Strategy Structure, Micro Foundations of Capability Configurations and Their Effects on Execution Gaps Under Volatility

STRATEGY AS CONFIGURATION: STRATEGY STRUCTURE, MICRO FOUNDATIONS OF CAPABILITY CONFIGURATIONS AND THEIR EFFECTS ON EXECUTION GAPS UNDER VOLATILITY

STANLEY IKENEDUM DIKE OMEIKE DM Nonprofit Management Fellow

Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy.

Weatherhead School of Management Designing Sustainable Systems

CASE WESTERN RESERVE UNIVERSITY

May, 2017

CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Stanley I.D. Omeike

candidate for the Doctor of Philosophy degree*.

(signed) Kalle Lyytinen (chair of the committee) Simon Peck

Bernard Bailey

Robin Gustafsson

(date) February 17, 2017

*We also certify that written approval has been obtained for any proprietary material contained therein.

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Dedication

To my loving family – my wife, Vivian, for her incredible resilience, kindness, love and support over the past quarter of a century, especially and specifically, including these past four years of doctoral studies; my children, Stanley, Stephenie, Stephen, Seth and Seal who endured countless months of their father being away studying and endless travels as

I completed this process; and also to my parents, Livinus and Cordelia Omeike who raised me to always strive to do my best. And to an awesome Boss, Edward Lamatek

Adamu without whose support and rare magnanimity this journey could never have been completed.

Table of Contents List of Tables ...... x List of Figures ...... xi Acknowledgements ...... xii Abstract ...... xiv Motivation ...... xvii CHAPTER 1: INTRODUCTION ...... 1 Problem of Practice and Research Question ...... 1 Relevance of Study Topic ...... 3 Theory and Literature Review ...... 4 View of Strategy Implementation in Literature ...... 5 Framing of Strategy Implementation Factors and Constructs ...... 6 Strategy and Orientations ...... 19 Volatility as an Antecedent of Variations in Strategy ...... 20 Composition of a Molar Strategy Structure ...... 22 Capability Configurations ...... 24 Research Questions and Design ...... 32 Research Questions...... 32 Overview of mixed methods design ...... 36 Summary and Results of Each Study ...... 38 Organization of Remainder of Dissertation ...... 45 CHAPTER 2: STRATEGY AS INTERACTION: IDENTIFYING LIVED EXPERIENCES OF C-SUITE CORPORATE EXECUTIVES WITH STARTEGY EXECUTION AT THE ORGANIZATIONAL LEVEL (Study #1.) ...... 46 Preface ...... 46 Introduction ...... 46 Literature Review ...... 47 View of Strategy Implementation in Literature ...... 48 Strategy Formulation and Implementation Constructs ...... 48 Research Question and Method ...... 53 Sample...... 54

Data Collection ...... 56 Data Analysis...... 56 Findings ...... 58 Finding 1: The interactions between Organizational shifts and Environmental shifts create a dynamic that constantly shapes the execution gap...... 59 Finding 2: The interactions between the strategy focusing and the gap reduction capabilities creates a dynamic that shapes organizations engagement with the environment as it seeks to reduce its execution gap...... 60 Finding 3: The interactions between the organization’s strategy configuration and its current execution capability create a dynamic that shapes its execution effectiveness...... 62 Discussion ...... 71 Limitations ...... 78 Future Research (See Study 2) ...... 79 CHAPTER 3: STRATEGY AS CONFIGURATION: HOW IS IMPLEMENTED STRATEGY STRUCTURED? - A QUANTITATIVE STUDY OF THE ANTECEDENT EFFECTS OF VOLATILITY ON STRATEGY STRUCTURE AND GAP REDUCTION (Study 2: Quant) ...... 80 Preface ...... 80 Introduction ...... 80 Theoretical Framework ...... 81 Hypothesis development ...... 82 Effect of Volatility on Strategy orientations and Execution Gap Reduction ...... 82 The effect of volatility on extent of engagement with strategy orientations...... 82 The effect of volatility on execution gap reduction effectiveness ...... 83 The Effect of Core Leapfrogging Strategy on Execution Gap ...... 85 Mediation Effects of Strategy orientations on Volatility...... 86 Moderation Effects of Volatility on the Strategy orientations ...... 87 Non-financial performance as a predictor of strategy implementation effectiveness performance ...... 88 Construct Operationalization...... 89 Measures: Dependent Variable ...... 90 Measures: Independent Variables ...... 90

Control Variables ...... 92 Instrument Development ...... 92 Pre-Testing ...... 92 Data Collection and Sample ...... 94 Data Analysis ...... 98 Data Screening ...... 98 Measurement Model Analysis ...... 99 Exploratory Factor Analyses (EFA) ...... 99 Confirmatory Factor Analyses (CFA) ...... 104 Common Method Bias ...... 105 Measurement Model Analysis in Smart Pls ...... 107 The Structural Model...... 109 Findings ...... 114 Discussion ...... 118 Summary of Main Effects Major Findings ...... 119 Volatility and Variance in Molar Structure of Strategy ...... 121 Volatility and Execution Gap Reduction ...... 127 Enabler and Growth Gap Reduction Performance ...... 129 Controls...... 130 Limitations and Future Research ...... 132 Conclusion and Implications for Practice ...... 134 CHAPTER 4: IMPACT OF VOLATILITY AND EXECUTION GAPS AND THE MEDIATING ROLE OF STRATEGY STRUCTURE ON CONFIGURATION OF DYNAMIC CAPABILITY (Study 3)...... 138 Preface ...... 138 Introduction ...... 138 Theoretical Background and Hypotheses ...... 139 How Does the Quality and Orientation of Formulated Strategy Affect the Configuration of Capability In The Presence Of Volatility? ...... 140 Effect of Volatility on Strategy Formulation and Gap Reduction...... 140 Volatility and Capability Configurations...... 142

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Volatility and Strategy orientations...... 144 Execution Gaps and Dimensions of Strategy...... 145 Execution Gaps and Dimensions of Capability...... 146 Intervening and Gap Reduction...... 146 Strategy orientations and Capability Configurations...... 147 Capability Configurations and Intervening...... 149 Mediation Effects Of Strategy orientations On The Relationship Between Volatility And Capability Configuration...... 150 Research Method and Sampling Strategy ...... 154 Construct Operationalization ...... 154 Measures: Independent Variables ...... 155 Instrument Development ...... 156 Analysis ...... 157 Measurement Model Analysis in Smart Pls ...... 158 The Structural Model...... 160 Findings ...... 163 Mediation Effects Of Strategy orientations On The Relationship Between Volatility And Capability Configuration...... 172 Moderation Effects ...... 174 Discussion ...... 185 The Effect of Strategy Formulation on Execution Gaps ...... 186 Volatility, Execution Gaps and Variations in Strategy Structure...... 188 Volatility, Execution Gaps and Variations in Capability Configuration ...... 192 Strategy Structure and Capability Configurations ...... 194 Capability Configurations Organization ...... 199 Recursive Organization of Gap Reduction Variable under Varying Volatility Levels ...... 200 CHAPTER 6: CORE FINDINGS AND INTEGRATED DISCUSSION...... 204 INTEGRATED ANALYSIS ...... 206 Implications and Contributions ...... 211 Future Research ...... 213

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Limitations ...... 214 Conclusion ...... 215 Appendix A. Qualitative Study Interview questions (Study 1) ...... 217 Part 1 – Initial / Background Questions: ...... 217 Part 2 – Core Questions:...... 217 Appendix B. Scales used in Quantitative Studies (Study 2 and 3) ...... 219 Appendix C: Common Method Bias Test Results ...... 224 APPENDIX D: CFA Measurement Models (AMOS) (Study 2 and 3) ...... 225 APPENDIX E: Structural Equation and Measurement Model (Smart PLS) ...... 226 REFERENCES ...... 227

List of Tables

Table 1 : Factors Influencing Strategy Implementation (Literature) ...... 7 Table 2: Strategy Implementation Frameworks from Literature ...... 11 Table 3 : Noble’s (1999) Strategy Implementation Framework ...... 14 Table 4 : Research questions and methods ...... 42 Table 5 : Strengths and weaknesses of the different methods used ...... 43 Table 6: Factors influencing Strategy Implementation (Literature) ...... 49 Table 7 : Strategy Implementation Frameworks from Literature ...... 51 Table 8 : Interview Respondents...... 55 Table 9 : Table of Organizational Execution Capability Factors ...... 65 Table 10 : Table of Contextual Immersion Sub Themes from Findings ...... 67 Table 11 : Marshaling Sub Themes ...... 69 Table 12 : Table of Organizational Balance Sub Themes from Findings ...... 71 TABLE 14 : Respondent Sample Characteristics ...... 96 Table 15 : Execution gap reduction Performance (EFA Measurement Model Results). 102 Table 16 : CFA Measurement Model Results (n = 557) ...... 106 Table 17 : Model Correlations Matrix ...... 108 Table 19 : Smart PLS Measurement Model Collinearity Assessment ...... 111 Table 18 : Smart PLS CFA Measurement Model Results (n = 557) ...... 112 Table 20 : Significance Testing Results of the Structural Model Path Coefficients ...... 117 Table 21 : Strategy Context -Execution gap reduction Performance Model Correlation Matrix (n = 557) ...... 159 Table 22 : Hypothesis Test Results ...... 171 Table 23 : High and Low Volatility Moderation Results & Group Difference Tests.... 179 Table 24 : Mediation Test Results ...... 182 Table 25 : Model f² Effects ...... 183 Table 26 : Model Endogenous Latent Variable R² and Q² Values ...... 184 Table 27 : Research Study Summary with Results ...... 205

List of Figures

Figure 1: Okomus’s (2001) Strategy Implementation Framework ...... 16 Figure 2. Dimensions of Molar Strategy...... 29 Figure 3: Linked Approach to Literature Gaps ...... 34 Figure 4 : Conceptual Framework ...... 35 Figure 5 : Mixed Methods Approach ...... 38 Figure 6 : Qualitative Data Structure Analysis ...... 58 Figure 7 : The Strategy Execution Gap Dynamic Model ...... 73 Figure 8 : The Strategy Execution Gap Dynamic Model ...... 74 Figure 9 : Hypothesized Model...... 81 Figure 10 : The flow of quantitative analysis ...... 100 Figure 11 : Main Effects Model ...... 117 Figure 12 : Hypothesized Final Model ...... 153 Figure 13 : SEM Results ...... 170 Figure 14 : Main Effects Model ...... 177 Figure 15 : High Volatility Effects Model ...... 177 Figure 16 : Low Volatility Effects Model...... 178

Acknowledgements

I must begin by acknowledging two individuals who played pivotal roles in igniting in me the enthusiasm for research and scholarship. Walter Ahrey who was the Director of

Strategy and Performance when I joined the Central Bank of Nigeria. Through leadership habit he demonstrated to me the superior value of relentless pursuit of knowledge and the application of learning in solving organizational challenges. He actively encouraged and facilitated my initial foray into academic scholarship. Also, Dare Lawal, who has been my life long mentor. He taught me the basic principle of tenacious focus and a curious mind. He prepared me for a future that only now becomes apparent. Without these individuals my path to becoming a practitioner scholar would not have been possible.

I must thank my dissertation committee chair, Kalle Lyytinen, for investing time and effort in me, constantly refining my scholarship. He sets a standard for academic excellence that has inspired and propelled me on. The perspectives and rigor Kalle brought into my work and my sessions with him helped steer me purposefully down the stream of scholarly exceptionalism. Kalle has a keen eye for identifying valuable contribution and very early on in my research saw well ahead of me the potential value of my research and has helped shape both my scholarly identity and the contributions this paper makes to the body of knowledge.

I would also like to thank the remaining members of my dissertation committee. Simon

Peck. He made very strong impact on my approach to framing enquiry and ensured I was thorough and exhaustive enough in my work. Benard Baily has been awesome. He

xii provided very refreshing practitioner-based insights that helped balance my work. Robin

Gustafsson provided invaluable critique of my research framings helping me balance them in the wider context of existing theory. His contributions helped refine this final work.

I must thank my friends and colleagues of the 2016 Doctor of Management Cohort that unselfishly shared so much of their lives and talents with me over the past 4 years. For without their support and encouragement this would never have been possible. Of them all, I appreciate the very supportive role played by Allen Curreri, Susanne Cromlish and

William Brake. These friends have been awesome in the roles they each played to get me to this point. Sue Nartker and Marilyn Chorman who supported me throughout the program. I also thank the more than 1000 corporate executives that participated in my research studies.

Finally, my dear wife Vivian endured the most. She sacrificed her professional career and spent much time without her husband while raising five wonderful children and never wavering in her support. My brother Bethel has been amazing, always willing to rescue me every now and then when I get stuck. I also want to thank my brother Joel for the awesome job he did with getting my surveys administered. Lilian Okocha and Charles

Ugwumba also deserve very special recognition for without their tireless effort I could not have completed this work. Thanks to all these people and the many more that I love who have helped me so very much throughout my life.

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Strategy as Configuration: Strategy Structure, Micro Foundations of Capability Configurations and Their Effects on Execution Gaps under Volatility.

Abstract

STANLEY IKENEDUM DIKE OMEIKE

Effective strategy execution depends on the organization’s capacity to understand the dynamics around strategy execution and to shape its strategy. The body of knowledge around strategy formulation is well established, but there is less clarity as to how organizations are to implement strategy to close the execution gap between their intent and reality. Although literature suggests that most successful organizations adapt significantly their strategy during implementation and ultimately realize a different strategy, we find that understanding the role of dynamic strategy process, which connects the firm’s strategic intent with the observed velocity of change and competitiveness within the environment is crucial but less understood. This calls for firm level dynamic capabilities and ambidextrous strategy implementation. Yet most studies of dynamic strategy processes focus less on connecting the interactions between volatility and strategy with the theory of dynamic capabilities to explain how this dynamic is expressed in the implemented strategy. Research largely ignores three important considerations: 1) how volatility and related variations in strategy jointly influence the effectiveness at closing the gap between strategic intent and implementation. 2) the mechanisms through

xiv which these factors interact and 3) how such interactions are expressed in the implemented strategy in relation to dynamic capabilities. We pose three related research questions: 1) how does volatility influence the configurations (molar structure) of the implemented strategy? 2) how does volatility affect the strength of each strategy orientation (operational, core expanding, core transforming) in the implemented strategy

(Strategy variation)? and 3) to what extent does the presence of each of the strategy orientations mediate the effects of volatility on reducing strategy execution gaps? We study these questions using a sample of 557 companies in the US and Nigeria operating in several industries. We approach organization’s strategy and capability as recursively organized and influenced by environmental volatility. We study specifically environmental factors that constitute the volatility construct and relationships between these environmental constructs and organizational responses as strategy compositions and the related capability configurations. We show that under volatility, variations in strategy and related configurations of capabilities can have a positive relationship with strategic impact and increase implementation effectiveness. The study surfaces variations in the strategy orientations and how they explain the extent to which the organization engages in each capability component. We further uncover patterns of strategic practices with a view to conceptualize how gap reduction happens and what mechanisms influence it.

For example, we observe a direct positive effect between volatility and strategy orientations; increasing levels of volatility leads to increased levels of discontinuous change in strategy in that the presence of regenerative change (core leapfrogging strategy) becomes stronger. Surprisingly, we find limited support for a direct negative effect of volatility on the gap reduction. Overall, implemented strategy has a molar xv structure, which varies to the extent to which organizations engage in each orientation of strategy when volatility changes. This research has several implications: first, it introduces a concept strategy configurations and their variations during strategy implementation; second, it directs executive’s attention towards salient mechanisms that influence the interactions between volatility and strategy structure and how these interactions connect to dynamic capabilities to influence implementation success. Finally, the study invites students of strategy to new research avenues to explore impactful interactions within volatile strategy contexts.

Key Words: Strategy, Strategy implementation, Strategy Execution, Dynamic

Capabilities, Strategy Execution Gap, Capability Configuration, Volatility,

Strategy Context, Capability Orders

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Motivation

For over 25 years I have worked as an external consultant or as an internal corporate strategist to facilitate organizational conversations and actions to formulate and implement the organization’s strategy. A common thread of experience across all the organizations I have been engaged with is the difficulty in effectively implementing the formulated strategy to achieve the deliberate intent. In contrast variance between the intent of the strategy and the implementation outcomes is significant. The transition from a formulated to an implemented strategy need to be mastered by organization’s leaders, because it is the implemented strategy which provides the means through which the organization harnesses its resources to achieve strategic outcomes- negative or positive.

Implementing the strategy prepares the organization to truly compete and develop related capabilities and thereby to meet the expectations of various stakeholders. Strategy, capability and environmental volatility are organized recursively which makes the organizations to struggle as they continuously orchestrate means to realize their strategic intent (Omeike and Lyytinen, 2015). Consequently, I have been for a while interested in the organizational level effects of the complex relationships between strategy, capabilities and environment during the strategy implementation.

But why should we study interactions at the organizational level using this framing as the study context? These interactions ultimately affect the organizations’ effectives in achieving its intent. Failing to properly understand how volatility influences the interactions between strategy and organizational capability or the mechanisms of their recursive organization and how they need to be consequently configured will lead to xvii inefficient mobilization of organization’s resources and ineffective responses to environmental change resulting in failed strategies, loss of competitive advantage and market leadership. This will result in falling profitability and ultimately organizational collapse (D’Aveni & Gunther, 1994; D’Aveni, 1999; McNamara et al., 2003; Hamel &

Välikangas, 2003; Christensen & Langhoff-Roos, 2003). Inspired by Walsh’ (1995: 286) assertion that “challenges should stimulate investigation, not serve as a rationale for research moratorium”, I accept the conceptual and empirical challenge to study such strategy implementation dynamics.

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CHAPTER 1: INTRODUCTION

Problem of Practice and Research Question

Most organizations struggle in realizing the intent of their strategy as echoed in the widely held views that the capability to execute strategy surpasses in importance the initial quality of the strategy itself (Hrebiniak, 2013. Christensen and Donovan, 2000).

Senior executives also recognize the importance of strategy implementation, but a majority admits that their companies fall short in narrowing the gap between the stated intent and the results. The Economist Intelligence Unit Report found out that 80% of managers state that executing strategic initiatives successfully will be “essential” or “very important” for their organizations’ competitiveness; yet 61% of the respondents acknowledge that the firm struggles to bridge the gap (The Economist Intelligence Unit

Report, 2013). Recent research has made some progress identifying myriads of factors that account for this challenge. One such factor is unpredicted volatility defined as the relative velocity of change, uncertainty and competitiveness in the environment.

(McNamara et al., 2003; Hamel & Välikangas, 2003; Christensen & Langhoff-Roos,

2003). Volatility induces environmental shifts that may erode the competitive advantages of firms (Markides, 1999; Thomas, 1996; Larsen et al., 2003; D’Aveni & Gunther, 1994,

Hamel & Prahalad, 1996) increasing the difficulties experienced during strategy implementation. Organizations have responded to these implementation challenges by seeking to reduce the ‘execution gap’ defined as the gap between the stated intent of the strategy and the implementation outcomes. This is accomplished by altering the focus of

strategy (Christensen and Langhoff-Roos, 2003; Omeike and Lyytinen, 2015) and related capabilities (Teece et al., 1997; Eisenhardt and Martin, 2000). Variations in strategy, related organizational capabilities and the nature of the environment are thus pivotal constructs for strategy research. This framing suggests intimate interdependency between the dimensions of strategy and the associated capabilities that must be configured to execute the strategy. It also suggests the idea of variation of both strategies and capabilities relative to the change in the environment. Hence, perceived volatility in the environment influences an organization’s strategy implemented over time (Christensen &

Langhoff-Roos, 2003). This, in turn, influences the ongoing configuration of organizational capabilities (Teece et al., 1997; Eisenhardt and Martin, 2000). The staging of dynamic capabilities can therefore be seen as an operationalization of the implemented strategy as a means to achieve a chosen set of strategic objectives.

This framing lines up with a growing body of research, which advocates a dynamic approach to strategy implementation where organizations are approached as adaptive systems which manifest non-linear negative and positive feedback loops connecting individuals, groups, functions and processes within the organization and connecting the organization to other systems in the environment (Levy 1994; Mintzberg and Quinn 1996; Pascale 1990; Stacey 1993, 1995, 1996a, 1996b; Theys 1998 and

Wheatley and Kellner-Rogers 1995). Prominent among these studies is the configuration school where strategy formation is seen as a process of transforming where the firm periodically reconfigures itself in response to internal or external stimuli. The adaptive model by Chafee (1985) views this lens as of three alternative models of strategy (Linear

Model, the Adaptive Model and the Interpretive Model). In adaptive model the focus is on continuous evaluation of the environment and subsequent adaptation and where the environment is seen as highly dynamic. Similar idea is echoed in the organic view by

Farjorn (2000) which adopts two alternate views of strategy- mechanistic and organic- where in the latter, the flow of strategy events is considered to be nonlinear, interactional and dynamic. All these studies perceive strategy execution to involve dynamic organization level interactions rather than it following a linear mechanistic train of actions. However, they provide inadequate explanations of organizational level factors involved in this interactions and the dynamics associated with their organization.

Following these considerations, the gaps at the organizational level in the literature fall into three sorts: strategy implementation variables, mechanisms of interaction, and void of a framework of their dynamic organization. This raises important questions in relation to strategy implementation variables. What are the elements which are likely to interact during strategy execution? What are their modes of interaction? What are the mechanisms by which the interactions happen? We use a three-phased mixed method approach to address these research questions.

Relevance of Study Topic

While the studies reviewed above have argued at conceptual level for view of strategy as dynamic set of organizational interactions little is known about the dimensions, elements or the actual dynamics of these interactions. We know even less to what extent such collective dynamics influence strategy implementation and its

3 outcomes. Therefore, the first two phases of this research seek to probe to what extent strategy implementation at the organization level proceeds through a series of organizational interactions that integrate three sets of factors dynamically. These are: 1) environmental volatility, 2) strategy and 3) the dynamic capabilities. The third phase builds upon the findings from the first two studies and seeks to theorize how varying dimensions of strategy influence the dynamic configurations of capabilities in the presence of volatility. This helps complete in the study a loop from the sensing side of the strategy model (focused on perceived volatility and execution gaps) to organizational response mechanisms (focused on strategy and capability relationship) that jointly shape strategy implementation and its outcomes.

Theory and Literature Review

In this section, I first review extant literature on strategy implementation and focus on factors that influence strategy implementation. I also review frameworks for strategy implementation as discussed in the literature. This forms a basis for identifying what factors influence strategy implementation at the organizational level and how they can be organized into a framework to understand the dynamics of organization’s implementation of a strategy. I next review extant studies that analyze various dimensions of strategy- especially I examine how these studies treat the effects of volatility on the efficacy of various strategy orientations in reaching specific outcomes.

I also review extant literature on dynamic capabilities as this theory forms a foundation for explaining how strategy variations come to be and how organizations over time

4 learn to orchestrate dynamically their capabilities as they implement their strategy. I also introduce the idea of configurations (Fiss, P. C. 2007) as a basis to frame and understand the simultaneous combination of several strategy orientations as a response to the change in the environment. Finally, building on this theoretical foundation I posit the main effects of volatility on each orientation of strategy and discuss associated capability configurations by articulating a set of hypotheses.

View of Strategy Implementation in Literature

Literature abounds with definitions of strategy (Porter, Haberburg and Rieple

2008; Kim and Mauborgne 2005; Porter 1980; Mintzberg and Lampel 1999; White 2004:

5; Mintzberg and Quinn 1991: 12) and their implementation approaches (Hrebiniak and

Joyce 1984; Aaker 1988; Floyd and Woolridge 1992; Kotler 1984; Bonoma 1984; Li,

Guohui, and Eppler 2008; Mintzberg 1978; Miller and Frieson 1980 and Pettigrew 1985).

The multitude of these definitions and perspectives influence how the strategy execution

‘gap’ and related strategy execution models have been defined nebulously. There is no common understanding how organizations engage in executing strategy. For example,

White (2004, p.5) defines strategy as “a coordinated series of actions which involve the deployment of resources to which one has access for the achievement of a given purpose”. This view of strategy emphasizes the need for dynamic deployment of organization’s resources relative to the observed change in the environment. It connects strategy, on the one hand, with the dynamic deployment of organizational capabilities and is therefore consistent with the resource-based view (Peteraf & Barney, 2003; Barney,

1991). On the other hand, it is also consistent with the contingency theory, which states 5 that organizations seek to achieve ‘fit’ within a turbulent environment by changing strategy and structuring (Scott, W.R., 1981; Jeong Chun Hai and Ibrahim, & Nor

Fadzlina Nawi, 2012). This view connects with Li et al., (2008)’s definition of strategy implementation as a dynamic, iterative and nonlinear learning process which comprises of a series of decisions and activities aimed at actualizing strategic intent. Put together, these definitions frames strategy execution as a dynamic and generative dance between the organization and its strategic context. This can be contrasted with the more traditional definition which often views strategy implementation as a linear, strict ‘march’ that converts a plan into a well-defined activity (Wind and Robertson 1983; Singh, 1998). I will assume the former view as I move forward.

Framing of Strategy Implementation Factors and Constructs

Studies into strategy implementation have progressed in two directions; the first stream studies individual or bi-variate factors influencing strategy implementation. For example, Li et al. (2008) identify nine recurring factors influencing strategy implementation: the strategy formulation process, the strategy executors (managers, employees), the organizational structure, the communication activities, the level of commitment for the strategy, the consensus regarding the strategy, the relationships among different units/departments and different strategy levels, the employed implementation tactics, and the administrative system. In Table 1, I summarize each of these factors. Although extant studies as shown in Table 1 evince a progress in understanding the impact of each of these factors they mostly focus one-sidedly on ‘soft’

6 people or ‘hard’ internal factors and do not explain the dynamic generative dance that take place between these factors during the execution or between the organization and its context. We still do not know how these factors interact over time and what mechanisms shape these interactions and influence strategy implementation. This calls for studies at the organizational level that focus on uncovering the elements that dynamically interact, the mechanisms of such interactions and how the elements change and are organized during implementation (Li et al, 2008).

Table 1 : Factors Influencing Strategy Implementation (Literature)

Factor Description Reference Strengths/Weakness  Strategy formulation Yang et al.  Showcase internal organization soft process influences its (2008), issues of strategy implementation (Li acceptability, legitimacy, Hrebiniak etal 2008). Strategy the level of consensus and (2006), Bantel  An element of the micro foundations of commitment. (1997), strategy implementation Formulation Alexander  Lacks pivotal relevance at the Process  classified into (1985), Allio, organizational level in shaping how the participative, Kim and organization responds to environmental Collaborative, Synthesis, Mauborgne, changes during implementation. Centralized, Specialized (1991, 1993), and Sole Ranger Singh (1998) Describs the role and  Internal organization soft people issues relevance of the following  Adds little value to strategy groups; Govindarajan implementation framework (Li etal  The Boards, (1998), Viseras 2008). Strategy  Top Management, Baines and  It lacks pivotal relevance at the Sweeney Executors  Senior Management, organizational level in shaping how the  Middle Management and (2005), Smith organization responds to environmental Non-management and Kofron changes during implementation. layers (Teams and Operational (1996)  An element of the micro foundations of staff strategy implementation

 Showcase internal organization soft Emphasizes alignment Heide, issues of strategy implementation (Li between strategy type and Grønhaug and etal 2008). structure (Structure types are: Johannessen Organizational  An element of the micro foundations of management dominant, (2002), Schaap strategy implementation Structure customer-centric innovators, (2006), White customer-centric cost (1986), Olson,  Lacks pivotal relevance at the controllers and middle Slater and Hult organizational level in shaping how the ground) (2005) organization responds to environmental changes during implementation.

Factor Description Reference Strengths/Weakness Alexander (1985),  Showcase internal Rapert and Wren organization soft (1998) Peng & issues of strategy Litteljohn, 2001; implementation Advocates that Communication influences Heide & Grønhaug & (Li etal 2008). Communication the management’s ability to create, Johannessen, 2002; disseminate and mobilize engagement.  An element of the Rapert & Velliquette micro foundations & Garretson, 2002; of strategy Forman & Argenti, implementation 2005; Schaap, 2006)  Lacks pivotal (Rapert & Velliquette relevance at the & Garretson, 2002), Emphasizes understanding and engagement organizational (Wooldridge & Floyd, of the middle management is seen critical level in shaping Commitment 1989, (Heracleous, in implementation. how the 2000). Alexander organization (1985), Noble & responds to Mokwa (1999) environmental Consensus Advocates agreement between top, middle-, Floyd and Wooldridge changes during and operating-level managers on the (1992), Noble, 1999, implementation. priorities of the organization. Dooley, Fryxell and  Judge‟s (2000) Consensus level varies from strong consensus, blind devotion, and informed skepticism to weak consensus. The relationship Advocates alignment among different Walker and Ruekert strategy execution levels influences (1987), Porter (1980), among different strategy implementation. (Gupta, 1987). Units/depts. Chimhanzi, 2004

Implementation Describes several implementation tactics Nutt (1986), tied to leadership styles; Bourgeois Ш and tactics  Leadership orientation (intervention, Brodwin (1984), participation, persuasion, and edict.), Lehner (2004),  Process View (Commander model, Change model, Collaborative model, Cultural model, Crescive model), and  behavioral model (command, change/politics, culture, collaboration and crescive/market ).

Advocates differentiated administrative systems to facilitate strategy. Govindarajan (1988), The  organizational structure systems (Drazin & (decentralization), administrative Howard, 1984;  control systems (budget evaluative Nilsson & Rapp, system style); 1999)  selection of managers (locus of control). Henry (2008, p.10) Effective strategy implementation demands (Heracleous 2000; Culture flexibility in organizational culture and Heide, Grønhaug and design. Johannessen 2002; Schaap 2006)

Another, more, recent stream of strategy implementation studies has highlighted the multidimensional nature of managing strategy execution (Cobbold and Irwin 2001; Zook and Allen 2001; Hrebiniak 2005; Mintzberg 2002 ; Johnson 2004; Hrebiniak 2006;

Mankins and Steele 2005). I adopt this multi-dimensional approach as it emphasizes strategy implementation as a process and focuses on emergent inter-relationships among organizational components during implementation. Studies in this stream describe strategy execution and its failures from multiple perspectives and have put forward several frameworks to explain how organizations engage while implementing strategy.

They do so in two distinct ways (summarized in Table 2 below): First, through simple categorizations of pivotal factors into groups of categories (such as the studies of:

Skivington & Daft, 1991; Noble, 1999b; Noble & Mokwa, 1999; Beer & Eisenstat, 2000;

Okumus, 2001). For example, Noble & Mokwa (1999), advocate a “strategic implementation model” made up of three dimensions; the structural view (firm structure and control mechanisms) and the interpersonal process view (strategic consensus, behaviors, organizational climate, communication and interaction processes) and the individual-level processes view (emphasizing cognition, organizational roles and commitment). While this approach clarifies a heuristic of how groups of implementation factors influence execution, frameworks in this category have been criticized for not adding adequate value to the current body of knowledge, because they do not sufficiently align with empirical research on strategy implementation. Second, they do not relate the identified variables to each other with a cogent conceptual logic (Li et al, 2008).

A second approach focuses on compiling multiple factors into a framework that demonstrates causal or temporal relationships between them (Noble, 1999a; Higgins,

2005; Qi, 2005; Brenes & Mena & Molina, 2007). I follow below this latter body of research. This body frames strategy implementation factors into dynamic frameworks or relational models; Studies in this stream of research come in two forms; those that advocate a process model of strategy implementation represented in the interactions, meanings, and sanctions and relationships among organizational components as the strategy implementation proceeds, and those that put forward a generic framework for strategy implementation represented in configurations of rules and resources that underpin the dynamic interactions and organizational responses to the internal and environmental triggers shaping strategy implementation (Li et al , 2008). These process models are well researched and represent mostly a simple value chain of activities that capture a linear progression of the strategy implementation along logically sequenced phases. For example; Noble, (1999) puts forward a framework (Table 3 below) which organizes the strategy implementation around four major stages (pre-implementation, organizing the implementation effort, managing the implementation process, maximizing cross-functional performance) and five managerial levers for these implementation phases (goals, organizational structure, leadership, communications, and incentives).

Although Noble emphasizes the importance of contextual variables and makes an attempt to frame execution around dynamic interactions by showing that the management of these factors changes through the sequenced implementation stages his focus is largely on internal organizational elements. The framework neither integrates nor explains the role of external environment as a means to account for emerging dynamics. This poses a 10 challenge with the offered framing of strategy execution dynamics; the internal context appears to play a key role in implementing strategic decisions. However, the external context has been shown to also play a pivotal role in how strategy is actually shaped and implemented (Miller, 1980; Murray, 1988; Wright, 1987; D’Aveni & Gunther, 1994;

Hamel & Prahalad, 1996; D’Aveni, 1999). Focusing on the internal implementation process alone and ignoring the wider context does not therefore provide a holistic picture of implementation challenges. In addition, this approach follows a linear flow and is therefore inadequate to describe dynamic and recursive interactions among the organizational elements and between the organization and its strategic context (Li et al,

2008).

Table 2: Strategy Implementation Frameworks from Literature

Framework Key Construct Gap Skivington &  Framework and process, with different content  Lack of s dynamic Daft in their categories. view of strategy ( Skivington &  Two generic types of strategic decisions – low implementation Daft, 1991) cost and differentiation – that need to be  No account for the implemented through two organizational dynamic interactions modalities, namely framework and process. between the organization and its strategic context Higgins 8S Structure, system and processes, leadership style, Fails to adequately account Model (Higgins, staff, resources and shared values and strategic for the dynamic 2005)) performance. interactions between the organization and its strategic context Li etal Strategic  Soft factors (people oriented factors: Fails to adequately account Management communications, consensus and commitment), for the dynamic Model  Hard factors (institutional factors: interactions between the organizational structure and administrative organization and its Y. Li, Guohui S., system) strategic context Eppler MJ (2008  Mixed factors (strategy formulation, SBU relationship among different hierarchical levels and strategy etc.)

TABLE 2 (Cont’d) Strategy Implementation Frameworks from Literature

Framework Key Construct Gap Noble‟s strategy  Structural view (firm structure and control  Lack of s implementation mechanisms) dynamic view framework  Interpersonal process view (strategic consensus, of strategy behaviors, organizational climate, communication and implementation Noble‟s (1999) interaction processes)  No account for  Individual-level processes view, emphasizing the dynamic cognition, organizational roles and commitment interactions  Model is organized around four stages of the between the implementation –pre-implementation, organizing the organization implementation effort, managing the implementation and its strategic process, maximizing cross-functional performance context  Five managerial levers for implementation each phase: goals, organizational structure, leadership, communications, and incentives Qi Model Seven factors: feedback systems, resources, leadership, Fails to adequately motivation, communication and coordination, company account for the Qi (2005) structure, company culture. dynamic interactions between the organization and its strategic context Brenes, Mena Five dimensions: strategy formulation process, systematic Fails to adequately and Molina execution, implementation control and follow-up, CEO’s account for the leadership, motivated management and employees, and, dynamic interactions Brenes, Mena corporate governance (board and shareholders) between the and Molina organization and its (2007) strategic context Beer and six silent killers of strategy implementation a collection of Eisenstat  top-down or laissez-faire senior management style strategy  unclear strategy and conflicting priorities implementation Beer and inhibiting factors  an ineffective senior management team Eisenstat (2000) but do not put  poor vertical communication forward any  poor coordination across functions, businesses or framework for borders strategy  inadequate down-the-line leadership skills and implementation development The six killers are grouped into three categories:  quality of direction,  quality of learning and  quality of implementation.

TABLE 2 (Cont’d) Strategy Implementation Frameworks from Literature

Framework Key Construct Gap Pettigrew  strategic content, most of the constructs are  context (consisting of organizational context: not based on empirical Pettigrew, organizational structure, organizational culture; analysis 1985; Pettigrew and environmental context: uncertainty in the et al., 1992) general and uncertainty in the task environment),  process (operational planning, resources, people, communication, control and feedback) and strategic outcome Okumus Extended Pettigrew et al., 1992 model to include most of the constructs are three new variables. not based on empirical Okumus (2001)  content (strategic decision, multiple project analysis implementation),  context (internal context: organizational structure, organizational culture, organizational learning; external context: environmental uncertainty in the general and task environment),  process (operational planning, resources allocation, people, communication, monitoring and feedback, external partners)  outcome (tangible and intangible outcomes of the project). Noble & three dimensions: Clusters factors on the basis Mokwa structural view, hierarchical relevance in the interpersonal process view organization but these Noble & Individual-level processes view. constructs provide little Mokwa (1999) value in explaining the implementation process or dynamics Okumus and five groups: planning Approach, learning approach, Presents classifications of Roper contingency approach, configurational approach, strategy implementation and the complexity approach approaches but Fails to Okumus and adequately account for the Roper (1999) dynamic interactions between the organization and its strategic context Kaplan and A six stage; strategy development, alignment, Fails to adequately account Nortorn operationalization, monitoring and adapting for the dynamic interactions between the organization and its strategic context

Table 3 : Noble’s (1999) Strategy Implementation Framework

STAGES Pre- Organizing the Managing the Maximizing LEVERS Implementation Implementation Implementation Cross-functional Effort Process Performance Ensure that all Introduce goals of the Maintain the Develop and focus on managers are aware strategy being flexibility to adapt common goals to Goals of the strategic goals implemented, incl. fit goals based on encourage cross- of the firm within firm’s broader environmental functional strategic vision changes cohesiveness Ensure that Establish a formal Ensure equal Temporarily suspend functional areas implementation unit representation by all key implementation have the slack and ensure its affected functional team members‟ normal Organizational resources needed to visibility throughout areas structure responsibilities to be able to contribute the firm allow them to focus to an on the implementation implementation effort effort Develop employees‟ Establish a Ensure that leaders Balance visible and knowledge and “champion” who show charismatic leadership appreciation of has both official equal attention to all with a maintenance of functional-level autonomy for Leadership multiple functional cross-functional authority and general concerns functional-level areas implementation respect in the firm efforts Maintain regular Discuss and resolve Update Communicate Communications cross-functional implementation implementation implementation communications details early in the team frequently on progress across the to foster process progress and changes entire organization to understanding and in objectives foster buy-in appreciation Reward the Develop time and Adjust incentives as Establish visible and development of performance-based strategy and consistent cross- cross-functional incentives for environmental functional rewards for Incentives skills implementation team conditions change successful while lessening during implementation traditional functional implementation efforts incentives

Over the last two decades, a small stream of literature has put forward frameworks describing higher level interactions between central elements of strategy during its implementation. Three studies illustrate well these approaches; 1). Okumus (2001) (see

Figure 1 below) extended an earlier framework by Pettigrew (Pettigrew, 1985; Pettigrew

14 et al., 1992) including four components: content (strategic decision, multiple project implementation), context (internal context: organizational structure, organizational culture, organizational learning; external context: environmental uncertainty in the general and task environment), process (operational planning, resources allocation, people, communication, monitoring and feedback, external partners) and outcome

(tangible and intangible outcomes of the project). 2); Brenes, Mena and Molina (2007) put forward a implementation model of five dimensions that consist of strategy formulation process, systematic execution, implementation control and follow-up, CEO’s leadership, motivated management and employees, and, corporate governance (board and shareholders); and finally 3) Higgins, (2005) emphasizes the role of alignment between organizational factors (structure, system and processes, leadership style, staff, resources and shared values) on strategic performance during strategy execution.

