that there was a significant association between individualized consideration and job satisfaction X2 (132, N = 347) = 385.123, p<.05). The individualized consideration variables were mentoring, support and delegation. The results show that mentorship, support and delegation influence job satisfaction. Belias and Koustelios (2014) stated that individualized consideration fosters the provision of support, encouragement, coaching, feedback mechanisms and delegation which play a big role in the follower’s personal development which in turn positively impacts job satisfaction. Further, Bass and Avolio (1994) noted that a leader demonstrates individualized consideration when providing the followers with support. The study went on to note that the improvement of individualized consideration around supportive and developmental leadership is likely to have a transformational impact (Long et al., 2014). Individualized consideration refers to the personal attention the leaders have towards the needs of the followers which makes the followers to feel valued; this explains the significant relationship between individualized consideration with job satisfaction. However, some studies show that bank jobs are characterized by long working hours, pressure from the job itself, poor treatment, non-conducive working environment, minimal promotion opportunities and unfairness (Sattar & Ali, 2014). These

182 studies show there is no significant relationship between individualized consideration and job satisfaction especially when the working environment is poor.

The One-way ANOVA test results showed that there was no significant difference on the mean values of all the respondents’ demographic information on individualized consideration and job satisfaction. However, the means for job satisfaction were significantly different across the age, education and number of years worked (p<.05). Hoboubi et al. (2017) found that there were significant differences in job satisfaction mean scores among three age groups of young, middle age and older people. Job satisfaction was found to be higher for older employees and the young employees but lower for the middle aged employees. They attributed this to young employees being motivated and older ones settling in their lives whereas the middle aged employees would be unhappy with repetitiveness of roles. They also found that the education level affected job satisfaction indicating that people who had good educational qualifications were more satisfied. Olorunsola (2012) found that age significantly influenced job satisfaction of administrative staff in Nigeria Universities. They attributed this to the values and expectations of the staff at the different ages.

Research by Alkahtani (2016) noted that the leader’s ability to create a supportive environment by listening, coaching and mentoring speaks volumes to the followers about how their leaders consider their needs by ensuring that as the organization grows the employees also grow in their areas of interest. The leaders also help the employees to get through their personal challenges because they are concerned not only about the work but also their followers well-being. Ahmad et al. (2014) stated that leaders train the followers on how to achieve the set goals and objectives. The accomplishment results in aspects of recognition which are key drivers of job satisfaction. Further, Muenjohn (2010) found out that encouragement from the leaders allows the followers to express themselves freely and also to implement their ideas.

The results of multiple linear regression showed that individualized consideration significantly influenced job satisfaction (R2 = .354, F (1, 138.779) = 188.851, p<.05). The analysis showed that individualized consideration statistically predicted job satisfaction (B = .827, (-.545) t = 13.742, p<.05). These results were similar to Mustafa and Lines (2014) who found out that supportive leadership has a positive impact on job

183 satisfaction which reaffirms that a leader’s characteristics and behaviors play an important role in boosting job satisfaction, ultimately leading to positive outcomes in the workplace. Kombo et al. (2014) found that delegation had a strong relationship with job satisfaction and performance through raised enthusiasm for the employees. Additionally, delegation was not only rewarding for the employees but it also raised the employees’ sense of accomplishment and self-esteem.

Horner (2017) carried out a study to establish whether mentoring based on Watson’s caring model positively influenced nurses’ job satisfaction. All the participants reported that mentor experience or relationship positively influenced job satisfaction. Additionally, job satisfaction was associated with reduced turnover of staff and improved patient retention. Hanaysha et al. (2012) conducted a study in Malaysia among administrative and clerical staff involved in graduate and postgraduate affairs in three universities. The findings revealed that individualized consideration was negatively related to job satisfaction which contradicts most research. It is however attributed to the fact that perhaps employees could not meet their leaders due to their busy schedules.

