Augmented reality and motion capture apparel e-shopping values and usage intention

Purpose – The purpose of this paper is to examine: whether monetary, convenience, emotional, and social values were related to utilitarian and hedonic performance expectancies, which were then related to usage intention of augmented reality and motion capture (ARMC) e-shopping via a webcam and whether ego involvement and cognitive effort moderated the links between performance expectancies and usage intention. Design/methodology/approach – The proposed model was based on Prospect Theory and the Value-Attitude-Behavior hierarchy consumer decision model. The participants were US online apparel shoppers (n=806) and were drawn from an online consumer panel. Structural equation modeling was employed to test the proposed model and research hypotheses. Findings – This study identified that utilitarian performance expectancy was positively related to usage intention. However, hedonic performance expectancy was not positively related to usage intention. Monetary, convenience, emotional, and social values had an in...

[1]  Carol Kaufman–Scarborough,et al.  E‐shopping in a multiple channel environment , 2002 .

[2]  Rex Eugene Perera.  Optimizing Human-Computer Interaction for the Electronic Commerce Environment , 2000, J. Electron. Commer. Res..

[3]  A. Fiore,et al.  For fun and profit: Hedonic value from image interactivity and responses toward an online store , 2005 .

[4]  L. Berry,et al.  Understanding Service Convenience , 2002 .

[5]  Herbjørn Nysveen,et al.  Drivers of Intention to Revisit the Websites of Well-Known Companies: The Role of Corporate Brand Loyalty , 2001 .

[6]  R. Keeney,et al.  The Value of Internet Commerce to the Customer , 1999 .

[7]  Rolph E. Anderson,et al.  E‐satisfaction and e‐loyalty: A contingency framework , 2003 .

[8]  R. Fazio.  On the power and functionality of attitudes: The role of attitude accessibility. , 1989 .

[9]  Namkee Park,et al.  Intention to upload video content on the internet: The role of social norms and ego-involvement , 2011, Comput. Hum. Behav..

[10]  G. Soutar,et al.  Consumer perceived value: The development of a multiple item scale , 2001 .

[11]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[12]  Tino Fenech,et al.  Internet users’ adoption of Web retailing: user and product dimensions , 2001 .

[13]  Hee-Woong Kim,et al.  Value‐driven Internet shopping: The mental accounting theory perspective , 2010 .

[14]  E. Y. Kim,et al.  Predicting online purchase intentions for clothing products , 2004 .

[15]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[16]  Chechen Liao,et al.  Shopping motivations on Internet: A study based on utilitarian and hedonic value , 2007 .

[17]  G. Laurent,et al.  Consumers' Involvement Profile: New Empirical Results , 1985 .

[18]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[19]  Peter A. Todd,et al.  Consumer Reactions to Electronic Shopping on the World Wide Web , 1996, Int. J. Electron. Commer..

[20]  Edward E. Rigdon,et al.  Experiential value: Conceptualization, measurement and application in the catalog and Internet shopping environment. , 2001 .

[21]  Sandra Forsythe,et al.  Examining drivers of online purchase intensity: Moderating role of adoption duration in sustaining post-adoption online shopping , 2011 .

[22]  P. Homer,et al.  A structural equation test of the value-attitude-behavior hierarchy. , 1988 .

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

[24]  Sung-joon Yoon,et al.  The antecedents and consequences of trust in online-purchase decisions , 2002 .

[25]  Il Im,et al.  The impact of product category on customer dissatisfaction in cyberspace , 2003, Bus. Process. Manag. J..

[26]  Rajiv N. Rimal,et al.  An Explication of Social Norms , 2005 .

[27]  Gregory J. Brush,et al.  Comparing the Perceived Value of Information and Entertainment Mobile Services , 2008 .

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

[29]  Jordan J. Louviere,et al.  Measuring values using best‐worst scaling: The LOV example , 2007 .

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

[31]  Thompson S. H. Teo,et al.  Online buying behavior:a transaction cost economics perspective , 2005 .

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

[33]  Tung‐Zong Chang,et al.  A descriptive model of online shopping process: some empirical results , 2003 .

[34]  Lisa R. Klein.  Evaluating the Potential of Interactive Media through a New Lens: Search versus Experience Goods , 1998 .

[35]  M. Gilly,et al.  Shopping Online for Freedom, Control, and Fun , 2001 .

[36]  K. Reynolds,et al.  Hedonic shopping motivations , 2003 .

[37]  ThaeMin Lee,et al.  THE IMPACT OF PERCEPTIONS OF INTERACTIVITY ON CUSTOMER TRUST AND TRANSACTION INTENTIONS IN MOBILE COMMERCE , 2005 .

[38]  R. B. Woodruff,et al.  Customer value: The next source for competitive advantage , 1997 .

[39]  Mary Ann Eastlick,et al.  An online prepurchase intentions model: The role of intention to search , 2001 .

[40]  S. Forsythe,et al.  Post‐adoption online shopping continuance , 2010 .

[41]  Taeshik Gong,et al.  The electronic service quality model: The moderating effect of customer self‐efficacy , 2008 .

[42]  Blair T. Johnson,et al.  Effects of involvement on persuasion: a meta-analysis , 1989 .

[43]  G. Laurent,et al.  Measuring Consumer Involvement Profiles , 1985 .