Understanding what determines consumers' expanded use of mobile videophones

The research proposes a framework incorporating the flow theory and the theory of planned behaviour to understand the intention of subscribers to expand their use of mobile videophones. The research model was tested with the partial least square graph software based on its structural equation modelling approach. The data used in this research were collected from online questionnaires done by 151 respondents. The findings show that the flow and the subjective norm are positively related to the expanded intention to use mobile videophones and the privacy concern is negatively related to that intention. The results also reveal that the effects of media characteristics such as interactivity and telepresence may have significant influences on the flow. Moreover, the effects of the flow, the subjective norm and the privacy concern are moderated by self-monitoring. Regardless of personality, people are likely to expand the intention to use mobile videophones when they immerse themselves in the flow. Several implications for research and practice are derived from these findings.

[1]  J. Noyes,et al.  Web Site Design, Self‐Monitoring Style, and Consumer Preference , 2007 .

[2]  Dan J. Kim,et al.  Self-Perception-Based Versus Transference-Based Trust Determinants in Computer-Mediated Transactions: A Cross-Cultural Comparison Study , 2008, J. Manag. Inf. Syst..

[3]  Mark E. Slama,et al.  Self-presentation and consumer interaction styles , 1995 .

[4]  Wynne W. Chin,et al.  A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic - Mail Emotion/Adoption Study , 2003, Inf. Syst. Res..

[5]  Per E. Pedersen,et al.  Intentions to use mobile services: Antecedents and cross-service comparisons , 2005 .

[6]  David Luna,et al.  Cross-cultural and cognitive aspects of web site navigation , 2002 .

[7]  I. Ajzen.  Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. , 2002 .

[8]  Christian Homburg,et al.  How Organizational Complaint Handling Drives Customer Loyalty: An Analysis of the Mechanistic and the Organic Approach , 2005 .

[9]  D. Carroll,et al.  The Influence of Affective and Instrumental Beliefs on Exercise Intentions and Behavior: A Longitudinal Analysis , 2002 .

[10]  Rolf T. Wigand,et al.  Optimal experience of Web activities , 1999 .

[11]  Avideh Zakhor,et al.  Mobile multimedia services , 2005, WWW '05.

[12]  Kenton O'Hara,et al.  Everyday practices with mobile video telephony , 2006, CHI.

[13]  M. Csíkszentmihályi.  Beyond boredom and anxiety , 1975 .

[14]  Ran Wei,et al.  Motivations for using the mobile phone for mass communications and entertainment , 2008, Telematics Informatics.

[15]  Vincenzo Russo,et al.  Privacy calculus model in e-commerce – a study of Italy and the United States , 2006, Eur. J. Inf. Syst..

[16]  M. Csíkszentmihályi,et al.  The measurement of flow in everyday life: toward a theory of emergent motivation. , 1992, Nebraska Symposium on Motivation. Nebraska Symposium on Motivation.

[17]  E. Y. Kim,et al.  Modeling consumer adoption of mobile shopping for fashion products in Korea , 2009 .

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

[19]  Giles D'Souza,et al.  Antecedents and consequences of consumer privacy concerns: An empirical investigation , 2001 .

[20]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[21]  Elena Karahanna,et al.  Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage , 2000, MIS Q..

[22]  Marios Koufaris,et al.  Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior , 2002, Inf. Syst. Res..

[23]  Manuel J. Sánchez-Franco,et al.  Web acceptance and usage model: A comparison between goal-directed and experiential web users , 2005, Internet Res..

[24]  Izak Benbasat,et al.  Addressing the What and How of Online Services: Positioning Supporting-Services Functionality and Service Quality for Business-to-Consumer Success , 2008, Inf. Syst. Res..

[25]  Shin-Yuan Hung,et al.  Critical factors of WAP services adoption: an empirical study , 2003, Electron. Commer. Res. Appl..

[26]  Chao-Min Chiu,et al.  Predicting electronic service continuance with a decomposed theory of planned behaviour , 2004, Behav. Inf. Technol..

[27]  M. Snyder.  Self-monitoring of expressive behavior. , 1974 .

[28]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[29]  Katherine N. Lemon,et al.  The Theoretical Underpinnings of Customer Asset Management , 2002 .

[30]  Soung Hie Kim,et al.  ERP training with a web-based electronic learning system: The flow theory perspective , 2007, Int. J. Hum. Comput. Stud..

[31]  A. Manstead,et al.  Evaluating and extending the theory of planned behaviour , 1995 .

[32]  J. Webster,et al.  Flow in Computer-Mediated Communication , 1992 .

[33]  Izak Benbasat,et al.  An investigation into the effects of Text-To-Speech voice and 3D avatars on the perception of presence and flow of live help in electronic commerce , 2005, TCHI.