5.3.3 Influence of Inspirational Motivation on Job Satisfaction

The third research question sought to examine the extent to which inspirational motivation influenced job satisfaction among employees in commercial banks in Kenya. The results of correlation analysis of inspirational motivation and job satisfaction showed a positive correlation on all the variables with job satisfaction: communication (r (347) =.893, p<.05), teamwork (r (347) =.915, p<.05); and motivation (r (347) =.917, p<.05). Other studies have found a positive correlation between inspirational motivation and job satisfaction. Kakakhel et al. (2015) carried out a study on the impact of organizational communication on organizational commitment and job satisfaction in Pakistan. The findings of the study indicated that organizational communication had a positive effect on job satisfaction. Monga et al. (2015) who studied job satisfaction of employees of ICICI bank found that among other factors like communication, attitudes of supervisors, job security and team work had an important role in determining employee job satisfaction. Rizwan et al. (2012) conducted an empirical study of employee job satisfaction and aimed to establish the crucial problems faced by employees and to find ways to enhance employee loyalty. Findings revealed a strong and positive relationship between team work and job satisfaction.

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The chi-square test was used to determine the strength of association between inspirational motivation and job satisfaction. The results showed that there was a significant association between inspirational motivation and job satisfaction X2 (156, N = 347) = 445.180, p<.05). The variables of inspirational motivation are communication, teamwork and support which have a significant association with job satisfaction. Research indicates that effective communication, teamwork and motivation influence job satisfaction. A study found that communication whether horizontal or vertical, formal or informal in any environment was an important factor that influenced the organization’s success which is a factor of satisfaction (Epure et al., 2013). Another study goes on to note that if employees receive proper communication about their roles, responsibilities and performance expectations, their satisfaction increases. Thus, the supervisor’s role on inspirational motivation cannot be overemphasized because of the significant impact it has on job satisfaction (Kakakhel et al., 2015).

Leaders need to ensure they promote job satisfaction and commitment yielding policies and practices. Employee commitment is beyond being passively loyal to being actively involved and being ready to transcend personal gain for organizational gain. This explains why inspirational motivation factors like teamwork, communication and motivation are individual based and have a significant association with job satisfaction. However, E.O Darko and T.O Darko (2015) research in Ghana noted that as a result of high competition in the banking industry, employees are expected to work harder to ensure they retain and attract new business regardless of the carder of the Bank. Such competition affects personal motivation at work which in turn affects the job satisfaction (Thirulogasundaram & Sahu, 2014). This greatly affects motivation and commitment and as stated by Yucel and Bektas (2012), the level of commitment can greatly influence the quality of service rendered to customers and influences job satisfaction. This research shows that the bank’s working environment can contribute to negative association between inspirational motivation and job satisfaction.

The One-way ANOVA test results showed that there was no significant difference on the mean value of all the respondents’ demographic information on inspirational motivation and job satisfaction (p<.05). The results revealed that personal individual background does not influence or relate to inspirational motivation. Teamwork as a factor that influences inspiration motivation is determined by personal individual skills, mutual

185 accountability and complements to each other. In order to create effective teamwork, there must be clear goals, relevant skills, mutual trust, commitment, effective communication, negotiation skills and good leadership with both internal and external support (Benrazavi & Silong, 2013; Musriha, 2013). Additionally, attributes of inspirational motivation enable a leader to instill pride in the followers and induces the follower’s interests beyond personal interests for the good of the organization (Guay, 2013). A leader’s optimistic talk about the future also helps to build hope in the followers because the leader provides an exciting image of organization’s future (Guay, 2013; Rao & Abdul, 2015; Bass, 1985). This supports the research finding that individual background does not influence inspirational motivation variables.

The results of multiple linear regression revealed that inspirational motivation significantly influenced job satisfaction (R2 = .344, F(1, 126.302) = 180.980, p<.05). The analysis showed inspirational motivation statistically predicted job satisfaction (B = .689, (2.117) t = 13.453, p<.05). Akpinar et al. (2013) stated that job satisfaction was a result of organizational commitment and not organizational communication. Additionally, results indicated that there was a positive relationship between employee’s perception of organizational communication and organizational commitment. However, unlike many studies, the results indicated communication to a greater extent predicted organizational commitment as opposed to job satisfaction. Shujaat et al. (2014) conducted a research to establish the impact of team work on employee job satisfaction. The results of the study revealed that there was a significant impact of team work on job satisfaction. This indicates that it is important for organizational leaders to build a team work culture, build team skills and hold it in high regard because of its significant effect on job satisfaction and achieving organizational goals. Rana (2015) sought to determine the job satisfaction factors affecting employees in the Bangladesh banking sector. The results indicated that there was a significant and positive relationship between human resource management practices like team work, job autonomy and leadership behavior on job satisfaction; however, team work was the most important factor affecting job satisfaction

5.3.4 Influence of Intellectual Stimulation on Job Satisfaction

The fourth research question sought to examine the extent to which intellectual stimulation influenced job satisfaction among employees in commercial banks in Kenya.

