Papadopoulou, 2012; Yaseen & Zayed, 2010; Zhou & Lu, 2011a). Other researcher ...... and Shareef (2013) explained social influence as the degree to which a person's social status is enhanced from ..... Manual management. E-commerce ...
MOBILE COMMERCE COMPETITIVE ADVANTAGE: A QUANTITATIVE STUDY OF VARIABLES THAT PREDICT M-COMMERCE PURCHASE INTENTIONS by Robert Blaise MARC MUCHNICK, PhD, Faculty Mentor and Chair GAIL FERREIRA PhD, Committee Member JOHN HERR PhD, Committee Member Barbara Butts Williams, PhD, Dean, School of Business and Technology
A Dissertation Presented in Partial Fulfillment Of the Requirements for the Degree Doctor of Philosophy
Capella University June 2016
ProQuest Number: 10148414
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ProQuest 10148414 Published by ProQuest LLC (2016). Copyright of the Dissertation is held by the Author. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code Microform Edition © ProQuest LLC. ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346
© Robert Blaise, 2016
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Abstract Electronic and web technologies include a significant economic and social force in contemporary life and business. Commercial activities conducted over computers and mobile networks empower business processes and add value to consumers by introducing unique channels for buying and exchanging information. Whereas past research expanded knowledge about attitudes and perceptions toward e-commerce that drive consumer purchase intentions and provide a competitive advantage, the fundamental behavioral dynamics associated with mcommerce requires further investigation. Based on the unified theory of acceptance and use of technology (UTAUT), this quantitative, survey-based study investigates adult American users of m-commerce to measure their perceptions of performance and effort expectancies, social influence, the facilitating conditions of m-commerce trust and perceived risk. This study surveyed 177 participants to measure their perceptions of performance and effort expectancies, social influence, the facilitating conditions of m-commerce trust and perceived risk, and their mcommerce purchase intentions. The results of this study indicated that performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce together predicted m-commerce purchase intentions at a statistically significant level regarding competitive advantage.
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Acknowledgments I would like to express my sincere appreciation to my mentor Dr. Marc Muchnick for his guidance, mentoring, and support throughout this journey. I would also like to extend my appreciation to my dissertation committee members: Dr. Gail Ferreira and Dr. John Herr. They presented timely and constructive feedback that were significant in helping improve my dissertation. Special recognition is given to my good friend Dr. Halloran for your personal encouragement throughout this process. I am especially grateful to my wife for her unwavering love, and support throughout this journey. To children and parents who were patience while I “finished my book,” I extend my most gracious thanks.
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Table of Contents Acknowledgment ..................................................................................................... ii List of Tables .......................................................................................................... vi List of Figures........................................................................................................ vii CHAPTER 1. INTRODUCTION ........................................................................................ 1 Introduction to the Problem ..................................................................................... 1 Background of the Study ......................................................................................... 3 Statement of the Problem ........................................................................................ 5 Purpose of the Study ................................................................................................ 6 Rationale .................................................................................................................. 6 Research Questions ................................................................................................. 7 Hypotheses .............................................................................................................. 8 Significance and Contributions ............................................................................... 9 Significance to Body of Research ......................................................................... 10 Significance to Organizations................................................................................ 11 Definition of Terms ............................................................................................... 11 Assumptions and Limitations ................................................................................ 13 Positivistic Research Study ................................................................................... 13 Nature of the Study / Theoretical and Conceptual Framework ............................. 14 Organization of the Remainder of the Study ......................................................... 16 CHAPTER 2. LITERATURE REVIEW ........................................................................... 18 Introduction ........................................................................................................... 18 Theoretical Frameworks for Mobile and Electronic Commerce Research ........... 18 Variables in User Acceptance of M-commerce..................................................... 27 The Nature of Mobile Commerce.......................................................................... 36
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Comparison of Traditional Commerce, E-commerce, and M-commerce ............. 37 Research on Technology Use ................................................................................ 40 M-Commerce Dimensions and Purchase Intentions ............................................. 46 Evaluation and Future Directions .......................................................................... 48 Summary and Conclusion...................................................................................... 49 CHAPTER 3. METHODOLOGY ..................................................................................... 52 Research Design .................................................................................................... 54 Sample .................................................................................................................. 55 Instrumentation / Measures ................................................................................... 56 Data Collection ...................................................................................................... 58 Data Analysis......................................................................................................... 59 Validity and Reliability ......................................................................................... 60 Ethical Considerations ........................................................................................... 61 CHAPTER 4. RESULTS................................................................................................... 64 Introduction ........................................................... Error! Bookmark not defined. Screening the Data ................................................................................................. 64 Validity and Reliability ......................................................................................... 64 Descriptive Statistics ............................................................................................. 67 Description of the Sample ..................................................................................... 67 Assumption Testing ............................................................................................... 69 Regression Analysis .............................................................................................. 75 Supplementary Analysis ........................................................................................ 81 Summary of Results and Conclusion .................................................................... 82 Conclusion ............................................................................................................. 84 CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS ................... 86
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Introduction ........................................................................................................... 86 Summary of the Results......................................................................................... 87 Discussion of the Results....................................................................................... 89 Implications of the Study Results .......................................................................... 94 Limitations ............................................................................................................. 99 Recommendations for Further Research ............................................................. 100 Conclusion ........................................................................................................... 102 REFERENCES ................................................................................................................ 104 APPENDIX ..................................................................................................................... 118
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List of Tables Table 1. Definition of Study Constructs ................................................................ 27 Table 2. Classification of M-Commerce Definitions Based on Literature ............ 37 Table 3. Comparison of Commerce Business Models .......................................... 38 Table 4. Significant Works on M-commerce Research......................................... 47 Table 5. Key Studies on Predictors of Purchase Intentions in M-Commerce ....... 47 Table 6. Confirmatory Factor Analysis of the Predictor Variable (N = 165) ........ 65 Table 7. Cronbach’s Alpha Reliability Results ..................................................... 66 Table 8. Means and Standard Deviations for Continuous Variables..................... 67 Table 9. Participant Demographics ....................................................................... 68 Table 10. Participant U.S. Regional Location ....................................................... 69 Table 11. Statistics Tests of Normality ................................................................. 70 Table 12. Skewness and Kurtosis Statistics .......................................................... 70 Table 13. Case-Wise Diagnostics for Outliers and Residuals ............................... 71 Table 14. Intercorrelation Between the Independent Variables ............................ 72 Table 15. Tolerance and VIF Multicollinearity Statistics for the Independent Variables ............................................................................... 72 Table 16. Durbin–Watson Test: Summary for Model ........................................... 75 Table 17. Regression Model Summary for the Effect of the Independent ............ 76 Table 18. Significance Test of the Regression Coefficients.................................. 76 Table 19. The Impact of Trust on Purchase Intentions ......................................... 77 Table 20. Significance Test of the Regression Coefficients for M-commerce Usage ......................................................................................................... 81
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List of Figures Figure 1. Pictorial view of UTAUT Model .......................................................... 15 Figure 2. TAM model adapted from “A theoretical extension of the technology acceptance model....................................................................................... 23 Figure 3. Scatter plot of residuals to test for of homoscedasticity in Purchasing Intentions ................................................................................................... 73 Figure 4. Frequency of the residuals for purchase intentions (PI) plotted to a normal curve .............................................................................................. 74 Figure 5. Cumulative Probabilities of the Expected Versus Observed Regression Residuals plotted to a Linear Relationship ................................................ 75 Figure 6. Scatter plot showing relation between Trust (T) and Purchase Intention (PI). ............................................................................................................ 77 Figure 7. Scatter plot showing relation between Effort Expectancy (EE) and Purchase Intention (PI). ............................................................................. 78 Figure 8. Scatter plot showing relation between Risk (R) and Purchase Intention (PI) ............................................................................................. 79 Figure 9. Scatter plot showing relation between Performance Expectancy (PE) and Purchase Intention (PI) ....................................................................... 86 Figure 10. Scatter plot showing relation between Social Influence (SI) and Purchase Intention (PI) .............................................................................. 86 Figure 11. Mean Purchase Intention as a Function of Age of Participants) ......... 82
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CHAPTER 1. INTRODUCTION Introduction to the Problem Electronic and web technologies include a significant economic and social force in contemporary life and business. In 2015, the U.S. Department of Commerce (2015) reported electronic commerce (e-commerce) accounted for approximately $340 billion in retail sales (U.S. Department of Commerce). Commercial activities conducted over computers and mobile networks empower business processes and add value to consumers by introducing unique channels for buying and exchanging information. Mobile commerce (m-commerce) is a steadily growing segment of digital commerce solutions predicted to reach $626 billion in sales by 2018 (ComScore, 2014). As noted by Swilley, Hofacker, and Lamont (2012), firms face increasing pressure to deploy m-commerce strategies as a source of sustained competitive advantage for attracting new and preserving existing customers. As a result, knowledge and intellectual capital pertaining to web and mobile technologies are crucial business assets and a source of competitive advantage (Lin, Lu, Wang, & Wei, 2011). In this context, business models are linked to technological innovation (Baden-Fuller & Haefliger, 2013), as such it is important firms understand how users of m-commerce perceive and utilize m-commerce to develop more efficient and effective technology interfaces and formulate their strategies. Within the current body of research on technology acceptance that relates to the context of a competitive advantage (Mahmood, Gemoets, Hall, López, & Mariadas, 2008), a range of established attitudes and perceptions related to predictive m-commerce purchase intentions exist. In several findings, trust and privacy concerns indicated reliable predictors of m-commerce purchase intentions (Chunxiang, 2014; Lai, Lai, & Jordan, 2009; Nassuora, 2013; Pelet & Papadopoulou, 2012; Yaseen & Zayed, 2010; Zhou & Lu, 2011a). Other researcher indicated
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that usefulness (performance expectancy) and ease of use (effort expectancy) predict mcommerce adoption behaviors (Jaradat, Mamoun, & Rababaa, 2013; Nassuora, 2013; Song, Koo, & Kim, 2008; Wang & Li, 2012; Wang, Wang, Kang & Sun, 2014; Yaseen & Zayed, 2010; Zhou & Lu, 2011b). Social influence, or the extent to which someone adopts m-commerce based on the views of others, also indicated a determinant of m-commerce purchase intentions (Pelet & Papadopoulou, 2012; Wang & Wang, 2010; Zhou & Lu, 2011a). Whereas past research expanded knowledge about attitudes and perceptions toward ecommerce that drive consumer purchase intentions and provide a competitive advantage (Coursaris & Kim, 2011; Foon & Fah, 2011; Hernández, Jiménez, & Martin, 2010; San-Martín & Camarero, 2012; Tuch, Roth, Hornbaek, Opwis, & Bargas-Avila, 2012; Vrechopoulos & Atherinos, 2009; Wang, Wang, & Liu, 2016). Budzanowska-Drzewiecka (2015) maintained the fundamental behavioral dynamics associated with m-commerce requires further investigation. From 2007 to 2014, among the published m–commerce studies (Alkhunaizan & Love, 2012; Chunxiang, 2014; Jaradat et al., 2013; Lin et al., 2014; Okazaki & Menendez, 2013; Zhang, Zhu, & Liu, 2012; Zhou & Lu, 2011b), none included investigation of the relationship between perceptions of m-commerce performance and effort expectancies, social influence, the facilitating conditions of m-commerce trust and perceived risk, and customer purchase intentions. The research problem of this study addresses the gap in knowledge on the impact of users’ perceptions about m-commerce performance and effort expectancies, trust, and perceived risk of their purchase intentions, which may be applied in business to develop competitive advantages. Scholarly evidence suggest that performance and effort expectancies, social influence, and facilitating conditions of trust and perceived risk may be significant factors in predicting m-
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commerce purchase intentions (Zhang et al., 2012; Zhou & Lu, 2011b). Facilitating conditions, in the present context, refers to the degree to which an individual believes that a technical infrastructure exists to support technology use. Facilitating conditions “reflects perceptions of internal and external constraints on behavior and encompasses self efficacy, resource facilitating conditions, and technology facilitating conditions” (Venkatesh, Morris, Davis, & Davis, 2003, p. 454). To date, no research has yet integrated these factors into one model to test their relative influence on m-commerce adoption. The research includes a design to address this gap in the research literature, thereby potentially identifying important competitive advantages that may be applied to m-commerce. Background of the Study A significant trend in e- and m-commerce research includes the use of behavioral models to predict user intentions and behaviors. A range of approaches exists in the research literature to understand the processes associated with adoption of mobile commerce (Chan & Yee-Loong Chong, 2013; Chong, Chang, & Ooi, 2012). The common aim is for researchers to gain insights into consumers’ perceptions of m-commerce and their subsequent behavioral intentions as a method of increasing conversion rates, which is an important metric used to determine the percentage of users who take a desired action (e.g. sales conversions). Through different theoretical frameworks, research studies revealed a number of predictors of behavioral intentions and conversion rates in the m-commerce domain (Okazaki & Mendez, 2013; Min, Ji, & Qu, 2008). The seminal work of Davis (1989) set the foundation for investigating the impact of consumer acceptance of technology adoption, while the research of Lederer, Maupin, Sena, and Zhuang (2000) was among the first to draw a correlation between ease of use and usefulness to
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predict applications usage of websites. As researchers continued to refine models to measure user intention and usage online, marketers sought to find ways to influence consumers to embrace newer developments in the m-commerce domain (Okazaki & Menendez, 2013). Researchers and scholars developed and discussed different theories and concepts related to e-commerce and mcommerce (San Martín & Herrero, 2012; Swilley et al., 2012). Some of the most relevant consumer behavioral theories in the context of m-commerce include those of user acceptance and usage, namely technology acceptance model (TAM), extended TAM (TAM2), the theory of reasoned action (TRA), the theory of planned behavior (TPB), and the unified theory of acceptance and use of technology (UTAUT). These models represent the cornerstone for subsequent m-commerce research, which has become a significant academic pursuit due to the exponential growth in web technologies. Electronic and web technologies are major economic and social forces in contemporary life and business (Jaradat et al., 2013). According to the U.S. Department of Commerce (2014), electronic commerce (e-commerce) accounted for approximately $300 billion in 2014 retail sales. M-commerce is a steadily growing business technology forecasted to reach $626 billion in sales by 2018 (ComScore, 2014). As a result, knowledge and the related intellectual capital of mobile technologies are indispensable business assets and a source of competitive advantage (Lorenzo-Romero, Constantinides & Alarcón-del-Amo, 2013; San Martín, López-Catalán, & Ramon-Jeronimo, 2012). An increased understanding of the predictors of m-commerce purchase intentions could be adopted to enhance and progress competitive advantages and growth opportunities in various business domains (Kim, Ferrin, & Rao, 2008; Zhou, Zhang, & Zimmermann, 2013).
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Research on the predictors of m-commerce between 2010 to 2012 established a clear link between the development of e-commerce and m-commerce capacities to gain an optimum competitive advantage (Benou, Vassilakis, & Vrechopoulos, 2012; Lee & Mills, 2010; Kuo, Yen, & Chen, 2011). Recent academic research on the applications of m-commerce from strategic lenses has been scarce (Chong, 2013), thus producing limited m-commerce strategic frameworks of reference. The research topic addressed in this study focuses on developing knowledge that can be applied to m-commerce to enhance competitive advantages by investigating what drives and facilitates m-commerce purchase intentions among American adult consumers. The research includes the framework of the unified theory of acceptance and use of technology (UTAUT; Venkatesh et al., 2003), which posits that performance expectancy, effort expectancy, social influence, and facilitating conditions impact user acceptance of the technology. Statement of the Problem Previous research has established that a range of important factors such as attitudes and expectations toward e-commerce predict consumer purchase intentions to provide knowledge about developing a competitive advantage (Foon & Fah, 2011; Hernández et al., 2010; SanMartín & Camarero, 2012; Vrechopoulos et al., 2009; Wang et al., 2016). Nevertheless, it is still relatively unclear if the same factors predict m-commerce purchase intentions; in short, there is less knowledge about how the fundamental behavioral dynamics associated with m-commerce translate into competitive advantages (Budzanowska-Drzewiecka, 2015). Research on the predictors of purchase intentions related to m–commerce exist (Alkhunaizan & Love, 2012; Chunxiang, 2014; Jaradat et al., 2013; Lin et al., 2014; Okazaki & Mendez, 2013); however, there is no study in the literature that has investigated the relationship between m-commerce
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performance and effort expectancies, social influence, the facilitating conditions of m-commerce trust and perceived risk, and customer purchase intentions. The research problem of this study is to address the gap in knowledge on the impact of users’ perceptions of m-commerce performance and effort expectancies, trust and perceived risk of their purchase intentions, which may be applied in business to develop competitive advantages. Purpose of the Study The purpose of the study is to address a gap in the research literature on m-commerce adoption by investigating the extent to which performance and effort expectancies, social influence, and facilitating conditions predict m-commerce purchase intentions within the context of competitive advantage. The central research questions and subquestion of this study focus on building an understanding as to what extent perceptions of m-commerce performance and effort expectancies, social influence, and the facilitating conditions of m-commerce trust and perceived risk can predict customer purchase intentions. The study used a quantitative, non-experimental predictive design, in which adult American users of m-commerce were asked to complete a questionnaire to measure their perceptions of performance and effort expectancies, social influence, the facilitating conditions of m-commerce trust and perceived risk, and their mcommerce purchase intentions. Results from the study are intended to provide knowledge for firms seeking to gain a competitive advantage through m-commerce business models. Rationale Prior information technology (IT) acceptance research supported the UTAUT model (Alkhunaizan & Love, 2012; Foon & Fah, 2011; Im, Hong, & Kang, 2011; Jaradat et al., 2013), yet limited research exists which clarifies the facilitating conditions that may impact user acceptance of m-commerce. The goal of this study was to generate insight as to how the specific
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facilitating conditions of m-commerce trust and perceived risk, as well as expectancies and social influence, relate to customer purchase intentions and translate into potential competitive advantages. Baden-Fuller and Haefliger (2013) maintained expanding awareness of these relationships indicated an essential avenue for developing competitive advantages in business management and technology, as m-commerce a critical business tool and investment opportunity (Benou et al., 2012; Giovannini, Ferreira, da Silva, & Ferreira, 2015). Research Questions In the context of competitive advantage, the research problem and topic of this study address the lack of knowledge on the influence of users’ perceptions of m-commerce performance and effort expectancies, social influence, and facilitating conditions on their purchase intentions. The variables in this study were measured by m-commerce user responses to validated and reliable subscales used in a comparable study by Escobar-Rodriguez and CarvajalTrujillo (2014). The main research question for this study is Main: To what extent do performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce predict m-commerce purchase intentions with regard to competitive advantage? The subquestions for this study are SubQ1: To what extent does performance expectancy predict m-commerce purchase intentions with regard to competitive advantage? SubQ2: To what extent does effort expectancy predict m-commerce purchase intentions with regard to competitive advantage? SubQ3: To what extent does social influence predict m-commerce purchase intentions with regard to competitive advantage?