These studies start to clarify strategy implementation to involve interdependencies that only can be captured in a relational framework which observes interactions among composite strategy factors. But these approaches fail to identify and explain higher level organizational level interactions and the pertinent issue of dynamic organization of the elements. They also fail to surface mechanisms of interactions between the elements through which ongoing configurations of organizational components such as strategy content change or capability shifts are invoked by changes in the organizational context.

For example, although Okomus’s model (Figure1 below) includes the external context described as influencing both strategic content (projects) and the strategy outcome; the model fails to adequately and empirically describe how these interactions happen or the

15 mechanisms through which organizational responses shape components of strategy and the internal context. Finally, they come short in explaining how organizations respond to these changes so as to shape strategy and the implementation mechanisms (Li etal, 2008;

Omeike and Lyytinen, 2015). My proposed study aims to contribute to filling these gaps in literature.

Figure 1: Okomus’s (2001) Strategy Implementation Framework

The strategy implementation framework by Okumus (2001)

Key 1. The characteristics of and developments in, the external environment influence the strategic context and force the companies to develop new initiatives

2. The problems and inconsistencies in the internal context require new projects

3. The project is implemented in the internal context and the characteristics of, and changes in, the context variables influence the process variables

4. All the process variables are used on a continuous basis

5. (a) The characteristics of, and changes in, the external and internal context have impacts on the outcomes; (b) The characteristics of the process variables, and how they are used, determine the outcomes of the project implementation

A number of pertinent research issues emerge out of this review. First, the dominant focus has been on single factors or lower level elements which fail to provide insight into the dynamics in strategy execution (Li et al 2008). This makes it difficult for corporate executives to understand how these factors can be combined or configured to orchestrate effective strategy. Second, most of the frameworks focus on the internal process view of

16 strategy but ignore the wider context of the dynamic interactions that shape both the process and organizational responses at the organizational level. Third, although it is now trendy to propose holistic frameworks of strategy implementation, most of them add primarily new factors to previous frameworks (as in frameworks put forward by;

Skivington & Daft, 1991; Noble, 1999b; Noble & Mokwa, 1999; Beer & Eisenstat, 2000;

Okumus, 2001) or re-group variables from a new angle (as in frameworks put forward by; Noble & Mokwa, 1999; Higgins, 2005; Qi, 2005; Brenes & Mena & Molina, 2007).

Neither of these approaches propose new logics to explain the dynamics of interactions.

In addition, Li et al (2008) assert that though some authors call their frameworks models, they cannot be tested empirically. Finally, although most of the models make some attempt to show that strategy execution effectiveness is achieved through attaining a match or alignment among constituent implementation factors, they, however, do not frame these constructs on the basis of the dynamic capabilities concept. Accordingly, they fail to include the environmental triggers that induce dynamic recursive organization of these elements and how organizations can respond to these triggers. We accordingly lack adequately focused models to examine key dynamic relationships, as well as high level comprehensive strategy implementation frameworks that can provide guidance to practicing managers at different levels.

In summary, research addressing how organizations engage in closing the strategy execution ‘gaps’ remains limited. We know a lot about individual factors influencing strategy execution, but we know less about the underlying mechanisms or modes of interaction. Studies highlight the need for valid constructs that help explain how

17 organizations engage dynamically in strategy execution. Yet, most studies assemble multiple factors in a single causal model that provide limited insight into complex interactions and dynamics. The process models provide little explanations of the dynamics associated with strategy execution (Noble, 1999; Higgins, 2005; Qi, 2005;

Brenes & Mena & Molina, 2007). To improve our understanding of interactions between elements and environmental context we need to articulate models that recognize interactions and account for the dynamics between the organization and its environment.

These models need to provide insights on how dynamics generate learning and continually shape strategy and its implementation. Therefore, I seek to uncover the dynamic as organizations engage with their environment to implement strategy. I additionally aim to articulate a framework of dynamic strategy implementation that will improve our understanding of the dynamics of strategy implementation.

Omeike and Lyytinen (2015) provide an alternative model of strategy implementation dynamics founded on the dynamic capabilities theory. They frame strategy implementation as a configurable triad of dynamics with volatility as the pivotal construct inducing variations and dynamic interactions among interdependent constructs of gap reduction, capability configuration, and a molar idea of strategy. Central to this framework are the interactions between the organization and its environment, which are induced by volatility and which trigger dynamically variance in execution gaps.

Organizations respond to this dynamic by continually shaping a molar strategy structure and orchestrating multi-dimensional set of dynamic capabilities. The framework consists of three sub-dynamics; Execution Gap Dynamic involving interactions between the

18 organization and its environment, which dynamically creates and alters the execution gap; Gap Reduction Dynamic involves the dynamic engagement with contextual shifts to focus both on the strategy and close execution gaps; and the Execution Focusing

Dynamic involves the ongoing configurations of strategy structure and related execution capabilities. The causal organization of these dynamics and their constituent constructs provide micro foundations of analyzing these constructs as presented in Tables 6,7 and 8 and Figure 6. I adopt this view as I move forward.

Strategy and Orientations

Several classifications of strategy exist in the literature. These classifications are typically based on singular characteristic of the firm’s orientation that dominates in a given environment (Porter, 1980; Treacy and Wiersema 1993; Chan and Mauborgne 1999;

March; 1991). These include the scope of strategy (lower cost, differentiated or focus types of strategy), underlying value discipline (operational excellence, product leadership and customer intimacy), or type of learning (management of today’s business - exploitation vs. coping with tomorrow’s varying demands- exploration). Such classifications fit poorly, however, with the idea of dynamic capabilities (Teece et al.,

1997), and they fail to show how diversified capabilities need to be staged dynamically during strategy execution. Recently, Omeike and Lyytinen, (2015) proposed a classification of the strategy variation founded on the idea of dynamic capabilities. They categorize strategy along three main orientations depending on to what extent environmental volatility is perceived or is in need of being created by managers. But at

19 the same time they do not approach strategy as uni-dimensional, but a pluralistic concept where multiple orientations are at the same time present. I will assume this later conceptualization of the implemented strategy and follow thereby a growing body of research, which posits that (implemented) strategy is a combination of multiple strategy orientations being simultaneously implemented (Omeike and Lyytinen, 2015; Reeves and

Routledge, 2013; Davis, 1984; Hill, 1988; Anderson 1997; Goldman et al. 1995; Pine

1993; Porter, 1988; March, 1991).

Overall, recent research has made progress deconstructing and discerning strategy orientations and explaining how volatility shape the environment (Hamel & Prahalad,

1996; D’Aveni, 1999; Hamel & Välikangas, 2003; Christensen & Langhoff-Roos, 2003;

Omeike and Lyytinen, 2015; Eisenhardt and Martin, 2000). But we do not know enough of how organizations configure multiple orientations of strategy as a response to volatility and its effects on organizational effectiveness.

Volatility as an Antecedent of Variations in Strategy

Research has made steady progress in framing volatility and its impact on strategy. As a result, our understanding of the dynamics around strategy volatility has evolved over time as well. McNamara et al., (2003) identifies the entry of a hostile entrant, an exogenous shock or an aggressive strategy shift by an incumbent as examples of environmental volatility changing the market equilibrium (Hamel & Prahalad, 1996; D’Aveni, 1999).

Such shifts have mostly adverse effects on incumbent firm’s competitive advantage,

20 because it creates increased variance between their strategy intent and chosen means

(McNamara et al., 2003). Volatility affects strategy implementation process and outcomes (Hamel & Välikangas, 2003; Christensen & Langhoff-Roos, 2003). Omeike and Lyytinen (2015) find support to this view; they found that three separate orientations of implemented strategy coexist and often mutually depend on one another in any implemented strategy structure. At the same time, each dimension varies in relative intensity depending on the level of environmental volatility. We describe as “strategy configuration” the relative combination of multiple strategy orientations in implemented strategy.

Overall, volatility affects the variations in strategy configuration and related implementation effectiveness (Omeike and Lyytinen 2015; Hamel & Välikangas, 2003;

Christensen & Langhoff-Roos, 2003). The literature suggests that certain types of strategy configurations are likely to be more potent under specific environmental contexts

(Jansen, van den Bosch, & Volberda, 2006). For example, survival in unpredictable markets calls for greater flexibility and the need to face any potential contingency

(Anderson 1997, Goldman et al. 1995, Pine 1993 cited by Radas 2005, p. 197). These insights connect volatility with variations in strategy. However, there is less clarity as to how strategy can be composed under varying levels of volatility to combat its potential adverse effects on implementation and its outcomes. Omeike and Lyytinen (2015) report that organizations struggle to find the right mix of configural strategies given their aspiration levels and the volatility of the context. This study sets out to improve our understanding of these interactions. I next introduce the concept of a molar structure of

21 strategy and relate this to the dynamic variations of strategy structure in the presence of volatility.

Composition of a Molar Strategy Structure

Our understanding of the structure of implemented strategy has evolved significantly in recent years. It emphasizes that volatility increases the need for ambidexterity and calls for orchestrating (new) dynamic capabilities during strategy implementation (Teece et al., 1997; Eisenhardt and Martin, 2000). And organizations progressively refine and marshal organizational resources to more effectively orchestrate intent through iterative strategy implementation as a learning activity (Pietersen, 2010).

In order to understand how these organizational actions are orchestrated to achieve performance, several scholars have advocated the organizational configuration view

(Fiss, 2007) as a more viable framing to understand organizational actions. Meyer, Tsui,

& Hinings, 1993 define organizational configuration as “any multidimensional constellation of conceptually distinct characteristics that commonly occur together”. This approach has occupied an important and central role in both organization theory and strategy research (e.g., Ketchen, D. J., Short, J. C. & Payne, T. G. 2008; Bensaou &

Venkatraman, 1995; Dess & Davis, 1984; Doty & Glick, 1994; Hambrick, 1984; Ketchen et al., 1997; Miller, 1986; Miller & Friesen, 1978, 1984; Mintzberg, 1979, 1980). And in essence, suggests that organizations are best understood as clusters of interconnected structures and practices, rather than as modular or loosely coupled entities whose components can be understood in isolation. The configuration theory therefore takes a 22 systemic and holistic view of organizations connecting patterns or profiles to organizational outcomes such as performance instead of the reductionist approach in which individual independent variables take on prominence (Delery & Doty, 1996). We extend this configuration-based view of organizations to explain how organizations can approach (implemented) strategy by combining multiple strategy orientations that are simultaneously implemented and related dynamic capabilities in response to volatility induced variances between strategic intent and execution outcomes (Omeike and

Lyytinen, 2015; Reeves and Routledge, 2013; Davis, 1984; Hill, 1988; Anderson 1997;

Goldman et al. 1995; Pine 1993; Porter, 1988; March, 1991). I refer to this approach as the “strategy configuration” approach and assume it as I move forward. It is founded on the idea that volatility influences to what extent companies need to engage in each orientation of strategy at any moment (Omeike and Lyytinen, 2015) and that combinations rather than individual components of these strategy orientations present stronger implementation outcomes. This view finds support in the configuration theory research stream (Fiss, 2007) where patterns or profiles rather than individual independent variables is a more viable construct to explain related organizational outcomes such as performance (Delery & Doty, 1996). Accordingly, this research makes three broad conclusions; 1) the simultaneous combinations of strategy orientations provide for improved effectiveness. (Reeves and Routledge, 2013; Davis, 1984; Hill, 1988; Anderson

1997, Goldman et al. 1995, Pine 1993; Porter, 1988); 2) changing the relative emphasis of each strategy orientation in volatile environments improves strategy execution effectiveness (Miller, 1992; Baden¬Fuller and Stopford, 1992); and 3) organizations are likely to adjust the combinations relative to the level of environmental shifts (Miller, 23

1980; Murray, 1988; Wright, 1987). Hence, perceived volatility in the environment influences an organization’s strategy implemented over time (Christensen & Langhoff-

Roos, 2003). This, in turn, influences the ongoing configuration of organizational capabilities (Teece et al., 1997; Eisenhardt and Martin, 2000). Overall, these insights connect variations in strategy composition with execution effectiveness under volatility.

But, we do not know enough of how strategy can be composed under varying levels of volatility to deal with its adverse effects on implementation outcomes. I next explore such strategy variations on the basis of the theory of dynamic capabilities.

Capability Configurations

Dynamic capabilities theory (Teece et al., 1997; Eisenhardt and Martin, 2000; Barney,

1986, 1991; Wernerfelt, 1984; Helfat & Peteraf, 2003, Eisenhardt & Martin, 2000;

Rindova & Kotha, 2001) has recently emerged as a main account to explain how internal characteristics of the firm influence its strategy performance. The essential proposition that the theory puts forward is that organizational processes and related resources need to be re-configured in volatile conditions (Teece et al., 1997). Eisenhardt & Martin (2000:

1107) refine dynamic capability as “the firm’s processes that use resources – specifically the processes to integrate, reconfigure, gain and release resources – to match and even create market change. Dynamic capabilities are fundamentally the organizational and strategic routines by which firms achieve new resource configurations as markets emerge, collide, split, evolve, and die”. Dynamic capabilities are defined typically as high-level routines capable of changing other organizational routines, resources and capabilities

(Zott, 2003; Winter, 2000). These are ‘antecedent’ routines by which managers alter the firm’s resource base while generating new value-creating strategies (Eisenhardt &

Martin, 2000). Accordingly, “a dynamic capability is a learned and stable pattern of collective activity through which the organization systematically generates and modifies its operating routines in pursuit of improved effectiveness” (Zollo & Winter, 2002: 340).

These framings associate dynamic capabilities with organizations’ strategy effectiveness and related implementation processes. The theory frames that the dynamics around organizational capabilities are proportional to environmental volatility. Winter (2003) and

Zahra et al. (2006) further argue that capabilities are hierarchically organized. They consist of incrementing capabilities, renewal capabilities, and regenerating capabilities. I accordingly posit that (dynamic) capabilities are composed of three levels; zero order

(incremental), first order (renewing) and second order (regenerative) capabilities. This hierarchical framing is consistent with the current theory of dynamic capabilities. (Helfat et al., 2007, p. 1; Eisenhardt and Martin, 2000; Helfat and Peteraf, 2003; Teece et al.,

1997; Zollo and Winter, 2002).

Incrementing capabilities (Winter 2003) are ‘zero-order’ capabilities, which enable the firm to ‘keep earning a living now’ by producing and selling the same outputs to the same customers. Zero-order capabilities consist of managerial and organizational processes that exhibit patterns of current practice and related past learning (Teece & Pisano, 1994). The capability is motivated by the search for operational excellence (Teece et al. 1997;

Eisenhardt and Martin‟s 2000). However, as the environment changes zero order capabilities need to be refreshed. This calls for what Christensen et al. (2002) advocate 25 as building an organizational ‘innovation engine’: a robust and repeatable process that creates and nurtures new business initiatives. Consequently, organizations that seek to operate in volatile environments need to develop a capability to constantly revise and redefine their zero order capabilities (Zott, 2000). Remaining competitive pressure requires cultivating the ‘capability to systematically adjust capabilities’ by specifically to

“extend, modify, or create ordinary capabilities”. This set of capabilities is what was above called ‘dynamic capabilities’ (Winter, 2003: 991). We, can also characterize this

“first order” dynamic capability as a set of ‘search routines’ (Nelson & Winter, 1982) which “represent a body of wisdom regarding any potential modification of its [the firm’s] business practices” (Zott, 2003: 105). Another way of stating this is to define first order capabilities as a capacity to reconfigure and transform the organization as a learned organizational skill (Teece & Pisano1994). Huber (2011) illustrates such capability using the idea of repeated experiments whereby the firm reduces its reconfiguration costs (Zott,

2003). In other words organizational processes producing such effects can be treated as learning mechanisms that create dynamic capabilities.

In volatile environments, organizations’ learning mechanisms need to become systematic, dynamic and persistent (Zollo & Winter (2002). I will refer to this dynamic capability as a “second order” capability (Eisenhardt & Martin’ 2000; Zollo & Winter 2002; Winter

2003). In order to maintain competitive advantage, the organization must garner systematic learning mechanisms (second order capability; learning to learn) that create and modify zero and first order capabilities when the environment changes (Zollo &

Winter, 2002).

The three capabilities apply to different organizational environments and their usefulness depends on the prevailing level and intensity of volatility. At the same time all three capabilities are present in any given organization, but with one typically dominating. In a stable environment, the dominant dimension would be zero order capability. This level of capability is required to stage a core preservation strategy associated with operational excellence assumed in stable conditions. However, as noted, increasing environmental volatility will weaken the competitive advantage created by existing operational excellence. In order to remain competitive, the organization need to renew its existing capabilities by staging first order capabilities which triggers variation in the implemented strategy as to a focus on core extension by garnering and deploying capabilities required to systematically adjust capabilities to extend, modify, or create ordinary capabilities.

(D’Aveni 1999). In order to maintain competitive advantage under these conditions, the organization must garner systematic learning mechanisms (learning to learn) that create and replace existing sets of dynamic capabilities when the environment changes (Zollo &

Winter, 2002). The need to regenerate dynamic capabilities calls for a variation in the implemented strategy and focus on the core-leapfrogging dimension that enables the organization to stage the capabilities required to reconfigure and transform its dynamic capabilities (D’Aveni 1999).

The value of capability configuration concept in the context of strategy implementation effectiveness has been recognized (Eisenhardt & Martin, 2000). In line with these view organizations are seen to enact specific variations of strategy orientations by dynamically staging capabilities necessary to close or reduce volatility induced execution gaps (Teece 27 et al., 1997). Capability configuration can then be understood as the constellation of the firm’s zero, first and second order capabilities in a given strategy context. As volatility generates shifts in market equilibrium these capabilities are redeployed to create and maintain competitive advantage. Consequently, the molar structure of strategy is continually reconfigured and variations in strategy is implemented by staging dynamic capabilities.

These theoretical insights suggest two things as I move forward; 1) implemented strategy involves a molar structure consisting of three dimensions of strategy, which are combined and simultaneously implemented. These three configurational orientations mutually coexist in any strategy configuration, but vary in relative intensity over time. 2)

Implemented strategy continually shifts through the variation of the relative strength of each of the orientations as the organization dynamically deploys and redeploys its capabilities in response to volatility. In line with Reeves and Routledge,(2013), Miller

(1992) and Baden-Fuller and Stopford (1992) I suggest that strategy orientations are not mutually exclusive and generating combinations of these dimensions given environmental shifts are critical for organization’s success. Volatility, therefore, triggers a sensing process that causes variations in the molar structure of strategy as organizations grapple with the perceived volatility and related induced gap between their strategy outcome and their strategic intent.

In summary, I propose that Implemented Strategy consists of three strategy orientations implemented as molar strategy structure. This molar view gives strategy implementation

28 a different flavor than a single-oriented strategy implementation approach. The organization can vary the configuration of its strategy by staging shifts in their dynamic capabilities. As a result, the presence and strength of each orientation of strategy varies with shifts in volatility (Miller, 1992; Baden•Fuller and Stopford, 1992). I consider these views foundational for approaching strategy as a molar structure. In the light of these findings, I view volatility as a critical antecedent of strategy shifts. Accordingly, molar strategy is a formative construct with three orientations which exhaustively define a firm’s strategy as a configuration (Figure 1). Moreover, the source of variance in this configuration is volatility.

Figure 2. Dimensions of Molar Strategy

Core The dimension of strategy focused on Preservation incremental improvements of the existing core processes of the organization with emphasis on operational excellence

Molar Core The dimension of strategy focused on Strategy Extension transformative change and extension of existing core processes to substantially improve capabilities

Core The dimension of strategy focused on Leapfrogging disruptive change resulting in new domains of competence that replace existing core processes

Given this framing I will next seek to explore the impact of such strategy variations under volatility on components of organizational capabilities. These components are orchestrated to implement strategy. By doing so I align with a growing body of literature in organizational and strategy literature that focuses strategy as an instance of complex adaptive systems. For this study, I use the idea of strategy as configuration to frame a novel approach to implementing strategy as a molar structure, which involves composing simultaneously strategy in three dimensions (Omeike and Lyytinen, 2015). I also adopt the configuration concept in the context of strategy implementation effectiveness

(Eisenhardt & Martin, 2000). In line with this view organizations are seen to enact specific variations of strategy orientations by dynamically staging capabilities necessary to close or reduce volatility induced execution gaps (Teece et al., 1997). Capability configurations are understood as constellations of the firm’s zero, first and second order capabilities in a given strategy context. As volatility generates shifts in the market equilibrium these capabilities are redeployed to create and maintain competitive advantage. Consequently, the molar structure of strategy is continually reconfigured and variations in strategy is implemented by staging dynamic capabilities. Research shows that strategy configuration and capability configuration are recursively organized and that these dynamics are influenced by volatility. Recognizing this relationship is important, but its impact has not yet been quantified. Demonstrating that perceived volatility in the environment influences an organization’s strategy implemented over time (Christensen &

Langhoff-Roos, 2003) requiring the simultaneous combinations of strategy orientations

(Reeves and Routledge, 2013), changing the relative emphasis of each strategy dimension

(Baden Fuller and Stopford, 1992) and the ongoing configuration of organizational 30 capabilities (Teece et al., 1997; Eisenhardt and Martin, 2000) will help improve understanding of the molar structure of strategy and to show how organizations adjust these combinations successfully relative to the level of environmental shifts (Miller,

1980). Demonstrating that variations in strategy composition (the molar structure of strategy) calls for configuring capabilities as to improve the gap reduction execution effectiveness would give organizations a tangible tool to increase strategy implementation effectiveness. Omeike and Lyytinen, (2015) identify three microfoundations of configurable strategy implementation capability based on the dynamic capabilities concept; the Organization’s contextual immersion, defined as the extent to which the organization is able to immerse itself in its environment and sense latent or real shifts (Teece, D. J. 2007). Contextual immersion also describes the extent to which the organization monitors and senses contextual volatility. The degree to which the organization is immersed or connected to its internal and external environment influences consequently its relative ability to reduce perceived gaps (Okomus and Roper, 1999). The organization’s contextual immersion is seen being influenced by several factors such as decision making norms, culture, strategic altitude and bandwidth, external/internal learning, external/internal risk management. Table 4 showcases details of these factors

(Omeike and Lyytinen, 2015).

The organization’s marshaling capability, defined as the degree to which the organization can harness and deploy its resources to either exploit or explore change/opportunity and/or hedge against environmental risks. It influences the organization’s ability to reduce gap during strategy implementation. Teece suggest a

31 similar notion of configuration capability (Teece, D. J. 2007, 2012. And Teece, D. J.,

Pisano, G., & Shuen, A. 1997). Marshaling acts like a dynamic concentrator of resources.

It is influenced by a number of factors in the internal and external environment including leadership quality and focus, organizational cohesion and mobility, strategy and execution pacing management, organization’s change appetite, organization’s opportunity identification and sizing effectiveness, change externalization effectiveness, effectiveness at harnessing organizational capability, knowledge dissemination effectiveness and organizational capability management (Omeike and Lyytinen, 2015).

Finally, the organization’s balancing across multiple dimensions, described as the organization’s capacity to achieve and maintain consistency between its strategic intent and its organizational capability given the dynamics of its strategic context (Omeike and

Lyytinen, 2015; Teece, D. J. 2007, 2012. And Teece, D. J., Pisano, G., & Shuen, A.

1997). Table 5 showcases details of these factors.

Research Questions and Design

Research Questions.

I noted that one major gap in the strategy research literature is the lack of holistic view of the organizational dynamics between the organization and its environment which shape the sensing and configuring mechanisms of the organization (Rumelt 1991) and related responses during strategy implementation (Li et al, 2008). While the examined literature provides several promising findings that have improved our understanding of how strategy implementation is influenced by the dynamics, a key challenge during the strategy implementation is that they are not framed in the context of factors that are likely 32 to drive strategy formation and implementation including environmental change.

Although all senior executives recognize the importance of dynamic strategy implementation, a majority admits that their companies fall short in narrowing the gap between the stated intent and results. This observation amplifies the importance of understanding the role of dynamics during strategy process, which successfully balances the firm’s strategic intent with the environmental volatility. Bowman and Helfat (2001) and McGahan & Porter (1997) emphasize the need for dynamism as to improve strategy effectiveness. Following these considerations, the gaps at the organizational level in the literature fall into three sorts: strategy implementation variables, mechanisms of interaction, and void of a framework of their recursive organization. For implementation variables, I want to know the following: What are the elements which are likely to interact during strategy execution? What are their modes of interaction? What are the mechanisms by which the interactions happen?

Implementation variables, mechanisms and frameworks are connected. Therefore, in order to establish this connectedness I need to know first, what the actual elements are that influence strategy implementation dynamics at the organizational level. I, therefore, adopt a holistic approach to answer these research gaps on the basis of the dynamic capabilities theory rooted in the Resource Based View by framing my study in terms of implementation factors, then by mechanisms of interaction among these factors, and finally, a framework describing their recursive organization. Figure 3 illustrates the connections between these constructs.

Figure 3: Linked Approach to Literature Gaps

The overarching research purpose is to explore how organizations implement strategies and close their execution gaps, the role of perceived volatility and execution gaps and the dynamic interactions of strategy and capability as recursively organized factors while closing the execution gaps. To address these issues, I use a sequential mixed methods study approach to explore a set of research questions informed by dynamic capabilities theory in three sequential research strands. How each study strand sequentially builds upon the other is demonstrated in Table 4 and Figure 5

Figure 4 : Conceptual Framework

Figure 4 illustrates the conceptual model including the 1st research strand around organization level interactions during strategy implementation that take place across the critical dimensions of organizational structure and dynamics. The 2nd research strand focuses on the composite nature of strategy and the interactions between volatility and variations in implemented strategy. Finally, the 3rd study focuses on the interdependencies and interactions between the dimensions of a composite strategy and the variables of strategy execution capabilities (that emerged in the first study) and the mediating effect of strategy orientations on the extent to which organizations engage in

35 each of these capabilities under volatility. I next outline the three study strands, justifications for the chosen inquiry modes and the key questions informing each study.

Overview of mixed methods design

Tashakkori & Teddlie (1998; 2003a) argue that particularly the sequential qualitative-quantitative (‘QUAL / QUAN’) design is appropriate when the researcher studies a relatively unexplored area. This is particularly applicable for the study of organizational level dynamics of strategy implementation. Few studies in this research stream has focused on improving understanding of the dynamics of strategy implementation. We therefore, lack adequately focused models to examine key relationships involved in strategy implementation, as well as comprehensive strategy implementation frameworks that can provide guidance to practicing managers (Li et al,

2008). My design’s main focus is indeed to ‘explore a phenomenon’ (Morgan, 1998a) of strategy implementation interactions and the organizational level responses to these interactions. This type of mixed method design has hence been referred to as a

‘sequential exploratory’ design (Creswell et al., 2003). I propose to use this design to firstly develop or refine my theory or model and then to test this (Morse, 2003). The

QUAL/QUAN design is a common type of sequencing (Morgan, 1998a), because in most quantitative survey research, close-ended instruments can only be developed after exploratory qualitative interviews or narrative data have been contextually content analyzed for face and content and ecological validity. This is especially the case when new research instruments are to be developed (Creswell et al., 2003).

I address the research questions in this study using a sequential mixed method approach. Given the complexity of social phenomena, mixed method research is thought to be especially appropriate in evaluative social science (Creswell et al., 2003), management (Currall & Towler, 2003) and also in strategic management research

(Creswell et al., 2002). I also follow Curral & Towler’s (2003) claim that a mixed method design is especially suitable when research questions tackle innovation issues.

Furthermore, they argue that mixing qualitative and quantitative methods can also prove useful to refine and test a nascent theory or theoretical model, which I propose as a significant consideration in my study. A mixed methods approach is also important, because the context is still quite undefined and the intended audience is both scholars that are currently exploring the impact of volatility on the dynamic variation of strategy and capability as well as practitioners who would like to understand not only the dynamics associated with implementing strategy under volatility, but also what response mechanism can be used to close volatility induced execution gaps and how to configure these mechanisms. A diagram of the mixed methods approach is illustrated and summarized in Figure 5.

Figure 5 : Mixed Methods Approach

Sequential Phase # & Method of Data Purpose Typology Path Collection & Analysis

Strand 1 Semi structured interviews with top To obtain an understanding management executives who play pivotal roles in strategy about the drivers of Data analysis through grounded strategy execution in theory analysis using Nvivo coding organizations software

Strand 2 Surveys administered to top To identify interactions and management executives who play relationships among volatility, pivotal roles in strategy strategy orientations and Gap Data analysis through Structural Equation Modeling using SPSS, AMOS reduction and Smart PLS

Strand 3 Surveys administered to top To identify interactions and management executives who play relationships among, strategy pivotal roles in strategy Data analysis through Structural orientations, Capability Equation Modeling using SPSS, AMOS Configurations and Gap reduction and Smart PLS in the presence of volatility

Summary and Results of Each Study

Study Strand 1: In this first strand I focus on exploring the “Dynamics between Strategy

Execution Effectiveness and Organizational Interactions during Strategy

Implementation” I use patterns of such interactions to uncover the significant factors that interact at the organizational level during strategy implementation. The overarching research question of this study is: What dynamics and mechanisms influence the organizational level interactions during strategy execution so that they can effectively reduce the execution gap? To address this question I further examine the following sub questions: What are the elements which are likely to interact during strategy execution? 38

What are their modes of interaction? What are the mechanisms by which the interactions happen? These questions represent an exploratory study in the nascent field of strategy implementation research. Literature shows that a qualitative inquiry mode best fits research that focus on discovering and understanding the experiences, perspectives, and thoughts of participants (Hiatt, 1986) or that attempts to make sense of, or interpret, phenomena in terms of the meanings people bring to them (Denzin & Lincoln, 2005, p. 3;

Lincoln & Guba, 1985; Creswell 2003). I therefore conduct an inductive, grounded theory study to address the posed questions. The research questions in this study strand represent a foundational step in establishing what the strategy implementation variables at the organization level are. A clear framing of these variables will enable us proceed with subsequent inquiries around the mechanisms of interaction, and a framework of their recursive organization. The research questions in study strand 2 and 3 build on this foundational questions to clarify the mechanisms of interaction of the identified variables and to propose a framework of their recursive organization.

Findings from this study surfaced three sub-dynamics that influence organizational level strategy implementation and suggest a recursive organization between environmental volatility, strategy structure and capability configurations involved in strategy implementation. I therefore, focused the next two studies (Quantitative) to theorize and validate the presence of dynamic interactions between these constructs.

Study Strand 2: In this second strand I focus on exploring “Environmental Volatility and Variations in the Implemented Strategy.” Arising from the outcomes of the first study strand which frame volatility, strategy and capability as pivotal constructs to explain 39 execution gaps in implemented strategy, understanding how organizations can configure their strategies and how this relates to the concept of dynamic capabilities is an important component of my research aim. Accordingly, I pose the following question; how do different levels of volatility influence the configurations of implemented strategy? I specifically pose the following questions: To what extent does the level of volatility affect the strength of strategy orientations in the implemented strategy (Strategy variation)? And, to what extent does each of the strategy orientations mediate the effect of volatility on execution gap reduction? These research questions call for an inquiry mode that focus on a deductive approach to testing existing theory using hypotheses and on causality with measurements that provide objective numerical information derived from statistical interpretations of data that can be mathematically manipulated and understood (Patton, 2012). A quantitative inquiry is well suited to address these questions because it is the best approach to use to test theory and identify factors that influence an outcome and understand the best predictors of these outcomes (Creswell J.W. 2009).

Accordingly, I formulate and test a structural equation model, which examines interactions between volatility and variations in implemented strategy orientations and the effect of engaging in each dimension on reducing execution gaps. These research questions begin to clarify how three of the identified constructs from the first study strand interact and the mechanisms of such interactions. This also sets the tone for a subsequent exploration of how the structure of strategy influences capability configuration under different levels of volatility as is posed by the research questions of the 3rd study strand.

Study Strand 3 (Quantitative Study 2): In my 3rd study strand I focus attention on exploring the micro foundations of capability configurations and their effects on strategy under volatility. The third phase extends the findings from the first two studies and seeks to theorize how varying dimensions of strategy influence the dynamic configurations of capabilities in the presence of volatility. Accordingly, I pose the following question; how do the different dimensions of strategy influence configuration of the sub-dimensions of capabilities? In order to effectively address this main question, I also address the following sub-questions; how is each dimension of capability (Capability Variation) affected by the strength of the different dimensions of strategy? How does strategy execution gaps influence the relationship between strategy orientations and capability configurations? And how does each of the strategy orientations mediate the effect of volatility on the strength of the sub-dimensions of capabilities? Creswell J.W. (2009) show that quantitative inquiry is well suited to address research questions that aim to test a theory and identify factors that can influence and are the best predictors of outcomes.

Accordingly, I formulate and test a structural equation model, which examines interactions between volatility and the micro foundations of dynamic capability identified in my first research strand and the effect of the dimensions of strategy on the relationship between volatility, gap reduction performance and the micro foundations of capability.

These research questions begin to clarify how three of the identified constructs from the first study strand interact and the mechanisms of such interactions.

Table 4 : Research questions and methods

Research Justification Of Inquiry Mode Research Questions Methods Method Specifics Phase QUAL To address this question I followed a 1. What dynamics Expert 20 interviews C-level qualitative, grounded theory in and mechanisms interviews executives of multinational uncovering the dynamics of influence the organizations (10 from USA organizational interaction during organizational and 10 from Nigeria) who strategy execution. I chose this level interactions have been in pivotal roles in approach as qualitative research during strategy executing strategy methods are well suited for execution so that they can understanding the contextual effectively reduce phenomena and processes where the execution dynamic actions take place (Maxwell gap? What are 2004). I followed grounded theory by the elements Glaser and Strauss (1967) to probe C- which are likely to Level executives’ lived experience of interact during strategy execution in its variation strategy across different stages. The data from execution? this study formed the basis for 2. What are their identifying and understanding if and modes of how these relationships exist interaction? (Bryman & Bell, 2003) as well as 3. What are the informing and developing a typology mechanisms by of antecedents for a quantitative study which the (Teddlie & Tashakkori, 2011). interactions happen? QUAN1 These research questions call for an 1. What how do Sample Survey of 557 C-level inquiry mode that focus on a different levels of survey executives who have been in deductive approach to testing existing volatility pivotal roles in executing theory using hypotheses and on influence the strategy in large causality with measurements that molar structure of multinational organizations provide objective numerical implemented across 13 industries information derived from statistical strategy? (Financial Services, Airlines, 2. To what extent Telecoms, Information interpretations of data that can be does the level of technology, fast moving mathematically manipulated and volatility affect Consumer Goods, understood(Patton MQ 2012). A the strength of Hospitality, Professional quantitative inquiry is well suited to each strategy Services, Oil and Gas, address these questions because it is dimension in the Health Care Services, the best approach to use to test theory implemented Agriculture/Mining, and identify factors that influence an strategy (Strategy Manufacturing, Non-Profit/ outcome and understand the best variation)? NGO and Government). I predictors of these outcomes 3. To what extent cluster these industries into (Creswell J.W. 2009). Accordingly, I does each of the three broad volatility level formulate and test a structural strategy groups (High Volatility, equation model, which examines orientations medium Volatility and Low interactions between volatility and mediate the effect Volatility) in order to variations in implemented strategy of volatility on surface the main effects in orientations and the effect of execution gap our postulated model under reduction? varying conditions of engaging in each dimension on volatility. reducing execution gaps 42

Research Justification Of Inquiry Research Questions Methods Method Specifics Phase Mode

QUAN2 Creswell J.W. (2009) show How do the different Sample Survey of 557 C-level that quantitative inquiry is dimensions of strategy survey executives who have been in well suited to address influence configuration of the pivotal roles in executing research questions that aim sub-dimensions of capabilities? strategy in large to test a theory and identify In order to effectively address multinational organizations factors that can influence this main question, I also across 13 industry (Financial and are the best predictors address the following sub- Services, Airlines, Telecoms, Information technology, Fast of outcomes. Accordingly, I questions; moving Consumer Goods, formulate and test a 1. How is each dimension of Hospitality, Professional structural equation model, capability (Capability Services, Oil And Gas, Variation) affected by the which examines Health Care Services, strength of the different interactions between Agriculture/Mining, dimensions of strategy? volatility and the micro Manufacturing, Non-Profit/ 2. How does strategy foundations of dynamic NGO and Government). I execution gaps influence capability cluster these industries into the relationship between three broad volatility level strategy orientations and groups (High Volatility, capability configurations? medium Volatility and Low 3. How does each of the Volatility) in order to surface strategy orientations the main effects in our mediate the effect of postulated model under volatility on the strength of varying conditions of the sub-dimensions of volatility. capabilities?

Table 5 : Strengths and weaknesses of the different methods used

Data Collection Method Strengths Weaknesses Interviews - Useful for measuring attitudes and most - Expensive other content - Time-consuming of interest - Reactor and investigator effects - Allow probing - Low perceived anonymity by - In-depth information respondents - Moderate measurement validity - Time-consuming data analysis - High response rates - Measures need further validation - Useful for both exploration and confirmation Questionnaire - Useful for measuring attitudes - Need validation - Inexpensive - Must be kept short - May be administered to probability sample - Possibly missing data - High perceived anonymity by respondents - Possibly reactive effects - Moderately high measurement validity - Nonresponse to selective items - Ease of data analysis - Low response rate

In summary, extant studies have argued at conceptual level for view of strategy as dynamic set of organizational interactions yet little is known about the dimensions, elements or the actual dynamics of these interactions. We know even less to what extent such collective dynamics influence strategy implementation and its outcomes and a framework to explain their recursive organization. I therefore, set out to fill this gap by exploring how organizations engage with volatility to implement strategy. Given the identified gaps in literature and the nascent nature of strategy implementation research, I use a mixed method approach to uncover the factors at play during strategy implementation, the mechanisms through which they interact and how they are recursively organized. The mixed methods study consists of three research sequential study strands. The first two research strands seek to probe to what extent strategy implementation at the organization level proceeds through a series of organizational interactions that integrate three sets of factors dynamically. These are: 1) environmental volatility, 2) strategy configurations and 3) the dynamic capabilities. The third research strand builds upon the findings from the first two studies and seeks to theorize how varying dimensions of strategy influence the dynamic configurations of capabilities in the presence of volatility. This helps complete in the study a loop from the sensing side of the strategy model (focused on perceived volatility and execution gaps) to organizational response mechanisms (focused on strategy and capability relationship) that jointly shape strategy implementation and its outcomes.

Organization of Remainder of Dissertation

The next sections of this paper are therefore organized as follows; first I present the findings of the first study and use these findings to frame the focus of the subsequent studies, I next present the findings of the second study. Next, I present the findings of the

3rd study. I finally integrate these findings into a coherent framing of the dynamics around strategy implementation and propose a model to explain their recursive organization. I conclude with the implications for theory and practice, limitations of the study and potential future research focus

CHAPTER 2: STRATEGY AS INTERACTION: IDENTIFYING LIVED EXPERIENCES OF C-SUITE CORPORATE EXECUTIVES WITH STARTEGY EXECUTION AT THE ORGANIZATIONAL LEVEL (Study #1.)1

Preface

As the first study in this stream of research on the factors influencing strategy execution effectiveness in organizations, this study offers a grounded theory approach to gain an understanding of the lived experiences of C-Suite corporate executives in industry through inductive exploration. This research focuses specifically on organization level interactions during strategy implementation that take place across the critical dimensions of organizational structure and dynamics. I use patterns of such interactions as a key to understand how organizations implement strategy and can close the execution gap. The results of this initial study set the groundwork for continuing exploration in studies 2-3 (Chapters 3-5).

