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Article10-TherelationshipbetweenChiefInformationOfficertransformationalleadershipandcomputingplatformoperatingsystems.pdf

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Walden University

COLLEGE OF MANAGEMENT AND TECHNOLOGY

This is to certify that the doctoral dissertation by

George W. Anderson

has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made.

Review Committee Dr. Lee W. Lee, Committee Chairperson,

Applied Management and Decision Sciences Faculty

Dr. Jimmy Brown, Committee Member, Applied Management and Decision Sciences Faculty

Dr. Xu Di, External Committee Member,

Applied Management and Decision Sciences Faculty

Chief Academic Officer

Denise DeZolt, Ph.D.

Walden University 2010

ABSTRACT

The Relationship Between Chief Information Officer Transformational Leadership and Computing Platform Operating Systems

George W. Anderson

M.B.A., Chaminade University, 1991 B.G.S., Roosevelt University, 1989

Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy Applied Management and Decision Sciences

Walden University February 2010

ABSTRACT

The purpose of this study was to relate the strength of Chief Information Officer (CIO)

transformational leadership behaviors to 1 of 5 computing platform operating systems

(OSs) that may be selected for a firm’s Enterprise Resource Planning (ERP) business

system. Research shows executive leader behaviors may promote innovation through the

use of information technology (IT), in turn affecting business performance. Responsible

for IT leadership, CIOs hold the greatest opportunity to influence IT innovation. Yet no

research explains the relationship between CIO leadership behaviors and the computing

platform selected for critical business applications. This literature gap is important

because innovative computing platforms influence a firm’s competitiveness and IT cost

structure. Research questions asked to what extent transformational leadership theory and

its subcomponents (independent variables) predict the selection of more or less

innovative OSs (dependent variables). Using the Multifactor Leadership Questionnaire

and 17 additional items, data representing 151 randomly selected North American CIOs

and their ERP computing platforms were studied using a theoretical framework

incorporating the influence of executive-level leadership on technology innovation.

Through analysis of variance, t tests, and descriptive statistics, the study uncovered

significant relationships between the strength of transformational leadership behaviors,

particularly intellectual stimulation and inspirational motivation, and the OS selected for

a firm’s ERP business system. The implications for social change include a clearer

understanding of how specific executive-level leadership behaviors may encourage IT

workplace innovation, enable IT teams to more effectively meet changing business needs,

and positively affect IT team longevity and company-wide business performance.

The Relationship Between Chief Information Officer Transformational Leadership and Computing Platform Operating Systems

George W. Anderson

M.B.A., Chaminade University, 1991 B.G.S., Roosevelt University, 1989

Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy Applied Management and Decision Sciences

Walden University February 2010

UMI Number: 3391435

INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted.

In the unlikely event that the author did not send a complete manuscript

and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion.

UMI 3391435

unauthorized copying under Title 17, United States Code.

ProQuest LLC 789 East Eisenhower Parkway

P.O. Box 1346 Ann Arbor, MI 48106-1346

DEDICATION

I dedicate this work to my wife, Michelle, and my children, Phillip, Ashley, and

Meagan, for giving me the time, opportunity, and freedom to pursue a PhD, and to my

Lord and Savior Jesus for giving me the endurance to complete it. May it bring honor and

glory to the One through whom all things are possible.

ACKNOWLEDGMENTS

Thank you to everyone who helped me during this long journey, especially my

family for all the sacrifices they made and their encouragement along the way. My

deepest gratitude and respect goes to Dr. Lee W. Lee, my faculty mentor and dissertation

committee chair who carefully guided my work during the course of my doctoral studies.

Thank you for taking special interest in ensuring my work was relevant, valid, logical,

and comprehensive. I also want to thank my committee member Dr. Jimmy Brown for his

subject matter expertise, outstanding direction, and tireless review of my work, my

committee member Dr. Xu Di for her good sense and commitment to excellence, Dr.

Shannon Lynch for her outstanding review and critical insight, and Dr. Melanie Brown

for her meticulous assessment and refinement of my work. I am tremendously grateful to

my wife for faithfully believing in me, my children for their patience and understanding,

my parents and siblings for their encouragement, and to my close friends and church

family who helped keep my eyes on the target. Finally, thank you to my colleagues in the

SAP and HP technical communities and management ranks for their assistance and

support throughout this process.

iii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. vi

LIST OF FIGURES ......................................................................................................... viii

CHAPTER 1: INTRODUCTION TO THE STUDY ...........................................................1 Introduction ....................................................................................................................1 Problem Statement .........................................................................................................3 Nature of the Study ........................................................................................................5 Purpose of the Study ......................................................................................................8 Research Questions ........................................................................................................9 Hypotheses ...................................................................................................................10 Theoretical Grounding .................................................................................................12 Operational Definitions ................................................................................................13 Assumptions, Limitations, Scope, and Delimitations ..................................................18 Significance of the Study, Gaps in the Literature, and Social Implications ................20 Summary ......................................................................................................................22

CHAPTER 2: LITERATURE REVIEW ...........................................................................24 Introduction ..................................................................................................................24 Foundations in Leadership Theory ..............................................................................26 Transformational Leadership .......................................................................................28

The Multifactor Leadership Questionnaire ........................................................... 30 Intellectual Stimulation ......................................................................................... 30 Charisma: Idealized Influence and Inspirational Motivation................................ 31 Individualized Consideration ................................................................................ 32 Intellectual Stimulation Synonymous with Transformational Leadership ........... 32

Innovation in Information Technology ........................................................................33 Operationalizing Innovation ................................................................................. 33 Executive Leadership and Information Technology Innovation .......................... 34 Innovation, Vision, and Climate ........................................................................... 37 Innovation, Risk Taking, Creativity, and Organizational Survival ...................... 38 Innovation and ERP Computing Platforms........................................................... 39

Computing Platforms and Innovation ..........................................................................40 Operationalizing the Information Technology Computing Platform .................... 41 Computing Platform Classifications: Legacy or Contemporary ........................... 42 Hybrids: Another Contemporary Computing Platform ........................................ 44 Computing Platform Operating Systems Innovation Continuum ......................... 46 Less Innovative Operating Systems: Legacy/Mainframe and UNIX ................... 47 More Innovative Operating Systems: Linux, Windows, and Hybrids .................. 48

SAP ERP Implementation Critical Success Factors ....................................................51 The Gap in the Literature .............................................................................................53 Counterarguments in the Literature .............................................................................55

Transformational Leadership Counterarguments ................................................. 55 Innovation Counterarguments ............................................................................... 56

Computing Platform Counterarguments ............................................................... 57 Research Review: Research Questions, Variables, and Hypotheses ...........................57 Most Important Theory, Primary Research, and Methods ...........................................58

Literature Review of Competing Methodologies ................................................. 60 Literature Review of Transformational Leadership Surveys ................................ 60 Literature Review of Computing Platform and OS Innovation Measures ............ 62

Narrowing the Gap to Promote Positive Social Change ..............................................64 Summary ......................................................................................................................65

CHAPTER 3: RESEARCH METHOD .............................................................................66 Introduction ..................................................................................................................66 Research Design and Approach ...................................................................................66

Justification for the Electronic Survey Method .................................................... 68 Justification for the MLQ 5X Instrument: Validity and Reliability ..................... 69 MLQ Subscale Sample Transformational Leadership Survey Items .................... 70 Justification for the Survey Items related to the Dependent Variable .................. 70

Setting and Sample ......................................................................................................71 Sample Size ........................................................................................................... 71 Sample Identification Methodology ..................................................................... 72 Data Collection Methodology ............................................................................... 73 Addressing Nonresponse Bias .............................................................................. 74 Justification for the Research Data Source ........................................................... 75

Study Variables and Details .........................................................................................76 Independent Variable Details and Discussion ...................................................... 76 Dependent Variable Details and Discussion ......................................................... 77 Confounding Variables: Justifying Demographic Survey Items .......................... 79

Measurement and Treatment........................................................................................81 Transformational Leadership MLQ 5X Measures ................................................ 81 Demographic, Computing Platform, and Innovation Measures ........................... 83

Data Analyses ..............................................................................................................83 Participant Rights, Assumptions, and Limitations .......................................................84 Summary ......................................................................................................................85

CHAPTER 4: RESULTS ...................................................................................................86 Overview ......................................................................................................................86 Research Tools: Instruments and Measures .................................................................87 Population, Sample, and Subsample Data ...................................................................88 Demonstrating Dependent Variable Validity...............................................................94 Calculating Cronbach’s Alpha .....................................................................................97 Leadership Variables Descriptive Statistics.................................................................98 Testing Hypotheses ....................................................................................................100

Research Question and Hypothesis 1 .................................................................. 101 Research Question and Hypothesis 2 .................................................................. 104 Research Question and Hypothesis 3 .................................................................. 106 Research Question and Hypothesis 4 .................................................................. 108

Research Question and Hypothesis 5 .................................................................. 110 Research Question and Hypothesis 6 .................................................................. 112

Confounding Variables Analyses ..............................................................................114 Age, Years of Experience, and Years with the Firm .......................................... 114 Basis Team Size and Annual Revenue ............................................................... 118 Categorical Confounding Variables .................................................................... 120

Research Questions: Themes, Findings, and Alternate Interpretations .....................127

CHAPTER 5: SUMMARY, CONCLUSION, AND RECOMMENDATIONS .............130 Study Summary ..........................................................................................................130 Conclusions ................................................................................................................131

Relationship between Transformational Leadership and Platforms ................... 131 Relationship between IS and Computing Platforms ........................................... 133 Relationship between IC and Computing Platforms ........................................... 134 Relationship between IIA and Computing Platforms ......................................... 134 Relationship between IIB and Computing Platforms ......................................... 135 Relationship between IM and Computing Platforms .......................................... 136 Relationship between Age and Computing Platforms ........................................ 137 Relationships between Revenue, Team Size, and Computing Platforms ........... 138 Relationships between Other Variables and Computing Platforms .................... 139

Recommendations ......................................................................................................140 Limitations .................................................................................................................145 Significance of the Study and Implications for Social Change .................................147 Concluding Statement ................................................................................................150

REFERENCES ................................................................................................................151

APPENDIX A: EMAIL ANNOUNCING RESEARCH STUDY ...................................172

APPENDIX B: INVITATION EMAIL TO SOLICIT SURVEY PARTICIPATION ...............................................................................................173

APPENDIX C: MULTIFACTOR LEADERSHIP QUESTONNAIRE RATER FORM (5X) SAMPLE QUESTIONS ..................................................................175

APPENDIX D: PERMISSION TO USE THE MLQ (RATER FORM 5X) INSTRUMENT ....................................................................................................176

APPENDIX E: DEMOGRAPHIC/COMPUTING PLATFORM SURVEY ITEMS ......177

APPENDIX F: THANK YOU EMAIL ...........................................................................182

CURRICULUM VITAE ..................................................................................................183

LIST OF TABLES

Table 1. OS Innovation Attributes Suggested by the Innovation and ERP Literature ..... 50 Table 2. Firm and Individual Participant Survey Responses by Computing Platform ..... 89 Table 3. Population and Sample Sizes for Firms by Computing Platform ....................... 89 Table 4. Transformational Leadership Items in the MLQ 5X Survey Instrument ............ 91 Table 5. Chi-square Tests for Normal Distribution of Mean Transformational Leadership

Scores by Computing Platform ................................................................................. 91 Table 6. Operating System Innovation Attribute Ratings by Relative Importance .......... 96 Table 7. Operating System Perceived Innovativeness by Evaluation Method ................. 97 Table 8. Research Study’s Internal Consistency: Cronbach’s Alpha ............................... 98 Table 9. Descriptive Statistics by Transformational Leadership Subscale ....................... 99 Table 10. Transformational Leadership Grand Mean Scores for Subscales by Operating

System ..................................................................................................................... 100 Table 11. One-Way ANOVA: Transformational Leadership Mean Scores by Subscale 102 Table 12. Transformational Leadership Variances by Operating System ...................... 103 Table 13. Kruskal-Wallis Transformational Leadership Results: Hypothesis 1............. 103 Table 14. Intellectual Stimulation Subscale Variances by Operating System ................ 105 Table 15. Kruskal-Wallis Intellectual Stimulation Results: Hypothesis 2 ..................... 105 Table 16. Individualized Consideration Subscale Variances by Operating System ....... 107 Table 17. Kruskal-Wallis Individualized Consideration Results: Hypothesis 3............. 107 Table 18. Idealized Influence Attributed Subscale Variances by Operating System ..... 109 Table 19. Kruskal-Wallis Idealized Influence Attributed Results: Hypothesis 4 ........... 109 Table 20. Idealized Influence Behavior Subscale Variances by Operating System ....... 111 Table 21. Kruskal-Wallis Idealized Influence Behavior Results: Hypothesis 5 ............. 111 Table 22. Inspirational Motivation Subscale Variances by Operating System .............. 113 Table 23. Kruskal-Wallis Inspirational Motivation Results: Hypothesis 6 .................... 113 Table 24. Descriptive Statistics for Time-Related Items and Measure of Computing

Platform Innovation ................................................................................................ 115 Table 25. Chi-square Testing for Independence by SAP Basis Professionals' Mean Age

and Operating System ............................................................................................. 116 Table 26. Chi-square Testing for Independence by Years of Experience and Operating

System ..................................................................................................................... 117 Table 27. Chi-square Testing for Independence by Years with Firm and Operating

System ..................................................................................................................... 117 Table 28. Chi-square Testing for Independence by SAP Basis Team Size and Operating

System ..................................................................................................................... 118 Table 29. Chi-square Testing for Independence by a Firm’s Annual Revenue and OS . 118 Table 30. Annual Firm Revenue per Employee by Operating System (Billions USD) . 119 Table 31. Results of t test Assuming Unequal Variance: Transformational Leadership

Mean Scores of CIOs who Inherited vs Influenced their Computing Platform Selection .................................................................................................................. 121

vii

Table 32. Results of t test Assuming Unequal Variance: Transformational Leadership Mean Scores of CIOs Responsible for Insourced vs Outsourced Computing Platforms ................................................................................................................. 122

Table 33. Results of t test Assuming Unequal Variance: Transformational Leadership Mean Scores of CIOs who Employ Innovation Sponsors vs no Innovation Sponsors ................................................................................................................................. 123

Table 34. Results of t test Assuming Unequal Variance: Transformational Leadership Mean Scores of CIOs Leading SAP Basis Teams with a Track Record of Innovation vs no Track Record of Innovation .......................................................................... 125

Table 35. Results of t test Assuming Unequal Variance: Transformational Leadership Mean Scores of CIOs Leading Teams that Employed a SAP Knowledge Management System vs no SAP Knowledge Management System ....................... 126

viii

LIST OF FIGURES

Figure 1. Theoretical framework describing leadership’s effect on innovation ................. 5 Figure 2. Visual map of the literature ............................................................................... 25 Figure 3. Bass and Avolio’s Transformational Leadership model ................................... 29 Figure 4. The ERP computing platform ............................................................................ 42 Figure 5. The ERP hybrid computing platform ................................................................ 44 Figure 6. Suggested by the ERP literature: The OS innovation continuum ..................... 46 Figure 7. Research design and conceptual model describing how CIO transformational

leadership may predict computing platform operating system outcomes ................. 67 Figure 8. Numbers and percentages of respondent firms categorized by industry segment

................................................................................................................................... 90 Figure 9. Mean CIO transformational leadership scores for CIOs responsible for UNIX-

based SAP ERP computing platforms ...................................................................... 92 Figure 10. Mean CIO transformational leadership scores by operating system ............... 93 Figure 11. Individual respondent survey responses by date ............................................. 94 Figure 12. Suggested by the study’s findings: Relative position of computing platform

OSs in terms of perceived innovation ....................................................................... 95 Figure 13. Respondent ratings of OS innovation attributes .............................................. 95 Figure 14. Confidence intervals for group means: Transformational leadership mean

scores by operating system ..................................................................................... 101 Figure 15. Confidence intervals for group means: Intellectual Stimulation subscale mean

scores by operating system ..................................................................................... 104 Figure 16. Confidence intervals for group means: Individualized Consideration subscale

mean scores by operating system ............................................................................ 106 Figure 17. Confidence intervals for group means: Idealized Influence Attributed subscale

mean scores by operating system ............................................................................ 108 Figure 18. Confidence intervals for group means: Idealized Influence Behavior subscale

mean scores by operating system ............................................................................ 110 Figure 19. Confidence intervals for group means: Inspirational Motivation subscale mean

scores by operating system ..................................................................................... 112 Figure 20. Possible near-term evolution of the operating system innovation continuum for

SAP ERP ................................................................................................................. 143

CHAPTER 1: INTRODUCTION TO THE STUDY

Introduction

Organizations are compelled to innovate lest they fade into a background of

mediocrity and compromise or disappear altogether (Anderson, 2004). For business

application owners and information technology (IT) leaders responsible for Enterprise

Resource Planning (ERP) systems, this threat is particularly true as the viability and

longevity of entire companies lies in the balance (Chang, 2004). ERP systems represent

the most critical business applications a firm deploys (Schubert, 2007; Umble & Umble,

2002). Unlike familiar office desktop applications including word processing and Internet

browsing tools commonly used by individuals to perform individual work (Peeling &

Stachell, 2001), ERP systems are used regularly by individuals to run an entire firm’s

financial systems, execute company-wide strategic logistics analyses, and examine

enterprise-wide sales and distribution system effectiveness. It is this company-wide scope

of ERP systems that makes them imperative to firm longevity (Anderson et al., 2009).

For the same reason, ERP systems are more critical than company-internal web sites,

database repositories, email systems, and other large computing systems or applications

(Berinato, 2001). Broadly adopted to replace collections of older, less-integrated, and

less-capable business applications (Parr & Shanks, 2000), ERP represents the top of the

business computer software hierarchy (Hoffman, 2008).

With millions of dollars and hundreds of thousands of work hours at stake

(Anderson, 2003), ERP business applications like those from market-leading software

vendor SAP require effective technical implementation to fully deliver on their intended

value (Nah, Zuckweiler, & Lau, 2003; Umble & Umble, 2002). Research revealed that

these complex change-enabling ERP systems often fail to live up to their potential,

succumbing to multimillion dollar cost overruns in the implementation process

(Kimberling, 2006; Parr & Shanks, 2000; Umble, Haft, & Umble, 2003). Research cited

ineffective IT executive leadership as one cause of failure (Chang, 2004; Gunson & de

Blasis, 2002; Schneider, 1999), suggesting a positive relationship between executive-

level leadership and successful leader-follower relationships is critical to ERP

deployment success. The literature further showed a relationship between

transformational leadership and follower creativity or innovation (Bass & Steidlmeier,

1998; Howell & Avolio, 1993). Transformational leadership has also been shown to be a

critical success factor in large-scale IT projects in general and a key factor affecting

organizational innovation (Nah et al., 2003).

Innovation has been shown to be directly related to IS, one of several

transformational leadership traits. Leaders who intellectually stimulate their followers

value intelligence, encourage them to rethink conventions, and promote rational problem

solving and new ideas (Moore & Benbasat, 1991; Lievens, Van Geit, & Coetsier, 1997).

Intellectually-stimulating leaders also encourage creativity and risk taking, stimulating

followers to innovate (Bass & Avolio, 1994). Such innovation has further been shown to

affect business performance as well. Computing platform technical innovations can

increase the platform’s ability to quickly implement business application changes which

in turn enable greater business agility and responsiveness (Anderson et al., 2008; SAP,

2005). However, a gap exists in the literature in that there is very little theoretical or

empirical data describing to what extent executive leader-derived transformational

leadership relates to computing platform innovation as evidenced by the operating system

(OS) underpinning business-critical software applications such as ERP systems.

The following sections contain a description of the problem statement, the nature

and purpose of the study, and a review of the study’s theoretical underpinnings. After a

review of operational definitions, the limitations and boundary conditions, assumptions,

and potential research project weaknesses are identified. Chapter 1 concludes with the

significance of this research project and how the study addresses several related gaps in

the literature and promotes positive wide-spread social change.

Problem Statement

IT organizations are tasked with innovatively supporting ERP and similar critical

business applications central to a firm’s livelihood. As the executive responsible for IT

decisions, the Chief Information Officer (CIO) role is the most critical technology

leadership position of influence and essential to organizational longevity (Byrnes, 2005;

Sebastian, 2007). The problem that this study addresses is the lack of understanding

regarding how CIO transformational leadership behaviors may influence the technology

platform selected for business-critical ERP systems. Innovative technology platforms

enable business system agility, simplify computing platform administration, reduce the

costs of IT, and provide IT organizations with the flexibility and speed they require to

respond quickly to changing business needs (SAP, 2005; Tallon, 2003), which in turn

positively affect a firm’s ability to compete in the market. Conversely, less innovative

technology platforms provide less business system agility and therefore less opportunity

to compete effectively in the market.

Operating systems (OSs) represent the primary technology component affecting

computing platform innovation (Anderson et al., 2009). Though less mature and riskier to

deploy, more innovative OSs offer technical and cost advantages as well as

administration benefits that can translate to business advantages (Dedrick & West, 2003).

CIOs who encourage technology innovation and technical risk-taking can positively

affect their organizations by enabling more agile business systems. In particular, CIOs

who practice strong transformational leadership behaviors may stimulate and promote IT

innovation without compromising organizational adaptability, financial performance,

information systems data integrity, or business longevity (Rusaw, 2001).

This study explored the relationship between the transformational leadership

behaviors ascribed to a firm’s CIO and the computing platform’s operating system

selected by the CIO’s team for the firm’s ERP business application or system. The

study’s independent variable was transformational leadership, and the dependent variable

was the OS selected for a firm’s ERP computing platform. Transformational leadership

“has been associated in [the] research with organizational effectiveness” (Katz &

Salaway, 2004, p. 13). The researcher speculated that CIOs who practice low

transformational leadership create a less effective environment in which innovation is

stifled and therefore less innovative computing platforms are deployed. Such leaders fail

to recognize technology’s power to propel the business forward (SAP, 2005). In the

absence of innovative business application computing platforms, these CIOs limit their

teams’ potential to reduce costs and increase revenue. Conversely, it was theorized that

innovation-inspiring CIOs create an IT environment in which business needs are better

addressed through the purposeful deployment of innovative computing platforms, thereby

providing a more agile and successful business application foundation.

This study analyzed the relationship between transformational leadership theory

and the operating system deployed by a firm for its ERP system. Adapted for this study,

Elenkov, Judge, and Wright’s (2005) theoretical framework (see Figure 1) describes the

influence of strategic or executive-level transformational leadership on innovation,

specifically administrative innovations such as new business applications. Within this

framework, the study employed a widely-held theoretical model (Bass & Avolio, 1995)

describing transformational leadership by way of several subcomponents to predict ERP

operating system outcomes. Whether the ERP system was perceived as successful was

irrelevant; the researcher sought to understand only to what extent a CIO’s

transformational leadership behaviors relate to which computing platform operating

system was selected for the firm’s ERP business system.

Figure 1. Theoretical framework describing leadership’s effect on innovation.

Nature of the Study

The processes of change, innovation, and survival are difficult as people and

organizations attempt to adapt to changing priorities, conditions, environmental factors,

competition, and more (Conger & Kanungo, 1999). An executive IT leader exhibiting

high transformational leadership may develop a culture in which innovation is the norm,

enabling the organization to successfully meet the needs of business and subsequently

grow and thrive (SAP, 2005). In the absence of innovative computing platform operating

systems, companies will be constrained in terms of how quickly and to what extent the IT

organization can support or enable changing business requirements (Anderson et al.,

2008; Barbacci, 2003; Rapoza, 2007; Riske, 2006).

Many factors converge to provide opportunities for business application

innovation. Computing platforms implemented for ERP business applications are

particularly subject to innovation benefits and limitations by virtue of the platform’s role,

scope, and footprint relative to enabling and supporting company-wide business. Due to

its significant up-front investment, an ERP computing platform cannot be converted or

replaced overnight. A firm is essentially held captive by its large-scale ERP computing

platform decision for 3 to 5 years or more (Anderson et al., 2008; Fichman, 2004). In the

meantime, the business impact germane to less innovative computing platforms can be

dramatic. Without the ability to deftly meet new business requirements, companies

unable to innovate through new market strategies or go-to-market offerings or unable to

provide financial and supply chain visibility to business leaders via well-integrated IT-

enabled business processes may be superseded by nimbler competitors. A lack of IT

innovation promulgates a firm’s inability to pursue business innovation (Anderson et al.,

2008; Dedrick & West, 2003; Karimi, Somers, & Bhattacherjee, 2007; Katz & Salaway,

2004; Teo & King, 1997).

Research has been conducted regarding leadership, the impact of IT, and the

effects of innovation. How and to what extent these factors relate to one another,

however, has been given only brief attention in the literature (Anderson, 2003; Howell &

Avolio, 1993; Howell & Hall-Marenda, 1999). Literature exploring how transformational

leadership and innovation in the context of an IT organization’s ERP computing platform

relate to one another is more limited (Anderson et al., 2008; Nah et al., 2003).

Though innovation has been linked to IT leadership, the degree to which IT

organizational leadership affects IT innovation, and under what conditions, remains

unclear. There has been little academic research completed on the effect of CIO

leadership behaviors with regard to technology innovation (Seddon, Walker, Reynolds, &

Willcocks, 2008). However, in a recent CIO leadership study, Katz and Salaway (2004)

found that “organizations with transformational leaders usually have organizations with

stronger innovation climates” (p. 14). More than half of theses CIOs exhibited high

transformational leadership scores, giving credence to the study of a firm’s IT executive

leader.

Measuring transformational leadership behaviors of CIOs rather than less senior

leaders was appropriate for the study because the CIO organizational role both directly

and indirectly affects all major IT decisions (Katz & Salaway, 2004). The CIO sets the

strategic direction pursued by the IT team and weighs in on the relative importance of

various computing platform characteristics (Seddon et al., 2008). In their executive role,

CIOs innately influence and encourage the degree of technical innovation and risk taking

practiced by the CIO’s technical staff who in turn deploy technology systems and make

other decisions affecting the firm’s technologies and ultimately its business agility.

The firm’s OS component of the overall computing platform was the focus of this

study for two reasons. First, the OS is the foundational component of an IT computing

platform (Frankel, 2005). Second, it is the OS rather than hardware and other technology

components through which innovation is most frequently enabled (Dedrick & West,

2003; Hunt & Brubacher, 1999). ERP software vendors such as SAP support five

possible outcomes with regard to computing platform operating systems. Such platforms

include Legacy/Mainframe, UNIX, Windows, Linux, and hybrids (the latter of which is a

mixed outcome reflecting a combination of two or more operating systems). The first two

outcomes are described as less innovative than the latter three outcomes (Anderson et al.,

2009, pp. 76-77), and are detailed further in chapter 2.

The transformational leadership-computing platform OS relationship has been

illuminated in the study through survey research and analyzed via descriptive and

inferential statistics. To assess transformational leadership, Bass and Avolio’s (1995)

Multifactor Leadership Questionnaire (MLQ) was used. Operating system outcomes and

other salient demographic details were collected by way of 17 survey items appended to

the MLQ. Details related to the research methodology may be found in chapter 3.

Purpose of the Study

The purpose of this quantitative study was to relate transformational leadership

behaviors attributed to a firm’s CIO technology leader to the computing platform

operating system selected for the firm’s ERP business application. It was theorized that

CIOs exhibiting greater transformational leadership would presumably encourage

followers to deploy more progressive, business-enabling, and innovative ERP computing

platforms. These systems may in turn enable IT teams to more effectively address

changing business priorities, better align technology investments with business outcomes,

and in the process have a positive effect on the business’s ability to compete in the

marketplace. On the other hand, it was theorized that CIOs exhibiting lower

transformational leadership may cultivate team environments comprised of followers

who are more risk-averse, conservative, and prone to deploying safe or mature operating

systems for the firm’s ERP systems, possibly sacrificing business agility and the

opportunity for reduced technology and personnel staffing costs as a result.

