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EVALUATING THE ACCEPTANCE OF CLOUD-BASED PRODUCTIVITY

COMPUTER SOLUTIONS IN SMALL AND MEDIUM ENTERPRISES

Alfredo Dominguez

BERNARD J. SHARUM, PhD, Faculty Mentor and Chair

MARTHA S. HOLLIS, PhD, Committee Member

JILL C. ALRED, PhD, Committee Member

Sue Talley, EdD, Dean, School of Business and Technology

A Dissertation Presented in Partial Fulfillment

Of the Requirements for the Degree

Doctor of Philosophy

Capella University

March, 2013

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UMI Number: 3557596

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Abstract

Cloud computing has emerged as a new paradigm for on-demand delivery and

consumption of shared IT resources over the Internet. Research has predicted that small

and medium organizations (SMEs) would be among the earliest adopters of cloud

solutions; however, this projection has not materialized. This study set out to investigate

if behavior factors from the unified theory of acceptance and use of technology could

explain the acceptance of cloud-based computing solutions within the context of SMEs.

The study found that performance expectancy, effort expectancy, and social influence can

predict 76% of the behavior intention of decision makers to accept the use of cloud

computing. The study highlights the impact of these findings for SMEs, for cloud

solutions providers, and for academia. Recommendations are also made for future

research.

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iii

Dedication

I want to dedicate this work to my wife Eyda, who has been a divine source of

inspiration every step of the way in this long journey. I also want to dedicate this work to

my mother who never stopped asking, "Como están las cosas mijito" when she wanted to

know how much progress I’ve made. Last, but not least, I want to dedicate this work to

my two kids, Alex and Johanna, who bring joy to my life every day and are the energy

that keeps me going.

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Acknowledgments

I want to acknowledge the support of my mentor, Dr. Bernie Sharum, who

provided wise advice and had the patience to hear my complaints, my achievements, my

motivations, and my depressions. I also want to acknowledge the members of my

dissertation committee: Dr. Hollis and Dr. Alred, who provided great guidance and

support throughout the entire dissertation process. Similarly, I want to acknowledge the

contributions of Matthew Benjamin who provided amazing support with my constant

questions about online surveys. Last, but not least, I want to acknowledge the many

friends and family who always asked how things were going and who constantly

provided encouraging words.

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Table of Contents

Acknowledgments iv

List of Tables viii

List of Figures ix

CHAPTER 1. INTRODUCTION 1

Introduction to the Problem 1

Background of the Study 2

Statement of the Problem 3

Purpose of the Study 4

Rationale 4

Research Questions and Hypotheses 8

Significance of the Study 12

Contributions of the Study 13

Definition of Terms 14

Assumptions and Limitations 16

Nature of the Study 18

Organization of the Remainder of the Study 20

CHAPTER 2. LITERATURE REVIEW 21

Introduction 21

Introduction to Cloud Computing 22

Introduction to the Unified Theory of Acceptance and use of Technology 31

Theories Used to Develop the UTAUT Model 32

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Conclusion to the Theoretical Background 47

Relevance of the UTAUT Model to the Study 48

Small and Medium Enterprise Populations 49

Research Approach and Methodology Selection 51

CHAPTER 3. METHODOLOGY 53

Introduction 53

Research Questions and Hypotheses 53

Research Design 55

Population and Sampling 65

Measures/Instrument 74

Data Collection 80

Statistical Analysis 82

Ethical Considerations 83

CHAPTER 4. RESULTS 86

Introduction 86

Instrument Reliability 86

Data Collection 86

Demographic Data 88

Exploratory Data Analysis 89

Using the Bootstrapping Method with Correlation and Regression Calculations 91

Descriptive Statistics 92

Research Questions and Hypothesis Testing 93

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vii

Assessing Assumptions of Multiple Regression Model 95

Chapter Summary 102

CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS 104

Discussion 104

Implications for SMEs 109

Implications for Cloud Computing Vendors 110

Implications for Academia 110

Recommendations for Future Research 111

Summary 112

REFERENCES 113

APPENDIX A. SURVEY INSTRUMENT 123

APPENDIX B. PERMISSION TO USE SURVEY 125

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viii

List of Tables

Table 1. Summary of Independent, Dependent, and Moderating Variables 11

Table 2. Unified Theory of Acceptance and use of Technology Construct Definition 48