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The results of correlation analysis test on intellectual stimulation and job satisfaction showed a positive correlation on all the variables: knowledge sharing (r (347) =.638, p<.05), creativity (r (347) =.547, p<.05) and risk taking (r (347) =.550, p<.05). Cheung and Wong (2011) examined the link between transformational leadership and employee creativity in Hong Kong. The results of the study revealed a positive and significant relationship between transformational leadership and employee creativity which in turn boosts employee job satisfaction. Raisi and Forutan (2017) conducted a study on the relationship between a knowledge sharing culture and job satisfaction in the context of Bank Sepah Branches in Shriraz, Iran. Results revealed a positive and significant relationship between a knowledge sharing culture and components of job satisfaction. Habib et al. (2014) sought to establish the impact of organizational culture on job satisfaction, employee commitment and turnover intention. The results of the study revealed that organization culture, specifically, innovation and risk taking highly influenced employee commitment, job satisfaction and retention. However, the study found that intellectual stimulation was positively correlated to job satisfaction because leaders foster inspiration through stimulation which in turn creates excitement

The chi-square test was used to determine the strength of association between intellectual stimulation and job satisfaction. The results showed that there was a significant association between intellectual stimulation and job satisfaction X2 (144, N = 347) = 426.404, p<.05). The variables under intellectual stimulation are knowledge sharing, creativity and risk-taking. Different researchers have noted the importance of intellectual stimulation variables that lead to job satisfaction. Chen et al. (2009) found that organizations need to provide a supportive process and environment for employees to be creative. Additionally, the organizations should provide challenges, involvement of staff and trust because these motivate employees to make contributions. An environment that allows creativity is catalyzed by some room for ambiguity, freedom and some room for risk taking (Chen et al., 2009). Iqbal et al. (2013) and Raju (2017) support the need for intellectual stimulation; an ethical organizational climate is a key enabler for creativity among employees in the organizations. Employees were more associated with organizations which encouraged creativity and provided a platform for freedom of expression. This shows how a work environment that encourages creativity, risk taking and knowledge sharing contributes to job satisfaction.

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The One-way ANOVA test results showed that there was no significant difference in the mean values of all the respondents’ demographic information on intellectual stimulation and job satisfaction (p<.05). The results indicate that organizations that encourage innovation and provide a supportive climate are likely to experience growth and retention of talent. This is attributed to the organizational leadership which provides employees with a conducive and supportive environment to enable them to be creative and innovative whilst allowing room for implementation of their innovations (Farrukh et al., 2014). The research conducted by Raisi and Forutan (2017) on a knowledge sharing culture and job satisfaction in the context of Bank Sepah Branches in Shriraz, Iran also showed a positive relationship between a knowledge sharing culture and intellectual stimulation. This shows that the culture of the organization influences intellectual stimulation variables and not individual background.

The results of the multiple linear regression revealed that intellectual stimulation significantly influenced job satisfaction (R2 = .292, F (1, 106.274) = 142.533, p<.05). The analysis showed that intellectual stimulation statistically predicted job satisfaction (B = .666 (2.842) t = 11.939, p<.05). Yee et al. (2014) conducted a study on the effect of a psychological climate for creativity on job satisfaction and work performance. The findings showed that a creative climate is a key predictor of job satisfaction and work performance among electrical engineers. In this regard, leaders need to create a culture and an environment which promotes creativity in their organizations since it is a predictor of job satisfaction. Kianto et al. (2016) sought to establish if knowledge management could be used to nurture job satisfaction and also examined how it could be used to increase individual employee job satisfaction. The results revealed that knowledge sharing was a key component of the knowledge management process which was found to have a positive correlation with job satisfaction. The overall study concludes that having knowledge management processes in place is linked to high job satisfaction. Abbaspour and Noghreh (2015) examined the relationship between organizational culture and job satisfaction of Tourism Bank employees in Iran. The results revealed that there was a relationship between organizational culture factors like risk taking and job satisfaction. Specifically, there was a relationship between risk-taking and job satisfaction which was not statistically significant.