Introduction

Research addressing how organizations engage in closing the strategy execution gap remains limited. We know a lot about the individual factors influencing strategy execution but we know less about the underlying mechanisms or modes of interaction.

Studies highlight the need for valid constructs that help explain how organizations engage in strategy execution. Yet most studies assemble multiple factors in a causal model that provide limited insight into complex interactions and dynamics. The process models provide little explanations of the dynamics associated with strategy execution (Noble,

1 This chapter was presented at the AOM 2015 Conference in Vancouver, Canada and a modified version was also presented at the 2015 Engaged Management Scholarship Conference 46

1999; Higgins, 2005; Qi, 2005; Brenes & Mena & Molina, 2007). To improve our understanding of interactions between elements and environmental context I need to articulate models that recognize continuous interactions and accounts for the dynamics between the organization and its environment. These models need to provide insights on how dynamics generate learning and continually shape strategy and its implementation.

Therefore, I seek to uncover the dynamic as organizations engage with their environment to implement strategy. I additionally aim to articulate a framework of dynamic strategy implementation that helps improve our understanding of the dynamics of strategy implementation.

Literature Review

We next review key theories and extant literature related to strategy implementation and identified challenges with strategy formulation/implementation. We also review the literature on frameworks for strategy execution that are relevant to understanding organizational approaches to strategy execution effectiveness. This theoretical framework provides background for the study by defining individual characteristics that are expected to influence strategy execution at the organizational level. From this theoretical background, we attempt to identify key factors that influence organizational effectiveness at implementing strategy. Thereafter, background on existing frameworks of strategy implementation is provided to articulate the significance of this research. Finally, the section concludes with brief review of the gaps in the framings in literature of the dynamics associated with strategy implementation and the consequent

47 inadequacy of the frameworks in explaining how organizations engage with their environment to execute strategy.

View of Strategy Implementation in Literature

Literature provides several definitions of strategy (Porter, Haberburg and Rieple

2008; Kim and Mauborgne 2005; Porter 1980; Mintzberg and Lampel 1999; White 2004:

5; Mintzberg and Quinn 1991: 12) and its implementation approach (Hrebiniak and Joyce

1984; Aaker 1988; Floyd and Woolridge 1992; Kotler 1984; Bonoma 1984; Li, Guohui, and Eppler 2008; Mintzberg 1978; Miller and Frieson 1980 and Pettigrew 1985). These varying definitions and perspectives of strategy influence how both the execution gap and the strategy execution model are defined. This is one of the reasons why there is no common understanding in the literature how organizations engage in executing strategy.

Li et al., (2008) define strategy implementation as a dynamic, iterative and nonlinear process comprising of a series of decisions and activities aimed at actualizing strategic intent. This definition attempts to frame strategy execution as a dynamic and generative dance between the organization and its strategic context when compared to the traditionally held view of strategy implementation as a linear, strict march that converts a plan into a well-defined activity (Wind and Robertson 1983). We will assume the former view as we move forward.

Strategy Formulation and Implementation Constructs

Most factors influencing implementation outcomes have been studied in their individual or bi-variate forms (Table 6). In addition to the dominant multi-factor

48 approach several scholars have recently highlighted multidimensional nature of managing strategy execution. The multi-dimensional approach emphasizes strategy implementation and the emerging inter-relationships among organizational components during implementation. Several more recent strategy implementation studies have advocated this approach. (Cobbold and Irwin 2001; Zook and Allen 2001; Hrebiniak 2005; Mintzberg

2002 ; Johnson 2004; Hrebiniak 2006; Mankins and Steele 2005). These researchers agree on the multidimensional nature of strategy implementation and they describe strategy execution and its failures from multiple and varying perspectives. These multidimensional studies have advanced several frameworks to explain how organizations engage while implementing strategy. Table 7 summarizes pertinent multi- dimension based frameworks.

We assume next the multi-dimensional approach to strategy implementation.

Table 6: Factors influencing Strategy Implementation (Literature)

Factor Description Reference Strategy The way a strategy is formulated influences its acceptability, Yang et al. (2008), legitimacy and the level of consensus and commitment. The Hrebiniak (2006), Formulation choice of strategy and how it is developed correlates Bantel (1997), Process positively with the implementation effectiveness. Process to Alexander (1985), arrive at the strategy are classified into: participative, Allio, Kim and Collaborative, Synthesis, Centralized, Specialized and Sole Mauborgne, (1991, Ranger 1993), Singh (1998) Strategy Strategy Executors are actors involved in strategy Govindarajan implementation (The Boards, Top Management, Senior (1998), Viseras Executors Management, Middle Management and Non-management Baines and Sweeney layers (Teams and Operational staff) critical to (2005), Smith and implementation. The emphasis is on skills, attitudes, Kofron (1996) capabilities, experiences related to a specific task or position the executor has during the implementation.

Factor Description Reference Organizational Strategy-structure alignment is viewed necessary Heide, Grønhaug and for successful implementation of the strategy. Johannessen (2002), Schaap Structure Different strategy types require different (2006), White (1986), Olson, organizational structures. Structure types Slater and Hult (2005) associated with strategy types are: management dominant, customer-centric innovators, customer-centric cost controllers and middle ground Communication Communication influences the management’s Alexander (1985), Rapert and ability to create, disseminate and mobilize Wren (1998) Peng & Litteljohn, engagement around pursued strategy. 2001; Heide & Grønhaug & Communication involves clarification of Johannessen, 2002; Rapert & strategic priorities, setting new goals and tasks Velliquette & Garretson, 2002; and providing justification why the change is Forman & Argenti, 2005; Schaap, introduced. 2006) Commitment The understanding and engagement of the middle (Rapert & Velliquette & management strategic goals is seen critical in Garretson, 2002), (Wooldridge & effective implementation. This covers Floyd, 1989, (Heracleous, 2000). management’s organizational commitment, Alexander (1985), Noble & strategy commitment and role commitment. Mokwa (1999) Consensus The agreement between top, middle-, and Floyd and Wooldridge (1992), operating-level managers on the priorities of the Noble, 1999, Dooley, Fryxell and organization. The lack of consensus creates Judge‟s (2000) obstacles to successful strategy implementation consensus can vary from strong consensus, blind devotion, informed skepticism to weak consensus. The relationship The relationship among involved Walker and Ruekert (1987), departments/Units and the alignment among Porter (1980), (Gupta, 1987). among different different strategy execution levels influences Chimhanzi, 2004 Units/depts. strategy implementation. Related business strategy behaviors have been classified into Prospectors, Differentiated defenders and low cost defenders. implementation The leadership style that drives strategy Nutt (1986), Bourgeois Ш and implementation covering three views; Leadership Brodwin (1984), Lehner (2004), tactics orientation (intervention, participation, persuasion, and edict.), Process View (Commander model, Change model, Collaborative model, Cultural model, Crescive model), and behavioral model (command, change/politics, culture, collaboration and crescive/market ). the implementation tactics is a mixed model leveraging leadership orientation, Process and Behavior that are determined largely by the type of strategy, the organization context and the strategic context

Factor Description Reference The Design of differentiated administrative systems Govindarajan (1988), systems facilitates the implementation of a variety of (Drazin & Howard, 1984; administrative strategies pursued. Three key administrative Nilsson & Rapp, 1999) system mechanisms can be used to cope with uncertainty: design of organizational structure (decentralization), design of control systems (budget evaluative style); selection of managers (locus of control). Culture Effective strategy implementation requires Henry (2008, p.10) (Heracleous sufficiently flexibility in organizational culture 2000; Heide, Grønhaug and and design. Johannessen 2002; Schaap 2006)

Table 7 : Strategy Implementation Frameworks from Literature

Framework Key Construct Reference Higgins 8S Examines alignment between organizational factors Higgins, Model (structure, system and processes, leadership style, staff, (2005) resources and shared values) during strategy execution and its influence on strategic performance. Li etal Categorizes factors during strategy implementation into soft Y. Li, Strategic factors (people oriented factors: communications, consensus Guohui S., Management and commitment), hard factors (institutional factors: Eppler MJ Model organizational structure and administrative system) and (2008) mixed factors (strategy formulation, SBU relationship among different hierarchical levels and strategy etc.) and examines their interactions. Noble‟s Advocates a “strategic implementation model” consisting of Noble‟s strategy two dimensions to strategy implementation; the structural (1999a) implementatio view (firm structure and control mechanisms) and the n framework interpersonal process view (strategic consensus, behaviors, organizational climate, communication and interaction processes). Model is organized around four stages of the implementation –pre-implementation, organizing the implementation effort, managing the implementation process, maximizing cross-functional performance. Five managerial levers for implementation each phase: goals, organizational structure, leadership, communications, and incentives which change during each phase.

Framework Key Construct Reference Qi Model Seven factors influence strategy implementation process: Qi (2005) feedback systems, resources, leadership, motivation, communication and coordination, company structure, company culture. Brenes, Mena Five dimensions of implementation: strategy formulation Brenes, Mena and Molina process, systematic execution, implementation control and and Molina follow-up, CEO’s leadership, motivated management and (2007) employees, and, corporate governance (board and shareholders). All dimensions must be managed comprehensibly to align them with the firm’s strategic choice. Okumus and Categorize strategy execution approaches into five groups: Okumus and Roper planning Approach, learning approach, contingency Roper (1999) approach, configurational approach, and the complexity approach Kaplan and A six stage process covering strategy development, Kaplan and Nortorn alignment, operationalization, monitoring and adapting Norton Execution Premium

Li et al., (2008) note that most of the studies (multi-factor and multi-dimensional) do not convey clear conclusions about the pertinent relationships between the implementation variables and implementation outcomes. There are few in-depth analyses on how the factors exactly influence strategy implementation. While these variables appear to be also mutually dependent, recursive and collectively influence the outcomes of the implementation, most of the studies focus their study on a single or a few factors.

They further emphasize the importance of contextual variables and observe that these have been largely unaccounted for in most studies which also fails to provide a clear understanding of the implementation challenges. Followed factor and multi-dimension based approach to explain strategy implementation outcomes provides thus only a partial view of how organizations execute successfully a strategy.

In summary, research addressing how organizations engage in closing the strategy execution gap remains limited. We know a lot about the individual factors influencing strategy execution but we know less about the underlying mechanisms or modes of interaction. Studies highlight the need for valid constructs that help explain how organizations engage in strategy execution. Yet most studies assemble multiple factors in a causal model that provide limited insight into complex interactions and dynamics. The process models provide little explanations of the dynamics associated with strategy execution (Noble, 1999; Higgins, 2005; Qi, 2005; Brenes & Mena & Molina, 2007). To improve our understanding of interactions between elements and environmental context we need to articulate models that recognize continuous interactions and accounts for the dynamics between the organization and its environment. These models need to provide insights on how dynamics generate learning and continually shape strategy and its implementation. Therefore, we seek to uncover the dynamic as organizations engage with their environment to implement strategy. We additionally aim to articulate a framework of dynamic strategy implementation that helps improve our understanding of the dynamics of strategy implementation.

Research Question and Method

My main research question sought to uncover critical antecedents that explain organizational interactions so that the organization can effectively close the execution gap. To address this question I asked the following sub-questions: 1) what are the elements which interact during strategy execution? 2) What are the modes of their

53 interaction? 3) What are the mechanisms underlying these interactions? I discuss next how my study addresses these questions

To address this question I followed a qualitative, grounded theory in uncovering the dynamics of organizational interaction during strategy execution. I chose this approach as qualitative research methods are well suited for understanding the contextual phenomena and processes where dynamic actions take place (Maxwell 2004). I followed grounded theory by Glaser and Strauss (1967) to probe C-Level executives’ lived experience of strategy execution in its variation across different stages.).

Sample

I theoretically sampled C-level executives who have been in pivotal roles in executing strategy in 20 multinational organizations (10 from USA and 10 from Nigeria). The sample was drawn from several industries including Financial, Manufacturing, Services and

Government sectors. I included the government sector in order to assess the effect of sectorial variation on strategy implementation practices. The main objective of the sampling approach was increase contextual and organizational variation and thereby help uncover broadly the dynamics of interactions that influence gap closure. I selected Nigeria and the USA, because they are far apart in their environmental volatility and I wanted to see if this has any effect on strategy execution approach. The summary of the sample and profiles of the interviewees are presented in Table below.

Table 8 : Interview Respondents

Respondent Organization Industry Organiz Role Country ation Size (Employ ees) Respondent #1 N1 Banking 20000 CEO Nigeria Respondent #2 N2 Banking 10000 CEO Nigeria Respondent #3 N3 Services 25000 CEO Nigeria (FMCG) Respondent #4 N4 Telecoms 37000 CEO Nigeria Respondent #5 N5 Telecoms 23000 COO Nigeria Respondent #6 N6 Regulatory 6700 Deputy Nigeria Governor Respondent #7 N7 Regulatory 3500 Managing Nigeria Director Respondent #8 N8 Manufacturing 14000 COO Nigeria Respondent #9 N9 Manufacturing 67000 COO Nigeria Respondent N10 Healthcare 5000 Head of Nigeria #10 Strategy Respondent A1 Banking 50000 CEO USA #11 Respondent A2 Finance 45000 CEO USA #12 Respondent A3 Mining 14000 COO USA #13 Respondent A4 Mining 35000 COO USA #14 Respondent A5 Pharmaceutic 130000 Group USA #15 als Head Human Resources Respondent A6 Insurance 8000 COO USA #16 Respondent A7 Healthcare 5800 Head of USA #17 Strategy Respondent A8 Finance 6000 CEO USA #18 Respondent A9 Services(FMC 14000 COO USA #19 G) Respondent A10 Insurance 7000 COO USA #20

Data Collection

We developed an interview protocol which aimed at eliciting the interviewee’s experiences with the dynamics and contextual interactions associated with strategy formulation, strategy implementation and its effectiveness. We also inquired how the respondent’s organization engaged with volatility. Data was gathered through interviews with open ended questions to allow the interview process to be flexible, and the conversation to flow and evolve (Teddlie & Tashakkori, 2011). The interviews were designed to elicit the interviewee’s experiences with the dynamics and contextual interactions associated with strategy formulation, strategy implementation and its effectiveness. I also inquired how the respondent’s organization engaged with volatility.

The interview protocol is presented in Appendix A.

The interviews (lasting between 40 and 90 minutes) were carried out between May 2014 and October 2014. Half of the interviews were face to face while the other half was conducted via skype. They were all audio recorded and transcribed resulting in 450 pages of transcribed text. We also made handwritten notes during the interviews to capture moments when body gestures, inflection, or tone conveyed additional information and these were consulted during data analysis. These comments were integrated into the transcripts during the validation process resulting in a robust record of the interview.

Data Analysis

I adopted a dynamic and iterative coding approach where I continuously checked for interactions between codable moments. The analysis was carried out in NVivo. I also 56 examined the literature as new codes emerged (theoretical triangulation) and gained prominence. During my initial coding I reviewed transcripts line-by-line and incident-by- incident making copious use of memos comparing the experiences of my participants (Charmaz 2006). I initially coded for factors associated with strategy formulation and execution and ended up with an initial set of 247 codes. I then focused axial coding to consolidate and cluster initial codes into higher level abstractions based on their underlying interaction patterns and associated dynamics (Charmaz 2006). This led to the emergence of higher level clusters and subsequent uncovering of dynamic interaction patterns within the organization, the environment and between the organization and the environment. This process reduced my code count into about 25 codes (first order concepts). I then organized my codes into 6 logical clusters (second order themes) associated with specific interaction factors; Marshaling, Balance, Contextual Sensitivity, Maintaining, Improving and Transforming. Based on this I then carried out theoretical coding as to summarize and tie these earlier codes together (Charmaz 2006). This process yielded three distinct dimensions of dynamics associated with strategy implementation: Contextual Execution Gap Dynamic, Gap Reduction Dynamic Engagement, and Execution Focusing Dynamic (Execution Capability and Strategy Configuration). Figure 1 and Figure 2 presents the data structure and associated analysis.

Figure 6 : Qualitative Data Structure Analysis

Findings

We next discuss three key dynamics observed associated with strategy execution: 1) the execution gap dynamic resulting from interactions between the organizational shifts and environmental shifts that shape the execution gap 2). The gap reduction dynamic resulting from the interactions between the strategy focusing and the gap reduction activities of the organization that shapes its dynamic engagement with the environment.

3) The execution focusing dynamic resulting from the interactions between the current strategy configuration and the firm’s execution capability shaping its execution effectiveness. Under this dynamic, three elements define the strategy configuration: core preservation, core extension and core leapfrogging; three lower level capabilities 58 influence the overall execution capability: contextual immersion, marshaling and balancing. We present and discuss our findings in this sequence, because of the endemic dependencies between the four dynamics as revealed by our study.

Finding 1: The interactions between Organizational shifts and Environmental shifts create a dynamic that constantly shapes the execution gap.

The organization and its environment continually experience shifts and counter shifts.

This volatility affects both the opportunities targeted by the organization’s strategy and assumptions upon which the strategy is anchored. This leads to constant variations between strategy implementation outcomes and strategic intent. We call this variation the execution gap. The execution gap emerges from the organization’s need to grapple with the volatility as it seeks to maintain both strategic focus and achieve effectiveness in executing its strategy against emerging opportunities.

Dynamic interactions between the environment and the organization affects constantly both the strategic opportunities and the outcomes of the strategy implementation thereby manifesting a variance between present strategic intent and execution results. Research participants described this phenomenon richly as seen in the following representative quotes;

RESPONDENT 2: “…you have to recognize that in a volatile environment there are many things at work, it is not necessarily the old things that work because if the old things work … volatility comes because there are new factors that you have not seen before and it comes as well because these factors are interacting at a fastest pace that you were not previously accustomed to.”

RESPONDENT 7: “Sometimes the strategy has a window, you have to hit it within this time frame or else the market changes the things happening and if 59

you don’t act in time to deal with those kinds of things, everything takes way much longer and is way much bigger than you think, and then people start to question the strategy”

Finding 2: The interactions between the strategy focusing and the gap reduction capabilities creates a dynamic that shapes organizations engagement with the environment as it seeks to reduce its execution gap.

We define dynamic engagement as the relative dynamism of the organization as it responds to the constant variation in the execution gap. We define strategy focusing as a dynamic capability whereby the organization can shift its strategic focus relative to contextual dynamics. (Teece, D. J. 2007, 2012. And Teece, D. J., Pisano, G., & Shuen, A.

1997). We define gap reduction capability as the organization’s capability to intervene in the dynamics of its environment as to reduce or entirely shift the execution gap. We found that the degree to which the organization was able to concurrently (re)focus its strategy relative to the contextual dynamics and its capability for gap reduction affected its ability to positively engage with the execution gap dynamic. Study participants described both gap reduction and strategy focusing capabilities as critical antecedents of their effectiveness in positively affecting the dynamic shifts.

Past literature identifies similarly dynamic capability which is called often as ambidexterity or dynamic focusing (including strategic anticipation) as key individual constructs affecting strategy execution (Drazin and Howard 1984, Heide & Grønhaug &

Johannessen 2002, Noble, 1999). Our finding suggests that the organization’s ability to positively engage with the environment to close the execution gap results from the reciprocal interactions between the two factors (Strategy focusing and Gap Reduction).

Both strategy focusing and gap reduction were engaged concurrently and interactively to achieve effectiveness in executing the strategy. The following quotes highlight the significance of these interactions:

RESPONDENT 18: I just try to think of a business where someone makes a huge acquisition that doesn’t work. There’s a strategy change, they buy the company, they do all the little things right, bring it together and the costumer doesn’t want what you have. So a way to think about the strategy is about what the customer wants, and execution is figuring out a way to deliver it to the costumer.”

RESPONDENT 7 “We got it wrong by, by setting up a goal; define operationalizing the goal in a way that did not solve customer problems. So we had a senior executive who said ‘this is the way we are going to improve’ and he was effective in getting the organization to do better in that very narrow definition of the strategic goal. And we all worked as a team doing this.”

RESPONDENT 14 “If you are going to manage that new and fast changing situation, you have to be able to get all hands on deck because different people will have different pieces of knowledge that you will not have and also because they are dealing with it at a closer level than you are because that is where (being the main piece of the puzzle) they may have ideas that you may not have thought about, so you have to have the skill of being able to include all, allowing all parties in the board to go ahead and contribute to it, so it has to be a mechanism whereby you allow many contributions to take place.”

RESPONDENT 19 “this company could buy this company, this could happen in the market, this company could over throw whatever and we plan on how to respond as a company to each of this things and we map that out, we actually map it out with a string, where we string it out on a board and that gets everybody into dealing with a volatile thing, because the world is volatile everything changes, but if you say “here is what we believe is going to happen and this is why we are going to invest on this one path” if that doesn’t happen, well you have got eggs on your face, you look like you don’t know what to do, but if you say, ”look here is 5 mega things that could happen and depending on what happens in 2years, 5years and 7years, here is how we are going to respond to it””

Finding 3: The interactions between the organization’s strategy configuration and its current execution capability create a dynamic that shapes its execution effectiveness.

We defined strategy configuration in terms of three elements: core preservation, core extension and core leapfrogging. It describes shifts in balancing strategy as to preserve the firm’s current core, as to improve by extending the core, or by transforming the core by leapfrogging. We define the core as the prevailing business operations of the organization specifying the business space in which the organization competes with its strategy. We observe from our findings that strategy focusing modify the configuration by remaining the core, extension or leapfrogging and these moves may follow one another over time. This three-dimensional configuration characterizes the shifts in the scope of strategy and how this is influenced by the organization’s strategy-focusing capability. Respondents described specifically three scope dimensions that characterize the current configuration of strategy and how its influences the ensuing strategy implementation; preserving the core of the business, extending the core of the business and leapfrogging the core of the business. These variations provided contextual focus for the organization during strategy implementation. Respondents described that these three alternatives were latently present to varying degrees in any strategy implementation situation. They also describe a continual organizational struggle to balance focus during execution across these priorities. This highlights the tension between the need to preserve the core versus extend making a leap. This invites all the time orchestrating potentially contradictory and divergent organizational capabilities that are required for each of foci

(O’ Reilly & Tushman, 2007)

One interviewee described this tension as follows: RESPONDENT 7: “I want to talk about capacity but in a different way : run the business, keep improving the business, and then grow the business, 3 things. Does your organization have the internal manpower or capacity? Can they do all that at the same time? Because before you came up with this big strategy, they were full time working and running the business and improving the business, so now you bring this new strategy in, you have to be able to set priorities to find that balance between those 3 different functions, and that is got to be leadership job… and the hard part is to figure out what’s the ratio, how much can you spend”

While literature on dynamic capabilities and ambidexterity highlights this constant challenge, no attempts have not been made to examine how this is reflected in the strategy execution models. For example, Lewis M. W., Andriopoulos C. and Smith K.

W (2014) advocate that leaders must encourage “paradoxical thinking,” in which a tension is identified, its contradictory elements and their links are explored, and new insights into existing problems are reached. But this in only emphasize its importance without linking it to any strategy implementation model

We also found that one of these three foci emerged as the dominant characterization of current organizational strategy at any point of time creating a sort of contextual ambidexterity. The following quotes highlights these three foci;

RESPONDENT 5:“You still have to run the existing business and so you have to come up with a realistic pace. Everything can’t get done at once and that’s what a lot of companies fall into.”

RESPONDENT 3:“…. but it’s also improving the business we have today because you always have cost pressures, you always have to get more efficient so you can’t just put the existing business on auto-pilot because to fund the growth, you have to get more efficient with what you do”

RESPONDENT 3:“I would say that there is running the Business we have today and Thinking about the Future piece …. its running the existing business improving the existing business and then start to build on the future pieces (and that’s a hard part)”

We define execution capability as the organization’s capacity to immerse itself in its

environment and dynamically harness its capabilities as a response to the execution

gap dynamics. This involves dynamic acquisition, harnessing, deploying and

releasing of organizational capabilities relative to the dynamics at play between the

organization and its environment. (Eisenhardt & Martin 2000). This places the

organization’s gap reducing capability under constant dynamic strain. Research

participants described their organization’s capability management as being influenced

by changes in the strategy, the aspiration, the environment and their relative ability to

effectively ramp up capabilities against current, emergent and anticipated shifts.

Ineffective capability management resulted often in a mismatch between the current

strategy configuration and continually shifting dynamic focus of the strategy. The

following quote highlights this;

RESPONDENT 11:“I think you have to be very good understanding what your entire capabilities are, know where you are strong, where you are weak and be pretty dedicated to boosting any of the capabilities particularly those that are needed for the future.”

Implementation effectiveness was achieved when the organization focused not just on the strategy but also dealt with the dynamic variance and appropriately engaged to reduce the gap through a combination of strategy focusing (through strategy configurations adaptation) and execution capability management (execution capability). While literature 64 on dynamic capabilities support this finding (Teece, D. J. 2007, 2012. And Teece, D. J.,

Pisano, G., & Shuen, A. 1997) we note that this finding is at variance with the more widely advocated approach in literature where a direct link between the strategy and aspiration with alignment is the primary aim. We could also solidify the necessary organizational capabilities for strategy execution into three groups: Contextual

Immersion, Marshaling and Balance as shown in Table 9 below

Table 9 : Table of Organizational Execution Capability Factors

Factors Our Findings contextual Organization’s contextual sensitivity is a distinguishing factor at executing immersion strategy. Contextual barriers to the acuity of this organizational trait are; the decision making norms and symmetry, cultural harmony, strategic altitude and bandwidth of the leadership. Organizations also derive acuity in sensitivity through management of their internal and external contextual immersion and learning, their risk management practices and their sensing and sense making capacity. This factor provides the organization the double loop learning for both configurational focusing and dynamic capabilities marshaling. Marshaling Marshaling capacity of the organization is related to the contextual volatility and represents the organization’s mechanism for engaging to preempt, protect or preserve its position against observed volatility as it explores the execution gap dynamic. The factors influencing marshaling were the quality and focus of the leadership, organizational cohesion and structural flexibility and the ability of the organization to pace the strategy execution relative to volatility. Balancing Balancing across dimensions influenced gap closure and was affected by the imprint of the organization, the quality and type of strategy selected and the orientation of the organization. Other factors include; External Execution Balance, External Consistency, Internal Execution Balance and Internal Consistency.

Organization’s contextual immersion is defined as the extent to which the organization is able to immerse itself in its environment and sense latent or real shifts (Teece, D. J.

2007). Contextual immersion also describes the extent to which the organization monitors and senses contextual volatility. The degree to which the organization is immersed or connected to its internal and external environment influences consequently its relative ability to reduce gap by effectively implementing strategy. Research participants described consistently higher levels of contextual immersion as enabling strategy execution effectiveness. Participants who had experienced implementation ineffectiveness described typically poor contextual immersion as a contributing factor

(c.f. Okomus and Roper, 1999). The organization’s contextual immersion was seen being influenced by several factors such as decision making norms, culture, strategic altitude and bandwidth, external/internal learning, external/internal risk management. Table 10 showcases details of these factors.

Ineffective contextual immersion contributed to the mismatch between the organizational state configuration and the focusing of strategy and between execution capacity and the execution gap. Several quotes characterize contextual immersion:

RESPONDENT 7:“You have to have a pretty informed point over you as to where your market is going not just in five years but in ten or fifteen years and I think you have to be extremely externally aware to keep ahead at the changes “

RESPONDENT 14:“I think the biggest problem you have with volatile environment is a disconnect between your senior team and the environment… That is a big mistake.

Table 10 : Table of Contextual Immersion Sub Themes from Findings

SUB THEME DEFINITION Decision making Decision making norms influence both the speed and the effectiveness Norms of organizations at responding to shifts in their environment. (Ying et al. 2008; Richter and Schmidt 2005) Culture Organization’s culture plays a critical role in the effectiveness at gap closure. Attempts to change the culture include either injecting significant number of new recruitments into the organization and try to “chase the culture” away or bringing in an outsider at the C-Level to shakeup the organization. Cultural diversity especially across geographies and even cultural asymmetry between individuals fuels conflict and weakens the ability of the organization to connect to its environment, or sense latent and real opportunities/risk and orchestrate change. (Heracleous 2000; Heide,and Grønhaug and Johannessen 2002; Schaap 2006) Strategic Altitude We define strategic altitude as the elevation level at which the and Bandwidth organization can stay focused on strategy in its conversations as opposed to operational and transactional details. Strategic bandwidth defines the relative attention span and time the leadership devotes to strategic level conversations when compared to operational detail. External/Internal Awareness of trends in the external environment as to moderate Context Awareness strategy relative to external trends and shifts. This helps achieve and Learning consistency between the strategy thrust and the environment. The organization must learn from the environment and take advantage of emerging opportunities or defend against emerging risks. The volatility requires organizational acuity to detect potential patterns in the environment and trigger responses. Sensitivity to unintended consequences of the strategic options emphasizes the need to be tuned- in to the tension between organizational actions and countervailing systemic forces. External/Internal Risk Management Effectiveness at anticipating and managing risks associated with the strategy that result from environmental shifts Amemba (2013) Active Sensing and sense making provide the mechanisms that enhances the External/internal ability to receive feedback from the environment and take appropriate Sensing and Sense- action either towards gap closure. (Neil (2007), Weick (1995), Thomas making et al. (1993), and Sackman (1991))

The organization’s marshaling capability is defined as the degree to which the organization can harnessing and deploying its resources to either exploit or explore change/opportunity and/or hedge against environmental risks. It influences the organization’s ability to reduce gap during strategy implementation. Teece suggest a 67 similar notion of configuration capability (Teece, D. J. 2007, 2012. And Teece, D. J.,

Pisano, G., & Shuen, A. 1997). Marshaling acts like a dynamic concentrator of resources.

Ineffective marshaling contributes to a mismatch between the organizational state and the focus of strategy and between the execution capacity and the execution gap.

Research participants described marshaling are as follows;

RESPONDENT 14:“they need to be the champions of execution in all functional areas. They need to walk out of the strategy room with their arms on it and their eyes fixed on their own organizations, and they need to be unified and consistent and supportive of how they implement the strategy.”

RESPONDENT 20: “Sometimes the strategy has a window, you have to hit it within this time frame or else the market changes the things happening and if you don’t act in time to deal with those kinds of things, everything takes way much longer and is way much bigger than you think, and then people start to question the strategy”

Table 11 : Marshaling Sub Themes

SUB THEME DEFINITION Leadership The quality of the leadership influences resource marshaling and consequently is Quality and critical to remaining focused and in achieving necessary intensity as expressed in Focus terms like champions of execution, unified and supportive, courageous decision makers, inspiring confidence, credibility, committed and providing focus and priority setting. When strategy execution failed a weak leadership was reported as a pivotal contributing factor. (See also Nutt 1987, Bourgeois and Brodwin 1984) Cohesion This refers to the extent to which the organization is able to get all layers of the organization committed to the strategy and the relative ease with which the organization changes in response to shifting direction. For example, Henry (2008) points that ‘effective implementations of strategies require the organization to be sufficiently flexible in its organizational design and White (2004,) asserts that Coordination, Communication, Command, Control and Conflict/consensus are five elements required to align varying parts of the organization with strategy Execution Execution pacing was deemed a critical component of executing the strategy. It Pacing involves two dimensions: the pace at which the organization can scale up its operations and capacity; and the pace at which components of the strategy are implemented relative to volatility (a.k.a. agility). This creates a need develop capability ramp-up relative to the strategy realization needs (Yang et al. 2008) Organization’s Organization need to have a will and capability to change its internal configurations Change when it tries to keep pace with the volatility. (Drazin and Howard (1984), Heide, Appetite Grønhaug and Johannessen (2002), Noble (1999b) Opportunity Effectiveness at marshaling organizational capability seems to be dependent in part Identification on the ability to accurately identify, scope and select opportunities. Organizational and Scoping strength in scouting for change opportunities and exploiting these conditions is vital Effectiveness to strategy execution. This was described as “pipelining”, “opportunity scouting”. The challenge highlighted by our research participants is the ability to adequately define a holistic view of the opportunity and understand negative implications of dealing with just a part of the whole. Change We define change externalization as the ability of the organization to influence its Externalization operating environment, externalizing its strength to intervene in the environment to Effectiveness either create enabling conditions for its strategy or creating turbulence to preserve its positioning. This is observed in the “pricing” strategy in which some of our respondents describe how they engage with the competition and the market through price intervention mechanisms Harnessing Harnessing organizational capability to orchestrate and balance the strategy execution organizational is critical to execution effectiveness. This was described in terms such as “milking the capability franchise”, “leveraging capability”, and “engaging the organization”. This describes the ability of the management to take advantage of inimitable firm competencies in driving strategy implementation Dissemination We define dissemination effectiveness as the extent to which the organization can Effectiveness engage across its functions, levels and stakeholder groups to focus on a single purpose. It also describes the extent to which the strategy is decomposed and communicated to all layers of the organization. Organizational Our research participants describe as important the organizations ability to plan and Capability manage the acquisition of required but internally lacking capabilities relative to Management change and implementation process. We refer to this as the Organizational Capability Management. This is influenced by changes in the strategy, aspiration levels and the ability to ramp up capabilities against future growth projections. Ineffective capability management results in a mismatch between the strategy payload and the organizational capability or the aspiration of the organization. 69

The organization’s balancing across multiple dimensions. We describe balancing

as the organization’s capability to achieve and maintain consistency between its

strategic intent and its organizational capability given the dynamics of its strategic

context (Teece, D. J. 2007, 2012. And Teece, D. J., Pisano, G., & Shuen, A. 1997).

The following describes the process of balancing:

RESPONDENT 7: “Taking a realistic look at your people and their capacity. ..do they have what we call ‘the run-way’ to continue to grow, or do we need to make some big changes? Do we need to bring some outside people or do we need to shuffle things around a little bit?”

Participants described their organization’s capacity to balance across the dimensions as being influenced by multiple factors: organizational imprint, quality and type of strategy, organizational orientation, external and internal execution balance, external and internal consistency, internal consistency and internal execution balance. Table 12 showcases these factors. Ineffective balancing contributed to the mismatch between the organizational state configuration and the focus of strategy and between execution capacity and the execution gap. The following quotes describe the balancing:

RESPONDENT 19: “So we are rising high, we are doing great, we are making all the investments and we started out making investments that were hundred million dollars, hundred and fifty million dollars and we got a little brave and made a seven hundred and fifty million dollar investment. It was a big investment and then we said let’s go further, we bought a 4.5 billion dollar property, we went from 750 (being the biggest we have ever done ever in 165 years) to 4.5 billion. It was bold and turns out wasn’t the smartest thing for us to do “

RESPONDENT 4: “So I think the two aspects for me in strategy has always been external, understanding your markets and internally, what strengths exist within the institution and how to capitalize on that, how to bring that to

bear with your strategy, once you knew that, you knew what kind of management team you wanted

Table 12 : Table of Organizational Balance Sub Themes from Findings

SUB THEMES DEFINITION Organizational Balancing is dependent on organization’s imprint: the effect of early conditioning Imprint and path dependence over time on strategy execution. Imprinting is a condition where the organization acquires a particular orientation, configuration (including operating structure, infrastructure and people) and approach to the market based on past experiences and exposure to industry or culture. Imprinting was described with such terms as “legacy” and “conforming” to tradition despite the need for transition (Bamford, Dean, and McDougal 2000; Boeker 1989; Kriauciunas and Kale 2006; Kimberly 1979; Schein 1983) Quality and Different strategy types have different requirements regarding organizational Type of Strategy structure. The fit between business unit strategy and the internal organization does have an effect on business unit performance. If the underpinning assumptions of strategy or its scale, orientation and content are inadequate then execution will be affected.. This observation is supported by the emergent view of strategy (Mintzberg etal, 1990), Li etal. (2008), White (1986); Olson & Slater & Hult ( 2005). Organizational This can be defined as the organizational outlook consisting of its leadership style, Orientation its structural rigidity, collaborative appetite, risk appetite and fit with the market profile. Risk appetite creates an orientation towards innovation and creativity and whereas orientation towards functional insulation imbues shallow strategic integration. External/ This is the relative fit between the execution speed and the velocity of change in the Internal environment, and the consistency between the strategic rigor and the organization’s Execution capability. Participants described the execution balance in terms of the Balance environmental opportunities and the firm positioning, the consistency between customer needs and their strategy offerings, and the consistency between the speed of decision making and external volatility. Research participants described the execution balance in the internal environment with terms like People fit, Organizational Capability, Leadership team fit and resource constraints. External/Internal This is the consistency between the strategy and the opportunities, and the Consistency consistency between the strategy and the organization. Research participants described the external consistency of the strategy as the fit between the environmental opportunities and strategy while they describe internal consistency of the strategy in terms of the fit with the organizational constraints, capacity and dynamics

Discussion

We observed at the outset that organizations experience significant difficulty in implementing strategy. This difficulty is caused in part by interactions between the

71 volatility in the external and internal environments. This leads to a gap between organization’s strategic intent and contextual reality defined here as the execution-gap. In strategy research strategy execution has been less studied and there is less models to understand strategy execution when compared to the preponderance of frameworks for strategy formulation (Altonen & Ikavalko 2002; Goold 1997). Our research focused on uncovering organizational level interactions during strategy execution and examining related dynamics that influence the effectiveness of the organization in reducing the execution gap. Our main research question sought to uncover critical antecedents that explain organizational interactions so that the organization can effectively close the execution gap. To address this question we asked the following sub-questions: 1) what are the elements which interact during strategy execution? 2) What are the modes of their interaction? 3) What are the mechanisms underlying these interactions? We discuss next how our study addresses these questions

Our findings suggest that organizations approach strategy implementation through a series of interconnected and mutually recursive interactions between the organization and its strategic context. Here the classic quest for alignment- alignment between strategy and the organization is only a part of these interactions. Yet, past research describes effectiveness in strategy execution mostly as an outcome of internal alignment between the organization and its strategy. Kaplan and Norton (2008).

Our findings suggest that both the organization and its environment experience volatility. And, recursive interactions continually unfold between the organization (internal capability shifts) and the environment (environmental shifts) due to their inherent 72 volatility. This dynamic (which we call the execution gap dynamic) continually tips off the balance and accounts for the existence of a dynamic variance between strategic intent and execution outcomes (which we call the execution gap). Because of the dynamic nature of the execution gap the organization continuously engages with its environment through strategy focusing and gap reduction with the aim to reduce or entirely shift the gap. As a result, strategy configurations and organizational execution capabilities constantly shift in response to these dynamics. Accordingly, organizational effectiveness during strategy execution need to be seen as a function of a shifting internal/external context; the dynamic interaction between the organization and the environment, which generates an execution gap dynamic as depicted in Figure 7.