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SubQ4: To what extent does the facilitating condition of trust in the use of m-commerce predict m-commerce purchase intentions with regard to competitive advantage? SubQ5: To what extent does the facilitating condition of perceived risk in the use of mcommerce predict m-commerce purchase intentions with regard to competitive advantage? Hypotheses The hypotheses for the stated research questions are as follows: Omnibus Hypothesis H00: Performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce do not predict m-commerce purchase intentions at a statistically significant level with regard to competitive advantage. HA0: Performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce predict m-commerce purchase intentions at a statistically significant level with regard to competitive advantage. Sub-Hypotheses H01: Performance expectancy in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. HA1: Performance expectancy in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. H02: Effort expectancy in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. HA2: Effort expectancy in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage.
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H03: Social influence does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage HA3: Social influence predicts m-commerce purchase intentions at a statistically significant level with regard to competitive advantage. H04: Trust in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. HA4: Trust in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. H05: Perceived risk in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level with regard to competitive advantage. HA5: Perceived risk in the use of m-commerce predicts m-commerce purchase intentions
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statistically significant level regarding competitive advantage. Significance and Contributions The study is significant for its capacity to refine and confirm the existing theoretical framework provided by the UTAUT model regarding competitive advantage in m-commerce. The model proposes four primary factors that influence acceptance of technology; performance expectancy, effort expectancy, social influence, and facilitating conditions. Whereas the first three elements are well-defined passive and operationalized in research, the facilitating conditions appear to lack conceptual clarity and precision (Escobar-Rodrigues & CarvajalTrujillo, 2014; Oliveira, Faria, Thomas, & Popovič, 2014). Facilitating conditions include definition in the UTAUT framework as factors that promote or remove barriers to the use of technology (Venkatesh et al., 2012). The definition of facilitating conditions allows for a wide range of possibilities, yet provides few clues as to which facilitating conditions are more or less
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meaningful predictors of user acceptance of technology. Existing literature confirmed facilitating conditions (Attuquayefio & Addo, 2014; Venkatesh et al., 2012), justified (Alkhunaizan & Love, 2012; Im et al., 2011), or omitted from the UTAUT model (Casey & Wilson-Evered, 2012; Pope, 2014; Wang & Wang, 2011). As such, facilitating conditions have little impact on technology acceptance (Escobar-Rodriguez and Carvajal-Trujillo, 2014; Foon & Fah, 2011; Jaradat et al., 2013; Oliveira et al., 2014; Venkatesh et al., 2012). Significance to Body of Research The study addresses the two facilitating conditions that have figured highly in the research: m-commerce perceived risk and trust issues (Cyr, Head, & Ivanov, 2006; Giovannini et al., 2015; Oliveira et al., 2014). Consistent with the UTAUT conception of facilitating conditions, perceived risk and trust issues reflect the means to promote and remove a barrier (Venkatesh et al., 2012). The research in this study has the potential to refine UTUAT by giving a clearer picture of the facilitating conditions of m-commerce perceived risk and trust issues. The research also may confirm further the predictive value of performance expectancy, effort expectancy, and social influence on m-commerce purchase intentions in the context of a competitive advantage. Altogether, the study should refine the definition and UTAUT conception of facilitating conditions by incorporating perceived risk and trust issues into the research focus (Venkatesh et al., 2012). The research will also contribute to the knowledge-base regarding direct and indirect relationships between facilitating conditions and other UTUAT constructs (performance expectancy, effort expectancy, and social influence). Similarly, the impact on predicting mcommerce purchase intentions (Escobar-Rodriguez & Carvajal-Trujillo, 2014; Foon & Fah, 2011; Jaradat et al., 2013; Oliveira et al., 2014; Venkatesh et al., 2012) may be adopted to
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enhance growth opportunities in various business domains. Finally, the implications for the generalizability of UTAUT specifically to the domain of m-commerce regarding competitive advantage are addressed in the study. Significance to Organizations Technology competence expands beyond the realm of tangible assets. Organizations must invariably seek growth through innovation and redefine their strategies in a digital world (Bharadwaj, El Sawy, Pavlou, & Venkatraman, 2013). As firms vie for a share of the uptake in digital commerce, knowledge and intellectual capital pertaining to web and mobile technologies is a crucial business asset and source of competitive advantage (Lin et al., 2011). In this context, the findings from the study may contribute practical implications and knowledge that can be applied in business technologies to design and develop more efficient and effective user interfaces and improve user experiences of m-commerce in the context of developing a competitive advantage. Definition of Terms In the context of this study, the following terms will apply: Competitive advantage. The way in which an organization implements a business strategy that results in cost leadership, product differentiation, or product focus (Porter, 1980). E-Commerce. Business activities conducted through an electronic medium. The two main attributes of e-commerce are aggregator of information and as a potential apparatus for the replacement of many business transactions once performed within the confines of enterprise (Terzi, 2011). Effort expectancy. The extent to which people believe using m-commerce would be free from effort and not difficult to use (Venkatesh et al., 2012).
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M-commerce. Business transactions conducted by wireless telecommunication networks (Zhang et al., 2012). Performance expectancy. The extent to which people believe m-commerce will help perform a task better (Venkatesh et al., 2012). Purchase intentions. The strength of person’s intention to make a purchase with mcommerce in the future or again (Venkatesh et al., 2012). Risk. The risk perceived with using m-commerce including fraud and product quality (Zhang et al., 2012). Social influence. The strength with which important others have influenced a person to adopt or use an m-commerce system (Venkatesh et al., 2012). Technology acceptance model (TAM). A valid and reliable measure that predicts the acceptance or adoption of information systems (Davis, 1989). Trust. The strength of a person’s belief that using m-commerce is secure and poses no privacy threats (Zhang et al., 2012). Unified theory of acceptance and use of technology (UTAUT). A consolidated a range of theoretically and empirically relevant constructs that explain user acceptance of information technology (UTAUT), developed by Venkatesh, Morris, Davis, and Davis (2003). The UTAUT model includes four main constructs to predict behavioral intentions and user behaviors: performance expectancy, effort expectancy, social influence, and facilitating conditions. To a certain degree, performance expectancy, and effort expectancy relates to the TAM constructs of perceived usefulness and ease of use. Venkatesh et al. (2003) explained the role of social influence as the extent to which a person believes how important others think he or she should
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adopt an IT system while facilitating conditions are conceptualized as factors to promote, or remove barriers to, the use of technology. Assumptions and Limitations The study proposes several of assumptions. The first assumption included that UTAUT provides a valid framework to investigate predictors of m-commerce purchase intentions. Next, that the tools and measures employed to capture the operational definitions of the main constructs of this study (effort and performance expectancy, social influence, the facilitating conditions of m-commerce risk and trust, and purchase intentions) are valid and reliable. In both cases, the conceptual framework and measures of the study assume a body of contemporary and valid research on m-commerce. The final assumption includes that survey participants delivered honest and accurate answers about their perceptions of m-commerce given the privacy and confidentiality of their responses would be insured. Positivistic Research Study The assumptions of this study reflect those put forth by Venkatesh et al. (2012) in their article, Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology (UTAUT), examining consumer acceptance and use of information technology. Venkatesh et al. (2012) made the following assumptions: Ontological assumption. Consumer acceptance and use of information technology can be observed and measured. One defined reality for these constructs and if measured, will be readily visible to all who observe it. Epistemological assumption. The acquisition of knowledge of consumer acceptance and use of information technology is an objective process, one that can be measured, and that measured and objective report is significant and useful knowledge.
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Axiological assumption. The measurements for consumer acceptance and information technology will objectively inform the theory of acceptance and use of technology, which is valuable to understand. Methodological assumption. Quantitative design—a new survey instrument was adapted from the UTAUT—a four input construct model of performance expectancy, effort expectancy, social influence, and facilitating conditions which influence behavioral intentions of use (Venkatesh et al., 2003). Limitations Some weaknesses to the research in this study exist. Because of the method of participant recruitment, participants to the SurveyMonkey panel may not consist of a representative sample of North American m-commerce users (Evans & Mathur, 2005). To compensate for any disparity a reasonable sample size was estimated, which broadly represented the North American population in terms of gender, age, education, ethnicity, and income. More generally, the correlative nature of the research does not produce information about definitive cause-effect relationships between m-commerce perceptions and purchase intentions to be concluded from the findings. Additionally, Wright (2005) argued, self-selection bias is another significant limitation of online survey research. Despite the limitations of the study format, the correlational method provided several practical benefits, including exploring a range of concepts simultaneously. The online questionnaire format provided a relatively valid and efficient means of data collection. Nature of the Study / Theoretical and Conceptual Framework The primary theoretical basis for the study is the unified theory of acceptance and use of technology (UTAUT; Venkatesh et al., 2012). The UTAUT model consolidates a range of
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theoretically and empirically relevant constructs to explain user acceptance of information technology as it pertains to a competitive advantage. The framework provided by the model and depicted in Figure 1 supports four main constructs to predict purchase intentions: performance expectancy, effort expectancy, social influence, and facilitating conditions.
Figure 1. Illustration of the Unified Theory of Acceptance and Use of Technology research model, showing the relationship between performance and effort expectancy, social influence and facilitating conditions with purchase intentions and the moderating influence of gender, age and experience. The model is adapted from “Unified Theory of Acceptance and Use of Technology: Toward a Unified View,” by V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, 2003, MIS Quarterly, 27(3), p. 447 Whereas performance expectancy is a concept defined and measured by user beliefs that technology will help them perform a task better (Venkatesh et al., 2012), effort expectancy is the belief that using a system will be free from effort and difficulty (Venkatesh et al., 2012). Social influence is the extent to which a person believes their adoption of an information technology (IT) system is significant to others. Correspondingly, facilitating conditions are factors to promote or remove barriers to the use of technology (Venkatesh et al., 2012). Venkatesh (2012)
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maintained, gender, age, and experience with technology moderate the relationship between performance and effort expectancy, social influence, facilitating conditions, and m-commerce purchase intentions. In various studies the UTAUT model has proved its capacity to predict a variety of mcommerce purchase intentions and behaviors related to the development of a competitive advantage (Foon & Fah, 2011; Im et al., 2011; Jaradat et al., 2013). In a test of UTAUT with 399 participants, Venkatesh et al. (2012) found the model explained 70% of the variance in user intentions to adopt new technology. Another study by Wang and Wang (2010) demonstrated that UTAUT predicted 65% of behavioral intentions to use mobile internet (m-internet) among a sample of Taiwanese participants. Other studies have shown UTAUT to predict approximately 62% of intentions to adopt mobile banking (Oliveira et al., 2014; Zhou, Lu, & Wang, 2010), 60% of m-commerce purchases of airplane flights (Escobar-Rodriguez & Carvajal-Trujillo, 2014), and 61% of behavioral intention to use business IT (Pope, 2014). Given its significant predictive capacity, the UTAUT model provides a suitable theoretical basis to address the research questions of this study and further knowledge of competitive advantages in m-commerce. Organization of the Remainder of the Study This study includes organization into in five chapters. The study first introduced issues related to the topic of this study. Second the study’s background, problem statement, and purpose of the study. The third discussions will incorporate the rationale for embarking on the study, the research questions, research hypothesis, and significance of the study. The final section chapter covered definitions of terms, assumptions, limitations as well as the nature and the theoretical and conceptual framework of this study.
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A literature review and evaluation of significant m-commerce theories with a particular focus on UTAUT and TAM conceptual frameworks to explain and predict m-commerce purchase intentions will be presented in Chapter 2. The literature review will also include an evaluation of previous research findings regarding relevant factors that predict customer purchase intentions. In Chapter 3, the methodological approach taken to address the research questions is undertaken. The chapter includes the conceptual model, hypotheses, research design, sampling method, participants, the validity and reliability of survey measures and tools, and research procedures. A further methodology will include mechanisms to ensure the study meets research ethics requirements and standards to assure the privacy and rights of participants. Chapter 4 provides a statistical analysis of the data collected. Chapter 5 concentrates on a discussion of the findings as they relate to the hypotheses, and their theoretical and practical implications in the context of future research and competitive advantage. There is also a discussion regarding limitations of the study and recommendations for future research found in this chapter.
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CHAPTER 2. LITERATURE REVIEW Introduction In this chapter, a literature review related to the research question and focus of this study is discussed. Next, the chapter includes the exposition of the literature, which covers the mcommerce theories relevant to the research question are reviewed, evaluated, and an outline and definition of the focus variables of this study are described. The study supported the use of the UTAUT model as the framework for exploring the relationship between m-commerce performance and effort expectancies, social influence, facilitating conditions, and m-commerce customer purchase intentions. In the next sections of the chapter, the nature of m-commerce, and comparison of traditional commerce, electronic commerce, and m-commerce are examined. Finally, the chapter includes an evaluation of research findings on factors that predict customer m-commerce purchase intentions and concludes with an overall perspective on the state of research on m-commerce purchase intentions to substantiate the focus of this study. Theoretical Frameworks for Mobile and Electronic Commerce Research The advent and growth of information technology (IT) drastically altered the way companies interact with customers. However, scholars have long acknowledged the barriers of adoption that is present in m-commerce (Chan et al., 2013; Chong et al., 2012). San Martín and Herrero (2012) posited, e-commerce became a significant field of interest, bringing new ways of interaction between retailers and customers. In particular, business leader and technology managers are under increased pressure to proactively implement cross-channel sale synergies to adapt to change brought about by wireless technologies. Al-Debei and Al-Lozi (2014) noted, the convergence of IT and mobile communication technology gave rise to the field of mobile commerce (m-commerce). M-commerce provides increased flexibility, mobility, and ubiquitous
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access to information, as business transactions can be carried out from mobile phones or personal digital assistants. Given the social and monetary impact of m-commerce, scholars proposed a number of models to study how individuals interact and react to mobile commerce (Ivanochko, Masiuk, & Gregus, 2015; Mehmood, 2015). Many of the business models are modifications of user acceptance theories initially intended for studying user acceptability in e-commerce. The most common theories are the technology acceptance model (TAM) (Davis, 1989), innovation technology, organization and environment (TOE) framework (Tornatzky & Fleischer, 1990), innovation diffusion theory (IDT) (Rogers, 1995), task technology fit (TTF) model (Goodhue & Thompson, 1995), and unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al., 2003). Though efforts include the ability to understand m-commerce user behavior using tools designed for e-commerce, research focusing on m-commerce received little attention (Püschel, Mazzon, & Hernandez, 2010). Some studies combining existing models include applications to the field of mobile usage (Njenga & Ndlovu, 2015) and mobile commerce in general (Riquelme & Rios, 2010). M-commerce is a relatively new field (Kourouthanassis & Giaglis, 2012), as limited understanding of customer behaviors can play a decisive role in the success or failure of m-commerce ventures (Coursaris & Kim, 2011), thereby warranting additional research toward framework development. Researchers have offered several approaches to explaining the processes associated with adoption of mobile commerce (Chan et al., 2013; Chong et al., 2012). The common aim is for researchers to gain insight into consumers’ adoption of m-commerce (Nassuora, 2013; Püschel et al., 2010) and subsequent purchase intentions as a method of increasing conversion rates (Verhagen & van Dolen, 2009; Wang & Li, 2012), which are a chief metric used to determine
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the percentage of users who make a preferred action (e.g., sales conversions), thus providing a competitive advantage. Through different theoretical frameworks, several research studies revealed a number of predictors of purchase intentions and conversion rates in the m-commerce domain (Okazaki & Menendez, 2013; Min et al., 2008). One of the earliest theoretical works on technology adoption was by Davis (1989), who showed that ease of use and usefulness were relevant predictors of user’s intention to accept and adopt new technology. Since then, researchers developed and utilized different theories and concepts to predict the uptake of e-commerce and m-commerce technologies (San Martín & Herrero, 2012; Swilley et al., 2012). Some of the most broadly employed theories in the context of m-commerce are presented in subsequent sections including the theories of reasoned action (TRA) and of planned behavior (TPB), the technology acceptance model (TAM), and the unified theory of acceptance and use of technology (UTAUT). These theories are the foundation upon which technology acceptance is explained and predicted (Okazaki & Menendez, 2013). Theory of Reasoned Action (TRA) and Planned Behavior (TPB) The TRA proposes that attitudinal and normative influences are central to the prediction of behavior intentions and behavior (Ajzen, 2011; Min et al., 2008). According to this theory, prediction of individual behavior can be made to some degree by recognizing a user's behavioral intentions, or commitment to behave in a particular manner. Southey (2011) maintained the TRA model offers potential benefits to predict the intention of individuals to perform specific behaviors based on their attitudes and normative beliefs. Similarly, Min et al. (2008) claimed the viewpoint of the person, and the subjective norm regarding the questioned behavior collectively determines the behavioral intention.