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5.3.5 Moderating Effect of Job Security on the Influence Transformational Leadership on Job Satisfaction

The fifth research question sought to examine the extent to which job security moderated the relationship between transformational leadership and job satisfaction among employees in commercial banks in Kenya. The results of the correlation analysis test revealed that there was a positive correlation on the moderating effect of job security and job satisfaction: ‘My leader encourages fair treatment to everyone’ was positively correlated with job satisfaction r (347) =.593, p<.05; ‘My leader’s behavior does not cause me stress’ was positively correlated with job satisfaction r (347) =.628, p<.05; and ‘My leader does not leave room for anxiety’ was positively correlated with job satisfaction r (347) =.660, p<.05. Anxiety manifests itself through future concerns and the inability to predict that future employment and career concerns all which have the possibility of affecting the employee’s judgments, perceptions, satisfaction and productivity. Studies have found these elements to be strong predictors of job satisfaction and thus a leader should endeavor to reassure employees through effective and accurate communication to dispel any anxieties for there to be job satisfaction (Kler et al., 2015).

Nadinloyi et al. (2013) sought to examine the relationship between job satisfaction and mental health. The findings based on their hypothesis indicate there was a relatively weak but significant correlation between anxiety and job satisfaction meaning leaders need to ensure there is minimum or no anxiety for job satisfaction to grow. However, according to Agarwal (2015) who measured the relationship of job stress and job satisfaction in the Indian IT Sector, there is no relationship between job stresses to job satisfaction. The discussion shows the results on the correlation differ though most studies indicate that stress is negatively correlated to job satisfaction as found in this study.

The chi-square test was used to determine the strength of association between job security as the moderating variable and job satisfaction. The results showed that there was a significant association between job security as the moderating variable and job satisfaction X2 (144, N = 347) = 664.814, p<.05). The job security variables were anxiety, fairness and stress. These results showed that anxiety, fairness and stress are job security variables which affect job satisfaction. Darko E.O and Darko (2015), in Ghana noted that as a result of high competition in the banking industry, employees are now expected to

189 work harder to ensure they retain and attract new business which affects their job security. This emanates from the fact that organizations depend on people to achieve their objectives and when there is no job satisfaction then the employees are faced with choices of whether to quit or remain on the job. This negatively affects the organizational effectiveness (Tetteh & Brenyah, 2016).

According to Islam and Rahman (2016), the banking industry has been curbed by problems like extended working hours, pressure, non-conducive working environments, lack of fairness, reducing career growth opportunities and poor treatment. All these have a significant impact on the level of organizational commitment and job satisfaction. Thorsteinsson et al. (2014) examined the association between stress, organizational support and staff health which incorporated anxiety, depression and fatigue together with work outcomes like turnover intentions, organizational commitment and job satisfaction. The findings indicate that high work stress was associated with worse staff health like anxiety, depression and fatigue all of which lead to negative work outcomes like low job satisfaction, high turnover intentions and less organizational commitment. These results are generalizable to the banking sector where job security affects job satisfaction.

The One-way ANOVA test results showed that there was no significant difference between the mean values of the entire respondent’s demographic data on job security and job satisfaction. The demographic information included; gender, age, education level, duration of working at the bank. Different researchers have found out that job satisfaction is not determined by an individual’s characteristics but by other factors. Umair et al. (2016) investigated the employee’s perception of fairness in the performance appraisal system and the effect this had on job satisfaction of the employees. They found that the perception of fairness consisted of distributive justice, procedural justice and interactional justice which had an impact on job satisfaction. Lin et al. (2014) conducted a study on role stress and job satisfaction among bank employees in Sabah, Malaysia. The results indicate that there was a significant role of stress attributed to bank characteristics of extended working hours, pressure, non-conducive working environments, lack of fairness, reducing career growth opportunities and poor treatment (Islam & Rahman, 2016). These discussions can be summarized by Yousef (1998) who found out that the importance of job security comes from its influence on work related outcomes for example employee health, turnover and job satisfaction.

