The Moderating Role of Consumer Technology Anxiety in Mobile Shopping Adoption: Differential Effects of Facilitating Conditions and Social Influences

ABSTRACTThis study examined whether consumers' levels of technology anxiety moderate the causal relationships among determinants of mobile shopping adoption in a modified Unified Theory of User Acceptance and Use of Technology (UTAUT) model. With the moderating role of technology anxiety, facilitating conditions were examined as an antecedent driver of utilitarian and hedonic performance expectancies in determining mobile shopping adoption in the modified UTAUT model. A sample of 400 mobile services users drawn from a purchased consumer panel participated in an online survey. Structural equation modeling analysis was used to examine the hypothesized paths in the adoption of mobile shopping. Results indicated that the effect of facilitating conditions on both utilitarian and hedonic performance expectancies is stronger for consumers with a low level of technology anxiety than for consumers with a high level of technology anxiety. Moreover, consumers with a high level of technology anxiety rely more on social influence in the use of mobile shopping than consumers with a low level of technology anxiety. The modified UTAUT model reveals insightful results and provides a holistic framework for predicting emerging mobile shopping adoption behavior.Keywords: Mobile shopping; Facilitating conditions; Social influences; Technology anxiety; UTAUT1. IntroductionThe ubiquitous characteristic of an Internet-enabled mobile phone is profoundly affecting the way people use services and information in their daily lives. This increased use of mobile internet and online services is enabling the creation of new services that promise alternative opportunities for companies [Yu 2012], In the retail industry, the rapid adoption of mobile Internet and smartphones has retailers attempting to capitalize on the promise of technology-mediated mobile services as a new and important channel to serve and connect with consumers [Liesse 2007], The mobile shopping channel has become a personal shopping assistant for consumers to enhance their shopping experiences and assist in making purchases across channels.While mobile shopping services may promise better consumer shopping experiences, there are concerns about whether consumers will actually adopt technology-mediated services when available. This new technology-mediated mobile shopping channel is different from traditional (e.g., in-store, catalog) and online shopping channels and it is not yet validated across consumer segments. Further, with the extremely private and personal nature of the mobile phone device, mobile shopping services often involve security and privacy issues resulting from transacting financial and personal information. Therefore, consumers' concerns for security and privacy may be higher in the mobile shopping channel than in other shopping channels. Due to its newness and uniqueness in shopping encounters (e.g., small screen size, using 4G & 5G mobile technologies), mobile shopping may provoke user anxiety in its embryonic stage of mobile shopping adoption. In that regard, consumer anxiety may be a significant barrier facing consumers at the moment when mobile shopping is used. If this is the case, consumer mobile shopping adoption might be leveraged by reducing consumer anxiety about using mobile shopping. Therefore, by understanding the relationships among consumers' underlying motivations to adopt mobile shopping and their associated anxieties, retailers may benefit by being proactive in designing mobile shopping services that help alleviate anxiety in the adoption stage.This study examines determinants of consumer mobile shopping adoption using measures suggested by Venkatesh et al. [2003]'s Unified Theory of User Acceptance and Use of Technology (UTAUT). Although extant technology acceptance models and theories are well-established and validated in previous studies [e.g., Dabholkar and Bagozzi 2002; Davis 1989; Davis et al. 1989; Venkatesh 1999], testing that is based upon one technology acceptance model may bring skewed and blurred outcomes, particularly when examining a new technology phenomenon. …

[1]  C. Yu.  Factors Affecting Individuals to Adopt Mobile Banking: Empirical Evidence from the Utaut Model , 2012 .

[2]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[3]  Christian Peter,et al.  Emotion in Human-Computer Interaction , 2012, Expanding the Frontiers of Visual Analytics and Visualization.

[4]  Songpol Kulviwat,et al.  The role of social influence on adoption of high tech innovations: The moderating effect of public/private consumption , 2009 .

[5]  I. Phau,et al.  Devil wears (counterfeit) Prada: a study of antecedents and outcomes of attitudes towards counterfeits of luxury brands , 2009 .

[6]  M.H.P. Kleijnen,et al.  Coupons going wireless: Determinants of consumer intentions to redeem mobile coupons , 2008 .