Figure 7 : The Strategy Execution Gap Dynamic Model

Figure 8 : The Strategy Execution Gap Dynamic Model

Figure 8 details the dynamics around these interactions as they emerged in our study. The execution gap dynamic model highlights three themes related to the organization’s ability to engage with its environment and close the strategy execution gap: 1). the execution gap dynamic resulting from interactions between the organizational shifts and environmental shifts that shapes constantly the execution gap. 2). The gap reduction dynamic resulting from the interactions between the strategy focusing and the gap reduction forces of the organization that shapes its dynamic engagement with the environment.; 3) the execution focusing dynamic resulting from the interactions between the strategy configuration and the execution capability of the organization that shapes its execution effectiveness. Under this dynamic, three components define the strategy configuration: core preservation, core improvement and core shift. And three factors

74 influence associated execution capability: contextual immersion, marshaling and balancing

We uncovered two activities that are leveraged by the organization as it engages with the environment to influence gap closure: strategy focusing and gap Reduction. The interactions between these two forces shape the dynamic engagement of the organization with its environment and the extent of its strategy effectiveness. The strategy focusing capability influences strategy configurations while the gap reducing capability influences execution capability. This highlights two elements that shape strategy execution;

Configuration (organizational and strategy) and Dynamic Capability (around contextual immersion, marshaling and balance) (Hodgkinson and Healy, 2011; O’Reilly and

Tushman, 2007)

Through the strategy focusing force the organization continually adapts its strategy configurations by leveraging three configurational/focusing modes: core preservation, core extension through improvements to remain efficient and competitive and core leapfrogging to take advantage of emergent change or opportunity. We also found that three configurational dimensions were mutually coexistent in any strategy configuration but varied in relative intensity and organizations struggled to find the right balance given their aspiration, and the execution gap dynamics of their context. Through the gap reducing force the organization influences execution capability by leveraging three factors to effect strategy execution; Contextual immersion, Marshaling and Balance. Table 9 presents these factors in detail. We relate these three execution factors to dynamic capabilities of the organization. Teece, (2007) corroborates this in his study on dynamic capabilities where 75 dynamic capabilities can be disaggregated into (a) to sense and shape opportunities and threats, (b) to seize opportunities, and (c) to maintain competitiveness through enhancing, combining, protecting, and, when necessary, reconfiguring the business enterprise’s intangible and tangible assets. Eisenhardt & Martin (2000) also support this in their definition of dynamic capabilities as the firm’s processes that use resources – specifically the processes to integrate, reconfigure, gain, and release resources to match and even create market change. Dynamic capabilities thus are the organizational and strategic routines by which firms achieve new resource configurations as markets emerge, collide, split, evolve, and die. Developed from the resource-based view (RBV) and evolutionary economics dynamic capabilities provide a framework to explain how firms develop and maintain competitive advantage over time. Although much of the work in this field is still preliminary and conceptual, March (1991) observes that what is missing in its constructs is clarity around the capabilities that facilitate ambidexterity. Our work uncovered a set of capabilities that collectively explain the dynamics at play during strategy execution. We therefore propose that dynamic capability as a construct can describe the dynamism in organizational capability through three key activities; contextual immersion, marshaling and balancing.

Regarding the interactions between the organizations focusing and execution capability, we uncovered a new dynamic. We refer to this dynamic as the organizational engagement dynamic. This dynamic affects organizational engagement during strategy execution and contributes to literature by highlighting the interactive effect of the dynamic capabilities and organizational dexterity in shaping the effectiveness of the strategy execution.

Several implications for theory building emerge from our study. The first is that organizations approach strategy as a series of iterative recursive interactions rather than as a sequential alignment. The process of strategy formulation and implementation is a double loop process in which the organization focuses on enhancing its strategy effectiveness

(configuration) and its execution capability effectiveness (dynamic capabilities) in order to close the execution gap.

Our findings uncover several insights on how different aspects of the interactive dynamics during strategy execution induce tension within organizational capabilities; for example, the Leap vs. core focus may require different capabilities and configurations which are in tension and are conditioned by a number of specific factors challenging the capacity of the organization for dynamic capabilities during strategy execution. Teece,

Pisano, & Shuen, 2007 describe this dynamic capability as the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments

We propose that viewing strategy -As-Interaction better explains strategy execution dynamics using the contextual execution gap dynamic and the organizational engagement dynamic (Figure 8). Our findings also extend the body of knowledge by putting forward a model that explains these dynamics and how organizations interact to close the execution gap. Finally, the research adds to the existing literature a new perspective from which to examine organizational interactions for strategy execution, Strategy configurations and dynamic capabilities.

In summary, from our study, three dynamics help explain how organizations execute strategy especially in volatile conditions; the execution gap dynamic which highlights the continuous nature of the interactions between the organization and its environment and the resulting effect on the execution gap. This dynamic emphasizes the need for execution to focus not only on strategy but on the organizational interactions that constantly shape the execution gap; the gap reduction dynamic highlights how organizations engage with the environment during strategy execution. Two constructs emerge from this dynamic to improve our understanding of strategy execution; strategy configuration (core preservation, core extension and core leapfrogging) and execution capability (contextual immersion, marshaling and balancing). These constructs highlight the process of strategy formulation and implementation as a double loop process in which the organization focuses on enhancing its strategy effectiveness (configuration) and its execution capability (dynamic capabilities) in order to close the execution gap. From our research findings therefore, strategy execution is an interactive iterative process through which the organization focuses both on strategy as well as on the organizational interactions that constantly shape the execution gap. Effective execution results from the capability of the organization to understand the dynamics around the execution gap and appropriately reconfigure its strategy while engage its capabilities to reduce or close the execution gap

Limitations

Our research has several limitations; it geographically concentrated on two locations Nigeria and the USA. Furthermore we focused only on C-level executives 78 directly involved with strategy. We tried to mitigate this in our interview protocol by asking participants to recall lived experiences in any other previous organizations. The researcher conducting this study has worked as a management consultant in the area of strategic planning and strategy implementation for over 20 years. Therefore, we acknowledge possible bias which we have tried to mitigate by consciously following a rigorous method in the design and implementation of this research and in several discussions with academic peers who are less involved in this field. In conclusion, we state that this study represents a preliminary conceptualization that can help explain how organizations interact during strategy execution to close the strategy-execution gap. Future research would improve on the validity of our construct by focusing on uncovering the nature of these dynamics in relation to organization type, size and industry. Another area where future study could further extend our work is to test the construct validity of our framework in a study of organizations implementing strategy

Future Research (See Study 2) The next study of these phenomena will be a quantitative study in which heterogeneity of a much larger sample (estimated at N=524) will be possible. It will attempt to establish the nomological network (i.e., representation of constructs of interest in a study and their inter-relationships, Cronbach & Meehl, 1995) for the factors identified as the five emergent themes herein. This will allow for the measurement and examination of causal relationships of these factors on outcome variables such as the strategy execution gap reduction performance of the organization.

CHAPTER 3: STRATEGY AS CONFIGURATION: HOW IS IMPLEMENTED STRATEGY STRUCTURED? - A QUANTITATIVE STUDY OF THE ANTECEDENT EFFECTS OF VOLATILITY ON STRATEGY STRUCTURE AND GAP REDUCTION (Study 2: Quant)

Preface

This study employed the use of quantitative methods to continue the stream of research into the dynamic interactions among the key factors that influence strategy execution at the organizational level (volatility and configurations of strategy and dynamic capabilities), building upon the findings of Chapter 2, with particular interest in understanding causal relationships between volatility and dimensions of strategy and to what extent their interactions influence implemented strategy composition and implementation effectiveness. These findings led to the third study in this series, presented in Chapter 4.

Introduction

The first study of the quantitative strand (Quant 1) was designed to detect causal relationships between volatility and dimensions of strategy and to what extent their interactions influence implemented strategy composition and effectiveness. It focused specifically on improving understanding around three major areas; first, understanding the main effects of volatility on the strength of each of the strategy orientations in implemented strategy and the consequent effect of these interactions on execution gap

80 reduction performance. Second, examining the mediating effect of each of the three dimensions of strategy on the relationship between perceived volatility and execution gap reduction in order to uncover how each of these strategy orientations interacts with volatility to affect execution gap reduction performance. Third, examining the variations in the strength of each of the strategy orientations in different levels of volatility in order to understand how varying levels of volatility influence strategy variation within the context of dynamic capabilities.

Theoretical Framework

The theoretical framework around the impact of volatility on the interactions between strategy variations and capability configurations, and the effect of these interactions on execution gap emerged through the qualitative process, supporting the development of a hypothesized model for the relationships I explored in Quant 1 as illustrated in figure 10.

Figure 9 : Hypothesized Model

Hypothesis development Effect of Volatility on Strategy orientations and Execution Gap Reduction

I approach my hypothesis development along two main themes. First, I articulate hypothesis around the main effects of volatility on the strength of each of the dimensions of strategy and their effects on execution gap reduction. Then, I examine the mediating effect of the dimensions of strategy on the effect of volatility on the level of execution gap. This analysis seeks to uncover to what extent each of these strategy orientations transfers or participates in the effect of volatility on the organization. I then review potentially varying effects of volatility levels (High, Medium and Low levels of volatility) on the postulated relationships between volatility and strategy structure and the effects of strategy structure on gap reduction.

The effect of volatility on extent of engagement with strategy orientations

Volatility and Strategy orientations. Shifts in the environment constantly exert a gap- inducing influence on strategy execution forcing a greater difference between strategy and execution outcomes because a given strategy becomes less effective with increased levels of volatility. Omeike and Lyytinen (2015; study 1) suggest that the three configurational dimensions of strategy vary in relative intensity depending on the level of environmental volatility. Zahra (1993), Christensen & Langhoff-Roos (2003) and

D’Aveni, (1999) show that increasing volatility reduces the effectiveness of a specific strategy and organizations look to innovation oriented strategies to cope with increasing

82 volatility. A core leapfrogging strategy orients the organization towards transformative change, which often requires the organization to orchestrate new capabilities that enable it deal with volatility. In contrast, a core preservation strategy will focus on building operational excellence and will as a result not create the capabilities required to cope with volatility. A core extension strategy will orient the organization towards capabilities that enable it innovate to improve existing practices. These capabilities may enable the organization cope with volatility. I therefore expect that volatility influences to what extent companies engages in Core Preservation strategy (less if volatility increases), Core extension strategy (more if volatility increases) and Core Leapfrogging strategy(more if volatility increases).

H1a-: Increased Volatility negatively influences the extent companies engage in Core Preservation strategy H1b+: Increased Volatility positively influences the extent companies engage in Core extension strategy H1c+: Increased Volatility positively influences the extent companies engage in Core Leapfrogging strategy

The effect of volatility on execution gap reduction effectiveness

The fluid and dynamic nature of the environment constantly exerts a gap-inducing influence on the strategy execution efforts of the organization, forcing variance between strategy and execution outcomes (McNamara et al., 2003). Strategy literature describes strategic outcomes in terms of growth related financial outcomes and non-financial outcomes often referred to as “enablers”. Performance measurement plays an important role in managing strategy execution Kaplan and Norton, 1996). These include translating

83 strategy into desired behaviors and results, communicating these expectations, monitoring progress, providing feedback, and motivating employees through performance-based rewards and sanctions, quantifying the execution gap and determining the extent of effectiveness at reducing or closing the execution gap. (Kaplan and Norton,

1996). I coin the variable “Execution Gap Reduction Performance” to quantify the extent of effectiveness at reducing the execution gap. Historically, managers had primarily used accounting-based measures for these purposes. However, with increasing volatility and complexity of the market space such as increased customization, flexibility, and rapid response to customer expectations, as well as new manufacturing practices such as Just in

Time and total quality management, many have argued that accounting-based performance measurement systems are no longer adequate (Kaplan and Norton, 1996). A wide variety of measures and systems have been proposed and implemented to overcome the limitations of accounting-based measures in these environments. These measurement systems include both financial and Non-financial performance measures which provide indication of a firm’s total value and its long-term Performance (Kaplan and Norton,

1996;. Hoffman, 2001). I therefore, analyze execution gap reduction performance from two distinct but interrelated angles; first, the strategic intent defined by the growth related financial goals of the strategy. I coin the term “Growth Gap Reduction Performance” to describe the variance between stated financial, customer goals (Growth goals related to profits, cash flow and market share), and implementation results. Second, the strategic intent can be defined by the strategy’s enabling effects on non-financial goals. I coin the term “Enabler Gap Reduction Performance” as to describe the variance between stated enabler goals (non financials related to behavioral measures such as frequency of 84 innovation, frequency of new products, reputation changes), and implementation results respectively. As volatility increases, execution gaps also widen manifesting in a weakening of Growth Gap Reduction Performance and Enabler Gap Reduction

Performance because of the inadequacy of existing capabilities. Hence,

H2a-: Volatility increases execution gaps, inducing a negative influence on the organization’s Growth Gap Reduction Performance. H2b-: Volatility increases execution gaps, inducing a negative influence on the organization’s Enabler Gap Reduction Performance.

The Effect of Core Leapfrogging Strategy on Execution Gap

As volatility increases, organizations must increasingly, stage dynamic capabilities required to create and replace existing dynamic capabilities (Zollo & Winter, 2002). A core leapfrogging strategy results in new domains of competence that provide the organization leadership in an uncontested market space. These will also have the strongest impact at improving Growth Gap Reduction Performance and Enabler Gap

Reduction Performance. A core preservation strategy, which focuses on improving operational excellence and incremental improvements, will be less potent at closing volatility-induced Growth Gap Reduction Performance and Enabler Gap Reduction

Performance. In addition, a core extension strategy, which focuses on renewing or refining existing capabilities, can be easily replicated by competitors further weakening any competitive advantages a firm has as volatility increases. I therefore anticipate that a core leapfrogging strategy would have a stronger positive effect than the other strategy

85 orientations on Growth Gap Reduction Performance and Enabler Gap Reduction

Performance.

H3+: Core leapfrogging strategy will have a stronger positive effect than other strategy orientations on Growth Gap Reduction Performance H4+: Core leapfrogging strategy will have a stronger positive effect than other strategy orientations on Enabler Gap Reduction Performance

Mediation Effects of Strategy orientations on Volatility.

Organizations respond to increasing volatility and the consequent erosion of competitive advantages by looking for fundamentally new ways to differentiate themselves (Styles &

Goddard, 2004; Day & Montgomery, 1999). Literature suggests that these responses represent new ways of playing the game and can be disruptive. Argyris & Schön, (1978) and (Teece & Pisano1994) support this view by advocating that organizations should acquire a capacity to reconfigure and transform the organization as a learned organizational skill as volatility increases. A core leapfrogging strategy represents a more effective strategy orientation to blunt the effect of volatility on the competitive advantages of the organization (Eisenhardt & Brown, 1997; Volberda et al., 2001b; Flier et al., 2003) for a number of reasons. First, it focuses on staging systematic learning mechanisms (second order capability; learning to learn) that create and modify zero and first order dynamic capabilities when the environment changes (Zollo & Winter, 2002).

Second, it orchestrates the capability of “learning to learn” which cannot be readily copied by competitors (Dickson, 1996). I therefore, expect that a core leapfrogging orientation of strategywill fully mediate the effect of volatility on two dimensions of execution gap reduction performance. A core extension strategy focuses on staging first

86 order capabilities required to “to extend, modify, or create ordinary capabilities”.

Volatility will weaken over time the effects of first order dynamic capabilities (Winter,

2003; Eisenhardt & Martin, 2000). I therefore, expect that a core extension strategy will only partially mediate the effect of volatility on two dimensions of execution gap reduction performance. A core preservation strategy focuses on staging zero order capabilities, which are easily replicated by competitors and does not create a sustainable competitive advantage. (Winter, 2003; Eisenhardt & Martin, 2000; D’Aveni (1999).

Therefore, a core preservation strategy will be ineffective in blunting the effects of volatility. I therefore, expect that a core preservation strategy will not mediate the effect of volatility on the two dimensions of execution gap reduction performance. Hence,

H5a: Core leapfrogging strategy dimension will fully mediate the effect of volatility on Growth execution gap reduction Performance. H5b: Core extension strategy dimension will partially mediate the effect of volatility on Growth execution gap reduction Performance H5c: core preservation strategy dimension will not mediate the effect of volatility on Growth execution gap reduction Performance. H5d: Core leapfrogging strategy dimension will fully mediate the effect of volatility on Enabler execution gap reduction Performance H5e: Core extension strategy dimension will partially mediate the effect of volatility on Enabler execution gap reduction Performance H5f: Core preservation strategy dimension will not mediate the effect of volatility on Enabler execution gap reduction Performance.

Moderation Effects of Volatility on the Strategy orientations

Organizations respond to the shifts in volatility by adapting or changing their strategy orientation somewhat differently (Christensen & Langhoff-Roos (2003). I therefore

87 expect that the main effects of Volatility on the dimensions of strategy (core preservation, core extension and core leapfrogging) and their mediating relationship with the two dimensions of execution gap reduction will be different for different levels of volatility.

Hence,

H6: The main effects of Volatility on the strength of each of the strategy orientations (core preservation, core extension and core leapfrogging) will be different such that only with high levels of volatility does Core Leapfrogging dimension become strong while core preservation strategy becomes insignificant.

Non-financial performance as a predictor of strategy implementation effectiveness performance

Organizational performance in implementing strategy is often measured using both financial and non-financial dimensions (Kaplan & Norton, 1998). Literature suggests that Non-financial performance represents organizational capability building outcomes that enable the organization to orchestrate both market and financial performance of their strategy (Kaplan and Norton, 1996). Hence, I frame organizational performance to close execution gaps as consisting of two related dimensions; “Enabler” gap reduction performance reflecting the nonfinancial orientation of strategy implementation outcomes and “Growth” gap reduction performance reflecting the market and financial dimensions of strategy implementation outcomes. I therefore anticipate that the extent to which the organization is able to close its Enabler related execution gaps would have a stronger positive effect on its performance at closing the Growth related execution gaps.

H7+: Enabler Gap Reduction Performance will have a stronger positive effect on Growth Gap Reduction Performance

My conceptual research model in Figure 10 reflects my theoretical framework and the findings of my earlier qualitative study on strategy execution dynamics.

Research Design and Methods

I use a quantitative approach to uncover the dynamics associated with Volatility and molar concept of strategy. I survey C-level executives who have been in pivotal roles in executing strategy in large multinational organizations across 13 industries (Financial

Services, Airlines, Telecoms, Information technology, fast moving Consumer Goods,

Hospitality, Professional Services, Oil And Gas, Health Care Services,

Agriculture/Mining, Manufacturing, Non-Profit/ NGO and Government). I cluster my data into two broad volatility level groups (High and Low Volatility) in order to surface the main effects in my postulated model under varying conditions of volatility.

Construct Operationalization

I consulted the literature to find previously validated scales to operationalize the key constructs. In most cases, I used existing scales with a few modifications. I developed new scales for the strategy orientations, as I did not find any suitable scales in the literature. I also added five new items to the volatility scale based on the earlier findings from my qualitative study, which suggested that internal organizational change generated also volatility that can influence effectiveness at execution gap reduction.

Measures: Dependent Variable

Execution gap reduction Performance (GRP): This construct is a perceptual measure of the degree to which the organization is able to close the gap between its strategic intent and execution outcomes. This construct reflects the effectiveness of the organization at implementing strategy by closing the execution gaps. I measure this dependent variable using 12 items from the scales developed by Chan, Yolande E., Sid L. Huff, and Donald

W. Barclay (1997), Venkatraman, N. (1985). See Table 2. I measured four dimensions of execution gap reduction. (1) Financial performance, the degree to which the organization was able to meet its financial goals; including revenue, profits and cash flow. (2) Market performance, the degree to which the organization was able to meet its market acquisition goals; including market share and revenue, (3) Product innovation performance, the degree to which the organization was able to meet its product innovation goals; including new product introductions, time to market and product performance. (4) Organizational performance, the degree to which the organization was able to meet its internal efficiency goals. The response anchor for the items in in my dependent variables was a 5-point

Likert scale where “1 = strongly disagree” and “5 = strongly agree.”

Measures: Independent Variables

Strategy orientations: I measured three dimensions of the strategy orientation (Omeike and Lyytinen, 2015). (1) Core Preservation (with four items), the degree to which the organizations’ strategy focused on incremental change or operational excellence. (2) Core

Extension (with three items), the degree to which the strategy of the organization focused on transformative change and (3) Core Leapfrogging (with three items), the degree to

90 which the strategy focused on game changing maneuvers resulting in the acquisition of entirely new capabilities or new domains. I found no suitable scales for strategy orientations in literature other than, the basic measures for business strategy types developed by Sabherwal, Rajiv, and Yolande E. Chan (2001) and therefore developed a

13-item scale by adapting scales developed by Yuan, Feirong, and Richard W. Woodman

(2010), and by Oh, Wonseok and Alain Pinsonneault (2007). The response anchor was a

5-point Likert scale where “1 = strongly disagree” and “5 = strongly agree.”

Volatility (VOL): This construct is a measure of the frequency, unpredictability and pace of environmental change. I identified four dimensions of volatility: (1) Technology, (2)

Market, (3) Environment (External) and (4) Organizational (Internal). Measurement scales were adapted from scales developed by Carson, Stephen J., Anoop Madhok, and

Tao Wu (2006), Technology (5-item) and Market Turbulence (4-item) scales developed by Lichtenthaler, Ulrich (2009). Environmental Uncertainty (Heterogeneity, dynamism, and hostility), (5-item) scale developed by Grover, Varun, and Martin D. Goslar (1993).

The response anchor was a 5-point Likert scale where “1 = strongly disagree” and “5 = strongly agree.”

I controlled for firm size and industry because prior research has shown that these relate to execution gap reduction performance of organizations (Singh, House, and Tucker,

1986; Mitchell. 1989; Delacroix and Swaminathan, 1991; Haveman, 1992; Amburgey,

Kelly, and Barnett, 1993). Thus, by controlling for these variables, any effects detected in

91 this study for strategy context cannot be attributed to potential industry and size differences among the organizations in this sample.

Control Variables

Industry. I measured industry using 13-item scale adapted from Karimi, Jahangir, Yash

P. Gupta, and Toni M. Somers (1996). Participants indicated their industry type by selecting one item from a listing of industry types. See Appendix B (Survey scales used in the quantitative study).

Size. I measured company size using a 2-factor construct consisting of number of employees and annual revenue with items scale adapted from Karimi, Jahangir, Yash P.

Gupta, and Toni M. Somers (1996). Participants indicated their firm size and annual revenue brackets by selecting from a listing of firm size groups and revenue groups. See

Appendix B.

Instrument Development

Pre-Testing

Q-sort. I tested validity of scales using a Q-sort technique (Thomas and Watson 2002) to see if Q-sort participants would group the items in the ways suggested by the scale authors. Participants comprising of two different groups were recruited through personal network to conduct a Q-sort. The first group comprising of 12 participants from eight organizations reviewed the first survey instrument containing 140 items. I achieved 85% agreement on scale items sorting outcomes with this group and ended up with a

92 measurement instrument of 90 items. I dropped or refined items flagged for poor clarity or having low group agreement. A second group made up of eight participants from five organizations conducted a second Q-sort to further refine the 90-item measurement instrument. This iteration further dropped or refined some items for clarity and precision and resulted in an 80-item instrument with 82% group agreement on sorting outcomes.

My Q-sort results showed some cross-loading of a few items on the three dimensions of strategy context (Core Preservation, Core Extension and Core Leapfrogging), and on the

Environmental and Technology sub-scales of Volatility Scales, but they were minor and did not justify changing or eliminating items or making other changes to the scales. The final instrument included perceptual measures of volatility, organizational performance as a proxy for gauging execution gap reduction effectiveness measured at two points of the strategy implementation process, measures of the strategy context, measures of emphasis and shifts in organizational capability, measures of volatility and measures of organizational size and industry (Appendix B).

Online Pre-Test of Questionnaire. Following the Q-sort, I developed my questionnaire and loaded the questions onto the Qualtrics online platform. I then sent email invitations to each potential participant with a link to the online survey and instructions that included an assurance that all responses were strictly confidential. The primary researcher then tested a pilot version of the online questionnaire with a group of

12 corporate executives from eight organizations. The test suggested the need for minor wording changes in a few of the questions to provide greater clarity. A second test was conducted with several business leaders, which revealed some minor issues and the need 93 to redesign the measurement scale for one set of questions around the Core Leapfrogging sub items of Strategy orientations.

Because most of my measures are self-reported perceptual measures, social desirability construct was included as a check for potential response bias. I introduced an 8-item short form of the Marlowe-Crowne social desirability scale adapted by Ray (1984). The reliability of this short social desirability scales is reflected in the satisfactory internal consistency and reliability (alpha of .77) achieved by the authors using items Nos. 6, 13,

15, 16, 19, 21, 34, and 35). I had the eight items scattered throughout the questionnaire and then analyzed using the scoring format provided by Ray. (1984).

Data Collection and Sample

Data was collected over a four-month period from mid-September 2015 to early

January 2016. The sample consisted of 557 cleaned cases representing responses from

319 organizations implementing strategy. The sample was drawn from the United States

(126 respondents, 112 organizations), surveyed directly through LinkedIn and representing a mix of organizations and countries but predominantly USA, and (431 respondents, 207 organizations) from Nigeria surveyed directly using paper based questionnaires. See table 14 below.

Because strategy implementation and organizational responses depended greatly on environment, these organizations were sampled to determine the extent to which the differences in the operating environment (Volatility) and practices between Nigeria and the US affected the organizational responses. Respondents were limited to members of 94 the C-Suite, Heads of Functions and heads of strategy of large organizations across sectors. A survey link, hosted on Qualtrics, was e-mailed to target participants from a hit list of organizations as well as through direct physical administration on a second sample of participants. In order to maximize response rates, we sent three reminder emails (one week apart) to individuals who had not completed the survey (Dillman, 2000)

The final set of participants were recruited through personal networks and through social media using a combination of directly administered paper-based surveys and emailed invitations both directly and through social media with an offer to share the reports of the research findings with respondents. I emailed 1500 respondents out of which 900 responded by initiating completion of the survey forms online to varying degrees of completion. I also distributed 547 copies of paper-based versions of the instrument to respondents in Nigeria out of which only 438 copies were received back.

I utilized a combination of Qualtrics survey platform and direct paper-based distribution to administer the survey. Each participant had to respond to a set of four initial qualifying questions (Appendix e.) to determine that they fall within the category of respondents the study targeted. As a precaution, if any intending participant responded to the first question indicating that his role in his organization falls outside the targeted roles the survey immediately ends and encourages the intending respondent to direct the survey to those within his organization more suited to respond to the survey.

Sample characteristics for the organizations represented in the survey are shown in Table

14. I followed the approach recommended by Armstrong & Overton (1977) to assess the possibility of non-response bias in my sample by comparing the responses of late 95 responders with earlier responders. I compared the item responses of the last 10% of survey participants to all other respondents by conducting a two-tailed t-Test. There were significant differences for only 5 of 66 item responses (7.6%) in the combined sample, suggesting that non-response bias does not represent a threat for my sample.

TABLE 13 : Respondent Sample Characteristics

ITEM CATEGORY TOTAL SAMPLE USA NIGERIA # % # % # % Location 557 100.0 126 22.6 431 77.4

Structure 1. By Product 31 5.6 11 8.7 20 4.6 2. By Customers 78 14.0 17 13.5 61 14.2 3. By Geographic Regions 36 6.5 8 6.3 28 6.5 4. By Functional Areas 233 41.8 39 31.0 194 45.0 5. Mixed 179 32.1 51 40.5 128 29.7

Strategy 1. Our strategy aimed to seal off a stable and Type predictable but narrow niche in our 69 12.4 19 15.1 50 11.6 industry by offering high-quality (but standard) products or services at low cost 2. Our strategy aimed to simultaneously minimize risk while maximizing opportunities for growth. It aimed to 387 69.5 80 63.5 307 71.2 maintain a stable domain of core products, while seeking new product/market opportunities. 3. Our strategy aimed to continuously seek new product/market opportunities, and 99 17.8 27 21.4 72 16.7 create change in our market. 4 2 .4 2 .5

Industry 1. Financial Services 168 30.2 16 12.7 152 35.3 2. Professional Services 50 9.0 28 22.2 22 5.1 3. Hospitality 11 2.0 3 2.4 8 1.9 4. Information technology 104 18.7 30 23.8 74 17.2 5. Fast moving Consumer Goods 5 .9 1 .8 4 .9 6. Telecommunications 12 2.2 10 7.9 2 .5 7. Health Care Services 10 1.8 5 4.0 5 1.2 8. Oil And Gas 12 2.2 3 2.4 9 2.1 9. Airlines 4 .7 2 1.6 2 .5 10. Agriculture/Mining 24 4.3 3 2.4 21 4.9 11. Manufacturing 9 1.6 7 5.6 2 .5 12. Non-Profit/ NGO 13 2.3 9 7.1 4 .9 13. Government 135 24.2 9 7.1 126 29.2

TABLE 14 : Respondent Sample Characteristics (Cont’d)

TOTAL ITEM CATEGORY SAMPLE USA NIGERIA # % # % # % Job Role 1. MD/CEO/President 31 5.6 27 21.4 4 .9 2. Executive Director 35 6.3 10 7.9 25 5.8 3. Chief Strategist/Head of Strategy 12 2.2 1 .8 11 2.6 4. COO 15 2.7 9 7.1 6 1.4 5. CIO 7 1.3 1 .8 6 1.4 6. Business unit Head 44 7.9 20 15.9 24 5.6 7. Functional Head 188 33.8 36 28.6 152 35.3 8. Head of Strategy 23 4.1 3 2.4 20 4.6 9. Other 202 36.3 19 15.1 183 42.5

Job Level 1. Board Member 29 5.2 19 15.1 10 2.3 2. Top Management 58 10.4 23 18.3 35 8.1 3. Senior Management 13 171 30.7 39 31.0 30.6 2 4. Middle Management 16 198 35.5 33 26.2 38.3 5 5. Operational Level Staff 101 18.1 12 9.5 89 20.6

Staff Strength 1. Fewer than 50 45 8.1 28 22.2 17 3.9 2. 51 to 100 41 7.4 18 14.3 23 5.3 3. 101 to 250 52 9.3 10 7.9 42 9.7 4. 251 to 500, 59 10.6 11 8.7 48 11.1 5. 501 to 1,000 31 5.6 15 11.9 16 3.7 6. 1,001 to 5,000 13 161 28.9 23 18.3 32.0 8 7. 5,001 to 10,000 13 147 26.4 12 9.5 31.3 5 8. more than 10,000 employees. 21 3.8 9 7.1 12 2.8

Revenue 1. Less than $25 million 112 20.1 44 34.9 68 15.8 2. $25 to 50 million 77 13.8 12 9.5 65 15.1 3. $51 to 1OO million 46 8.3 12 9.5 34 7.9 4. $101 to 250 million 11 131 23.5 20 15.9 25.8 1 5. $251 to 500 million 39 7.0 9 7.1 30 7.0 6. $501 to 1,000 million 41 7.4 7 5.6 34 7.9 7. more than $1 ,000 million. 111 19.9 22 17.5 89 20.6

TABLE 14 : Respondent Sample Characteristics (Cont’d)

ITEM CATEGORY TOTAL SAMPLE USA NIGERIA # % # % # % Have Strategy 1. No 22 3.9 7 5.6 15 3.5 2. Yes 535 96.1 119 94.4 416 96.5

Is Implementing 1. No 27 4.8 5 4.0 22 5.1 Strategy 2. Yes 530 95.2 121 96.0 409 94.9

Strategy Life 1. Below 3 years 74 13.3 23 18.3 51 11.8 2. 3 years 101 18.1 25 19.8 76 17.6 3. Not sure 162 29.1 33 26.2 129 29.9 4. 5 years 141 25.3 30 23.8 111 25.8 5. Above 5 years 79 14.2 15 11.9 64 14.8

Strategy Level 1. Enterprise/Corporate Level 321 57.6 77 61.1 244 56.6 2. Business/Product Level 101 18.1 28 22.2 73 16.9 3. Functional Level 135 24.2 21 16.7 114 26.5

Organization 62 11.1 12 9.5 50 11.6 Scale 1. National 332 59.6 50 39.7 282 65.4 2. Regional/Continental 75 13.5 33 26.2 42 9.7 3. Multinational/Global 86 15.4 31 24.6 55 12.8 5 2 .4 2 .5

Data Analysis

A detailed explanation of my data analysis procedures follows. See Figure 8 for a flow chart of the process followed.

Data Screening

I conducted data cleaning and missing data treatments and dropped 715 cases with more than 10% missing data. Total missing data for the remaining variables was only

4.8%. I imputed missing values using the mean replacement method. Since I used

Likert-type scales, I did not remove outliers. I tested for skewness, kurtosis, linearity, and homoscedasticity and found that all variables exhibited homoscedasticity. However, a number of variables exhibited skewness and/or kurtosis. Because my data set was 98 sufficiently large to reduce the effects of skewness and kurtosis on results (Hair, Black,

Babin, Anderson, & Tatham, 2010), I did not transform data for the measurement model tests that follow. I ran the model in PLS without transformed variables. The results reported in the SEM analysis section of the paper use untransformed variables. I also tested for linearity and found all relationships between variables to be linear in nature.

Measurement Model Analysis

I conducted exploratory (EFA) and confirmatory factor analyses (CFA) on the full data set using IBM SPSS Statistics Amos 22 software.

Exploratory Factor Analyses (EFA)

All EFAs were conducted using principal component analysis with Promax rotation. I used principal components analysis (PCA) because my goal was data reduction for prediction purposes, as my data set was large. PCA identifies the minimum number of factors necessary to account for the maximum amount of total variance in the variables

(Hair et al., 2010). I used Promax rotation, because as an oblique rotation method it is more realistic when the underlying theoretical dimensions are assumed to be correlated

(Hair et al., 2010). As it turned out, an exploratory factor analysis of my data revealed that three of the questions I added form an independent scale of an emergent factor reflecting the relative level of difficulty experienced by the organization implementing strategy under volatility. The exploratory factor analysis also revealed that my constructs for strategy context describe three unique factors that reflect the strategy orientations that make up the molar structure of strategy - a finding I consider a meaningful contribution

99 of my study and that supports earlier findings of my Qualitative study. The exploratory factor analysis also revealed that my constructs for execution gap reduction performance constitute two separate factors that reflect the dimensions of execution gap reduction- a finding I consider a meaningful contribution of my study. Final scales used to measure the constructs in my study are as contained in Appendix B. Results of the EFA and CFA analyses for the respondents and the constructs about which they answered questions follow.

Figure 10 : The flow of quantitative analysis

FIGURE 4 Data Analysis Flow Chart

Data Collection

Self-Report Direct Survey Design. n = 557

Data Screening.

Measurement Model Analysis in SPSS & AMOS

Strategy Context Volatility (VOL)

All Respondents All Respondents

Gap Reduction Rigor (GRR), Gap Reduction Performance (GRP)

All Respondents All Respondents

Create SEM in PLS (n = 557)

Measurement Model Analysis in PLS

Collinearity Assessment of Predictor Variables

Structural Equation Model (SEM) Analysis in PLS

Calculate Path Coefficients Bootstrap 5,000 Samples to Determine t Values

Evaluate Hypotheses Conduct Mediation Tests Trim Non-Significant Paths

Conduct Main Effects and Moderation Effects Tests in SmartPLS

Evaluate Total Effects Calculate R² Values Calculate Q² Values Calculate f² Effects

Assess Controls

Execution gap reduction (n=557). Strategy execution questions were answered by all survey participants, so I used the full data set for this measurement model. Using

100

Eigenvalues greater than one as my criteria for the EFA of Strategy Execution Model

(SEM), the initial run of the data returned a clean ten-factor pattern matrix that explained

64% of the total variance. Communalities ranged from .451 to .811 with all items above the minimum acceptable threshold of .50 (Hair et al., 2010), lowest loading item being

SC_CL1 (.590). The Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) value was .894 and the Bartlett's Test of Sphericity was significant (x² = 11829.305, df = 946, p

< .000), indicating sufficient inter-correlations for factoring. All items loaded on their respective factors with values greater than .50 (Hair et al., 2010). With my sample size of

557, a loading above .30 is statistically significant and acceptable for structure interpretation (Hair et al., 2010), so I retained SC_CL1 for the confirmatory factor analysis. There were only four cross-loadings and they all differed from the value of the loading on the primary factor by more than .20, indicating sufficient discriminate validity. Cronbach’s Alphas were all well above the .70 (Hair et al. 2010). Three items loaded separately from volatility to create a new factor. Since the items on this factor seem to describe level of difficulty experienced with implementing strategy, I labeled this factor Execution Gap Reduction Force (GRF). Execution gap reduction Performance items also loaded into two distinct factors. The items under the first factor describe financial and market performance. I labeled this as Growth Execution gap reduction

Performance (GROWTH_GRP) while the items loading on the second factor describe product innovation and internal organizational performance. I labeled this factor as

Enabler Execution gap reduction Performance (ENABLER_GRP). See Table 15 for a summary of EFA results.

101

Table 14 : Execution gap reduction Performance (EFA Measurement Model Results) (n = 557) Factor/ Items Loading No. of Cronbach’ Construct Items s Alpha GROWTH_ . 6 GRP 0.913 Market Performance -Revenue growth .918 GR_M3 Market Performance -Net profits. .903 GR_F1 Market Performance -Sales growth rate. .836 GR_M2 Market Performance -Market share gains .784 GR_M1 Market Performance -Return on investment .704 GR_F3 Market Performance -Cash flow. .661 Gr_F4 ENABLER 5 _GRP 0.86 Market Performance -Frequency of new product or service . 862 GR_PI2 introduction Market Performance -Technological developments and/or .774 GR_PI4 other innovations in business operations: Market Performance -New product/service market .731 GR_PI3 performance Market Performance -Reputation among major customer .662 GR_OE1 segments: Market Performance -Strategic projects delivery and .644 GR_OE2 performance: 7 SC_CE 0.874 Strategy Changes -Our strategy focused primarily on 849 SC_CE4 making significant improvements to our operational efficiency Strategy Changes -Our strategy focused primarily on .778 maintaining the efficiency of our operations (turnaround times, SC_CP1 quality levels) Strategy Changes -Our strategy focused mainly on ensuring .753 SC_CP3 we meet operating targets Strategy Changes -Our strategy focused .747 primarily on taking advantage of emergent change or SC_CL3 opportunity and exploring or exploiting new domains. Strategy Changes -Our strategy focused primarily on .716 ensuring our operations continued to run, focusing on managing business processes and maintaining controls and SC_CP2 compliance 5 SC_CP 0.864 Capability Changes -Capability building placed emphasis on .868 CAP_ZO1 business and operations management capability Capability Changes -Capability building placed emphasis on .854 CAP_FO4 business process turnarounds Capability Changes -Capability building placed emphasis on .749 CAP_FO3 cost management capability Capability Changes -Capability building placed emphasis on .727 CAP_FO2 quality improvement capability. Capability Changes -Capability building placed emphasis on .618 CAP_ZO3 functional and business skills 102

Factor/ Items Loadi No. of Cronbac Construct ng Items h’s Alpha 3 SC_CL 0.795 Strategy Changes -Our strategy focused primarily on driving .871 growth, mergers and acquisitions and maintaining game-changing SC_CL2 market leadership Capability Changes -Capability building focused primarily on .821 driving growth, mergers and acquisitions and maintaining game- CAP_SO2 changing market leadership Strategy Changes -Our strategy focused primarily on new .590 SC_CL1 products /service innovation 5 VOL_ORG 0.801 Changes in your organization -We experienced significant .808 VOL_O4 turnover in our operational level staff (operational staff turnover) Changes in your organization -We experienced significant .786 VOL_O5 turnover in our strategic job families Changes in your organization -We experienced significant .755 turnover in our middle management (middle management VOL_O3 turnover) 3 VOL_ENV 0.74 Changes in the environment -The economic (buying power and .806 VOL_E2 vibrancy) environment changed often and was unpredictable Changes in the environment -The socio-political environment .772 VOL_E1 changed often and was unpredictable Changes in the environment -The legal/regulatory environment .761 VOL_E3 changed often and was unpredictable 3 VOL_MKT 0.703 Changes in the market -New customers tended to have product- .823 VOL_M2 related needs that were different from those of existing customers. Changes in the market -In our markets, customers’ preferences .764 VOL_M1 changed relatively fast. Vol_M3 Changes in the market -products/services became obsolete in our .702 industry very fast 3 VOL_TECH 0.614 Changes in Technology -The technology in our markets changed .773 VOL_T1 rapidly. Changes in Technology -Changes in technologies significantly .710 VOL_T3 affected our products/services and strategy Changes in Technology -It was very difficult to forecast where .689 the technologies in our markets would be in the next three to five VOL_T2 years 4 GRF 0.728 Effect of Turbulence -Increased market competitiveness .798 GRD_T4 significantly affected our competitive stance and tactics Effect of Turbulence -our existing organizational capability to .722 GRD_T3 perform was significantly stretched and challenged Effect of Turbulence -We experienced significant increase in the .710 GRD_T1 effort required to compete in the market Effect of Turbulence -Changes in our environment significantly .707 GRD_T6 affected our strategy

Execution 44 gap reduction 0.908 103

Confirmatory Factor Analyses (CFA)

I conducted a confirmatory factor analysis (CFA) in AMOS based on the EFA findings. I consulted the modification indices and co-varied error terms for items that loaded on the same factor and were theoretically related when it improved the model fit (Byrne, 2010).