Table 3. Construct Names, Variable Names, Levels of Measurement,

and Variable Types 63

Table 4. List of U.S. SMEs Organized by Employee Size 66

Table 5. Output of Power Calculation 72

Table 6. Reliability Coefficients 87

Table 7. Skewness and Kurtosis Statistics 90

Table 8. Descriptive Statistics 93

Table 9. Multiple Correlation coefficients 95

Table 10. Correlation Matrix of Predictor Variables 97

Table 11. Summary of Research Questions and Hypotheses 105

Table 12. Summary of Findings 107

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List of Figures

Figure 1. Unified theory of acceptance and usage of technology model (UTAUT) 6

Figure 2. Core factors under investigation 8

Figure 3. Factors under investigation and associated alternative hypotheses

considered in the study 11

Figure 4. Conceptual framework of intention to accept technology 19

Figure 5. Theory of reasoned action 33

Figure 6. Theory of planned behavior 35

Figure 7. Technology acceptance model 37

Figure 8. Combined TAM and TBM model 40

Figure 9. Model of PC utilization 41

Figure 10. SCT model for computer usage 46

Figure 11. Theoretical framework showing the different constructs of the model 62

Figure 12. Matrix scatterplot 96

Figure 13. Scatterplot of residuals to test homescedasticity 97

Figure 14. Test of normality of residuals 98

Figure 15. P-Plot of residuals’ distribution 99

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CHAPTER 1. INTRODUCTION

Introduction to the Problem

Cloud computing has emerged as a new technological paradigm for on-demand

delivery and consumption of shared information technology (IT) resources over the

Internet. As a new innovation, cloud computing is considered one of the most significant

advances in technology evolution of recent years (Marston, Li, Bandyopadhyay, Zhang,

& Ghalsasi, 2011). This phenomena is associated with the cloud's potential for

dramatically altering and transforming how businesses provision, consume, and release

computing power, make use of data storages and computer applications, and enable users

to engage in access to various forms of networking infrastructures (Buyya, Yeo,

Venugopal, Broberg, & Brandic, 2009; Sultan, 2010a).

These transformational process should make it possible for organizations of all

sizes, particularly small and medium enterprises, to derive a significant number of

technical, financial, and strategic benefits from the adoption of cloud computer services

(Armbrust et al., 2010). One of the benefits is the possibility of lowering total ownership

costs associated with the procurement of expensive technology. Similarly, businesses

could derive better resource utilization as well as gain access to scalable computational

power if and when the need arises for working with large volumes of data. At the same

time, organizations would have with options for returning unused resources back to the

cloud when they are not required anymore as well as avoiding paying premium prices for

such transactions. This last unique feature of cloud computing–known as elasticity–is a

completely new technical and economical model that breaks away from traditional IT

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operational practices, making possible a move towards a pay-per-use approach similar to

a utility service like water, gas, or electricity (Armbrust et al., 2010).

With multiple technical and financial benefits proposed by cloud vendors, small

and medium enterprises (SMEs) are anticipated to be among the earliest and largest

organizational groups adopting cloud computing solutions worldwide (Enisa, 2009). This

should not be surprising, as it is well known that SMEs lack the resources, technical

expertise, and overall resources to plan, access, implement, and support complex

technology (Premkumar, 2003). Consequently, newer innovations that help SMEs gain

access to complex IT infrastructure at a fraction of the cost could be seen as valuable

opportunities for leveraging their respective competitive landscapes.

Background of the Study

Despite much anticipated projections regarding SMEs' leading role in adoption of

cloud solutions, this event has not fully materialized. Recent surveys suggest that

adoption of cloud computing offerings by smaller firms is not taking place at the

predicted rate or with the intensity that was earlier predicted (Benlian, Hess, & Buxmann,

2009; Wu, Lan, & Lee, 2011). Although cloud computing has the potential for increasing

SMEs’ organizational performance, the SMEs do not seem to be realizing such value. As

Venkatesh, Morris, Davis, and Davis (2003) suggested, "For technologies to improve

productivity, they must [first] be accepted and used by employees in organizations" (p.

426).

While there are multiple delivery methods for cloud solutions, the emphasis of

investigation for this research is placed on cloud-based solutions intended to replace or

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complement traditional productivity applications commonly installed and used in most

corporate and business environments. Examples of such applications include various

forms of office suites such as word processors, spreadsheets, presentations, email, and

collaboration packages. Also, online backup systems, file storage systems, and enterprise

applications such as enterprise resource planning (ERP) or client resource management

(CRM) applications are applicable. Within the realm of cloud computing, such alternative

solutions fall under Software as a Service, also known as SaaS (Benlian & Hess, 2011).