Research Questions

1. How does the strength of transformational leadership behaviors (Bass &

Avolio, 1995) of CIOs relate to a firm’s ERP business system’s computing platform

operating system outcomes?

2. How does the strength of CIO intellectual stimulation (IS) behaviors, a

component of transformational leadership (Bass & Avolio, 1995), relate to a firm’s ERP

business system’s computing platform operating system outcomes?

3. How does the strength of CIO individualized consideration (IC) behaviors,

a component of transformational leadership (Bass & Avolio, 1995), relate to a firm’s

ERP business system’s computing platform operating system outcomes?

4. How does the strength of CIO idealized influence attributed (IIA)

behaviors, a component of transformational leadership (Bass & Avolio, 1995), relate to a

firm’s ERP business system’s computing platform operating system outcomes?

5. How does the strength of CIO idealized influence attributed (IIB)

behaviors, a component of transformational leadership (Bass & Avolio, 1995), relate to a

firm’s ERP business system’s computing platform operating system outcomes?

6. How does the strength of CIO inspirational motivation (IM) behaviors, a

component of transformational leadership (Bass & Avolio, 1995), relate to a firm’s ERP

business system’s computing platform operating system outcomes?

Hypotheses

Null Hypothesis 1 (H0): Transformational leadership of CIOs is not associated or

is negatively associated with the operating system selected for a firm’s ERP business

system.

Alternative Hypothesis 1 (H1): Higher transformational leadership of CIOs is

positively associated with the OS selected for a firm’s ERP business system.

H0: µt∞i ≤ 0

H1: µt∞i > 0

For hypothesis 1, µt∞i is the relationship between CIO transformational leadership

and the firm’s ERP OS.

Null Hypothesis 2 (H0): IS of CIOs is not associated or is negatively associated

with the operating system selected for a firm’s ERP business system.

Alternative Hypothesis 2 (H1): Higher IS of CIOs is positively associated with the

operating system selected for a firm’s ERP business system.

H0: µt∞i ≤ 0

H1: µt∞i > 0

For hypothesis 2, µt∞i is the relationship between CIO IS and the firm’s ERP OS.

Null Hypothesis 3 (H0): IC of CIOs is not associated or is negatively associated

with the operating system selected for a firm’s ERP business system.

Alternative Hypothesis 3 (H1): Higher IC of CIOs is associated with the operating

system selected for a firm’s ERP business system.

H0: µt∞i ≤ 0

H1: µt∞i > 0

For hypothesis 3, µt∞i is the relationship between CIO IC and the firm’s ERP OS.

Null Hypothesis 4 (H0): IIA of CIOs is not associated or is negatively associated

with the operating system selected for a firm’s ERP business system.

Alternative Hypothesis 4 (H1): Higher IIA of CIOs is positively associated with

the operating system selected for a firm’s ERP business system.

H0: µt∞i ≤ 0

H1: µt∞i > 0

For hypothesis 4, µt∞i is the relationship between CIO IIA and the firm’s ERP OS.

Null Hypothesis 5 (H0): IIB of CIOs is not associated or is negatively associated

with the operating system selected for a firm’s ERP business system.

Alternative Hypothesis 5 (H1): Higher IIB of CIOs is positively associated with

the operating system selected for a firm’s ERP business system.

H0: µt∞i ≤ 0

H1: µt∞i > 0

For hypothesis 5, µt∞i is the relationship between CIO IIB and the firm’s ERP OS.

Null Hypothesis 6 (H0): IM of CIOs is not associated or is negatively associated

with the operating system selected for a firm’s ERP business system.

Alternative Hypothesis 6 (H1): Higher IM of CIOs is positively associated with

the operating system selected for a firm’s ERP business system.

H0: µt∞i ≤ 0

H1: µt∞i > 0

For hypothesis 6, µt∞i is the relationship between CIO IM and the firm’s ERP OS.

Theoretical Grounding

Elenkov, Judge, and Wright (2005) described a theoretical framework

incorporating the influence of strategic or executive-level leadership on technology

innovation. Transformational leadership theory (Bass, 1985; Burns, 1978) includes the

breadth of strategic leadership behaviors required by this framework. In particular,

transformational leadership encompasses the leadership components most germane to the

study: IM or the ability to cast a vision, communication, IC, and IS (Elenkov et al., 2005).

With respect to IS, leaders who intellectually stimulate their teams promote creativity and

innovation, encouraging their followers to take calculated risks (Bass & Steidlmeier,

1998; Howell & Avolio, 1993).

From a technology innovation perspective, risk taking is contrary to deploying

conservative business-critical ERP software systems, where business application

availability is essential to maintaining company-wide business operations (Anderson,

2003). Conservative computing platforms are commonplace despite limited findings

concluding that computing platform innovation may play a role in realizing successful

ERP implementation (Nah et al., 2003; Murray & Coffin, 2001), enabling greater

revenue-enhancing business agility (Anderson et al., 2009), and decreasing the cost of

technology and staff necessary to deploy and operate business applications (Anderson et

al., 2009; Chang, 2004).

More so than computer hardware and database software, the computing platform

component capable of delivering the greatest level of innovation is the operating system

(Apte, 2008; Irvine, 1997; Newman, 1998; Riske, 2006; Wheeler, 2006). With some

exceptions, the hardware underpinning the operating system provides less differentiation

and therefore less opportunity for innovation (Anderson et al., 2009). Similarly,

computing platform database software offers less opportunity for innovation given that

business applications provided from software vendors such as SAP abstract the database

layer, eliminating much of the database software’s potential for realized innovation

(Anderson, 2003).

Operational Definitions

In the context of this research study, several broad areas of the literature spanning

transformational leadership, innovation, computing platforms, and ERP business

applications have been brought together. The following operational definitions are used:

Application compatibility: Attribute of an operating system that more quickly or

completely supports new business application servers, database servers, or both (Dedrick

& West, 2003)

Availability: The ability of an OS to help create a highly available computing

platform that remains up and accessible to end users (Anderson, 2003).

Benefit: The degree to which the deployment or ability to support a particular

computing platform, practice, or process is beneficial to a worker’s job, role, or task

(Moore & Benbasat, 1991).

Business applications: The software programs employed by large business entities

to support accounting, finance, manufacturing, inventory control, human resources

management, payroll, and similar activities necessary to run a business (Chang, 2004).

ERP systems are one type of business application. Others include Customer Relationship

Management, Product Lifecycle Management, Supply Chain Management, and more.

Charisma: A transformational leadership trait that comprises idealized influence

(attributed and behavior), and inspirational motivation (Bass & Avolio, 1995).

Computing platform: The combination of operating system, hardware, and

database software technology elements upon which a business software application or

program runs (Dedrick & West, 2003).

Diffusion: The process for sharing innovations with others (Rogers, 2003).

Early adopters: Information technology (IT) organizations that have deployed

what are perceived as new or innovative technologies or platforms (Rogers, 2003).

Enterprise resource planning: Business application software that enables a

company to manage the efficient and effective use of its materials, human resources,

financial assets, and other resources (Nah et al., 2003).

Hybrid: A mixed computing platform reflecting a combination of two or more

different operating systems (Anderson et al., 2008).

Idealized influence attributed: A transformational leadership (Bass & Avolio,

1995) attribute of leaders who instill pride in being associated with the leader and the

leader’s organization (Avolio & Bass, 2004).

Idealized influence behavior: A transformational leadership (Bass & Avolio,

1995) behavior focused on promoting shared values, beliefs, and ethical decision-making

(Avolio & Bass, 2004).

Individualized consideration: A transformational leadership (Bass & Avolio,

1995) attribute whereby (through mentoring and developing the follower’s strengths) the

leader tends to a follower’s individual desire to grow and contribute (Avolio & Bass,

2004).

Innovation: A technology, approach, practice, or computing platform component

or dimension that is perceived as new or unique in a manner deemed potentially positive

or beneficial (Rogers, 2003).

Inspirational motivation: A transformational leadership attribute whereby leaders

encourage followers and develop team spirit by describing a possible future state or

vision (Avolio & Bass, 2004). Inspirational motivation is synonymous with visionary

leadership (Elenkov et al., 2005).

Integrated innovation: Incorporating or layering in innovative features from

competing products to create a new product boasting features and benefits greater than

the sum of its parts (Microsoft, 2004).

Intellectual stimulation: A transformational leadership attribute that values

intelligence, encourages followers to re-examine conventions and assumptions and seek

new perspectives, and promotes rational problem solving and assumptions (Avolio &

Bass, 2004; Lievens et al., 1997).

Open systems: Computing platforms based on ubiquitous standards (Zhu et al.,

2006) or a Linux operating system variant (West & Dedrick, 2001).

Operating system: Software that manages, automates, and extends a computer’s

hardware resources, making these resources available to one or more applications

(Auslander, Larkin, & Scherr, 1981).

Operating system outcomes: One of five possible computing platform selections

upon which ERP systems are installed and run. These include Legacy/Mainframe, UNIX,

Windows, Linux, and hybrids (SAP, 2005).

Organizational endurance: The ability of an operating system or computing

platform to provide IT organizations with a choice, acting as a successfully introduced

change agent (Tallon, 2003).

Population: “The set of all measurements in which the investigator is interested”

(Aczel & Sounderpandian, 2002, p. 25).

Portability: The ability of an OS to execute on different hardware platforms

(Wheeler, 2006).

Sample: A subset of measurements collected from the population reflecting all

interested measurements (Aczel & Sounderpandian, 2002).

SAP AG: A multinational German software company competing primarily against

Oracle Corporation and Microsoft Corporation in the business applications packaged

software market (Anderson et al., 2008). Note that AG is the German equivalent of

Incorporated.

SAP basis: The combination of SAP infrastructure and system administration

skills used to technically deploy and manage an SAP computing platform (Anderson,

2003).

SAP basis team: A subset of a firm’s IT organization tasked with deploying and

supporting an SAP computing platform from a technical installation, support, and

administration perspective (Anderson, 2003).

SAP ERP: A business application provided by software vendor SAP which

provides support for managing materials, sales, warehouses, financials, logistics, and

similar firm-wide business functions (Nah et al., 2003).

SAP ERP computing platform: The combination of hardware, one or more

operating systems, and database software necessary to run an ERP business application

(Anderson et al., 2008).

Subsample: A portion of a larger sample (Aczel & Sounderpandian, 2002).

Technical flexibility: The ability of an OS to be easily repurposed, changed, or

integrated to facilitate change or support new technologies (Anderson, 2003).

Technology stack: The successive technical layers of a computing platform

comprised of hardware, an operating system, database software, integration software

(middleware), and business application software. A technology stack is broader than a

computing platform, as the technology stack includes the business application’s technical

components as well as the computing platform technology stacks (Anderson, 2003).

Transformational leadership: A leadership style characterized by leaders and

followers who raise “one another to higher levels of motivation and morality” (Burns,

1978, p. 20), where leaders change followers’ priorities to reflect a team orientation more

than an individual orientation (Avolio & Bass, 2004).

Visionary leadership: Style of leadership synonymous with inspirational

motivation (Elenkov et al., 2005).

Assumptions, Limitations, Scope, and Delimitations

To enable other researchers the ability to replicate, expand, or otherwise leverage

this study’s methodology and findings, a number of assumptions, weaknesses, and

bounds have been explored. It was assumed that only participants aged 18 years or older

with at least 1 year of experience at the firm being assessed have been included in this

research project and that any North American ERP implementations was studied

regardless of whether the implementation might be perceived a success or failure. It was

assumed that SAP Basis professionals working in their respective IT organizations are

capable of describing their perceptions of their CIO’s transformational leadership

behaviors, that an adequate subsample sizes allowed analyses within and between

dependent variable outcomes, that the number of survey items would not greatly

discourage participation, and that linear relations have been assumed between IS, IC, IIA,

IIB, and IM, respectively, to the five possible computing platform operating system

outcomes relevant to ERP business systems.

Research Study Weaknesses and Limitations

Several research study weaknesses and limitations exist. ERP business application

computing platforms may be perceived as innovative due to factors other than the

deployed computing platform operating system (Anderson et al., 2009). Innovative

technology configuration and deployment practices, specific computing hardware

capabilities, and other descriptive dimensions unrelated to the operating system may

constitute actual or perceived innovation. Additional survey method limitations also

apply to this study, including the following: (a) volunteer respondents, (b) unknown

respondent motivation towards completing the survey, (c) respondents who may provide

socially desirable responses, (d) unknown demographic survey item validity, and (e)

unknown respondent reliability with regard to their responses. Finally, the research

design drew exclusively on demographic data found in Hewlett-Packard’s SAP-specific

Customer Solutions Database. This data may or may not approximate a normal

distribution with regard to the universe of SAP implementations.

Scope and Bounds of the Study

Software vendor SAP AG’s primary business application was selected for this

study because (a) SAP AG has been the ERP business application software market leader

for more than a decade (Anderson et al., 2008; Evelson & Hamerman, 2007; SAP, 2006),

and (b) ERP systems are among the most critical of a firm’s business applications

(Berinato, 2001). This research project was therefore bounded in that it only included

firms that had deployed SAP’s ERP systems. Similar ERP solutions from Oracle,

Microsoft, and other business application software vendors were excluded from

consideration, as were the populations of non-ERP business applications. This study was

also bounded by the perception surrounding, and operational definition of, technical

innovation which is difficult to describe and quantify. Finally, only Hewlett-Packard

Company’s customers or prospects running software vendor SAP AG’s ERP business

application were surveyed.

Significance of the Study, Gaps in the Literature, and Social Implications

The researcher sought to identify how the strength of CIO transformational

leadership behaviors might predict the operating system selected for a firm’s ERP

system. Research has been conducted to assess how executive-level leadership impacts

innovation (Finkelstein & Hambrick, 1996; Quinn, 1985). Leadership’s effect on

innovation has also been studied (Halbesleben, Novicevic, Harvey, & Buckley, 2003;

Sharma & Rai, 2003; West et al., 2003). However, several gaps exist in the literature.

First, ERP systems have not been studied from this perspective despite the fact that a

primary source of organizational innovation rests in the deployment of “new and more

efficient administrative mechanisms [including] new systems for strategic planning and

control” (Elenkov et al., 2005, p. 669). Second, much of the existing research focused on

general leadership behaviors or attributes rather than on specific roles (Antonakis &

House, 2002; Sebastian & Korrapati, 2007; Yukl, 2002). As the executive responsible for

IT, and given the ongoing debate of this role’s impact in large organizations (Byrnes,

2005; Iyengar, 2007), a critical examination of the intersection of CIO leadership and

innovation outcomes serves to narrow this gap. Third, while a CIO’s effectiveness has

been shown to be dependant on their leadership and particularly their ability to cast a

vision (Earl, 2004), how CIO leadership behaviors affect ERP technology innovation

outcomes is unclear (Elenkov et al.). Finally, with regard to transformational leadership,

the literature has focused more extensively on assessing lower-level managers rather than

executive leaders (Antonakis & House; Katz & Salaway, 2004; Yukl). Assessing the

CIO’s degree of transformational leadership assists in narrowing this gap as well.

In addressing the aforementioned gaps in the leadership and innovation literature,

the research study’s application and overall business will be of significance to CIOs and

other executive leaders who exercise transformational leadership behaviors with their

technical team. Such CIOs may foster an innovative working environment where (a)

technology enables businesses to compete better, (b) technology risk-taking is more

culturally acceptable, (c) ongoing technology costs are lower, and (d) particular IT

staffing models are purposely pursued or avoided, any of which may equate to significant

cost savings and competitive advantage on behalf of the firm. In this way, this study

should assist executives in creating a more rewarding workplace for IT employees while

facilitating improved business agility and company-wide business performance.

Implications for positive social change are broad-based. Key implications include

uncovering knowledge useful to executive leadership tasked with implementing

transformational change or ensuring organizational longevity, for IT leaders concerned

with fostering workplace innovation and creating a satisfying work environment, and for

IT organizations tasked with better meeting changing business needs. Transformational

leadership in the hands of an informed CIO holds the potential to influence a firm’s

business performance through increased technology innovation and subsequent business

flexibility, increased profitability, decreased technology and business-related costs,

decreased employee hiring costs in light of greater retention, greater employee fulfillment

and productivity, and ultimately greater organizational longevity.

This study helps narrow the gap between current CIO leadership practices and

those that may transform an organization for the positive benefit of society. Through

analyzing the research study’s outcomes, the ability of a firm to deliver goods and

services more efficiently to the benefit of its myriad stakeholders is enhanced. Lessons

learned through this study will also prove useful in broader domains where leadership

and technology intersect, creating agents of social change and enabling increased

business performance well beyond the bounds of this study. Impact of this magnitude

made the study worthy of conducting as its outcomes hold the promise of positively

serving individuals, IT organizations, businesses, and society.

Summary

There was significant value in conducting the research study, primarily due to the

statistical relationship found between high CIO transformational leadership and greater

IT innovation as evidenced in the computing platform OS deployed for ERP. This study

showed a positive relationship between CIOs who practice transformational leadership

and the operating systems deployed by their technical teams. Developing CIOs to hone

their transformational leadership skills could therefore enable both IT organizations and

the business applications supported by IT to be more effective, creating a better

workplace and subsequently higher employee retention rates while enabling improved

business performance in the process.

In chapter 2 of this dissertation, the leadership and IT innovation literature are

critically reviewed, including how these bodies intersect with the ERP and computing

platform literature. Chapter 3 details this study’s research design and methodology,

followed by instrumentation details, study variables, and data collection and analysis

procedures. Chapter 4 highlights this study’s results while chapter 5 draws conclusions

and suggests new research gaps worthy of further examination.

CHAPTER 2: LITERATURE REVIEW

Introduction

Chapter 2 includes a review of the literature corresponding to several relevant

content areas of this research study, the study’s design and methodology, and the study’s

variables. Each of these discussions constitutes respective sections of chapter 2. After

examining the general leadership literature and its evolution towards transformational

leadership (Bass & Avolio, 1995; Burns, 1978; Downton, 1974), the innovation literature

is presented in review. A review of the computing platform literature follows and a

subsequent synthesis of the transformational leadership, innovation, and computing

platform technology literature is also included. The next section of the literature review

includes the gaps identified by the researcher at the intersection of the aforementioned

three bodies of literature along with several other relevant findings and

counterarguments. A critical review of the research variables, research questions, and the

method of study, followed by how the study addresses important gaps in the literature

while effecting positive social change, concludes the literature review. Figure 2 is a map

of the literature intersecting leadership, innovation, computing platforms, and Enterprise

Resource Planning (business software application) systems.

Figure 2. Visual map of the literature.

To ensure a comprehensive and fair literature review, several search approaches

were employed. The EBSCO and ProQuest electronic databases available through the

Walden library and several other university and local libraries were used to locate current

peer-reviewed journals and additional scholarly materials. Information and research

studies related to keywords such as transformational leadership, intellectual stimulation,

ERP, computing platforms, and administrative systems innovation in general were

collected. Other keywords including executive leadership, full range leadership,

visionary leadership, administration innovation, IT innovation, IT infrastructure,

computing platform, information systems innovation, innovative IT architecture, SAP

architecture, SAP implementation, technology stack, ERP, MRP, MRP II, diffusion, and

CIO afforded greater breadth to the review.

In limited cases, popular or professional literature proved useful, particularly with

regard to software marketshare data and innovation perceptions. In example, data from

industry-recognized institutions, including Gartner Group, AMR, Forrester Research, and

Ventana Research, to support ERP platform positioning with respect to innovation and

marketshare perspectives were collected. Finally, software vendor SAP AG also provided

data useful in supporting population and characteristics assumptions.

Foundations in Leadership Theory

In the last century, leadership theory and its effect on organizational change or

innovation have been studied extensively. The breadth of this literature may be divided

into industrial and postindustrial leadership theories (Vinger, 2005). The evolution to

postindustrial leadership resulted from several factors. First, researchers began to view

leadership as a relationship between people rather than as an attribute held or developed

by an individual (Shriberg, Shriberg, & Lloyd, 2002). This perspective represented a

departure from traditional trait, personal characteristics, or great man theories in which

leaders were deemed born and not made. It also differed from the concepts of task

orientation and leadership behaviors described by Fiedler (1967).

Second, leadership theory evolved postindustrially to recognize that leadership

was not confined to those with positional authority or authority based on a particular

organizational role. Leadership could be practiced effectively by anyone in any position

based on a set of practiced behaviors (Sarros, Cooper, & Santora, 2008). Finally,

leadership began to be viewed as a mechanism for introducing and managing change

rather than as a mechanism for simply managing people and resources. Among others,

Damanpour and Schneider (2006) found that executive and other senior leaders

influenced organizational outcomes by developing the aptitude for change and

innovation. Germinal theories based on leadership style, including autocratic, democratic,

and laissez-faire gave way to a leadership framework extended in the postindustrial

literature to include situational theories (Stogdill, 1974). First proposed in Downton

(1974), by the early 1980s these theories evolved to encompass transactional and

transformational leadership theories (Bass, 1985; Burns, 1978; Yukl, 2002) including

their effect on innovation. Pearce (2004) summarized this effect well when he wrote that

the “authoritarian control of knowledge workers can stifle the very innovation and

creativity that one desires from them” (p. 55). By the end of the 20th century, the

collective state of leadership theory reflected a distinct departure from previously held

industrial and administrative views (Fayol, 1916).

A synthesis of the postindustrial leadership and innovation literature underscores

the role of leaders as change managers or innovation leaders. Elenkov et al. (2005)

asserted that several genres of leadership styles influenced innovation outcomes,

particularly visionary leadership theory and full range leadership theory. Visionary

leadership was depicted as the conceptual cousin to transformational leadership and noted

as an organizational enabler for changing the status quo (Bennis & Nanus, 1985). Full

range leadership theory put forth by Avolio (1999) described the continuum of

transformational and transactional leadership behaviors postulated earlier by Barns

(1978) who proposed that the two sets of behaviors were mutually exclusive. Later

studies revealed transformational and transactional behaviors were frequently practiced

together and complemented one another (Bass, 1985; Howell & Avolio, 1993). Rost and

Smith (1992) recognized transformational change processes and emphasized specific

personality traits, characteristics, and behaviors shown to enable transformation. As early

as 1994, Brown conjectured that transformational leadership was most appropriate for a

technologically-focused rapidly changing society. Northouse (2001) described

transformational leaders as change agents. More recent literature has revealed a

relationship between transformational leadership, organizational innovation (Sarros et al.,

2008), and technology innovation (Anderson et al., 2009). Transformational leadership

not only represents the pinnacle of contemporary leadership theory (Goho, 2005) but

reflects the organizational leadership required by a culturally diverse and geographically

distributed workforce tasked with implementing technological innovation. As such,

transformational leadership was deemed the most appropriate leadership lens through

which to conduct this study.

Transformational Leadership

The leadership literature recurrently cites transformational leadership’s effect on

organizational outcomes (Avolio & Bass, 1999; Bass & Avolio, 1990a, 1990b, 1994). In

1978, Burns described a leadership style in which leaders looked beyond transactional

and economic needs to understand human motivational elements and subsequently help

followers transition to realize a higher level of need. Burns called this transforming

leadership. Bass (1985) and Yukl (2002) further developed Burns’s work and relabeled it

transformational leadership.

Yukl (2002) defined transformational leadership as a process used to bring

together and empower individuals to pursue an organization’s objectives.

Transformational leadership as initially described by Bass and Avolio (1995) comprised

several attributes and behaviors used by leaders to help followers realize their aspirations

while simultaneously changing their attitudes, aligning personal needs with change-

derived organizational goals, and developing a shared culture (see Figure 3).

Figure 3. Bass and Avolio’s Transformational Leadership model.

Bass and Avolio (2004) also took into account outcome measures assessing the

impact of leadership rather than the leadership style itself. This approach provided well-

balanced insight into a leader’s effectiveness despite the weighting and combination of

transformational and transactional traits (Bass & Avolio). The absence of leadership was

also measured as an outcome termed laissez-faire leadership. Research has shown laissez-

faire leadership, though an effective control variable (Elenkov et al., 2005), to be the least

effective and least satisfying to followers leadership style or behavior (Bass, 1990).

The Multifactor Leadership Questionnaire

As described by Bass and Avolio (1995), transformational leadership with regard

to the MLQ comprises several components, each of which relates to an MLQ subscale.

Bass’s (1985) earliest MLQ comprised four transformational factors: inspirational

leadership, IS, IC, and charisma. Ten years later, Bass and Avolio broadened the

charisma subscale and in the MLQ 5X identified five transformational components and

related subscales: IS, IC, IIA, IIB, and IM, the first of which was of most interest to this

study and detailed next.

Intellectual Stimulation

Despite Bass and Avolio’s (1995) transformational leadership subscale changes

over the years, the definition of IS has remained consistent. Leaders who give weight and

consideration to intelligence, knowledge, and rational thought are said to be intellectually

stimulating; such leaders encourage followers to deconstruct problems creatively and

then reconstruct new, innovative solutions (Bass & Avolio). These leaders practice

behaviors that value intelligence, encourage followers to rethink conventions, and

promote rational problem solving and new ideas (Lievens et al., 1997).

Given this study’s focus on innovation, the IS component of transformational

leadership was the most intriguing. Bass (1999) viewed IS as soliciting new ideas or

creative solutions to assist or encourage followers to become more creative and inventive.

Litwin and Stringer (1966) found that leaders who exhibited leadership behaviors

approximating IS realized the greatest sales, new product market entrants, and overall

innovations. IS has been found to influence subordinates’ perceptions of competence

thereby leading to greater individual performance (Bass, 1999). It was also found useful

in providing employees incentives for challenging existing assumptions, traditions, or

beliefs, encouraging followers to find new methods of solving current problems (Davies,

2004, p. 42). By seeking different perspectives relative to problem solving, it stimulated

individuals to change, promoted intelligence and rationality (Bass, 1990), and fostered

incremental change and widespread continuous improvement (Davies, p. 197).

Charisma: Idealized Influence and Inspirational Motivation

The transformational leadership literature examined charisma. Bass and Avolio

(1995) defined charisma as a combination of three components: IIA, IIB, and IM. While

these components are oftentimes bundled together in the literature to reflect the broader

charisma construct, Bass and Avolio’s MLQ divides charisma into its three constituent

components to simplify analyses.

A survey of several hundred firms that recently deployed ERP found charisma to

be the single most important leadership behavior responsible for improving

organizational cohesiveness and performance (Wang et al., 2005). Hofstede (1980) and

more recently Brain and Lewis (2004) identified charisma as the most critical

transformational leadership dimension. Bass (1985, 1990) found that followers of

charismatic leaders were more enthusiastic in regards to their work, had a greater sense of

mission or esprit de corp, and were generally more loyal. None of these findings;

however, were related to innovation or the empowerment to explore new ideas. An

exhaustive review of the literature could not substantiate charisma’s relationship to

innovation outside of the premise that effective leaders themselves must be innovative

(Cohen, 1990) and that there is a relationship, however small, between leaders who

exhibit greater charisma and those who achieve a higher level of IS (Bass, 1999).

Individualized Consideration

Like charisma, the relationship between the IC component of transformational

leadership and innovation is also lacking in the literature. While Bass (1999)

communicated a connection between IS and charisma, no similar connection between IS

and IC has been noted. IC also appears unconnected to innovation (Bass, p. 21). Instead,

the transformational leadership literature attributes IC to empathy, mentoring, and

coaching (Bass, 1990; Judge & Piccolo, 2004).