I removed Vol_T2_1 (lowest loading at .43 and low communality < .50 at .463),

GRF_T6 (low loading at .50 and communality < .50 at .433, Vol_M3 (low loading at .55 and communality < .50 at .433, and Vol_O9 (low loading at .53), and achieved a good model fit with CMIN/DF = 1.907, GFI = .910, CFI = .948, RMSEA = .040, and PCLOSE

= 1.000 (see Table 16 for a summary of CFA results).

I tested for convergent validity of the factors that comprise the execution gap reduction construct using three tests recommended by Fornell and Larker (1981): item reliability, composite reliability, and average variance extracted. After removing the items detailed above, all remaining items demonstrated standardized loadings on their respective factors greater than .50, demonstrating item reliability (Hair et al., 2010).

Composite reliability for each of the three factors was greater than .70, indicating internal consistency (Hair et al., 2010). Average variance extracted (AVE) for each of the three factors was greater than .50, the minimum threshold recommend by Hair et al. (2010), indicating that the variance captured by the factor is greater than variance due to measurement error. A summary of test results and the correlation matrix are shown in

Table 16. For all constructs, AVE is greater than the maximum-shared variance (MSV) with any other construct as well as the average shared variance (ASV) with all the other constructs in the model. 104

Discriminate validity is demonstrated when the variance shared between a construct and any other construct in a structural equation model (SEM) is less than the variance shared between the construct and its measures (Fornell, Tellis, & Zinkhan,

1982). Discriminate validity is assessed by comparing the square root of a construct’s average variance extracted with that construct’s correlations with the other constructs in the model. If the square root of the AVE is greater than the correlations with other constructs in the model (the off-diagonals in a correlation matrix), then discriminate validity is demonstrated (Fornell & Larcker, 1981; Hair et al., 2010). A summary of my test results and the correlation matrix are shown in Table 16. The square root of the

AVEs is entered on the diagonals. For all constructs, AVE is greater than the maximum shared variance (MSV) with any other construct as well as the average shared variance

(ASV) with all the other constructs in the model.

Common Method Bias

Finally, I tested for common method bias by adding a common latent factor to the model and comparing standardized regression weights of factor loadings with and without the common latent factor (Podsakoff et al., 2003). Differences in factor loadings in the models with and without the common latent factor were all significantly less than

.20, indicating the lack of meaningful common method bias. A summary of CFA results is shown in Table 16. My final CFA measurement model for Strategy Context -

Execution gap reduction Performance is shown in Appendix C.

105

Table 15 : CFA Measurement Model Results (n = 557)

Standardized Average Maximum Average Regression Cronbach's Composite Variance Shared Shared Constructs/Items Mean SD Weights Alpha Reliability Extracted Variance Variance Criteria >.50 >.70 >.70 >.50

Gap Reduction performance 0.908 GR_Ext 0.913 GR_M3P2 0.793 GR_F1P2 0.039 0.784 GR_M2P2 0.048 0.798 GR_M1P2 0.053 0.77 GR_F3P2 0.05 0.874 GR_F4P2 0.052 0.851 CAP_FO 0.874 SC_CE4 0.716 SC_CE3 0.066 0.657 SC_CP1 0.063 0.739 SC_CP3 0.067 0.764 SC_CL3 0.074 0.68 SC_CP2 0.068 0.711 SC_CE2 0.07 0.656 CAP_ZO 0.864 CAP_ZO1 0.736 CAP_FO4 0.054 0.716 CAP_FO3 0.077 0.761 CAP_FO2 0.066 0.798 CAP_ZO3 0.072 0.773 Vol_ORG 0.801 Vol_O4 0.753 Vol_O5 0.075 0.688 Vol_O3 0.092 0.749 Vol_O2 0.074 0.623 GR_Int 0.86 GR_PI2P2 0.701 GR_PI4P2 0.07 0.732 GR_PI3P2 0.064 0.802 GR_OE1P2 0.075 0.761 GR_OE2P2 0.072 0.774 GRD 0.728 GRD_T4 0.732 GRD_T3 0.091 0.615 GRD_T1 0.101 0.782 Vol_ENV 0.74 Vol_E2 0.813 Vol_E1 0.073 0.713 Vol_E3 0.067 0.586 CAP_SO 0.795 SC_CL2 0.732 CAP_SO2 0.061 0.706 SC_CL1 0.065 0.781 VOL_MKT 0.703 Vol_M2 0.696 Vol_M1 0.101 0.792 VOL_TECH 0.614 Vol_T1 0.685 Vol_T3 0.106 0.693 Model Fit Statistic Threshold Results Reference Chi Square

Degrees of Freedom

CMIN/DF <3.0 1.855 Carmines & McIver GFI >.90 0.904 Hair et al. (2010:649) CFI >.92 0.946 Hair et al. (2010:649) RMSEA (LO 90/HI .039(.036 90) <.07 .043 Hair et al. (2010:649) PCLOSE <.05 1

Standardized RMR = .0397 Correlations Matrix

CR AVE MSV ASV CAP_SO GR_Ext CAP_FO CAP_ZO VOL_ORG GR_Int GRD VOL_ENV VOL_MKT CAP_SO 0.786 0.551 0.407 0.193 0.742 GR_Ext 0.921 0.660 0.542 0.130 0.435 0.812 CAP_FO 0.848 0.528 0.493 0.187 0.638 0.377 0.726 CAP_ZO 0.870 0.572 0.493 0.180 0.626 0.341 0.702 0.756 VOL_ORG 0.796 0.566 0.169 0.044 0.237 0.087 0.030 0.124 0.752 GR_Int 0.869 0.570 0.542 0.169 0.520 0.736 0.508 0.450 0.015 0.755 GRD 0.754 0.508 0.169 0.057 0.173 -0.100 0.160 0.207 0.411 -0.048 0.713 VOL_ENV 0.750 0.505 0.121 0.045 0.202 0.120 0.199 0.308 0.112 0.104 0.155 0.710 VOL_MKT 0.712 0.553 0.155 0.102 0.394 0.147 0.358 0.285 0.303 0.259 0.390 0.348 0.743

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Measurement Model Analysis in Smart Pls

Smart PLS assesses the measurement model and structural model simultaneously.

I used Partial Least Squares Structural Equation Modeling (PLS-SEM) to build the structural model. It is superior to other SEM approaches for my study, because my study is an early exploration of the field of strategy execution, which lacks strong predictive theory. In addition, my structural model is complex with formative constructs which justifies the use of PLS and my sample size of 557 is not that large given the complexity of my model (Hair Jr et al., 2013). I used Smart PLS 3.2.4, the most current version of the software at the time of my study. In conducting my analysis I investigated the main effects of volatility on my model and also conducted multi-group moderation effect analysis on the moderation effects of varying levels of volatility on my structural model as to determine whether the effect between volatility and each of the three strategy context factors and on execution gap reduction performance is significantly different for different levels of volatility.

I used outer model statistics calculated by Smart PLS to assess the measurement model for my data set, which confirmed my findings for the measurement model I created in AMOS for the full data set. Indicator reliability is demonstrated by outer loadings of .709 or higher on the latent variables in a PLS model (Hair Jr et al., 2013).

All of the indicators in my final measurement model met this standard except GRF_T3

(.69). Hair et al. (2013) recommend removing indicators with outer loadings between .40 and .70 only if doing so results in an increase in the composite reliability and/or average variance extracted for the latent variable on which they load above their suggested

107 threshold values. Since this was not the case for these indicators, I left them in the model.

Composite reliability was above the recommended threshold of .708 for all constructs

(Hair Jr et al., 2013). Average variance extracted was above the .50 threshold (Hair Jr et al., 2013) for all constructs except Vol_Tech (.47), which was very close to the threshold level.

Discriminate validity for all constructs was demonstrated using two tests. First, I examined the cross loadings table that is part of the Smart PLS output and determined that the loading of each indicator on its primary construct was higher than its loading on any other construct (Hair Jr et al., 2013). Using the test recommended by Fornell &

Larcker (1981), I determined that the average variance extracted for each construct was greater than its maximum shared variance with any other construct. This is determined by squaring the highest correlation with any other construct and comparing the result to the AVE. It can also be determined by comparing the square root of the AVE for a construct with its highest correlation with any other construct as I have done in the correlations matrix shown in Table 16 and 17.

Table 16 : Model Correlations Matrix

Growth_GRP Enabler_GRP Ind SC_CE SC_CL SC_CP Size VOL Growth_GRP 0.835 Enabler_GRP 0.671 0.801 Ind -0.112 -0.149 1 SC_CE 0.344 0.456 -0.11 0.756 SC_CL 0.375 0.442 -0.146 0.521 0.842 SC_CP 0.315 0.405 -0.12 0.611 0.512 0.806 Size 0.105 0.097 -0.08 0.152 -0.003 0.103 0.91 - VOL 0.186 0.235 -0.061 0.347 0.392 0.366 0.004 0.734

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The Structural Model

Based on the results of the EFA and CFA, I modeled Execution gap reduction

Performance as two latent constructs reflecting Enabler Gap Reduction Performance and

Growth Gap Reduction performance; Volatility as a second-order construct reflecting

Market Volatility, Organizational Volatility and Environmental Volatility and

Technology Volatility. I included size of Company as measured by annual revenue and staff strength and Industry type as controls in all analyses of the structural equation model. I conducted invariance test on my measurement and structural models to determine that my model is invariant across the groups of high and low volatility. We were slightly metric invariant, but the models are structurally invariant since the Chi- square difference between the two models was insignificant.

I created a main effects structural equation model that reflected my hypothesized relationships including the mediators and controls (see Figure 7). I ran the model to calculate regression coefficients and conducted bootstrapping using 5,000 samples of the data (Hair Jr et al., 2013) to determine significant relationships for mediation. I trimmed the model of any paths whose regression coefficients were not significant. All remaining paths were significant at a 99% confidence level, as indicated by t-values of at least 2.57

(p < .01), except for the relationships between SC_CP and GROWTH_GRP (t = 2.11, p <

.05) which were all significant at a 95% confidence level. One of the latent variables,

Vol_Tech showed weak composite reliability and average variance extracted (CR=0.644 and AVE=0.475). I tested its effects on results by running the analysis with and without

109 it. I decided to remove it from my model because the results were not significantly different. I anticipated this during the Q-sort as VOL_Tech overlapped with the

VOL_Env factor. In the final SEM model, I included mediators (dimensions of strategy) and controls (firm size and industry). The structural model is shown in Appendix E.

Multi-Collinearity Assessment of Predictor Variables. The first step on analyzing an SEM in Smart PLS is to test for collinearity of predictor variables to ensure that they are sufficiently distinct. Tolerance and its inverse, the variance inflation factor

(VIF), measure collinearity. Tolerance is simply the amount of variance in an independent variable that is not explained by the other independent predictor variables.

Tolerance values below .20 and VIF values above 5 indicate potential multi-collinearity problems (Hair Jr et al., 2013). Smart PLS does not provide tolerance or VIF values, so I used IBM SPSS Statistics to perform a multi-collinearity analysis on the predictor variables. All constructs and variables demonstrated tolerance and VIF values within acceptable limits. See Table 19 for a summary of the multi-collinearity assessments of the predictor variable for each endogenous construct in the model.

Finally, R² values for the endogenous variables in a Smart PLS model are important in assessing model fit as they measure the amount of variance in the construct explained by the exogenous variables in the model. While exact interpretations of R² values are dependent upon the complexity of a model and research discipline, in general,

R² values of .75 and above are considered substantial, values of .50 - .74 are considered moderate, and values of .25 - .49 are considered weak (Hair Jr et al., 2013). In my model, the constructs of primary interest are the dependent variables of Growth Gap

110

Reduction Performance, Enabler Gap Reduction Performance and the hypothesized mediating variables, strategy orientations. The main effects model achieved a moderate

R² value of 0.61(0.60 without mediators) for Growth Gap Reduction Performance, 0.39

(0.12 without mediators) for Enabler Gap Reduction Performance, a somewhat weak R² value of .23 for Core Leapfrogging, 0.19 for core Extension and 0.15 for Core

Preservation, although as pointed out by Hair et al.(2013), in some disciplines, this might be considered moderate or strong. Overall, the model exhibits adequate fit for meaningful evaluation of my hypotheses. A summary of my measurement model evaluation in Smart PLS (essentially a Smart PLS CFA) is shown in Table 18

Table 17 : Smart PLS Measurement Model Collinearity Assessment

Execution gap reduction Execution gap reduction Performance EXT Performance INT Tolerance VIF Tolerance VIF Volatility Volatility VOL_MKT .997 1.003 .997 1.003 VOL_ENV .987 1.013 .987 1.013 VOL_ORG .984 1.016 .984 1.016

Strategy Dimension Strategy Dimension SC_CP .989 1.011 .989 1.011 SC_CE .995 1.005 .995 1.005 SC_CL .926 1.080 .926 1.080

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Table 18 : Smart PLS CFA Measurement Model Results (n = 557)

Standardized Average Maximum Constructs Regression Standard t- Composite Variance Shared (Items) Weights Deviation statistic Reliability Extracted Variance R2 Criteria >.708 >1.96 >.708 >.50

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I controlled for the Industry of the respondents in my study, as well as for the size of their organizations as measured by revenue and staff strength. My final step in the analysis was to examine the size and significance of the path coefficients of the hypothesized relationships in the structural model. This was done by using bootstrapping in Smart PLS.

I conducted bootstrapping using 5,000 samples as recommended by Hair et al. (2013) to determine the significant relationships in the model as shown in Table 20.

Mediation Analysis. I used the Preacher and Hayes (Preacher & Hayes, 2004, 2008) approach for mediation testing, as it is perfectly suited for Smart PLS-SEM analysis.

This method involves bootstrapping the sampling distribution of the indirect effects and can be applied to small sample sizes with greater confidence than other methods (Hair Jr et al., 2013). The first step is to determine that a direct effect of an exogenous variable

(X) on an endogenous variable (Y) is significant when no mediator is included in the model. The next step is to test for the significance of the indirect effect of X on Y through a third mediating variable (A). The paths from X → A and from A → Y must both be significant. In my tests for mediation, I conducted bootstrapping on 5,000 subsamples for each path in a proposed mediated relationship. I then multiplied the path coefficients from X → A by the path coefficients from A → Y to determine the coefficient of the indirect effect of X on Y for each of the 5,000 subsamples. Next, I calculated the standard deviation of the indirect effects of the subsamples in Microsoft

Excel. I then conducted Soble Test to test the significance of mediation effects in my model. T-values greater than 1.96 indicate the presence of mediation 113

Findings

My study showed that volatility predicts the extent to which organizations engage in each of the strategy orientations: Core Preservation (.40, p < .01), Core Extension (.43, p <

.01) and Core Leapfrogging (.48, p < .01). These findings support two important conclusions. First, that organizations respond to perceived level of volatility by varying the extent of engagement in each of the strategy orientations. Second, with increasing levels of volatility, organizations engage more in core leapfrogging strategy dimension than they do in a core preservation or core extension strategy. In this regard the core leapfrogging strategy dimension participates fully in the effects of volatility on the execution gap reduction performance of the organization much more than the other strategy orientations. It is therefore effective as a strategy orientation for confronting highly volatile conditions. These conclusions are supported by earlier studies, which suggest that volatility weakens competitive advantages of the organization and requires a strategy orientation to stage discontinuous change (Hamel & Välikangas, 2003;

Christensen & Langhoff-Roos, 2003; Omeike and Lyytinen, 2015; Eisenhardt and

Martin, 2000).

I also observe that all three dimensions of strategy are simultaneously present to varying degrees under conditions of volatility. They all have a positive relationship with volatility but exert varying effects on execution gap reduction. I framed this phenomenon as the molar strategy structure. This finding has implications for both theory and practice. A molar structure of strategy in conditions of volatility requires simultaneous combination

114 of multiple strategy orientations to varying degrees as an effective response to volatility.

Earlier studies support this finding by suggesting that strategy orientations are not mutually exclusive and that generating combinations of these dimensions relative to the environment are critical for organizational success (Reeves and Routledge, 2013; Miller

1992 and Baden-Fuller and Stopford, 1992). In order to enhance dynamism corporate executives can do a better job if they shift their thinking to strategy as a configuration by implementing a configuration of molar structured strategy. Corporate executives may benefit from realizing that the highly volatile environment requires agile sensing mechanisms and appropriate responses at the organizational level to configure and redeploy strategy with simultaneous combination of multiple dimensions of strategy relative to shifts in volatility. This calls for dynamic and multi-positioning of the strategy of the organization to both explore and exploit change.

Although the literature suggests that increasing levels of volatility exert a negative influence on execution gap reduction (McNamara et al., 2003; Hamel & Välikangas,

2003; Markides, 1999a; Thomas, 1996; Larsen et al.,2003), my findings show that the effect of volatility on Growth Gap Reduction and Enabler Gap Reduction Performance are fully mediated by the core extension and core leapfrogging dimensions. Core extension strategy dimension does not participate in the effect of volatility on the financial and market performance of the organization. Core preservation strategy does not participate in and does not transfer the effects of volatility on execution gap reduction performance. My findings have important implications for theory building as they suggest that various dimensions of strategy participate to varying degrees and transfer the 115 effects of volatility on the execution gap reduction performance of the organization differently. This surfaces a foundational mechanism through which strategy orientations interact with volatility to either improve or weaken the effectiveness of the organization at closing execution gaps.

Finally, moderation effect of volatility levels uncovered another unique finding; each of the three dimensions of strategy show increasing strength with increasing volatility levels such that core leapfrogging strategy has the strongest effect among the three strategy orientations when volatility is high. In addition, while a Core Preservation strategy positively affects Enabler gap reduction in conditions of low volatility but it shows a negative effect under conditions of increased volatility. This suggests that a Core

Preservation strategy is more effective in stable or low volatility environments but is counterproductive as volatility levels increase. This result reinforces my earlier conclusion that in the presence of volatility, core leapfrogging strategy enhances execution gap reduction more than a core preservation or core extension strategy orientations. This conclusion is supported by results from other studies that indicate that as volatility increases, organizations need to acquire the regenerative capacity to reconfigure and transform the organization and accordingly deploy systematic learning mechanisms (learning to learn) that create and replace existing dynamic capabilities

(Argyris & Schön, 1978; Teece & Pisano,1994; Zollo & Winter, 2002).

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Table 19 : Significance Testing Results of the Structural Model Path Coefficients

Path Sample Standard Significance Path t Value P Values Coefficient Mean Deviation Level Enabler_GRP -> *** 0.766 0.767 0.031 24.971 0.000 Growth_GRP Ind -> Growth_GRP 0.006 0.006 0.026 0.213 0.832 ns Ind -> Enabler_GRP -0.047 -0.047 0.034 1.357 0.175 ns SC_CE -> Growth_GRP -0.099 -0.098 0.045 2.178 0.029 ** SC_CE -> Enabler_GRP 0.270 0.270 0.051 5.348 0.000 *** SC_CL -> Growth_GRP 0.129 0.128 0.049 2.639 0.008 *** SC_CL -> Enabler_GRP 0.383 0.382 0.048 7.968 0.000 *** Size -> Growth_GRP 0.042 0.044 0.030 1.410 0.159 ns Size -> Enabler_GRP 0.068 0.072 0.035 1.938 0.053 ns VOL -> Growth_GRP -0.059 -0.060 0.036 1.632 0.103 ns VOL -> SC_CE 0.430 0.432 0.040 10.660 0.000 *** VOL -> SC_CL 0.480 0.483 0.038 12.569 0.000 *** VOL -> SC_CP 0.397 0.399 0.042 9.363 0.000 *** **p < .05, ***p < .01

Figure 11 : Main Effects Model

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Discussion

We noted at the outset that several studies have been carried out which improve our understanding of volatility and its effect on organizational strategy (Christensen et al.,

2002; Govindarajan & Gupta, 2001; Raisch & Hotz, 2008; Zahra, 1993, Reeves and

Routledge, 2013; Omeike and Lyytinen, 2015; Anderson 1997, Goldman et al. 1995, Pine

1993 cited by Radas 2005, p. 197). Although these studies show that volatility challenges the effectiveness of strategy and consequently dynamic capabilities and ambidexterity of organizations during strategy implementation, we know little about the systematic effect of volatility on strategy variations and execution gap reduction performance. We therefore set out to examine factors that theory and prior research, including our own qualitative study, suggest influence variations in implemented and realized strategy in organizations. To bridge this gap in literature, our study focused on connecting the interactions between volatility and strategy with the theory of dynamic capabilities to explain how this dynamic is expressed in the implemented strategy. We specifically aimed at improving our understanding around three major areas; first, understanding the main effects of volatility on the strength of each of the strategy orientations in implemented strategy and the consequent effect of these interactions on execution gap reduction performance. Second, examining the mediating effect of each of the three dimensions of strategy on the relationship between perceived volatility and execution gap reduction in order to uncover how each of these strategy orientations interacts with volatility to affect execution gap reduction performance. Third, examining the variations in the strength of each of the strategy orientations in different levels of

118 volatility in order to understand how varying levels of volatility influence strategy variation. We summarize our major findings as follows;

Summary of Main Effects Major Findings

 There are significant positive direct relationships between Volatility and Core

Preservation (.40, p < .01), between Volatility and Core Extension (.43, p < .01)

and between Volatility and Core Leapfrogging (.48, p < .01). These positive path

coefficients provide support for our hypothesis that Volatility predicts the extent

to which organizations engage in each dimension of strategy. However, these

findings fail to support our hypothesis on the direction of the relationship between

volatility and core preservation.

 In the presence of volatility, organizations engage more in core leapfrogging

strategy dimension than they do in a core preservation or core extension strategy

orientations.

 Volatility shows a negative direct effect on both Growth Gap Reduction,

Growth_GRP (-0.06, p=.10) and Enabler Gap Reduction, Enabler_GRP (-0.01,

p=.78). However, these findings fail to support our hypothesis on the significance

of these effects and suggests potential mediation of the direct effect of volatility

on execution gap reduction by the strategy orientations in our model.

 In high volatility conditions, Volatility shows a significant negative effect on

GrowthGRP (-0.05, p<.05) but shows a significant positive effect on EnablerGRP

(0.11, p<.01)

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 Enabler_GRP showed a very strong positive relationship with Growth_GRP(.77,

p < .01) suggesting that the degree to which organizations achieve non-financial

performance goals positively influence their financial and market performance

(Growth Gap Reduction performance).

 Under varying levels of volatility, each of the three dimensions of strategy show

increasing strength with increasing volatility levels such that core leapfrogging

strategy is stronger than the other strategy orientations when volatility is high

 Core Preservation strategy positively influences Enabler gap reduction

performance under conditions of low volatility but negatively influences Enabler

gap reduction performance with increasing volatility.

We noted at the outset that the Contingency Theory, Resource Based View and Dynamic

Capabilities Theory have two key elements in common. First, they all describe the operating environment of the organization as being volatile, changing and influencing the organization and its strategic intent. Second, these theories suggest that as organizations respond to environmental change they also experience variations in their strategy leading to shifts in their internal configuration and structure. More specifically, these theories assert that a relationship exists among environmental volatility (Change), strategy focus

(direction or intent), capability emphasis (organization assets) and organizational performance. Based on these theoretical underpinnings we next explore the significance of our findings and discuss the implications of the observed interactions and effects

120 among the variables in our model and the impact on the Enabler and Growth Gap

Reduction performance of the organizations in our study.

Volatility and Variance in Molar Structure of Strategy

We had hypothesized a number of effects involving volatility and the dimensions of strategy. We hypothesized that volatility predicts the extent to which organizations engage in each orientation of strategy and that volatility as an antecedent of the strategy orientations will have a significant positive effect on core extension and core leapfrogging strategy orientations and a negative effect on core preservation strategy. Our findings show that there are significant positive direct relationships between Volatility and Core Extension (.43, p < .01) and between Volatility and Core Leapfrogging (.48, p <

.01). These significant path coefficients provide support for our hypothesis that Volatility predicts the extent to which organizations engage in each dimension of strategy. It also provides support for the hypothesized positive direct effect of volatility on core extension and core leapfrogging strategy orientations.

This finding is supported by earlier studies, which show that organizations look to innovation oriented strategies as volatility increases especially as the need to stage strategic change is required to regain competitive advantage (Christensen & Langhoff-

Roos, 2003; Miller, 1992; BadenFuller and Stopford, 1992). However, our findings show that a positive relationship exists between Volatility and Core Preservation (.40, p < .01) instead of our hypothesized negative effect. This may suggest ‘incorrect’ response by management. Organizations tend to focus more on what they are good at doing which is operational excellence but this may not reduce the implementation gap. Levinthal and 121

March (1993) describe this phenomenon as “competency trap”, the tendency to maintain and intensify the use of existing and familiar competencies, technologies and processes resulting from an over commitment to a core competence (Prahalad, and Hamel, 1990).

Predictive relevance of our variables is also strong. Cohen (1988) suggests that f² values of .02, .15, and .35 respectively, represent small, medium, and large effects and, predictive relevance measured using Stone-Geisser’s Q² value (Geisser, 1974; Stone,

1974) indicate that Q² values larger than zero for an endogenous latent variable indicate that the paths in the model leading to that construct exhibit predictive relevance.

Volatility’s large f² effect sizes on each of the three strategy orientations in the order of

SC_CP (0.19), SC_CE (0.23), SC_CL(0.30). And the Q² values of each of the strategy orientations (SC_CP (0.15), SC_CE (0.18), SC_CL(0.22)) further demonstrate the importance of volatility in explaining the degree to which organizations engage in each of the strategy orientations in our study. These findings support two important conclusions.

First, that organizations respond to perceived increase in the level of volatility by incrementing the extent of engagement of each of these strategy orientations. Second, that in the presence of volatility, organizations engage more in core leapfrogging strategy dimension than they do in a core preservation or core extension strategy orientations.

Other studies support these conclusions by suggesting that 1). Varying the relative emphasis of each strategy dimension in implemented strategy improves strategy efficacy in volatile environments (Miller, 1992; BadenFuller and Stopford, 1992). 2). organizations continuously adjust these combinations relative to environmental shifts

(Miller, 1980; Murray, 1988; Wright, 1987) and 3). Volatility weakens competitive 122 advantage of the organization requiring discontinuous change in both strategy and related capabilities (Hamel & Välikangas, 2003; Christensen & Langhoff-Roos, 2003; Omeike and Lyytinen, 2015; Eisenhardt and Martin, 2000).

We also theorized that with in the presence of volatility, a core leapfrogging strategy have the strongest impact on Enabler and Growth execution gap reduction performance because it leads to disruptive change resulting in new domains of competence that provide the organization leadership in an uncontested market space. In addition, a core preservation strategy, will be less effective at closing volatility-induced gaps because it focuses on operational excellence and incremental improvements which are ineffective responses to high levels of volatility. We therefore anticipated that a core leapfrogging strategy would have a stronger positive effect than the other strategy types on the organization’s effectiveness at closing external execution gaps in the presence of volatility. Results from our findings show that in the presence of volatility, Core

Leapfrogging strategy dimension exerted a stronger positive effect on Growth Gap

Reduction, Growth_GRP (0.13, p<.01) when compared to the effects of core extension

SC_CE (-.01, p<.05). Core leapfrogging also had a stronger positive effect on Enabler

Gap Reduction, Enabler_GRP(.38, p<.01) when compared to the effect core extension has on Enabler_GRP(.27, p<.01). Core preservation, SC_CP had no direct effect on both

Enabler and Growth execution gap reduction. These results provide support for our hypothesis and suggest that in the presence of volatility, core leapfrogging strategy enhances execution gap reduction performance much more than a core preservation or core extension strategy dimension. This view is supported by findings in literature which 123 advocate that in order to maintain competitive advantage as volatility intensifies, organizations must acquire the regenerative capacity to reconfigure and transform the organization as a learned organizational skill (Argyris & Schön, 1978; Teece &

Pisano,1994)). They must also garner systematic learning mechanisms (learning to learn) that create and replace existing sets of dynamic capabilities when the environment changes (Zollo & Winter, 2002).

We also observe that all three dimensions of strategy are simultaneously present to varying degrees under conditions of volatility. They all have a positive relationship with volatility but exert varying effects on execution gap reduction. Predictive relevance of these variables are also strong. Cohen (1988) suggests that f² values of .02, .15, and .35 respectively, represent small, medium, and large effects and, predictive relevance measured using Stone-Geisser’s Q² value (Geisser, 1974; Stone, 1974) indicate that Q² values larger than zero for an endogenous latent variable indicate that the paths in the model leading to that construct exhibit predictive relevance. The f² values of the variables in our study show that SC_CL had a small but stronger effect on ENABLER_GRP (0.11) than it did on GROWTH_GRP (0.02). SC_CE had a small but stronger effect on

ENABLER_GRP (0.06) than it did on GROWTH_GRP (0.01). SC_CP had very negligible effect on ENABLER_GRP and GROWTH_GRP. The Q² Value of

Growth_GRP(.59) and Enabler_GRP (.39) further demonstrate the predictive relevance of the three strategy orientations in explaining the degree to which organizations achieve execution gap reduction.

124

These findings support an important conclusion. Implemented strategy is essentially molar in structure. In addition, under conditions of volatility implemented strategy or realized strategy will often be a simultaneous combination of multiple strategy orientations to varying degrees. And, as volatility increases, organizations increase the extent of their engagement in these strategy orientations but with a greater focus on staging strategic change at the macro or organizational level. This focus reflects an orientation towards a core leapfrogging strategy. Earlier studies support this view by advocating that strategy orientations are not mutually exclusive and organizational success at implementing strategy may require generating various combinations of these dimensions relative to the environment (Omeike and Lyytinen, 2015; Reeves and

Routledge, 2013; Miller 1992 and Baden-Fuller and Stopford, 1992).

We also theorized that the main effects of Volatility on the strength of each of the strategy orientations (core preservation, core extension and core leapfrogging) would be different, for different levels of volatility such that only with high levels of volatility does

Core Leapfrogging orientation of strategy become strong while core preservation strategy becomes insignificant. Our findings showed variations in the strength of each of the strategy orientations (core preservation, core extension and core leapfrogging) with changes in the level of volatility such that the effect of core leapfrogging strategy dimension was strong in the order of high volatility (0.48, p<.01) and low volatility (0.35, p<.01). In addition, when volatility was high, the core leapfrogging had a stronger effect when compared with the other dimensions of strategy. In addition, whereas the effects of core leapfrogging and core extension strategy orientations on Enabler Gap Reduction

125 performance was strong positive and significant under high volatility (0.20, p<.01), only core leapfrogging showed significant positive effect on Growth Gap Reduction under conditions of high volatility. Core preservation strategy showed no significant effect on

Growth Gap Reduction in our main effects model and in any of the volatility groups but showed significant negative effect on Enabler_GRP under high volatility and a significant positive effect on Enabler_GRP under conditions of low volatility.

From our findings, we conclude that under varying levels of volatility, each of the three dimensions of strategy show increasing strength with increasing volatility levels such that core leapfrogging strategy is strongest of the three strategy orientations when volatility is high. This result further reinforces our earlier conclusion that suggest that in the presence of volatility, core leapfrogging strategy enhances execution gap reduction much more than a core preservation or core extension strategy orientations. On the other hand, our findings also suggest that a core preservation strategy is more effective under low volatility or stable conditions but is counterproductive as a focal strategy when volatility increases. These conclusions are supported by results from other studies that suggest that higher levels of volatility weakens competitive advantage of the organization requiring discontinuous change in both strategy and related capabilities (Hamel & Välikangas,

2003; Christensen & Langhoff-Roos, 2003; Omeike and Lyytinen, 2015; Eisenhardt and

Martin, 2000). Under these conditions organizations adopt a strategy that enables them acquire the regenerative capacity to reconfigure and transform the organization and deploy systematic learning mechanisms (learning to learn) that create and replace existing sets of dynamic capabilities (Argyris & Schön, 1978; Teece & Pisano,1994;

Zollo & Winter, 2002). 126

Volatility and Execution Gap Reduction

We had theorized that Volatility increases execution gaps, inducing a negative influence on the effectiveness of the organization at closing execution gaps. Our findings showed that Volatility has a direct negative effect on Enabler Gap Reduction

Performance (Enabler_GRP) and Growth Gap Reduction performance (Growth_GRP) in the presence of the strategy orientations. However, contrary to our hypothesis these effects were not significant. We initially thought this might be due to suppression effects in our data resulting from respondents with high performance in execution gap reduction and those with low performance. We analyzed this by splitting our Dependent variables

(DV), Enabler Gap Reduction Performance (Enabler_GRP) and Growth Gap Reduction performance (Growth_GRP) data into high (Likert scale responses 0f 4 and 5) and low performers (Likert scale responses below 4.0) to see if it may provide additional insight on whether the impact remains the same. The effects remained the same for these two

DVs under conditions of low performance. However, the effect of volatility on the DVs reversed direction and became positive with high performance in execution gap reduction. In both cases, the effect of volatility remained insignificant.

Literature and theory suggests that increasing volatility exerts a negative influence on execution gap reduction ability of the organization. (McNamara et al., 2003;

Hamel & Välikangas, 2003; Markides, 1999a; Thomas, 1996; Larsen et al.,2003) and many studies in other contexts advocate the importance of volatility to the perceived effectiveness of organizations at closing emerging execution gaps caused by the market forces and the dynamic nature of the operating environment (D’Aveni & Gunther, 1994;

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Hamel & Prahalad, 1996; D’Aveni, 1999). Indeed, in the absence of the strategy orientations as mediators volatility showed a significant and negative unmediated direct effect on Growth_GRP ( -0.106, p<.01, t=2.4). In order to explain the observed insignificant direct effect of volatility on our DVs in the presence of the strategy orientations, we examined potential mediation effects of the strategy orientations on the relationship between volatility and execution gap reduction. The existence of a full mediation effect or an indirect mediation effect would explain this phenomenon. We make three important observations about our findings on the mediation effect of the dimensions of strategy on volatility in our study. First, the indirect effects of volatility on

Growth_GRP( .20, p < .01, t-value=4.75) and Enabler_GRP ( .19, p < .01, t-value=6.50) were both positive and significant with core leapfrogging strategy dimension (SC_CL) as mediator. As expected, we found that Core Leapfrogging strategy fully mediated the effect of volatility on Growth Gap Reduction and on Enabler Gap Reduction. This result suggests that a core leapfrogging strategy dimension fully transfers the effect of volatility or fully participates in the effect of volatility on the organization such that it reverses and blunts its effect execution gap reduction. Second, The indirect effects of volatility on

Growth_GRP (.08, p=.06, t-value=1.86) is insignificant while its effect on Enabler_GRP

(.12, p < .01, t-value=4.04) is positive and significant with core extension strategy dimension (SC_CE) as mediator. Contrary to our expectation, Core Extension strategy did not mediate the effect of volatility on Growth Gap Reduction Performance but as expected, fully mediated the effect of volatility on Enabler Gap Reduction Performance.

This result suggests two things. First, that a core extension strategy dimension fully transfers the effect of volatility or fully participates in the effect of volatility on the 128 organization such that it reverses and blunts its effect on the non-financial performance of the organization. Second, core extension strategy dimension does not participate in the effects of volatility on financial performance of the organization and is therefore ineffective as a strategy orientation for driving financial and market performance in highly volatile conditions. Third, The indirect effects of volatility on Growth_GRP (-.02, p =.57, t-value=.57) and on Enabler_GRP (.0, p =.99, t-value=.01) are both insignificant with core preservation strategy dimension (SC_CP) as mediator. As expected, Core

Preservation strategy did not mediate the effects of volatility on Growth Gap Reduction and Enabler Gap Reduction. This result suggests that core preservation strategy dimension does not participate in the effects of volatility on both nonfinancial and financial performance of the organization and is therefore ineffective as a strategy orientation for closing strategy execution gaps in highly volatile conditions.

These mediation effect results suggest that the dimensions of strategy participate in or transfer the effects of volatility to varying degrees depending on the level of volatility such that as volatility increases, a core leapfrogging strategy is the most effective orientation of strategy at blunting the effect of execution gap reduction performance of the organization.

Enabler and Growth Gap Reduction Performance

Enabler GRP showed a very strong positive relationship with Growth_GRP in our main effects model, High volatility model and Low Volatility models ((.77, p < .01; .62, p <

.01; 0.65, p < .01 and 0.58, p < .01 respectively). The predictive relevance of

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Enabler_GRP is also demonstrated by the very strong f² values for its effects on

GROWTH_GRP (0.91). These findings suggest that the degree to which organizations achieve their nonfinancial goals (Enabler Gap Reduction goals) will positively influence their performance with the financial and market goals (Growth Gap Reduction performance). Literature supports this finding. Non-financial performance represents organizational capability building outcomes that enable the organization to orchestrate both market and financial performance of their strategy (Kaplan and Norton, 1996)

Controls.

We controlled for firm size and industry because prior research has shown that these relate to execution gap reduction performance of organizations (Singh, House, and

Tucker, 1986; Mitchell. 1989; Delacroix and Swaminathan, 1991; Haveman, 1992;

Amburgey, Kelly, and Barnett, 1993). Thus, by controlling for these variables, any effects detected in this study for strategy context cannot be attributed to potential industry and size differences among the organizations in this sample.

Industry. We measured industry using 13-item scale adapted from Karimi, Jahangir,

Yash P. Gupta, and Toni M. Somers (1996). Participants indicated their industry type by selecting one item from a listing of industry types. See Appendix C.

Size. We measured company size using a 2-factor construct consisting of number of employees and annual revenue with items scale adapted from Karimi, Jahangir, Yash P.

Gupta, and Toni M. Somers (1996). Participants indicated their firm size and annual

130 revenue brackets by selecting from a listing of firm size groups and revenue groups. See

Appendix C.

Industry type and firm size had no significant direct effects on any of the endogenous variables in our model. These results suggest that there are no significant differences across industry types on how volatility influences the relationship between strategy context and execution gap reduction performance. This is important because it provides additional confirmation that the principles and practice of strategy is universal.