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In the TRA model, attitudes refer to the evaluation of a particular course of action or the subjective probability that a certain conduct will result in a precise result or consequence, whereas subjective norms refer to the perceptions significant to others have towards performing a target behavior (Min et al., 2008). The TRA model includes identification of two potentially vital facets of behavioral intentions towards m-commerce: attitudes and norms incorporated into subsequent models of m-commerce behavior. Albeit, scholars acknowledged that despite best intentions, an individual may not have full control over their behaviors from a lack of confidence or influence over the behavior (Pavlou, 2003). As such, the construct of perceived behavior control was added to the TRA model, resulting in the development of the TPB. The TPB (Ajzen, 1985) incorporates the dimension of behavior control to account for scenarios in which individuals lack substantial influence over a certain behavior. Thus, TPB explains how the behavior of a person can be defined by their behavioral intention, influenced by perceived behavioral control, subjective norms, and attitudes. Conversely, attitude refers to the overall evaluation of performing a behavior. Also, subjective norms include definition as the perception an individual has about the opinions of others. Min et al. (2008) maintained, perceived behavioral control includes concern with the perceptions of a person as to the availability of resources or opportunities needed to perform a behavior. Research TRA/TPB. In one technology adoption investigation of the TPB model, Pavlou and Fygenson (2006) tested the model’s efficacy when predicting consumers’ inclination toward using e-commerce. Participants (N = 312) completed questionnaire measures of the attitudes, subjective norms, behavioral control, intentions, and behaviors toward purchasing from an online store. Pavlou and Fygenson findings showed that TPB variables together were significant predictors, accounting for 56% of the variance in behavior. Despite the predictive
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capacity of TPB, the model lacks specificity regarding m-commerce. TRA and TPB were intended to be applied to health behaviors, yet employed to investigate other topics (Sheppard, Hartwick, & Warshaw, 1988). As a consequence, the trend for contemporary researchers develops alternative models that address more specific variables that account for e-commerce behaviors. Evaluation TRA/TPB. The TRA/TPB model was one of the earliest research frameworks to be deployed for conceptualizing the potential predictors of technology acceptance. Additional variables have been added to the model to extend its predictive capacity (Pavlou & Fygenson, 2006) such that the model has been extended to fit the phenomenon of technology acceptance. At the same time, the capacity of the TRA/TPB model to predict technology acceptance is moderate at best. Whereas TRA/TPB provides a significant model of the predictors of behavioral choices in general, its application to technology acceptance is questionable. More recent modeling has addressed these issues with a focus on the idiosyncrasies of technology acceptance. Technology Acceptance Model Davis (1989) developed the user acceptance of IT model or Technology Acceptance Model (TAM), which conceptualized one of the earliest and more influential approaches to the distinct questions of technology use. Reflecting upon the initial stage of IT development, the focus of the research was on users’ acceptance of e-mail. Davis proposed that the users’ perceived usefulness (PU) and perceived ease of use (PEOU) of systems are causal linkages between intention to use and the behavior towards using a new information system, as shown in Figure 2. Perceived usefulness is a concept defined and measured by users’ beliefs that
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technology will help them perform their job better, whereas perceived ease of use was defined as the belief using a system would be free from effort and not arduous to use.
Figure 2. Illustration of the Technology Acceptance Model (TAM) showing the relationship between perceived usefulness, ease of use and intentions to use with usage behavior. The model is adapted from “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” by V. Venkatesh and F.D. Davis, 2000, Management Science, 46(2), pp.186-204 TAM research. In Study 1 of Davis’s (1989) research program, participants were 112 Canadian employees asked to self-report on the perceived usefulness, ease of use, and current usage of e-mail technology via responses to a range of questions measured on a Likert-type scale. Evidence from factor and reliability analysis, as well as, tests of discriminant and convergent validity strongly supported two relatively independent 6-item scales to measure perceived usefulness and ease of use, respectively. More importantly, the research by Davis in Study 1 and the follow-up Study 2 tested the effect of perceived usefulness and ease of use on actual usage of e-mail via regression analysis. In both studies conducted by Davis (1989), perceived usefulness significantly predicted participant’s usage of e-mail, after controlling for ease of use. In contrast, the effect of perceived ease of use was not significant after controlling for usefulness, even though ease of use included independent correlations with usage. These findings supported the idea that ease of use affects usage indirectly through usefulness. Moreover, regression analysis revealed that the full model
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explained approximately 40% of the variance in usage. The use of self-report measures, correlational designs, and regression and statistical modeling is a fundamental approach to research in a wide range of studies (Bhatti, 2007; Chen & Chang, 2013). Such tactics are used to quantify the adoption of IT beyond e-mail applications into the e-commerce and m-commerce domains (Schierz, Schike, & Wirtz, 2010; Vrechopoulos et al., 2009). Wang, Lin, and Luarn (2006) integrated the TAM model with TPB to investigate consumer intentions to use mobile banking. Taiwanese participants (N = 258) completed a survey to measure the effects of self-efficacy, perceived financial resources, perceived usefulness, perceived ease of use, and perceived credibility on behavioral intention to use internet banking. These factors explained 69% of the variance in behavioral intention, with perceived usefulness illuminating the largest amount of variance, yet the authors suggested social norms should also be measured as an important factor in e-commerce. An extended version of TAM (TAM 2) by Venkatesh and Davis (2000) added organizational and social factors like impression and subjective norms to the original model. Evaluation of TAM. Overall, the TAM models include extensive testing and applications across many dimensions of m-commerce and managed to stand out as useful theoretical models for examining m-commerce adoption by consumers (Min et al., 2008; Wu & Wang, 2005). Adapted TAM models predicted 46% of m-commerce user intentions (Song et al., 2008), 57% of intentions to adopt m-services with the addition of social influence into a structural model (Yang, Lu, Gupta, Cao, & Zhang, 2012), 55% of user’s m-commerce adoption intentions (Chunxiang, 2014), and 69% of the variance in behavioral intention with regard to mobile banking (m-banking) by augmenting TAM with measures of perceived credibility and self-efficacy (Wang et al., 2006). As such, the variables identified in TAM postulate a suitable
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understanding of the predictors of m-commerce usage and may be applied to enhance and increase growth opportunities in various business domains. Nevertheless, a recent model of technology acceptance successfully integrated TAM, TPB, and other user acceptance models to provide improved reliability and a more complete basis to comprehend user acceptance and behaviors towards m-commerce. Unified Theory of Acceptance and Use of Technology In a crucial contribution to the research on Internet commerce, Venkatesh et al. (2003) synthesized TAM and several other models of user acceptance including TRA, TAM, and TPB to yield the unified theory of acceptance and use of technology (UTAUT). Zhou and Lu (2011) maintained UTAUT’s theoretical underpinning also reflecting aspects of the motivational model (MM), the model combining the technology acceptance model and theory of planned behavior (C-TAM-TPB), the model of personal computer utilization (MPCU), innovation diffusion theory (IDT), and social cognitive theory (SCT). Venkatesh et al. proposed that the behavioral intentions of users are determined by social influence, performance expectancy, facilitating conditions, and effort expectancy. Whereas performance expectancy and effort expectancy relate to the TAM constructs of perceived usefulness and ease of use, social influence is defined as the extent to which a person believes how important others think he or she should adopt an IT system, and facilitating conditions are conceptualized as factors to promote or remove barriers to the use of technology. Research on UTAUT. In the domain of m-commerce, the UTAUT model found support and successful application in several studies (Alkhunaizan & Love, 2012; Jaradat et al., 2013; Lai et al., 2009). UTAUT included use to study user acceptance of technology in a range of varied cultural contexts, including Finland (Bouwman, Carlsson, Molina-Castillo, & Walden,
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2007) and China (Park, Yang, & Lehto, 2007). Wang and Wang (2010) conducted an investigation of the determinants of mobile Internet usage intention by employing UTAUT with the additional constructs of playfulness, value, and palm-sized self-efficacy; (N= 343) Taiwanese survey participants. The results revealed strong support for UTAUT with 65% of the variance in usage intention explained; apart from playfulness, all the variables had a moderately significant effect on mobile use intentions. In using empirical data to extend and modify the UTAUT, the findings provided an understanding of the perceptions of potential adopters. Evaluation of UTAUT. The UTAUT model includes testing in a set of studies, which provided preliminary evidence of its strong capacity to predict a variety of m-commerce purchase intentions and behaviors (Alkhunaizan & Love, 2012; Escobar-Rodriguez & CarvajalTrujillo, 2014; Foon & Fah, 2011; Im et al., 2011; Jaradat et al., 2013; Oliveira et al., 2014; Pope, 2014; Zhou, Lu, & Wang, 2010). Min et al. (2008) proclaimed the UTAUT model was the most comprehensive among the IT adoption models. According to Lai et al., (2009), extensive test concluded the UTAUT model is the most definitive model in most scenarios as it is able to synthesize the significant predictors of m-commerce purchase intentions and offers guidance into future research about technology adoption. A review of the trends in scholarly research on information systems acceptance and usage suggests that UTAUT emerged as one of the most influential models to explain and predict technology acceptance and use (Alkhunaizan & Love, 2012; Foon & Fah, 2011; Im et al., 2011; Jaradat et al., 2013). The variables of this study are based on the UTAUT framework, wherein facilitating conditions encompass trust and risk associated with m-commerce because of their relevance to m-commerce purchase intentions (Aboelmaged & Gebba, 2013). There is a need to understand m-commerce adoption through examining factors that influence user’s intention. Such
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knowledge may guide strategic planning and inform decision making that relates to the context of a competitive advantage (Coursaris & Kim, 2011; Kuo et al., 2011). Table 1 indicates the definition of each variable concerning m-commerce based on a careful examination of the literature. Table 1. Definition of Study Constructs Construct
Definition
Literature
Performance Expectancy
Expectations that technology will help perform a task better.
Venkatesh et al., (2012)
Effort Expectancy
Expectation that using a system would be free from effort and not difficult to use.
Venkatesh et al., (2012)
Social Influence
The degree to which important others have been an influence on m-commerce use.
Venkatesh et al., (2012)
Trust
The extent an individual believes that using technology is secure and has no privacy threats.
Zhang et al., (2012)
Perceived Risk
The risk perceived with using m-commerce including fraud and product quality.
Zhang et al., (2012)
Purchase Intentions
The strength of a person’s intention to use the technology in the future or again.
Venkatesh et al., (2012)
Variables in User Acceptance of M-commerce Venkatesh et al. (2003) asserted few key variables used to study user acceptability in the context of m-commerce, which varies according to the theoretical framework. For example, UTAUT uses effort expectancy, performance expectancy, facilitating conditions, and social influence as parameters for studying user acceptance, while TTF uses task characteristics, technology characteristics, task technology fit, and use to characterize user acceptability (Oliveira et al., 2014). Researchers often classify m-commerce as a subset of e-commerce (Gupta
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& Vyas, 2014; Hu, Lu, & Tzeng, 2015). However, m-commerce has significantly different characteristics when compared to e-commerce. Privacy and security issues and concerns are higher in m-commerce, as wireless data transfer over public networks increases the risk of data theft or fraud (Benou et al., 2012). Similarly, since m-commerce is still in a formative stage, limited availability of appropriate models exist (Chan et al., 2013). Therefore to study and define the variables aiding user acceptance in the unique context of m-commerce is necessary (Al-Debei & Al-Lozi, 2014). Based on the UTAUT model and the nature of m-commerce, the variables of effort expectancy, performance expectancy, social influence, trust, perceived risk, and purchase intention were selected for this study. In this section, each of these variables was analyzed, defined, and justified for modeling user acceptance in m-commerce applications. Effort Expectancy Effort expectancy measures the degree of effort the user perceives in using a particular technology and has been studied in detail in the context of e-commerce by Davis (1989). The construct of effort expectancy is explored in a wide number of applications like the Intranet (Chiu & Wang, 2008), e-banking (Oliveira et al., 2014), wireless Internet (Tsai & LaRose, 2015), and in the domain of m-commerce (Lin et al., 2011); Kim & Garrison, 2009). In many existing models using constructs such as self-efficacy, perceived ease of use (TAM), ease of use (IDT), and complexity (Attuquayefio & Addo, 2014), effort expectancy can be found (Lin et al., 2011). Additionally, effort expectancy is related to individual difference variables like gender, age, and experience of the user. However, Yu (2012) noted effort expectancy effects on gender, age, and experience of the user has a higher significance if the user is older with less technology experience.
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The effect of effort expectancy in determining user behavior regarding technology is the focus of various studies. Multiple definitions of effort expectancy can be found in the literature. Davis (1989) defined effort expectancy as perceived ease of use and the degree to which a person believes that using a system would be effort-free, whereas Thompson, Higgins, and Howell, (1991) termed this concept as complexity or the degree to which a system includes the perception of difficulty in understanding and use. Moore and Benbasat (1991) defined effort expectancy as ease of use: the degree to which an innovation is perceived as being difficult to use. Venkatesh et al. (2003) defined effort expectancy specifically for mobile systems as the extent of ease linked with system utilization. While the definitions vary, lower effort expectancy contributes to more extensive utilization of technology (Attuquayefio & Addo, 2014; Oliveira et al., 2014; Tsai & LaRose, 2015). Integrating the literature, effort expectancy for m-commerce can be defined as: the degree of effort the user perceives in the utilization of mobile technology for the purpose of commercial transactions. Performance Expectancy Performance Expectancy measures the positive effect of using a system on the user’s job performance. Venkatesh et al. (2003) defined as a combination of constructs from different models. Performance expectancy, along with perceived ease of use is the two key parameters determining technology adaptation. Performance expectancy correlates strongly with purchase intention (Davis, Bagozzi, & Warshaw, 1992). However, it has been theorized that this relationship is moderated by factors such as the age of user population and gender. For example, men, being more task oriented, would place higher weight on performance expectancy (Cyr et al., 2006).