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The results of multiple linear regression revealed that job security had a significant moderating effect between transformational leadership and job satisfaction (R2 = .446, F(5, 27.760) = 54.780, p<.05). Additionally, the analysis revealed that the regression coefficient for job security was statistically significant (β = .384, t (.622) = 6.011, p<.05). This showed that with the moderating effect of job security, transformational leadership predicted job satisfaction. Thus, job security is a key factor in determining job satisfaction. Studies have revealed that lack of job security has consequences among them turnover intentions which consequently affect the job satisfaction. This also affects the quality of service rendered to customers, productivity and ultimately the overall organizational success. Knowledge of this and a vision of the bigger picture of the consequences of turnover and job satisfaction should spur leaders into providing the best environments to obviate the lack of job security (Joarder & Ashraf, 2012).

Allan et al. (2016) conducted a research on meaningful work and mental health with job satisfaction as a moderator. The study found that having meaningful work was associated with better mental health meaning lower rates of depression, anxiety and stress. Meaningful work predicted lower depression but did not significantly predict anxiety or stress. Thus, meaningful work contributes to the level of job satisfaction. Khan et al. (2013) conducted a research to establish whether job satisfaction of operational staff in Islamic banks was determined through organizational climate, occupational stress, age and gender. The results revealed that organizational climate and occupational stress have a significant impact on the level of job satisfaction. These research findings support the results of this study which indicate that job security influences job satisfaction.

5.4 Conclusions

This section presents the conclusions based on the findings of the study. The presentation is done according to the research questions.

5.4.1 Influence of Idealized Influence on Job Satisfaction

The multiple linear regression test results revealed that idealized influence had a significant influence on job satisfaction (R2 = .246, F(1, 97.750) = 112.421, p<.05). As a result, the null hypothesis that there was no significant influence of idealized influence on job satisfaction among employees in commercial banks in Kenya was rejected. The study concluded that idealized influence significantly influenced job satisfaction among employees in commercial banks in Kenya.

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5.4.2 Influence of Individualized Consideration on Job Satisfaction

The multiple linear regression test results revealed that individualized consideration had a significant influence on job satisfaction (R2 = .354, F(1, 138.779) = 188.851, p<.05). As a result, the null hypothesis that there was no significant influence of individualized consideration on job satisfaction among employees in commercial banks in Kenya was rejected. The study concluded that individualized consideration significantly influenced job satisfaction among employees in commercial banks in Kenya.

5.4.3 Influence of Inspirational Motivation on Job Satisfaction

The multiple linear regression test results revealed that inspirational motivation had a significant influence on job satisfaction (R2 = .344, F(1, 126.302) = 180.980, p<.05). As a result, the null hypothesis that there was no significant influence of inspirational motivation on job satisfaction among employees in commercial banks in Kenya was rejected. The study concluded that inspirational motivation significantly influenced job satisfaction among employees in commercial banks in Kenya.

5.4.4 Influence of Intellectual Stimulation on Job Satisfaction

The multiple linear regression test results revealed that intellectual stimulation significantly influenced job satisfaction (R2 = .292, F(1, 106.274) = 142.533, p<.05). As a result, the null hypothesis that there was no significant influence of intellectual stimulation on job satisfaction among employees in commercial banks in Kenya was rejected. The study concluded that intellectual stimulation significantly influenced job satisfaction among employees in commercial banks in Kenya.

5.4.5 Moderating effect of Job Security on the Influence of Transformational Leadership on Job Satisfaction

The multiple linear regression test revealed that job security had a significant moderating effect between transformational leadership and job satisfaction (R2 = .446, F(5, 27.760) = 54.780, p <.05). As a result, the null hypothesis that there was no significant moderating effect of job security between transformational leadership and job satisfaction among employees in commercial banks in Kenya was rejected. The study concluded that job security has a significant moderating effect on the influence of transformational leadership and job satisfaction among employees in commercial banks in Kenya.