Our findings on the effect of size on execution gap reduction varies with literature. Several theories have over time framed how firm size affects change (e.g.,

Singh, House, and Tucker, 1986; Mitchell. 1989; Delacroix and Swaminathan, 1991;

Haveman, 1992; Amburgey, Kelly, and Barnett, 1993). In particular, organizational ecology (Aldrich, 1979; Hannan and Freeman, 1977, 1984, 1989) holds that organizational forms are subject to strong structural inertia, so that change in organizational structures and activities will be slower paced than environmental change .

If organizational size indicates political insulation and degree of bureaucratization, then large organizations will change less than small organizations and the above observations will apply. However, if organizational size is related to the possession of slack resources, differentiated and decentralized structures, and market power, then large organizations will be more fluid than small organizations. Research results suggest that both market power and bureaucratization operate simultaneously but that the market-power process dominates the bureaucratization process. (Haveman, 1993: 20-50). In our study, we measured Size using Revenue and Staff Strength. Both dimensions, slack resources

(Revenue) and bureaucratization (Staff strength) are accounted for and our research 131 findings show that firm size has no significant effect on execution gap reduction performance of the organization (the use of slack resources to achieve organizational fluidity). We conclude that market power processes representing the use of slack resources to achieve organizational fluidity was perhaps a much more dominant factor in the organizations we studied. Size, therefore would have no effect on Execution gap reduction.

Limitations and Future Research

A limitation of our study is that our analysis and findings are not based on a longitudinal study of the 13 industry environments we sampled to obtain data on variations of strategy with actual variations in volatility. We addressed this challenge by clustering the 13 industry sectors in our study into three groups based on similarity in volatility levels (High, Medium and Low Volatility environments) and used this to examine the relative variations of strategy across these different volatility environments.

Moreover, we did not focus on empirical evidence around a specific formulated strategy in the organizations we studied in order to track the strategy implementation process and the consequent variations of the implemented strategy as volatility unfolds. Our data is self-reported by our respondents and represents the lived experiences with implementing strategy in the organization. We also ensured that participants are senior executives of organizations whose roles are pivotal to the implementation of their organization’s strategy.

Our results suggest a number of interesting possibilities for further research.

Although we learned a great deal from our study about how varying levels of volatility

132 influence the extent to which organizations engage in individual dimensions of strategy and the antecedent role of the strategy orientations on execution gap reduction , we would like to know more about how variations in strategy influence organizational capabilities, the mechanisms of their interaction. Because we did not include organizational capability configuration in our model and our analysis, another meaningful follow-up study would be a quantitative study that aims to study the relationship between capability configurations of organizations as response mechanisms under conditions of volatility.

Capability configuration rooted in the dynamic capability and ambidexterity fields is a key component of strategy execution effectiveness (Teece et al., 1997: 516; Galunic &

Eisenhardt, 2001: 1229; Eisenhardt & Martin, 2000: 1107; Styles & Goddard, 2004;

Day & Montgomery, 1999). Therefore, it would be interesting to learn how capability configuration interacts with the other factors of strategy execution effectiveness under volatility and the mechanism by which these interactions occur.

Because strategy context influenced so many of the other variables related to execution gap reduction effectiveness in our study, further research on how each of the factors of strategy context interact with one another and how dominant dimensions of strategy focus emerge and influence strategy implementation effectiveness would be useful. Such research might be particularly valuable to corporate executives with pivotal roles in the strategy formulation and execution of their organization and who require greater insight into how to leverage the concept of dominant strategy contexts to improve their strategy execution effectiveness. Our research suggests that the current practice of responding to volatility by altering micro strategy that is common among many organizations and supported by literature may be unsustainable and inefficient as 133 volatility increases and our understanding of the role of a molar structure of strategy and

Capability Configuration improves.

Conclusion and Implications for Practice

Our study shows that the choice of strategy orientation influences the level of organization level effectiveness with implemented strategy under conditions of volatility.

There are three major stories in our study. First, our research demonstrates the importance of a molar structure of strategy under volatility and the extent to which organizations engage in each orientation of strategy with varying levels of volatility.

Second, the study reveals the interaction effects of the dimensions of strategy on how volatility influences execution gap reduction. Third, the study reveals the impact of varying levels of volatility, on the strength of each dimension of strategy.

Our study showed that volatility predicts the extent to which organizations engage in each of the strategy orientations. These findings support two important conclusions. First, that organizations respond to perceived changed in the level of volatility by varying the extent of engagement of each of the strategy orientations. Second, with increasing levels of volatility, organizations engage more in core leapfrogging strategy dimension than they do in a core preservation or core extension strategy. This is so because as we found, a core leapfrogging strategy dimension participates fully in the effects of volatility on the execution gap reduction performance of the organization much more than the other strategy orientations. It is therefore, effective as a strategy orientation for confronting highly volatile conditions. These conclusions are supported by studies, which suggest that

134 volatility weakens competitive advantages of the organization and requires a strategy orientation to stage discontinuous change (Hamel & Välikangas, 2003; Christensen &

Langhoff-Roos, 2003; Omeike and Lyytinen, 2015; Eisenhardt and Martin, 2000).

We also observe that all three dimensions of strategy are simultaneously present to varying degrees under conditions of volatility. They all have a positive relationship with volatility but exert varying effects on execution gap reduction. We frame this phenomenon as the molar strategy structure. This finding has implications for both theory and practice. A molar structure of strategy in conditions of volatility in which implemented strategy or realized strategy requires the simultaneous combination of multiple strategy orientations to varying degrees as an effective response to volatility.

Earlier studies support this finding by suggesting that strategy orientations are not mutually exclusive and that generating various combinations of these dimensions relative to the environment are critical for organizational success (Reeves and Routledge, 2013;

Miller 1992 and Baden-Fuller and Stopford, 1992). In order to enhance organizational dynamism to shifts in volatility corporate executives can do a better job of implementing strategy under volatile conditions if they shift paradigm (from implementing a single dimensioned strategy) to strategy as configuration by implementing a configuration of molar structured strategy. Corporate executives may benefit more from realizing that the highly volatile environment requires agile sensing mechanisms and appropriate response first at the organizational level to configure and redeploy strategy and capability and follow through with the simultaneous combination of multiple dimensions of strategy relative to shifts in volatility. This application of molar strategy in response to volatility

135 calls for dynamic and multi-positioning of the strategy of the organization to both explore and exploit change.

Although Literature and theory suggests that increasing levels of volatility exerts a negative influence on execution gap reduction ability of the organization. (McNamara et al., 2003; Hamel & Välikangas, 2003; Markides, 1999a; Thomas, 1996; Larsen et al.,2003), our findings show that the effect of volatility on Growth Gap Reduction and

Enabler Gap Reduction Performance are fully mediated by the core extension and core leapfrogging dimensions of strategy. Core extension strategy dimension does not participate in the effect of volatility on the financial and market performance of the organization. And, core preservation strategy does not participate in and does not transfer the effects of volatility on execution gap reduction performance. Our findings have important implications for theory building as they suggest that the dimensions of strategy participate to varying degrees and transfer the effects of volatility on the execution gap reduction performance of the organization. This Finding surfaces a foundational mechanism through which strategy orientations interact with volatility to either improve or weaken the effectiveness of the organization at closing volatility induced execution gaps.

Finally, moderation effect of volatility levels on our model uncovered a unique finding; each of the three dimensions of strategy show increasing strength with increasing volatility levels such that core leapfrogging strategy is strongest of the three strategy orientations when volatility is high. In addition, while a Core Preservation strategy seems to positively affect Enabler gap reduction in conditions of low volatility it showed a

136 negative effect under conditions of increased volatility. This suggests that a Core

Preservation strategy is more effective in stable or low volatility environments but is counterproductive as volatility levels increase This result further reinforces our earlier conclusion that suggest that in the presence of volatility, core leapfrogging strategy enhances execution gap reduction much more than a core preservation or core extension strategy orientations. This conclusion is also supported by results from other studies that suggest that as volatility increases, organizations must acquire the regenerative capacity to reconfigure and transform the organization and deploy systematic learning mechanisms (learning to learn) that create and replace existing sets of dynamic capabilities (Argyris & Schön, 1978; Teece & Pisano,1994; Zollo & Winter, 2002).

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CHAPTER 4: IMPACT OF VOLATILITY AND EXECUTION GAPS AND THE MEDIATING ROLE OF STRATEGY STRUCTURE ON CONFIGURATION OF DYNAMIC CAPABILITY (Study 3).

Preface

This is a third study in a series of inquiries into factors influencing strategy execution at the organizational level. The study examines a mediated model of strategy orientations and how they impacts volatility and strategy execution gaps on the sub capabilities

(marshaling, contextual immersion, balancing and intervening). The work simplifies and further clarifies findings from both the first (Chapter 2) and second (Chapter 3) studies, presented previously.

Introduction

The overarching purpose of this quantitative study is to explore how organizations implement strategies and close their execution gaps, the role of perceived volatility and execution gaps and the dynamic interactions of strategy and capability as recursively organized factors while closing the execution gaps. My main research question to address this issue sought to uncover critical antecedents that explain organizational interactions so that the organization can effectively close the execution gap. To address this question,

I asked the following sub-questions: 1) what are the elements which interact during strategy execution? 2) What are the modes of their interaction? 3) What are the mechanisms underlying these interactions? Although I learned from previous quantitative study a great deal about how varying levels of volatility influence the extent to which 138 organizations engage in dimensions of strategy and the antecedents of the strategy orientations my qualitative study surfaced two findings that were not tested in this quantitative study; First, configurable execution capabilities are rooted in the dynamic capabilities and notion of ambidexterity and this forms a key component of strategy execution effectiveness (Teece et al., 1997: 516; Galunic & Eisenhardt, 2001: 1229;

Eisenhardt & Martin, 2000: 1107; Styles & Goddard, 2004; Day & Montgomery, 1999).

Second, formulated strategy influences the variance of execution gaps while implementing the strategy. Therefore, it is pivotal to validate the presence and effects of these relationships. The first two studies created a foundation to explore further whether the formulated strategy and the ongoing configuration of strategy orientations in the presence of volatility truly have an effect on the configurations of execution capabilities and the mechanisms by which these interactions occur. This would reveal the recursive organization that affects execution effectiveness under volatility.

Theoretical Background and Hypotheses

I posit my hypothesis along two main themes. First, I articulate hypothesis around the main effects of formulated strategy (strategy fit and strategic orientation) on the execution gaps and discuss the antecedent effects of execution gap and dimensions of implemented strategy on the extent to which organizations engage in each of their execution capabilities. Next, I examine the mediating effect of the dimensions of implemented strategy on relationships between execution gaps and capability configurations. This analysis seeks to uncover to what extent each of these strategy orientations predicts the extent to which organizations will engage specific execution 139 capabilities in the presence of volatility and address formulated strategy induced gaps. I then review potentially varying effects of volatility levels (High and Low levels of volatility) on the postulated relationships between execution capability and strategy structure and the effects of formulated strategy on gap reduction.

How Does the Quality and Orientation of Formulated Strategy Affect the Configuration of Capability In The Presence Of Volatility?

Effect of Volatility on Strategy Formulation and Gap Reduction.

Earlier studies suggest that volatility weakens the effects of formulated strategy and calls for the constant realignment of strategy to generate a greater fit with environmental dynamics (Hamel & Välikangas, 2003; Christensen & Langhoff-Roos, 2003; Omeike and

Lyytinen, 2015; Eisenhardt and Martin, 2000). The extent of achieved fit with prevailing environmental contexts influences the level of success in closing execution gaps.

Informed by my qualitative research findings, two factors emerged as strongly shaping the formulated strategy; strategy fit, defined as the extent of fit or alignment between articulated strategy and organizational capability relative to the environment in which the strategy is implemented, and the strategic orientation of the organization defined to reflect the strategic directions chosen by the firm to create behaviors that generate continuously superior performance (Narver and Slater, 1990). A number of scholars support the view that increasing levels of volatility adversely influences strategy fit

(Hamel & Välikangas, 2003; Christensen & Langhoff-Roos, 2003; Omeike and Lyytinen,

2015; Eisenhardt and Martin, 2000). Organizations respond to this change by realigning their strategy to regain competitiveness (De Avini, 1999). Strategic orientation has been

140 widely used in the research field of strategic management, entrepreneurship and marketing. For example, Venkatraman (1989) first used the term strategic orientation and defined it through the dimensions of strategic aggressiveness, analysis, defensiveness, futurity, proactiveness and riskiness. He suggested that the strategic orientation of an organization may be measured through managerial perceptions and beliefs in the organizational processes on these six dimensions. Lumpkin and Dess (1996) then refined it to include autonomy, proactiveness, aggressiveness, risk-taking and innovativeness.

Several scholars have built on this framing to further clarify the nature and implication of strategic orientation for organization’s strategy. For example, Narver and Slater, (1990) associate a firm’s strategic orientation with the sensing of shifts in the environment and an ongoing sense making process to guide dynamic investment in organizational resources that reflect emergent strategic issues and focus. Eisenhardt and Martin, (2000) argue that organizations must orchestrate higher levels of dynamism and alertness and continually re-orient their resources as increasing levels of volatility induce unpredictability to their environment. These framings motivate the expectation that as volatility levels increase, organizational strategic orientation will increase in intensity and this will in turn act to improve strategy fit. I therefore, expect that increased Volatility will have a negative effect on strategy fit while exerting a much stronger and positive effect on strategic orientation. Moreover, the strategic orientation of the firm shapes its resource allocation priorities and therefore it’s strategic focus in the face of change and will have a direct positive effect on strategy fit. Hence,

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H1a+: Increased Volatility positively influences the extent companies are strategy oriented

H1b-: Increased Volatility negatively influences the extent of fit of strategy developed by the firm

H1c+: Increased strategy orientation positively influences the extent companies are able to close execution gaps

H1d+: Increased strategy fit positively influences the extent companies are able to close execution gaps

H1e+: Increased strategy Orientation positively influences the quality of strategy formulated by companies.

H1f+: Increased Volatility will have a stronger positive effect on strategy orientation than on strategy fit

H1g+: Strategy Fit will have a stronger positive effect on execution gap reduction than will the strategy orientation of the organization

Volatility and Capability Configurations.

Shifts in the environment constantly exert a gap-inducing influence on strategy execution.

Increasing levels of volatility means increasing levels of unpredictability and change generating potential for shocks that affect the competitive advantage (McNamara et al.,

2003). Organizations respond to this unpredictability by increasing the level of their alertness to their environment. These shifts also mean that organizational resources need to be constantly deployed and redeployed (Eisenhardt and Martin, 2000). Because marshalling execution capability is the degree to which the organization can harness and

142 deploy its resources to either exploit or explore change/opportunity and/or hedge against environmental risks, it influences the organization’s ability to reduce gap during strategy implementation (Teece, D. J. 2007, 2012. And Teece, D. J., Pisano, G., & Shuen, A.

1997). I therefore anticipate that increased levels of volatility will intensify the marshalling capability of the organization. I anticipate that because the organization is engaged in an ever shifting generative dance with the elements of its environment, its responses can also be more disruptive (Christensen et al., 2002, Govindarajan & Gupta,

2001). Therefore this will negatively influence organizational balancing execution capability. In addition to this, the increasing level of volatility may call on the organization to intervene more in its internal and external environment as it seeks to disruptively redefine its context (Christensen & Langhoff-Roos, 2003; Omeike and

Lyytinen, 2015). Hence,

H2a+: Increased Volatility positively influence the extent companies

engage in a Contextual Immersion capability

H2b+: Increased Volatility positively influence the extent companies

engage in a Marshaling capability

H2c-: Increased Volatility negatively influence the extent companies

engage in a Balancing capability

H2d+: Increased Volatility positively influence the extent companies

engage in an Intervening capability

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H2e+: Increased Volatility will have a stronger positive influence on

Marshaling strategy execution capability than on the other dimensions of execution capability

Volatility and Strategy orientations.

Shifts in the environment constantly exert a gap-inducing influence on strategy execution forcing a greater difference between strategy and execution outcomes, because a given strategy becomes less effective with increased levels of volatility. Omeike and Lyytinen

(2015) suggest that the three configurational dimensions of strategy vary in relative intensity depending on the level of environmental volatility. Zahra (1993), Christensen &

Langhoff-Roos (2003) and D’Aveni, (1999) show that increasing volatility reduces the effectiveness of a specific strategy and organizations look to innovation oriented strategies to cope with increased volatility. A core leapfrogging strategy orients the organization towards transformative change. This requires the organization to orchestrate new capabilities that enable it deal with volatility. In contrast, a core preservation strategy will focus on building operational excellence and will as a result not create the capabilities required to cope with volatility. A core extension strategy will orient the organization towards capabilities that enable it innovate to improve existing practices.

These capabilities may enable the organization cope with volatility. I therefore expect that volatility influences to what extent companies engages in Core Preservation strategy

(less if volatility increases), Core extension strategy(more if volatility increases) and

Core Leapfrogging strategy(more if volatility increases).

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H2f+: Increased Volatility positively influences the extent companies

engage in a Core Leapfrogging strategy

H2g+: Increased Volatility positively influences the extent companies

engage in a Core Extension strategy

H2h+: Increased Volatility negatively influences the extent companies engage in a Core Preservation strategy

H2i+: Increased Volatility will have a stronger positive influence on Core

Leapfrogging strategy than on the other two strategy orientations

Execution Gaps and Dimensions of Strategy.

As volatility increases, organizations must increasingly stage dynamic capabilities necessary to create new and replace existing dynamic capabilities (Zollo & Winter,

2002). A core leapfrogging strategy results in new domains of competence that provide the organization the leadership in an uncontested market space. These will also have the strongest impact at improving Gap Reduction Performance. A core preservation strategy, which focuses on improving operational excellence and incremental improvements, will be less potent at closing volatility-induced Gap Reduction Performance. In addition, a core extension strategy, which focuses on renewing or refining existing capabilities, can be easily replicated by competitors further weakening any competitive advantages a firm has as volatility increases. I therefore anticipate that a core leapfrogging strategy would have a stronger positive relationship with Gap Reduction Performance than the other strategy orientations in the presence of volatility. Hence,

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H3+: Increased Gap Reduction Performance in the presence of volatility will have a stronger positive relationship with Core Leapfrogging strategy than the other two strategy orientations

Gap Reduction Performance will have positive relationship with strategy orientations such that the extent to which the organization engages in a Core

Leapfrogging strategy will be stronger than its extent of engagement with the core preservation and core extension strategy orientations)

Execution Gaps and Dimensions of Capability.

Marshaling capacity acts as a dynamic concentrator of organization’s resources and is thus pivotal in handling contextual volatility. It represents the organization’s mechanism for engaging to preempt, protect or preserve its position against observed volatility as it engages with the execution gap dynamic (Omeike and Lyytinen, 2015).

This orientation of strategy execution capability therefore, would have a stronger impact on gap reduction performance when compared to the effects of contextual immersion and balancing capabilities. Hence,

H4a+: Increased Gap Reduction Performance in the presence of volatility positively predicts the extent to which the organization engages in Contextual Immersion capability H4b+: Increased Gap Reduction Performance in the presence of volatility positively predicts the extent to which the organization engages in Marshaling capability H4c+: Increased Gap Reduction Performance in the presence of volatility positively predicts the extent to which the organization engages in Balancing capability

Intervening and Gap Reduction.

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Organizations often enact disruptive strategies that represent new ways of doing things and which aim to reshape the environment in order to reinstate competitive advantage.

The intervening execution capability requires that the organization is able to engage with volatility to stage a core leapfrogging strategy. Because intervening capability aims to disrupt or perturb existing environmental patterns, I anticipate that it would have a negative effect on gap reduction. Hence,

H4d+: Increased Gap Reduction Performance in the presence of volatility positively predicts the extent to which the organization engages in Intervening capability

Strategy orientations and Capability Configurations.

I noted in my theory building section that the strategy implementation involves ongoing configuration and orchestration of dynamic capabilities. Past studies suggest that organizations can respond to environmental challenges by altering their strategy configurations (Miller, 1980; Murray, 1988; Wright, 1987; D’Aveni & Gunther, 1994;

Hamel & Prahalad, 1996; D’Aveni, 1999). These views posit that volatility increases the need for ambidexterity and calls for orchestrating (new) dynamic capabilities during strategy implementation (Eisenhardt and Martin, 2000). Omeike and Lyttinen (2015) propose that dynamic capability as a construct can describe this dynamism in organizational capability through three key activities; contextual immersion, marshaling and balancing. These three dimensions of dynamic capabilities apply to different organizational environments and their usefulness depends on the prevailing level and intensity of volatility. In a stable environment, a zero order capability is required to stage a core preservation strategy associated with operational excellence assumed in stable 147 conditions. Under this condition Balancing execution capability would become more dominant as the organization focuses on operational excellence maintaining existing capabilities as to continually achieve incremental improvements (Omeike and Lyytinen,

2015). However, increasing environmental volatility will weaken the competitive advantage created by existing operational excellence. The organization needs to renew its existing capabilities as to remain competitive by staging first order capabilities. These trigger variation in the implemented strategy as to focus on core extension by garnering and deploying capabilities required to systematically adjust capabilities to extend, modify, or create ordinary capabilities. (D’Aveni 1999). Under the conditions of volatility, marshalling execution capability would have the strongest impact on a core extension strategy as the organization scrambles to harness, deploy and redeploy resources in response to emerging imbalance in its competitive advantages. In order to maintain competitive advantage under these conditions, the organization must garner systematic learning mechanisms (learning to learn) that create and replace existing sets of dynamic capabilities when the environment changes (Zollo & Winter, 2002). The need to regenerate dynamic capabilities calls for a variation in the implemented strategy. The focus on the core-leapfrogging dimension enables the organization to stage the capabilities required to reconfigure and transform its dynamic capabilities (D’Aveni

1999). Under the heightened conditions of volatility, contextual immersion and intervening execution capabilities would become more prominent as the organization engages with its environment as to understand the patterns of change and seek to influence it. Hence,

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H5+: Core Extension Strategy dimension positively affects the extent to which the organization engages in each capability such that its effect will be strongest on Marshaling Capability H6+: Core Leapfrogging Strategy dimension positively affects the extent to which the organization engages in each capability such that its effect will be strongest on Intervening Capability H7+: Core Preservation Strategy dimension positively affects the extent to which the organization engages in each capability such that its effect will be strongest on Balancing Capability

Capability Configurations and Intervening.

As volatility levels increase organizations often enact disruptive strategies that represent new ways of doing things and which aim to reshape the environment to reinstate competitive advantage (Christensen & Langhoff-Roos, 2003; Omeike and Lyytinen,

2015). These intervening capabilities require that the organization is able to engage with volatility to stage a core leapfrogging strategy. Marshaling execution capability of the organization is related to velocity of change in the environment and represents the organization’s mechanism for engaging to preempt, protect or preserve its position against observed volatility as it explores the execution gap dynamic (Omeike and

Lyytinen, 2015). This orientation of strategy implementation capability would have a stronger impact on intervening capability when compared to the effects of contextual immersion and balancing capabilities. Hence,

H8+: Execution capabilities will have a positive influence on the Intervening capability of the organization such that the Marshalling execution capability will have a stronger

149 positive effect than other execution capabilities on Intervening execution capability in the presence of volatility

Mediation Effects Of Strategy orientations On The Relationship Between Volatility And Capability Configuration.

Literature suggests that organizations often respond to increasing volatility by seeking to differentiate themselves (Styles & Goddard, 2004; Day & Montgomery, 1999). These responses represent new ways of playing the game and seeking to be disruptive

(Christensen et al., 2002, Govindarajan & Gupta, 2001). Argyris & Schön, (1978). Teece

& Pisano (1994) support this view by advocating that organizations should acquire a capacity to reconfigure and transform the organization as a learned organizational skill as volatility increases. A core leapfrogging strategy represents a more effective strategy orientation to thwart the effect of volatility on the competitive advantage (Eisenhardt &

Brown, 1997; Volberda et al., 2001b; Flier et al., 2003) for a number of reasons. First, it focuses on staging systematic learning mechanisms (second order capability; learning to learn) that create and modify zero and first order dynamic capabilities (Zollo & Winter,

2002). Second, it orchestrates the capability of “learning to learn” which cannot be readily copied by competitors (Dickson, 1996). I therefore, expect that a core leapfrogging orientation of strategy will fully mediate the effect of Marshalling and

Intervening execution capabilities on execution gap reduction performance. A core extension strategy focuses on staging first order capabilities required to “to extend, modify, or create ordinary capabilities”. Volatility will weaken over time the effects of first order dynamic capabilities (Winter, 2003; Eisenhardt & Martin, 2000). I therefore, 150 expect that a core extension strategy will only partially mediate the effect of Marshalling and Intervening on execution gap reduction performance. A core preservation strategy focuses on staging zero order capabilities, which are more easily replicated by competitors and does not create a sustainable competitive advantage. (Winter, 2003;

Eisenhardt & Martin, 2000; D’Aveni (1999). Therefore, a core preservation strategy will not mediate the effects of Intervening and Marshalling but would mediate the effects of

Balancing on gap reduction performance. Hence,

H9a: Core preservation strategy dimension will partially mediate the effect of

Volatility on Contextual Immersion execution capability.

H9b: Core preservation strategy dimension will partially mediate the effect of

Volatility on Marshalling execution capability.

H9c: Core preservation strategy dimension will fully mediate the effect of

Volatility on Balancing execution capability.

H9d: Core preservation strategy dimension will not mediate the effect of

Volatility on Intervening execution capability.

H10a: Core extension strategy dimension will fully mediate the effect of Volatility on Contextual Immersion execution capability.

H10b: Core extension strategy dimension will partially mediate the effect of

Volatility on Marshalling execution capability

H10c: Core extension strategy dimension will partially mediate the effect of

Volatility on Balancing execution capability.

H10d: Core extension strategy dimension will not mediate the effect of Volatility 151 on Intervening execution capability.

H11a: Core leapfrogging strategy dimension will fully mediate the effect of

Volatility on Contextual Immersion execution capability.

H11b: Core leapfrogging strategy dimension will partially mediate the effect of

Volatility on Marshalling execution capability

H11c: Core leapfrogging strategy dimension will not mediate the effect of

Volatility on Balancing execution capability.

H11d: Core leapfrogging strategy dimension will fully mediate the effect of

Volatility on Intervening execution capability.

Moderation Effects of Volatility On The Capability Configurations

Organizations respond to the shifts in volatility by adapting or changing their strategy orientation somewhat differently (Christensen & Langhoff-Roos (2003). This, in turn, influences the ongoing configuration of organizational capabilities (Teece et al., 1997;

Eisenhardt and Martin, 2000). Reconfiguration of dynamic capabilities are such that the

Intervening and marshalling capabilities of the organization are associated with organizational responses to increased levels of volatility while the Balancing and contextual immersion capabilities as associated with more stable conditions. I therefore expect that the main effects of Volatility on the dimensions of capability (contextual immersion, marshalling, balance and intervening) and their relationship with execution gap reduction will be different for different levels of volatility. Hence,

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H12a: The main effects of a core extension strategy dimension under high volatility will be stronger on Intervening capability than on the other capability dimensions.

H12b: The main effects of a core leapfrogging strategy dimension under high volatility will be stronger on Marshaling capability than on the other capability dimensions

H12c: The main effects of core preservation strategy dimension under high volatility will be stronger on Balancing capability than on the other capability dimensions

The hypothesized model for the relationships I explored in this research strand is illustrated in figure 13.

Figure 12 : Hypothesized Final Model

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Research Method and Sampling Strategy

In this research strand, I use the same psychometric techniques used in the second research strand to validate the research model since this approach is well suited for this type of research. I do not develop any new instruments nor engage in any further EFA and CFA analysis as this based on the data and factor analysis conducted in the second study strand. The target group of respondents are the C-Suite, heads of strategy and functional heads and I use a survey based approach to obtain responses from the target group. Respondents in each organization answered questions that probed their organizations’ experience with volatility, closing execution gaps during strategy implementation, and capability configurations as the strategy unfolded. I also collected data from two different environments (USA and Nigeria) as a means of comparing the variance (if any) in the effect of volatility on the relationship between strategy context and execution gap reduction performance in the models. This approach also ensured further bias minimization in my data given that using different sources to assess key measures as I have done in the study is the best ex ante procedure to avoid potential common method variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).

Construct Operationalization

Scales for most constructs in my model (Volatility, Strategy orientations, Gap Reduction

Performance, Firm Size and Industry) were taken from the scales developed in my second research. These include scales for the dependent variable, Gap Reduction Performance

(GRP) and scales for the independent variables; Volatility (VOL), Strategy orientations

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(Core Preservation, Core Extension and Core Leapfrogging). The response anchor for each of these scales was a 5-point Likert scale where “1 = strongly disagree” and “5 = strongly agree.” These variables have been extensively discussed in the second research strand. I therefore focus next only on the additional new constructs.

In addition to the above listed variables, two new independent variables are introduced in the model (formulated strategy and capability dimensions). I consulted the literature to find previously validated scales to operationalize these new variables. In most cases, I used existing scales with a few modifications.

Measures: Independent Variables

Execution Capabilities: I measured four components of the strategy execution capability; three derived from my qualitative study (Omeike and Lyytinen, 2015). (1)

Marshaling (with four items), represents the organization’s mechanism for engaging to preempt, protect or preserve its position against observed volatility as it tries to close or reduce execution gaps. (2) Contextual Immersion (with three items), the degree to which the organization is connected to its environment to sense and make sense of potential change and (3) Balancing (with six items), the relative fit between the strategy, environmental volatility and organizational capability. A fourth component factored out of my EFA and CFA analysis which I labeled Intervening Capability (with three items) the degree to which the strategy focused on game changing maneuvers resulting in the acquisition of entirely new capabilities or new domains. The response anchor was a 5- point Likert scale where “1 = strongly disagree” and “5 = strongly agree.” 155

Formulated Strategy: I measured two components of formulated strategty (Omeike and

Lyytinen, 2015). (1) Strategic Orientation (with six items), reflecting the strategic directions implemented by a firm to create the proper behaviors for the continuous superior performance of the business in the presence of ongoing environmental change.

(2) Strategy Fit (with two items), defined as the extent of fit or alignment between articulated strategy and organizational capability relative to the environment in which the strategy is implemented. The response anchor was a 5-point Likert scale where “1 = strongly disagree” and “5 = strongly agree.”

Instrument Development I use a psychometric study to validate the research model since this approach is well suited for this type of research. I defined out target group as consisting of the C-Suite, heads of strategy and functional heads. Each corporate executive participating in this study completed an online survey instrument. Respondents in each organization answered questions that probed their organizations’ experience with volatility, closing execution gaps during strategy implementation, and capability configurations as the strategy unfolded. I also collected data from two different environments (USA and Nigeria) as a means of comparing the variance (if any) in the effect of volatility on the relationship between strategy context and execution gap reduction performance in my models. This approach also ensured further bias minimization in my data given that using different sources to assess key measures as I have done in my study is the best ex ante procedure to avoid potential common method variance (Podsakoff, MacKenzie, Lee, & Podsakoff,

2003). Because most of the measures are self-reported perceptual measures, social 156 desirability was included in my analysis as a check for potential response bias. I introduced an 8-item short form of the Marlowe-Crowne social desirability scale adapted by Ray J. J. (1984). The reliability of this short social desirability scales is reflected in the satisfactory internal consistency and reliability (alpha of .77) achieved by the authors using items Nos. 6, 13, 15, 16, 19, 21, 34, and 35). I had the eight items scattered throughout the questionnaire and then analyzed using the scoring format provided by Ray

J. J. (1984). I utilized a combination of Qualtrics survey platform and direct paper-based distribution to administer the survey. Each participant had to respond to a set of four initial qualifying questions (Appendix e.) to determine that they fall within the category of respondents the study targeted. As a precaution, if any intending participant responded to the first question indicating that his role in his organization falls outside the targeted roles the survey immediately ends and encourages the intending respondent to direct the survey to those within his organization more suited to respond to the survey.

Analysis

The data collected from the quant strand was examined using a rigorous process of screening the data, identifying the structure of the relationships, the validity and the reliability of the measures through exploratory factor analysis (EFA), testing for model fit through confirmatory factor analysis (CFA) and interpreting the model and testing the hypotheses through structure equation modeling (SEM). I used SPSS to create and analyze the EFA and Amos to analyze the CFA and then used Smart PLS to create, analyze and interpret the SEM.

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Measurement Model Analysis in Smart Pls

Smart PLS assesses the measurement model and structural model simultaneously.

I used Partial Least Squares Structural Equation Modeling (PLS-SEM) to build my structural model. It is superior to other SEM approaches for my study, because my study is an early exploration of the field of strategy execution, which lacks strong predictive theory. In addition, my structural model is complex with formative constructs which justifies the use of PLS and my sample size of 557 is not that large given the complexity of my model (Hair Jr et al., 2013). I used Smart PLS 3.2.4, the most current version of the software at the time of my study. In conducting my analysis I investigated the main effects of volatility on my model and also conducted multi-group moderation effect analysis on the moderation effects of varying levels of volatility on my structural model as to determine whether the effect between volatility and each of the three strategy context factors and on execution gap reduction performance is significantly different for different levels of volatility.

All of the indicators in my final measurement model met this standard except GRF_T3

158 threshold values (see Table 4). Since this was not the case for these indicators, I left them in the model. Composite reliability was above the recommended threshold of .708 for all constructs (Hair Jr et al., 2013). Average variance extracted was above the .50 threshold

(Hair Jr et al., 2013) for all constructs except Vol_Tech (.47), which was very close to the threshold level.

Table 20 : Strategy Context -Execution gap reduction Performance Model Correlation Matrix (n = 557)

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The Structural Model

Based on the results of my EFA and CFA, I modeled Execution gap reduction

Performance as two latent constructs reflecting Enabler Gap Reduction Performance and

Growth Gap Reduction performance; Volatility as a second-order construct reflecting

Market Volatility, Organizational Volatility and Environmental Volatility and

I created a main effects structural equation model that reflected my hypothesized relationships including the mediators and controls (see Figure 13). I ran the model to calculate regression coefficients and conducted bootstrapping using 5,000 samples of the data (Hair Jr et al., 2013) to determine significant relationships for mediation. I trimmed the model of any paths whose regression coefficients were not significant. All remaining paths were significant at a 99% confidence level, as indicated by t-values of at least 2.57

(p < .01), except for the relationships between SC_CP and GROWTH_GRP (t = 2.11, p <

.05) which were all significant at a 95% confidence level. One of the latent variables,

Vol_Tech showed weak composite reliability and average variance extracted (CR=0.644 and AVE=0.475). I tested its effects on results by running the analysis with and without it. I decided to remove it from the model because the results were not significantly 160 different. I anticipated this during my Q-sort as VOL_Tech overlapped with the

VOL_Env factor. In the final SEM model, I included mediators (dimensions of strategy) and controls (firm size and industry). The structural model is shown in Appendix F.

(VIF), measure collinearity. Tolerance is simply the amount of variance in an independent variable that is not explained by the other independent predictor variables.

Tolerance values below .20 and VIF values above 5 indicate potential multi-collinearity problems (Hair Jr et al., 2013). Smart PLS does not provide tolerance or VIF values, so I used IBM SPSS Statistics to perform a multi-collinearity analysis on the predictor variables. All constructs and variables demonstrated tolerance and VIF values within acceptable limits. See Table 14 for a summary of the multi-collinearity assessments of the predictor variable for each endogenous construct in the model.

Reduction Performance, Enabler Gap Reduction Performance and my hypothesized

161 mediating variables, strategy orientations. My main effects model achieved a moderate R² value of 0.61(0.60 without mediators) for Growth Gap Reduction Performance, 0.39

(0.12 without mediators) for Enabler Gap Reduction Performance, a somewhat weak R² value of .23 for Core Leapfrogging, 0.19 for core Extension and 0.15 for Core

Preservation, although as pointed out by Hair et al.(2013), in some disciplines, this might be considered moderate or strong. Overall, my model exhibits adequate fit for meaningful evaluation of my hypotheses. A summary of my measurement model evaluation in Smart PLS (essentially a Smart PLS CFA) is shown in Table 13.

I controlled for the Industry of the respondents in my study, as well as for the size of their organizations as measured by revenue and staff strength. My final step in the analysis was to examine the size and significance of the path coefficients of the hypothesized relationships in my structural model. This was done by using bootstrapping in Smart

PLS. I conducted bootstrapping using 5,000 samples as recommended by Hair et al.

(2013) to determine the significant relationships in my model as shown in Table 17.

Mediation Analysis. I used the Preacher and Hayes (Preacher & Hayes, 2004, 2008) approach for mediation testing, as it is perfectly suited for Smart PLS-SEM analysis.

(X) on an endogenous variable (Y) is significant when no mediator is included in the model. The next step is to test for the significance of the indirect effect of X on Y through a third mediating variable (A). The paths from X → A and from A → Y must 162 both be significant. In my tests for mediation, I conducted bootstrapping on 5,000 subsamples for each path in a proposed mediated relationship. I then multiplied the path coefficients from X → A by the path coefficients from A → Y to determine the coefficient of the indirect effect of X on Y for each of the 5,000 subsamples. Next, I calculated the standard deviation of the indirect effects of the subsamples in Microsoft

Excel. I then conducted Soble Test to test the significance of mediation effects in my model. T-values greater than 1.96 indicate the presence of mediation

Findings

In this section, I present my findings in the following order; First, I present results of the main effects analysis of my structural model to determine the effect of strategy fit and strategic orientation of the organization on the relationship between volatility and execution gaps, next I present results of the main effects analysis of my structural model to determine the effect of execution gaps in the presence of volatility on the strength of each of the implemented strategy orientations and the antecedent effects of these interactions on the strength of the sub-components of execution capabilities. Then I present results for the mediation analysis to determine the effect of the strategy orientations in influencing the relationship between volatility and execution gap reduction. I finally present results for multi-group moderation effect analysis of my structural model across two volatility groups representing, High and Low volatility groups (by splitting my data into the two volatility groups) to examine variations in strength of each of the strategy orientations and capabilities in different levels of

163 volatility A summary of hypothesis test results is provided in Table 22. Results were as follows:

Controls. Industry type had no significant direct effects on any of the endogenous variables in my model. These results suggest that there are no significant differences across industry types on how volatility influences the relationship between strategy context and execution gap reduction performance. This provides additional confirmation that some principles and practice of strategy is universal. However, firm size had significant effect on the intervening capability in my complete model (-0.01, p<0.05) and on the low volatility group (-0.19, p<0.01) and on marshaling capability in my complete model (-0.10, p<0.05) and on the low volatility group (-0.12, p<0.05). Firm size had no significant effect on these variables in the high volatility group and also showed no significant effect on all the other endogenous variables in my model.