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Davis et al. (1989) defined performance expectancy as perceived ease of use or the degree to which a person believes that using a particular system would enhance his/her job performance. Davis et al. (1992) used another aspect of performance expectancy as Extrinsic Motivation: the perception that the use of a technology in an activity can aid in achieving goals other than the activity itself, such as increased pay, improved job performance etc. Thompson et al. (1991) defined performance expectancy as Job fit: the ability of a system to improve the user’s job performance, while Moore and Benbasat (1991) termed it as Relative Advantage: the degree to which an innovation is perceived as being better than its precursor. Tseng and Kuo (2014) defined a similar concept Outcome Expectancy, which is a combination of performance expectancy and personal expectancy. Though a specific definition of personal expectancy is not provided, performance expectancy for Tseng and Kuo is the perception that using a system would improve on job effectiveness and quality of output while reducing time spent on routine tasks. In the context of m-commerce, performance expectancy can be defined as the degree to which the use of m-commerce is perceived to improve job performance in terms of improved effectiveness, quality of output, time use etc. Social Influence Societal conditions often play a significant role in determining users' perception and approach to technology. Social influence figures prominently in most studies pertaining to technology use behavior (Attuquayefio & Addo, 2014; Chong et al., 2012; Foon & Fah, 2011). Social influence is a composite of factors such as peer influence and self and social image, and can be broadly divided into two categories; social norms and critical mass (Wang et al., 2014). Social norms include informal influences and normative influences. Informal influence involves the user accepting information from peers as evidence about reality and forming an opinion on
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its basis (Bapna, & Umyarov, 2015). Normative influences account for instances where the user decides to conform to avoid a negative perception in the social setting (Safeena, Hundewale, & Kamani, 2011). Critical mass occurs when a technology reaches significant market of penetration, its perceived value in society increases, thereby attracting more users, and accelerating adoption. Safeena et al. maintained social influence has a direct positive effect on user attitude towards m-commerce, as user perceived advanced technology would improve their image, status, and performance in the society. As the effect of social influence on customer behavior is an important explanatory factor, however researchers have different definitions. Ajzen (1985) studied social influence as subjective norms in the TRA / TPB model where social influence is defined as the user's perception that people important to him think he should, or should not, perform a certain behavior. Thompson et al. (1991) defined social influence as social factors an individual's internalization of reference group's subjective culture, and specific interpersonal agreements between the user and members of the group. Similarly, Weerakkody, El-Haddadeh, Al-Sobhi, and Shareef (2013) explained social influence as the degree to which a person's social status is enhanced from the use of a technology or innovation. The UTAUT model includes the definition of social influence as the extent to which consumers perceive that important others (e.g., family and friends) believe they should use a particular technology. In the context of m-commerce, social influence can be defined as the extent to which the user perceives that the important members in his social circle support the use of an m-commerce technology. Trust Various definitions of trust have been put forward in the literature, such as Rousseau, Sitkin, Burt, & Camerer, (1998), who defined trust as a psychological state comprising the
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intention to accept vulnerability based upon positive expectations of the intentions or behavior of another. Yang et al. (2015) defined trust as: a set of specific beliefs dealing primarily with the integrity benevolence, competence and predictability of a particular vendor. Joubert and Van Belle (2013) later extended the same definition to m-commerce where trust is a relatively more important predictor of purchasing because direct interaction between a client and seller are infrequent and has been shown to have a direct positive effect on user behavior (Chen & Chang, 2013). Based on relevant literature, trust in m-commerce can be defined as: a customer's belief in the security and reliability of the m-commerce platform and in the seller's ability and motivation to provide quality product and service. According to Pavlou (2003), when customers trust a system, interaction will occur on most occasions, and therefore the buyer-seller transactions are more likely to increase, as trust gives customers high expectations of system reliability. The study by Pavlou confirmed the TAM variables PEOU and PU as fundamental factors in e-commerce acceptance, while other factors include trust and perceived risk. A similar study conducted by San Martín and Herrero (2012) finds established technology risk as negative determinant to online purchasing intention, and may have a negative influence on attitude towards the website use. Pavlou’s and San Martín & Herrero’s research is consistent with recent studies on e-commerce, as the findings imply the direct antecedents of intention to transact to be perceived risk and trust. McKnight and Chervany (2001) characterized trust as three-dimensional, consisting of disposition to trust, structural assurance, and trust belief. Disposition of trust is a personality trait, which varies based on user and is the tendency of a person to trust in general. Structural assurance deals with a person's perceived trust in the environment, and trust belief assumes that the trustworthiness of the vendor consists of beliefs about their integrity, benevolence, and
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competence (Yang, Pang, Liu, Yen, & Tarn, 2015). Trust can also be classified as trusting beliefs (Yang et al., 2015) and trusting intentions (Chen & Chang, 2013), which make a user comfortable in the information provided by the vendor, leading to a purchase. Trust can also act as an indirect antecedent of transaction intentions through perceived ease of use, perceived usefulness, and perceived risk. A recent study by Alkhunaizan and Love (2012) employing UTAUT demonstrates consistency with previous findings, which identified trust as an essential variable of enhancing customer satisfaction and consumer loyalty in mcommerce. Chunxiang (2014) maintained, increased customer trust can increase users perception of value and perceived cost. According to Min et al. (2008) trust emerged as a main determinant of user acceptance as m-commerce businesses that earned that trust associates with success. Additionally, within the context of competitive advantage Hu et al. (2015) posit, companies can improve their m-commerce adoptions enhancing consumer trust via integrity. Developing this perspective would suggest that future m-commerce studies should incorporate the trust variable in their research. Perceived Risk Perceived risk is positively related to trust, as research has confirmed that perception of risk influences trust, and eventually the willingness to engage in a transaction (Joubert & Van Belle, 2013). Trust is often viewed as a mirror image of perceived risk, and risk is often viewed in relation to cost of outcomes. Perceived risk correlates to a person's trust beliefs, environmental uncertainty, and potential loss. Because of the complex nature of perceived risk, many researchers ignored the role perceived risk plays in user behavior (Lin et al., 2014). Despite the large number of transactions and complicated safety mechanisms, consumers still experience
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anxiety, while taking part in online transactions (Hille, Walsh, & Cleveland, 2015; Yang, Chye, Fern, & Kang, 2015). A central ethical issue for Internet commerce is the emphasis of research regarding the risk associated with online transactions. Pavlou (2003) explored consumer uncertainty around online transactions claiming a key risk concern is monetary loss from transactions, which links to consumers’ vulnerability to distort or fail to complete information in the context of online platforms. The second risk identified by Pavlou is the loss of privacy linked to the provision of personal data to online retailers. According to Sreenivasan and Noor (2010), loss of privacy issues with online transactions have been exacerbated that marketers obtain consumers’ personal information using online forms. Lin et al., (2011) argued the open nature of Internet transaction highlight risk and trust as significant elements of e-commerce and related research. Palka, Pousttchi, and Wiedemann (2007) argued perceived risk can be defined as: a function of perceived trustworthiness, perceived confidentiality, and perceived security. Pavlou (2003) further defined trust as the user's subjective expectation of suffering a loss in pursuit of a desired outcome. In addition to the perception of risk associated with fraud and product quality in the m-commerce platform, perceived risk should also include the risks associated with data from immature technology and product performance (Truong, Klink, Fort-Rioche, & Athaide, 2014). In this context, perceived risk can be defined as a user's expectation of loss in terms of product quality, data security or information theft, while engaging in an m-commerce transaction. Purchase Intentions Purchase intention is a measure of a user's plan to purchase a product or service in the near future. Purchase intention is influenced by a number of factors ranging from the usefulness
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of the product, ease of access, and enjoyment associated with shopping (Yu, 2012). Within ecommerce, purchase intention is likely to be influenced by website layout and online store imagery (Dedeke, 2016; Verhagen & van Dolen, 2009). It has also been found that an appealing and enjoyable website often mitigates the effects of poor content, quality, and usability (Nilashi, Ibrahim, Mirabi, Ebrahimi, & Zare, 2015). In m-commerce, purchase intentions is influenced by the selling platform's compatibility with mobile technology and the user's skill and familiarity with mobile technology (Maity & Dass, 2014). Purchase intention is regarded the most significant characteristic in the study of digital commerce user behavior (Dedeke, 2016; EscobarRodriguez & Carvajal-Trujillo, 2014; Kuo & Wu, 2012), as such understanding of purchase intention will give sellers comprehensive insights regarding customer behavior. Researchers have offered several definitions of purchase intentions regarding mcommerce. Kuo and Wu (2012) defined purchase intentions as an individual`s readiness and willingness to purchase a certain product or service. In the context of m-commerce, purchase intention can be defined as the willingness of an individual to acquire a product or service over a mobile platform. Escobar-Rodriguez and Carvajal-Trujillo, 2014 approaches the definition of purchase intentions from the lenses of innovativeness, whereas purchase intention is described as the willingness of an individual to try out any new IT. Wang & Li, (2012) argued purchase intention is defined as consumers propensity to purchase a good, product or services is predicated on brand equity, then share their experiences with others. In the context of mcommerce, purchase intention can be defined as the willingness of an individual to acquire a product or service over a mobile platform.
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The Nature of Mobile Commerce To examine the nature of m-commerce, several fundamental differences between traditional commerce, m-commerce and Internet-based e-commerce are discussed. Based on this extension of traditional commerce, e-commerce models, new definitions of transactions and business models are explored and classified as they relate to m-commerce. Scholarly articles abound with m-commerce definitions, however literature analyses of m-commerce research are scant (Kourouthanassis & Giaglis, 2012). Since 2014, published studies (Gupta & Vyas, 2014; Hu et al., 2015; Turban, King, Lee, Liang, & Turban 2015), include m–commerce classification as a subset or an extension to the concepts of e-commerce. Other researchers argued characteristics of electronic e-commerce could be generally applied to m-commerce (Maity & Dass, 2014; Lin et al., 2011), as barriers typically associated with the adoption of new technology such as trust have both direct and indirect influences on usage behavior. Several researchers suggested that m-commerce should be broadly defined as any form of business transaction or activity of value that occurs on or through a mobile network (Kumar, Rishi, & Kumar, 2013; Sharma, Kansal, & Tomar, 2015; Yang, Chye, Fern, & Kang, 2015). Following this taxonomy, the next generation of m–commerce definitions may be classified on their ecosystem (e.g., mobile phones, tablet, operating system, and apps) as effective business models for emerging m-commerce services (Mehmood, 2015; Ivanochko et al., 2015; Mahajan & Agarwal, 2015). Congruent with the aforementioned examples, the definition and classification of mcommerce is complicated and continues to evolve (Omonedo & Bocij, 2014). Consequently, some authors formulated definitions, constructed as a combination of classifications mentioned above. These new and refined definitions of m-commerce combine the concepts of transaction,
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ecosystem, and m-commerce. For a clear overview, the various definitions of m-commerce and their respective authors are displayed in Table 2. Table 2. Classification of M-Commerce Definitions Based on Literature Classification
Definition
Literature
Subset form of ecommerce
M-commerce is a subset e-commerce; therefore, all the aspects involved can be extended and applied to mcommerce
Gupta & Vyas, 2014; Hu, Lu, & Tzeng, (2014);Turban, King, Lee, Liang, & Turban, (2015)
Transactions
M-commerce is any form of business transaction or activity of value that occurs on through a mobile network
Kumar, Rishi, & Kumar, (2013), Sharma, Kansal, & Tomar, (2015), Yang, Chye, Fern, & Kang, (2015)
Business Model Ecosystem
M-commerce is defined in part by the ecosystem (e.g., marketplace, Appbased services)
Mehmood, (2015); Ivanochko, Masiuk, & Gregus, (2015); Mahajan & Agarwal, (2015)
Comparison of Traditional Commerce, E-commerce, and M-commerce The rapid growth of m-commerce created unique business models for mobile operators, retail organization, and consumers (Chong, 2013; Khan, Talib, & Faisal, 2015). As firms formulated their business model correlation with technology (Baden-Fuller & Haefliger, 2013), the lack of framework for identifying opportunities resulted in strategic inefficiencies (Girotra & Netessine, 2014). As such, firms seeking a competitive advantage in digital commerce have adapted traditional commerce and e-commerce strategies to m-commerce-based business models. Zott and Amit (2013) explored the difference among traditional commerce, e-commerce and m-
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commerce from the lenses of business model content, structure, and governance, as shown in Table 3. Table 3. Comparison of Commerce Business Models Traditional Commerce
E-commerce
M-commerce
Business Model Content
Tangible product
Tangible product, digital product, information service, revenue sharing, advertising & marketing
Tangible product, digital product, information service, revenue sharing, advertising, marketing, and improved efficiency
Business Model Structure
Traditional information medium (cable television, radio, newspapers, magazines and books)
Internet platform (e.g., Internet); Internet transaction Digital payments and on-the-spot payment
Mobile internet platform (Web portal of cell phones, information kiosk)
Traditional transactions. Physical exchange of currency
Data storage, cloud and social media
Mobile transaction (mobile terminal payment); Mobile internet payment and location based payment
Manual management.
E-commerce platform
M-commerce ecosystem
Business model Governance
Business Model Perspective The study of business models is an expansive topic covering numerous aspects from all industries (DaSilva & Trkman, 2014). However, Osterwalder and Pigneur (2010) provided a general perspective of the business model concept and defined business models as a firm’s underlying core logic and strategic choices used for creating and capturing value. From the business model perspective, Zott and Amit (2013) posited that enterprises include concern with
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the production and delivery of tangible products, while e-commerce and m-commerce models supply information service with actual products or pure information service. Such change of product content can improve the allure of products for customers and meet their demands for information service. Business Model Structure The m-commerce model facilitates a business structure in which consumers obtained information or purchased products without the disadvantage of fixed location constraints typically associated with e-commerce (Bang, Le, Han, Hwang, & Ahn, 2013). The nature of wireless communication enables customers to achieve instant satisfaction of obtaining information or making purchases (Drossos, Giaglis, Vlachos, Zamani, & Lekakos, 2013), and may thereby influence consumer’s purchasing intentions. Researchers suggest the attributes of trust, attitude and mobile service are significant factors for m-commerce adoption (Oliveira et al., 2014; Thakur & Srivastava, 2014; Wang & Li, 2012). Organizations could use of these identified factors to enhance the firm’s mobile commerce adoption and increase their competitiveness, which may lead to a competitive advantage. Business Model Governance Zott and Amit (2013) also suggested that content, structure, and governance described the architecture of a business model. Traditional business models included concern with the monetization products through the prices charged to customer (Mehmood, 2015); however, Ivanochko et al., 2015 argued m-commerce provided new ways to monetize product offerings by providing a way for customers to obtain revenue from sponsors (e.g., advertisers). Business model governance under the m-commerce business model enables enterprises to instantly
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manage customers’ information and transaction process of products, subsequently producing a sustainable competitive advantage (Wu, Chen, & Guo, 2008). Research on Technology Use Research on the influence of multiple perceptual predictors of purchase intentions and conversion rates in the m-commerce domain employed several methodological approaches. Most research surveyed users of m-commerce with quantitative methods such as validated questionnaires and surveys to determine their perceptions of m-commerce (BudzanowskaDrzewiecka, 2015; Cyr et al., 2006; Bhatti, 2007; Escobar-Rodriguez & Carvajal-Trujillo, 2014; Venkatesh et al., 2003). Perceptions included such factors as performance and effort expectancies, usefulness, ease of use, social and normative influence, aesthetic preferences, and facilitating conditions to determine their influence on m-commerce intentions and behaviors. In a range of studies, researchers applied correlational, regression, and path modeling designs to test the power of perceptions to predict m-commerce purchase intentions and uptake (Alkhunaizan & Love, 2012; Joubert & Van Belle, 2013; Nilashi et al., 2015). As shown in Appendix A, research has demonstrated a range of factors associated with m-commerce purchase intentions including accessibility, intrinsic motivation, risk, usefulness and trust. The utility of the modeling research approach allowed researchers since Davis (1989) to develop sophisticated models incorporating a wider range of theoretically relevant factors to predict intentions and usage of information technology. Pavlou (2003) synthesized the literature on TRA and TAM with the important ethical issues of risk and trust in the field of e-commerce. Another set of studies investigated the relationship between website design aesthetics, usability, and purchase intentions (Coursaris & Kim, 2011; Tuch et al., 2012). Cyr et al. (2006) extended TAM to discover how visual design aesthetics affect ease of use, enjoyment, and perceived
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usefulness, and subsequently the behavioral intentions of users. Cyr et al. suggested that TAM can incorporate both utilitarian and hedonic factors of aesthetic perceptions. Hedonic factors resonate with the pleasure, enjoyment, and fun experienced by consumers when they utilize an information system. Cyr et al. identified a lack of understanding of the influence of design elements on the experience of mobile users, which affected consumers’ loyalty towards utilizing the service. The researchers predicted that consumer loyalty in mobile commerce links to the enjoyment and usefulness of the products from that business. Apart from security issues and aesthetics, research exposed a range of other factors appended to user acceptance models. Wu and Wang (2005) sighted compatibility as a significant determining factor of the intention to use a technology. Perdesen (2005) extended TAM by incorporating behavioral control and subjective norms to yield a decomposed theory of planned behavior, which proved useful in explaining the behaviors of early adopters of m-commerce. Contrasting research by Zhou, Lu, and Wang (2011) further identified personality traits as an important factor in user adoption to m-commerce. The researchers argued that mobile service providers should consider the need to conduct their market segmentation based on the personality traits of users as this approach would account for the individual character of users to better tailor their products and services. The findings of other empirical studies also supported the suitability of an extended TAM framework of user acceptance in analyzing the adoption of mobile payment (m-payments). The research by Zmijewska and Lawrence (2005) and colleagues Zmijewska, Lawrence, and Steele, (2004) confirmed that the success of m-payments depends on the features of technology that influence the decision of potential users and other success determinants associated with the technological infrastructure. Swilley and Goldsmith (2007) found a range of constructs
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predictive of consumer involvement with m-commerce using a modified version of TAM. Findings from the empirical study of Yaseen and Zayed (2010) revealed the critical determinants to the adoption of m-commerce in the Jordanian marketplace involve variables within technology adoption/acceptance models. One can find other variables to explain m-commerce attitudes, intentions, and behaviors from the perspective of UTAUT. Wu, & Wang, (2005) maintained cognitive and affective factors are important variables that prevent people from trusting online services. In one investigation of the Jordanian market, Jaradat et al., (2013) revealed the prediction of mcommerce adoption can be derived from behavioral intentions that are predicted by social influence, performance expectancy, and effort expectancy; however, social influence was found to be the most significant predictor. In contrast, Zhou and Lu (2011a) found that personality traits such as openness to new experiences, agreeableness, neuroticism, and extraversion significantly affect trust within the e-commerce or m-commerce context, but agreeableness and neuroticism are the only significant factors affecting perceived usefulness. Cost Alkhunaizan and Love (2012) identified cost as a significant predictor of usage intention in consumers when making decisions regarding purchasing through the m-commerce platforms. Anil, Ting, Moe, and Jonathan (2003) suggested the failure of individuals in Singapore and Australia to embrace Internet banking has been mostly attributed to cost. Zmijewska and Lawrence (2005) argued that the adoption of mobile payments is impeded by associated costs. Following this logic, online retailers should consider pricing as one of the most significant elements in their marketing mix, which can be leveraged as a source of competitive advantage.