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5.5 Recommendations

This section presents suggestions for improvement based on the findings of the study and also presents suggestions for further research.

5.5.1 Suggestions for Improvement

5.5.1.1 Influence of Idealized Influence on Job Satisfaction

The study established that idealized influence significantly influenced job satisfaction among employees in commercial banks in Kenya. Based on the finding, the leaders of banks should leverage on idealized influence to enhance job satisfaction among the employees. To achieve this, they can demonstrate or model how the employees should behave through charisma. Additionally, being trustworthy and ethical will help to enhance job satisfaction among the employees.

5.5.1.2 Influence of Individualized Consideration on Job Satisfaction

The study established that individualized consideration significantly influenced job satisfaction among employees in commercial banks in Kenya. Based on the finding, leaders in the commercial banks should leverage on individualized consideration to drive job satisfaction among the employees. This can be achieved through mentorship, support and delegation. These aspects demonstrate concern and care for the employees needs. This helps in boosting the job satisfaction among the employees.

5.5.1.3 Influence of Inspirational Motivation on Job Satisfaction

The study established that inspirational motivation significantly influenced job satisfaction among employees in commercial banks in Kenya. As a result, the leaders in the commercial banks should use inspirational motivation elements to drive and sustain job satisfaction. Motivation can be achieved through communication, encouraging teamwork and providing motivational elements in the workplace which promote job satisfaction among the employees.

5.5.1.4 Influence of Intellectual Stimulation on Job Satisfaction

The study established that intellectual stimulation significantly influenced job satisfaction among employees in commercial banks in Kenya. Therefore, leaders in commercial banks 193 need to stimulate their employees in order to enhance and sustain their job satisfaction. This can be achieved through knowledge sharing and allowing creativity among the employees. As much as some of the jobs in commercial banks can be routine, the leadership needs to develop ways of stimulating the intellect of the employees. This will in turn drive job satisfaction among the employees.

5.5.1.5 Moderating Effect of Job Security on the Influence of Transformational Leadership on Job Satisfaction

The study established that job security significantly moderated the relationship between transformational leadership and job satisfaction among employees in commercial banks in Kenya. It is therefore important that leaders in commercial banks ensure employees have job security which positively influences job satisfaction significantly. The lack of job security negatively influences job satisfaction. Job security can be provided by ensuring there is fairness, effective communication to reduce anxiety, no stress and that the work environment is habitable. Aspects of lack of fairness, factors leading to anxiety and stress should be eliminated in order to enhance job security among the employees.

5.5.2 Suggestions for Further Research

This study sought to establish the influence of transformational leadership on job satisfaction among employees in commercial banks in Kenya. This was a cross-sectional study carried out when commercial banks in Kenya were going through a lot of turbulence due to new legislation which capped the interest rates. As a result, this led to a reduction in the profitability of the commercial banks. Additionally, the country was going through an electioneering period which resulted in reduced business for the commercial banks. Based on this, most banks had to review their strategies to cut on costs and most chose to leverage on electronic platforms and reduce investments in brick and motor. As a result, most banks were downsizing through staff lay-offs and early retirement programs which caused a lot of anxiety among the employees in the industry due to lack of job security. This situation could have influenced the lack of job satisfaction among the employees in the banking sector at the particular time this study was carried out. Therefore, future research should carry out a study on the influence of transformational leadership on job satisfaction among employees in other sectors such as the microfinance institutions in Kenya at a time when the industry will have stabilized.

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APPENDICES

Appendix I: Cover Letter

4th August, 2017

Dear Respondent,

I am a Doctoral candidate in the Business Administration program at United States International University of Africa (USIU – Africa). As part of my doctoral degree requirement, I am expected to successfully conduct Applied Research on a relevant topic in my area of concentration which is Leadership and Change Management.

This study will focus on The Influence of Transformational Leadership on Job Satisfaction among employees in Kenyan Banks.

I would like to humbly request that you spend some time (10-15 minutes) to complete this questionnaire to the best of your knowledge. Thank you in advance for accepting to be a positive contributor to our society. I assure you that your responses will be treated with utmost confidentiality.

