Understanding the adoption of location-based recommendation agents among active users of social networking sites

Nowadays, using increasingly granular data, from real-time location information and detailed demographics to consumers-generated content on the social networking sites (SNSs), businesses are starting to offer precise location-based product recommendation services through mobile devices. Based on the technology acceptance model (TAM), this paper develops a theoretical model to examine the adoption intention of active SNS users toward location-based recommendation agents (LBRAs). The research model was tested by using the Partial Least Squares (PLS) technique.The results show that perceived usefulness, perceived control, and perceived institutional assurance are important in developing adoption intention. Perceived effort saving, special treatment, and social benefit have influences on the adoption intention through the mediating effect of perceived usefulness. Perceived accuracy has direct influence on adoption intention.

[1]  Tao Zhou.  An empirical examination of user adoption of location-based services , 2013, Electron. Commer. Res..

[2]  Gordon R. Foxall,et al.  Technology acceptance: a meta‐analysis of the TAM: Part 1 , 2007 .

[3]  Izak Benbasat,et al.  E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact , 2007, MIS Q..

[4]  Min-Sun Kim,et al.  An Analysis of Self-Construals, Motivations, Facebook Use, and User Satisfaction , 2010, Int. J. Hum. Comput. Interact..

[5]  Param Vir Singh,et al.  A Hidden Markov Model for Collaborative Filtering , 2010, MIS Q..

[6]  William B. Dodds,et al.  Effects of Price, Brand, and Store Information on Buyers’ Product Evaluations: , 1991 .

[7]  Dhruv Grewal,et al.  Fix It or Leave It? Customer Recovery from Self-service Technology Failures , 2013 .

[8]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

[9]  Clive Sanford,et al.  The roles of self-concept clarity and psychological reactance in compliance with product and service recommendations , 2010, Comput. Hum. Behav..

[10]  Torsten J. Gerpott,et al.  Determinants of the Willingness to Use Mobile Location-Based Services , 2011, Bus. Inf. Syst. Eng..

[11]  I. Benbasat,et al.  Research Note---The Influence of Trade-off Difficulty Caused by Preference Elicitation Methods on User Acceptance of Recommendation Agents Across Loss and Gain Conditions , 2011 .

[12]  Ya-Ping Chang,et al.  The role of perceived social capital and flow experience in building users' continuance intention to social networking sites in China , 2012, Comput. Hum. Behav..

[13]  Cliff Lampe,et al.  Facebook as a toolkit: A uses and gratification approach to unbundling feature use , 2011, Comput. Hum. Behav..

[14]  J. Zenger,et al.  Making yourself indispensable. , 2011, Harvard business review.

[15]  Izak Benbasat,et al.  The Effects of Personalizaion and Familiarity on Trust and Adoption of Recommendation Agents , 2006, MIS Q..

[16]  Ya-Ping Chang,et al.  Understanding social networking sites adoption in China: A comparison of pre-adoption and post-adoption , 2011, Comput. Hum. Behav..

[17]  Daniel L. Sherrell,et al.  Examining the influence of control and convenience in a self-service setting , 2010 .

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

[19]  Judy Chuan-Chuan Lin,et al.  Antecedents and consequences of trust in online product recommendation: An empirical study in social shopping , 2010, Online Inf. Rev..

[20]  Iris Vessey,et al.  Cognitive Fit: A Theory‐Based Analysis of the Graphs Versus Tables Literature* , 1991 .

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

[22]  Fernando Ortega,et al.  Incorporating group recommendations to recommender systems: Alternatives and performance , 2013, Inf. Process. Manag..

[23]  David M. Woisetschläger,et al.  Consequences of customer loyalty to the loyalty program and to the company , 2012 .

[24]  Izak Benbasat,et al.  A study of demographic embodiments of product recommendation agents in electronic commerce , 2010, Int. J. Hum. Comput. Stud..

[25]  Hock-Hai Teo,et al.  Research Note - Effects of Individual Self-Protection, Industry Self-Regulation, and Government Regulation on Privacy Concerns: A Study of Location-Based Services , 2012, Inf. Syst. Res..

[26]  Wynne W. Chin,et al.  Adoption intention in GSS: relative importance of beliefs , 1995, DATB.

[27]  Meredith E. David,et al.  Do relationship benefits and maintenance drive commitment and loyalty , 2011 .

[28]  Wolfgang Maass,et al.  In-store consumer behavior: How mobile recommendation agents influence usage intentions, product purchases, and store preferences , 2010, Comput. Hum. Behav..