Intellectual Stimulation Synonymous with Transformational Leadership

Bass (1985) found that IS, IC, and charisma were key components to

transformational leadership. Analogous to inspiring creativity and innovation (Elenkov et

al., 2005), IS has also been shown synonymous with higher-order transforming leadership

in a subset of the literature (Grisham, 2006). Based on his research, Grisham concluded

that IS appears tantamount to transformation and, consequently, is the primary

component of transformational leadership. IS was noted to increase follower awareness

with regard to problems and present-day solutions. This focus on intellectually

stimulating followers was shown to encourage them to rethink new solutions to existing

problems (Yukl, 2002). In light of this relationship, intellectually stimulated innovation

and transformation were found to be interchangeable (Kimmel, 2000), underscoring how

well Bass and Avolio’s (1995) early transformational leadership work continues to apply

to contemporary IT projects and teams (Moon, 2007). Because organizational leadership

has been shown to be related to IT-based innovation (Bass & Steidlmeier, 1998; Boynton,

Zmud, & Jacobs, 1994; Elenkov et al., 2005), a detailed review of the innovation

literature is in order.

Innovation in Information Technology

Noting that innovation is only successful when used or diffused, Kanter (1988)

described innovation as the “creation and exploitation of new ideas” (p. 170). Rogers’s

(2003) conceptual framework outlined five characteristics of innovation found

throughout the innovation literature: compatibility, complexity, observability, relative

advantage, and trialability. These characteristics (and others added through the years)

continue to be measured by innovation researchers today. From an implementation

perspective, the literature revealed that innovation diffusion and adoption are different;

the former is unexceptional while the latter is much more difficult to accomplish

(Rogers). Researchers also found that the adoption of innovation constitutes a primary IT

implementation consideration regardless of the diverse theoretical constructs surrounding

innovation diffusion (Johnson & Rice, 1987). The notion of operationalizing or adopting

innovation is outlined next.

Operationalizing Innovation

Moore and Benbasat (1991) suggested that innovation comprises something new

introduced into an environment, whether a tool or instrument, a new approach or practice,

or new capabilities relative to a dimension of an existing technology or approach. To

bridge the gap between introducing something new and introducing authentic innovation;

however, they went on to share that true innovation holds the promise of positive or

beneficial change. Conversely, other researchers showed that innovation is the

introduction of change or the presence of creativity, whether beneficial or not (Davila,

Epstein, & Shelton, 2006). This gap in perspective defines disparate views of innovation.

Given these differences, conceptualizing innovation varies in the literature. Most

researchers concurred that innovation differs from invention. Beyond simple invention,

innovation introduces or applies invention to a situation in order to address a need

(Fagerberg, 2004). Innovation is also described as not only a newly invented idea, but

one that is diffused and adopted (Chesbrough, 2003). Luecke and Katz (2003) concluded

that innovation implied not just the introduction of a change to meet a need but the

successful introduction of that change. Serving as a change agent, innovation may be

implemented in incremental steps or en masse; either way, change is introduced. Davila

et al. (2006) noted innovation was similar to other business functions, representing a

method of conducting business requiring oversight by leadership and discipline on behalf

of an organization to navigate change successfully. Ling (2003) further noted that

successful innovation required visionary leadership. Since the primary task of the CIO is

to enable a firm’s business functions to navigate change successfully through the

application of technology, a discussion of the CIO’s executive leadership role with regard

to innovation is merited.

Executive Leadership and Information Technology Innovation

Researchers have been studying the relationship between leadership behaviors

and innovation for several decades. A review of early IT innovation literature uncovered

a relationship between leader expectations of innovation and innovative work behavior

on behalf of followers (Scott & Bruce, 1994), suggesting that a follower’s perception of

leadership helps facilitate an innovative climate. More recently, Carmeli and

Schaubroeck (2007) conducted a similar study and found comparable results: When a

follower perceives high leader expectations regarding innovation, increased follower

creativity and individual innovative behavior often result, consistent with additional

contemporary findings (De Jong, 2007; De Jong & Hartog, 2007).

For CIOs tasked with managing the people and technologies with the greatest

ability to affect an entire firm’s business agility through technology innovation, these

findings are particularly relevant. One of transformational leadership’s tenets is its ability

to introduce change to bring about a new level of achievement and leadership (Crawford,

Gould, & Scott, 2003). In multiple studies conducted by Howell and Higgins (1990a,

1990b, 1990c), innovation was found to enable transformation. This finding is important

to CIOs and other senior IT leaders concerned with more than merely managing

resources but rather transforming their IT organizations to the firm’s betterment.

Transformation, innovation, and change can therefore be viewed as synonymous in the

context of organizations seeking to successfully navigate the unknown. CIOs who

understand and actively encourage innovation provide themselves and their teams the

incentive to keep pace with the very business-enabling technologies they are tasked to

deploy and support (Bassellier, Benbasat, & Reich, 2003).

Mupepi (2005) defined technology as changing the status quo by introducing

innovation into an environment to satisfy new needs. By relating technology and

innovation, the two may be viewed as interchangeable in that either construct may

stimulate change. Contemporary IT innovation literature identifies several antecedents

relative to enabling change through improved business processes or the application of

technology (Chang & Shaw, 2005; Neary, 2007; Tallon, 2007). Innovation is but one of

these antecedents. According to Moore and Benbasat (1991), innovation in the context of

IT is not limited exclusively to technology but may also encompass a particular method,

approach, or practice that is both unique and beneficial to IT. A technical computing

platform or platform dimension perceived as new or unique and of positive impact

therefore meets this tenet, as would a new process, methodology, or taxonomy useful in

enabling technology organizations to deliver their intended value more efficiently or

effectively.

The breadth of technology and innovation adoption may vary based on an

organization’s position on the bell-shaped IT innovation diffusion curve introduced by

Fichman (2000) and later refined by Rogers (2003). Rogers outlined adoption or

assimilation categories spanning a continuum anchored at one end by innovators and

early adopters (representing 2.5% and 13.5% of a population, respectively). Laggards

(representing 16%) anchored the opposite end of the continuum, while the middle 68%

was occupied by early and late majorities (each representing 34%). Organizations may be

described in light of their propensity to diffuse innovation based on their relative position

on this innovation diffusion curve, significant given that earlier studies (Grover & Goslar,

1993; Zmud, 1982), including one by Fichman and colleague Kemerer (1999) had been

inconclusive in this regard.

Elenkov et al. (2005) stated that communication is a critical factor in IT

innovation. Kimmel (2000) described innovation as akin to communication and

impossible without it, explaining that ill-communicated innovation by its very nature is

undiffused and therefore unworthy of being described as innovative. Communication was

also noted as one of several necessary skills enabling an organization to learn and

transform itself. Other researchers (Dalton et al., 2002; Grisham, 2006) further noted that

the role of innovator (comprised of communication, transformation, and power

dimensions) represented one of several innovation dimensions.

Though many researchers agree communication is paramount to innovation, the

literature is inconclusive with regard to what comprises the best mix of innovation-

inspiring organizational characteristics. Damanpour’s (1991) meta-analysis on the

determinants of organizational innovation sought to emphasize ten statistically significant

dimensions. Rogers (2003) cited low correlations between much of Damanpour’s work,

however. Despite a lack of consensus, the literature generally allows that innovation,

organizational vision, and work climate are related to one another as outlined next.

Innovation, Vision, and Climate

Successful innovators share an innovation-based future-oriented vision (De Jong,

2007). Innovative teams are unafraid to innovate in part due to the vision communicated

by organizational leadership. When business organizations perceive innovation as

paramount to achieving their firm’s vision, they are more likely to adopt innovative

technologies or business practices (Edmondson, Bohmer, & Pisano, 2001). Jung et al.

(2003) found that a well-articulated vision led to greater innovative contributions,

consistent with visionary leadership theory. Podsakoff, MacKenzie, and Bommer (1996)

shared similar results: articulating vision and intellectually stimulating followers

comprised two of several critical transformational leadership tenets. Further, Hartog, Van

Muijen, and Koopman (1996) determined that transformational leaders emphasized

innovation and support above other transformational leadership components, instilling

organizational vision and creating an innovative work climate in the process.

Innovation, Risk Taking, Creativity, and Organizational Survival

Innovation has long been correlated to risk taking and creative problem solving

(Crawford et al., 2003; O’Reilly et al., 1991). Fry (2003) demonstrated a relationship

between innovation, organizational performance, and longevity, citing IS as one of

several important dimensions. Mumford’s (2003) creativity study uncovered a relevant

gap in the literature related to knowledge-based professions. Specifically, computer

programmers, engineers, designers, and other IT knowledge workers were overlooked in

the innovation literature, while artists, musicians, and similar creative professions were

studied more frequently by virtue of their job roles being perceived as more innovative.

In the IT literature, innovation is synonymous not only with adopting new

technology but with enabling organizational survival. Harney (2002) attributed

organizational survival to the ability of constituent communities of practice to innovate.

An important buttress against competition, Harney’s study showed that innovation

enabled a firm to adapt to a changing IT landscape and position itself for long term

survival. Newly introduced technologies were shown to bolster innovation (Crawford et

al., 2003), further sustaining a culture of change desired and promoted by innovative

organizations. Technologically innovative leadership was accordingly viewed as

delivered in two forms: by innovation champions and by innovative technical leaders.

The former was perceived as a world changer while the latter was perceived as limited to

affecting local change at best. Regardless of their breadth of impact, findings across the

innovation and ERP computing platform literature (Anderson et al., 2009; Rogers, 2003)

generally corroborate these findings, explored next.

Innovation and ERP Computing Platforms

Fichman (2004) identified computing platform innovation as one approach to

enabling innovation and change, adding that the “innovation process begins with some

positioning investment in the platform, which can take the form of a pilot project,

prototype, establishment of necessary infrastructure, or some baseline implementation of

the platform itself” (p. 135). During this preliminary proof-of-concept investment in the

computing platform, the organization is tasked with validating whether the platform not

only meets the firm’s business needs but aligns with the IT organization’s technical

competencies, direction, expectations, and budget constraints. The IT organization must

also determine whether it can realistically and effectively address its internally-held

technology biases and adopt an innovation (Anderson, 2003). Organizations incapable of

or discouraged with regard to adopting new technology standards are less likely to

develop a track record of innovation (Fichman). Fichman’s financial modeling

perspective on IT and ERP platform innovation was not widely held in the literature, but

was compelling in its rigor and quantifiably comparative outcomes. Factoring in expected

values of return, net present value (NPV), and traditional discounted cash flow (DCF)

calculations, his work remains a rational means of calculating an ERP computing

platform’s value. Such an approach enables an IT organization to measure on two fronts

the value associated with adopting a new platform. First, an organization may measure

the extent to which a new computing platform might itself be innately more cost-effective

than an incumbent or proposed platform. Such even comparisons enable total cost of

ownership (TCO) evaluation predicated on generally accepted return on investment

(ROI) metrics. Second and more related to computing platform innovation, Fichman’s

financial modeling perspective makes it possible to apply a financial estimate to a

platform’s differing computing platform dimensions (agility, flexibility, availability,

performance, and so on). Quantifying impact in this manner provides greater quantitative

illumination as to how an ERP computing platform may enable business process agility

within a particular cost model. From Fichman’s perspective, if a computing platform can

better arm the business to address inevitable change and organizational transformation,

such a platform should reap a positive financial impact compared to its less innovative

and therefore less business-enabling computing platform counterparts. The next section

further addresses the roles and intersection of computing platforms and adopted

innovation.

Computing Platforms and Innovation

A review of the literature yielded gaps regarding the role and importance of the

computing platform upon which an ERP business application is installed. Researchers

generally agreed that system performance is an important factor to both computing

platform and business application success (Chang, 2004; Mansfield, 2005; Sherer &

Alter, 2004). Most IT adoption and innovation diffusion literature either failed to

recognize, or inadequately distinguished, the relationship between the technology

underneath an application and the application itself. In this oversight, much of the

computing platform literature overlooked the business agility the platform could innately

enable or discourage, along with the impact that such agility could have on the IT

organization responsible for the platform and consequently the IT organization’s affect

on the business (SAP, 2005). Despite differences in how computing platforms were

described or defined, a review of the literature showed reasonable consensus surrounding

what constituted computing platform innovation. These findings are outlined next.

Operationalizing the Information Technology Computing Platform

Dedrick and West (2003) outlined the premise of an IT computing platform,

describing it as “a processor, operating system (OS), and associated peripherals” (p. 6).

Other researchers acknowledged the configuration of a computing platform consisting of

hardware, networking, and various software products necessary to run middleware, a

database, or an application (Bresnahan & Greenstein, 1999; Duncan, 1995; Prior, 2007;

Shi, 2007). Additional labels in the literature included “computer hardware,” “general

purpose technology infrastructure,” “software infrastructure,” “technology solution

stack” and various references to specific hardware, operating systems, or middleware

components making up a larger technology stack or set of architectural standards required

for a business application to be installed and run (Morris & Ferguson, 1993; Zhu et al.,

2006). Zhu and Kraemer (2005) clarified that sound integration between and within the

technology stack layers of an IT computing platform were critical to the platform’s

usefulness.

For purposes of this study, the term computing platform comprises only the

computing hardware, operating system software, and database software necessary for

installing and operating a business applicationin this case ERP business systems (see

Figure 4). Other business applications could have included email systems, customer

relationship management systems, or collaboration systems.

Figure 4. The ERP computing platform. Computing Platform Classifications: Legacy or Contemporary

Anderson et al. (2008) noted that computing platforms are often classified as

either legacy or contemporary platforms based on the platform’s operating system

element. Today, legacy platforms tend to describe long-lived proprietary mono-vendor

computing platforms tied to specific operating systems which in turn are supported on

one or very few hardware platforms (Mansfield, 2005). IBM’s z/OS operating system

running on IBM’s zSeries mainframe is a good example of a legacy system. Long-time

commercial and proprietary OSs like Hewlett-Packard’s HP-UX and IBM’s AIX are

similarly often viewed as legacy (Dedrick & West, 2003).

By way of their enduring nature, legacy platforms are naturally the most mature

of all computing platforms. Legacy or mainframe platforms were the first to be supported

by business application vendor SAP, for example, followed later by UNIX, Microsoft

Windows, and finally Linux-based platforms. Legacy platform drawbacks are numerous

and generally self-evident. Legacy platforms grow increasingly expensive to operate year

over year, are generally less flexible relative to integrating with more contemporary

technologies, and thus are generally less capable of or desirable with regard to supporting

newly released business applications (Anderson, et al., 2009). These unfavorable

characteristics are countered by a superior record of system reliability, availability, and

serviceability (RAS), low risk with regard to deployment, application support, and

ongoing operations, and excellent scalability (Anderson et al., 2008). In the wake of their

limits and despite their advantages, legacy platforms reflect declining market share.

In contrast, the computing platform innovation literature revealed another class of

computing platforms viewed as more contemporary. Similar to how legacy platforms

earned their title, the contemporary label is most often applied based on the computing

platform’s underlying operating system rather than by its hardware or database software

provider. Anderson et al. (2008) explained that contemporary computing platforms are

often synonymous with low-cost Intel-based or AMD-based processor platforms sold by

many different hardware platform vendors. The Microsoft Windows operating system

installed atop an Intel processor-based or AMD processor-based computing platform

(Wintel), and the Linux operating system installed atop an Intel processor-based or AMD

processor-based computing platform (Lintel), are the two most common examples of

low-cost and contemporary computing platforms. Wintel and Lintel computing platforms

manufactured by Dell, Fujitsu, IBM, Hewlett-Packard, Sun, Unisys, and other hardware

vendors are often labeled in the literature as volume, industry standard, and commodity

platforms (Carr, 2004; Dedrick & West, 2003). These contemporary platforms reflect

growing market share at the expense of their legacy counterparts (Anderson et al., 2008).

Hybrids: Another Contemporary Computing Platform

The contemporary or commodity computing platforms outlined previously have

reinvented how computing platforms may be assembled (Anderson, 2003). Rather than

exclusively from a single computer company, contemporary computing platforms may

also represent a mix of several vendors (Dedrick & West, 2003). When applied to ERP

system architectures, such mixed platforms combine to form what is often termed in the

literature a “hybrid computing platform” (Anderson et al., 2008). Illustrated in Figure 5,

hybrids are created when a business application’s computing platform is subdivided into

two or more platforms based on OS: one OS used to host the system’s database software,

and one or more different OSs used to host the system’s application servers (servers

executing the system’s business application logic).

Figure 5. The ERP hybrid computing platform.

Chau and Tam (1997) conducted a study investigating how hybrids might provide

benefits similar to that provided by contemporary homogenous systems (systems based

exclusively on Lintel or Wintel computing platforms). They found, and Tallon (2003)

agreed, that a hybrid’s technical flexibility and ability to provide an organization with

technology alternatives afforded another positive outcome: organizational endurance.

Anderson (2003) agreed and wrote further of the potentially significant cost savings

inherent to hybrids when legacy database server “back-end” computing platforms were

coupled with commodity “front-end” application server computing platforms. Such

hybrids exhibit many of the desirable traits of legacy and more contemporary computing

platform. West and Dedrick (2001) also observed that the adoption of open systems by IT

organizations might include favorably-dimensioned hybrid computing platforms.

Hybrids can be a compelling choice for risk-averse, forward-looking, or budget-

constrained IT organizations tasked with deploying critical business applications

(Anderson et al., 2009). By deploying a hybrid, an IT organization can take advantage of

its long-standing legacy computing platforms shown useful for hosting critical business

data while introducing less-expensive computing platforms to run an application’s

business logic. In this way, the IT organization may incrementally introduce innovative

platforms in a risk-averse manner. The company’s data remains protected by virtue of the

legacy system’s mature technologies, resilience, system scalability, and performance

inherent to these proven computing platforms. Similarly, the contemporary components

of the hybrid computing platform can significantly reduce platform acquisition and

maintenance costs and implementation risk while providing innovative virtualization,

resource management, workload management, scale-out capabilities, and morea

compelling computing platform alternative for even the most risk-averse CIOs.

Computing Platform Operating Systems Innovation Continuum

Though never empirically validated, Anderson et al. (2009) suggested a simple

method of categorizing operating systems across a continuum of innovation spanning less

innovative to more innovative (see Figure 6).

Figure 6. Suggested by the ERP literature: The OS innovation continuum.

In this way, otherwise complex computing platforms may be grouped into one of

five broad categories based on operating system as explained:

The relative degree of innovativeness for SAP’s four families of supported OSs can be viewed as lying on a continuum where mainframe/legacy OSs are defined as least innovative followed by UNIX, Windows, and finally the most innovative OS for SAPLinux. A fifth OS variant, hybrids (typically composed of a mature though less-innovative database server software OS surrounded by application servers running more innovative OSs), sits squarely in the middle of the other four OSs. (Anderson et al., 2009, p. 77)

How computing platform OSs may be ranked by innovation attributes is explored

next, concluding with the need for an empirically valid and reliable ranking method.

Less Innovative Operating Systems: Legacy/Mainframe and UNIX

A review of the ERP and computing platform literature uncovered several

common themes indicating that Legacy/Mainframe and UNIX operating systems are less

innovative than their contemporary counterparts. First, Legacy/Mainframe and UNIX

operating systems are not “open” in that their source code cannot be easily or legally

changed by others (Spocker, 2008; Wheeler, 2006). UNIX’s popularity in the 1970s was

attributed to open source code at that time (Peeling & Stachell, 2001). However, none of

the major ERP-supported UNIX-based operating systems are open today. Second, the

ability of an operating system to execute on different hardware platforms, an attribute

termed portability, is uncommon in UNIX and Legacy/Mainframe operating systems.

Portability represents innovation (Wheeler), yet the only major portable UNIX operating

system today is Sun Solaris (Riske, 2006). Competing UNIX operating systems are

implicitly coupled to vendor-specific hardware platforms and therefore cannot run on

competing hardware platforms. The literature viewed such proprietary or mono-vendor

computing platforms as technical roadblocks to innovation (Chang & Shaw, 2005). The

HP-UX operating system only runs on Hewlett-Packard Company platforms, for

example, while IBM’s AIX operating system runs exclusively on IBM hardware

platforms (Spocker).

Cost and pricing are also viewed as innovation differentiators (Dedrick & West,

2003). For example, Legacy/Mainframe and UNIX operating systems are significantly

more expensive to acquire, as are the proprietary hardware platforms and unique database

software versions required to support these OSs, than their contemporary counterparts

(SAP, 2006; Spocker, 2008). Application compatibility is another innovation related to an

operating system’s support for a business application (Dedrick & West; Rogers, 2003).

SAP develops its new business applications on the Linux and Windows operating

systems first (Anderson, 2004), making these OSs compatible more quickly with SAP’s

applications than other OSs. In other cases, SAP develops business applications to be

compatible with only a subset of the more broadly available OSs.

More Innovative Operating Systems: Linux, Windows, and Hybrids

A review of the ERP and computing platform literature uncovered several

common factors indicating that the Linux operating systems and to a lesser extent the

Microsoft Windows operating systems are perceived as more innovative than their

Legacy/Mainframe and UNIX counterparts, summarized here:

More so than innovative attributes held by hardware and middleware, true computing platform innovation is embodied at the OS level. Innovative OS attributes tend to either exist or be absent, and include portability, source code openness, cost, compatibility, integrated innovation attributes (integrating others’ innovations), virtualization capabilities, clustering support, manageability, and new-sales marketshare position. (Anderson et al., 2009, pp. 76-77)

Linux and Windows operating systems are exceptionally portable (Riske, 2006;

Wheeler, 2006). Linux has been ported to nearly every hardware vendor’s commodity

platform (those based on Intel’s x86 and x64 processor families and AMD’s family of

processors) as well as previously proprietary computing platforms (such as select IBM

mainframes and high-end HP UNIX computers). Microsoft Windows is similarly

portable, supported on commodity platforms as well as high-end otherwise proprietary

platforms like Intel’s IA64 and the now-defunct Alpha platform (Anderson, 2003).

Linux and Windows are less expensive to acquire and often perceived as less

expensive to maintain than their less innovative counterparts (Rapoza, 2007). OS

licensing for Linux and Windows operating systems is a fraction of the license fees for

UNIX and Legacy/Mainframe operating systems (Anderson, 2004). Hardware supporting

Linux and Windows is similarly less expensive to purchase. According to Apte (2008),

these differences in cost constitute pricing innovation.

Further, Linux and Windows are innovative by virtue of what Microsoft (2004)

described as layered or integrated innovation: the ability of a product to integrate

competitors’ innovations in a manner that makes the features easier to use or available at

lower cost. Such an approach yields a product presumably greater than the sum of its

parts. The Linux and Windows operating systems are innovative in part due to built-in

tools that enable services and applications to be virtualized (Irvine, 1997), utilities that

allow hardware resources to be easily manipulated and managed (Yamada & Kono,

2007), support for clustering and file systems that decrease unplanned downtime

(Anderson et al., 2009), and the ability of these operating systems to be easily or quickly

modified in response to new technology needs or business requirements (Barbacci, 2003;

Newman, 1998; West & Dedrick, 2001). These innovative characteristics are consistent

with findings by Yamada and Kono (2007) who stated that investment in innovative

resource management tools and policies is necessary for operating system vendors

seeking to address new or changing business application requirements. Finally, Peeling

and Stachell’s (2001) market share observation with regard to innovative technologies

applies: Linux and Windows OSs are gaining market share at the expense of their

Legacy/Mainframe and UNIX competitors (Anderson et al., 2008).

Because hybrids exhibit many of the benefits ascribed to innovative computing

platforms, practitioners and researchers have positioned hybrids as incrementally more

innovative than their Legacy/Mainframe and UNIX counterparts (Anderson et al., 2009).

Hybrids are therefore often classified alongside Windows-based and Linux-based

computing platforms as contemporary platforms despite the fact that one of the two

underlying OSs is likely, though not necessarily, UNIX or Legacy/Mainframe. See Table

1 for a synopsis of operating system innovation attributes and descriptions gathered from

the ERP and innovation literature. By measuring these attributes, an empirically valid and

reliable method of ranking OS innovativeness is possible.

Table 1

OS Innovation Attributes Suggested by the Innovation and ERP Literature

Attribute Description Application compatibility Ability of the OS to support SAP Enterprise Resource Planning software as the OS for application servers, database servers, or both Availability Ability of an OS to help create a highly available computing platform that remains up and accessible to end users Integrated innovation Innovative OS features layered together from competing OSs Market share OS popularity for SAP applications, often growing at the perceived expense of competing OSs Open source code OS source code that can be easily and legally changed Organizational endurance Ability of the OS to provide an existing IT organization with a choice, acting as a change agent for successful innovation Portability Ability of an OS to execute on two or more hardware vendor’s computer platforms Pricing OS TCO, which includes the cost of acquiring the OS as well as the cost of ongoing OS maintenance Technical flexibility Ability of the OS to be easily repurposed, changed, or integrated to facilitate change or to support new technologies Tools Inclusion of effective resource management, workload management, and virtualization tools within the OS Note. TCO = Total Cost of Ownership.

SAP ERP Implementation Critical Success Factors

The rise in importance of the IT organization throughout the late 20th century

aligns with the progressively more complex business and economic landscape

characteristic to this period. Increasingly robust computing platforms enabled richer

business software to be developed, culminating in the development of broad-based ERP

systems. ERP is recognized as one of several mechanisms for introducing organizational

and other firm-wide business-enabling changes through technological innovation (Chang

& Shaw, 2005; Ross & Vitale, 2000). Successful ERP implementation is difficult,

explaining why general implementation success factors frequent the literature (Chung &

Snyder, 2000; Parr & Shanks, 2000). Researchers found attitude to change (Kwahk,

2006) and resistance to change (Gale, 2002) greatly influenced adaptability and in turn

successful ERP implementation. Formal change management programs and attention to

managing change, including the need for a change management program adapted to an

organization’s unique culture (Nah et al., 2003) led by a dedicated change management

leader (Roy & Aubert, 2002) were found central to successful ERP implementation.

Studies concluded organizational fit factors (Hong & Kim, 2001) and developing and

refining technical, cognitive, and soft-skills (Gale) were also important. Maintaining a

balanced mix of business, interpersonal, and technical skills was cited (Hawking & Stein,

2003), along with attention to maximizing motivation (Sousa & Goodhue, 2003) and the

need for relevant past ERP implementation experience (King, 2005). Molla and Loukis

(2005) found that cultural factors played a key role as well, specifically with regard to a

lack of congruence between the ERP solution’s implementation methodology or

perspective (system culture) and the culture of the firm implementing the ERP (host

culture).

The high incidence of ERP implementation failure (Appleton, 1997; Chang, 2004;

Fallon, 2005) demands a more thorough understanding of the reasons behind these

failures. ERP implementation is expensive even in the best of cases—when it is

successful. Research reveals significantly more than half of all implementations fail

altogether (Gargeya & Brady, 2005; Moon, 2007; Scott & Vessey, 2000). Beyond all the

aforementioned general success factors, the ERP implementation literature revealed

several critical success factors shown paramount to ERP’s success, all of which support

why ERP failures continue to mount despite a growing body of ERP implementation

knowledge and lessons learned (Moon; Ross & Vitale, 2000; Stefanou, 2001). Consistent

with previously noted innovation diffusion, many researchers cited the overwhelming

importance of executive communication (Chang; Ramirez & Garcia, 2005). Other

researchers suggested that the ERP implementation process itself and inadequate

postimplementation outcomes were two of the most critical factors (Al-Mashari et al.,

2003; Gable, Sedera, & Chan, 2003). Chang further noted that an inadequate

understanding of ERP’s lifecycle, particularly the requirement for iterative functional

upgrades, was to blame. Finally, the literature cited fundamental project management

skills as another common ERP implementation success factor (Scott & Vessey; Umble et

al., 2003).