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A study conducted by Chunxiang (2014) combined variables from different models (TRA, TPB & TAM) to test their effect on perceived value of m-commerce and adoption intention. The findings with Chinese participants revealed that adoption intentions was significantly predicted by perceived value which in turn was predicted by free connection, usefulness, and enjoyment. Perceived cost, technicality and trust also have a direct effect on perceived value. The total model explained 55.3% of user’s adoption intentions. Performance and Effort Sanakulov and Karjaluoto (2015) confirmed performance expectancy (PE) of UTAUT would explain the behavior of consumers from an m-commerce perspective. Other empirical studies offered further support for this finding, such that performance expectancy predicts the perceived advantages associated with the adoption of m-commerce (Khalifa & Shen, 2008; Kim, Choi, & Han, 2009; Chan et al., 2012). Alkhunaizan and Love (2012) asserted effort expectancy (EE) is a similar concept to TAM’s perceived ease of use factor, and social influence reflects the subjective norm factor of TBP and TRA. Fan, Saliba, Kendall, and Newmarch (2005) clarified that social influence can be divided into mass media and interpersonal influence, where the latter is derived from social networks via peers, superiors, and friends, while the former includes television, newspapers, Internet, radio, magazines, and other media. According to Venkatesh et al. (2003), the presence of technical and organizational frameworks for the purpose of supporting system utilization is an important factor in mcommerce. Under the UTAUT model, frameworks such as facilitating conditions embody compatibility, whereas perceived behavioral control reflects similar concepts from the TPB, TAM, MPCU, and IDT models (Alkhunaizan & Love, 2012). The research study by Alkhunaizan and Love recognized that studies about technological implementations viewed
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behavioral intention as a predictive factor of technological adoption. The behavioral intention of a user towards using a system also refers to the function of attitude and usefulness. While analyzing influences on consumers’ intention to use mobile payment, scholars derived valuable alternative perspectives from their empirical studies. Alkhunaizan and Love’s (2012) research revealed a strong correlation exists between perceived trustworthiness of mobile payment service providers and perceived confidentiality of payment details. The findings also suggested that users who find mobile payments easy to use consider it more useful. Okazaki and Mendez (2013) explored gender differences in the use of m-commerce. The research suggested that ease of use and extrinsic attributes of a mobile device significant predictors of m-commerce usage for males than females. Sreenivasan and Noor (2010) analyzed trust and privacy issues connected to the use and acceptance of m-commerce in the Malaysian market, finding that these factors enrich usage behavior and support the UTAUT model of m-commerce acceptance. Longitudinal and Qualitative Findings In one of the very few longitudinal studies m-commerce research, Lin et al., (2011) investigated consumer trust development in mobile banking. Based on extended valence theory, self-perception theory, and the information systems expectation confirmation theory, pre-use trust in mobile banking was presumed to predict perceived risk and benefit, which were anticipated to impact usage. Lin, Wang, Wang, & Lu, (2014) suggested, the extended valence theory suggests that consumers are motivated to minimize such risks by avoiding behaviors where such risks are considered high. Usage was then expected to influence perceived usefulness and confirmation, which together would predict satisfaction and post-use trust. Customers of a Chinese bank (N = 332) completed a questionnaire prior to use of mbanking and again after 2 months post-use with the data analysis showing 57% of the variance in
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post-use of m-banking was predicted by the combination of pre-trust in m-banking and satisfaction during the 2-month usage phase. In other words, trust leads to m-banking usage, which then affects satisfaction and enhanced trust, ultimately causing increased usage (Foon & Fah, 2011; Püschel et al., 2010). Although this research provides one of the only longitudinal studies of m-commerce perceptions and usage, it was limited to a focus on the impact of trust on usage. The model of pre-trust, usage, satisfaction, and post-use trust was rather circular, and there may have been external variables that produced such results, which is a general weakness of longitudinal designs. As with longitudinal research of m-commerce usage and behaviors, there have been few qualitative studies in this domain. Pelet and Papadopoulou (2012) conducted a qualitative study by interviewing 21 students to gauge their perceptions of using mobile phones for commerce and Social Network Systems (SNS). Only two participants indicated they used their phone for shopping on m-commerce sites, with the majority using phones for SNS. The findings showed a range of issues moderated their potential use of m-commerce, including trust, security, and reputation from others. Considering the main variables of the TAM model, participants identified ease of use and usefulness as facilitating factors of m-commerce and SNS adoption. The interview findings also revealed aesthetics and professional designs of m-commerce and SNS to be significant factors in their usage (Pelet & Papadopoulou, 2012). Zmijewska and Lawrence (2005) conducted a qualitative study of m-commerce exploring barriers to the success of mobile payment services to identify the most critical issues to the slow uptake of m-commerce. Experts in m-commerce projects (N = 46) were asked via a web-based qualitative questionnaire to identify the barriers to the success of mobile payments and the most critical issues to the uptake of m-commerce. Two raters who discussed and resolved any
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differences in their coding independently coded participant’s responses. The findings revealed a number of perceived barriers to m-commerce including security / trust, ease of use, usefulness, cost, and a series of infrastructure factors such as cooperation between services and regulatory barriers to m-commerce adoption. Again, these findings may confirm impediment factors to mcommerce that are consistent with models such as TAM or UTAUT. Zhang et al. (2012) conducted a meta-analysis of mobile commerce adoption and the moderating effect of culture. A total of 58 studies were included with a sample size of 19,334 participants; 35 studies were classified as being from an Eastern and 23 as a Western background. The set of studies included variables from the technology acceptance model (TAM), which include actual use, behavioral intention, attitude towards using, perceived use, and perceived ease of use. Other variables were from the theory of planned behavior (TPB) (subjective norm and perceived behavior control), from the innovation diffusion theory (innovativeness and compatibility), and from a review of other tested constructs in the literature (perceived cost, perceived risk, trust, perceived enjoyment). Findings showed that the extended TAM model provides a good predictor of behavioral intentions and actual use of mobile commerce. M-Commerce Dimensions and Purchase Intentions Although research reviewed in this chapter has broadly drawn from findings on technology use more generally, there are a range of significant studies on m-commerce dimensions specifically that are summarized in Table 4. The results of these are presented based on a scheme that consists of 3 distinct dimensions: classification of m-commerce theory and research, competitive factors that drive growth, and m-Commerce revenue models.
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Table 4. Significant Works on M-Commerce Research Dimensions
Author
Classified m-commerce literature into five dimensions: mobile commerce theory and research; wireless network infrastructure; and mobile cases and applications.
Ngai & Gunasekaran, (2007)
Developed a map of m-commerce research based on an analysis of published sources and empirical work. The map classifies m-commerce research into three dimensions, namely technology (infrastructure and devices), service (applications, content, payments), and value (business models).
Fouskas et al., (2005)
Found competitive factors (traditional payment services and barriers to entry), new e-payment services substitutes, and mobile payment service providers drive the development of mobile payment services markets and determine market structures.
Dahlberg, Mallat, Ondrus, & Zmijewska, (2008)
Conceptualizing m-business is a key research activity that needs execution.
Wang & Li, (2012)
First attempted to explain factors influencing the adoption of Coursaris & Hassanein, M-commerce, m-Commerce business applications, m(2002) Commerce value network, and m-Commerce revenue models.
It is important to note the key studies that relate m-commerce to purchase intentions given the focus of this study. As shown in Table 5, there is a pattern of main predictors of purchase intentions to include ease of use, usefulness, and the attributes of the vendor and customer. As such, investigations of m-commerce purchase intentions from the perspective of UTAUT would help clarify the relative importance of potential predictors of m-commerce purchase intentions.
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Table 5. Key Studies on Predictors of Purchase Intentions in M-Commerce Predictors of Purchase Intentions
Author
Ability, integrity, and benevolence of the vendors when handling the consumer’s transaction.
Lin & Wang, (2006)
Ease of use, usefulness, and aesthetics when being involved in the interactive systems.
Okazaki & Mendez, (2013)
Higher rewards (monetary values in terms of compensation) are attributed to activities with higher risks (firm's uncertainties).
Xu, Luo, Carroll, & Rosson, (2011)
Physical and socio-psychological attributes and beliefs affect customers’ perceptions of the brand and the meaning they attribute to it.
Shao Yeh & Li, (2009)
Defined three key attributes that describe a successful service: perceived usefulness, ease of use and cost-effectiveness.
Pagani, (2004)
Argued modeling the perceived value of a product solely on price is an important but insufficient conceptualization because most of the time customers consider attributes other than price, such as perceived quality of the product.
Kim, Chan, & Gupta (2007)
Design characteristics of interactive systems, users perceptions and evaluation of various attributes of the system (e.g., ease of use, usefulness), including its aesthetics.
Tractinsky, (1997)
Evaluation and Future Directions Although ample support exists for the UTAUT model in explaining the factors associated with technology acceptance (Alkhunaizan & Love, 2012; Foon & Fah, 2011; Im et al., 2011; Jaradat et al., 2013), there is diminutive research on its application to the area of m-commerce. On the surface, the main variables of UTAUT would appear to relate to m-commerce adoption, especially social influence and facilitating conditions. Mobile phones have become an important aspect of social communication and one would predict that social influence has an impact on mobile use and the use of related applications such as m-commerce (Attuquayefio & Addo,
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2014). Understanding the facilitating conditions to promote or remove barriers to the use of mcommerce would appear to be an important research direction. The uncertainty created by the open nature of Internet transactions made risk and trust important elements of m-commerce and related research (Lin et al., 2011). Chong et al. (2012) maintained, issues of trust and risk relate to user intentions and behaviors to the adoption of mcommerce are worthy of further investigation. As Pavlou (2003) found, consumer uncertainty of online transactions and the risk of monetary loss from transactions, as well as the risk of loss of privacy linked to the provision of personal data to online retailers are significant factors to the usage behaviors. Within the UTAUT framework, risk and trust factors appear to be facilitating conditions that can either promote or impede m-commerce use. Investigation of such a possibility would be a worthwhile research direction to develop the UTAUT model further and contribute to knowledge about the ethical issues associated with m-commerce. Summary and Conclusion The influence of multiple perceptual predictors of purchase and behavioral intentions and conversion rates in the m-commerce domain are discussed from various viewpoints, however are additional research topics worth exploring. The significant theoretical frameworks and main concepts that guided research on electronic and mobile commerce warrant further investigation and refinement. The main theoretical frameworks discussed in this study guiding Internet commerce research include the technology acceptance model (TAM), the theory of reasoned action (TRA), the theory of planned behavior (TPB), and the unified theory of acceptance and use of technology (UTAUT). Some scholars suggested that the TRA model offers potential benefits to predict the intention of individuals performing a certain behavior based on their attitudes and beliefs. TRA
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led to the further refinement of TPB and more specific technology adoption models like TAM and UTAUT. Behavior of an individual can be explained through his or her behavioral intention, which is influenced by perceived behavioral control, subjective norms, and attitude. In contrast, the TAM model proposes that a user favors or does not favor a certain information system as a result of the impact created by the ease of use and the usefulness of the system on the attitude of a user towards using the system. The UTAUT model included the argument that the behavioral intentions of users include determination by social influence, performance expectancy, facilitating conditions, and effort expectancy. Past research offered empirical support to the identified models of technology adoption and has revealed that UTAUT provides a comprehensive framework for predicting user intentions and behavior. Still, little research exists regarding the application of UTUAT to mcommerce and how facilitating conditions such the ethical issues of risk and trust may moderate m-commerce intentions and behavior. Continued examination of the specific antecedents of the UTAUT predictors of m-commerce intentions and behavior associated with m-commerce risk and trust within the mobile domain is likely to lead to further improvements in the performance of m-commerce and provide suitable competitive advantages for associated businesses. The purpose of the research in this study is to address these gaps in the research literature on m-commerce adoption by investigating the extent to which performance and effort expectancies, social influence, and facilitating conditions predict m-commerce purchase intentions within the context of competitive advantage. The main research question and subquestions of this study focus on building an understanding as to what extent perceptions of m-commerce performance and effort expectancies, social influence, and the facilitating conditions of m-commerce trust and perceived risk predict customer purchase intentions. Results
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from the study are intended to provide knowledge that may be applied to m-commerce companies seeking a competitive advantage, as well as guide future research. In this chapter, a detailed review of the literature pertinent to this study was conducted. The nature of m-commerce, comparison of commerce models and theoretical framework for mcommerce and e-commerce research was reviewed before a review of the UTAUT model was conducted. Additional frameworks discussed in this study guiding Internet commerce research include the technology acceptance model (TAM), the theory of reasoned action (TRA), and the theory of planned behavior. From various viewpoints the influence of multiple perceptual predictors of purchase and behavioral intentions and conversion rates in the m-commerce domain were discussed. Lastly, the problem of the study was restated in order to properly explain this study’s area of contribution to the body of knowledge.
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CHAPTER 3. METHODOLOGY This chapter focuses on the research methodology of this study. Within the context of competitive advantage, the purpose of this study is to address the lack of knowledge on the influence of users’ perceptions of m-commerce performance and effort expectancies, social influence, and facilitating conditions on their purchase intentions. A quantitative, nonexperimental survey approach was used to test the hypothesis for this study. The research questions with their respective null and alternative hypotheses of this study were Main RQ: To what extent do performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce predict m-commerce purchase intentions regarding competitive advantage? H00: Performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce do not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. HA0: Performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce predict mcommerce purchase intentions at a statistically significant level regarding competitive advantage. SubQ1: To what extent does performance expectancy predict m-commerce purchase intentions regarding competitive advantage? H01: Performance expectancy in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage.
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HA1: Performance expectancy in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. SubQ2: To what extent does effort expectancy predict m-commerce purchase intentions regarding competitive advantage? H02: Effort expectancy in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. HA2: Effort expectancy in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. SubQ3: To what extent does social influence predict m-commerce purchase intentions regarding competitive advantage? H03: Social influence does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. HA3: Social influence predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. SubQ4: To what extent does the facilitating condition of trust in the use of m-commerce predict m-commerce purchase intentions regarding competitive advantage? H04: Trust in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. HA4: Trust in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. SubQ5: To what extent does the facilitating condition of perceived risk in the use of mcommerce predict m-commerce purchase intentions regarding competitive advantage?
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H05: Perceived risk in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. HA5: Perceived risk in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The methodology adopted to address the research questions and test the hypotheses of this study is addressed in this chapter. The research design, the sample selection methods, the materials and instruments used in the study, and the data collection and analysis methods are also presented in this chapter. Finally the methods employed to address ethics and research validity issues are reviewed in this chapter. Research Design The research design for this study employed a quantitative, predictive study design using survey methods. This design is based on similar UTAUT studies (Venkatesh et al., 2012) to enable a test of the predictive capacity of performance and effort expectancies, social influence, and facilitating conditions on m-commerce performance expectancies. Adult participants were invited to participate in an online survey to measure their m-commerce performance and effort expectancies, social influence, facilitating conditions (trust and perceived risk), and m-commerce purchase intentions. As noted by Wright (2005) advantages of online surveys include: (a) access to unique populations, (b) reduction in time, (c) relatively valid, (d) cost efficient, and (e) means of data collection. Data analysis entailed the use of the ordinary least squares (OLS) linear regression model to test the predictive capacity of mobile-commerce performance and effort expectancies, social influence, facilitating conditions (trust and perceived risk) and m-commerce purchase intentions in the context of a competitive advantage. The OLS regression method was selected because of
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use in previous m-commerce research to explain the variance in m-commerce purchase intentions that reflect certain competitive advantages (Escobar-Rodrigues & Carvajal-Trujillo, 2014; Schenkman & Jonsson, 2000; Schierz et al., 2010; Venkatesh et al., 2012). The research approach and methodology adopted in this study are based on the positivist paradigm, which makes the ontological assumption that observations of the real world are the most reliable basis for generating knowledge (Erlingsson & Brysiewicz, 2013). However, ontological assumption accepts that unobserved hypothetical, or theoretical constructs can play useful roles in scientific theories (Antonenko, 2015). Based on the positivist perspective, this study adopts the scientific method to test empirical questions, conduct systematic observation via valid and reliable methodologies, gather data and submit to quantitative analysis, and integrate the information to form an interpretation of the findings regarding the research questions and hypotheses (Tsang, 2014). In the case of the study, assumptions included that the methodology will adequately test the impact of the predictor variables (performance and effort expectancies, social influence, and facilitating conditions) on the outcome variable of m-commerce purchase intentions. Sample The target population of interest for the study included adults at various stages of adopting m-commerce use and other new consumer technologies. The focus was on North American consumers within an already well-developed base of IT users (U.S. Department of Commerce, 2015). The following characteristics describe the sample frame and inclusion criteria: ● Demographics: Although the study research problem and research question do not directly focus on the impact of demographic variables on m-commerce purchase
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intentions, a range of demographic data was collected to ensure the sample is gender balanced and represents a broad range of age groups, and income levels. ● Inclusion criteria: Participants were 21 years of age and over. ● Exclusion criteria: Anyone who is under 21 years of age was not permitted to participate in the study because of their inability to give independent consent. As a facet of the study’s quantitative approach, the study used predictive design and random sampling to recruit participants through the utilization of the SurveyMonkey Audience Panel (SurveyMonkey, 2015). Random sampling was employed as probability sampling is appropriate for quantitative survey research such that each person in the study population has an equal chance of being recruited into the sample (Teddlie & Yu, 2007). SurveyMonkey distributed invitations to the population of potential participants via its SurveyMonkey Audience Panel, which has over 30 million members demographically representative of the United States (SurveyMonkey, 2015). An a-priori power analysis was conducted to determine an appropriate sample size using the G*Power 3.1.2 software, which covers a broad range of study designs and reflects the research design parameters put forward by Cohen (1988). OLS linear regression analysis was used for investigating the relationship between the independent variables and the dependent variables. G*Power analysis showed that a sample size of 146 participants provides a power of 0.95 with a medium effect size of .15. The final sample consisted of 165 participants, including 98 women and 67 men who came from a wide range of age groups, income levels and U.S. regions (a full description of the participants is provided in the results section). Instrumentation / Measures The data collection instrument subscales for this study were adapted from EscobarRodriguez & Carvajal-Trujillo (2014) to test the application of the UTAUT to purchase
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intentions. Six validated subscales from this instrument were used to measure the constructs of this study; the other subscales of this instrument were not employed in this study, as they were outside the scope of the research problem. The details of each subscale are described below: 1. Performance expectancy: measured by The Performance Expectancy Subscale (Escobar-Rodriguez & Carvajal-Trujillo, 2014), a 4-item measure of the strength people believes m-commerce will help them perform a task better. Responses to each item were measured by a 7-point Likert-type scale from 1 – Strongly disagree to 7 – Strongly agree. 2. Effort expectancy: measured by The Effort Expectancy Subscale (Escobar-Rodriguez & Carvajal-Trujillo, 2014), a 4-item measure of the belief that using m-commerce is free from effort and easy to use. Responses to each item were measured by a 7-point Likert-type scale from 1 – Strongly disagree to 7 – Strongly agree. 3. Social influence: measured by The Social Influence Subscale (Escobar-Rodriguez & Carvajal-Trujillo, 2014), a 3-item measure of the strength with which important others have influenced a person to adopt or use an m-commerce system. Responses to questions were measured by a 7-point Likert-type scale from 1 – Strongly disagree to 7 – Strongly agree. 4. Trust: the strength of a person’s belief that using m-commerce is secure and has no privacy threats was measured by a 3-item m-Commerce Trust Subscale (EscobarRodriguez & Carvajal-Trujillo, 2014). Responses to questions were measured by a 7point Likert-type scale from 1 – Strongly disagree to 7 – Strongly agree. 5. Perceived risk: the risk perceived with using m-commerce including fraud and product quality, was measured with a 3-item m-Commerce Perceived Risk Subscale
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(Escobar-Rodriguez & Carvajal-Trujillo, 2014). Responses to questions were measured by a 7-point Likert-type scale from 1 – Strongly disagree to 7 – Strongly agree. 6. Purchase intentions: measured by 3-item Purchase Intentions Subscale, which measures the strength of a person’s intentions to purchase with m-commerce in the future (Escobar-Rodriguez & Carvajal-Trujillo, 2014). Responses to questions were rated on a 7-point Likert-type scale from 1 - Strongly disagree to 7 – Strongly agree. Although outside of the central focus of this study, demographic information was also collected from participants and will include questions about their age bracket, gender, income bracket, U.S. regional location, and level of m-commerce usage answered as Infrequent, Occasional, Frequent, or Very Frequent. Data Collection Data collection commenced when an invitation to undertake the study was sent to members of the SurveyMonkey Audience Panel. Participants were self-selected. The use of the online site ensured via an initial screening process that participants could only proceed with the study if they met the selection criteria of being a North American adult, 21 years of age or older. If potential participants met the selection criteria and wanted to complete the study, they were directed to an online informed consent letter to read and complete before participation in the study. If participants did not meet the selection criteria, they were informed as such, thanked for their interest, and were not permitted to participate in the study. Once participants completed the informed consent form, they were directed to an online site for administration of the research questionnaire via SurveyMonkey (2015). Sampling was completed when the desired number of participants fully completed the questionnaire.