H1a+: Increased Volatility positively influences the extent companies are strategy oriented. My findings support this hypothesis. As expected, volatility had a strong significant and positive effect on the extent of an organizations strategy orientation (Strat_OR; 0.37, p<0.01) H1b+: Increased Volatility negatively influences the extent of fit of strategy developed by the firm. My findings did not support this hypothesis. Contrary to my expectations, volatility had no significant weakening effect (0.06, ns) on the extent of fit of the strategy formulated by the organization. This may show ‘incorrect’ response by management- a tendency to not focus on the strategy realignment as volatility increases but focusing more on tactical responses to change but this may not reduce the implementation gap! 164

H1c+: Increased strategy orientation positively influences the extent companies are able to close execution gaps. My findings support this hypothesis. As expected, strategy orientation (Strat_OR) had a strong significant and positive effect on the extent organizations are able to reduce execution gaps (0.23, p<0.01) H1d+: Increased strategy fit positively influences the extent companies are able to close execution gaps. My findings support this hypothesis. As expected, strategy quality (StratFit) had a strong significant and positive effect on the extent organizations are able to reduce execution gaps (0.32, p<0.01). H1e+: Increased strategy Orientation positively influences the quality of strategy formulated by companies. My findings support this hypothesis. As expected, strategy orientation (Strat_OR) had a strong, significant and positive effect on the extent organizations are able to develop the right strategy in the presence of volatility (Strat_Fit; 0.57, p<0.01) H1f+: Increased Volatility will have a stronger positive effect on strategy orientation than on strategy fit. My findings support this hypothesis. As expected, volatility had a stronger significant and positive effect on the extent of an organizations strategy orientation intensity (Strat_OR; 0.37, p<0.01) than it does on the extent of fit of the strategy (StratFit; 0.06 (ns))

H1g+: Strategy Fit will have a stronger positive effect on execution gap reduction than will the strategy orientation of the organization. My findings support this hypothesis. As expected, strategy fit (StratFit) (0.32, p<0.01) had a stronger significant and positive effect on the extent organizations are able to reduce execution gaps when compared to the effect of the strategy orientation (Strat_OR) of the organization (0.23, p<0.01). F test analysis further show that F-Statistic (1.18) for the difference in the path coefficients of these variables is larger than the F Critical (1.16). Therefore the path coefficients are significantly different for the effect of strategy fit and strategy orientation on Gap reduction such that strategy fit has a stronger significant and positive effect than Strategy Orientation on execution gaps reduction performance.

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H2a+: Increased Volatility positively influence the extent companies engage in a Contextual Immersion capability. I find no support this hypothesis. Contrary to my expectations, volatility has a significant but negative effect (-0.13, p<.01) on the extent organizations engage in contextual immersion capability. (This suggests a tendency among responding organizations to quickly respond with tactical actions instead of focusing on rigorous assessment of change drivers and strategic implications) H2b+: Increased Volatility positively influence the extent companies engage in a Marshaling capability. I find no support for this hypothesis. Contrary to my expectations, volatility had no significant effect (0.08, ns) on the extent organizations engage in marshaling capability. H2c+: Increased Volatility positively influence the extent companies engage in a Balancing capability. My findings support this hypothesis. As expected, volatility (VOL) had a significant and positive effect on the extent organizations engage in Balancing capability (0.10, p<0.01) H2d+: Increased Volatility positively influence the extent companies engage in an Intervening capability. My findings do not support this hypothesis. Contrary to my expectations, volatility had no significant effect (0.04, ns) on the extent organizations engage in intervening capability. H2e+: Increased Volatility will have a stronger positive influence on Marshaling strategy execution capability than on the other dimensions of execution capability. My findings do not support this hypothesis. Contrary to my expectations, volatility had no significant effect (0.08, ns) on the extent organizations engage in Marshaling capability and exhibits a stronger negative effect on Contextual Immersion(- 0.13, p<.01) and on Balancing capability (0.10, p<0.01). H2f+: Increased Volatility positively influences the extent companies engage in a Core Leapfrogging strategy. My findings support this hypothesis. As expected, volatility (VOL) had a strong significant and positive effect on the extent organizations engage in a core leapfrogging strategy (Core_LP; 0.36, p<0.01) H2g+: Increased Volatility positively influences the extent companies engage in a Core Extension strategy. My findings support this hypothesis. As expected, volatility 166

(VOL) had a strong significant and positive effect on the extent organizations engage in a core extension strategy (Core_EXT; 0.44, p<0.01) H2h+: Increased Volatility negatively influences the extent companies engage in a Core Preservation strategy. My findings do not support this hypothesis. Contrary to my expectations, volatility (VOL) had a strong significant and positive effect on the extent organizations engage in a core preservation strategy (Core_PRE; 0.53, p<0.01) H2i+: Increased Volatility will have a stronger positive influence on Core Leapfrogging strategy than on the other two strategy orientations. My findings do not support this hypothesis. Contrary to my expectations, increased volatility had no stronger significant positive effect (0.36, p<0.01) on core leapfrogging strategy when compared to the effects on core extension (0.44, p<0.01) and core preservation (0.53, p<0.01). This may show ‘incorrect’ response by management- a tendency to do what they are good at which is operational excellence but this may not reduce the implementation gap! H3+: Increased Gap Reduction Performance in the presence of volatility will have a stronger positive relationship with Core Leapfrogging strategy than with the other two strategy orientations (Gap Reduction Performance will have positive relationship with strategy orientations such that the extent to which the organization engages in a Core Leapfrogging strategy with increasing gap reduction performance will be stronger than its extent of engagement with the core preservation and core extension strategy orientations). My findings support this hypothesis. As expected, increased gap reduction performance (GRP) had a stronger significant and positive relationship with the extent organizations engage in Core Leapfrogging strategy (SC_CL; 0.44, P<.01) than it does on Core Extension (0.22, p<0.01) and Core Preservation strategies (0.27, p<0.01) H4a+: Increased Gap Reduction Performance in the presence of volatility positively predicts the extent to which the organization engages in Contextual Immersion capability. My findings do not support this hypothesis. Contrary to my expectations, gap reduction performance had no significant effect (-0.002, ns) on the extent organizations engage in contextual immersion capability. H4b+: Increased Gap Reduction Performance in the presence of volatility positively predicts the extent to which the organization engages in Marshaling capability. 167

My findings support this hypothesis. As expected, gap reduction performance (GRP) had a significant and positive effect on the extent organizations engage in Marshaling capability (0.32, p<0.01) H4c+: Increased Gap Reduction Performance in the presence of volatility positively predicts the extent to which the organization engages in Balancing capability. My findings support this hypothesis. As expected, gap reduction performance (GRP) had a significant and positive effect on the extent organizations engage in Balancing capability (0.29, p<0.01) H4d-: Increased Gap Reduction Performance in the presence of volatility negatively predicts the extent to which the organization engages in Intervening capability. My findings support this hypothesis. As expected, gap reduction performance (GRP) had a significant and negative effect on the extent organizations engage in Intervening capability (-0.23, p<0.01) H5+: Core Extension Strategy dimension predicts the extent to which the organization engages in each capability such that its effect will be strongest on Marshaling Capability. My findings support this hypothesis. As expected, a core extension strategy (SC_CE) had a stronger significant and positive effect on the extent organizations engage in marshaling capability (MAR; 0.30, p<0.01) than it does on Intervening capability (INT; 0.10, p<0.01), Contextual Immersion capability (CI; -0.23, p<0.01) and Balancing capability (BAL; -0.07, ns). A core extension strategy however showed a negative relationship with the Contextual Immersion capability of the organization and had a negative but none significant effect on the extent to which the organization engaged with its Balancing capability H6+: Core Leapfrogging Strategy dimension predicts the extent to which the organization engages in each capability such that its effect will be strongest on Intervening Capability. My findings do not support this hypothesis. Contrary to my expectations, a core leapfrogging strategy (SC_CL ) had a stronger significant and positive effect on the extent organizations engage in Contextual Immersion capability (CI; 0.76, p <.001) than it does on Intervening capability(INT; -0.31, p<0.01), Marshaling capability(MAR; -0.43, p<0.01) and Balancing capability(BAL; 0.16, p<0.05). A core 168 leapfrogging strategy however showed a negative and weaker relationship with the Intervening capability of the organization when compared to the extent of engagement with the other capabilities. H7+: Core Preservation Strategy dimension predicts the extent to which the organization engages in each capability such that its effect will be strongest on Balancing Capability. My findings do not support this hypothesis. Contrary to my expectations, a core preservation strategy (SC_CP) had a stronger significant and positive effect on the extent organizations engage in Intervening capability (INT; 0.60, p<0.01) than it does on Contextual Immersion capability (CI; -0.48, p<0.01), Marshaling capability (MAR; -0.24, p<0.01) and Balancing capability (BAL; 0.01, ns). A core preservation strategy showed a negative relationship with the Marshaling and Contextual Immersion capabilities of the organization and had no significant effect on the extent to which the organization engaged with its Balancing capability H8+: Execution capabilities will have a positive influence on the Intervening capability of the organization such that the Marshalling execution capability will have a stronger positive effect than other execution capabilities on Intervening execution capability in the presence of volatility. My findings do not support this hypothesis. Contrary to my expectations, Contextual Immersion had the strongest effect on the Intervening capability (0.41, p<0.01) when compared to the effects of Marshaling (0.40, P<.01) and Balancing (BAL; 0.31, p<0.01). F test analysis further show that F-Statistic (1.19) for the difference in the path coefficients of these variables is larger than the F Critical (1.16). Therefore the path coefficients are significantly different for the effect of contextual immersion and marshaling capabilities on intervening capability such that contextual immersion has a stronger significant and positive effect than marshaling capability on the intervening capability of the organization. However, the difference in the effects of both Marshaling and Contextual Immersion on Intervening seems marginal suggesting that corporate executives tend to rely on environmental sensitivity and their ability to respond by reallocating resources to shape organizational agility and ability to intervene during change.

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Figure 13 : SEM Results

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Table 21 : Hypothesis Test Results

Hypothesis Path T Statistic Direct effect Coefficient Support for Direct Effect Hypothesis

H1a+ VOL -> Strat_OR 8.757 0.37*** Yes H1b+ VOL -> StratFit 1.742 0.06(ns) No H1c- VOL -> GRP 2.102 0.10** No H1d+ Strat_OR -> GRP 4.534 0.23*** Yes H1e+ StratFit -> GRP 6.816 0.32*** Yes H1f Strat_OR -> StratFit 16.835 0.57*** Yes H2e+ VOL -> Core_LP 10.32 0.36*** Yes H2f+ VOL -> Core_EXT 10.624 0.44*** Yes H2g+ VOL -> Core_PRE 15.079 0.53*** No H2a+ VOL -> CI 3.122 -0.13*** No H2b+ VOL -> MAR 1.881 0.08(ns) No H2c+ VOL -> Bal 2.641 0.10*** Yes H2d+ VOL -> INT 1.299 0.04(ns) No

H3+ GRP -> Core_EXT 4.883 0.22*** Yes

H3+ GRP -> Core_LP 11.489 0.41*** Yes

H3+ GRP -> Core_PRE 6.676 0.27*** Yes

H4a+ GRP -> CI 0.062 -0.002(ns) No

H4b+ GRP -> MAR 9.19 0.32*** Yes

H4c+ GRP -> Bal 6.745 0.29*** Yes

H4d+ GRP -> INT 6.984 -0.23*** Yes

H5 Core_EXT -> Bal 1.36 -0.07(ns) No

H5 Core_EXT -> CI 4.38 -0.23*** Yes

H5 Core_EXT -> INT 2.712 0.10*** Yes

H5 Core_EXT -> MAR 6.153 0.30*** Yes

H6 Core_LP -> Bal 2.399 0.16** Yes

H6 Core_LP -> CI 15.652 0.76*** Yes H6 Core_LP -> INT 6.182 -0.31*** No H6 Core_LP -> MAR 7.348 0.43*** Yes H7a+ Core_PRE -> CI 8.008 -0.48*** Yes

H7b+ Core_PRE -> MAR 4.142 -0.24*** Yes

H7c+ Core_PRE -> INT 14.182 0.60*** Yes

H7d+ Core_PRE -> Bal 0.202 0.012(ns) No

H8+ Bal -> INT 8.719 0.31*** Yes

H8+ CI -> INT 12.948 0.41*** Yes

H8+ MAR -> INT 11.517 0.40*** No H9a+ CI -> Bal 0.317 -0.01(ns) No H9b+ CI -> MAR 0.271 -0.01(ns) No

H10 MAR -> Bal 6.6 0.41*** Yes

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Mediation Effects Of Strategy orientations On The Relationship Between Volatility And Capability Configuration.

Strategy orientations Mediation Results. In my model, each of the three dimensions of strategy (SC_CP, SC_CE and SC_CL) and Gap Reduction Performance

(GRP) served as a mediator for the indirect effects of Volatility (VOL) on the dimensions of execution capabilities (Marshaling, Balancing, Contextual Immersion and Intervening)

(shown on Table 16). Specific Mediation hypothesis test results follow. I will comment on this in the discussion section of the paper. A summary of the results of my mediation tests is shown in Table 9.

H9a: Core preservation strategy dimension will partially mediate the effect of Volatility on Contextual Immersion execution capability. Contrary to my expectations, I found that Core Preservation strategy fully mediated the effect of volatility on Contextual Immersion Capability. The indirect effects of volatility on Contextual Immersion (-.05, p =0.22, t-value=1.24) was not significant with core preservation strategy dimension (SC_CP) as mediator. H9b: Core preservation strategy dimension will partially mediate the effect of Volatility on Marshalling execution capability. As expected, I found that Core Preservation strategy partially mediated the effect of volatility on Marshaling Capability. The indirect effects of volatility on Marshaling (.30, p <.01, t-value=8.19) was significant with core preservation strategy dimension (SC_CP) as mediator. H9c: core preservation strategy dimension will fully mediate the effect of Volatility on Balancing execution capability. Contrary to my expectations, Core Preservation strategy did not mediate the effect of volatility on Balancing Capability. The indirect effects of volatility on Balancing (0.28, p <.01, t-value=7.51) was significant with core preservation strategy dimension (SC_CP) as mediator. H9d: core preservation strategy dimension will not mediate the effect of Volatility 172 on Intervening execution capability. Contrary to my expectations, Core Preservation strategy fully mediated the effect of volatility on Intervening Capability. The indirect effects of volatility on Intervening (0.39, p <.01, t-value=11.47) was significant with core preservation strategy dimension (SC_CP) as mediator. H10a: Core extension strategy dimension will fully mediate the effect of Volatility on Contextual Immersion execution capability. Core Extension strategy partially mediated the effect of volatility on Contextual Immersion Capability. The indirect effects of volatility on Contextual Immersion (-.05, p =0.22, t-value=1.24) was not significant with core preservation strategy dimension (SC_CE) as mediator. H10b: Core extension strategy dimension will partially mediate the effect of Volatility on Marshalling execution capability. As expected, I found that Core Extension strategy partially mediated the effect of volatility on Marshaling Capability. The indirect effects of volatility on Marshaling (.30, p <.01, t-value=8.19) was significant with core preservation strategy dimension (SC_CE) as mediator. H10c: core extension strategy dimension will partially mediate the effect of Volatility on Balancing execution capability. Contrary to my expectation, I found that Core Extension strategy did not mediate the effect of volatility on Balancing Capability. The indirect effects of volatility on Balancing (0.28, p <.01, t-value=7.51) was significant with core extension strategy dimension (SC_CE) as mediator. H10d: core extension strategy dimension will not mediate the effect of Volatility on Intervening execution capability. Contrary to my expectations, Core Extension strategy partially mediated the effect of volatility on Intervening Capability. The indirect effects of volatility on Intervening (0.39, p <.01, t-value=11.47) was significant with core extension strategy dimension (SC_CE) as mediator.

H11a: Core leapfrogging strategy dimension will fully mediate the effect of Volatility on Contextual Immersion execution capability. Core Leapfrogging strategy only partially mediated the effect of volatility on Contextual Immersion Capability. The indirect effects of volatility on Contextual Immersion (-.05, p =0.22, t-value=1.24) was

173 not significant with core leap-frogging strategy dimension (SC_CL) as mediator. H11b: Core leapfrogging strategy dimension will partially mediate the effect of Volatility on Marshalling execution capability. Core Leapfrogging strategy fully mediated the effect of volatility on Marshaling Capability. The indirect effects of volatility on Marshaling (.30, p <.01, t-value=8.19) was significant with core leapfrogging strategy dimension (SC_CL) as mediator. H11c: core leapfrogging strategy dimension will not mediate the effect of Volatility on Balancing execution capability. Contrary to my expectations, Core Leapfrogging strategy partially mediated the effect of volatility on Balancing Capability. The indirect effects of volatility on Balancing (.28, p <.01, t-value=7.51) was positive and significant with core leapfrogging strategy dimension (SC_CL) as mediator. H11d: core leapfrogging strategy dimension will fully mediate the effect of

Volatility on Intervening execution capability. Core Leapfrogging strategy only partially mediated the effect of volatility on Intervening Capability. The indirect effects of volatility on Intervening (.39, p <.01, t-value=11.47) was both positive significant with core leapfrogging strategy dimension (SC_CL) as mediator.

Moderation Effects

Significance of Path Coefficients for High and Low Volatility Models. My next step was to examine the size and significance of the path coefficients of the hypothesized relationships in my model for High Volatility and Low Volatility groups as shown in

Table 23 below. The significance test of the direct path coefficients of the model produced some meaningful results.

H12a: The main effects of a core extension strategy dimension on the strength of each of the capability configuration (contextual immersion, marshalling, balance and intervening) will be different, for different levels of volatility such that only with high levels of volatility does Intervening dimension become strongest. The effect of Core 174

Extension strategy on each of the dimensions of capability was significant and strong in my main effects model (CI, -0.23, p < .01; INT, 0.10, p < .01; and MAR, 0.31, p < .01. Core Extension had no significant effect on Balancing Capability). These effects remained significant under high Volatility (CI, -0.24, p < .01; INT, 0.11, p < .01; and MAR, 0.27, p < .01. Core Extension had no significant effect on Balancing Capability). However, under conditions of low volatility, I find that while Core Extension Strategy showed consistent strength and significance in its relationship with CI and Marshaling capabilities (CI, -0.22, p < .01 and MAR, 0.38, p < .01) its effect on INT reversed direction and dropped out of significance in the low volatility group to show a negative direct effect on INT (-0.01, p=0.86). Group difference test results for the effects in my model under conditions of high and low volatility surface no significantly different relationships for the effect of Core Extension on the dimensions of capabilities in the two groups. H12b: The main effects of a core leapfrogging strategy dimension on the strength of each of the capability configuration (contextual immersion, marshalling, balance and intervening) will be different, for different levels of volatility such that only with high levels of volatility does Marshaling capability become strongest. Strongest. The effect of Core Leapfrogging strategy on each of the dimensions of capability was significant and strong in my main effects model (CI, 0.76, p < .01; INT, -0.30, p < .01; and MAR, 0.44, p < .01. BAL, 0.14, p < .05. ) . These effects remained significant under high Volatility (CI, 0.68, p < .01; INT, -0.36, p < .01; and MAR, 0.36, p < .01. BAL, 0.17, p < .05. ). However, under conditions of low volatility, I find that while Core Leapfrogging Strategy showed consistent strength and significance in its relationship with CI and Marshaling capabilities (CI, 0.86, p < .01 and MAR, 0.50, p < .01) its effect on INT (-0.04, p=0.74) remained negative but dropped out of significance while its effect on BAL (-0.03, p=0.74) reversed direction and dropped out of significance in the low volatility group to suggest a negative direct effect. I also find that there is a significant group differences in the relationship between Core Leapfrogging and the Intervening dimensions of capabilities in the two groups of volatility(Path Coeff-diff ( High Vol-Low Vol) 0.32, T- Stat=2.72, p<0.01). Group difference test results for the effects in my model under 175 conditions of high and low volatility surface no significantly different relationships for the effect of Core Leapfrogging strategy on CI, BAL and MAR in the two groups. However, the effect of the relationships between Core Leapfrogging and Intervening strategy dimension is significantly different (t statistic =2.72, p=0.007). I can say with 95% confidence that the effect between SC_CL and INT in my model is significantly different for conditions of High volatility (.36, p=0.000, t value=6.59) than for conditions of Low volatility (-0.04, p=0.74, t value=0.35) such that the effect is stronger under conditions of high volatility. H12c: The main effects of core preservation strategy dimension on the strength of each of the capability configuration (contextual immersion, marshalling, balance and intervening) will be different, for different levels of volatility such that only with high levels of volatility does Balancing capability become strongest. The effect of Core Preservation strategy on each of the dimensions of capability was significant and strong in my main effects model (CI, -0.48, p < .01; INT, 0.62, p < .01; and MAR, -0.20, p < .01. Core Preservation had no significant effect on Balancing Capability). These effects remained significant under high Volatility for CI(-0.41, p < .01) and INT(0.62, p < .01) but dropped out of significance for MAR (-0.08, p =0.25). Core Preservation had no significant effect on Balancing Capability. However, under conditions of low volatility, I find that while Core Preservation strategy showed consistent strength and significance in its relationship with the dimensions of capabilities (CI, -0.52, p < .01; INT, 0.56, p < .01; and MAR, -0.45, p < .01) Core Preservation had no significant effect on Balancing Capability. I also find that there is a significant group differences in the relationship between Core Preservation and the Marshaling dimensions of capabilities in the two groups of volatility(Path Coeff-diff ( High Vol-Low Vol) 0.37, T-Stat=2.99, p<0.01). Group difference test results for the effects in my model under conditions of high and low volatility surface no significantly different relationships for the effect of Core Preservation strategy on CI and INT in the two groups. However, the effect of the relationships between Core Preservation and Marshaling strategy dimension is significantly different (t statistic =2.99, p=0.003). I can say with 95% confidence that the effect between SC_CP and MAR in my model is significantly different for conditions of 176

High volatility (-0.08, p=0.24, t value=1.17) than for conditions of Low volatility (-0.45, p=0.00, t value=5.04) such that the effect is stronger under conditions of low volatility. Detailed group test results are presented on Table 23 below.

Figure 14 : Main Effects Model

Figure 15 : High Volatility Effects Model

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Figure 16 : Low Volatility Effects Model

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Table 22 : High and Low Volatility Moderation Results & Group Difference Tests

Effect Size f². The next step in evaluating the predictive power of a structural equation model in Smart PLS is to calculate the relative contribution of each exogenous

179 variable in the model to the coefficient of determination (R² value) of the endogenous variable it predicts. The formula for calculating f² value is as follows:

f² = R² included - R² excluded, 1 - R² included

where R² included and R² excluded are the R² values of an endogenous latent variable when a selected exogenous latent variable is included in or excluded from the model

(Hair Jr et al., 2013). Cohen (1988) suggests that f² values of .02, .15, and .35 respectively, represent small, medium, and large effects.

There were several meaningful f² values in my model. First, I note that the f² values for the effects of Strat_OR on Strat_Fit (0.53) was the largest in my model.

Second, Volatility had a medium but stronger effect on SC_CP (0.46) when compared to its effects on SC_CE (0.26) and SC_CL (0.19). Third, GRP had a much stronger effect on SC_CL (0.25) when compared to its effects on the other strategy orientations; SC_CE

(0.06) and SC_CP (0.12). Fourth, Contextual Immersion had a medium but stronger effect on the Intervening Capability (0.36) when compared to the effects of the other capabilities on Intervening; Balancing (0.15), Marshaling (0.22). Fifth, Core Preservation had large and stronger effect on Intervening (0.43) when compared to its effects on the other capabilities; Marshaling(0.03), CI(0.13). Sixth, Core Leapfrogging had a large and stronger effect on CI(0.48) when compared to its effects on the other capabilities;

Bal(0.02), INT(0.09) and MAR(0.12). A summary of f² values for the model is shown in

Table 20. Both Strategy Fit and Strategy Orientation had small effect on the execution gap (0.09 and 0.05 respectively) while Volatility had a medium effect on strategy orientation (0.16) and a small effect on Gap Reduction (0.01) 180

Predictive Relevance. In addition to examining the coefficient of determination

(R²) values of the endogenous latent variables in a Smart PLS structural equation model as I did in the measurement model section, the predictive relevance of the model should also be evaluated (Hair Jr et al., 2013). A model that demonstrates predictive relevance is one that accurately predicts the data points of indicators of reflective endogenous constructs like the ones in my model. Predictive relevance is measured using Stone-

Geisser’s Q² value (Geisser, 1974; Stone, 1974). Q² values larger than zero for an endogenous latent variable indicate that the paths in the model leading to that construct exhibit predictive relevance. I used the blindfolding feature in Smart PLS to calculate Q² values for each of the endogenous latent variables in my model. All variables exhibited

Q² values greater than zero demonstrating the predictive relevance of my model. Q² and

R² values are shown in Table 17.I calculated Q² values for the endogenous variables in my revised model. All Q² values are above zero, indicating the predictive relevance for the model as a whole. Q² and R² values are shown in Table 25.

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Table 23 : Mediation Test Results

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Table 24 : Model f² Effects

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Table 25 : Model Endogenous Latent Variable R² and Q² Values

R² Value Q² Value Complete High Vol Low Vol Complete High Vol Low Vol Bal 0.550 0.540 0.566 0.529 0.515 0.529 CI 0.364 0.253 0.673 0.347 0.234 0.628 SC_CE 0.298 0.319 0.165 0.287 0.308 0.132 SC_CL 0.373 0.378 0.286 0.365 0.367 0.258 SC_CP 0.434 0.496 0.220 0.421 0.485 0.195 GRP 0.288 0.297 0.233 0.270 0.281 0.180 INT 0.715 0.710 0.722 0.690 0.681 0.648 MAR 0.538 0.528 0.568 0.518 0.487 0.519 StratFit 0.345 0.334 0.362 0.288 0.278 0.290 Strat_OR 0.139 0.175 0.000 0.063 0.076 -0.001

Industry had no significant effect on any of the execution capabilities. However, Size had strong negative and significant effects on INT (-0.06, P<0.05) and MAR (-0.1, P<0.01) in my main effects model. Table x below presents these results.

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Discussion

This third study explores how strategy formulation influences execution gaps in the presence of volatility and whether the ongoing configuration of strategy as a result of the gap inducing effects of volatility influences the configuration of the component execution capabilities that constitute dynamic capabilities. I also explore the mechanism by which these interactions occur. In this way I hoped to close my research loop by seeking to understand how separate components of strategy implementation are organized under volatility and the mechanisms of their interactions.

I noted at the outset that the Contingency Theory, Resource Based View and Dynamic

Capabilities Theory have two key elements in common. First, they all describe the operating environment of the organization as being volatile, changing and influencing the organization and its strategic intent. Second, these theories suggest that as organizations respond to environmental change they experience variations in their strategy leading to shifts in their internal configuration and structure. More specifically, these theories assert that a relationship exists among environmental volatility (Change), strategy focus

(direction or intent), capability emphasis (organization assets) and organizational performance. Based on these theoretical underpinnings I next explore the significance of the findings and discuss the implications of the observed interactions and effects among the variables in my model and the impact on capability configurations of the organizations in my study.

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The Effect of Strategy Formulation on Execution Gaps

I had hypothesized a number of effects involving volatility and execution gap on the one hand and the effect of formulated strategy expressed as the strategic orientation and strategy fit on the other hand. I hypothesized that volatility positively affects the extent of an organizations strategic orientation and negatively affects strategy fit. I also hypothesized that volatility as an antecedent of strategy implementation effectiveness will have a significant negative effect on the extent companies are able to close execution gaps. I surface very important findings here; My findings show that there are significant positive direct relationships between Volatility and Strategic Orientation (0.37, p<0.01) and between Volatility and Gap Reduction Performance (0.10, p<.05), but no significant relationship between Volatility and Strategy Fit (0.06, ns). While these positive path coefficients provide support for an important element of my hypothesis, that increased volatility will have a stronger positive effect on the strategic orientation of the organization than on its strategy fit. My findings fail to support my hypothesis on the effect of volatility on gap reduction performance and on the extent of fit of the strategy as they show that; 1) volatility does not significantly influence the extent of strategy fit and

2) volatility does not exert a direct negative effect on gap reduction performance.

Mediation effect results from this study however show that both strategy fit (Sobel Test

Stat. = 6.15, p<0.01) and strategic orientation (Sobel Test Stat. = 6.67, p<0.01) partially mediate the effects of volatility on execution gaps during strategy implementation.

Earlier studies support my findings on the strong positive effect of volatility on the extent organization show strategic orientation and the positive relationship between strategic

186 orientation and gap reduction performance. Anderson (1997), Goldman et al. (1995) and

Pine (1993) cited by Radas 2005, p. 197 show that survival in unpredictable markets requires flexibility at a level where the firm prepares to face any contingency.

Christensen et al. (2002) and Govindarajan & Gupta (2001) show that volatility challenges the effectiveness of implemented strategy. Bowman and Helfat (2001);

McGahan & Porter (1997) and Rumelt (1991) emphasize the need for dynamism in order to improve strategy effectiveness. Several other studies suggest that organizations can respond to volatility by altering their strategy (Miller, 1980; Murray, 1988; Wright, 1987;

D’Aveni & Gunther, 1994; Hamel & Prahalad, 1996; D’Aveni, 1999). These extant studies advocate an organization level view of strategy as a means to enhance the sensing and configuring mechanisms of the organization. They, on the one hand connect the strategic orientation of the organization with strategy fit in the presence of volatility and on the other hand connect this on the consequent positive effect on gap reduction performance. They therefore suggest that as volatility increases, organizations search for a stronger strategic orientation to be able to sense environmental changes (Hamel &

Prahalad, 1996; D’Aveni, 1999) and make sense of the antecedent effect of this dynamic on the variance between their strategy intent and chosen means (McNamara et al., 2003).

These in turn influence organizational responses to refine strategy to achieve greater fit in closing or reducing the execution gap (Miller, 1980; Murray, 1988; Wright, 1987;

D’Aveni & Gunther, 1994; Hamel & Prahalad, 1996; D’Aveni, 1999). These findings also explain the partial mediation effects of both strategic orientation and strategy fit on the effect of volatility on execution gap reduction performance.

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Predictive relevance of my variables is also strong which strengthens the quality of the causal inference. R² and Q² Values for strategic orientation, strategy fit and gap reduction

( 0.14,0.06; 0.35,0.29; 0.29,0.27 respectively) and Volatility’s medium f² effect size on strategic orientation(0.16) and the very large f² effect size of strategic orientation on strategy fit (0.53) the small f² effect sizes of strategic orientation(0.05) and strategy fit(0.09) on gap reduction performance demonstrate the importance of strategy orientation and strategy fit in explaining the gap reduction performance of organizations under volatility. These findings support two important conclusions. First, organizations respond to perceived increase in the level of volatility by incrementing their level of engagement in strategic orientation. Second, in the presence of volatility, organizations engage in an ongoing refinement of strategy to effectively close execution gaps (Miller,

1992; BadenFuller and Stopford, 1992, Miller, 1980; Murray, 1988; Wright, 1987; Hamel

& Välikangas, 2003; Christensen & Langhoff-Roos, 2003; Omeike and Lyytinen, 2015;

Eisenhardt and Martin, 2000).

Volatility, Execution Gaps and Variations in Strategy Structure

I hypothesized that increased volatility positively influences the extent companies engage in a Core Leapfrogging strategy and a Core Extension strategy. And that it negatively influences the extent companies engage in a Core Preservation strategy. I also hypothesized that Gap Reduction Performance will have positive relationship with strategy orientations such that the extent to which the organization engages in a Core

Leapfrogging strategy with increasing gap reduction performance will be stronger than its

188 extent of engagement with the core preservation or core extension strategy orientations.

My findings show that there are significant positive direct relationships between volatility and core leapfrogging strategy; (0.36, p<0.01), between Volatility and core extension strategy (0.44, p<0.01), and between volatility and core preservation strategy (0.53, p<0.01). These positive path coefficients provide support for a central theme of my strategy structure hypothesis; that implemented strategy is of a molar structure and organizations simultaneously implement combinations of these strategy orientations to varying degrees with increasing volatility. However, these findings fail to support my hypothesis on the effect of volatility on the structure of implemented strategy such that with increased volatility organizations will increasingly focus on a Core Leapfrogging strategy when compared to the use of other two strategy orientations. They show that volatility has the strongest effect on core preservation and the weakest effect on core leapfrogging strategy. This may suggest cognitive bias by management and the existence of competency traps as organizations tend to focus more on what they are good at doing which is operational excellence but this may not reduce the implementation gap.

Levinthal and March (1993) describe this phenomenon as “competency trap”, the tendency to maintain and intensify the use of existing and familiar competencies, technologies and processes resulting from an over commitment to a core competence

(Prahalad, and Hamel, 1990). It also suggests that organizations grapple with the challenges of ambidexterity as volatility increases; the ability to be aligned and efficient in the management of today’s business demands as well as being adaptive to changes in the environment at the same time (Duncan, R. 1976). March, J. G. (1991), further

189 described this as the ability to balance exploration and exploitation which enables the organization become creative and adaptable (pursuing variation, risk taking, experimentation, flexibility, discovery and innovation) while simultaneously focusing on production, efficiency, selection, implementation, and execution.

Although literature suggests that organizations look to innovation oriented strategies as volatility increases especially as the need to stage strategic change is required to regain competitive advantage (Christensen & Langhoff-Roos, 2003; Miller, 1992; Baden¬Fuller and Stopford, 1992). My findings show that a positive relationship exists between

Volatility and Core Preservation (.53, p < .01) instead of my hypothesized negative effect. This may suggest challenges with ambidexterity (Duncan, R. 1976; March, J. G.

1991) and competency trap (Levinthal and March 1993) as already highlighted above ‘

There are significant positive direct relationships between gap reduction performance and core leapfrogging strategy; (0.44, p<0.01), between gap reduction performance and core extension strategy (0.22, p<0.01), and between gap reduction performance and core preservation strategy (0.27, p<0.01). These positive path coefficients support a central claim of my strategy structure hypothesis in relation to execution gaps: organizations respond to volatility induced execution gaps by varying the extent to which they engage in each of the strategy orientations such that the extent to which the organization engages in a Core Leapfrogging strategy with increasing gap reduction performance will be stronger than its extent of engagement with the core preservation and core extension strategy orientations. These results provide support for my hypothesis and suggest that in 190 the presence of volatility, gap reduction performance affects the extent to which the organization engages in a core leapfrogging strategy such that it is much more likely to engage in it than for a core preservation or core extension strategy dimension. This view is supported by findings in literature which advocate that in order to maintain competitive advantage as volatility intensifies, organizations must acquire the regenerative capacity to reconfigure and transform the organization as a learned organizational skill (Argyris &

Schön, 1978; Teece & Pisano,1994)). They must also garner systematic learning mechanisms (learning to learn) that create and replace existing sets of dynamic capabilities when the environment changes (Zollo & Winter, 2002).

I also observe that all three dimensions of strategy are simultaneously present to varying degrees under conditions of volatility. They all have a positive relationship with gap reduction, but exert varying effects on the execution gap reduction. Predictive relevance of these variables are also strong. Cohen (1988). The f² values of the variables show that

GRP had a medium but stronger effects on Core_LP (0.25) than it did on Core_EXT

(0.06) and Core_PRE (0.12). The Q² Value of Core_LP (0.37), Core_EXT (0.29) and

Core_PRE (0.42) further demonstrate the predictive relevance of volatility and execution gaps in explaining the degree to which organizations engage in each dimension of strategy. These findings support an important conclusion. Implemented strategy is essentially molar in structure. In addition, under conditions of volatility implemented strategy will be a simultaneous combination of multiple strategy orientations to varying degrees. And, as volatility increases, organizations increase the extent of their engagement in all strategy orientations but with a greater focus on staging strategic 191 change at the organizational level. This focus reflects an increased orientation towards a core leapfrogging strategy. Earlier studies support this view by advocating that strategy orientations are not mutually exclusive and organizational success at implementing strategy may require generating various combinations of these dimensions relative to the environment (Omeike and Lyytinen, 2015; Reeves and Routledge, 2013; Miller 1992 and

Baden-Fuller and Stopford, 1992).

Volatility, Execution Gaps and Variations in Capability Configuration

I had hypothesized a number of effects involving; 1) volatility and execution gaps on the capability configuration of the organization on the one hand, and 2) the mediation effects of the strategy structure on the relationship between volatility and execution capabilities.

I hypothesized that volatility positively influences the extent companies engage in execution capabilities (Balancing, Intervening, Marshaling and Contextual Immersion capability) and that volatility as an antecedent of capability configurations will have a stronger positive influence on Intervening execution capability than on the other dimensions of execution capability. I also hypothesized that Increased Gap Reduction

Performance in the presence of volatility positively predicts the extent to which the organization engages in Contextual Immersion, Marshaling and Balancing execution capabilities but will negatively predict the extent to which the organization engage in

Intervening capability.

My findings show that the relationship between volatility and balancing execution capability is significant and positive (0.10, p<0.01) while being significant and negative 192 for contextual immersion (-0.13, p<.01). Volatility has no significant positive effect on marshaling capability (0.08, ns), and on intervening capability (0.04, ns). The positive path coefficient between volatility and balancing capability provide support for one of my hypothesis that increasing volatility positively influences the extent to which organizations engage in balancing capability. The significant but negative path coefficient between volatility and contextual immersion fail to support my hypothesis on this relationship and suggest a relationship in which organizations become more internally focused as volatility increases suggesting a tendency among responding organizations to become increasingly reactive internally towards change. The findings also fail to support my hypothesis on the effect of volatility on the extent to which the organizations engage in marshaling and intervening capabilities. The results show that volatility does not induce organizational engagement in building these capabilities. The findings also fail to support my hypothesis on the effect of volatility on capability configurations such that increasing volatility will have a stronger positive influence on intervening strategy execution capability than on the other dimensions of execution capability. They instead show that volatility has a stronger effect on contextual immersion than on balancing capability.

The relationship between gap reduction performance and the execution capabilities also surfaced interesting results. They show that there is a significant positive direct relationship between gap reduction performance and marshaling capability (0.32, p<0.01), and with balancing capability (0.29, p<0.01). A significant negative direct relationship exists between gap reduction performance and intervening capability (-0.23, 193 p<0.01). However, gap reduction performance has no significant positive effect on contextual immersion capability (-0.002, ns). The positive path coefficient provide support for my hypothesis that increasing gap reduction performance positively influences the extent to which organizations engage in a balancing and marshaling capabilities. The significant but negative path coefficient between gap reduction performance and intervening capability also supports my hypothesis on this relationship and uncover a relationship in which organizations intervene less in the internal and external context as execution gaps reduce. The findings also fail to support my hypothesis on the effect of gap reduction performance on the extent to which the organizations engage in contextual immersion capability. The results show that gap reduction performance does not predict organizational engagement with contextual immersion capability.

Strategy Structure and Capability Configurations

I had hypothesized a number of effects involving dimensions of strategy structure and components of capability configuration; I hypothesized three key relationships; 1) that

Core Extension Strategy dimension predicts the extent to which the organization engages in each capability such that its effect will be strongest on Marshaling Capability. 2) Core

Leapfrogging Strategy dimension predicts the extent to which the organization engages in each capability such that its effect will be strongest on Intervening Capability and 3) Core

Preservation Strategy dimension predicts the extent to which the organization engages in each capability such that its effect will be strongest on Balancing Capability. My findings 194 show that Core extension strategy showed a stronger significant and positive effect on the extent organizations engage in marshaling capability (0.30, P<.01) than it does on intervening capability (0.10, p<0.01), contextual immersion capability (-0.23, p<0.01) and balancing capability (BAL; -0.07, ns). These path coefficients provide support for my hypothesis that Core Extension Strategy dimension predicts the extent to which the organization engages in each capability such that its effect will be strongest on

Marshaling Capability. Core extension strategy however, showed a negative relationship with the Contextual Immersion capability of the organization and a negative but none significant effect on the extent to which the organization engaged in its balancing capability. These findings suggest that as organizations emphasize a core extension strategy the dominant execution capabilities become marshaling, contextual immersion, intervening in that order. Balancing capability appears muted.