Interesting in its absence is the lack of research showing to what extent the ERP

computing platform represents a critical success factor. Despite the foundational role

played by ERP computing platform technology, there is very little mention in the

literature of the importance of selecting the appropriate technical groundwork for ERP.

With few exceptions, much of the literature ignores ERP computing platform innovation

or the ramifications inherent to IT architecture limitations. In recent years, though, a

growing body of research has found that attention to proper IT architecture, ERP

technology, or the ERP computing platform represents yet another ERP critical success

factor (Jones, 2004; Nah et al., 2003). The combination of hardware, operating system,

and database software that together forms the technical foundation for ERP was found in

limited cases to affect implementation success markedly (Murray & Coffin, 2001; Scheer

& Habermann, 2000). Selecting and deploying the appropriate ERP platform or

architecture represented an increasingly visible and growing, though still

underemphasized, critical success factor gap in the literature (Sherer & Alter, 2004).

With this critical success factor in mind, the gap in the intersection of the leadership,

innovation, ERP, and computing platform bodies of literature is examined next.

The Gap in the Literature

The gap in the literature is significant in that the four bodies of transformational

leadership, IT innovation, computing platform OS, and ERP literature intersect

insufficiently with one another. The scholarly literature revealed a positive relationship

between technical innovation and transforming leadership (Fichman & Kemerer, 1999),

supporting Quinn’s (1985) non-directional hypothesis that a relationship exists between

transformational leaders and an organization’s adoption of technology innovations.

Innovative leaders were found not only to implement innovation more quickly but also to

use innovation to their advantage more completely (Schein, 1994). Furthermore, IT in the

hands of innovation-focused leaders was found to transform an organization (Henderson

& Venkatraman, 1993; Klenke, 1994) and therefore to change the organization’s

competitive position within its industry despite confounding business and technology

factors. Yet only a small body of empirical evidence explored the relationships between

executive-level transformational leadership and technology innovation. Based in part on

Quinn’s conjectured relationship between transformational leaders and technology

innovation, a few contemporary researchers (Elenkov et al., 2005) have developed a

sound theoretical framework for exploring this gap found in the intersection of executive

leadership and innovation. No published studies, however, have explored the CIO’s effect

on technology innovation as evidenced in the selection of an ERP system’s technical

computing platform. The literature focuses instead on general leadership behaviors or

attributes (Antonakis & House, 2002; Sebastian & Korrapati, 2007; Yukl, 2002).

CIOs’ effectiveness has been shown to rest largely in their leadership and ability

to cast a vision (Earl, 2004). How CIO leadership traits affect ERP technology innovation

outcomes, however, is unclear. A literature review by Elkins and Keller (2003) revealed

innovation measures related to how leadership affects the perception of an innovative

environment, but they failed to connect transformational CIO leadership with IT or ERP

computing platforms. And while the contemporary literature readily acknowledged the

ongoing need for ERP innovation (Karimi et al., 2007; McKie, 2005), an exhaustive

review of the literature uncovered no relationship between executive leadership and ERP

computing platform innovation. Another review conducted by Elenkov and associates

(2005) of the organizational innovation literature with respect to new administrative

business systems deployment was also inadequate in that the researchers failed to

specifically examine the CIO position.

Finally, with regard to transformational leadership, the literature has focused more

extensively on assessing lower-level managers rather than executive leaders (Antonakis

& House, 2002; Katz & Salaway, 2004; Yukl, 2002). As such, CIO transformational

leadership has been measured infrequently despite popular and scholarly literature

indicating that innovation cannot be laid atop an organization but rather needs to be

introduced through organizational leadership acting as a change agent (Avolio, Kahai, &

Dodge, 2001; Drucker, 2001). The CIO is the natural change agent in this regard.

Counterarguments in the Literature

Transformational Leadership Counterarguments

As one of several competing theoretical frameworks, transformational leadership

cannot alone describe leadership’s innate diversity. As addressed earlier in this literature

review, the literature outlines various leadership styles that might better encourage

innovation, among them visionary leadership (Nanus, 1992), situational and contingency

styles of leadership (Stogdill, 1975), and various great man theories (Kaplan & Kaiser,

2003; Van Seters & Fields, 2000).

Transformational leadership’s IS component is directly related to stimulating

follower creativity and innovation (Amabile, 1997). A search of the literature found that

IS practiced by leaders may actually have a negative impact on followers. For example,

Seltzer, Numerof, and Bass (1989) uncovered a positive relationship between IS and

employee burnout. In environments characterized by high stress levels, high IS

contributed to role overload and has also been associated with greater failure rates in IT

implementations. The practice of encouraging innovation diffusion therefore may not

consistently correlate to the successful adoption of that innovation (Johnson & Rice,

1987).

Innovation Counterarguments

CIOs are not alone in their leadership role of encouraging innovation. Chief

technology officers (CTOs), domain-specific chief technologists, first-line managers,

mentors, senior colleagues, and other senior leaders may champion innovation as much or

more than the CIO (Anderson et al., 2009; Crawford et al., 2003). More than effective

transformational leadership influences innovation, too. Cost constraints, technology

standards, a firm’s resistance to change and risk, and strategic technology

customer/vendor agreements also shape innovation outcomes (Anderson, 2003). The

literature revealed studies shown to relate organizational support with innovation

adoption (Beatty, Shim, & Jones, 2001; Premkumar & Roberts, 1999). Harney (2002)

noted that innovation typically had no long-lasting result in organizations constrained by

their own bureaucracy. In such cases, it was found that bureaucratic though nonetheless

successful organizations were forced to deploy innovation incubators (focused

technology think-tanks) chartered with facilitating organizational innovation as a way of

coping with change otherwise hampered by the organization’s culture. Given that any or

all of these factors may be present in a firm that has implemented an ERP business

application, all of these factors may be confounding.

Computing Platform Counterarguments

While computing platform innovation admittedly occurs beyond the OS, all

technology, administrative, and other innovations become ordinary and eventually

outdated with time (Mansfield, 2005; Turban, McLean, & Wetherbe, 2001). In the

literature, innovation is often described as relational, dynamic, new, and social in nature

(Crawford, 2004; Van de Ven, 1986). Zhu et al. (2006) found that peer adoption of

innovative technology or a new technology standard drove broader industry adoption. By

its very adoption, then, successful innovation becomes commonplace over time. For

example, in 1993 Morris and Ferguson described the strong adoption rates of UNIX

computing platforms. Soon after, Bresnahan and Greenstein (1999) noted the continued

success of specialized computing platforms (UNIX) from vendors like IBM, Hewlett-

Packard, and Sun in fulfilling special or niche needs, such as serving as the technology

foundation for critical business applications. Given their publication dates, these findings

unsurprisingly counter more recent claims that UNIX-based computing platforms are

losing marketshare to contemporary and innovative computing platforms (Anderson et

al., 2009). After all, despite their record of past innovation, UNIX and Legacy/Mainframe

OSs are simply no longer new.

Research Review: Research Questions, Variables, and Hypotheses

A review of the early ERP implementation literature found that most

organizations rarely experienced problems with IT hardware or the overall ERP technical

platform (Benders et al., 2006; Wheatley, 2000). The computing platform was found to

be a factor to successful ERP implementation in contemporary literature from a total cost

of ownership (TCO) or performance perspective, but not a factor related to innovation

(Jones, 2004; Murray & Coffin, 2001; Scheer & Habermann, 2000). Widespread

implementation and usability issues with ERP systems in general and software vendor

SAP’s ERP technical platform in particular illustrate the significance and magnitude of

this study’s problem statement (Fallon, 2005; Kwahk, 2006; Sousa & Goodhue, 2003).

Similar research questions, variables, and hypotheses were observed throughout a small

but important subset of the respective leadership, innovation, and ERP literature, further

underscoring this study’s importance (Al-Mashari et al., 2003; Chang, 2004; Kansal,

2006; Motwani et al., 2002; Ramirez & Garcia, 2005; Ross & Vitale, 2000; Stefanou,

2001).

Most Important Theory, Primary Research, and Methods

The theoretical framework described by Elenkov et al. (2005), which in turn

accommodates transformational leadership theory, provided the basis for this research

study. Outside of the general transformational leadership construct, the most important

aspect of transformational leadership theory relative to this study is IS’s role in predicting

the computing platform operating system installed for a firm’s ERP business application.

If the strength of IS alone could singularly predict ERP platform innovation with a

reasonable degree of certainty, subsequent research studies might be simpler to design

and administer.

No single theory meshed precisely with the goal to measure ERP computing

platform innovation. Existing instruments outlined later in this chapter were found

inadequate to quantify ERP computing platform innovation. Based on the literature

review’s findings, however, computing platform innovation was shown to be nearly

synonymous with the platform’s operating system element (Anderson et al., 2008; Riske,

2006). Computing platforms based on Legacy/Mainframe and UNIX operating systems

were viewed as less innovative than computing platforms based on vendor-independent,

open, portable, and generally more cost effective operating system alternatives like

Windows and Linux (Dedrick & West, 2003).

Several primary research methods (which involve gathering new data first-hand)

exist, spanning quantitative and qualitative approaches. Where primary research data

have not been observed or collected previously, secondary research assesses existing data

to draw conclusions. Primary research is therefore akin to exploratory research while

secondary research is explanatory in nature. The survey method is a popular quantitative

research approach. Qualitative methods are also available for conducting primary

research and include in-depth interviews, focus groups, and projective techniques.

Surveys are generally focused on obtaining feedback to specific questions or situations,

whereas in-depth interviews are focused on gathering rich data through a process of

open-ended questioning (Creswell, 2005, p. 47). In-depth interviews could also be used

for targeted data collection while projective techniques designed to measure the attitudes

of respondents (rather than their actual spoken or written words) capture qualitative and

difficult-to-obtain data. Similarly, physiological measures, like instruments used to gather

pulse rates, eye movement, and so on, might be used to corroborate projective techniques

or add a qualitative dimension to collected quantitative data (Diamond, 2007).

Literature Review of Competing Methodologies

Though survey-based research was most prevalent in the relevant literature, the

leadership, ERP, and computing platform innovation literature often cited case studies as

well (Conger, 1996; De Jong, 2007; Ehrlich, Meindl, & Viellieu, 1990; Gulledge &

Simon, 2005; Leverman, 2008), which can be particularly useful for investigating causal

relationships (Yin, 1994). Contemporary ERP researchers (Orlikowski & Hofman, 1997)

and transformational leadership researchers (Rada, 1999) also employed ethnographic

and autoethnographic studies. Other research methodologies noted in the literature

included limited phenomenological (Chang, 2004), grounded theory (Al-Mashari & Al-

Mudimigh, 2003; Dedrick & West, 2003), and to a lesser extent biographical studies

(Gunson & de Blasis, 2002). However, these studies generally explored users of ERP or

large IT systems rather than the CIOs or technical professionals tasked with deploying

and supporting ERP. In light of this literature, the researcher’s purpose to predict an ERP

computing platform based on the strength of a CIO’s transformational leadership, and the

need for primary data from a large population, a quantitative survey-based research

method was deemed most appropriate. Several relevant survey instruments of varying

reliability and validity are explored next.

Literature Review of Transformational Leadership Surveys

After Burns (1978) first described what he termed transactional and transforming

leadership, other researchers built upon his theoretical foundation and developed new

instruments useful in predicting or measuring transformational leadership (Avolio et al.,

2004; Keller, 1992). From Burke’s (1994) Leadership Assessment Inventory (LAI) to

Carless, Wearing, and Mann’s (2000) Global Transformational Leadership scale (GTL),

Rafferty and Griffin’s (2004) transformational leadership instrument, and Sashkin and

Burke’s (1990) Leadership Behavior Questionnaire (LBQ), researchers have several valid

and reliable instruments upon which to draw. Used in many transformational leadership

research studies, Bass’s (1985) Multifactor Leadership Questionnaire or MLQ remains

the gold standard for evaluating transformational leadership (Block, 2003). Updated in

subsequent years with Avolio, the MLQ is the most validated and reliable leadership

instrument used today (Northouse, 2001, p. 154; Tejeda, 2001, p. 32). Howell and

Higgins (1990a, 1990b, 1990c) have found the MLQ valid and reliable both as a self-

report measure and when used by followers to measure the strength of a leader’s

leadership behaviors.

Various versions of the MLQ offer different strengths and challenges. The 5R

release (Bass & Avolio, 1990b) features more items than its 5X (1995) successor but in a

different format using different subscales, thus complicating the earlier instrument’s

ability to correlate to 5X-based studies. Vinger (2005) utilized the MLQ 6S (Bass &

Avolio, 1992) over its more comprehensive 5X counterpart and found that results from

the 6S could not be as easily generalized. Essentially a shortened form of the MLQ 5X,

the MLQ 6S comprises only 21 items and possesses lower validity than the MLQ 5X,

making it a less precise measure of transformational leadership (Northouse, 2001, p.

155). Further, the MLQ 6S does not benefit from the same breadth and depth of coverage

in the transformational leadership literature as the MLQ 5X, and version 6S contains only

three items related to IS whereas version 5X contains four. Finally, the MLQ 6S is

designed as a self-assessment form only and is thus unacceptable for measuring follower-

assessed leadership behaviors.

Literature Review of Computing Platform and OS Innovation Measures

Within the survey research literature, several methods and instruments have been

shown useful in quantifying the impact of technical computing platform innovation. None

have been adapted specifically for ERP, however. The Moore and Benbasat (1991)

Innovation Instrument has been both widely used and shown to be valid and reliable in

assessing technology innovations. Crawford, Gould, and Scott (2003) carried out research

using an instrument developed by Crawford and Strohkirch (1997) aimed at quantifying

the adoption of innovation through innovative technologies. The Acceptance of

Technological Innovation (ATI) instrument was shown to be valid and reliable in pilot

studies. An exhaustive search through the literature could not confirm this 30-item

instrument was ever used again, nor could the instrument be located in the public domain

or its developers contacted.

Rusaw’s (2001) Innovative Organizational Audit, also referred to as the

Multifactor Assessment of Innovation Climate in a subset of the literature, held promise.

Composed of 25 items, Rusaw’s instrument appeared well-documented, easy to

administer, simple to analyze, and relevant to ERP. Only two studies could be found in

the literature (Katz & Salaway, 2004; Nelson, 2004) employing this instrument. Given its

brief mention, the Innovative Organizational Audit did not merit a role in this study.

Another instrument targeting administrative, technical, and information systems

environments, Swanson’s (1994) Tri-core Model of IT Innovation, held promise as well.

Cited in several studies (Grover, Fiedler, & Teng, 1997; Mitchell & Nault, 2005; Nelson,

2004), this three-factor instrument was found to be more appropriate to custom developed

software applications rather than packaged business application implementation projects

(Mitchell & Nault, 2005). Byrd, Lewis, and Bradley (2006) also developed a

measurement tool correlating IT leadership with three facets of IT infrastructure

(application functionality, technical integration, and data integration). These researchers,

however, did not specifically identify or align their findings with IT innovation. Work

completed by early researchers established much of the theoretical foundation later built

upon by Rusaw (2001), Crawford (2004), and others. IT innovation and adoption models

developed by DiPietro, Wiarda, and Fleischer (1990) looked to correlate Rogers’ (2003)

five innovation attributes with organizational and environmental factors. But these

researchers did not leave the body of innovation literature with the instruments or tools

necessary to quantify the impact of ERP computing platform innovation.

While conducting the literature review, this researcher noted that the operating

system installed for ERP business applications embodied many of the innovation

dimensions outlined in the literature. Nah et al. (2003) conducted research with ERP

executives and concluded that computing platform technology played an

underappreciated role in successful SAP ERP implementations. Other researchers

concluded that the computing platform element capable of delivering the greatest level of

innovation was the operating system (Anderson et al., 2008; Apte, 2008; Irvine, 1997;

Newman, 1998; Riske, 2006; Wheeler, 2006). Innovative mechanisms for increasing a

computing platform’s availability (Anderson, 2003), portability (Wheeler), system

resource management and workload management capabilities (Yamada & Kono, 2007),

and virtualization capabilities (Irvine) were all introduced or simplified through their

inclusion in operating systems rather than through other computing platform components.

Newer Windows and Linux operating systems also leveraged the concepts of integrated

innovation and innovative pricing strategies to gain market share over their UNIX and

Legacy/Mainframe operating system competitors (Anderson et al., 2009). Popularized by

Microsoft (2004), integrated innovation describes an innovation dimension based on

smoothly incorporating features and functionality previously introduced by others, while

pricing innovation reflects competitive pricing models (Apte). These findings reinforce

the theory that computing platform innovation may be simply represented by the

operating system selected for a computing platform. Measuring OS outcomes does not

require a special instrument, too, giving further credence to this research study’s

approach based on measuring the relationship concerning transformational leadership

behaviors and OS selection.

Narrowing the Gap to Promote Positive Social Change

This study significantly contributes to and narrows the gap found in several

bodies of literature. Many executive leaders either underestimate or fail to understand the

value of the computing platform underpinning ERP. By demonstrating how the strength

and direction of CIO transformational leadership behaviors relates to ERP computing

platform OS outcomes, this study provides much-needed clarification and support within

the boundaries of the study’s theoretical framework. In terms of positive social change,

the research benefits senior technology leaders seeking to improve working

environments. By showing how transformational leadership affects positive social change

through enabling greater technology agility and consequently improved company-wide

business performance, this study also yields a simple model useful for predicting IT

innovation outcomes in light of the degree of exercised transformational leadership and

the strength and mix of its underpinning components.

Summary

After a brief introduction, the literature review was organized in the following

way: (a) foundations in leadership theory, (b) transformational leadership, (c) innovation

in IT, (d) computing platform roles and innovation, (e) SAP ERP implementation critical

success factors, (f) transformational leadership, innovation, and the computing platform

gap, (g) counterarguments in the literature, (h) research review (research questions,

variables, and hypotheses), (i) defining the most important theory aspects, (j) primary

research and the survey method, (k) review of competing methodologies, and (l)

narrowing the gap to promote positive social change. The literature review provides

context for the research methods used to conduct the study. Research design and

methodology, sample specifics, study variables, measurement tools, and data analysis

procedures are explored in the next chapter.

CHAPTER 3: RESEARCH METHOD

Introduction

Chapter 3 includes a description of the study’s design and methods, followed by

an examination of the population, sampling procedure, and measurement process.

Instrumentation is then detailed, along with matters related to data analyses. Chapter 3

concludes with details explaining participant rights protection and a summary of the

study’s assumptions and limitations.

Research Design and Approach

The researcher designed the study to investigate whether greater transformational

leadership on behalf of CIOs encourages IT teams to deploy more innovative computing

platform OSs for their ERP business systems. Showing such a relationship would prove a

useful first step toward later demonstrating causation. A quantitative design was used to

evaluate CIO leadership behaviors and their relationship to the operating system chosen

for the firm’s ERP business application. This objective was accomplished by studying the

relationship between transformational leadership, its five subscales, and the five possible

operating system outcomes for ERP (see Figure 7). The independent variables

(transformational leadership and its subscales) are ratios. The dependent variable, that is,

one of five ERP computing platform operating system outcomes, each of which may be

classified as more innovative or less innovative, was a categorical variable. Given the

latter categorical variable, in which equality is the only relation or operation possible,

quantitative analysis was deemed the most fitting and appropriate research method by

which to conduct this study.

Figure 7. Research design and conceptual model describing how CIO leadership may predict computing platform operating system outcomes.

A relational design enabled through survey research was used to investigate the

relationship between the independent and dependent variables. To collect data to measure

the independent variables, permission was obtained to use the electronic version of Bass

and Avolio’s (1995) 45-item MLQ, shown to be valid and reliable in effectively

evaluating transformational leadership (Bass & Avolio, 2004). Transformational

leadership subscale scores were obtained via the rater 5X-short form of the MLQ. To

measure the dependent variable, items were created (based on the literature review in

chapter 2) to collect ERP computing platform data, innovation perceptions, and CIO

technical support team data. These survey items were appended to the MLQ to create a

single web-based survey instrument of 62 items. A link to this web-based survey was

distributed by email to 1,602 prospective respondents employed by 500 randomly

selected North American firms. Prospective respondents held a position in their

respective firms’ SAP technical or SAP Basis teams. These respondents rated their

respective CIOs’ transformational leadership behaviors, and they provided the

demographic, computing platform, innovation perceptions, and team-related data

necessary for the study.

Justification for the Electronic Survey Method

Administering the study’s survey electronically rather than through traditional

mass mailing methods was determined the best approach for this quantitative research

study. Church and Waclawski (1998) noted that electronic surveys are simple to

disseminate, administer, and analyze, quickly enabling researchers to obtain data from a

representative sample of a large and potentially geographically distributed population.

Low cost was cited as one of its greatest advantages (Babbie, 2004). With no need for

printing, obtaining envelopes and postage, or investing time in physically mailing paper

surveys, or meeting personally with respondents, electronic surveys were noted as

relatively inexpensive to produce and deliver (Knoke, Bohrnstedt, & Mee, 2002).

The greatest disadvantages associated with electronic surveys relate to computer

access and computer literacy on behalf of the respondents (Church & Waclawski, 1998).

The electronic survey method precludes involvement of respondents without a computer

or without access to email or an Internet-based survey hosting site. Though electronic

surveys by their nature eliminate a portion of the potential population, this disadvantage

was deemed minimal: Given prospective respondents’ high economic status and role in

their firm’s IT organization, they would typically have ready access to both computers

and Internet connections from work as well as from home (Brown, 2007).

Justification for the MLQ 5X Instrument: Validity and Reliability

The MLQ has been shown in the literature to be a valid, reliable, and internally

consistent transformational leadership measure across hundreds of research studies

spanning different industries, geographies, and domains (Den Hartog, van Muijen, &

Koopman, 1997; Lowe, Kroeck, & Sivasubramaniam, 1996; Tejeda, 2001). In 1990, Bass

and Avolio established the MLQ’s reliability. Bass and Avolio (1995, p. 9) later validated

the MLQ 5X through a number of methods, including factor analyses, partial least

squares (PLS) analysis, and additional analyses leveraging Howell and Avolio’s (1993)

findings using the MLQ 5X’s predecessor 5R. Bass and Avolio (1997, pp. 53-55)

published findings showing high correlations for all five transformational leadership

subscales. Hartog et al. (1997, pp. 27-28) reviewed the MLQ’s internal consistency and

similarly validated its subscale alphas.

Using confirmatory factor analyses, Avolio and Bass (2004) conducted a cross-

validation examination of the MLQ and found the subscales generally adequate. Howell

and Hall-Marenda (1999, p. 29) and Avolio and Bass (2004) also tested the MLQ’s

reliability and validity across all transformational leadership subscales. All of these

reliability findings are conclusive: High reliabilities across all subscales demonstrate that

the MLQ exceeds minimum reliability requirements. In light of the instrument’s strong

reliability, the MLQ-based research was conducted in more than 300 doctoral

dissertations between 1995 and 2004 (Bass & Avolio, 2004). These high reliabilities

demonstrated that the MLQ scales were reliable enough to proceed with their use.

MLQ Subscale Sample Transformational Leadership Survey Items

For each MLQ subscale measuring transformational leadership behaviors, there

are four related survey items. Sample IS survey items include (a) suggests new ways of

looking at how to complete assignments and (b) re-examines critical assumptions to

question whether they are appropriate. Two IC survey items are (a) spends time in

teaching and coaching and (b) helps me develop my strengths. IIA sample items include

(a) goes beyond self-interest for the good of the group and (b) acts in a way that builds

my respect. Sample survey items measuring IIB include (a) talks about their most

important values and beliefs and (b) considers the moral and ethical consequences of

decisions. Finally, two sample survey items that measure IM include (a) talks

optimistically about the future and (b) expresses confidence that goals will be achieved.

Justification for the Survey Items related to the Dependent Variable

To address issues of unknown dependent variable validity and reliability cited in

chapter 2, two OS innovation-related survey items were included in the survey. The first

asked respondents to weigh the relative importance of each of the 10 OS innovation

attributes cited in the innovation and ERP computing platform literature. The second

survey item asked respondents to rank the perceived degree of innovation associated with

each attribute for each of the five OSs. In this way, the OS innovation continuum

suggested by Anderson et al. (2009) could be validated.

Several demographic and personnel-related or team-specific data necessary for

characterizing and controlling the study’s dependent variable were identified in chapter 2.

These data were collected by way of survey items and included (a) number of years

experience as an SAP Basis (technical support) professional, (b) time in years with the

firm represented in the study, (c) age of the survey respondent, (d) confirmation that an

ERP system (and not another business application) was assessed, (e) identification of the

ERP operating system(s) selected by the firm’s IT organization, (f) the size of the

respondent’s SAP Basis team, (g) whether the firm employed any SAP-focused

innovation sponsors or innovation champions, (h) whether the SAP Basis team had a

track record of successfully implementing or adopting innovation with regard to the SAP

computing platform, (i) whether the SAP Basis team used a company-internal knowledge

management system, (j) whether the CIO inherited the computing platform was in

position when it was selected, (k) whether the SAP Basis team was insourced (staffed by

internal IT) or outsourced (staffed by a third party), and (l) annual revenue of the firm.

Setting and Sample

The research project focused on CIOs and SAP Basis or technical support teams

working for firms operating in North America, specifically Canada and the United States.

The overall population comprised 3,296 public and private firms, companies, and other

large organizations (e.g., federal or state government and public education systems).

Sample Size

To determine minimum sample size, the study’s two primary data analyses tools

were evaluated using the G*Power power analysis statistical utility version 3.0.10.

Minimum participation by 40 different firms was noted given an ANOVA executed for

repeated measures (within-between interaction) across five groups reflecting the study’s

five possible OS outcomes with medium effect size f = 0.30 and an alpha of 0.05.

However, participation by a minimum of 134 different firms was determined necessary

given a two-tailed t test study (correlation test reflecting a point biserial model) with

medium effect size r = 0.30 and an alpha of 0.05. Due to the t test’s more conservative

requirements, the study’s minimum sample size was designated as n = 134. Ultimately, a

random sample of n = 151 unique firms was collected, exceeding the minimum required

sample size by 12.69%. By way of comparison, the same t test study designed for a large

effect size would have required only 42 rather than 134 unique firms.

Sample Identification Methodology

To randomly select 500 firms for the study, each firm in the population of 3,296

extracted from the HPCSD, the Hewlett-Packard Customer Solutions Database outlined

later in this section, was ordered alphabetically and numbered sequentially. The first

number in Aczel and Sounderpandian’s (2002, p. 809) random number table was used as

the random seed in Microsoft Excel 2003’s Random Number Generation data analysis

tool to generate a list of 500 numbers in the range of 1 to 3,296. These numbers were

matched to the alphabetically numbered population to identify the 500 to be contacted for

the research study. Using the HPCSD, email address contact information was researched

and obtained. Contact information was available for between 1 and 8 prospective

respondents per firm. Two to 3 prospective respondents per firm was typical. Email

addresses for 1,602 prospective respondents were eventually collected.

Data Collection Methodology

Once the 500 firms and 1,602 prospective survey respondents were identified,

initial contact was made via email (see Appendix A). This initial email also verified

correct email addresses and gave prospective respondents the opportunity to decline

participation. Unreachable firms, that is, those with incorrect or otherwise unreachable

participant email addresses, were removed from this study and replaced with new firms

using the next number in the random number table and the selection process outlined

previously. Several prospective firms were unreachable and needed to be replaced with

new randomly selected firms.