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The de-identified data is stored in a password-protected computer file, located on the principal researcher’s computer. Data will be kept for a minimum of 7 years, at which time the data will be destroyed based on The National Institute of Standards and Technology guidance on data sanitation, in accordance with best practices for clearing, purging, and destroying research data. The online survey and data are secured on SurveyMonkey (2015) via a password, protected from anyone other than the researcher accessing the information. Data Analysis Data was prepared for analysis by first examining each case for a range of potential participant response biases (Peer & Gamliel, 2011), such as an acquiescence bias or extreme responding wherein a participant has completed all the survey items with the same response. From the raw data, a mean score was computed for each scale and each scale was examined included examination for skewness or kurtosis to ensure they meet the assumption of normality, required to perform inferential statistics (Fink, 2009). Factor analysis was also conducted with the items from each subscale to determine their validity and the Cronbach’s alpha reliability test was run to assess the internal consistency of each subscale for the study sample (Thurber et al., 2014). Data analysis to test the hypotheses entailed OLS linear regression analysis to determine the extent to which performance and effort expectancies, social influence, and facilitating conditions (trust and perceived risk) predicted m-commerce purchase intentions. The OLS regression method was selected (Chan & Oksanen, 1987) because of use in previous mcommerce research to explain the variance in m-commerce purchase intentions that reflect certain competitive advantages (Escobar-Rodrigues & Carvajal-Trujillo, 2014; Schierz et al., 2010; Venkatesh et al., 2012).
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Validity and Reliability Overall, the survey questionnaire employed for data collection indicated significant construct validity and test reliability. This outcome is evidenced by the research of EscobarRodrigues and Carvajal-Trujillo (2014), who reported tests of the validity and reliability of the questionnaire with a sample of 1,096 adult users of Internet commerce. The validity of reliability for each subscale indicated following findings: 1. The 4-item Performance Expectancy Subscale produces an interval scale that indicated a reliability coefficient of α > .89 and factor loadings of each item between .88 and .92. Low to moderate inter-correlations between the subscales of the full instrument showed that performance expectancy was a distinct measure indicative of high levels of discriminant validity (Escobar-Rodrigues & Carvajal-Trujillo, 2014). 2. The 4-item Effort Expectancy Subscale produces an interval scale with a reliability coefficient of α > .89 and factor loadings of each item between .84 and .92. Low to moderate inter-correlations between the subscales of the full instrument showed that effort expectancy was a distinct measure indicative of high levels of discriminant validity (Escobar-Rodrigues & Carvajal-Trujillo, 2014). 3. The 3-item Social Influence Subscale produces an interval scale indicated a reliability coefficient of α > .94 and factor loadings of each item between .94 and .96. Low to moderate inter-correlations between the subscales of the full instrument showed that social influence was a distinct measure indicative of high levels of discriminant validity (Escobar-Rodrigues & Carvajal-Trujillo, 2014). 4. The m-Commerce Trust Subscale measures the strength of a person’s belief that using m-commerce is secure and has no privacy threats. The 3-item scale produced an
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interval scale which has a reliability coefficient of α > .92 and factor loadings of each item between .84 and .88. Low to moderate inter-correlations between the subscales of the full instrument showed that trust was a distinct measure indicative of high levels of discriminant validity (Escobar-Rodrigues & Carvajal-Trujillo, 2014). 5. The m-Commerce Perceived Risk Subscale measures the perceived risk with using m-commerce including fraud and product quality. The 3-item scale produced an interval scale which has shown a reliability coefficient of α > .92 and factor loadings of each item between .89 and .92. Low to moderate inter-correlations between the subscales of the full instrument showed that perceived risk was a distinct measure indicative of high levels of discriminant validity (Escobar-Rodrigues & CarvajalTrujillo, 2014). 6. The Purchase Intentions Subscale measures the strength of a person’s intentions to use the technology in the future. The 3 item scale produces an interval scale which has shown a reliability coefficient of α > .96 and factor loadings of each item between .93 and .95. Low to moderate inter-correlations between the subscales of the full instrument showed that purchase intentions were a distinct measure indicative of high levels of discriminant validity (Escobar-Rodrigues & Carvajal-Trujillo, 2014). Ethical Considerations There are some ethical issues to consider in research with human participants (Sales & Folkman, 2000). These include the method for recruiting participants, the ability to consent, anonymity and confidentiality issues with participation, and the potential harm participants may experience as a result of participation. Ethical considerations in the study were based on the
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Belmont Report (1979) principles and study procedures, ensuring the following issues were addressed with concerning the research with human participants via an informed consent letter: ● Participant ability to consent to undertake the study. ● Participants were recruited if they were 21 years of age or over, with the ability to consent to undertake the study. ● Respect for persons and equitable treatment. ● Participants were given information about the study before they commenced to ensure their consent was informed. ● To protect anonymity, there was no request for personal information from the participants. Individual participant data was only utilized by the researcher for the purposes of this study and will not be distributed to any other person or organization. ● An assessment of the risks and benefits of the study indicated that participation will not cause any undue harm, as the study was relatively innocuous and did not elicit any emotionally unpleasant responses from participants. The benefit of generating more knowledge about m-commerce purchase intentions outweighed any potential risks of the study. ● Prior to commencement of the study, the research was vetted by the institutional review board (IRB) to insure it met the ethical standards for research at Capella University (2016). Overall, meeting the challenge of addressing these issues was addressed by ensuring participants received relevant information about the research before consenting and undertaking the research (Frels, & Onwuegbuzie, 2013). The informed consent process insured participants were informed about the purpose of the research, the expected duration and procedures, their
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right to decline to participate or to withdraw from the research once started, as well as the anticipated consequences of doing so. Participants were also informed about potential risks, discomfort or adverse effects of the research, although none were expected given the innocuous nature of the research materials. Participants were also informed of any prospective research benefits, and if there are any limits to confidentiality, such as data coding, disposal, sharing, and archiving. The research methods insured participants’ privacy was respected and their responses were confidential as their responses were de-identified and were only used for the purposes of the research.
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CHAPTER 4. RESULTS Chapter 4 includes an analysis of the findings from the survey as they relate to the research questions and hypotheses of this study are presented. Procedural details relating to screening of the data, testing the validity, and reliability of the scales and measures, descriptive statistics, and the characteristics of the sample of participants who completed the survey instrument are included in this chapter. Next the results from various tests of assumptions to substantiate regression analysis and presents the details of regression analysis as they relate to the research hypotheses are outlined. Overall, the results provide a unique set of findings that generally supports the research hypotheses of this study. Screening the Data As noted by Field (2009) incomplete survey questionnaires present potential complications during data analysis and may compromise the validity and reliability of survey. As such, data screening was conducted to identify and address any potential missing data issues. The target population for this study included 177 people who consented to complete the survey; however only 172 completed the surveys. These cases included screening for any anomalous data, wherein two participants had only partially completed the survey and five participant responses reflected cases of a response bias demonstrating extreme responding across all items. After these seven cases were removed from the data set, the final sample included 165 valid responses to the survey. Validity and Reliability The first step towards establishing validity and reliability of the survey data was to conduct a principal components analysis (PCA) to establish the multidimensionality of scale items relating to the five predictor variables: Performance Expectancy, Effort Expectancy, Social
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Influence, Risk, and Trust. The study used a direct oblimin rotation as there was an expectation that the variables would be moderately correlated (Reio & Shuck, 2014). For ease of interpretation, factor loading below .5 were suppressed in the factor analysis output. Factor analysis confirmed the expected factor structure of the items (Thurber et al., 2014), as each item loaded on its respective dimension and there were no significant cross loadings of items on alternative factors (see Table 6). The rotated solution supported a five factor structure that explained 77.9% of the variance in item responses. Effort expectancy (factor 1) explained 43.86 of the total variance, Risk (factor 2) explained 13.0%, Performance Expectancy (factor 3) explained 8.87% of the variance, Social Influence (factor 4) explained 7.35%, and Trust (factor 5) explained 4.78% of the variance in item responses. The factor intercorrelations were low to moderate and further support the construct validity of the scales used to measure the predictor variables. Table 6. Confirmatory Factor Analysis of the Predictor Variable (N = 165)
Items EE1 EE2 EE3 EE4 R1 R2 R3 PE1 PE2 PE3 PE4 SI1 SI2 SI3
Effort Expectancy .92 .93 .93 .62
Factor Loadings Performance Risk Expectancy
Social Influence
.78 .86 .84 .66 .50 .67 .60 .80 .58 .79
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Trust
Table 6. Confirmatory Factor Analysis of the Predictor Variable (N = 165)(continued) Factor Loadings Performance Risk Expectancy
Effort Expectancy
Items
Social Influence
T1 T2 T3
Trust .75 .63 .80
Intercorrelations of dimensions Dimension Risk Performance Expectancy Social Influence Trust
.08 -.32 .36 .53
-.10 .01 -.08
-.23 -.26
.23
1.00
Reliability analysis was then conducted by calculating the internal consistency of the questionnaire items in terms of the respective dimensions. Table 7 indicated that Cronbach's Alpha values were mostly good to very good (Cronbach & Shavelson, 2004) and there was clear overall evidence of internal consistency in all the measures. Table 7. Cronbach’s Alpha Reliability Results α
Performance Expectancy
.91
4
α Comparison study1 .91
Effort Expectancy
.92
4
.92
Social Influence
.74
3
.94
Trust
.74
3
.88
Risk
.77
3
.88
Purchase Intention
.87
3
.91
Scale
Present study
No. of items
Note. Cronbach’s Alpha Reliability Results compared to Escobar-Rodriguez and Carvajal-Trujillo (2014).
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Descriptive Statistics A mean score of endorsement of items with respect to each measure was calculated and the findings are shown in Table 8. These data showed that participants were quite high on risk showing safety and security concerns for conducting m-commerce. Similarly, participants were high on effort expectancy believing that m-commerce is easy to use and free from hassles. Conversely, people were relatively low on social influence or the extent other people influence them in their m-commerce activity, and they only showed moderate purchase intentions and, similarly, were occasional users of m-commerce. Table 8. Means and Standard Deviations for Continuous Variables Variable
M
SD
Trust
4.54
1.24
Effort Expectancy
4.91
1.32
Risk
4.97
1.25
Performance Expectancy
4.64
1.39
Purchase Intention
4.18
1.48
Social Influence
3.73
1.29
M-Commerce Usage
2.00
0.89
Note. N = 165;
1
n= 37
Description of the Sample In terms of the sample for this study, the following table (Table 9) illustrated the frequency distribution of sex, age, income, and device type used to complete the survey. Even though there were more female participants by a factor of 1.5, there were equally distributed percentage of participants from almost all the age groups with 45-59 year olds being the most frequent age group of participants. The spread of total household income was varied. Most participants (almost 40%) reported income between $25k and 100K per year, with the remainder of
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participants evenly spread among lower and higher income categories. Most participants (66.5%) completed the survey via a desktop or laptop PC device. Table 9. Participant Demographics n
Percent
Gender Male Female
67 98
39.4% 57.6%
Age (in years) < 21 21–29 30–44 45-59 > 60
0 38 44 59 24
0.0% 22.4% 25.9% 34.7% 14.1%
Money earned last year $0 to $9,999 $10,000 to $24,999 $25,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000 to $124,999 $125,000 to $149,999 $150,000 to $174,999 $175,000 to $199,999 $200,000 and up Prefer not to answer
9 14 30 19 15 25 6 6 4 15 22
5.3% 8.2% 17.6% 11.2% 8.8% 14.7% 3.5% 3.5% 2.4% 8.8% 12.9%
Device Types IOS Android Phone/Tablet Windows/ Desktop MacOS Desktop Other
19 21 113 11 1
11.2% 12.4% 66.5% 6.5% 0.6%
Note. N = 165.
Analysis of participant location shown in Table 10 demonstrates a relatively even spread of participants across U.S regional location. Nevertheless, there were very few participants from New England, West North Central, and East South Central.
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Table 10. Participant U.S. Regional Location U.S. region
n
Percent
New England Middle Atlantic
10 20
5.9% 11.8%
East North Central
29
17.1%
West North Central
5
2.9%
33
19.4%
East South Central
5
2.9%
West South Central
16
9.4%
Mountain
16
9.4%
Pacific
31
18.2%
South Atlantic
Note. N = 165.
Assumption Testing A range of assumptions about correlational data is required to be met to conduct valid inferential statistics and regression analysis (Venkatesh et al., 2012). These included assumptions that the variables are normally distributed, that there are no issues with multicollinearity where the independent variables are correlated, and the assumption that the error terms are independent. Normality To check the assumption of normality of the independent variables, the KolmogorovSmirnov (K-S) and Shapiro-Wilk tests were conducted with the results shown in Table 11. Although the test statistics were statistically significant for all the independent variables indicating a potential issue with normality, multiple regression analysis would be robust to violation of this assumption (Mason & Perreault, 1991).
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Table 11. Statistics Tests of Normality Kolmogorov-Smirnova
Shapiro-Wilk
Statistic
df
Sig.
Statistic
df
Sig.
Trust
.100
165
.000
.976
165
.005
Effort Expectancy
.122
165
.000
.964
165
.000
Risk
.091
165
.002
.968
165
.001
Performance Expectancy
.111
165
.000
.966
165
.000
Social Influence
.116
165
.000
.980
165
.019
Note. Lilliefors Significance Correction
A further test of the assumption of normality was conducted by an examination of the potential skewness and kurtosis of the variables, displayed in Table 12 The assumption of normality was analyzed by dividing the skewness and kurtosis statistics for each variable by their standard errors to determine if the values fell below the criterion of Z = 3.29, p < .001 (Tabachnick & Fidell, 1996). Analysis showed no value was higher than 3.29 and the skewness and kurtosis statistics fell between -1.0 to 1.0 to indicate no significant violation of the assumption of normality. Despite the results from K-S and Shapiro-Wilk test, the variables did not show significant skewness or kurtosis supporting the assumption of normality among the variables. Table 12. Skewness and Kurtosis Statistics Statistic
Std. Error
Trust
Mean Skewness Kurtosis
4.54 -0.42 -.241
.097 .189 .376
Effort Expectancy
Mean Skewness Kurtosis
4.91 -0.53 -0.11
.103 .189 .376
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Table 12. Skewness and Kurtosis Statistics (continued) Statistic
Std. Error
Risk
Mean Skewness Kurtosis
4.97 -0.31 -0.18
.098 .189 .376
Performance Expectancy
Mean Skewness
4.64 -0.52
.108 .189
Social Influence
Mean Skewness Kurtosis
3.73 -0.14 -0.40
.101 .189 .376
The potential for outliers was investigated through examination of standardized residual limit with respect to the relationship of the independent variables to the dependent variable, purchase intentions. The standardized residual limit was set to ±3 standard deviations with Table 13 showing two cases (156 and 160) could be probable outliers for purchase intentions. However, the cases have been retained in the analysis as their scores were within acceptable limits and removing their influence would account for little change to analysis. Table 13. Case-Wise Diagnostics for Outliers and Residuals Case Number
Std. Residual
PI
Predicted Value
Residual
156
3.414
3
0.59
2.411
160
-3.211
1
3.27
-2.268
Note. PI = Purchase Intentions
Multicollinearity Analysis was conducted to determine if there was multicollinearity between the predictor independent variables wherein there is a strong correlation between two or more variables. Table 14 indicated the correlation between the five independent variables. From this table, the independent variable risk had no significant correlation with any other of independent variables,
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whereas the remaining variables were moderately correlated included a moderate correlation. Based on this analysis, there is little issue with multicollinearity. Table 14. Intercorrelation Between the Independent Variables Effort Expectancy
Trust
Trust 1.0
Effort Expectancy
.64**
1.0
Risk
-.10
.08
1.0
Performance Expectancy
.64**
.70**
.12
1.0
**
**
.03
.62**
Social Influence
.41
.49
Risk
Performance Expectancy
Social Influence
1.0
Multicollinearity was also investigated by calculating the Variance Inflation Factor (VIF) with respect to the relationship between each independent variable and the dependent variable, purchase intentions. According to Table 15, the VIF value for all the variables is less than 3 and the tolerance values are higher than .3, where Tolerance = 1/VIF. These set of findings thus confirm that there are no issues of multicollinearity among the independent variables (O’Brien, 2007). Table 15. Tolerance and VIF Multicollinearity Statistics for the Independent Variables Variable
Tolerance
VIF
Trust
.482
2.074
Effort Expectancy
.443
2.255
Risk
.921
1.086
Performance Expectancy
.352
2.837
Social Influence
.605
1.653
Note. VIF = Variance Inflation Factor; DV = PI (Purchase Intentions)
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Independence of Error The independence of error among the independent variables and purchasing intention was tested via the Durbin-Watson test. From the model summary shown in Table 16, the Durbin Watson value of 1.987 is within the recommended limit (1.5-2.5). Thus, there were no serial autocorrelation among the variables and the assumption of independence of error has been met. Table 16. Durbin–Watson Test: Summary for Model Model 1
R
R Square
.883a
.779
Adjusted R Square .772
Std. Error of the Estimate .706
DurbinWatson 1.987
Note. a. Predictors: (Constant), SI, R, T, EE, & PE, b. Dependent Variable: PI
Homoscedasticity and Normal Distribution of Error The assumption of homoscedasticity is that there is homogeneity of variance in the residuals or error terms of the dependent variable. According to Figure 3, the residuals are distributed evenly indicative of homogeneity of variance meeting the homoscedasticity assumption for regression analysis.
Figure 3: Scatter plot of residuals to test for of homoscedasticity in Purchasing Intentions (PI).
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Importantly, frequency analysis also showed the residuals included normal distribution. As shown in Figure 4, a histogram plot indicated the residuals for the dependent variable purchase intentions included normal distribution.