[34]  Mark Snyder,et al.  Appeals to image and claims about quality: Understanding the psychology of advertising. , 1985 .

[35]  Donna L. Hoffman,et al.  Measuring the Customer Experience in Online Environments: A Structural Modeling Approach , 2000 .

[36]  A. Tenbrunsel,et al.  Organizational Behavior and Human Decision Processes , 2013 .

[37]  Mohamed Khalifa,et al.  Drivers for Transactional B2C M-Commerce Adoption: Extended Theory of Planned Behavior , 2008, J. Comput. Inf. Syst..

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

[39]  Aron O'Cass,et al.  A psychometric evaluation of a revised version of the Lennox and Wolfe revised self-monitoring scale , 2000 .

[40]  Dennis L. Hoffman,et al.  Marketing in Hypermedia Computer-Mediated Environments : Conceptual Foundations 1 ) , 1998 .

[41]  Anol Bhattacherjee,et al.  Acceptance of e-commerce services: the case of electronic brokerages , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[42]  Tamara Dinev,et al.  Internet Privacy Concerns and Social Awareness as Determinants of Intention to Transact , 2005, Int. J. Electron. Commer..

[43]  J. Webster,et al.  The Dimensionality and Correlates of Flow in Human-Computer Interactions. , 1993 .

[44]  Robert W. Zmud,et al.  The Nature and Determinants of IT Acceptance, Routinization, and Infusion , 1993, Diffusion, Transfer and Implementation of Information Technology.

[45]  R. Frank Falk,et al.  A Primer for Soft Modeling , 1992 .

[46]  Subhash Sharma,et al.  Identification and Analysis of Moderator Variables , 1981 .

[47]  Wynne W. Chin.  The partial least squares approach for structural equation modeling. , 1998 .

[48]  I. Ajzen.  Attitudes, Personality and Behavior , 1988 .

[49]  Jyh-Jeng Wu,et al.  Towards understanding members' interactivity, trust, and flow in online travel community , 2005, Ind. Manag. Data Syst..

[50]  J. J. Po-An Hsieh,et al.  ScholarWorks @ Georgia State University , 2016 .

[51]  Stephen Sutton,et al.  The Importance of Affective Beliefs and Attitudes in the Theory of Planned Behavior: Predicting Intention to Increase Physical Activity , 2005 .

[52]  Kar Yan Tam,et al.  Understanding Continued Information Technology Usage Behavior: A Comparison of Three Models in the Context of Mobile Internet , 2006, Decis. Support Syst..

[53]  Kwok Kee Wei,et al.  Understanding competing application usage with the theory of planned behavior , 2006, J. Assoc. Inf. Sci. Technol..

[54]  Nicola Green,et al.  On the Move: Technology, Mobility, and the Mediation of Social Time and Space , 2002, Inf. Soc..

[55]  Thompson S. H. Teo,et al.  Adoption of the Internet and WAP-enabled phones in Singapore , 2003, Behav. Inf. Technol..

[56]  Carl H. Botan.  Communication work and electronic surveillance: A model for predicting panoptic effects , 1996 .

[57]  Jonathan Steuer,et al.  Defining virtual reality: dimensions determining telepresence , 1992 .

[58]  Bernard C. Y. Tan,et al.  A Cross-Cultural Study on Escalation of Commitment Behavior in Software Projects , 2000, MIS Q..

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

[60]  M. Goldberg,et al.  What to Convey in Antismoking Advertisements for Adolescents: The use of Protection Motivation Theory to Identify Effective Message Themes , 2003 .

[61]  M. Ahearne,et al.  The ABC's of ACB: Unveiling a Clear and Present Danger in the Sales Force , 2006 .

[62]  Mark Andrejevic,et al.  The Discipline of Watching: Detection, Risk, and Lateral Surveillance , 2006 .

[63]  Jinwoo Kim,et al.  Why People Continue to Play Online Games: In Search of Critical Design Factors to Increase Customer Loyalty to Online Contents , 2004, Cyberpsychology Behav. Soc. Netw..

[64]  Ronald E. Rice,et al.  Video as a technology for informal communication , 1993, CACM.

[65]  F. Bookstein,et al.  Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory , 1982 .

[66]  David Lyon,et al.  The Electronic Eye: The Rise of Surveillance Society , 1994 .

[67]  M. Sherif,et al.  The psychology of attitudes. , 1946, Psychological review.

[68]  Kar Yan Tam,et al.  Understanding the behavior of mobile data services consumers , 2008, Inf. Syst. Frontiers.

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

[70]  Robert E. Kraut,et al.  Prospects for video telephony , 1995 .

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