Statement of the Problem

For the purposes of this study, the fact that SMEs are not adopting cloud

computing solutions as predicted is one of the primary concerns leading the research;

however, it is not the only one. As suggested by Benlian et al. (2009), practitioner

surveys place much emphasis in finding technical issues as the main causes of the

problem, but fail to consider additional factors such as users' individual reactions and

intention for accepting and adopting the technology. These are themes that have long

been considered underlying aspects of various user acceptance models (Venkatesh et al.,

2003). Therefore, an additional research problem addressed by this investigation is

identifying which predictor variables better explain the potential acceptance of cloud

computing solutions within the context of SMEs.

This last research problem stems from deficiencies in the academic literature.

Interesting to note is that despite mounting interest in cloud computing themes, academic

research on the area of acceptance and adoption from the perspective of SMEs still lag

behind compared to other more technical investigations. This result is even more

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surprising since SMEs have been predicted to be preferred candidates for early adoption

of cloud solutions (National Institute of Standards and Technology; NIST, 2011).

Purpose of the Study

The main purpose of this study is to explore the acceptance of cloud-based

computing solutions within the context of SMEs, using as a reference core factors

described by the unified theory of acceptance and use of technology (UTAUT) model

(Venkatesh et al., 2003). Specifically, the study evaluates the potential correlation

between scores associated with performance expectancy (PE), effort expectancy (EE),

and social influence (SI)--considered the direct determinants or independent variables--

and scores associated with behavioral intention (BI)--considered the outcome or

dependent variable. The study also applies multiple regression analysis to evaluate if BI

could be predicted based on the combined influence of the direct determinants.

Rationale

The fact that SMEs are not adopting cloud computing solutions as predicted in

major industry reports (Enisa, 2009) provides the primary rationale for engaging in the

present research; however, additional factors should be explored. Benlian et al.’s (2009)

research suggested technical issues as the primary reason SMEs have not adopted cloud

technology; however, users' individual reactions and intentions for accepting and

adopting the technology have not be explored. These are themes which have long been

considered underlying aspects of various user acceptance models (Venkatesh et al.,

2003). Therefore, an additional research problem addressed by this investigation

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includes identifying predictor variables that better explain the potential acceptance of

cloud computing solutions within the context of SMEs.

In 2003, Venkatesh et al. developed the UTAUT model in an effort to combine a

large number of earlier theories and current models to study technology adoption. The

theories and models synthesized in the UTAUT model include the theory of reasoned

action (TRA), the technology acceptance model (TAM), the motivational model (MM),

the theory of planned behavior (TPB), an earlier model combining TAM and TPB, the

model of pc utilization (MPCU), the innovation diffusion theory (IDT), and the social

cognitive theory (SCT; Venkatesh et al., 2003).

The resultant model from this combination was the unified theory of acceptance

and use of technology (UTAUT) model, which consist of four core determinants of

intention and usage (Venkatesh et al., 2003). These determinants are performance

expectancy (PE), effort expectation (EE), social influence (SI), and facilitating conditions

(FC). Behavioral intention (BI) and usage behavior (UB) are considered outcome factors.

In addition, the model has four moderator factors that influence all key relationships.

These moderating factors are gender, age, experience, and voluntariness. Figure 1

depicts the original UTAUT model and shows the relationship lines between the core

constructs.

The UTAUT model was empirically validated using a longitudinal approach that

captured data from several businesses operating in dissimilar industries and in various

stages of adoption (Venkatesh et al., 2003). The results of this investigation revealed that

the UTAUT model was capable of explaining 70% of technology acceptance behavior

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(Venkatesh et al., 2003). In contrast, previous models explained only 30% of acceptance

on average (Venkatesh et al., 2003).

Figure 1. Unified theory of acceptance and usage of technology model (UTAUT).

Reprinted with permission from “User Acceptance of Information Technology: Toward a

Unified View,” by V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, 2003, MIS

Minnesota. Used with permission.