Figure 4. Frequency of the residuals for purchase intentions (PI) plotted to a normal curve. A further standard check on normality was conducted by plotting the relationship between the expected and observed cumulative probabilities of standardized residuals. Figure 5 demonstrates the required linear relationship to support the assumption of normality in the regression residuals of the dependent measure, purchasing intentions.
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Figure 5. Cumulative probabilities of the expected versus observed regression residuals plotted to a linear relationship. Regression Analysis Having met the recommended assumptions, regression analysis was conducted to test H00 that performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce do not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. Regression analysis showed that the independent variables explained a significant level of variance in purchase intentions. As shown in Table 16, the independent variables explained a significant amount of variance in purchase intentions, F(5, 164) = 112.02, p < .001. Table 16. Analysis of Variance: Sum of Squares and Degree of Freedom Model Regression Residual Total
Sum of Squares 279.449 79.328 358.777
df
Mean Square
5 159 164
55.890 .499
Note. Dependent Variable = PI; Predictors = SI, R, T, EE, & PE
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F 112.023
Sig. .000b
In terms of the strength of the model, Table 17 indicated that the model explained a very high 77.9% and significant amount of variance in purchase intentions. Thus, H00 is rejected in favor of HA0 that performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce predict m-commerce purchase intentions at a statistically significant level with regard to competitive advantage. Table 17. Regression Model Summary for the Effect of the Independent Variables on Purchase Intentions Model R R Square Adjusted R Square 1 .883a .779 .772 Note. a Predictors: Social Influence, Risk, Trust, Effort Expectancy, and Performance Expectancy
The impact of each independent variable was determined by an examination of the significance of the regression beta weights. Table 18 indicated that effort expectancy (p = 0.003), performance expectancy (p < 0.001) and social influence (p < 0.001) demonstrated a significant positive relationship with purchase intentions. Thus, ease of effort, expectations that mcommerce will help perform a task better and the influence of others has a positive impact on higher purchase intentions. Whereas trust has a somewhat weaker positive relationship with purchasing intentions that approached significance, risk was not significantly related to purchase intention. Table 18. Significance Test of the Regression Coefficients (Intercept) Trust Effort Expectancy Risk Performance Expectancy Social Influence
B
SE
-.619 .116 .190 -.046
.330 .064 .063 .046
.546 .278
t
Sig.
.097 .169 -.039
-1.879 1.808 3.024 -1.004
.062 .072 .003 .317
.067
.514
8.185
.000
.055
.243
5.074
.000
Note. Dependent Variable: Purchase Intentions
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Standardized B
Follow up regression analysis was then conducted to test the independent influence of trust on purchase intentions. As shown in Table 19, trust on its own explained a significant 40.9% of the variance in purchase intentions, F(1, 164) = 112.995, p < .001. Thus, trust does impact on purchase intentions independent of the other predictor variables. Table 19. The Impact of Trust on Purchase Intentions B
SE
(Intercept)
.720
.337
Trust
.763
.072
Standardized B .640
t
Sig.
2.133
.034
10.630
.000
Note. Dependent Variable = Purchase Intentions (PI); F(164) = 112.995, R2 = .409 .p = .0001
A further set of analyses was conducted on the relationship between each independent variable and purchase intentions via partial regression scatterplots. The first plot in Figure 6 shows a weak positive relationship between trust and purchase intentions, such that more trust in m-commerce is related to higher purchase intentions.
Figure 6. Scatter plot showing relation between Trust (T) and Purchase Intention (PI).
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The relationship between effort expectancy and purchase intentions is shown in Figure 7. The scatterplot shows a positive relationship, wherein ease of effort was significantly related to purchase intentions.
Figure 7. Scatter plot showing relation between Effort Expectancy (EE) and Purchase Intention (PI).
In contrast to other results, risk showed a weak negative relationship with purchase intentions, albeit non-significant. The scatterplot in Figure 8 indicated that higher perceptions of m-commerce risk were associated with lower purchase intentions.
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Figure 8. Scatter plot showing relation between Risk (R) and Purchase Intention (PI). The clearest predictor of purchase intentions was performance expectancy. As shown in Figure 9, performance expectancy was strongly related strongly relates to purchase intentions such that higher expectations that m-commerce will help achieve tasks was related to higher purchase intentions.
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Figure 9. Scatter plot showing relation between Performance Expectancy (PE) and Purchase Intention (PI). The relationship between social influence and purchase intentions is shown in Figure 10; as can be seen from the scatterplot, there was a significant relationship showing that higher social influence positively related to higher purchase intentions.
Figure 10. Scatter plot showing relation between Social Influence (SI) and Purchase Intention (PI).
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Supplementary Analysis Supplementary regression analysis was conducted on the effect of the independent variables on m-commerce usage. Although the analysis related to a reduced data set (N = 36), the findings showed that the independent variables overall explained a significant amount of variance in m-commerce usage with an R2 = .422, F(1, 36) = 4.528, p = .003. However, as shown in Table 20, the only independent variable that appeared to be related to m-commerce usage was risk such that higher risk was associated with lower m-commerce usage, although the beta coefficient was only approaching significance. Analysis also indicated purchase intentions and m-commerce usage included high correlations (r = .637, p < .001). Table 20. Significance Test of the Regression Coefficients for M-Commerce Usage B
SE
(Intercept)
.728
.616
Trust
.159
.145
Effort Expectancy
.052
Risk
Standardized B
t
Sig.
1.182
.246
.227
1.085
.286
.134
.089
0.390
.699
-.202
.109
-.332
-1.849
.074
Performance Expectancy
.341
.199
.581
1.713
.097
Social Influence
-.095
.135
-.161
-0.701
.487
Note. Dependent Variable = M-commerce Usage
Supplementary analysis was also conducted on the relationship between demographic factors and purchase intentions. Analysis via independent t-test showed purchase intentions did not significantly differ between men (M = 4.095) and women (M = 4.238), t(163) = 0.622, p = .542. Moreover, one-way Analysis of Variance showed purchase intentions did not differ between income categories (F = 1.591, p = .124) or regional location of respondents (F = 0.451, p = .889). In contrast, there was some indication of a difference in purchase intentions as a function of age, F(3, 164) = 2.477, p = .063. As shown in Figure 11, older participants (60+
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years) reported much lower m-commerce purchase intentions than other age groups, especially those 21-30 years of age. Finally, a one-way ANOVA showed purchase intentions differed as a function of device type, F(1, 164) = 2.848, p = .026. Participants who used an IOS/phone device showed higher purchase intentions (M = 5.09) than those who used an Android phone (M = 4.16), a Windows desktop/laptop (M = 4.11) or an Apple desktop/laptop (M = 3.36).
Figure 11. Mean purchase intention as a function of age of participants Summary of Results and Conclusion Overall, the results provide a valid representation of the factors that are associated with m-commerce purchase intentions. A good size sample of respondents completed the survey questionnaire and they broadly reflected the demographic characteristics of the North American population in terms of age, sex, income and regional location. The scales to measure the independent and dependent variable showed adequate validity and reliability and met the assumptions for regression analysis. Regression analysis showed significant support for the hypotheses of this study and the following decisions were made pertaining to the null and alternate omnibus and sub-hypotheses.
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Omnibus Hypothesis H00: Performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce do not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The null hypothesis was rejected. HA0: Performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce predict mcommerce purchase intentions at a statistically significant level regarding competitive advantage. The alternate hypothesis was not rejected. Sub-Hypotheses H01: Performance expectancy in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The null hypothesis was rejected. HA1: Performance expectancy in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The alternate hypothesis was not rejected. H02: Effort expectancy in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The null hypothesis was rejected. HA2: Effort expectancy in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The alternate hypothesis was not rejected.
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H03: Social influence does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The null hypothesis was rejected. HA3: Social influence predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The alternate hypothesis was rejected. H04: Trust in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The null hypothesis was rejected. HA4: Trust in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The alternate hypothesis was not rejected. H05: Perceived risk in the use of m-commerce does not predict m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The null hypothesis was not rejected. HA5: Perceived risk in the use of m-commerce predicts m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The alternate hypothesis was rejected. Conclusion In conclusion, the results of this study provide evidence to answer the main research question of this study: to what extent do performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce predict mcommerce purchase intentions with regard to competitive advantage? The findings supported the idea that performance and effort expectancies, social influence, and the facilitating conditions of
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trust strongly predict m-commerce purchase intentions. In contrast, risk was unrelated to purchase intentions. The research, theoretical, and practical implication of these findings for knowledge on m-commerce competitive advantage are discussed in the following and final chapter of this study.
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CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS Introduction This chapter includes a summary of the results of the current research presented in Chapter 4 and a comparison to the literature presented in Chapter 2. This chapter will also discuss the answers to the following research questions: Main Research Question Main RQ: To what extent do performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of mcommerce predict m-commerce purchase intentions with regard to competitive advantage? Research Subquestions SubQ1: To what extent does performance expectancy predict m-commerce purchase intentions with regard to competitive advantage? SubQ2: To what extent does effort expectancy predict m-commerce purchase intentions with regard to competitive advantage? SubQ3: To what extent does social influence predict m-commerce purchase intentions with regard to competitive advantage? SubQ4: To what extent does the facilitating condition of trust in the use of mcommerce predict m-commerce purchase intentions with regard to competitive advantage? SubQ5: To what extent does the facilitating condition of perceived risk in the use of m-commerce predict m-commerce purchase intentions with regard to competitive advantage?
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This chapter presents the results relating to these questions; their implications include discussion from a research, theoretical, and practical perspective. The limitations of the research as identified in Chapter 1 also include consideration in this chapter, as well as directions for future research. Summary of the Results Strong consumer demands and the increasing variety of goods and services available online characterized the growth in electronic and web business technologies as consumers globally continue to discover the convenience and practicality of conducting online transactions (U.S. Department of Commerce, 2015). Mobile commerce is a rapidly growing segment of the electronic business markets projected to reach $626 billion in sales by 2018 (ComScore, 2014). Businesses face the challenge of deploying m-commerce strategies to develop a competitive advantage over rivals, increase sales, retain their existing customer base and attract new customers (Swilley et al., 2012). Knowledge and intellectual capital pertaining to web and mobile technologies are crucial business assets and a source of competitive advantage (Lin et al., 2011). Therefore, it is important that firms investigate consumers’ perception and engagement of m-commerce to develop more efficient and effective technology interface between the company and its customers (Baden-Fuller & Haefliger, 2013). Research regarding technology interfaces indicated a range of central factors that facilitate e-commerce technology acceptance to provide knowledge about developing a competitive advantage (Foon & Fah, 2011; Hernández et al., 2010; San-Martín & Camarero, 2012; Vrechopoulos et al., 2009; Wang et al., 2016). Yet, knowledge about which factors predict m-commerce purchase intentions is comparatively underdeveloped; it is not entirely clear how the fundamental user perceptions associated with m-commerce translate into competitive
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advantages (Budzanowska-Drzewiecka, 2015). Although research on the predictors of mcommerce purchase intentions exists (Alkhunaizan & Love, 2013; Chunxiang, 2014; Jaradat et al., 2013; Lin et al., 2014; Okazaki & Menendez, 2013), there is no study in the literature including investigation of the specific relationship between m-commerce performance and effort expectancies, social influence, facilitating conditions, and m-commerce customer purchase intentions. The relationship between m-commerce performance and effort expectancies, social influence, facilitating conditions, and m-commerce customer purchase intentions is the central focus of the UTAUT theoretical framework (Alkhunaizan & Love, 2013; Foon & Fah, 2011; Im et al., 2011; Jaradat et al., 2013). However, limited research exists which clarifies the facilitating conditions that may impact user acceptance of m-commerce. The goal of this study was to generate insight as to how the specific facilitating conditions of m-commerce trust and perceived risk, as well as expectancies and social influence, related to customer purchase intentions that translate into potential competitive advantages. Expanding awareness of these relationships provides an essential avenue for developing competitive advantages in business management and technology, as m-commerce represents a critical business tool and investment opportunity (Benou et al., 2012; Giovannini et al., 2015). The research problem of this study focused on the gap in knowledge regarding the impact of users’ perceptions of m-commerce performance and effort expectancies, social influence, trust, and perceived risk of their purchase intentions, which may be applied in business to develop competitive advantages. The study included a sample of North American adult mcommerce users asked to complete a survey questionnaire to measure their m-commerce performance and effort expectancies, social influence, trust, and perceived risk of m-commerce
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and their m-commerce purchase intentions. The results of the study are significant for their capacity to contribute to the refinement and confirmation of the existing theoretical framework provided by the UTAUT model regarding competitive advantage in m-commerce and for contributing knowledge on the conditions that facilitate (or impede) users’ m-commerce purchase intentions. The findings from this study supported the hypotheses, while substantially addressing the research questions. In support of the main research question, findings indicated that performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce together predicted m-commerce purchase intentions at a statistically significant level regarding competitive advantage. The strongest significant predictors of m-commerce intentions were performance expectancy and social influence, whereas effort expectancy and trust were significant albeit weaker predictors of m-commerce purchase intentions. The next section includes discussion with respect to their relationship to previous research on m-commerce purchase intentions in the context of a competitive advantage. Discussion of the Results Main Research Question The main focus of this study was to explore the extent to which performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce predict m-commerce purchase intentions regarding competitive advantage. Regression analysis indicated that, together, performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce explained a significant amount of variance in m-commerce purchase intentions. Indeed, a very high 77.9% of the variance in purchase intentions was explained by the UTAUT variables, which
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compares favorably to other UTAUT research findings. For example, Venkatesh et al. (2012) found the UTAUT model explained 70% of the variance in user intentions to adopt new technology, Wang and Wang (2010) showed that UTAUT predicted 65% of behavioral intentions to use m-internet, and Escobar-Rodriguez and Carvajal-Trujillo (2014) reported that the UTAUT model explained 60% of m-commerce purchases of airplane flights. Consequently, the findings relating to the main research question and supporting the main hypothesis of this study were significant when compared to previous research. Subquestion R1 The first subquestion focused on the extent performance expectancy predicts mcommerce purchase intentions. The results from regression analysis indicated that performance expectancy was the strongest individual predictor of purchase intentions with linear regression data showing performance expectancy explained 29.6% in purchase intentions at a significant level. Other research indicated performance expectancy to be a comparatively strong predictor in the UTAUT model. For example, Alkhunaizan and Love (2012) found performance expectancy to be the strongest UTAUT predictor of intentions to use m-commerce. Similarly, structural equation modeling by Escobar-Rodriguez and Carvajal-Trujillo (2014) showed performance expectancy as the strongest predictor of behavioral intentions to use m-commerce for purchase of airplane tickets. The findings from this and other studies indicated that performance expectancy is a strong predictor of m-commerce purchase intentions, consistent with the conceptual strength of the performance expectancy construct. Performance expectancy includes the definition as the extent to which people believe m-commerce will help perform a task better (Venkatesh et al., 2012), conceptually related to the TAM construct of perceived usefulness. In the context of this
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study, performance expectancy is about m-commerce improving the performance of purchasing tasks, which would appear to be the main competitive advantage of m-commerce. As such, the capacity for m-commerce to help users perform purchasing tasks better than other electronic or physical means would appear to be a comparatively strong selling point. Subquestion R2 The second subquestion investigated to what extent effort expectancy predicts mcommerce purchase intentions regarding competitive advantage. The results from analysis indicated that effort expectancy was an individual, but weak predictor of purchase intentions with linear regression data showing effort expectancy explained 5.0% in purchase intentions at a significant level. Other studies found similar results indicating effort expectancy to be a significant, albeit weak predictor of m-commerce purchase intentions (Alkhunaizan & Love, 2012; Escobar-Rodriguez & Carvajal-Trujillo, 2014). Effort expectancy is the extent to which people believe using m-commerce would be free from effort and not difficult to use (Venkatesh et al., 2012), conceptually related to the TAM concept of ease of use. The relatively low importance of effort expectancy to explaining mcommerce purchase intentions appears to relate to findings by Davis (1989) that the effect of perceived ease of use was not significant after controlling for the variable usefulness. Although effort expectancy or ease of use related to purchase intentions in the current study, its impact as a competitive advantage appears to be minimal, somewhat tied to the effect of performance expectancy on purchase intentions. Subquestion R3 The third subquestion investigated to what extent effort social influence predicts mcommerce purchase intentions with regard to competitive advantage. The results from analysis
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indicated that social influence was a moderately effective individual predictor of purchase intentions with linear regression data showing social influence explained 13.9% in purchase intentions at a significant level. Previous research also indicated social influence to be an important determinant of Internet and m-commerce usage intentions. For example, Foon and Fah (2011) found social influence to be among the strongest predictors of Internet banking adoption, whereas, Lu et al. (2005) reported that social influence had a direct positive impact on intention to adopt WMIT. In contrast, Alkhunaizan and Love (2012) found that social influence had little impact on behavior intention to use m-commerce and Escobar-Rodriguez and Carvajal-Trujillo (2014) reported a weak relationship, but significant relationship between social influence and intentions to use m-commerce for airline ticket purchases. Social influence includes definition as the extent to which a person believes how important others think he or she should adopt an IT system (Venkatesh et al., 2012), conceptually related to normative influence in the TRA / TPB models of decision-making behavior. According to Safeena et al. (2011), social influence has a direct positive effect on user attitude towards mcommerce, as users perceive advanced technology would improve their image, status, and performance in the society. Despite some equivocal findings in the literature, social influence was found in the results of this study to exert some influence over m-commerce user intentions. As such, facilitating the impact of social influence on m-commerce adoption and use in the purchasing process would appear to provide a potential point of competitive advantage. Subquestion R4 The fourth subquestion investigated to what extent effort does the facilitating condition of trust in the use of m-commerce predict m-commerce purchase intentions with regard to competitive advantage. Although the influence of trust on m-commerce purchase intentions was