To maintain anonymity, I request that you do not write your name on the questionnaire.

The findings of this study will go help bank managers institute the effective leadership style which will positively influence job satisfaction among employees in the banking sector.

Yours Sincerely,

Andrew Njiraini Njiinu (Doctoral Candidate)

For more information, please contact me on: Cell: 0723 869 792 Email: [email protected]

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Appendix II: Questionnaire

This questionnaire has five sections which will cover general and demographic data, influence of idealized influence on job satisfaction, influence of individualized consideration on job satisfaction, influence of inspirational motivation on job satisfaction, influence of intellectual stimulation on job satisfaction and the moderating effect of job security on the influence of transformational leadership on job satisfaction.

Instructions: Please tick in the appropriate box. You are requested to complete this questionnaire as honestly and objectively as possible.

SECTION A: DEMOGRAPHIC AND GENERAL INFORMATION

Please tick (√) appropriately within the box provided

1. What is your gender?

Male [ ] Female [ ]

2. What is your age bracket?

21-29 years [ ] 30-39 years [ ] 40-49 years [ ] 50-59 years [ ] Over 60 years [ ]

3. What is your highest level of education?

Certificate [ ] Diploma [ ] Bachelor’s Degree [ ] Master’s Degree [ ] PhD [ ]

4. How long have you worked in the Bank?

0-5 years [ ] 6-10 years [ ] 11-15 years [ ] 16-20 years [ ] Over 20 years [ ]

5. What is the Tier of your bank?

Tier I [ ] Tier II [ ] Tier III [ ]

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SECTION B: INFLUENCE OF IDEALIZED INFLUENCE ON JOB SATISFACTION

This section focuses on the influence of idealized influence on job satisfaction.

Idealized influence is the capability to exert influence by serving as a role model. It brings out positive emotions from employees and makes them desire to emulate the leader who is a role model. It is achieved through charisma, trust and ethics. Job satisfaction is an emotional state or a pleasurable experience of an employee from the expectations of the job and the reality of the job situation.

Using a scale of 1-5, where one 1 = Strongly Disagree (SD), 2 = Disagree (D), 3 = Neutral (N), 4 = Agree (A) and 5 = Strongly Agree (SA), indicate the extent to which you agree or disagree with the following statements by ticking the box that best represents your opinion on each statement.

1 2 3 4 5 Idealized Influence

SD D N A SA

My leader has charismatic attributes

My leader demonstrates trust in my abilities

My leader is ethical in the workplace

Influence of Idealized Influence on Job Satisfaction

I am committed to the organization because my leader has charismatic attributes

I am hardly absent from work because my leader demonstrates trust in my abilities

I have no intentions of leaving my job because my leader is ethical in the workplace

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SECTION C: INFLUENCE OF INDIVIDUALIZED CONSIDERATION ON JOB SATISFACTION

This section focuses on the influence of individualized consideration on job satisfaction.

Individualized consideration is the degree to which the leader attends to the needs of the employees by displaying attention to their developmental needs. It involves leaders offering mentorship, coaching and support to the needs of the employees. Job satisfaction is an emotional state or a pleasurable experience of an employee from the expectations of the job and the reality of the job situation.

1 2 3 4 5 Individualized Consideration

SD D N A SA

My leader mentors me in the workplace

My leader supports me in my work

My leader delegates work to me

Influence of Individualized Consideration on Job Satisfaction

I am committed to the organization because my leader mentors me in the workplace

I am hardly absent from work because my leader supports me in my work

I have no intentions of leaving my job because my leader delegates work to me

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SECTION D: INFLUENCE OF INSPIRATIONAL MOTIVATION ON JOB SATISFACTION

This section focuses on the influence of inspirational motivation on job satisfaction.

Inspirational motivation is the ability of a leader to behave in a way that inspires, motivates and generates enthusiasm from employees. It is achieved through communication, teamwork and motivation. Job satisfaction is an emotional state or a pleasurable experience of an employee from the expectations of the job and the reality of the job situation.