Five days after sending the email announcing the study, an invitation was sent to

all prospective survey respondents representing 500 different North American firms

running SAP ERP. The announcement email outlined the study and requested

participation (see Appendix B). This email provided the web link to the study’s survey

hosted by the ZipSurvey survey utility. Prospective respondents were required to read

and electronically sign a consent form preceding the survey prior to participating. They

had an unlimited timeframe to complete the combined survey instrument (an abbreviated

version of which is shared in Appendix C). This survey instrument was approved for use

(see Appendix D) and appended with demographic, innovation, and computing platform-

specific items (see Appendix E). Prospective survey respondents could skip any items

and terminate the survey at any time. At the conclusion of the survey, respondents were

again debriefed regarding the intent of the study and thanked for their participation.

Survey results were reviewed every week. During this review period, the number

of completed survey responses, including those that were usable, was noted. Multiple

responses from the same unique identifier were investigated and discarded as necessary,

and multiple responses for the same firm were noted and later during data analyses were

averaged. Surveys with incomplete respondent or firm identifiers, and surveys from

respondents with less than 1 year with their employer, were discarded. All other

responses were deemed usable regardless of the remainder of the survey’s completeness.

Every week, regardless of survey completeness an email thanking the respondent

for participating was sent to respondents who had provided an email address (see

Appendix F). A slightly modified email notification reminding prospective respondents

of the study and encouraging their participation was also sent every week for 3 weeks.

The entire data collection process consumed 5 weeks: 1 week to contact the 1,602

participants representing 500 different firms, and 4 additional weeks to obtain participant

responses. Five weeks after the study commenced, 174 usable surveys representing 151

different firms and therefore 151 different CIOs were collected. The survey was closed

and all data were transferred to a personal secure laptop for analysis. Had fewer than 134

firms been represented, a second round of 500 firms would have been surveyed and the

sample selection process and data collection methodology outlined previously would

have been repeated until a minimum of 134 usable surveys were collected.

Addressing Nonresponse Bias

Nonresponse bias, or the percentage of a selected survey sample that cannot be

reached, choose not to participate, forget to complete a survey, or are incapable of

accurately completing the survey (Singer, 2006; Singleton & Straits, 2005), impedes a

researcher from generalizing a survey’s results relative to the population under study

(Mitchell & Jolley, 2007). By surveying members of a firm’s SAP Basis team, who by

way of their role are responsible for selecting, installing, and maintaining the SAP

computing platform, the potential for incorrect answers to computing platform questions

was minimal. This approach likely addressed up to one-third of potential nonresponse

bias (Sjostrom, Holst, & Lind, 1999). By ensuring the study’s prospective respondents

had one or more years of service with the firm, the risk of surveying respondents with

little knowledge of their CIO’s leadership style was reduced as well. Multiple rounds of

data collection and reminder emails also minimized respondent forgetfulness.

Justification for the Research Data Source

An SAP-specific customer contact database maintained by the Hewlett-Packard

Company (HP) was used to collect contact and demographic information of firms to be

studied. Termed the HP Customer Solutions Database (HPCSD), this customer contact

database is not publicly available but is accessible by any Hewlett-Packard Company

employee. The HPCSD is updated regularly by HP’s sales, consulting, and other

customer-facing teams. Effective and easy to utilize, the tool’s web-based front-end

provides the ability to sort SAP ERP customers and prospects by geography, platform,

and more.

Using the HPCSD, prospective North American firms representing a population

of 3,296 were quickly identified. The sampling frame was limited in that the HPCSD

database contains data only for HP customer firms that run SAP’s business software.

Non-HP customers or prospects, and customers or prospects operating a different ERP

system, are not included in the database. This frame was acceptable and adequate,

however, in terms of population size and scope. The 3,296 North American firms

identified by the HPCSD reflected all five computing platform operating systems;

spanned numerous industries and business sectors reflecting public, privately held, and

government- or education-oriented entities; and represented a realistic cross-section of

small, medium, and large firms as defined by the Aberdeen analyst firm (Jutras, 2008).

Study Variables and Details

For the conducted study, transformational leadership and its subscales as

described by Bass and Avolio (2004), along with computing platform operating system

outcomes and relevant data describing a firm’s business application and its personnel

support team, were measured. Demographic items were collected to describe the

dependent and confounding variables. A discussion of the study’s variables follows.

Independent Variable Details and Discussion

The study’s independent variables included transformational leadership and its

five subscales: IS, IC, IIA, IIB, and IM. Bass and Avolio’s (1995) MLQ 5X was

employed to measure the independent variables. While several versions of the MLQ

transformational leadership scales were available, Bass and Avolio’s MLQ 5X rater form

was selected. The more comprehensive though dated MLQ 5R (Bass & Avolio, 1990b)

was noted in chapter 2 as rarely used any longer in contemporary research studies, while

the MLQ 6S (Bass & Avolio, 1992) was noted to contain less than half the survey items

of the MLQ 5X and thus shown to be less precise in measuring transformational

leadership (Northouse, 2001). Refer to chapter 2 for a detailed assessment of the MLQ

instrument, its variations, and the widely-held view that the MLQ is the gold standard for

measuring transformational leadership (Block, 2003).

Dependent Variable Details and Discussion

The study’s dependent variable was the computing platform operating system

selected for a firm’s ERP business application. Like many software vendors, SAP ERP

supports five distinct operating systems: Legacy/Mainframe, UNIX, Windows, Linux,

and hybrids (Anderson et al., 2008; Prior, 2007). Like operating systems, computing

hardware and database software elements may include innovative attributes such as

clustering, virtualization, dynamic partitioning, other features that increase organizational

flexibility or reduce computing platform deployment time, and the ability to quickly

change computing platform settings without incurring downtime (Apte, 2008; Irvine,

1997; Wheeler, 2006; Yamada & Kono, 2007). As detailed in chapter 2, however,

researchers and practitioners generally agree that computing platform innovation is most

often associated with the platform’s operating system (Anderson et al., 2009; Risk, 2006).

Linux, Windows, and hybrid operating systems were described as more innovative by

virtue of their portability, open or nonproprietary source code, lower costs, technical

flexibility, superior management tools, superior integrated innovation, and market share

leadership at the expense of less innovative operating systems. For these reasons and the

previously outlined discussions in chapter 2 regarding OS innovation, measuring OS

outcomes was deemed a reasonable method of measuring computing platform innovation.

Few studies, however, outlined valid and reliable methods of measuring and

ranking OS outcomes. Levary (2009) suggested the Analytical Hierarchy Process (AHP),

Balli and Korukoglu (2009) used fuzzy AHP, and other researchers used weighted

measures or processes of varying, unpublished, or unknown validity and reliability

(Anderson, 2003; Anderson et al., 2009; Saaty, 1990). For these reasons, the study’s

survey included an item asking the respondents (experienced SAP Basis professionals) to

weight the components of OS innovation by rating the following ten innovation attributes

described in the literature on a scale of 1 to 10 (least important to most important): (a)

compatibility, (b) availability, (c) integrated innovation, (d) market share, (e) open

source, (f) organizational endurance, (g) portability, (h) pricing innovation, (i) technical

flexibility, and (j) tools perceived as innovative (consisting of resource management,

workload management, and virtualization). A second survey item asked respondents to

rank all five operating systems from least innovative (1) to most innovative (5) with

regard to each of the ten previously weighted innovation attributes. Higher rankings

suggested more innovative OSs. Choices for this item were identified as mutually

exclusive: For example, for every attribute, only one OS could be ranked the least

innovative (with a 1) or the most innovative (with a 5).

Using the data collected from the two innovation-related survey items, measuring

the attributes of the operating system dependent variable was straightforward.

Availability and compatibility held the greatest importance of the ten OS innovation

attributes, while open source code and an OS’s market share were perceived as least

important. With these and the other innovation attributes weighted and ranked by OS,

respondent perceptions of OS innovation were measured. The OS innovation continuum

suggested by Anderson et al. (2009) and previously illustrated in Figure 6 was shown to

be valid. See chapter 4 for detailed results.

Confounding Variables: Justifying Demographic Survey Items

To address and control for confounding variables, several specific items were

appended to the survey. Fichman (2004) confirmed that companies varied markedly with

respect to IT platform capabilities and innovation perspectives. Differences included the

presence of innovation sponsors and champions, the presence of a culture accustomed to

innovation, the extent to which a team possessed a successful record of accomplishment

relative to the innovation adoption process (Rogers, 2003; Tornatzky & Fleischer, 1990;

Wolfe, 1994), and size in terms of revenue (Jutras, 2008) or headcount (Damanpour,

1992; Fichman, 2000; Yao et al., 2003). Because firms with these characteristics have

been found to innovate more successfully or easily than other firms, an item for each was

included in the survey.

The age of the individuals comprising the SAP Basis (technical support)

organization was noted as another confounding variable. Zhu and He (2002) noted that

younger employees were three times more likely to be early adopters of innovative

technologies than their more senior counterparts. Time employed by a firm (Burton-Jones

& Hubona, 2005) and the presence of a knowledge repository or knowledge management

system used for aggregating the organization’s knowledge (Anderson, 2003; Cohen &

Levinthal, 1990) were also identified as confounding. Survey items were created to

collect data for each of these potentially confounding variables.

Several potentially confounding factors did not require special attention in the

survey. Executive leaders with knowledge of IT comprehend its value and more

frequently support IT innovation (Bassellier et al., 2003). Because of their technology

leadership role, there was little need to consider this factor on behalf of CIOs. Two

additional CIO attributes were also shown to be confounding in the literature: the ability

to communicate (Elenkov et al., 2005; Kimmel, 2000) and the ability to cast a vision

(Hambrick, 1989; Judge, 1999). Both of these factors, however, were previously

addressed by items germane to the MLQ 5X instrument.

The method by which a computing platform is hosted for ERP business

applications may affect long-term innovation (Anderson et al., 2008). Platforms may be

deployed internally by a firm’s own IT organization or outsourced to a hosting provider,

potentially affecting the level of control the CIO and SAP Basis team have with regard to

computing platform decisions. Additionally, outsourcing models may penalize IT

departments that make changes (innovative or otherwise) to an existing system

(Anderson, 2003). These incremental penalties (costs) could therefore discourage firms

from making computing platform changes. To control for these factors, an item

identifying the SAP Basis team’s staffing model was included in the survey.

Several other variables in the transformational leadership literature were noted as

neither confounding nor directly related to innovation diffusion. These variables included

employee gender (Rosenbusch & Townsend, 2004), education levels (Hoover, 2003), and

mean salaries (though the relationship between higher salaries and greater seniority

generally implied that salaries could be indirectly confounding). Nothing in the literature

indicated that political affiliations, military background, veteran status, or religious

background affected IT innovation in a meaningful way regarding the survey.

All the aforementioned potentially confounding variables were addressed by

adding demographic and platform-specific items to the survey instrument. For sensitive

data points, participants were reminded that all data would remain confidential and

published in aggregated form only. Demographic items were placed towards the end of

the survey based on insight shared by Singleton and Straits (2005, pp. 283-285), who

realized improved electronic survey response rates compared to placing demographic

items at the beginning of surveys.

Measurement and Treatment

A single survey instrument consisting of the rater 5X-short form of the MLQ 5X

(Bass & Avolio, 1995) transformational leadership assessment instrument appended by

17 demographic and computing platform-specific items was employed. The rater form

was used because the study required CIOs be rated by their subordinates. To avoid

misrepresenting the strength of their transformational leadership behaviors, CIOs were

therefore not granted the opportunity to self-assess or self-rate.

Transformational Leadership MLQ 5X Measures

The MLQ 5X measures how frequently, or to what degree, followers believe their

leaders employ specific leadership actions or behaviors. The instrument is self-scoring

and employs 45 items across a number of leadership subscales, five of which are relevant

to this study. Respondents rated their CIOs against a 5-point Likert scale and assessment

rubric (provided by the MLQ’s copyright holder) with anchors labeled as (a) 0 = not at

all, (b) 1 = once in a while, (c) 2 = sometimes, (d) 3 = fairly often, and (e) 4 = frequently,

if not always. Individual leadership scores comprised means, or the sum of respective

subscales divided by the number of answered items germane to each subscale. The

maximum score that could be achieved in any subscale was 4 and the minimum was 0. If

a respondent left an item blank, then the total score for that scale was divided only by the

number of items answered. Multiple survey responses from the same person for the same

firm did not exist. Therefore no survey responses needed to be discarded for this reason.

Multiple responses from respondents representing the same firm were averaged.

Interestingly, 17 of the 174 SAP Basis respondents worked for the same firms.

To assess the CIO’s overall transformational leadership score, all MLQ items

pertaining to the five transformational leadership subscales (IS, IC, IIA, IIB, and IM)

were summed and averaged. This averaging enabled leaders exhibiting overall high and

low transformational leadership to be differentiated. CIOs with a mean transformational

leadership score between 0.0 and 2.0 were assigned a low transformational leadership

score: CIOs with a mean transformational leadership score greater than 2.0 were assigned

a high score. This approach was consistent with a similar study conducted by Ali (2005,

p. 65), enabling these two categories of low and high transformational leadership (as well

as each transformational leadership subscale) to be analyzed through t tests. Additionally,

ordinal scale data for transformational leadership were also collected, consistent with a

similar study conducted by Vinger (2005).

Demographic, Computing Platform, and Innovation Measures

Demographic, computing platform, and OS innovation data were collected by

way of items appended to the MLQ survey. Respondents could decline to answer any

item. Respondent eligibility was based on role and time with the firm. Only SAP Basis

team members, team leaders, or technical managers with a minimum of 1 year with their

respective firms were eligible. Surveys completed by respondents with less than 1 year

with their employer or with incomplete computing platform demographic data were

eliminated from analyses to preserve the study’s integrity (Iyengar, 2007). Much of the

demographic and computing platform data were described in mutually exclusive terms to

protect participant rights, maximize usable surveys, and simplify analyses.

Data Analyses

All survey data were maintained on the ZipSurvey website until the study was

closed. The data were then securely downloaded, protected, and analyzed locally on a

personal laptop. After three unusable survey responses were removed from the sample,

the collected data were analyzed using version 16.0 of Statistical Package for Social

Sciences (SPSS) for Windows and Microsoft Excel 2003. Alpha levels were set to p <

.05. Data have been and will continue to be safeguarded in accordance with Walden

University’s IRB requirements (the approval number for this study is 06-10-09-0330396).

Data analyses included randomness testing followed by calculating reliability by way of

Cronbach’s coefficient alpha. Descriptive and correlational analyses were performed to

identify and describe the data and their relationships. These analyses included calculating

means, modes, standard deviations, frequency distributions, executing chi-square tests for

independence, and executing t tests between pairs of data as necessary. Finally, to explore

the study’s six hypotheses, analysis of variance and correlational analysis using ANOVA

and Kruskal-Wallis tests were conducted, followed by an analysis of confounding

variables using descriptive statistics, chi-square testing for independence, and t tests. See

chapter 4 for detailed test results and statistical outcomes.

Participant Rights, Assumptions, and Limitations

Measures were taken to protect survey participant rights. Significant rigorous

review was performed of this research study while in the proposal stage in an effort to

avoid unforeseen harm (Smyth & Murray, 2000), including review of the potential for

compromised dignity or self-esteem (Bier et al., 1996). Given the electronic nature of the

survey method used for this study, the opportunity to obtain distress-related visual or

auditory cues was unavailable (Azar, 2000), underscoring the importance of providing

clear directions and the researcher’s telephone number and email contact information.

Prospective respondents were encouraged to read and required to consent to the informed

consent page posted on the survey site prior to accessing the survey. Consistent with

Childress and Asamen (1998), researcher accessibility will continue to be maintained five

years after the research project has been concluded. Further, the unique identities for each

participant and firm involved in the study will continue to be safeguarded. Finally,

because the study’s results have been published in aggregate, no individually-identifying

or firm-specific data are available to compromise respondent or firm identities.

Assumptions and limitations necessary to enable others to replicate, expand, or

otherwise leverage the study’s methodology included the following: (a) the research

design assumed that computing platforms, firm industries, geographic distribution, and

overall completeness of contact data maintained in the HPCSD approximated a normal

distribution; (b) linear relations were assumed for all transformational leadership subscale

relationships; and (c) typical survey method limitations were deemed acceptable,

including the presence of volunteer respondents, unknown respondent motivation to

complete the surveys, unknown respondent reliability with regard to their responses, and

the fact that respondents might provide socially desirable responses.

Summary

Chapter 3 included an outline of the research study’s design, approach, and

justification for the survey method and instrument used. Sample identification and

randomization procedures, setting, population, sample size considerations and rationale,

data collection methodology, study variables, measurement, and instrumentation

considerations were then explored. Next, the rationale for the study’s geographical

boundaries, how and why prospective respondents were selected, and population and

sample frame details were explained. The chapter concluded with transformational

leadership (independent variable) measures, demographic and computing platform

innovation (dependent variable) measures, processes used for data analyses, the study’s

assumptions and limitations, and how participant rights were safeguarded. Chapter 4

presents the study’s results while chapter 5 provides an overall summary, research

conclusions, and recommendations for action and further study.

CHAPTER 4: RESULTS

Overview

This study was conducted with the objective of developing, testing, and

measuring a theoretical model reflecting the impact of executive-level IT leadership

behaviors on computing platform decisions affecting innovation. To collect data, the

MLQ 5X was used. Seventeen demographic computing platform-, innovation-, and team-

related items were appended to the MLQ to collect dependent and potentially

confounding variable data. The study’s data were used to measure the strength of

relationships between the variables outlined in chapters 2 and 3.

This chapter is organized around several themes. First, research instruments and

measures used to collect the data are reviewed, followed by a review of the surveyed

population and sample. After establishing the randomness of the collected data,

Cronbach’s coefficient alpha is calculated. Descriptive statistics are then employed to

describe the dependent variable. Each research question and hypothesis is explored

through one-way ANOVA. In cases where variances differ (thus violating a required

ANOVA assumption), the nonparametric equivalent of ANOVA, the Kruskal-Wallis test,

enables further analysis of the relationship between this study’s independent and

dependent variables. Next, the text is sequentially arranged around this study’s

potentially confounding variables and examined via chi-square or t test analyses. A

review of key themes, observed consistencies and inconsistencies, and several

interpretations concludes this chapter.

Research Tools: Instruments and Measures

The web-based version of Bass and Avolio’s (1995) MLQ 5X instrument was

employed for this study. No special adaptation was necessary. Seventeen items related to

demographic data and potentially confounding variables as described in chapters 2 and 3

were appended to the MLQ instrument. Two of these items validated the strength and

relative ranking of operating systems in terms of their level of innovation.

Five hundred firms from a population of 3,296 were randomly selected using the

methodology described in chapter 3. Email addresses for one or more SAP Basis

(technical support) professionals representing each of the 500 firms were appropriated,

and initial contact with each prospective respondent was made. A follow-up email

provided the survey instrument’s web link, and subsequent weekly emails acted as

regular reminders to complete the survey.

On a weekly basis, survey results were reviewed, and the number of usable survey

responses was tracked. Multiple responses from the same respondent (based on a unique

identifier requested for each survey) were investigated and discarded as necessary, and

multiple responses for the same firm were noted and later averaged during data analyses.

Surveys with incomplete respondent or firm identifiers, or respondents with less than one

year with their employer, were also discarded. All other responses were deemed usable.

Once the survey was closed, all data were transferred to a secured laptop for analysis

using SPSS for Windows and Microsoft Excel.

Population, Sample, and Subsample Data

The study was based on 174 individual survey respondents representing 151

different firms and CIOs. The sample was obtained from 500 randomly selected North

American firms represented by 1,602 prospective survey respondents. Among those

individuals invited to participate in the study, 122 were unable to be contacted (e.g.,

emails were returned as “undeliverable”), 90 requested to be removed from participation,

and 13 felt unqualified to complete the survey. Of the remaining 1,377 potential

respondents, 177 individuals self-administered the survey. Three of the respondents failed

to complete the computing platform survey items, requiring their responses to be

discarded. The 174 of 1,377 respondents successfully completing the survey represented

a 12.6% response rate, while valid responses from 151 of 500 surveyed firms suggested a

more robust 30.2% response rate (mean of 1.15 respondents per firm).

Valid responses from 151 different companies exceeded the 134 minimum

required. Of the 174 individual responses, slightly more than 84% had deployed either

UNIX- or Windows-based computing platforms for their ERP systems, while 9% had

deployed hybrid platforms. The lowest and highest ends of the OS computing platform

innovation continuum (Legacy/Mainframe and Linux, respectively), as earlier illustrated

in Figure 6, were represented by five firms each (see Table 2).

Table 2

Firm and Individual Participant Survey Responses by Computing Platform

Firms Individual Participants

n % n %

Legacy/MF 5 (3.31) 5 (2.82)

UNIX 65 (43.05) 74 (41.81)

Hybrid 14 (9.27) 14 (8.19)

Windows 62 (41.06) 76 (42.94)

Linux 5 (3.31) 5 (2.82)

Invalid Responses N/A 3 (1.70)

Note. n = 151 for firms, n = 177 for individual participants, N/A = Not Applicable (none).

Sample statistics generally agreed with population statistics as recorded in Table

3. Note that the percentage of hybrids represented in the sample was markedly greater

than the population, presumably due to inaccurate or stale computing platform data

maintained in the HP Customer Solutions Database (HPCSD).

Table 3

Population and Sample Sizes for Firms by Computing Platform

Population Sample

N % n %

Legacy/MF 56 (1.70) 5 (3.31)

UNIX 1182 (35.86) 65 (43.05)

Hybrid 23 (0.70) 14 (9.27)

Windows 1877 (56.95) 62 (41.06)

Linux 158 (4.79) 5 (3.31)

Note. N = 3296 population, n = 151 sample size.

Of the 174 valid survey responses, four represented three different Canadian

firms. The balance of responses represented U.S. firms. Respondents worked for a

diverse base of industries: 71 manufacturing, 34 energy, 21 high-tech, 12 pharmaceutical,

11 government, 10 consumer/public goods (CPG), 6 utilities, 4 educational institutions, 3

financial institutions, and 2 unknown industries (see Figure 8). Seventy (46.36%) of the

samples firms (n = 151) reflected less innovative Legacy/Mainframe and UNIX

platforms, while 81 firms (53.64%) reflected more innovative Windows, Linux, and

hybrid platforms.

Figure 8. Numbers and percentages of respondent firms categorized by industry segment.

This study made use of the data from the 20 survey items related to

transformational leadership as outlined in Table 4. Through these items, the leadership

data required to measure the strength of CIO transformational leadership actions or

behaviors (as described in the five relevant subscales) were collected.

Table 4

Transformational Leadership Items in the MLQ 5X Survey Instrument

Transformational Subcomponent Acronym Survey Item Number

Intellectual stimulation IS 2, 8, 30, 32

Individualized consideration IC 15, 19, 29, 31

Idealized influence attributed IIA 10, 18, 21, 25

Idealized influence behavior IIB 6, 14, 23, 34

Inspirational motivation IM 9, 13, 26, 36

Note. Transformational Leadership items comprise 20 of 45 total items in the MLQ 5X.

Descriptive statistics and chi-square tests for assessing normal distribution were

used to characterize computing platform distribution characteristics. Spanning the entire

OS innovation continuum for ERP systems, the overall sample (n = 151) reflected a

normal distribution of CIO mean transformational leadership scores (see Table 5). The p-

value for all combined OSs was a low 0.00001.

Table 5

Chi-square Tests for Normal Distribution of Mean Transformational Leadership Scores by Computing Platform

Size Mean STD Dev X2 p-value Normal

All OSs n = 151 2.295 0.986 26.012 0.00001 Yes

Legacy/MF n = 5 1.253 0.566 1.032 0.7935 No

UNIX n = 65 1.993 1.008 3.757 0.2890 No

Hybrid n = 14 2.568 0.889 8.030 0.0454 Yes

Windows n = 62 2.543 0.858 10.687 0.0135 Yes

Linux n = 5 3.410 0.097 3.274 0.3512 No

Note. STD Dev = Standard Deviation, Normal = Normal Distribution, OSs = Operating Systems.

CIO mean transformational leadership scores related to the Windows and hybrid

subsamples were normally distributed. Several smaller subsamples (Linux and

Legacy/Mainframe) violated normal distribution characteristics (p-value > 0.05),

however, likely due to their small subsample sizes (n = 5 in both cases). The distribution

of mean transformational leadership scores of CIOs responsible for UNIX computing

platforms was moderately skewed and irregularly shaped (see Figure 9) despite its

reasonable subsample size (n = 65).

Figure 9. Mean CIO transformational leadership scores for CIOs responsible for UNIX- based SAP ERP computing platforms.

Although overall sample mean leadership scores reflected a normal distribution,

CIO leadership scores organized by computing platform OS outcome clustered around

different locations. While more detailed analysis of variance is necessary to demonstrate

that all five of these group means are indeed not equal, the scatter plot depicted in Figure

10 illustrates the marked differences in platform-segregated CIO mean transformational

leadership scores.

Figure 10. Mean CIO transformational leadership scores by operating system.

Before analysis of variance could be conducted, it was important first to recognize

to what extent the collected survey data reflected randomly responding participants. The

goal was to show that the survey was not biased towards one subset of the population,

such as those who had deployed the Windows computing platform for ERP versus those

who had deployed the UNIX computing platform. To show this impartiality, randomness

testing was performed using the Runs test explained by Aczel and Sounderpandian (2002,

pp. 647-650). Sample data randomness was analyzed based on the order these data were

entered into the survey utility by survey respondents. Because the Runs test mandates all

data be analyzed in one of two states (plus or minus), all individual (rather than firm-

related) responses were grouped into less innovative (n = 82) and more innovative (n =

92) groups as described in chapter 2. This approach yielded 94 runs in the data, a p-value

of 0.3374, and a z test statistic of 0.9592. At an alpha of 0.05 and using a standard normal

table, the null hypothesis that the data were provided at random was not rejected:

participants supporting a random distribution of computing platform operating systems

was observed in that the z test statistic’s absolute value did not exceed 1.96 (Aczel &

Sounderpandian, 2002).

Survey response rates were also analyzed by date to detect response anomalies.

Responses naturally peaked after the survey commenced and again after each reminder

email was distributed (see Figure 11). In this regard, no anomalies were observed.

Figure 11. Individual respondent survey responses by date.

Demonstrating Dependent Variable Validity

Before data analyses could commence, it was important to validate to some extent

the OS innovation continuum described by Anderson et al. (2009). In this way, the

strength of innovation and subsequent ranking for each OS relative to one another could

be empirically depicted. The data showed that the OS rankings illustrated earlier in

Figure 6 were sound. Legacy/Mainframe and UNIX OSs were perceived as significantly

less innovative than Windows and Linux OSs for SAP, while hybrids were positioned in

the middle.

The OS innovation continuum suggested by the study’s data did not reflect the

evenly spaced OSs earlier depicted in Figure 6. Instead, Linux and Windows OSs

occupied relatively close positions on the innovation continuum, positions associated

with markedly more innovation than their hybrid and UNIX counterparts which occupied

nearly identical positions near the middle of the continuum (see Figure 12).

Legacy/Mainframe OSs anchored the leftmost edge of the innovation continuum.

Figure 12. Suggested by the study’s findings: Relative position of computing platform OSs in terms of perceived innovation.

To better understand each OS’s position on the innovation continuum, OS

attributes were individually rated by respondents on a scale of 1 to 10, where 10 was

described as most important. Figure 13 illustrates the 10 OS innovation attributes and

their perceived importance as rated by SAP Basis professionals responding to the survey.