Three main determinant factors, including PE, EE, and SI, are described in the

UTAUT model as having a direct influence over BI (Venkatesh et al., 2003). The present

study limits research to exclusively investigating these three factors. The factors or

constructs are defined as (a) performance expectancy (PE) represents the degree to which

a person believes that using a system–a computer system since UTAUT is primarily

concerned with testing IT and information systems (IS) solutions– will increase job

Performance

Expectancy ((PE)

Effort

Expectancy (EE)

Social Influence

(SI)

Facilitating

Conditions (FC)

Ge nder

Age

Performance

Expectancy (PE)

Effort

Expectancy (EE)

Social Influence

(SI)

Facilitating

Conditions (FC)

Behavioral

Intention (BI) Use Behavior

(UB)

Gender Age Experience Voluntariness

of use

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performance; (b) effort expectancy (EF) is the degree of ease associated with the use of

the system; (c) social influence (SI) is the degree to which a user believes that important

or influential people want him or her to use the system. Two additional core variables are

included in the UTAUT model: facilitating conditions (FC) and use behavior (UB).

These constructs relate to measuring the actual adoption of technology after behavioral

intention has been formed. However, FC and UB will not be measured by this study,

since this study is concerned with the early stages of the decision-making process prior to

technology adoption.

The model developed by Venkatesh et al. (2003) was based on a longitudinal field

study involving several organizations engaged in the deployment of a new technology

system at each respective location. The study measured various user reactions to the new

technology (Venkatesh et al., 2003). The data collection process began during the early

stages of technology introduction and expanded to cover advanced stages of user

experience with the systems (Venkatesh et al., 2003). The investigation placed the focus

of attention on understanding behavioral intention as well as on the actual technology

usage behaviors throughout the entire process.

In order to accomplish the goals of the study, Venkatesh et al. (2003) developed a

questionnaire containing four items for each of the independent variables and three items

for BI. The scales used in the development of the questionnaire were derived and

validated from previous research models (Venkatesh et al., 2003). Whereas Venkatesh et

al. conducted a longitudinal field study and captured real-time data from technology

usage over several adoption phases; the present study focuses on investigating potential

technology acceptance during the initial stages of the decision-making process. In

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particular, this research will focus on understanding the influence of the three main core

factors proposed by the UTAUT model that were proven to directly influence behavioral

intention–PE, EE, and SI by Venkatesh et al. Consequently, the research excludes the

analysis of the relationships between BI and UB, and FC and BI as well as the influence

of all moderating factors. For clarity, Figure 2 provides a graphical depiction of the

factors that will be investigated in this study.

Figure 2. Core factors under investigation. Adapter with permission from “User

Acceptance of Information Technology: Toward a Unified View,” by V. Venkatesh, M.

G. Morris, G. B. Davis, and F. D. Davis, 2003, MIS Quarterly, 27(3), pp. 425-478.

Research Questions and Hypotheses

The research questions presented are fundamentally associated with the problems

identified with the acceptance of cloud technology by SMEs. At its most basic, the study

strives to infer the statistical significance of particular social factors on the acceptance of

cloud computing solutions within the context of SMEs. Answers to the research

questions could produce practical recommendations for adopters as well as for providers

of cloud computing services. At the same time, the results will expand the extant

Performance

Expectancy (PE)

Effort

Expectancy (EE)

Social Influence

(SI)

Behavioral

Intention (BI)

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literature with a fresh understanding of technology adoption under a completely new

scenario and a set of conditions that have not been fully explored prior.

Research Question

RQ 1. What is the relationship of performance expectancy, effort expectancy, and

social influence as potential determinants of behavioral intentions to accept the use of

cloud computing systems within the context of SMEs?

Sub Research Questions

Three additional research sub-questions are formulated separately to better

understand the individual relationships between separate pairs of variables.

RQ 2. What is the relationship between performance expectancy and behavioral

intention?

RQ 3. What is the relationship between effort expectancy and behavioral

intention?

RQ 4. What is the relationship between social influence and behavioral intention?

Hypotheses

Ho1. Performance expectancy, effort expectancy, and social influence are not

statistically significant predictors of behavioral intention to accept the use of cloud

computing systems within the context of SMEs.

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Ha1. Performance expectancy, effort expectancy, and social influence are

statically significant predictors of behavioral intention to accept the use of cloud

computing systems within the context of SMEs.

Ho2. Performance expectancy is not a statistically significant predictor of

behavioral intentions to accept the use of cloud computing systems within the context of

SMEs.

Ha2. Performance expectancy is a statistically significant predictor of behavioral