1 2 3 4 5 Inspirational Motivation

SD D N A SA

My leader encourages two-way communication

My leader promotes teamwork among employees

My leader’s behavior motivates me at work

Influence of Inspirational Motivation on Job Satisfaction

I am committed to the organization because my leader encourages two-way communication

I am hardly absent from work because my leader promotes teamwork among employees

I have no intentions of leaving my job because my leader’s behavior motivates me at work

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SECTION E: INFLUENCE OF INTELLECTUAL STIMULATION ON JOB SATISFACTION

This section focuses on the influence of intellectual stimulation on job satisfaction.

Intellectual stimulation refers to the leader’s actions which persuade the employees to use their sense of logic to analyze situations using their creative thinking to find solutions. It involves creativity and innovation, risk taking and knowledge sharing. Job satisfaction is an emotional state or a pleasurable experience of an employee from the expectations of the job and the reality of the job situation.

1 2 3 4 5 Intellectual Stimulation

SD D N A SA

My leader encourages knowledge sharing among employees

My leader permits me to be creative in my job

My leader allows me to take risks in my job

Influence of Intellectual Stimulation on Job Satisfaction

I am committed to the organization because my leader encourages knowledge sharing among employees

I am hardly absent from work because my leader permits me to be creative in my job

I have no intentions of leaving my job because my leader allows me to take risks in my job

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SECTION F: MODERATING EFFECT OF JOB SECURITY ON THE INFLUENCE OF TRANSFORMATIONAL LEADERSHIP ON JOB SATISFACTION

This section focuses on the moderating effect of job security on the influence of transformational leadership on job satisfaction.

Job security refers to expectations regarding the continuity of a job situation and goes over and above the loss or retention of a job to the continuation or loss of certain desirable job features such as promotion opportunities and favorable working condition. It is influenced by anxiety, fairness and stress. Job satisfaction is an emotional state or a pleasurable experience of an employee from the expectations of the job and the reality of the job situation.

1 2 3 4 5 Job Security SD D N A SA

My leader encourages fair treatment to everyone

My leader’s behavior does not cause me stress

My leader does not leave room for anxiety

Influence of Job Security on Job Satisfaction

I am committed to the organization because my leader encourages fair treatment to everyone

I am hardly absent from work because my leader’s behavior does not cause me stress

I have no intentions of leaving my job because my leader does not leave room for anxiety

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Appendix III: USIU Research Introduction Letter

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Appendix IV: NACOSTI Research Permit

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Appendix V: Classification of Banks in Tiers

Tier One Banks

No Bank Name 1 Kenya Commercial Bank 2 Standard Chartered Bank Kenya Limited 3 Barclays Bank of Kenya Limited 4 Commercial Bank of Africa Limited 5 Co-operative Bank of Kenya Limited 6 Diamond Trust Bank Limited 7 Equity Bank Limited

Tier Two Banks

No Bank Name 1 National Bank of Kenya Limited 2 Citibank N A 3 Bank of Africa Kenya Limited 4 Chase Bank Limited 5 Stanbic Bank Kenya Limited 6 NIC Bank Limited 7 ECO Bank Limited 8 I&M Bank Limited 9 Housing Finance Bank 10 Family Bank Ltd 11 Bank of India 12 Bank of Baroda (Kenya Limited) 13 Prime Bank Limited 14 Imperial Bank Limited

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Tier Three Banks

No Bank Name 1 Habib Bank Limited 2 M-Oriental Bank Limited 3 Habib Bank A G Zurich 4 Middle East Bank Kenya Limited 5 Consolidated Bank of Kenya Limited 6 Credit Bank Limited 7 Trans-National Bank Limited 8 African Banking Corp. Bank Ltd 9 Giro Commercial Bank Limited 10 Spire Bank Ltd 11 Paramount Universal Bank Limited 12 Jamii Bora Bank 13 Guaranty Trust Bank ( Kenya) Ltd. 14 Victoria Commercial Bank Limited 15 Guardian Bank Limited 16 Development Bank of Kenya Limited 17 Fidelity Commercial Bank Limited 18 Sidian Bank Limited 19 Gulf African Bank Ltd 20 First Community Bank 21 UBA Kenya Bank Ltd 22 Dubai Bank of Kenya Ltd

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