[7]  Sang-Hoon Kim,et al.  Do early adopters upgrade early? Role of post-adoption behavior in the purchase of next-generation products , 2008 .

[8]  Y. Tao,et al.  Using UTAUT to explore the behavior of 3G mobile communication users , 2007, 2007 IEEE International Conference on Industrial Engineering and Engineering Management.

[9]  K. Ruyter,et al.  An assessment of value creation in mobile service delivery and the moderating role of time consciousness , 2007 .

[10]  Jason M. Carpenter,et al.  Consumer shopping value for retail brands , 2005 .

[11]  Wynne W. Chin,et al.  Factors motivating software piracy: a longitudinal study , 2004, IEEE Transactions on Engineering Management.

[12]  B. Hourahine,et al.  Money on the move: Opportunities for financial service providers in the ‘third space’ , 2004 .

[13]  Younes Benslimane,et al.  An exploration of wireless computing risks: Development of a risk taxonomy , 2004, Inf. Manag. Comput. Secur..

[14]  Mary Jo Bitner,et al.  The influence of technology anxiety on consumer use and experiences with self-service technologies , 2003 .

[15]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[16]  Irvine Clarke,et al.  Mobile portals: the development of M-commerce gateways , 2003 .

[17]  Hans van der Heijden,et al.  Measuring attitudes towards mobile information services: an empirical validation of the HED/UT scale , 2003, ECIS.

[18]  R. Bagozzi,et al.  An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors , 2002 .

[19]  M. Holbrook.  Introduction to consumer value , 2002 .

[20]  Paul A. Pavlou,et al.  What Drives Electronic Commerce across Cultures? Across-Cultural Empirical Investigation of the Theory of Planned Behavior , 2002, J. Electron. Commer. Res..

[21]  Jinwoo Kim,et al.  A Cross-Cultural Study on the Value Structure of Mobile Internet Usage: Comparison Between Korea and Japan , 2002, J. Electron. Commer. Res..

[22]  N. Nohria,et al.  A Moving Target: the Mobile-Commerce Customer , 2001 .

[23]  Deborah Compeau,et al.  Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study , 1999, MIS Q..

[24]  Viswanath Venkatesh,et al.  Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation , 1999, MIS Q..

[25]  Hsing K. Cheng,et al.  To Purchase or to Pirate Software: An Empirical Study , 1997, J. Manag. Inf. Syst..

[26]  D. Hoffman,et al.  Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations , 1996 .

[27]  Peter A. Todd,et al.  Assessing IT usage: the role of prior experience , 1995 .

[28]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[29]  Deborah Compeau,et al.  Application of Social Cognitive Theory to Training for Computer Skills , 1995, Inf. Syst. Res..

[30]  William R. Darden,et al.  Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value , 1994 .

[31]  R. Oliver,et al.  Assessing the Dimensionality and Structure of the Consumption Experience: Evaluation, Feeling, and Satisfaction , 1993 .

[32]  Terry L. Childers,et al.  The Influence of Familial and Peer-Based Reference Groups on Consumer Decisions , 1992 .

[33]  Fred D. Davis,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1 , 1992 .

[34]  I. Ajzen.  The theory of planned behavior , 1991 .

[35]  Kieran Mathieson,et al.  Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior , 1991, Inf. Syst. Res..

[36]  R. Green,et al.  Cross-Cultural Examination of the Fishbein Behavioral Intentions Model , 1991 .

[37]  Suzanne Rivard,et al.  Investigating the Support Role of the Information Center , 1990, MIS Q..

[38]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[39]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[40]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[41]  T. S. Robertson,et al.  A Propositional Inventory for New Diffusion Research , 1985 .

[42]  Michael J. Ryan.  Behavioral intention formation : the interdependency of attitudinal and social influence variables , 1982 .

[43]  R. Bagozzi.  Attitudes, intentions, and behavior: A test of some key hypotheses. , 1981 .

[44]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[45]  R. Gorsuch,et al.  Beliefs, attitudes, and values , 1968 .

[46]  Richard,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace , 2022 .