0.000

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

9.000 Availability

Compatibility

Technical Flexibility

Pricing Innovation

Tools

Integrated Innovation

Org Endurance

Portability

Market Share

Open Source

Figure 13. Respondent ratings of OS innovation attributes.

The ability of an OS to affect system availability and be compatible with SAP

were rated as most important, while open source code and market share were rated least

important. Once these ratings were established, OS attributes were weighted by mean

scores and by using the Analytical Hierarchy Process (AHP) described by Levary (2009)

and others (see Table 6).

Table 6

Operating System Innovation Attribute Ratings by Relative Importance

OS Attribute Mean Percent Percent Weighted Percent Weighted Scores not Weighted by Mean by AHP (0 to 10) (0 to 100) (0 to 100) (0 to 100) Availability 8.418 10% 12.88% 18.18% Application compatibility 7.910 10% 12.10% 16.36% Technical flexibility 7.015 10% 10.73% 14.55% Pricing innovation 6.836 10% 10.46% 12.73% Tools 6.537 10% 10.00% 10.91% Integrated innovation 6.194 10% 9.48% 9.09% Organizational endurance 6.149 10% 9.41% 7.27% Portability 6.060 10% 9.27% 5.45% Market share 5.239 10% 8.02% 3.64% Open source 5.000 10% 7.65% 1.82% Note. See Table 1 for descriptions of each innovation attribute, OS = Operating System, n= 67.

Applying the unweighted or raw scores associated with OS innovation attributes

suggested a ranking in order of least to most innovative of Mainframe/Legacy, UNIX,

hybrids, Windows, and Linux. However, weighting each OS by innovation attribute by

means as a percent of total or by applying the forced ranking method embodied by AHP

placed UNIX slightly ahead of hybrids in terms of perceived innovativeness (see Table

7). All other OSs remained in the same relative positions on the OS innovation

continuum.

Table 7

Operating System Perceived Innovativeness by Evaluation Method

Legacy/MF UNIX Hybrid Windows Linux

Not Weighted, scores 0-50 20.115 31.295 31.344 34.590 37.344

Not Weighted, % of 100 13.00 20.23 20.26 22.36 24.14

Weighted by Mean, % of 100 13.43 20.30 20.18 22.18 23.86

Weighted by AHP, % of 100 14.28 20.63 20.03 21.83 23.23 Note. Legacy/MF = Legacy/Mainframe, n= 61.

While the positional ranking illustrated previously is generally consistent with

contemporary ERP literature (Anderson et al., 2009, pp. 76-77; Dedrick & West, 2003),

the relative location of the positions and perceived strength of each attribute have

provided much-needed substance to the computing platform, innovation, and ERP bodies

of literature. The methods used for weighting OS innovation attributes revealed how the

strength of several attributes such as availability and compatibility deeply influenced all

five OSs from a perceived innovation perspective. Once the validity of the OS innovation

continuum was empirically validated, data analyses commenced. Cronbach’s alpha was

first determined to judge the survey instrument’s internal consistency, as described next.

Calculating Cronbach’s Alpha

To measure the internal consistency and reliability of the MLQ 5X as

administered, Cronbach’s alphas for the overall MLQ as well as the transformational

leadership subscales within the MLQ were calculated. Overall leadership measured

across all 45 items of the MLQ was 0.9223, while the transformational leadership

component of the MLQ 5X (representing 20 items) was 0.9555 (see Table 8).

Table 8

Research Study’s Internal Consistency: Cronbach’s Alpha

Items (K) Sum Var Test Var Cronbach’s Alpha

All Transformational Leadership Scales 45 77.14 785.46 0.9223

Transformational Leadership 20 37.12 402.17 0.9555

Note. Sum Var = Sum of the Item Variances, Test Var = Test Variance.

The study’s results exceeded Nunnally’s (1978) suggested minimum value of 0.60

or greater necessary to indicate adequate reliability. According to Pallant (2007),

Cronbach alpha values with regard to short subscales (those comprised of less than 10

items) may show lower precision. Given that each of the MLQ’s five transformational

leadership subscales is comprised of four items, the strength of the study’s observed

alpha values was unexpected but welcomed. With the internal consistency of the survey

instrument substantiated, analysis by way of descriptive statistics was conducted. Results

are outlined in the next several sections.

Leadership Variables Descriptive Statistics

Descriptive statistics were calculated (see Table 9) for the transformational

leadership independent variable and its five subscales. Standard deviations, standard

errors, and sample variances were similar for each subscale. Despite the sample’s overall

high transformational leadership scores, marked differences in means, medians, and

modes suggested further analysis was in order.

Table 9

Descriptive Statistics by Transformational Leadership Subscale

All IS IC IIA IIB IM

Mean 2.305 2.367 1.874 2.488 2.123 2.694

Standard Deviation 0.989 1.140 1.134 1.020 1.166 1.128

Standard Error 0.080 0.093 0.092 0.083 0.095 0.092

Median 2.5 2.5 2 2.75 2.25 3

Mode 3.2 4 3.25 3.25 1.75 4

Sample Variance 0.978 1.299 1.286 1.040 1.361 1.272

Note. All = Sum of all five transformational leadership scales, IS = intellectual stimulation, IC = individualized consideration, IIA = idealized influence attributed, IIB = idealized influence behavior, IM = inspirational motivation, n = 151.

Transformational leadership grand means scores for subscales organized by

operating system outcome were calculated (see Table 10). Across the sample,

transformational leadership grand means were strong with mean averages exceeding

2.000 for all but one subscale. The grand mean for items related to IS was 2.367, for IIA

was 2.488, and for IIB was 2.123. The grand mean for the items related to IM was 2.694.

At 1.874, only the grand mean for IC was lower than 2.0. The total grand mean for all

items related to transformational leadership was 2.305. Despite the strength of

transformational leadership behaviors across the overall sample, the differences in mean

scores between the computing platforms operating systems outcomes across nearly all

subscales were significant.

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Table 10

Transformational Leadership Grand Mean Scores for Subscales by Operating System

All Subscales IS IC IIA IIB IM

All OSs 2.305 2.367 1.874 2.488 2.123 2.694

Legacy/MF 1.253 1.317 1.200 2.150 0.600 1.500

UNIX 1.993 1.885 1.490 2.198 2.007 2.386

Hybrid 2.568 2.839 2.036 2.804 2.161 3.000

Windows 2.569 2.747 2.220 2.683 2.251 2.950

Linux 3.520 3.650 2.800 3.300 3.450 3.850

Note. All Subscales = Sum of all five transformational leadership scales, IS = intellectual stimulation, IC = individualized consideration, IIA = idealized influence attributed, IIB = idealized influence behavior, IM = inspirational motivation, Legacy/MF = Legacy/Mainframe, n = 151.

Though individual subscale strength varied, transformational leadership scores

(and all five of its subscales) appeared to predict computing platform innovation

outcomes as hypothesized in this dissertation. Each research question and hypothesis is

explored in more detail in the next section.

Testing Hypotheses

With the sample data and relationships between variables understood, the six

research questions were reviewed and related hypotheses tested. The data were analyzed

using one-way ANOVA yielding an F test, which tests whether the means of three or

more normally distributed populations are equal (and therefore that their coefficients are

jointly zero). When the absolute value of the F statistic exceeded Fcritical, the null

hypothesis was rejected. When grouped and analyzed by operating system outcomes, the

data showed they generally met required ANOVA assumptions that all groups be

normally distributed and all variances be equal. However, when grouped and analyzed by

101

MLQ 5X subscales, the assumption of normal distribution was generally preserved (as

noted previously), but variances differed. To account for this violation of a required

ANOVA assumption, the nonparametric equivalent of ANOVA, the Kruskal-Wallis test,

was employed to analyze the data further. The results of these analyses follow.

Research Question and Hypothesis 1

How does the strength of transformational leadership behaviors of CIOs relate to

a firm’s ERP computing platform OS outcomes?

Null Hypothesis 1 (H0): CIO transformational leadership is not associated or is

negatively associated with the OS selected for a firm’s ERP business system.

Alternative Hypothesis 1 (H1): Higher transformational leadership of CIOs is

positively associated with the OS selected for a firm’s ERP business system.

One-way ANOVA showed group means were not equal (see Figure 15), F was

significantly greater than Fcritical (refer again to Table 11), and the resulting p-value was

significantly below the alpha of 0.05. Thus, the null hypothesis was rejected.

Figure 14. Confidence intervals for group means: Transformational leadership mean scores by operating system.

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Table 11

One-Way ANOVA: Transformational Leadership Mean Scores by Subscale

Scale/Subscale Mean STD Dev STD Err F p-value

All Subscales 2.305 0.989 0.080 6.576 0.000068

IS Subscale 2.367 1.140 0.093 9.702 0.000001

IC Subscale 1.874 1.134 0.092 5.056 0.000762

IIA Subscale 2.488 1.020 0.083 3.343 0.011863

IIB Subscale 2.123 1.166 0.095 4.481 0.001916

IM Subscale 2.694 1.128 0.092 5.595 0.000322

Note. IS = intellectual stimulation, IC = individualized consideration, IIA = idealized influence attributed, IIB = idealized influence behavior, IM = inspirational motivation, STD Dev = standard deviation, STD Err = standard error, n = 151, Fcritical = 2.434.

For each subscale, F significantly exceeded Fcritical and, thus, all six null

hypotheses were rejected: There was a significantly greater difference between the

groups than within the groups. All p-values were substantially lower than 0.05. Though

the transformational leadership scale and its subscales were normally distributed as

outlined earlier in this chapter, analysis to confirm the assumption of equal variance

required by ANOVA revealed significant differences (see Table 12). Variances ranged

from 0.012 to 1.032.

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Table 12

Transformational Leadership Variances by Operating System

Legacy/MF UNIX Hybrid Windows Linux

Sample Variance 0.400 1.032 0.852 0.749 0.012

Mean 1.253 1.993 2.568 2.543 3.410

Standard Deviation 0.633 1.016 0.923 0.865 0.108

Standard Error 0.283 0.126 0.247 0.110 0.048

Subsample Size 5 65 14 62 5

Note. Legacy/MF = Legacy/Mainframe, n = 151.

Because the variances differed markedly (and to further account for the

differences in subsample sizes), the Kruskal-Wallis rank sum test, a nonparametric

equivalent of ANOVA, was conducted to analyze these data. The p-value remained very

low, indicating little overlap between the distributions. The null hypothesis was still

rejected. Results are displayed in Table 13.

Table 13

Kruskal-Wallis Transformational Leadership Results: Hypothesis 1

Group Sum R n Mean R Test Statistics

Legacy/MF 157.5 5 31.5

UNIX 4081.0 65 62.785

Hybrid 1235.5 14 88.25

Windows 5330.0 62 85.968

Linux 672.0 5 134.4 H = 24.347, p-value = 0.00007

Note. Legacy/MF = Legacy/Mainframe, R = Ranks, H = Kruskal-Wallis statistic.

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Research Question and Hypothesis 2

How does the strength of IS behaviors, a component of transformational

leadership, relate to a firm’s ERP computing platform OS outcome?

Null Hypothesis 2 (H0): CIO IS is not associated or is negatively associated with

the OS selected for a firm’s ERP business system.

Alternative Hypothesis 2 (H1): Higher CIO IS is positively associated with the OS

selected for a firm’s ERP business system.

One-way ANOVA showed group means were not equal (see Figure 15), F was

significantly greater than Fcritical (refer again to Table 11), and the resulting p-value was

significantly below the alpha of 0.05. Thus, the null hypothesis was rejected.

Figure 15. Confidence intervals for group means: Intellectual Stimulation subscale mean scores by operating system.

Though the IS subscale was normally distributed, analysis to confirm the

assumption of equal variance required by ANOVA revealed significant differences (see

Table 14). Variances ranged from 0.425 to 1.259.

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Table 14

Intellectual Stimulation Subscale Variances by Operating System

Legacy/MF UNIX Hybrid Windows Linux

Sample Variance 0.616 1.259 0.737 0.978 0.425

Mean 1.317 1.885 2.839 2.747 3.650

Standard Deviation 0.785 1.122 0.858 0.989 0.652

Standard Error 0.351 0.139 0.229 0.126 0.292

Subsample Size 5 65 14 62 5

Note. Legacy/MF = Legacy/Mainframe, n = 151.

Because the variances differed markedly (and to further account for the

differences in subsample sizes), the Kruskal-Wallis test was employed to analyze these

data. The p-value remained very low, indicating little overlap between the distributions.

The null hypothesis was still rejected. Results are displayed in Table 15.

Table 15

Kruskal-Wallis Intellectual Stimulation Results: Hypothesis 2

Group Sum R n Mean R Test Statistics

Legacy/MF 176.5 5 35.3

UNIX 3755.0 65 57.769

Hybrid 1298.0 14 92.714

Windows 5605.5 62 90.411

Linux 641.0 5 128.2 H = 31.525, p-value = 0.000002

Note. Legacy/MF = Legacy/Mainframe, R = Ranks, H = Kruskal-Wallis statistic.

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Research Question and Hypothesis 3

How does the strength of IC behaviors, a component of transformational

leadership, relate to a firm’s ERP computing platform OS outcome?

Null Hypothesis 3 (H0): CIO IC is not associated or is negatively associated with

the OS selected for a firm’s ERP business system.

Alternative Hypothesis 3 (H1): Higher CIO IC is associated with the OS selected

for a firm’s ERP business system.

One-way ANOVA showed group means were not equal (see Figure 16), F was

significantly greater than Fcritical (refer again to Table 11), and the resulting p-value was

significantly below the alpha of 0.05. Thus, the null hypothesis was rejected.

Figure 16. Confidence intervals for group means: Individualized Consideration subscale mean scores by operating system.

Though the IC subscale was normally distributed, analysis to confirm the

assumption of equal variance required by ANOVA revealed significant differences (see

Table 16). Variances ranged from 0.075 to 1.180.

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Table 16

Individualized Consideration Subscale Variances by Operating System

Legacy/MF UNIX Hybrid Windows Linux

Sample Variance 0.575 1.225 1.393 1.128 0.075

Mean 1.200 1.490 2.026 2.244 2.800

Standard Deviation 0.758 1.107 1.180 1.062 0.274

Standard Error 0.339 0.137 0.315 0.136 0.122

Subsample Size 5 65 14 62 5

Note. Legacy/MF = Legacy/Mainframe, n = 151.

Because the variances differed markedly (and to further account for the

differences in subsample sizes), the Kruskal-Wallis test was employed to analyze these

data. The p-value remained low, indicating little overlap between the distributions. The

null hypothesis was still rejected. Results are displayed in Table 17.

Table 17

Kruskal-Wallis Individualized Consideration Results: Hypothesis 3

Group Sum R n Mean R Test Statistics

Legacy/MF 252.5 5 50.5

UNIX 4004.5 65 61.608

Hybrid 1152.5 14 82.321

Windows 5511.0 62 88.887

Linux 555.5 5 111.1 H = 17.636, p-value = 0.00145

Note. Legacy/MF = Legacy/Mainframe, R = Ranks, H = Kruskal-Wallis statistic.

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Research Question and Hypothesis 4

How does the strength of IIA behaviors, a component of transformational

leadership, relate to a firm’s ERP computing platform OS outcome?

Null Hypothesis 4 (H0): CIO IIA is not associated or is negatively associated with

the OS selected for a firm’s ERP business system.

Alternative Hypothesis 4 (H1): Higher CIO IIA is positively associated with the

OS selected for a firm’s ERP business system.

One-way ANOVA showed group means were not equal (see Figure 17), F was

significantly greater than Fcritical (refer again to Table 11), and the resulting p-value was

significantly below the alpha of 0.05. Thus, the null hypothesis was rejected.

Figure 17. Confidence intervals for group means: Idealized Influence Attributed subscale mean scores by operating system.

Though the IIA subscale was normally distributed, analysis to confirm the

assumption of equal variance required by ANOVA revealed significant differences (see

Table 18). Variances ranged from 0.169 to 1.156.

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Table 18

Idealized Influence Attributed Subscale Variances by Operating System

Legacy/MF UNIX Hybrid Windows Linux

Sample Variance 1.144 1.036 1.156 0.924 0.169

Mean 2.150 2.198 2.804 2.683 3.300

Standard Deviation 1.069 1.018 1.075 0.961 0.411

Standard Error 0.478 0.126 0.287 0.122 0.184

Subsample Size 5 65 14 62 5

Note. Legacy/MF = Legacy/Mainframe, n = 151.

Because the variances differed markedly (and to further account for the

differences in subsample sizes), the Kruskal-Wallis test was employed to analyze these

data. The p-value remained very low, indicating little overlap between the distributions.

The null hypothesis was still rejected. Results are displayed in Table 19.

Table 19

Kruskal-Wallis Idealized Influence Attributed Results: Hypothesis 4

Group Sum R n Mean R Test Statistics

Legacy/MF 316.5 5 63.3

UNIX 4090.0 65 62.923

Hybrid 1279.0 14 91.357

Windows 5238.5 62 84.492

Linux 552.0 5 110.4 H = 13.39, p-value = 0.00952

Note. Legacy/MF = Legacy/Mainframe, R = Ranks, H = Kruskal-Wallis statistic.

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Research Question and Hypothesis 5

How does the strength of IIB behaviors, a component of transformational

leadership, relate to a firm’s ERP computing platform OS outcome?

Null Hypothesis 5 (H0): CIO IIB is not associated or is negatively associated with

the OS selected for a firm’s ERP business system.

Alternative Hypothesis 5 (H1): Higher CIO IIB is positively associated with the

OS selected for a firm’s ERP business system.

One-way ANOVA showed group means were not equal (see Figure 18), F was

greater than Fcritical (refer again to Table 11) and the resulting p-value was significantly

below the alpha of 0.05. Thus, the null hypothesis was rejected.

Figure 18. Confidence intervals for group means: Idealized Influence Behavior subscale mean scores by operating system.

Though the IIB subscale was normally distributed, analysis to confirm the

assumption of equal variance required by ANOVA revealed significant differences (see

Table 20). Variances ranged from 0.144 to 1.727.

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Table 20

Idealized Influence Behavior Subscale Variances by Operating System

Legacy/MF UNIX Hybrid Windows Linux

Sample Variance 0.144 1.464 1.727 1.054 0.200

Mean 0.600 2.007 2.161 2.251 3.450

Standard Deviation 0.379 1.210 1.314 1.026 0.447

Standard Error 0.170 0.150 0.351 0.130 0.200

Subsample Size 5 65 14 62 5

Note. Legacy/MF = Legacy/Mainframe, n = 151.

Because the variances differed markedly (and to further account for the

differences in subsample sizes), the Kruskal-Wallis test was employed to analyze these

data. The p-value remained very low, indicating little overlap between the distributions.

The null hypothesis was still rejected. Results are displayed in Table 21.

Table 21

Kruskal-Wallis Idealized Influence Behavior Results: Hypothesis 5

Group Sum R n Mean R Test Statistics

Legacy/MF 110.5 5 22.1

UNIX 4693.0 65 72.2

Hybrid 1086.0 14 77.571

Windows 4946.0 62 79.774

Linux 640.5 5 128.1 H = 15.661, p-value = 0.00351

Note. Legacy/MF = Legacy/Mainframe, R = Ranks, H = Kruskal-Wallis statistic.

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Research Question and Hypothesis 6

How does the strength of IM behaviors, a component of transformational

leadership, relate to a firm’s ERP computing platform OS outcome?

Null Hypothesis 6 (H0): CIO IM is not associated or is negatively associated with

the OS selected for a firm’s ERP business system.

Alternative Hypothesis 6 (H1): Higher CIO IM is positively associated with the

OS selected for a firm’s ERP business system.

One-way ANOVA showed group means were not equal (see Figure 19), F was

significantly greater than Fcritical (refer again to Table 11), and the resulting p-value was

significantly below the alpha of 0.05. Thus, the null hypothesis was rejected.

Figure 19. Confidence intervals for group means: Inspirational Motivation subscale mean scores by operating system.

Though the IM subscale was normally distributed, analysis to confirm the

assumption of equal variance required by ANOVA revealed significant differences (see

Table 22). Variances ranged from 0.113 to 1.507.

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Table 22

Inspirational Motivation Subscale Variances by Operating System

Legacy/MF UNIX Hybrid Windows Linux

Sample Variance 0.906 1.507 1.144 0.821 0.113

Mean 1.500 2.386 3.000 2.950 3.850

Standard Deviation 0.952 1.228 1.070 0.906 0.335

Standard Error 0.426 0.152 0.286 0.115 0.150

Subsample Size 5 65 14 62 5

Note. Legacy/MF = Legacy/Mainframe, n = 151.

Because the variances differed markedly (and to further account for the

differences in subsample sizes), the Kruskal-Wallis test was employed to analyze these

data. The p-value remained very low, indicating little overlap between the distributions.

The null hypothesis was still rejected. Results are displayed in Table 23.

Table 23

Kruskal-Wallis Inspirational Motivation Results: Hypothesis 6

Group Sum R n Mean R Test Statistics

Legacy/MF 152.5 5 30.5

UNIX 4226.0 65 65.015

Hybrid 1237.5 14 88.393

Windows 5205.5 62 83.96

Linux 654.5 5 130.9 H = 20.569, p-value = 0.00039

Note. Legacy/MF = Legacy/Mainframe, R = Ranks, H = Kruskal-Wallis statistic.

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Confounding Variables Analyses

This section explores the relationship between the dependent variable (operating

system outcomes) and the potentially confounding variables outlined in the chapter 2

literature review. Data for each of the potentially confounding variables were collected

through demographic and computing platform-related survey instrument. To analyze the

five computing platform operating system outcomes taking into account these potentially

confounding variables, one or more of the following tests were conducted: descriptive

statistics, t tests, one-way ANOVA, and chi-square tests for independence.

Age, Years of Experience, and Years with the Firm

Demographic items related to time were analyzed, including years of SAP Basis

experience held by participants, years the participants have been with their respective

firms, and the age of participants. Analyzing these time-related items via descriptive

statistics uncovered several trends (see Table 24). Descriptive statistics indicated that less

innovative platforms (based on Legacy/Mainframe and UNIX operating systems) were

generally supported by older individuals with more experience and employee seniority,

while more innovative platforms (based on Windows, Linux, and hybrid operating

systems) tended to be supported by younger individuals with less experience and less

seniority.

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Table 24

Descriptive Statistics for Time-Related Items and Measure of Computing Platform Innovation

Yrs Basis Experience Yrs with Firm Age (Yrs) of Participant

All Less More All Less More All Less More

Mean 8.1 9.0 7.3 5.5 6.0 5.1 41.4 43.2 39.9

Standard Deviation 3.46 3.29 3.39 6.05 6.30 5.85 6.76 6.53 6.62

Standard Error 0.26 0.37 0.35 0.46 0.72 0.60 0.52 0.75 0.68

Median 8.0 10.0 7.0 3.5 4.0 3.0 40.0 41.0 38

Mode 10.0 10.0 7.0 2.0 2.0 3.0 40.0 40.0 38

Sample Variance 11.94 10.82 11.50 36.62 39.63 34.22 45.74 42.64 43.81

Range 14 14 14 29 29 28 36 28 36

Minimum 1 1 1 1 1 1 28 32 28

Maximum 15 15 15 30 30 29 64 60 64

Individual Responses 172 78 94 170 76 94 169 75 94

Note. Yrs = Years, All = Entire Sample, Less = Less Innovative subsample, More = More Innovative subsample, n = 151 firms.

To quantify the apparent relationships noted in Table 24, chi-square tests for

independence were employed. Per chi-square convention, several groups (4) of

approximately the same size were created to facilitate the analysis. Chi-square testing for

independence further confirmed that participant age and computing platform OS were

related (see Table 25).

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Table 25

Chi-square Testing for Independence by SAP Basis Professionals’ Mean Age and Operating System

Age Legacy/MF UNIX Hybrid Windows Linux

>44 2 19 3 16 2

40-44 3 30 3 13 2

37-39 0 16 2 20 0

<37 0 6 6 26 0 X2 = 30.706

p-value = 0.0022

Note. Legacy/MF = Legacy/Mainframe, df = 12.

Because the p-value was less than 1% (Aczel & Sounderpandian, 2002, p. 692),

the null hypothesis that age (n = 169) and OS platform are not related was rejected. This

finding aligned with the innovation literature outlined in chapter 2: Younger workers tend

to be associated with greater innovation diffusion than their more senior counterparts.

Similar chi-square testing for independence revealed no statistical relationship,

however, between a respondent’s years of SAP Basis experience and computing platform

OS (Table 26) or between a respondent’s years employed with the firm and computing

platform OS (Table 27).

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Table 26

Chi-square Testing for Independence by Years of Experience and Operating System

Years Exp Legacy/MF UNIX Hybrid Windows Linux

>10 1 26 1 14 0

8-10 2 24 3 21 1

6-7 0 12 2 18 2

<6 1 13 7 23 1 X2 = 18.264

p-value = 0.1079

Note. Legacy/MF = Legacy/Mainframe, df = 12.

Because the p-value was greater than 1% (though marginally so), the null

hypothesis was not rejected. Analyzing seniority or years of experience (n = 172) and the

computing platform OS did not show a significant relationship.

Table 27

Chi-square Testing for Independence by Years with Firm and Operating System

Years w/Firm Legacy/MF UNIX Hybrid Windows Linux

>6 3 18 1 18 0

4-6 1 20 7 16 1

2-3 0 22 5 27 2

<2 0 12 1 15 1 X2 = 14.879

p-value = 0.2481

Note. Legacy/MF = Legacy/Mainframe, df = 12.

Given the p-value was greater than 1%, the null hypothesis was not rejected. The

relationship between the number of years employed with the firm (n = 170) and the

computing platform OS did not indicate a significant relationship.

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Basis Team Size and Annual Revenue

Significant differences in OS outcomes with regard to SAP Basis team size (see

Table 28) as well as a firm’s size measured in annual revenue (see Table 29) were noted.

The research study was generally at odds with the innovation literature (Fichman, 2000;

Rogers, 2003) in that greater size was not positively related to more innovative outcomes.

Table 28

Chi-square Testing for Independence by SAP Basis Team Size and Operating System

Team Size Legacy/MF UNIX Hybrid Windows Linux

>7 1 25 5 9 1

6-7 0 15 6 10 0

4-5 3 16 2 19 3

<4 0 7 0 24 0 X2 = 37.375

p-value = 0.0002

Note. Legacy/MF = Legacy/Mainframe, df = 12.

Table 29

Chi-square Testing for Independence by a Firm’s Annual Revenue and OS

Firm Revenue Legacy/MF UNIX Hybrid Windows Linux

>$20B 0 21 3 10 1

$5-20B 2 18 5 13 0

$2-4.9B 1 16 3 17 3

<$2B 2 9 2 22 0 X2 = 19.601

p-value = 0.0750

Note. Legacy/MF = Legacy/Mainframe, df = 12.

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Because both p-values were significantly less than 1%, the null hypotheses that

the SAP Basis team size (n = 146) and computing platform OS are not related and that

annual firm revenue (n = 148) and OS computing platform are not related were not

rejected. In both cases, a significant relationship was confirmed.

Further analysis of data related to size, including annual revenue and Basis team

size, showed that while teams supporting less innovative platforms employed greater

mean SAP Basis headcount, these same teams were actually leaner in terms of revenue

per employee ($1.2B to $3.0B). In contrast, more innovative environments claimed only

$1.61B to $2.18B in revenue per person. See Table 30 for an analysis of revenue per

employee and other descriptive statistics grouped by computing platform OS.