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not significant in multiple regression analysis, trust in the use of m-commerce nonetheless independently predicted m-commerce purchase intentions at a statistically significant level. Similarly, Rodriguez and Carvajal-Trujillo (2014) found trust in information quality, security, and privacy significantly predicted behavioral intentions to use m-commerce for airline tickets. Lin et al. (2014) also found pre-use trust predicts m-banking usage which then affects satisfaction and enhanced trust, ultimately causing increased usage. In contrast, Alkhunaizan and Love (2012) found trust did not predict m-commerce acceptance when part of a multiple regression that included other UTAUT predictors. The concept of trust reflects the strength of a person’s belief that using m-commerce is secure and poses no privacy threats (Zhang et al., 2012). The findings of this study indicated that trust in m-commerce is an important independent factor to m-commerce purchase intentions, but loses its significance when considered amongst other variables, such as effort expectancy. Indeed, trust and effort expectancy were significantly correlated, suggesting that effort expectancy accounts somewhat for trust in predicting m-commerce purchase intentions. Conceptually, trust in commerce and effort expectancy or ease of use may be related that both reflect the fact that users prefer systems that are hassle free in terms of trust and ease of use. Nonetheless, the findings support the view that trust in m-commerce is a competitive advantage that facilitates m-commerce purchase intentions to a certain degree. Subquestion R5 The final subquestion investigated to what extent the facilitating condition of perceived risk in the use of m-commerce predicts m-commerce purchase intentions with regard to competitive advantage. The results from regression analysis indicated that the facilitating condition of risk did not predict m-commerce purchase intentions at a statistically significant
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level regarding competitive advantage. Moreover, risk was independently uncorrelated with mcommerce purchase intentions, although risk was in the expected negative direction, wherein higher risk related to lower purchase intentions. Risk includes definition as the risk perceived with using m-commerce including fraud and product quality (Zhang et al., 2012). Previous research found some relationship between risk and acceptance of e-commerce (Lin et al., 2014; Pavlou, 2003). For example, Pavlou (2003) added risk to the TAM model and reported risk to explain a significant amount of variance in acceptance of e-commerce. However, other research by Joubert and Van Belle (2013) found risk had no impact on mobile commerce adoption per se. The current study was one of the first to incorporate risk as one of the UTAUT facilitating conditions of m-commerce purchasing intentions. Risk had little bearing on m-commerce purchase intentions in this study, suggesting that m-commerce is a domain in which risk is not a major issue and does not readily reflect a competitive advantage. Implications of the Study Results The results from this study raise several theoretical and practical implications. From a theoretical viewpoint, several models and conceptual frameworks provide the potential to develop knowledge and understanding of m-commerce purchasing intentions. The TRA/TRP framework has some applicability to m-commerce, wherein perceived behavioral control, subjective norms, and attitudes are proposed to be the central determinants of behavior (Ajzen, 2011). Nevertheless, the TRA/TPB model lacks specificity regarding m-commerce and the model is only a moderate predictor of e-commerce purchase intentions (Pavlou & Fygenson, 2006). In contrast, the TAM model of technology acceptance includes increased applicability to m-commerce, positing that perceived usefulness and perceived ease of use are the main
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predictors of technology acceptance (Davis, 1989). Consistent with the focus of TAM, the model is a significant predictor of technology acceptance. Empirical findings confirmed that TAM predicts an average of 50% in m-commerce user intentions (Chunxiang, 2014; Song et al., 2008). Nonetheless, the predictive capacity of TAM includes enhancements with additional independent variables, such as social influence (Lu et al, 2005) and perceived credibility and self-efficacy (Wang et al., 2007). Theoretical work synthesized TRA / TPB and TAM to account for a range of consistent predictors of technology intentions, acceptance, and behavior. The UTUAT model of technology acceptance proposed that performance and effort expectancies, social influence, and facilitating conditions are the focal predictors of technology acceptance (Venkatesh et al., 2012). In a range of studies, UTAUT indicated a superior predictor of technology acceptance across a various domains explaining between 61% (Pope, 2014) and 70% (Venkatesh et al., 2012) of technology acceptance across a range of domains, such as adoption of new technology (Venkatesh et al., 2012), behavioral intentions to use m-internet (Wang & Wang, 2010), adoption of mobile banking (Oliveira et al., 2014), and m-commerce purchases of airplane flights (EscobarRodriguez & Carvajal-Trujillo, 2014). Given its strong predictive capacity and conceptual specificity, the UTAUT model provided a suitable theoretical basis to investigate the predictors of m-commerce purchase intentions in this study. The predictive capacity of UTAUT in this study, clearly indicated performance and effort expectancies; social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce explained 77.9% of variance in m-commerce purchase intentions. Analysis also showed that the strongest significant predictors of m-commerce intentions were performance expectancy and social influence, whereas effort expectancy and trust were
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significant albeit weaker predictors of m-commerce purchase intentions. In contrast, perceived risk was not a significant predictor of m-commerce purchase intentions. Altogether, the results of the study confirmed the relevance and predictive capacity of UTUAT regarding m-commerce intentions. The results of the study also provided some clarification to the UTAUT concept of facilitating conditions. Facilitating conditions as defined in the UTAUT framework included factors that promote or remove barriers to the use of technology (Venkatesh et al., 2012). As such, the definition of facilitating conditions allows for a wide range of possibilities, yet provides few clues as to which facilitating conditions are more or less meaningful predictors of user acceptance of technology. Based on previous research (Lin et al., 2014; Pavlou, 2003; EscobarRodriguez & Carvajal-Trujillo, 2014), the specific facilitating conditions of m-commerce trust and perceived risk relates to m-commerce customer purchase intentions with trust and risk reflecting factors that promote or remove barriers respectively, consistent with the definition of facilitating conditions. The findings from regression analysis showed that m-commerce trust was a significant predictor of purchase intentions, however, perceived risk did not relate to m-commerce purchase intentions. Similarly, previous research generally found users’ trust in a system to be a consistent predictor of m-commerce purchasing intentions (Lin et al, 2014; Escobar-Rodriguez & CarvajalTrujillo, 2014), whereas the relationship between perceived risk and purchase intentions is equivocal in the research literature (Joubert & Van Belle, 2013; Pavlou, 2003). Together, previous findings and those from the current study suggested that facilitating conditions that promote m-commerce purchase intentions, such as trust, are perhaps more important than factors that can operate as barriers to m-commerce, that include perceived risk.
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Consistent with approach / avoidance goal theory (Elliot, 2006), the facilitating condition of m-commerce trust reflects a goal approach orientation and an intrinsic motivation associated with a desired outcome, such as satisfaction with a purchase of product or service. In terms of the current findings, m-commerce purchase intentions do not relate to the avoidance goal of reducing m-commerce risks. Moreover, the notion of facilitating conditions in the UTUAT framework may be strongly defined by factors that reflect an approach goal orientation and intrinsic mcommerce motivations rather than including those oriented towards removing barriers to mcommerce or focus on avoidance oriented goals associated with m-commerce. Indeed previous research found that intrinsic motivations such as website aesthetics and pleasure-seeking motives associate with technology acceptance and use (Cyr et al., 2006, Escobar-Rodriguez & CarvajalTrujillo, 2014), whereas barriers such as cost are weak predictors of usage intentions (Alkhunaizan & Love, 2012). Future research might seek to investigate the approach and avoidance goals and motivations behind m-commerce purchase intentions to provide further clarification of the facilitating conditions construct within the UTAUT theoretical framework. In addition to the theoretical implications of the findings, the results of the study raise certain practical implications in terms of their relationship to competitive advantages. A competitive advantage includes definition as the way in which an organization implements a business strategy that results in cost leadership, product differentiation, or product focus (Porter, 1980). The findings of this study showed that performance expectancy was the strongest predictor of m-commerce purchase intentions. From a practical perspective, this finding implies that businesses relying on m-commerce transactions may gain a competitive advantage by developing the utilitarian or practical aspects of their m-commerce services.
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The findings also showed social influence to be a comparatively strong predictor of mcommerce purchase intentions. Social influence reflects the strength with which important others have influenced a person to adopt or use an m-commerce system (Venkatesh et al., 2012). The implication from the findings is that providers of m-commerce services may gain a competitive advantage by communicating to existing and potential customers what other people are doing when it comes to making their purchases. Social influence may be employed as a bridge between m-commerce purchases intentions and other online applications, such as social media, for online services to develop a competitive advantage. To a lesser degree, the results showed that effort expectancy and m-commerce trust positively associate with m-commerce purchase intentions. From a practical perspective, the findings imply that providers of m-commerce may gain a competitive advantage to the extent that their systems are free from effort and not difficult to use (Venkatesh et al., 2012). Moreover, the design of m-commerce service delivery should demonstrate and promote trust between users and technology to facilitate a competitive advantage with information such as third-party security verifications (Casey & Wilson-Evered, 2012). Finally, the results from the study imply that perceptions of m-commerce risk do not predict purchase intentions. These findings imply that assurances to customers that a system is free from risk do not necessarily provide a competitive advantage to m-commerce businesses. Nonetheless, that minimizing m-commerce risks is a precondition or basic requirement to developing trust in m-commerce and facilitating purchase intentions and behaviors (Escobar-Rodriguez & Carvajal-Trujillo, 2014). In summary, the findings of the study provide a range of theoretical and practical implications that may be applied to developing m-commerce knowledge and competitive advantages. The UTAUT framework was a significant predictor of m-commerce purchase
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intentions and a relevant model for investigating competitive advantages in m-commerce. The findings also provided some clarification of the facilitating conditions concept in UTAUT and suggested that factors that promote the use of m-commerce are more important to purchase intentions than potential barriers. From a practical perspective, the findings suggested that mcommerce providers can gain competitive advantages by improving the performance aspect of m-commerce sites and employing social influence to facilitate m-commerce usage. A system that performs well consistent with what other people do provides competitive advantages to the business of m-commerce. Limitations Despite the theoretical and pragmatic implications of the results of this study, several methodological limitations to the study design and execution that impact on the generalizability of the findings. In terms of the sample of participants, the number of participants to the survey questionnaire limits the findings. Moreover, participants to the SurveyMonkey panel may not be a representative sample of North American m-commerce users (Evans & Mathur, 2005). Additionally, self-selection bias is another potential limitation of online survey research (Wright, 2005). Despite these issues, the study used a reasonable sample size for the study, which broadly represented the North American population in terms of gender, age, geographic location, and income. A further limitation of the findings is that the correlational nature of the research design does not produce information about definitive cause-effect relationships between m-commerce perceptions and purchase intentions. Indeed, correlational research is quite common in technology acceptance research and is a general limitation of the research field (Venkatesh et al., 2012), where there is a need for more experimental and longitudinal research designs (Casey &
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Wilson-Evered, 2012). Nevertheless, the correlational method provided several practical and pragmatic benefits in this study, including the capacity to investigate a range of concepts related to m-commerce purchase intentions simultaneously. As shown by the high reliabilities of the measures of m-commerce perceptions and purchase intentions, the online questionnaire format provided a relatively valid and efficient means of data collection. The findings of this study include limitations that participants were asked to rate their perceptions of m-commerce in a general way, rather than by referring to a specific m-commerce experience. Furthermore, asking participants to rate their m-commerce purchase intentions does not provide information about their actual m-commerce usage and behaviors. Nevertheless, similar research does show that purchase intentions are a good predictor of usage behavior (Escobar-Rodriguez & Carvajal-Trujillo, 2014). Recommendations include that when making inferences about how m-commerce purchase intentions may translate to actual purchase behavior. Similarly, inferences about how general perceptions of m-commerce relate to specific m-commerce domains should be made with an appropriate level of qualification. Recommendations for Further Research The findings of the study provide several directions for future research endeavors. Although a reasonable volume exists of m-commerce research in the literature, knowledge of the predictors of m-commerce purchase intentions is in its relative infancy (Oliveira et al., 2014). The findings suggest that future research may benefit from deploying the UTAUT framework, seemingly providing a foundation for developing knowledge about the factors that predict mcommerce purchase intentions. To improve the generalizability of the UTUAT framework, further research to extend upon the findings of this study would investigate m-commerce purchase intentions with a more varied set of participants from different backgrounds and
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culture. Extending the findings beyond the North American population would be a worthy research pursuit as m-commerce appears to be a global business opportunity. Whereas the findings from this research provide some clarification to the facilitating conditions construct in the UTAUT framework, further research is needed in this direction. The findings suggested that the notion of facilitating conditions may be strongly defined by factors that reflect an approach goal orientation (Elliot, 2006), as well as an intrinsic m-commerce motivations, rather than including those oriented towards removing barriers to m-commerce or focus on avoidance oriented goals associated with m-commerce. Future research might include investigation of the motives behind m-commerce purchase intentions to further clarify the nature of m-commerce facilitating conditions. From the nature of the findings of this study, expectations include that that achievement goals and intrinsic motivations such as website aesthetics and pleasure-seeking motives (Cyr et al., 2006; Escobar-Rodriguez & CarvajalTrujillo, 2014) are stronger predictors of technology acceptance and use than barriers to usage intentions, such as cost (Alkhunaizan & Love, 2012). Moreover, research to clarify the role of approach and avoidance goals and motivations behind m-commerce purchase intentions include relevancy to the development of a competitive advantage in the business of m-commerce. Similar to most research methods in the technology acceptance empirical literature (Escobar-Rodriguez & Carvajal-Trujillo, 2014), this study employed a correlational design to test the effects of m-commerce perceptions and attitudes on subsequent purchase intentions. Indeed, there are very few research studies with use in experimental or longitudinal investigations of m-commerce purchase intentions and none investigated cause-effect relationships from the UTAUT perspective (Venkatesh et al., 2012). Although the findings from correlational designs provide certain methodological benefits, they lack the capacity to show
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causal relationships. Future experimental research that manipulates performance and effort expectancies, social influence, and facilitating conditions to determine their independent effects on m-commerce purchase intentions would be worthwhile. Future research might also log usage of m-commerce for an extended period to see how performance and effort expectancies, social influence, and facilitating conditions may change over time. As m-commerce continues to grow and develop in the market place, knowledge developed from longitudinal research about how people relate to m-commerce over time may provide a significant competitive advantage. Conclusion The aim of this research was to contribute to knowledge about the predictors of mcommerce purchase intentions in the context of a competitive advantage. M-commerce is becoming an important domain for conducting purchasing transactions (ComScore, 2014), yet knowledge about how businesses gain a competitive advantage as providers of m-commerce services is limited. Drawing on the UTUAT technology acceptance framework (Venkatesh et al., 2003; 2012), the research conducted in this study investigated the question of what extent do performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce predict m-commerce purchase intentions regarding competitive advantage. The study used a correlational design to address this research question wherein mcommerce users completed a survey questionnaire to measure their perceptions of m-commerce performance and effort expectancies, social influence, and facilitating conditions, as well as their m-commerce purchase intentions. The findings from the survey showed that performance and effort expectancies, social influence, and the facilitating conditions of trust and perceived risk in the use of m-commerce together predicted m-commerce purchase intentions at a statistically
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significant level regarding competitive advantage. Moreover, findings indicated that performance expectancy and social influence are the strongest predictors of m-commerce purchase intentions. In conclusion, the findings of the research provide support to the efficacy of the UTAUT framework for developing knowledge about the predictors of m-commerce purchase intentions. The results also provided further clarification of the facilitating conditions of m-commerce purchases suggesting that implicit motivations and approach-oriented goals might be significantly associated with m-commerce purchase intentions. Despite some limitations of the findings, the results provide solid knowledge and implications about the predictors of mcommerce intentions and how they may translate into competitive advantages for m-commerce providers. Future research can be conducted to further develop knowledge about the extent of the relationship between m-commerce performance expectancies, social influence, implicit motivations, and purchase intentions. Developing knowledge on m-commerce purchase intentions has the potential to provide leverage to businesses seeking to gain a competitive advantage through their m-commerce purchasing domains.
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APPENDIX Research findings of factors associated with m-commerce purchase intentions Author
Factor Accessibility
Findings
Lu, H. P., & Yu-Jen Su, P., (2009)
The results demonstrate that anxiety, which is an effective barrier against using innovative systems, is a key negative predictor of a customer’s intentions to use mobile phones.
Sivunen & Valo, (2006)
People’s acceptance of different communication technologies depends on the available tools and their dispersion in the marketplace. Intrinsic Motivation Compatibility
Davis et al., (1992)
Both extrinsic and intrinsic factors affect the motivation to use information technology systems.
Venkatesh & Davis, (2000)
The authors present a theoretical extension of Davis’s technology acceptance model (TAM) and contributed toward foundational user adoption behavior.
Venkatesh, Morris, Davis, & Davis, (2003)
Performance expectancy, effort expectancy, social influence, and facilitating conditions impact user acceptance of technology. Risk
Joubert & Van Belle, (2013)
Consumers demonstrate a lack of enthusiasm, possibly due to a lack trust.
Nilashi, Ibrahim, Mirabi, Ebrahimi, & Zare, (2015)
Gaining customer trust in mobile commerce is a particularly daunting task and plays a major influence on a customer's decision-making behavior. A trusted website can provide mobile commerce with powerful competitive advantages.
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Research findings of factors associated with m-commerce purchase intentions (continued) Author
Factor
Findings
Usefulness Cyr et al., (2006)
Enhancing the design elements of a mobile device would improve user’s impression and their perceived usefulness.
Lu et al., (2005)
Intention to adopt wireless services via mobile technology is mostly determined by perceived usefulness and ease of use. Trust
Alkhunaizan & Love, (2012)
Trust is an essential variable of enhancing customer satisfaction and consumer loyalty in m-commerce.
Joubert, J., & Van Belle, J. (2013)
Trust has emerged as a fundamental factor for user acceptance as mcommerce businesses that have earned that trust are associated with success.
Pavlou, (2003)
Synthesized the literature on TRA and TAM with the important ethical issues of risk and trust in the field of ecommerce
Schmidt-Belz, (2003)
Lack of trust has been found to be a significant factor influencing the uptake of mobile commerce services.
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