Core leapfrogging strategy showed a stronger significant and positive effect on the extent organizations engage in contextual immersion capability (0.76, p<0.01) than it does on intervening capability (-0.31, p<0.01), marshaling capability (-0.43, p<0.01) or balancing capability (0.16, p<0.05). Core leapfrogging strategy however showed a negative and weaker relationship with the intervening capability of the organization when compared to the extent of engagement with the other capabilities. These path coefficients fail to provide support for my hypothesis that Core Leapfrogging Strategy dimension predicts the extent to which the organization engages in each capability such that its effect will be strongest on intervening capability. Core leapfrogging strategy however, showed a negative relationship with the intervening capability of the organization and the extent 195 to which the organization engaged in its marshaling capability. These findings suggest that as organizations emphasize a core leapfrogging strategy the dominant execution capabilities become contextual immersion, marshaling, intervening and balance in that order.

Core preservation strategy showed a stronger significant and positive effect on the extent organizations engage in Intervening capability (INT; 0.60, P<.01) than it does on

Contextual Immersion capability (CI; -0.48, p<0.01), Marshaling capability (MAR; -0.24, p<0.01) and Balancing capability (BAL; 0.01, ns). A core preservation strategy however, showed a negative relationship with the Marshaling and Contextual Immersion capabilities of the organization and had no significant effect on the extent to which the organization engaged with its Balancing capability. These path coefficients fail to provide support for my hypothesis that Core preservation Strategy dimension predicts the extent to which the organization engages in each capability such that its effect will be strongest on balancing capability. These findings suggest that as organizations emphasize a core preservation strategy in which focus is mainly on operational efficiency the dominant execution capabilities become intervening, contextual immersion and marshaling in that order. Balancing capability appears muted

Overall, my findings on the relationships between strategy structure and the component execution capabilities support my hypothesis on the effect of strategy structure on capability configurations under volatility such that organizations respond to variations in strategy structure by reconfiguring their execution capabilities. These findings are 196 supported by other studies which suggest that organizations can respond to volatility by altering their strategy configurations (Miller, 1980; Murray, 1988; Wright, 1987;

D’Aveni & Gunther, 1994; Hamel & Prahalad, 1996; D’Aveni, 1999) and this, in turn, increases the need for ambidexterity and for orchestrating (new) dynamic capabilities during strategy implementation (Eisenhardt and Martin, 2000). Other studies also advocate that volatility affects strategy in ways where volatility weakens competitive advantage of the organization requiring discontinuous change in both strategy and related capabilities (Hamel & Välikangas, 2003; Christensen & Langhoff-Roos, 2003; Omeike and Lyytinen, 2015; Eisenhardt and Martin, 2000).

I also found that the component dimensions of strategy structure mediated the effects of volatility on the execution capabilities. There are significant mediation effects of core preservation strategy orientations on the relationships between volatility and the execution capabilities as shown by my analysis. Core preservation strategy fully mediated the effects of volatility on contextual immersion and intervening capabilities. It also partially mediated the effect of volatility on marshaling capability but did not mediate the effect of volatility on balancing capability. There are significant mediation effects of core extension strategy dimension on the relationships between volatility and the execution capabilities. Core extension strategy partially mediated the effects of volatility on contextual immersion, marshaling and intervening capabilities. It however, did not mediate the effect of volatility on balancing capability. There are significant mediation effects of core leapfrogging strategy dimension on the relationships between volatility and the execution capabilities in my model; Core leapfrogging strategy fully mediated the 197 effects of volatility on marshaling capability but only partially mediated the effects on contextual immersion, balancing and intervening capabilities.

These findings are supported by my theorized effects of the three strategy orientations.

Core preservation and core extension both showed no mediating effects on the balancing capability of the organisation. I had defined balancing capability as the organization’s capacity to achieve and maintain consistency between its strategic intent and its organizational capability given the dynamics of its strategic context (Omeike and

Lyytinen, 2015; Teece, D. J. 2007, 2012. And Teece, D. J., Pisano, G., & Shuen, A.

1997). These mediation effect findings suggest that as volatility increases, these dimensions of strategy become less effective at preserving the competitive advantages of the firm. Core leapfrogging mediated the effects of volatility on the balancing capability and therefore, is shown as a more effective strategy to stage competitive advantage as volatility increases. My findings are supported by other studies in literature which suggest that Volatility induces environmental shifts that may erode the competitive advantages of firms (Markides, 1999; Thomas, 1996; Larsen et al., 2003; D’Aveni &

Gunther, 1994, Hamel & Prahalad, 1996) and that this increases the need for ambidexterity and calls for orchestrating (new) dynamic capabilities during strategy implementation (Teece et al., 1997; Eisenhardt and Martin, 2000).

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Capability Configurations Organization

I had hypothesized a number of effects involving the effects of marshaling, balancing and contextual immersion capabilities on the extent to which the organization is able to intervene in its environment. I hypothesized that execution capabilities will have a positive influence on the intervening capability of the organization such that the

Marshalling execution capability will have a stronger positive effect than other execution capabilities on Intervening execution capability in the presence of volatility. My findings show that there are significant positive direct relationships between components of capability configuration; Contextual Immersion, Marshaling and Balancing capabilities.

All showed positive significant relationships with Intervening capability (0.41, p<0.01,

0.40, P<.01 and 0.31, p<0.01 respectively). These positive path coefficients fail to provide support for my hypothesis that execution capabilities will have a positive influence on the Intervening capability of the organization such that the Marshalling capability will have a stronger positive effect than other execution capabilities on

Intervening execution capability under volatility.

My results suggest that the difference in the effects of both Marshaling and Contextual

Immersion on Intervening are marginal. This reveals that corporate executives tend to rely on environmental sensitivity and their ability to respond by reallocating resources to shape organizational agility and ability to intervene during change.

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Recursive Organization of Gap Reduction Variable under Varying Volatility Levels

I hypothesized a number of effects involving volatility and execution gap on the one hand and the effect of the quality of formulated strategy on this relationship on the other. I hypothesized that: 1) the main effects of a core extension strategy dimension on the strength of each of the capability configuration (contextual immersion, marshalling, balance and intervening) will be different, for different levels of volatility such that only with high levels of volatility does Intervening dimension become strongest. 2) The main effects of a core leapfrogging strategy dimension on the strength of each of the capability configuration (contextual immersion, marshalling, balance and intervening) will be different, for different levels of volatility such that only with high levels of volatility does

Marshaling capability become strongest. Strongest. 3) The main effects of core preservation strategy dimension on the strength of each of the capability configuration

(contextual immersion, marshalling, balance and intervening) will be different, for different levels of volatility such that only with high levels of volatility does Balancing capability become strongest. My findings show that there are significant multi-group moderation effects of volatility in my structural model;

Core extension strategy had varying effects on the dimensions of capability under different levels of volatility. The effect of Core Extension strategy on each of the dimensions of capability was significant and strong in my main effects model (CI, -0.23, p < .01; INT, 0.10, p < .01; and MAR, 0.31, p < .01. These effects remained significant under high Volatility (CI, -0.24, p < .01; INT, 0.11, p < .01; and MAR, 0.27, p < .01.).

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These effects remained consistently strong and significant under low volatility for CI and

Marshaling capabilities (CI, -0.22, p < .01 and MAR, 0.38, p < .01) but reversed direction and dropped out of significance for INT (-0.01, p=0.86). Core Extension had no significant effect on Balancing Capability in all the groups. There were no significant group difference in the relationships in the two groups.

Core leapfrogging strategy had varying effects on the dimensions of capability under different levels of volatility. The effect of Core leapfrogging strategy on each of the dimensions of capability was significant and strong in my main effects model (CI, 0.76, p

< .01; INT, -0.30, p < .01; and MAR, 0.44, p < .01. BAL, 0.14, p < .05. ) . These effects remained significant under high Volatility (CI, 0.68, p < .01; INT, -0.36, p < .01; and

MAR, 0.36, p < .01. BAL, 0.17, p < .05. ). These effects remained consistently strong and significant under low volatility for CI and Marshaling capabilities (CI, 0.86, p < .01 and

MAR, 0.50, p < .01) its effect on INT (-0.04, p=0.74) remained negative but dropped out of significance while its effect on BAL(-0.03, p=0.74) reversed direction and dropped out of significance in the low volatility group to show a negative direct effect.

There is also a significant group difference in the relationship between Core

Leapfrogging and the Intervening dimensions of capabilities in the two groups of volatility(Path Coeff-diff ( High Vol-Low Vol) 0.32, T-Stat=2.72, p<0.01) such that the effect between SC_CL and INT in my model is significantly different and higher for conditions of High volatility (.36, p=0.000, t value=6.59) than for conditions of Low volatility (-0.04, p=0.74, t value=0.35).

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Core preservation strategy had varying effects on the dimensions of capability under different levels of volatility. The effect of Core preservation strategy on each of the dimensions of capability was significant and strong in my main effects model ((CI, -0.48, p < .01; INT, 0.62, p < .01; and MAR, -0.20, p < .01.). These effects remained significant under high Volatility for CI(-0.41, p < .01) and INT(0.62, p < .01) but dropped out of significance for MAR (-0.08, p =0.25). However, under conditions of low volatility, the effects showed consistent strength and significance for marshaling contextual immersion and intervening capabilities (CI, -0.52, p < .01; INT, 0.56, p < .01; and MAR, -0.45, p <

.01). Core Preservation had no significant effect on balancing capability in any of the groups. There is also a significant group difference in the relationship between Core

Preservation and the Marshaling dimensions of capabilities in the two groups of volatility(Path Coeff-diff ( High Vol-Low Vol) 0.37, T-Stat=2.99, p<0.01) such that the effect between SC_CP and MAR in my model is significantly different and lower for conditions of High volatility (-0.08, p=0.24, t value=1.17) than for conditions of Low volatility (-0.45, p=0.00, t value=5.04).

My findings on the effect of size on execution gap reduction varies with literature.

Several theories have over time framed how firm size affects change (e.g., Singh, House, and Tucker, 1986; Mitchell. 1989; Delacroix and Swaminathan, 1991; Haveman, 1992;

Amburgey, Kelly, and Barnett, 1993). In particular, organizational ecology (Aldrich,

1979; Hannan and Freeman, 1977, 1984, 1989) holds that organizational forms are subject to strong structural inertia, so that change in organizational structures and activities will be slower paced than environmental change . If organizational size 202 indicates political insulation and degree of bureaucratization, then large organizations will change less than small organizations and the above observations will apply.

However, if organizational size is related to the possession of slack resources, differentiated and decentralized structures, and market power, then large organizations will be more fluid than small organizations. Research results suggest that both market power and bureaucratization operate simultaneously but that the market-power process dominates the bureaucratization process. (Haveman, 1993: 20-50). In my study, I measured Size using Revenue and Staff Strength. Both dimensions, slack resources

(Revenue) and bureaucratization (Staff strength) are accounted for and my research findings show that firm size has no significant effect on execution gap reduction performance of the organization (the use of slack resources to achieve organizational fluidity). I conclude that market power processes representing the use of slack resources to achieve organizational fluidity was perhaps a much more dominant factor in the organizations I studied. Size, therefore would have no effect on Execution gap reduction.

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CHAPTER 6: CORE FINDINGS AND INTEGRATED DISCUSSION

Collectively, the results of the preceding studies provide valuable understanding of the key factors influencing strategy implementation and how these factors are organized at the organizational level. The purpose of this final integrative chapter is to discuss the impact of the results of these three studies, as well as explore future areas for research.

This is not meant to be a recapitulation of the preceding chapters, but rather this chapter will focus on the most salient results and provide new insights on the most practical potential applications to practice and theory. I start by providing an updated mixed method summary table revised from Chapter 1, but now the table is more complete as it includes study results (see Table 27).

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Table 26 : Research Study Summary with Results

Study Research Rationale Targeted Results Phase and Questions Sample Method Phase 1 – What dynamics Qualitative, grounded Semi structured We find that strategy Qualitative and mechanisms theory was used to interviews with execution proceeds as a influence the uncover the dynamics of top series of organizational organizational organizational interaction management level interactions level during strategy execution executives who involving environmental interactions because this is well suited play pivotal volatility, strategy during strategy for understanding the roles in strategy orientations and dynamic execution so that contextual phenomena 20 interviews capabilities. These they can and processes where C-level interactions are centered effectively dynamic actions take executives of at closing execution gaps reduce the place (Maxwell 2004). multinational execution gap? organizations Phase 2 – How do We use a quantitative Survey of 557 Volatility predicts the Quantitative different levels inquiry as this is well C-level extent to which of volatility suited to address these executives who organizations engage in influence the questions because it is the have been in each of the strategy molar structure best approach to use to pivotal roles in orientations. of implemented test theory and identify executing Organizations respond to strategy? factors that influence an strategy in large perceived changed in the outcome and understand multinational level of volatility by the best predictors of organizations varying the extent of these outcomes (Creswell across 13 engagement of each of J.W. 2009). industries the strategy orientations..

Phase 3 – How do the We use a quantitative Survey of 557 Organizations respond to Quantitative different inquiry as this is well C-level variations in strategy dimensions of suited to address these executives who structure by reconfiguring strategy questions because it is the have been in their execution influence best approach to use to pivotal roles in capabilities. configuration of test theory and identify executing the sub- factors that influence an strategy in large dimensions of outcome and understand multinational capabilities? the best predictors of organizations , these outcomes (Creswell across 13 J.W. 2009). industry Triangulatio No research This phase brings The Results of the meta- n Phase questions at this together all of the combination of inference analysis phase, but rather previous research samples suggests that the Integration integration of questions and objectives provided above configurations theory of data qualitative and to provide a more detailed in this table. better explains how through quantitative understanding of how organizations respond to triangulatio inferences. This organizations engage with volatility induced n triangulation of their environment to execution gaps to vary empirical implement strategy. their strategy orientations Meta- evidence is Integration of study and related dynamic inference aimed at real mixed methods provides capabilities. The results based on world practical holistic evidence toward also suggest recursive results of solutions for answering research organization of the Phases 1, 2 organizations. questions. organizational level & 3 factors influencing strategy execution effectiveness

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STRATEGY AS CONFIGURATION RECURSIVE ORGANIZATION OF STRATEGY IMPLEMENTATION FACTORS AND THEIR EFFECTS ON STRATEGY EXECUTION EFFECTIVENESS.

INTEGRATED ANALYSIS

Because of the synergistic nature of the research questions in my mixed methods study, the results of the first qualitative study were integrated into the development of the survey used for both quantitative studies.

My Qualitative study explored three key questions and my analysis focused on uncovering the dynamics and mechanisms which influence the organizational level interactions during strategy execution so that they can effectively reduce the execution gap? I addressed this question by further answering the following questions: What are the elements which are likely to interact during strategy execution? What are their modes of interaction? What are the mechanisms by which the interactions happen? My findings showed that strategy execution at the organizational level is influenced by three broad constructs; Volatility induced execution gaps, Strategy Orientation and Capability configuration. Analysis of the results further show that these elements are recursively organized around three dynamics; Execution gap dynamic, Gap reduction Dynamic and execution focusing dynamic. My Qualitative study therefore demonstrated that strategy execution is an interactive iterative process through which the organization focuses both on strategy as well as on the organizational interactions that constantly shape the execution gap. Effective execution results from the capacity of the organization to understand the dynamics around the execution gap and appropriately reconfigure its 206 strategy while engaging its capabilities to reduce or close the execution gap. The first quantitative study supported this finding and further illuminated understanding of the role of volatility in inducing these gaps and in predicting the extent to which organizations engage in the three dimensions of strategy.

Building on this broad finding, the first Quantitative Study focused on extending my findings examining how different levels of volatility influence the composite structure of implemented strategy as established in the qualitative study. I explored two questions: To what extent does the level of volatility affect the strength of each strategy dimension in the implemented strategy (Strategy variation)? And To what extent does each of the strategy orientations mediate the effect of volatility on execution gap reduction?

Analysis of the findings of this research strand showed that volatility predicts the extent to which organizations engage in each of the strategy orientations. These findings support two important conclusions. First, that organizations respond to perceived changed in the level of volatility by varying the extent of engagement of each of the strategy orientations. Second, that in the presence of volatility, organizations engage more in core leapfrogging strategy dimension than they do in a core preservation or core extension strategy orientations. These findings on their own right are interesting, but I anticipate that once the second quantitative study is layered in, I will be able to identify “meta- inferences” from across all three strands (Teddlie & Tashakkor, 2009).

In order to uncover how the micro level capabilities influence the different dimensions of strategy therefore, my quantitative research strand addressed the following sub-questions;

To what extent does each dimension of capability (Capability Variation) affect the 207 strength of the different dimensions of strategy? To what extent do different dimensions of capability affect gap reduction? To what extent does each of the strategy orientations moderate the effect micro level capabilities on gap reduction? The third study attempted to surface how the extent to which the organization engages in each component of capability are related to variations in the strategy orientations, as well as determining the nature of interaction between the molar structure of strategy and capability configurations under volatility on the same organizations measured in the first quantitative study. This surfaced several important findings; 1) Formulated strategy as expressed in the strategic orientation of the organization and the extent of fit of the strategy influenced execution gaps and in combination with volatility and the selected execution approach had ongoing effect on execution gaps. In relation to this interactions I found that volatility does not exert a direct significant effect on the gap reduction performance of the organization.

However, strategy orientations, strategic orientation and strategy fit all mediate the effects of volatility on execution gaps such that core leapfrogging g strategy on the execution side and the strategic orientation of the organization on the strategy formulation side both exert the strongest effect reducing the effect of volatility on execution gaps. There are implications of this for both theory and practice. Corporate executives need to recognize that with increasing volatility a stronger focus on strategic orientation and a core leapfrogging strategy improves the ability of the organization to stage the dynamic capabilities required to effectively close execution gaps. These conclusions are supported by other studies in literature (Hamel & Prahalad, 1996;

D’Aveni, 1999). 2) I had expected to find that volatility would influence strategy structure (the relative strength of each orientation of strategy in the implemented strategy) 208 such that organizations focus more on a core leapfrogging strategy to stage innovation I find that organizations suffer cognitive bias and competency traps (Levinthal and March,

1993) as they tend to focus more on what they are good at doing which is operational excellence even with increasing volatility. Organizations also grapple with the challenges of ambidexterity as volatility increases (Duncan, R. 1976). March, J. G. (1991), and as a result corporate executives increasingly face the challenge of how to configure strategy and dynamic capabilities in order to stage the ambidexterity required to remain competitive under volatility. 3) My mixed methods study also surface that the structure of implemented strategy has implications for the configuration of dynamic capabilities during strategy implementation in the presence of volatility. This finding further validates results from other studies which suggest that organizations can respond to volatility by altering their strategy configurations (Miller, 1980; Murray, 1988; Wright,

1987; D’Aveni & Gunther, 1994; Hamel & Prahalad, 1996; D’Aveni, 1999) and this, in turn, increases the need for ambidexterity and for orchestrating (new) dynamic capabilities during strategy implementation (Eisenhardt and Martin, 2000). An interesting find here is the relative effect of strategy configuration in reducing the effects of volatility in eroding the competitive advantages of the firm. As volatility increases, a core leapfrogging strategy is more effective at accentuating the balancing capability of the organization.

In summary, this mixed methods study puts forward a number of key findings.

First, that strategy implementation proceeds as a series of ongoing configuration of

209 strategy structure and capability configuration as volatility impacts formulated strategy and execution gaps. This view challenges the linear mechanistic view of strategy implementation as a sequential train of actions and in its place proposes recursive organization of the strategy implementation mechanics. Second, it challenges the widely held view that volatility directly impacts strategy execution outcomes as is suggested in some streams of research. It proposes that volatility erodes competitive advantages by directly influencing the efficacy of a current structure of strategy and the configured capabilities. A misalignment between these configurations and volatility levels will induce execution gaps. Third, implemented strategy is molar in structure and I add to literature by positing that implemented strategy has three component dimensions; core preservation, core extension and core leapfrogging. These three dimensions are combined and simultaneously implemented to varying degrees relative to volatility by organizations. Forth, Volatility induces execution gaps and organizations respond by altering strategic orientation and strategy fit in order to appropriately configure strategy orientations and capability configuration. The implication of this view is that formulated strategy and the configuration of implementation variables collectively impact the ability of the organization to close execution gaps. Fifth, dynamic capability in relation to strategy execution is made up of four component capabilities; contextual immersion, marshaling, intervening and balancing capabilities. I view these capabilities as the micro foundations of dynamic capability and consider it an important addition to literature.

Sixth, that changes in the molar structure of implemented strategy directly influences how these micro foundations of dynamic capability are configured in response to volatility induced execution gaps. Seventh, this study also surfaces a number of 210 challenges associated with organizational sensing and response mechanisms during strategy implementation; I find that organizations tend to exhibit cognitive biases in sensing and responding to shifts in the environment. These biases are influenced by competency traps and the ambidexterity dilemma that collectively lead to inappropriate responses to volatility and the ineffective formulation of strategy, a weak strategy structure and a mis-aligned configuration of capability. Finally, this mixed methods study completes the study loop from the sensing side of the strategy model (focused on perceived volatility and execution gaps) to organizational response mechanisms (focused on strategy and capability relationship) that jointly shape strategy implementation and its outcomes and their recursive organization.

Implications and Contributions

Several implications for theory building emerge from my study. The first is that organizations approach strategy as a series of iterative recursive interactions rather than as a sequential alignment. The process of strategy formulation and implementation is a double loop process in which the organization focuses on enhancing its strategy effectiveness (configuration) and its execution capability effectiveness (dynamic capabilities) in order to close the execution gap.

My findings uncover several insights on how different aspects of the interactive dynamics during strategy execution induce tension within organizational capabilities. For example, the leap vs. core focus may require different capabilities and configurations which are in tension and are conditioned by a number of specific factors challenging the

211 capacity of the organization for dynamic capabilities during strategy execution. Teece,

Pisano, & Shuen, 2007 describe this dynamic capability as the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments

I propose that viewing strategy-as-interaction better explains strategy execution dynamics using the contextual execution gap dynamic and the organizational engagement dynamic (Figure 4). My findings also extend the body of knowledge by putting forward a model that explains these dynamics and how organizations interact to close the execution gap. The research adds to the existing literature a new perspective from which to examine organizational interactions for strategy execution, Strategy configurations and dynamic capabilities.

Through my research findings I use the idea of strategy as configuration to frame a new approach to implementing strategy as a molar structure, which involves composing simultaneously strategy in three dimensions (Omeike and Lyytinen, 2015). The significance of these findings is that understanding how volatility affects strategy orientations and the effect of these interactions on gap reduction provides corporate executives with insight as to how to vary their strategy during implementation and what the effects of these variations are for the strategy effects. It further clarifies understanding of how volatility influences the presence and level of each of the strategy orientations defined as unique orientations involved in each strategy. As a result, the sensing capability of organization becomes strengthened and strategy implementation becomes effective.

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The theoretical and practical implications to this research are significant. On the one hand, it advances a molar structure of strategy which advocates that implemented strategy is composed of three dimensions which collectively improve organizational ambidexterity and response to volatility. On the other hand, it also presents capabilities at the higher level as a configurable mix of zero, first and second order dynamic capabilities with sub components of contextual immersion, marshaling, intervening and balance, it also showcases the interconnectedness of these factors and how their recursive organizations can be leveraged to effectively close execution gaps under conditions of volatility.

Future Research

This study is preliminary exploration in a nascent field of strategy execution management. The exercise of conducting a rigorous, mixed method study is illuminating additional opportunities for future research. This study is being done at the organization level from the perspective, and the perception of, corporate executives with pivotal roles in the formulation and implementation of their strategy. If the relationships between volatility, strategy and capability are identified and objectively quantified, it will open up opportunities to explore this relationship from different perspectives. For example, an additional strand of study to further explore the learning mechanisms of the organization and how this relates to capability configuration and the choice of strategy. Further research could be done to understand the correlation between organization learning mechanisms and capability configuration.

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Limitations

A number of limitations constrain the findings of my study; First, There are measurement and endogeneity problems in the chosen design- Although I study the dynamic phenomenon of strategy implementation I use a static design approach instead of a longitudinal study that recognizes variations of both strategy and organizational performance over time. Data collection is an important component of research design as it affects generalizability and even reliability of collected data (Yin, 1994). Therefore, a longitudinal research design to deal with the issue of variations in organizational capabilities and the time lag required for such organizational responses to reflect in performance is recommended. There may also be significant measurement bias as the

DV and IV’s were measured at the same time with the same method. Second, because this was an empirical study, possible meanings of these discoveries must be inferred in light of former research. Additional studies will be needed to truly comprehend and test these interpretations. This study was also limited to organizations within the United

States and Nigeria and therefore can’t be extended to a global representation. More research would need to be done before this could be applied to the greater population of organizations. Third, Strategy variations happen over time. This study only captures a point in time in which the corporate executives were required to relive their experiences with implementing strategy. Additional longitudinal research should be done to take into account the variations in both strategy and capability configuration that play as the organization grapples with volatility. Finally, limitations on generalizability of the findings of this study arise also from the void of addressing the quality of the strategy and

214 related issues like ambition levels of strategic intent and related reach goals (these constructs were not addressed in this study). Generalizability is also constrained by the limited factors controlled for. Although we controlled for firm size and industry, two factors that literature suggests influences strategy execution dynamics we did not control for other potentially significant factors like Country.

Conclusion

Although literature and the first two strands of my research show that strategy configuration and capability configuration are recursively organized and that these dynamics are influenced by volatility, the mechanisms and the impact of these interaction had not yet been quantified. Demonstrating that having the dynamic variations of strategy and an associated ongoing configuration of capabilities under varying levels of volatility has a positive relationship with gap reduction, would give organizations a tangible tool to increase strategy implementation effectiveness in volatile environments. Understanding that volatility influences the extent to which organizations engage in strategy orientations is unchartered water. Understanding that implemented strategy is largely molar in structure and requiring the simultaneous combination of three dimensions of strategy and that this structure is recursively related to capability configurations the enact the strategy is advancing the knowledge in this timely and relevant field as well as providing practitioners with tangible tools that could improve strategy implementation effectiveness. Additional research in this area could break the code to helping organizations deal with increasing volatility to stage sustainable competitive advantages

215 through unique variations of their strategy and capability configurations by discovering how these variables interact and the mechanism through which they interact.

Because this area involves an exploration of organizational behaviors that have not yet been clearly defined as well as the desired outcome of validating the application across a larger population of organizations, a mixed methods approach is very appropriate. The rigorous process of applying two different techniques will support significant advancements in the empirical understanding of the mechanisms underlying the recursive organization of these variables as well as support practical applications.

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Appendix A. Qualitative Study Interview questions (Study 1)

Part 1 – Initial / Background Questions:

1. Please tell me about yourself, both personally and professionally? 2. Please describe your current roles and responsibilities Part 2 – Core Questions:

1. Tell me of a time when your organization created a compelling and inspiring vision that formed the foundation for developing the strategy. By vision I mean a dynamic and compelling view or positioning of the organization at some point in the future which provides a deeply inspirational or emotional drive to accomplish something great that those in the organization can get behind and drive toward. and by strategy I mean The entire sequence of plans, methods and actions chosen by the organization to deliberately achieve its desired aspirations, positioning, vision or desired future state, discuss especially what decisions/considerations that went into crafting this vision? Describe your experience of this process from the beginning to creation of the vision and in as much detail as you can (e.g., who were involved; how the team engaged; how the plan unfolded…) Additional probing inquiries: a. How did your organization engage its people in this effort? I. Who participated? II. Why were they involved? III. In what role did they get involved? b. How did your organization engage its systems in this effort? c. How did your organization engage its processes in this effort? d. How did you all work as a team 2. Tell me of a time when your organization succeeded in its strategy. What happened, why was it effective, how did your team engage in this process, Go into as much detail as you can. Additional probing inquiries: a. How did your organization engage its people in this effort? I. Who participated? II. Why were they involved? III. In what role did they get involved? b. How did your organization engage its systems in this effort? c. How did your organization engage its processes in this effort? d. How did you all work as a team? 217

3. Tell me of a time when you did not succeed or you were ineffective in your strategy- especially in its implementation. What happened, what characteristics made this ineffective? Did you change your strategy or vision or did you change the way you implement it? Go into as much detail as you can Additional probing inquiries: Additional probing inquiries: a. How did your organization engage its people in this effort? I. Who participated? II. Why were they involved? III. In what role did they get involved? b. How did your organization engage its systems in this effort? c. How did your organization engage its processes in this effort? d. How did you all work as a team? 4. From your experience what skills are important or relevant to ensure successful formulation and implementation of strategy that will ensure that the organization can fulfil its vision and strategy? 5. From your experience what skills, engagement, or processes are important or relevant for the leadership team (or teams) to ensure successful formulation and especially implementation of strategy that will ensure or help organizations thrive in our current global volatile environment?

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Appendix B. Scales used in Quantitative Studies (Study 2 and 3)

ITEMS DESCRIPTION CODE Volatility Technology Vol_T1 Changes in Technology -The technology in our markets changed rapidly. Changes in Technology -It was very difficult to forecast where the technologies Vol_T2 in our markets would be in the next three to five years. Changes in Technology -Changes in technologies significantly affected our Vol_T3 products/services and strategy Market Volatility Changes in the market -In our markets, customers’ preferences changed Vol_M1 relatively fast. Changes in the market -New customers tended to have product-related needs that Vol_M2 were different from those of existing customers. Changes in the market -products/services became obsolete in our industry very Vol_M3 fast Changes in the market -Changes in our markets significantly affected our Vol_M4 products/services and strategy Vol_M5 Changes in the market -The actions of competitors in our market were predictable Changes in the market -The actions of competitors significantly shifted the rules Vol_M6 of the game in our market Environmental Volatility Changes in the environment -The socio-political environment changed often and Vol_E1 was unpredictable Changes in the environment -The economic (buying power and vibrancy) Vol_E2 environment changed often and was unpredictable Changes in the environment -The legal/regulatory environment changed often and Vol_E3 was unpredictable Changes in the environment -Changes in the socio-political and economic Vol_E4 environment significantly affected our products/services and strategy Organizational Volatility Changes in your organization -The composition of our executive team changed Vol_O1 often (executive team turnover) Changes in your organization -The composition of our board changed often Vol_O2 (board turnover) Changes in your organization -We experienced significant turnover in our middle Vol_O3 management (middle management turnover)

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ITEM DESCRIPTION Changes in your organization -We experienced significant turnover in our Vol_O4 operational level staff (operational staff turnover) Changes in your organization -We experienced significant turnover in our Vol_O5 strategic job families

Changes in your organization -The time required To recruit into most mission Vol_O6 critical roles (for each role type) was significantly high

Changes in your organization -Recruitment error and recruitment failure rate Vol_O7 were high in our organization

Vol_O8 Changes in your organization -Our organization structure changed often

Changes in your organization -We tended to shuffle key personnel in middle Vol_O9 and senior management roles a couple of times cross the functions and locations of our organizatiuon Changes in your organization -Changes in our organization significantly affected Vol_O10 our products/services and strategy Execution gap reduction Force Effect of Turbulence -We experienced significant increase in the effort required GRF_T1 to compete in the market

Effect of Turbulence -We experienced significant shifts in technology that GRF_T2 challenged our strategic assumptions and beliefs about the market

Effect of Turbulence -our existing organizational capability to perform was GRF_T3 significantly stretched and challenged Effect of Turbulence -Increased market competitiveness significantly affected GRF_T4 our competitive stance and tactics

Effect of Turbulence -Unanticipated environmental(socio-political, economic GRF_T5 and legal/regulatory) changes challenged our existing configurations

Effect of Turbulence -Changes in our environment significantly affected our GRF_T6 strategy Effect of Turbulence -Labor turnover and loss of key personnel significantly GRF_T7 affected our capability to execute our strategy Effect of Turbulence -Internal group actions like unions significantly affected GRF_T8 our capability to execute our strategy

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ITEM DESCRIPTION Execution gap reduction Performance (Market) GR_M1 Market Performance -Market share gains: GR_M2 Market Performance -Sales growth rate. GR_M3 Market Performance -Revenue growth Execution gap reduction Performance (Financial) GR_F1 Market Performance -Net profits. GR_F2 Market Performance -Financial liquidity GR_F3 Market Performance -Return on investment. GR_F4 Market Performance -Cash flow. Execution gap reduction Performance (Innovation) GR_PI1 Market Performance -New product and service development.

GR_PI2 Market Performance -Frequency of new product or service introduction:

GR_PI3 Market Performance -New product/service market performance Market Performance -Technological developments and/or other innovations in GR_PI4 business operations: Execution gap reduction Performance (Organization)

GR_OE1 Market Performance -Reputation among major customer segments:

GR_OE2 Market Performance -Strategic projects delivery and performance:

GR_OE3 Market Performance -Operational excellence performance Strategy Context (Core Extension) Strategy Changes -Our strategy focused primarily on Implementing and SC_CE1 embedding technology in our operations Strategy Changes -Our strategy focused primarily on enhancing product and SC_CE2 service features and quality. Strategy Changes -Our strategy focused primarily on reducing the cost of our SC_CE3 operations and cost structure Strategy Changes -Our strategy focused primarily on making significant SC_CE4 improvements to our operational efficiency

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ITEM DESCRIPTION Strategy Context (Core Preservation)

Strategy Changes -Our strategy focused primarily on maintaining the SC_CP1 efficiency of our operations (turn around times, quality levels)

Strategy Changes -Our strategy focused primarily on ensuring our operations SC_CP2 continued to run, focusing on managing business processes and maintaining controls and compliance Strategy Changes -Our strategy focused mainly on ensuring we meet operating SC_CP3 targets Strategy Context (Core Leapfrogging)

Strategy Changes -Our strategy focused primarily on taking advantage of SC_CL3 emergent change or opportunity and exploring or exploiting new domains.

Strategy Changes -Our strategy focused primarily on new products /service SC_CL1 innovation

Strategy Changes -Our strategy focused primarily on driving growth, mergers SC_CL2 and acquisitions and maintaining game-changing market leadership

Strategy Changes -Our strategy focused primarily on shifting the rules of the SC_CL4 game in our market and creating continuous or periodic turbulence

Capability Changes -Capability building placed emphasis on technology CAP_FO1 capability to enhance product/service value Capability Changes -Capability building placed emphasis on quality CAP_FO2 improvement capability. Capability Changes -Capability building placed emphasis on cost management CAP_FO3 capability Capability Changes -Capability building placed emphasis on business process CAP_FO4 turnarounds Capability Changes -Capability building placed emphasis on business and CAP_ZO1 operations management capability Capability Changes -Capability building placed emphasis on Improving middle CAP_ZO2 management skills Capability Changes -Capability building placed emphasis on functional and CAP_ZO3 business skills

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ITEM DESCRIPTION

Capability Changes -Capability building placed emphasis on new products CAP_SO1 /service innovation expertise

Capability Changes -Capability building focused primarily on driving growth, CAP_SO2 mergers and acquisitions and maintaining game-changing market leadership

Capability Changes -Capability building placed emphasis on the CAP_SO3 entrepreneurial skills required to take advantage of emergent change or opportunity and exploring or exploiting new domains.

Q16 organization structure Q20 Type of strategy Q21 organizations' Industry Sector. Q22 job role in your organization Q24 your level in your organization. Q25 your Organization's Staff Strength Q26 your Organization's gross annual revenue Q27 your Organization's scale of operations. STRAT_1 Do you have a strategy? STRAT_2 Is your organization currently implementing a strategy? Q29 what is the lifespan of your organization's current strategy?

Q30 At what level of the organization is the current strategy being implemented?

SD_1 Have there been occasions when you took advantage of someone?

SD_2 Have you sometimes taken unfair advantage of another person? SD_3 Are you always willing to admit when you make a mistake? SD_4 Are you quick to admit making a mistake?

SD_5 Do you sometimes try to get even rather than forgive and forget?

SD_6 Do you sometimes feel resentful when you don't get you own way?

SD_7 Are you always courteous, even to people who are disagreeable?

SD_8 Are you always a good listener, no matter whom you are talking to?

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Appendix C: Common Method Bias Test Results

Common Method Bias Test (All Respondents) n=557 Std. Reg. Std. Reg. Weight Path Weight Without With CLF CLF Delta Threshold GR_M3P2 <--- GR_Ext 0.793 0.777 0.016 0.2 GR_F1P2 <--- GR_Ext 0.783 0.727 0.056 0.2 GR_M2P2 <--- GR_Ext 0.798 0.759 0.039 0.2 GR_M1P2 <--- GR_Ext 0.77 0.732 0.038 0.2 GR_F3P2 <--- GR_Ext 0.874 0.851 0.023 0.2 GR_F4P2 <--- GR_Ext 0.852 0.835 0.017 0.2 SC_CE4 <--- CAP_FO 0.716 0.67 0.046 0.2 SC_CP1 <--- CAP_FO 0.751 0.728 0.023 0.2 SC_CP3 <--- CAP_FO 0.769 0.759 0.01 0.2 SC_CL3 <--- CAP_FO 0.672 0.627 0.045 0.2 SC_CP2 <--- CAP_FO 0.722 0.695 0.027 0.2 CAP_ZO1 <--- CAP_ZO 0.74 0.74 0 0.2 CAP_FO4 <--- CAP_ZO 0.72 0.684 0.036 0.2 CAP_FO3 <--- CAP_ZO 0.755 0.753 0.002 0.2 CAP_FO2 <--- CAP_ZO 0.795 0.761 0.034 0.2 CAP_ZO3 <--- CAP_ZO 0.768 0.752 0.016 0.2 Vol_O4 <--- VOL_ORG 0.768 0.778 -0.01 0.2 Vol_O5 <--- VOL_ORG 0.75 0.763 -0.013 0.2 Vol_O3 <--- VOL_ORG 0.737 0.735 0.002 0.2 GR_PI2P2 <--- GR_Int 0.701 0.78 -0.079 0.2 GR_PI4P2 <--- GR_Int 0.732 0.693 0.039 0.2 GR_PI3P2 <--- GR_Int 0.802 0.841 -0.039 0.2 GR_OE1P2 <--- GR_Int 0.759 0.741 0.018 0.2 GR_OE2P2 <--- GR_Int 0.774 0.727 0.047 0.2 GRD_T4 <--- GRD 0.728 0.729 -0.001 0.2 GRD_T3 <--- GRD 0.623 0.629 -0.006 0.2 GRD_T1 <--- GRD 0.783 0.778 0.005 0.2 Vol_E2 <--- VOL_ENV 0.813 0.804 0.009 0.2 Vol_E1 <--- VOL_ENV 0.713 0.715 -0.002 0.2 Vol_E3 <--- VOL_ENV 0.585 0.58 0.005 0.2 SC_CL2 <--- CAP_SO 0.736 0.723 0.013 0.2 CAP_SO2 <--- CAP_SO 0.711 0.726 -0.015 0.2 SC_CL1 <--- CAP_SO 0.777 0.791 -0.014 0.2 Vol_M2 <--- VOL_MKT 0.699 0.758 -0.059 0.2 Vol_M1 <--- VOL_MKT 0.789 0.755 0.034 0.2 Vol_T1 <--- VOL_TECH 0.68 0.668 0.012 0.2 Vol_T3 <--- VOL_TECH 0.698 0.674 0.024 0.2

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APPENDIX D: CFA Measurement Models (AMOS) (Study 2 and 3)

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APPENDIX E: Structural Equation and Measurement Model (Smart PLS) CFA and SEM Analyses (Study 2)

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