Table 30

Annual Firm Revenue per Employee by Operating System (Billions USD)

Legacy/MF UNIX Hybrid Windows Linux

Mean Revenue $6.244 $25.802 $11.507 $10.850 $10.255

Standard Dev $9.231 $53.600 $14.807 $19.707 $12.311

Headcount 5.25 8.59 7.15 4.97 5.00

Revenue/Employee $1.189 $3.004 $1.608 $2.184 $2.051

Note. Legacy/MF = Legacy/Mainframe.

The remaining five potentially confounding variables analyzed in this study

reflect mutually exclusive nominal or categorical data. These data were analyzed using

limited descriptive statistics and t tests.

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Categorical Confounding Variables

A significant difference was found between firms whose CIOs inherited their ERP

computing platforms and those CIOs who were in position when the platform was

selected. A t test was employed to assess two groups with regard to transformational

leadership scores assuming unequal variance. Group 1 was the group of participants

whose CIO inherited the firm’s ERP computing platform. Group 2 was the group of

participants whose CIO was in position when the current ERP computing platform was

selected, presumably influencing the computing platform decision by virtue of their

influence on the SAP Basis team. Table 31 shows the results of the t test combined with

limited descriptive statistics. CIOs of inherited ERP computing platforms reflected lower

mean transformational leadership scores.

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Table 31

Results of t test Assuming Unequal Variance: Transformational Leadership Mean Scores of CIOs who Inherited vs Influenced their Computing Platform Selection

Inherited Influenced

Mean 2.061 2.427

Standard Deviation 0.954 0.988

Variance 0.909 0.976

Test Statistic (t Stat) -2.2111

p-value (T<=t) one-tail 0.0146, rejected

t Critical one-tail 1.6587

p-value (T<=t) two-tail 0.0291, rejected

t Critical two-tail 1.9816

Confidence Interval -0.3662 ± 0.3282

Note. n = 53 for Inherited, n = 95 for Influenced.

At an alpha of 5%, both p-values indicated the null hypothesis (that there is no

difference in population means) was rejected. Note that mean transformational leadership

scores of CIOs who influenced the selection of their ERP computing platforms were

17.76% higher than those who inherited their platform from a predecessor CIO.

With regard to another potentially confounding variable, the data did not show a

significant difference between staffing models and the OS computing platform outcome.

A t test was employed to assess two groups assuming unequal variance. Group 1 was the

group of participants representing firms that insourced or internally managed their ERP

computing platform. Group 2 was the group of participants representing firms that

outsourced their computing platform to a third party hosting provider. Table 32 shows the

results of the t test combined with limited descriptive statistics. CIOs who had outsourced

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their ERP computing platforms showed slightly lower though not statistically significant

mean transformational leadership scores.

Table 32

Results of t test Assuming Unequal Variance: Transformational Leadership Mean Scores of CIOs Responsible for Insourced vs Outsourced Computing Platforms

Insourced Outsourced

Mean 2.341 2.186

Standard Dev 0.990 0.991

Variance 0.980 0.981

Test Statistic (t Stat) 0.8143

p-value (T<=t) one-tail 0.2095, not rejected

t Critical one-tail 1.6725

p-value (T<=t) two-tail 0.4189, not rejected

t Critical two-tail 2.0032

Confidence Interval -0.1555 ± 0.3826

Note. n = 116 for Insourced computing platforms, n = 35 for Outsourced computing platforms.

At an alpha of 5%, both p-values indicated the null hypothesis (that there is no

difference in population means) was not rejected. The difference was not significant

enough to reject the null hypothesis. However, note that mean transformational

leadership scores of CIOs with internally managed ERP computing platforms were 7.09%

higher than those with outsourced computing platforms.

The study failed to uncover a statistically significant difference in mean

leadership scores of CIOs who employed SAP-focused innovation sponsors or champions

(such as a chief technology officer, chief technologist, or an SAP technical leadership

organization tasked with promoting technical innovation) and CIOs who did not. A t test

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was employed to assess two groups with regard to transformational leadership scores

assuming unequal variance. Group 1 was the group of participants representing firms

who employed innovation sponsors; group 2 was the group of participants representing

firms who did not employ innovation sponsors. Table 33 displays the results of the t test

combined with limited descriptive statistics.

Table 33

Results of t test Assuming Unequal Variance: Transformational Leadership Mean Scores of CIOs who Employ Innovation Sponsors vs no Innovation Sponsors

Innovation Sponsor No Sponsor

Mean 2.450 2.179

Standard Deviation 0.909 1.031

Variance 0.827 1.063

Observations 76 72

Test Statistic (t Stat) 1.6926

p-value (T<=t) one-tail 0.0464, not rejected

t Critical one-tail 1.6557

p-value (T<=t) two-tail 0.0928, not rejected

t Critical two-tail 1.9769

Confidence Interval 0.2710 ± 0.3166

Note. n = 76 for firms that employ Innovation Sponsors, n = 72 for firms that do not employ Innovation Sponsors.

At an alpha of 5%, both p-values above indicated the null hypothesis (that there is

no difference in population means) was not rejected. However, the p-value (0.0464)

associated with H0: m1 - m2 <= 0 indicated a difference in population means. Combined

with the sample statistics showing that mean transformational leadership scores of CIOs

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who employed innovation sponsors were 12.44% higher than those without innovation

sponsors, the data suggested a marked if not significant difference.

A significant difference was found, however, between CIOs leading SAP Basis

teams with a track record of innovation (such as successfully implementing innovation

with regard to the SAP computing platform, or having experience in the process of

adopting platform innovation) and those with no such track record. A t test to assess two

groups with regard to transformational leadership scores assuming unequal variance was

employed. Group 1 was the group of participants representing CIOs leading teams with a

track record of innovation; group 2 was the group of participants representing CIOs

leading teams with no such track record. Table 34 displays the results of the t test

combined with limited descriptive statistics. CIOs leading teams with a track record of

innovation reflected significantly higher mean transformational leadership scores than

their counterparts with no such track record.

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Table 34

Results of t test Assuming Unequal Variance: Transformational Leadership Mean Scores of CIOs Leading SAP Basis Teams with a Track Record of Innovation vs no Track Record of Innovation

Track Record No Track Record

Mean 2.449 1.910

Standard Dev 0.900 1.099

Variance 0.811 1.208

Test Statistic (t Stat) 2.6687

p-value (T<=t) one-tail 0.0051, rejected

t Critical one-tail 1.6753

p-value (T<=t) two-tail 0.0102, rejected

t Critical two-tail 2.0076

Confidence Interval 0.5391 ± 0.4057

Note. n = 112 for SAP Basis teams with a Track Record of Innovation, n = 36 for SAP Basis teams with no Track Record of Innovation.

At an alpha of 5%, both p-values indicated the null hypothesis (that there is no

difference in population means) was rejected. Mean transformational leadership scores of

CIOs leading SAP Basis teams with a track record of innovation were 28.22% higher

than those with no such record.

A significant difference was noted between CIOs leading teams that employed a

company-internal SAP knowledge management (KM) system to support the SAP Basis

team’s lessons learned and knowledge gained relative to SAP implementation and

support and those that did not. A t test to assess two groups with regard to

transformational leadership scores assuming unequal variance was employed. Group 1

was the group of CIOs leading teams who employed a KM system; group 2 was the

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group of CIOs leading teams without a KM system. Table 35 shows the results of the t

test combined with limited descriptive statistics. CIOs leading teams who had employed

KM systems reflected higher mean transformational leadership scores than CIOs leading

teams with no such KM system.

Table 35

Results of t test Assuming Unequal Variance: Transformational Leadership Mean Scores of CIOs Leading Teams that Employed a SAP Knowledge Management System vs no SAP Knowledge Management System

KM System Employed No KM System

Mean 2.498 2.129

Standard Deviation 0.958 0.966

Variance 0.918 0.934

Test Statistic (t Stat) 2.332

p-value (T<=t) one-tail 0.011, rejected

t Critical one-tail 1.655

p-value (T<=t) two-tail 0.021, rejected

t Critical two-tail 1.976

Confidence Interval 0.3691 ± 0.3128

Note. n = 76 for firms with a SAP KM system, n = 72 for firms with no SAP KM system.

At an alpha of 5%, both p-values indicated that the null hypothesis (that there is

no difference in population means) was rejected. Mean transformational leadership scores

of CIOs leading teams that had employed an SAP knowledge management system were

17.33% higher than those leading teams with no such system.

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Research Questions: Themes, Findings, and Alternate Interpretations

It was theorized that strong transformational leadership behaviors, particularly

high IS, could predict more innovative computing platform operating system outcomes.

The research study confirmed this prediction and further showed that all five

transformational leadership subscales could predict whether a less innovative or more

innovative OS was deployed. IM was found to be a stronger predictor of innovation than

IS. The IM grand mean was well above 2.0, or the point at which transformational

leadership scores transition from a rating of low to high. With regard to OS outcomes, the

study’s findings aligned well with the ERP and computing platform literature (Anderson

et al., 2008; Anderson et al., 2009; Dedrick & West, 2003). Representing the lower end

of the OS innovation continuum, Legacy/Mainframe and UNIX platforms were shown to

be related to lower CIO mean transformational leadership scores. Conversely,

representing the higher end of the OS innovation continuum, Windows, Linux, and

hybrid platforms were shown to be related to higher CIO mean transformational

leadership scores.

Contrary to what was expected based on the literature (Anderson et al., 2009),

mean transformational leadership scores of CIOs responsible for hybrid computing

platforms exceeded scores of CIOs responsible for Windows-based platforms, suggesting

hybrids might represent a more innovative outcome. Their potential to reduce costs

despite their inherent technical complexity might also explain why hybrids appear to be

gaining market share (a topic explored in more detail in chapter 5). Alternatively,

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technical teams supporting hybrids may be more inclined to participate in innovation

surveys.

An SAP Basis professional’s years of company service and years of SAP Basis

experience also failed to show a significant relationship to computing platform outcomes,

though mean scores implied a moderate relationship in both cases. The level of broad IT

experience necessary to successfully deploy business-critical applications may call for

generally senior IT professionals regardless of the computing platform selected.

Consistent with the innovation literature, younger SAP professionals correlated

well to Windows, Linux, and hybrid-based computing platforms. This finding might be

explained, however, by the tendency of firms new to SAP to deploy smaller business

units predisposed to selecting less-costly computing platforms like Windows and Linux

first. Similarly, smaller SAP Basis teams (with regard to head count) might imply

relatively small SAP implementations regardless of computing platform while small

firms (with regard to annual revenue) may be naturally drawn to low-cost computing

platform alternatives rather than those platforms perceived as more innovative.

Consistent with the innovation literature, SAP Basis team size and a firm’s annual

revenue were both related to innovation outcomes. However, the relationship was

negative in direction rather than the expected positive. Like the relationship to age, this

finding could be explained by the tendency of small firms or those new to SAP to deploy

smaller business units predisposed to selecting less-costly computing platforms like

Windows and Linux first.

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Several of the ordinal or categorical potentially confounding variables

demonstrated a relationship to computing platform outcomes. These included CIO

platform inheritance, SAP Basis teams possessing a track record of innovation, and teams

that employed a SAP-centric knowledge management (KM) system for troubleshooting

and resolving technical issues. Other potentially confounding variables failed to show a

statistically significant relationship to OS computing platform outcomes, though all of the

studied variables revealed at least a moderate relationship. By way of example, mean

transformational leadership scores of CIOs managing outsourced ERP computing

platforms varied less than expected from their internally-staffed counterparts. In the same

way, neither years of respondent experience nor individual respondent seniority proved to

be strong confounding factor. These findings and inconsistencies suggest similar studies

encompassing a larger sample size might uncover a significant relationship. In chapter 5,

the study’s findings, themes, additional inconsistencies, and conclusions are explored

further and interpreted.

CHAPTER 5: SUMMARY, CONCLUSION, AND RECOMMENDATIONS

Study Summary

The purpose of this study was to relate the strength of CIO transformational

leadership behaviors to the computing platform operating system selected for a firm’s

ERP business system. The researcher investigated to what extent transformational

leadership theory predicted the OS deployed for ERP, using a valid and reliable

instrument appended with 17 items to collect demographic, platform, and potentially

confounding variable data. Previous research showed that executive leaders who

practiced strong transformational leadership behaviors stimulated organizational

innovation from an IT business system perspective. As the executives tasked with

deploying and managing technology, CIOs are held most accountable for strategic IT

investments. However, previous studies failed to explain the relationship between CIO

leadership behaviors and the IT computing platform selected for critical business

applications, an important gap in the literature because innovative computing platforms

enable business system agility which in turn affects a firm’s competitiveness, market

position, and longevity.

The study collected data from 151 North American firms over a 5-week period

and employed descriptive statistics, analysis of variance, Kruskal-Wallis tests, chi-square

tests, and t tests as its primary statistical analysis tools. Empirical findings suggest that

the strength of transformational leadership, including all five subscales of Bass and

Avolio’s (1995) MLQ, strongly predict OS computing platform outcomes. High

transformational leadership scores positively related to more innovative computing

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platforms (Windows, Linux, and hybrids) while low transformational leadership scores

related to less innovative platforms (Legacy/Mainframe and UNIX). The IM, IS, and IC

subscales correlated best. This research contributed to the literature by demonstrating the

strength and direction of relationships between these leadership and OS variables,

including the effect of several potentially confounding variables including SAP Basis

team member age, experience, seniority, team size, firm annual revenue, and more.

Conclusions

The data analyzed in this study revealed a positive relationship between

transformational leadership behaviors and technical innovation. Strong transformational

leadership behaviors predicted greater innovation, while weaker transformational

leadership behaviors predicted less innovation. Each of the six null hypotheses associated

with the six research questions examined in the research study are explored in the next

section.

Relationship between Transformational Leadership and Platforms

The first null hypothesis stated that CIO transformational leadership is not

associated or is negatively associated with the computing platform operating system

selected for a firm’s ERP business system. If true, then there would be no significant

difference between the strength of leadership behaviors and the OS outcome. The study’s

results demonstrated that more innovative computing platforms were deployed in the

wake of stronger CIO transformational leadership behaviors. The results demonstrated

the contrary was also true: Weaker transformational leadership behaviors were related to

the deployment of less innovative computing platforms.

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From a sample data perspective, chi-square analysis demonstrated a normal

distribution. Comparing computing platform outcomes revealed significant differences in

means, medians, and modes, including significant differences in the grand means of

transformational leadership and its subscales. One-way ANOVA yielded a very low p-

value of 0.000068, and at an alpha of 0.05 the F statistic was well above Fcritical. These

results indicated the null hypothesis was rejected due to a very strong relationship

between transformational leadership and OS computing platform outcomes (refer again to

Table 11). However, sample variances ranged dramatically (from 0.012 to as much as

1.032), violating a critical assumption of ANOVA. Thus, the Kruskal-Wallis rank sum

test, a nonparametric equivalent of ANOVA, was employed to analyze the data further.

The Kruskal-Wallis test yielded a similarly low p-value of 0.000070, also indicating the

null hypothesis was rejected.

Throughout the sample, overall CIO transformational leadership scores were

actually quite strong, reflecting a mean of 2.305. Even the CIOs responsible for less

innovative environments (n = 70) earned a mean 1.959 score on a scale of 0 to 4, where 2

represented the midpoint between low and high scores. CIOs responsible for more

innovative environments (n = 81) yielded a transformational leadership mean score of

2.620, or 33.74% greater than their counterparts managing less innovative platforms.

The strength of transformational leadership was thus shown to predict the

computing platform operating system deployed for ERP. Individual subscale strengths

varied, however. The five transformational leadership subscales are explored next.

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Relationship between IS and Computing Platforms

The second null hypothesis stated that CIO IS is not associated or is negatively

associated with the operating system selected for a firm’s ERP business system. If true,

then there would be no significant difference between the strength of IS behaviors and the

OS outcome. The study’s results demonstrated that more innovative platforms were

deployed in the wake of stronger CIO IS leadership behaviors and that the converse was

true as well. The grand mean for transformational leadership IS scores of CIOs

responsible for more innovative platforms was significantly greater (2.818) than CIOs

scores of less innovative platforms (1.876). Due to this large delta (a full point and more

than 50%), the IS component of transformational leadership might be employed as a

simple and effective computing platform predictor. Instead of seeking responses to all 45

questions posed by the MLQ 5X, future studies might find it more expedient to

investigate the four IS subscale items of transformational leadership.

In support of this notion, one-way ANOVA analyses yielded the lowest p-value

observed (0.000001, well below alpha of 0.05) and the F statistic was well above Fcritical,

indicating the null hypothesis was rejected due to a very strong relationship between IS

and OS computing platform outcomes (refer again to Table 11). However, sample

variances ranged dramatically (from 0.425 to 1.259), violating a critical assumption of

ANOVA. Thus, the Kruskal-Wallis test was conducted which yielded a similarly low p-

value of 0.000002, also indicating the null hypothesis was rejected.

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Relationship between IC and Computing Platforms

The third null hypothesis tested stated that CIO IC is not associated or is

negatively associated with the operating system selected for a firm’s ERP business

system. If true, then there would be no significant difference between the strength of IC

behaviors and the OS outcome. The study’s results demonstrated that more innovative

platforms were deployed in the wake of stronger CIO IC behaviors and that the converse

was true as well. One-way ANOVA analyses yielded a low p-value of 0.000762, and at

an alpha of 0.05 the F statistic was well above Fcritical, indicating the null hypothesis was

rejected due to a very strong relationship between IC and OS computing platform

outcomes (refer again to Table 11). However, sample variances ranged dramatically

(from 0.075 to 1.180), violating a critical assumption of ANOVA. Thus, the Kruskal-

Wallis test was conducted which yielded a low p-value of 0.001454, also indicating the

null hypothesis was rejected.

The grand mean for transformational leadership IC scores of CIOs responsible for

more innovative platforms was significantly greater (2.224) than CIOs scores of less

innovative platforms (1.484), representing a 49.87% delta. Given the strength of the

difference, IC subscale scores might prove a more effective and equally simple predictor

of computing platform outcomes than the IS subscale.

Relationship between IIA and Computing Platforms

The fourth null hypothesis tested stated that CIO IIA is not associated or is

negatively associated with the operating system selected for a firm’s ERP business

system. If true, then there would be no significant difference between the strength of IIA

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behaviors and the OS outcome. The study’s results demonstrated that more innovative

platforms were deployed by CIOs exercising stronger IIA behaviors and that the converse

was true as well. One-way ANOVA analyses yielded a p-value of 0.011863, and at an

alpha of 0.05 the F statistic was well above Fcritical. The results indicated the null

hypothesis was rejected due to a very strong relationship between IIA and OS computing

platform outcomes (refer again to Table 11). However, sample variances ranged

dramatically (from 0.169 to 1.156), violating a critical assumption of ANOVA. Thus, the

Kruskal-Wallis test was conducted which yielded an even lower p-value of 0.009518,

also indicating the null hypothesis was rejected.

The grand mean for transformational leadership IIA scores of CIOs responsible

for more innovative platforms was significantly greater (2.742) than scores of CIOs

responsible for less innovative platforms (2.209). The difference reflects a 24.13% delta.

Like the IS and IC subscales, IIA scores might also serve as a useful predictor of

computing platform outcomes.

Relationship between IIB and Computing Platforms

The fifth null hypothesis tested stated that CIO IIB is not associated or is

negatively associated with the operating system selected for a firm’s ERP business

system. If true, then there would be no significant difference between the strength of IIB

behaviors and the OS outcome. The study’s results demonstrated that more innovative

platforms were deployed in the wake of stronger CIO IIB behaviors and that the converse

was true as well. One-way ANOVA analyses yielded a p-value of 0.001916, and at an

alpha of 0.05 the F statistic was well above Fcritical, indicating the null hypothesis was

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rejected due to a very strong relationship between IIB and OS computing platform

outcomes (refer again to Table 11). However, sample variances ranged dramatically

(from 0.144 to 1.727), violating a critical assumption of ANOVA. Thus, the Kruskal-

Wallis test was conducted which yielded a low p-value of 0.003509, also indicating the

null hypothesis was rejected.

Further evidence was noted in the differences in grand means for transformational

leadership IIB mean scores of CIOs responsible for more innovative platforms (2.310)

versus mean scores of CIOs responsible for less innovative platforms (1.907),

representing a 21.13% delta. Although IIB scores might be the least useful of the five

transformational leadership subscales with regard to predicting computing platform OS

outcomes, they were shown by this study to predict the outcome nonetheless.

Relationship between IM and Computing Platforms

The sixth and final null hypothesis tested stated that CIO IM is not associated or

is negatively associated with the operating system selected for a firm’s ERP business

system. If true, then there would be no significant difference between the strength of IM

behaviors and the OS outcome. The study’s results demonstrated that more innovative

platforms were deployed by CIOs exercising strong IM behaviors and that the converse

was true as well. One-way ANOVA analyses yielded a p-value of 0.000322, and at an

alpha of 0.05 the F statistic was well above Fcritical, indicating the null hypothesis was

rejected due to a very strong relationship between IM and OS computing platform

outcomes (refer again to Table 11). However, sample variances ranged dramatically

(from 0.113 to 1.507), violating a critical assumption of ANOVA. Thus, the Kruskal-

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Wallis test was conducted which yielded a similar p-value of 0.000385, also indicating

the null hypothesis was rejected.

Interestingly, the grand mean for transformational leadership IM scores of CIOs

responsible for more innovative platforms was significantly greater (3.014) than the

scores of CIOs responsible for less innovative platforms (2.355), representing a 27.98%

delta. From this perspective, IM scores appeared to be perhaps the most useful predictor

of the five transformational leadership subscales of computing platform OS outcomes,

even more so than IS and IC scores. This outcome agrees with executive leadership

literature stating that CIO success has been shown to rest in large part on the executive

leader’s ability to cast a vision (Earl, 2004) or provide visionary leadership (Elenkov et

al., 2005), both of which are tantamount to IM.

Relationship between Age and Computing Platforms

Beyond the study’s six hypotheses, several conclusions could be drawn with

regard to a number of confounding variables. Age was shown to be one of the most

significantly confounding factors (p-value = 0.0022) across three of the five OS

computing platform outcomes. Interestingly, a greater number of participants (n = 94)

supporting more innovative platforms participated in this study than participants (n = 75)

supporting less innovative platforms, a difference of 25.33%. The nature of the study

(investigating the intersection of leadership and innovation) may have innately appealed

more strongly to participants actively supporting more innovative environments.

The mean age of all participants (n = 169) was 41.4 years (ranging from 28 to 64).

The study revealed that more innovative platforms were supported by younger

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employees. The mean age of team members supporting less innovative computing

platforms (Legacy/Mainframe and UNIX) was 43.2 compared to 39.9 for more

innovative platforms (hybrids, Windows, and Linux). The former were on average only

8.27% older than the latter. A two tailed t test of the two groups assuming unequal

variance, however, confirmed significant differences between their respective means (p-

value = 0.001428, well below the tcritical of 1.9749). Larger subsample sizes might

describe an even broader delta between ages.

Relationships between Revenue, Team Size, and Computing Platforms

A firm’s annual revenue was shown to be a confounding factor in t tests where

more innovative platforms were deployed by generally smaller firms, while less

innovative platforms like UNIX were more frequently deployed by larger firms.

However, an analysis of variance of all five computing platforms yielded a low F statistic

compared to Fcritical, and a relatively high p-value, indicating less difference between than

within the means. Mean annual revenue across the sample was $17.237 billion, but a

standard deviation of $38.387B indicated wide dispersion and a very flat bell curve.

The mean annual revenue of firms that deployed less innovative computing

platforms for ERP (Legacy/Mainframe and UNIX) was $24.651B, 2.25 times larger than

the mean annual revenue of firms that deployed more innovative platforms (hybrids,

Windows, and Linux). A mean headcount of 8.39 (versus 5.33) enabled calculating mean

revenue per SAP Basis employee of $6.225B for less innovative computing platforms

versus $3.469B for more innovative computing platforms. Thus, it could be inferred that

SAP Basis teams supporting less innovative computing platforms realized 179% greater

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revenue per Basis employee than their more innovative counterparts, indicating SAP

Basis teams supporting less innovative platforms are actually leaner than those

supporting more innovative platforms. SAP Basis team size (headcount) was shown to be

a confounding factor (p-value = 0.0002) across three of the five OS computing platform

outcomes. More innovative platforms were supported by significantly leaner teams than

their less innovative counterparts.

Relationships between Other Variables and Computing Platforms

Several other variables were shown to have a relationship to computing platform

OS outcomes, including CIO influence versus inheritance. CIOs who held their

executive-level IT leadership position when the ERP computing platform was selected

was shown to be a confounding factor potentially affecting OS computing platform

outcomes. Regardless of platform, the mean transformational leadership scores of CIOs

in position when their computing platform was selected were 17.75% higher than scores

of CIOs who inherited the ERP platform from a predecessor. This relationship might

imply that CIOs who are in position to influence the computing platform selection

process may benefit from a certain amount of joint decision-making and subsequent

camaraderie unavailable to CIOs who simply inherit their ERP computing platform.

Mean transformational leadership scores of CIOs responsible for SAP Basis teams

with a track record of innovation were shown to be 28.22% higher than those with no

such record. Similarly, mean transformational leadership scores of CIOs working for

firms that had employed a SAP-centric knowledge management system were 17.33%

higher than those working for firms with no such system. These findings and the other

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conclusions drawn here generally confirmed findings observed in both the leadership and

innovation bodies of literature. The study’s theoretical construct outlined earlier in

chapter 1that strategic leadership behaviors on behalf of executive leaders influence

innovation in the form of the technology platform deployed for business applicationsis

preserved as well. To put these new findings into action, several recommendations

spanning methodological opportunities, gaps in the literature warranting additional

research, and specific follow-up steps a CIO or executive board should consider, have

been assembled. These recommendations are covered in the next section.

Recommendations

This study has shown that transformational leadership and its five subscales are

sound predictors of the computing platform operating system deployed for ERP systems.

From methodological opportunities to gaps in the literature warranting additional

research and steps to be considered by executive leadership, the study uncovered several

areas demanding closer examination.

Methodological Recommendations

From a methodology perspective, several matters should be considered by future

researchers. First, researchers should consider designing studies using social or

professional networking sites to attract and contact prospective respondents. In this way,

particular disciplines may be targeted and individual email addresses and other contact

information obtained without the need to gain special approvals. In the same way,

targeting profession-oriented technology support and networking sites that provide

domain-specific forums may prove useful in easily contacting prospective respondents.

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While the study’s sample size was adequate and normally distributed, subsample

sizes and distribution characteristics varied considerably. Future researchers should

consider methodologies that require minimum subsample sizes as well as sample size. In

the case of this dissertation study, from a population perspective, the study benefited

from better-than-expected hybrid, Mainframe/Legacy, and UNIX response rates as a

percentage of the population. However, poorer response rates in terms of population

percentages were obtained from respondents working for firms that deployed Windows-

based and particularly Linux-based ERP systems. Determining and enforcing a minimum

subsample size for each operating system would have provided a stronger base for

statistical analyses.

When using email as a method of initially contacting respondents, researchers

need to remind prospective respondents to check their email spam filters and allow

subsequent emails to pass. Because the first email may be caught by the filter (and only

seen by the respondent much later by chance, if at all, when emptying their email junk

folder), it is imperative for researchers to send multiple email reminders. Researchers

should also avoid sending emails that use words commonly screened by filters.

Finally, rather than creating a single reminder email (sent weekly to remind

prospective respondents to participate in the survey), future researchers should construct

a new email for every weekly reminder. This approach might more successfully

circumvent spam filters while better drawing the attention of prospective respondents.

More importantly, updated email reminders could be crafted to preserve the study’s

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integrity while addressing issues or questions previously posed by other prospective

respondents, minimizing nonresponse bias and increasing participation in the process.

Recommendations for Further Study

In the course of conducting this study, several themes or potential trends worthy

of further study were noted, including (a) CIO transactional leadership scores (not

studied) appeared to be inversely related to CIO transformational leadership scores, (b)

strong mean laissez-faire leadership behaviors (not studied) were more common in the

less innovative subsample, (c) there is a need to understand the leadership styles of CIOs

who have successfully backsourced their IT environments from hosting providers back to

their firm-internal IT organizations, (d) revenue per employee for SAP Basis teams

supporting less versus more innovative environments needs to be further analyzed in

terms of business workload (e.g., by comparing SAP online user counts), (e) the OS

innovation continuum needs to be further explored and characterized, and (f) hybrids

represented a significantly greater subsample of the sample than predicted by population

percentages. Beyond its greater-than-expected subsample size, CIOs responsible for

hybrid-based ERP computing platforms also exhibited greater transformational leadership

scores than CIOs responsible for Windows-based platforms. This finding implies that

hybrids may be more innovative than suggested by the study’s results. Hybrids may be

gaining in both popularity and general awareness as firms seek to lower their ERP

platform total cost of ownership (TCO) at minimal risk by replacing aging

Legacy/Mainframe-based and UNIX-based ERP application servers with Windows- or

Linux-based application servers. Such a trend might foretell a change to the ERP

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computing platform operating system innovation continuum with regard to the placement

of hybrids. Rather than “sitting squarely in the middle of the other four OSs” (Anderson

et al., 2009, p. 77), with greater awareness and continued adoption hybrids may be more

accurately positioned to the right of center. Figure 20 illustrates how the OS innovation

continuum might evolve in the near team as ongoing Linux and hybrid operating system

innovations are expected to encourage additional shifting to the right.

Figure 20. Possible near-term evolution of the operating system innovation continuum for SAP ERP.

Researching the intersection of leadership and contemporary trends in computing

platforms and hosting paradigms, including the currently most innovative Infrastructure-

as-a-Service (IaaS) cloud computing models, could also prove useful in identifying the

most and least effective executive leadership behaviors. Such cloud-based IaaS

approaches to providing operating system infrastructures might eventually affect the OS

innovation continuum by adding another confounding factora cloud computing

dimension. In the long run, pending cloud-aware operating systems like Microsoft Azure

and VMware Virtual Datacenter Operating System (VDC-OS) will probably extend the

OS innovation continuum itself to include new classes of OSs spanning an increasingly

broader continuum.

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Replicating this study on a broader basis would be useful. Researchers should

consider conducting a leadership-innovation study similar to that described here but

focused on the broader IT organization and similarly broader computing platforms

deployed by a firm for email, collaboration, web hosting, file-sharing applications, and

other business applications. Finally, the study’s methodology and analyses should be

replicated in different geographies and with different populations to understand to what

extent North American CIOs differ from their South American, European, and Asian

counterparts, for example. Replicating the study focusing on different sized firms (for

example, only Fortune 500 or only small or medium-sized businesses) or specific

industries would provide additional dimensions to the current body of transformational

leadership literature. A researcher might consider having CIOs self-assess their

leadership behaviors as well, especially when data are available for comparison from

direct reports who have similarly assessed the CIO’s leadership behaviors.

Additional Recommendations for Action

Empirical evidence gleaned from this study indicates the need for leadership

training focused on assisting CIOs and other technology leaders in identifying and

practicing specific behaviors. The study has shown that leadership behaviors described by

transformational leadership theory as IM, IS, and IC reflect particularly strong

relationships to computing platform outcomes spanning the OS innovation continuum.

Low scores related to less innovative outcomes while high scores related to more

innovative outcomes. CIOs tasked with transforming their enterprise business systems

should be trained to understand and practice specific leadership behaviors shown to

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motivate team members and encourage innovation. See chapter 3 for sample survey items

for all transformational leadership subscales. One or more simple surveys comprised of 4,

8, or all 12 of the items related to these three particular subscales might serve as a simple

yet powerful tool for not only assessing CIO leadership behaviors but also predicting the

degree of innovation implemented by a CIO’s team.

CIOs tasked with introducing technology changes would also benefit from

training with regard to transformational management techniques and leadership behaviors

that encourage risk taking. “Risk, after all, brings progress” (Nash, 2009, p. 28). One-day

leadership seminars, articles posted in popular journals and web-based resources

frequented by CIOs, and perhaps a concise text book would all be effective means of

communicating the study’s findings, conclusions, and recommendations with regard to

specific leadership behaviors that relate to team innovation and thoughtful risk taking.

CIOs concerned with transformational change also need to give consideration to

staffing models, staffing demographics, the use of knowledge management systems, the

team’s track record of innovation, and whether the CIO has inherited the present

computing platform standard or helped influence its selection and deployment. All of

these variables have been shown to have a significant confounding relationship to

computing platform outcomes.

Limitations

Despite this study’s contributions to the leadership, innovation, and ERP bodies

of knowledge, there are several limitations that need to be acknowledged. First, the

study’s findings may only be generalized to firms operating in North America,

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particularly those that support their ERP computing platforms with North American

resources. Second, while the overall sample size was more than reasonable given its

medium effect size (r = 0.30) and 0.05 alpha levels, a larger subsample of CIOs

responsible for ERP computing platforms based on Legacy/Mainframe (n = 5), hybrids (n

= 14), and Linux operating systems (n = 5) is necessary to draw stronger conclusions

related to these specific outcomes. Third, because the study broke new ground there is

little empirical evidence with which to compare the results. Fourth, a majority of

respondents worked in the manufacturing industry. In comparison, respondents

supporting consumer/public goods (CPG), utilities, educational institutions, and financial

institutions were poorly represented. Fifth, a number of prospective respondents (who in

the firm’s organizational hierarchy may have been far removed from regular CIO contact)

communicated they had weak relationships with their respective CIOs and thus were

uncomfortable evaluating CIO leadership behaviors. Obtaining CIO transformational

leadership evaluations exclusively from CIO direct reports would add another dimension

to the leadership literature. Sixth, though communicated beforehand, the survey’s 62

items may have discouraged more than 100 prospective respondents who viewed the

survey but never actually completed it. Seventh, the study did not include data collection

reflecting a firm’s aversion to or tolerance for risk in the form of the computing platform

deployed for its mission-critical business applications. Finally, while annual revenue and

SAP Basis team size data were collected, the study did not include ERP workload data

that in hindsight may have been another useful size dimension for comparing firms to one

another.

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Significance of the Study and Implications for Social Change

The significance of transformational leadership’s relationship to computing

platform innovation outcomes was confirmed by this study. Organizations interested in

maintaining the status quo in terms of their ERP systems and requisite technology

footprint may have little interest in the empirical evidence provided by this study. But for

the thousands of CIOs across North America interested in overcoming the challenges

associated with introducing new business applications in the midst of rapidly eroding

technology infrastructures and changing hosting paradigms, this study provides valuable

insight with regard to change-enabling leadership behaviors. In terms of positive social

change, this study could tangibly benefit North American CIOs seeking to improve

business performance through technical innovation. Specific leadership behaviors that

encourage technical innovation and thoughtful risk taking are identified. Moreover, this

study shows that while outsourced SAP Basis operations tend to favor less innovative

environments, this inclination is not statistically significant. Conversely, while IT staffing

models for firms that deployed ERP on more innovative operating systems are

significantly leaner than the mean, revenue per staff member is also significantly less,

implying that large deployments are perhaps not as overstaffed as previously described in

the literature. Finally, by demonstrating how transformational leadership behaviors

encourage positive social change through innovative delivery of technology-based

corporate business services to a firm’s stakeholders, the study provides several models

useful for predicting ERP computing platform innovation. In this way, future studies may

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be conducted potentially more expediently while presumably experiencing less

nonresponse bias.

This study’s findings have the potential to influence business success both

directly and indirectly. Business success requires providing a firm’s goods and services at

the right place, at the right time, using the best manufacturing processes, sourcing

models, and distribution channels to achieve the lowest costs. A well-delivered business

application and its underlying technology infrastructure help make such a complex

proposition possible. CIOs who can deliver such a technically agile business-enabling

system could provide tangible benefits, including organizational longevity, business

workflow efficiency, return on investment, and employment opportunity, to a breadth of

stakeholders. However, in the absence of knowledge regarding specific leadership

practices and behaviors that innately promote innovation and intelligent risk taking, CIOs

may fail to encourage, or, worse, they may discourage, the very innovation required not

only to preserve the firm’s viability but to improve its business performance. CIOs who

practice and promote the impactful transformational leadership behaviors outlined in this

study may therefore serve an important role in encouraging positive social change across

a firm’s workplace, its owners, local community and business stakeholders, and the

overall global business ecosystem comprising suppliers, vendors, sales channels,

partners, and consumers.

This research could also be used to inform CIOs and other technology leaders

about several specific leadership behaviors shown to have a strong relationship to

computing platform outcomes and team innovation in general. The research study has

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yielded several simple models useful for predicting whether a particular CIO has

established a track record of specific leadership behaviors shown to positively influence

technology teams and ultimately help improve a firm’s business agility. Boards of

directors, executive hiring boards, and CIOs themselves may use this information to

assess CIO leadership strengths and areas for improvement, particularly with regard to

behaviors that promote intellectual stimulation and embody inspirational motivation.

By addressing several of the gaps in the literature, the researcher sought to ensure

that study’s application and overall business implications could prove significant, helping

CIOs create environments in which technology enables firms to compete better, risk

taking in technology is more culturally acceptable, IT staffing models are potentially

leaner, and the firm’s improved business agility helps secure its future. These additional

implications for positive social change apply most directly to executives tasked with

implementing transformational change or ensuring organizational longevity. Beyond the

executive ranks, though, this study’s implications for positive social change may apply to

IT line managers concerned with fostering workplace innovation and creating a satisfying

work environment as IT seeks to aid the firm in navigating a rapidly changing business

and economic backdrop through the innovative application of technology.

This study therefore narrows the gap between a broad spectrum of possible CIO

leadership behaviors and those behaviors that may authentically help transform a

technology organization for the positive benefit of society. Lessons learned through this

study could also prove useful in broader domains where leadership and technology

intersect, creating agents of social change and increasing business performance in the

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process. Impact of this magnitude reinforces the study’s value, as its outcomes hold the

promise of positively serving individuals, IT organizations, businesses, and society.

Concluding Statement

More than ever, the ability of today’s businesses to efficiently provide goods and

services spells success or disaster not only for the firm itself but for its ecosystem of

suppliers, vendors, and more. Business software applications enabled by agile technology

infrastructures help make such business success possible. However, in the absence of

knowledge regarding specific leadership practices and behaviors that innately promote

innovation and intelligent risk taking, CIOs may fail to promote the very innovation

required to improve business performance and remain economically viable. This research

study has contributed to the literature by demonstrating a profound relationship between

several specific transformational leadership behaviors and the presence of innovative

business computing platforms. CIOs who practice these leadership behaviors may

therefore serve an important role in encouraging positive social change through the

innovative delivery of technology-based corporate business services to the betterment of

the firm’s immediate stakeholders and its potentially expansive business and community

ecosystem.

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APPENDIX A: EMAIL ANNOUNCING RESEARCH STUDY

Title: RESEARCH STUDY: CIO LEADERSHIP AND ERP PLATFORMS Firm/Company: MULTIPLE Dear <Participant>:

You are receiving this email because you are a valued contact of George

Anderson and a current or former member or leader of an SAP Basis team (employee,

contractor, or consultant). In the next several days, you will receive an invitation to

participate in a North American survey assessing the intersection of leadership behaviors

and ERP platforms. The survey is intended to show the strength of relationship between

your Chief Information Officer’s leadership behaviors and the computing platform

deployed by your firm for SAP ERP. This research project is for my doctoral dissertation,

and is unrelated to my employment at Hewlett-Packard Company. The information to be

sent to you will outline the parameters of the study and point you to a secured website to

participate in an online survey. Your participation is strictly voluntary. No personal or

firm-specific identifying information will be published. Thank you in advance for your

time and consideration of this request. If you would like to be excluded from future email

communiqués and not be considered for this study, please respond to this email with

“Please remove me from participation.” Otherwise, I look forward to contacting you in

the next few days with the survey link.

Sincerely,

George W. Anderson PhD Candidate Walden University

APPENDIX B: INVITATION EMAIL TO SOLICIT SURVEY PARTICIPATION

Title: LINK TO RESEARCH STUDY: CIO LEADERSHIP AND ERP PLATFORMS

Dear Friend and Colleague:

As a current or former member or leader of an SAP Basis team (employee,

contractor, or consultant), you are being asked to participate in a North American survey

intended to show the strength of relationship between your Chief Information Officer’s

(CIO’s) leadership behaviors and the computing platform deployed by your firm for SAP

ERP. This research project is for my doctoral dissertation, which is completely unrelated

to my employment at Hewlett-Packard Company. If you agree to participate in the study,

please use the following website URL to access the survey form:

http://www.zipsurvey.com/LaunchSurvey.aspx?suid=38872&key=813F59E2. The survey

website provides a Consent Statement outlining the background of this study, procedures,

risks, benefits, and more. Before you can complete the survey, you will be asked to

indicate your consent to participate in this study. Your participation is strictly voluntary,

and there is no compensation or any benefits provided to you for your participation in this

study (though I will share my completed dissertation/findings upon request). Further,

there are no personal risks associated with participating in this study.

Please keep a copy of this email should you need to contact me as it contains my

email address and phone number. After starting the survey, if for any reason you

reconsider taking part in this study you may withdraw from participation. Otherwise, it is

requested that the survey be completed within the next seven (7) days. The records of this

study will be kept private. In my dissertation as well as any report of this study that might

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be published elsewhere, I will not include any information that will make it possible to

identify you or your company/firm or organization. All published results will be grouped

and aggregated. Research records and backups of those records will be maintained in a

locked file cabinet for five years and then destroyed. Only I will have access to this

locked file cabinet.

If you have questions please contact me via e-mail at <removed> or via telephone

at <removed>. My Dissertation Committee Chair is Dr. Lee W. Lee, who may be reached

at [email protected]. The Research Participant Advocate at Walden University is

Leilani Endicott, who may be contacted at 1-800-925-3368, extension 1210, should you

have questions about your participation in this study.

I know you are busy and I truly value your participation in this survey. Thank you

in advance for your help in better understanding this important leadership/ERP

computing platform relationship. Best regards,

George W. Anderson PhD Candidate Walden University

APPENDIX C: MULTIFACTOR LEADERSHIP QUESTONNAIRE RATER FORM

(5X) SAMPLE QUESTIONS

THE CIO I AM RATING. . . 1. Provides me with assistance in exchange for my efforts ...................................0 1 2 3 4

2. Re-examines critical assumptions to question whether they are appropriate.....0 1 2 3 4

3. Fails to interfere until problems become serious................................................0 1 2 3 4

4. Focuses attention on irregularities, mistakes, exceptions, and deviations from

standards.................................................................................................................0 1 2 3 4

5. Avoids getting involved when important issues arise........................................0 1 2 3 4

The following rating scale is used:

0 = Not at all

1 = Once in a while

2 = Sometimes

3 = Fairly often

4 = Frequently, if not always

The MLQ 5X’s copyright prohibits publishing the instrument’s items in its entirety (refer

to Appendix D).

APPENDIX D: PERMISSION TO USE THE MLQ (RATER FORM 5X)

INSTRUMENT

APPENDIX E: DEMOGRAPHIC/COMPUTING PLATFORM SURVEY ITEMS

Thank you for completing the survey up to this point. The remaining questions

relate to you, your team, and the computing platform you support(ed). Please complete as

many of the questions as you are comfortable completing. You may leave any question

blank if you believe it is too personal. As a reminder, all collected data will be maintained

in the strictest of confidence and published only in aggregated form. Your individual

responses will be kept completely confidential and your name and company will never be

identified as a participant of this study.

46. Approximate total number of years experience as an SAP Basis professional (member

or leader): ___

47. Your time in years with the company/firm or group CIO that you are assessing: ___

48. Your age in years: ___

49. The SAP ERP system you are assessing (select only one; complete multiple surveys if

your company/firm or group runs two or more different SAP ERP systems on two or

more different computing platforms):

___ R/3 (any version) ___ ECC/ERP (any version)

50. Identify the Operating System platform (select only one) used by the SAP ERP

DATABASE Server:

___ UNIX (AIX, HP-UX, Solaris, or Tru64)

___ Legacy/Mainframe OS (i5/OS, z/OS, or similar)

___ Linux (RedHat or SuSE)

___ Windows (NT, 2000, 2003, or 2008)

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51. Identify the Operating System platform (select all that apply) used by the SAP ERP

APPLICATION Server(s):

___ UNIX (AIX, HP-UX, Solaris, or Tru64)

___ Legacy/Mainframe OS (i5/OS, z/OS, or similar)

___ Linux (RedHat or SuSE)

___ Windows (NT, 2000, 2003, or 2008)

52. Identify the size (2 to 15 is typical) of your SAP Basis Team in terms of full-time

employees, contractors, or consulting personnel (SAP Basis technologists and leaders

only; does not include Computer Operators, Data Center personnel, Network Specialists,

Database Administrators, SAN Specialists, or Server/OS Specialists, unless they also

fulfill SAP Basis functions): ___

53. Within your IT organization, do you employ any SAP-focused innovation sponsors or

innovation champions (not including your CIO), such as a Chief Technology Officer,

Chief Technologist, or SAP technology leadership organization tasked with promoting

technical innovation?

___ Yes ___ No

54. Does your SAP Basis team have a track record of successfully implementing

innovation with regard to the SAP computing platform, or experience in the process of

adopting computing platform innovation? For this study, innovation is defined as “a

technology, approach, practice, or computing platform component or dimension that is

perceived as new or unique in a manner deemed potentially positive or beneficial.”

___ Yes ___ No

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55. Does your SAP Basis team use a company-internal Knowledge Management System

to support the team’s lessons learned and knowledge gained relative to SAP

implementation and/or support? (SAP Notes, IT Toolbox, Google, and similar resources

not directly managed by your company do not apply)

___ Yes ___ No

56. Identify the IT Staffing Model that best reflects your SAP Basis team:

___ Internally staffed by your firm’s in-house or contracted IT organization

___ Outsourced or staffed by a third party (not the firm’s in-house IT organization)

57. Identify your firm’s approximate annual revenue in MILLIONS USD (i.e. $500

Million = 500, while $20 Billon = 20000): ___

58. Did the CIO evaluated in the leadership portion of this survey “inherit” the current

SAP computing platform? (if “No” then it is assumed that the CIO was in place when the

decision to deploy or refresh the current SAP computing platform was made).

___ Yes ___ No

59. Enter your company/firm or group name (this information will never be published; it

is captured only to ensure that responses reflecting a single company/firm or group are

averaged and therefore that your CIO is assessed accurately): ____

60. Enter your name, initials, or any other unique identifier (this information is captured

only for tracking purposes related to this study and will never be published): ____

If you would like an electronic copy of the researcher’s completed dissertation, please

provide your email address: ______________________

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Thank you for completing the survey up to this point. The final two questions

relate to your perceptions of operating system innovation. Please complete as many of the

questions as you are comfortable completing. You may leave a question blank if you

believe it is too personal. As a reminder, all collected data will be maintained in the

strictest of confidence and published only in aggregated form. Your individual responses

will be kept completely confidential and your name and company will never be identified

as a participant of this study.

61. Rate the following ten Operating System (OS) innovation attributes from least

important (1) to most important (10). Attributes may share the same rating.

1 - - - 2 - - - 3 - - - 4 - - - 5 - - - 6 - - - 7 - - - 8 - - - 9 - - - 10 Least Important somewhat important very important Most important

___ Application compatibility (ability of the OS to support SAP Enterprise Resource

Planning software as the OS for application servers, database servers, or both)

___ Availability (ability of an OS to help create a highly available computing platform

that remains up and accessible to end users)

___ Integrated innovation (the OS integrates innovative features from competing OSs)

___ Market share (OS popularity for SAP applications)

___ Open source code (OS source code can be easily and legally changed)

___ Organizational endurance (ability of the OS to provide an existing IT organization

with a choice, acting as a change agent for successful innovation)

___ Portability (ability of an OS to execute on two or more hardware vendor’s platforms)

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___ Pricing innovation (OS total cost of ownership, which includes the cost of acquiring

the OS as well as the cost of ongoing OS maintenance)

___ Technical flexibility (ability of the OS to be easily repurposed, changed, or

integrated to facilitate change or support new technologies)

___ Tools (inclusion of effective resource management, workload management, and

virtualization tools)

62. For each innovation attribute, please rank all five Operating Systems from least

innovative (1) to most innovative (5). Note that the higher the number, the more

innovative the OS. Choices are mutually exclusive in that only one OS may be ranked the

best (with a 5), for example.

Innovation Attributes Computing Platform Operating Systems Unix

(AIX, HP-UX, Solaris, Tru64)

Mainframe (z/OS or i5/OS)

Linux (RedHat or

SuSE)

Windows (Server Edition)

Hybrid (mix of 2 OSs)

Application compatibility (compatibility with SAP Apps)

Availability (OS enables a highly available platform)

Integrated innovation (incorporates other OS features)

Market share (OS popularity for SAP)

Open source code (easily/legally changeable)

Organizational endurance (gives IT org a good choice)

Portability (OS runs on two or more vendor platforms)

Pricing innovation (OS total cost of ownership)

Technical flexibility (OS supports new technologies)

Tools (resource/workload mgmt and virtualization tools)

Note. See Question 61 for detailed definitions of each OS innovation attribute.

APPENDIX F: THANK YOU EMAIL

Participant Name Organization <email address>

Dear <Participant>:

Thank you so much for participating in my CIO leadership and SAP ERP

computing platform Operating System study. I recognize how busy you are and truly

appreciate your time and effort. The results of the study are currently being assembled

and analyzed. I have taken note of whether or not you requested a copy of my dissertation

upon its completion, and look forward to sharing the compiled results very soon.

Sincerely, and with much gratitude!

George W. Anderson PhD Candidate Walden University

CURRICULUM VITAE

GEORGE W. ANDERSON

Chief Strategist & Distinguished Technologist, Office of the CTO Hewlett-Packard Company

Passionate in the sale, design, delivery, management, and strategic development of innovative ERP enterprise

services and solutions used to solve complex business problems, George seeks a position of global impact where he can develop and manage the strategies and processes necessary to transform client ERP infrastructures, staffing

models, and processes. George has years of experience working at an executive level to pursue, develop, and deliver innovative enterprise ERP consulting services across a breadth of industry verticals. He has made an enduring impact on HP’s products and services in terms of new customer logos, revenue, margin, solution repeatability, solution and product roadmaps, and creating new offeringsengaging countless other HP organizations and

geographies in the process. Currently influencing IP and solution accelerator development for consulting services delivered by more than 15,000 HP consultants, George possesses a broad record of accomplishment. Combined with

proven program and project management skills, George has the credibility and experience required to create, compete, win, and implement game-changing services offerings. Competencies and skills include:

• Consulting, teaching, leading, & selling • Developing transformational business & IT strategies

• Leading innovation/IP development initiatives • Performing EA application architecture & design

• Developing winning RFPs & SOWs • Providing large-scale project management services

• Implementing/upgrading business applications • Transforming IT and end-user environments

• Assessing computing platform TCO & ROI • Developing new business practices & processes

Professional Experience and Qualification Highlights

Anderson is a frequent speaker at a variety of events and customer forums, including conferences, analyst briefings, and CIO/technical roundtables, and has authored or co-authored seven books, numerous journal/magazine articles, and other papers relevant to ERP implementations, technical migrations, TCO analysis, achieving SAP operational excellence, and more. A former U.S. Marine with 24 years combined IT experience spanning mission-critical mainframe, client-server, Internet, SOA, and various cloud environments, recent highlights include:

• Developed and delivered innovative services offerings, pushing into the realm of high-value business consulting. Offerings reflected IT ERP staffing and organizational assessments, TCO analyses, technology/business peer analyses, high availability/recoverability assessments, SAP access strategy assessments, and more.

• Pursued, designed, closed, and provided project management and delivery of four complex end-to-end SAP business application implementations, from initial solution architecture through go-live support. Retained team personnel through personal attention and pan-team relationship building.

• Pursued, closed, and provided oversight to complex SAP functional upgrades, including technology platform refreshes. Attention to detail and superior delivery ensured minimum downtime. Also pursued and instrumental in closing many large SAP ERP, BI, and SCM OS/DB migrations from competing platforms to HP. Created reusable processes and project plans, netting millions in services and hardware sales.

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Chronological Experience and Qualifications

2000—2010 HP’s Office of the CTO & Applications Services

Chief Technologist, Master Business Consultant, and Enterprise Applications Project Manager � Provided thought leadership and strategic direction spanning HP, SAP, Oracle, and Microsoft technologies/business

solutions. Mentored and taught others (consultants, sales teams, others), assisted others in implementing our strategy by developing collateral and tools, actively sold, developed case studies to showcase impact, and presented on behalf of HP to show industry credibility. Aggressive client retention evidenced through significant change orders and repeat customers. Responsible for identifying trends and co-developing intellectual property (sales, solutioning, and delivery) culminating in a steady run-rate of HP and partner-provided professional services, software, and hardware sales.

1998—2000 Compaq SAP Competency Center

SAP Technologist

� Designed, planned for, and to a limited extent provided postsales implementation and optimization support for large manufacturing, financial, and K-12 environments reflecting SAP ERP, BW, SCM, and SRM/EBP. Provided support for PeopleSoft, i2, Siebel, & Baan business applications. Demonstrated outstanding partnering skills.

1997—1998 Compaq Enterprise Consulting Services

Enterprise Consultant

� Performed delivery and significant project management expertise related to upgrading and tuning SAP R/3 and other business application environments. Helped pursue, close, plan for, and execute multiple pre-go-live stress-testing engagements and post-go-live tuning and upgrade engagements. Studied Gartner’s approach to TCO Analysis and was quickly successful in using it as a tool for influencing the purchase of Compaq hardware and software solutions for ERP.

1995—1997 Inacom/Vanstar

District Professional Services Manager and Managing Consultant

� Managed the Southwest Professional Services Organization, growing in 2 years from 8 Houston-based systems engineers to 56 consultants, engineers, and project managers across the district. Consistently exceeded forecast expectations while re-engineering staffing and internal processes every 6 months to retain and grow both the client and resource pool. By focusing on delivering business solutions atop Microsoft, Novell, SAP, Peoplesoft, Cisco, and other hot technology solutions of the day, maintained close to 40% consulting margins and 80-90% billable utilization rates.

Certifications, Education, Military Experience, and Selected Publications

• PMI Project Management Professional (PMP), 1999

• Master ASE/HP Professional, 1999

• SAP Technical Certified Consultant (CTC) , 1999

• Microsoft Certified Systems Engineer (MCSE), 1997

• Compaq ASE/HP Professional, 1994

• MBA, Chaminade University of Honolulu, 1991

• BGS, Roosevelt University, 1989

• SAP Implementation Unleashed: A Business and Technical Roadmap to Deploying SAP (2009)

• Teach Yourself SAP in 24 Hours, 3rd Edition (2008)

• Teach Yourself SAP in 24 Hours, 2nd Edition (2005)

• MySAP Tool Bag for Performance Tuning and Stress Testing (2004)

• SAP Planning: Best Practices in Implementation (2003)

• U.S. Marine (enlisted), 1985-1993, honorably discharged, earned Associate’s, Bachelor’s, and Master’s

degrees while serving on Active Duty at Quantico, VA, Camp Pendleton, CA, and Camp Smith, HI.