Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems

This study investigates how consumers assess the quality of two types of recommender systems-collaborative filtering and content-based—in the context of e-commerce by using a modified version of the unified theory of acceptance and use of technology (UTAUT) model. Specifically, the concept of trust in the technological artifact is adapted to examine the intention to use recommender systems. Additionally, this study also considers hedonic and utilitarian product characteristics with the goal of presenting a comprehensive picture on recommender systems literature. This study utilized a 2 × 2 crossover within-subjects experimental design involving a total of 80 participants, who all evaluated each recommender system. The results suggest that the type of recommender system significantly moderates many relationships of the determinants of customer behavioral intent on behavioral intention to use recommender systems. Surprisingly, the type of product does not moderate any relationship on behavioral intention. This study holds importance in explaining the factors contributing to the use of recommender systems and understanding the relative influence of the two types of recommender systems on customer behavioral intention to use recommender systems. The finding also sheds light for designers on how to provide more effective recommender systems.

[1]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  Pasquale Lops,et al.  Improving Collaborative Recommender Systems by Means of User Profiles , 2004, Designing Personalized User Experiences in eCommerce.

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

[4]  Gurpreet Dhillon,et al.  A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research , 2014, Inf. Syst. Res..

[5]  Pär Åhlström,et al.  Longitudinal Field Studies , 2009 .

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

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

[8]  Keith E. Muller,et al.  Linear Model Theory: Univariate, Multivariate, and Mixed Models , 2006 .

[9]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[10]  Kirsten Swearingen,et al.  Interaction Design for Recommender Systems , 2002 .

[11]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[12]  Rashmi R. Sinha,et al.  Comparing Recommendations Made by Online Systems and Friends , 2001, DELOS.

[13]  Viswanath Venkatesh,et al.  Predicting Collaboration Technology Use: Integrating Technology Adoption and Collaboration Research , 2010, J. Manag. Inf. Syst..

[14]  Pearl Pu,et al.  User Technology Adoption Issues in Recommender Systems , 2007 .

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

[16]  Khaled Hassanein,et al.  The Impact of Infusing Social Presence in the Web Interface : An Investigation Across Different Products ” , 2006 .

[17]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[18]  S. Sénécal,et al.  The influence of online product recommendations on consumers' online choices , 2004 .

[19]  Terry L. Childers,et al.  HEDONIC AND UTILITARIAN MOTIVATIONS FOR ONLINE RETAIL SHOPPING BEHAVIOR , 2001 .

[20]  David Gefen,et al.  Managing User Trust in B2C e-Services , 2003 .

[21]  Joseph M. Jones,et al.  Do Internet Shopping Aids Make a Difference? An Empirical Investigation , 2001, Electron. Mark..

[22]  D. Gefen,et al.  Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services , 2004 .

[23]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[24]  Alexander Tuzhilin,et al.  Towards the Next Generation of Recommender Systems , 2010, ICE-B 2010.

[25]  Tor Guimaraes,et al.  Assessing the impact of internet agent on end users' performance , 2005, Decis. Support Syst..

[26]  G IpeirotisPanagiotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews , 2011 .

[27]  Yi-Cheng Ku,et al.  Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings , 2006, J. Manag. Inf. Syst..

[28]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[29]  Charles J. Kacmar,et al.  Developing and Validating Trust Measures for e-Commerce: An Integrative Typology , 2002, Inf. Syst. Res..

[30]  N. Anderson,et al.  Primacy effects in personality impression formation. , 1961, Journal of abnormal and social psychology.

[31]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[32]  E. Horvitz,et al.  Personalised hypermedia presentation techniques for improving online customer relationships , 2001, The Knowledge Engineering Review.

[33]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[34]  Izak Benbasat,et al.  Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs , 2007, J. Manag. Inf. Syst..

[35]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[36]  J. Bettman.  An information processing theory of consumer choice , 1979 .

[37]  Lawrence Feick,et al.  The role of product type and country‐of‐origin in decisions about choice of endorser ethnicity in advertising , 2006 .

[38]  E. Hirschman,et al.  The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun , 1982 .

[39]  Loren Terveen,et al.  Beyond Recommender Systems: Helping People Help Each Other , 2001 .

[40]  Rajiv Vaidyanathan,et al.  The Perceived Effectiveness of Virtual Shopping Agents For Search Vs. Experience Goods , 2003 .

[41]  Hong Joo Lee,et al.  The Influence of Social Presence on Customer Intention to Reuse Online Recommender Systems: The Roles of Personalization and Product Type , 2011, Int. J. Electron. Commer..

[42]  W. Bearden,et al.  Reference Group Influence on Product and Brand Purchase Decisions , 1982 .

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

[44]  Izak Benbasat,et al.  Trust In and Adoption of Online Recommendation Agents , 2005, J. Assoc. Inf. Syst..

[45]  Li Chen,et al.  Trust building with explanation interfaces , 2006, IUI '06.

[46]  Alfred Kobsa,et al.  Personalised hypermedia presentation techniques for improving online customer relationships , 2001, The Knowledge Engineering Review.

[47]  R. Dhar,et al.  Consumer Choice between Hedonic and Utilitarian Goods , 2000 .

[48]  Maryon King,et al.  The effects of expertise, end goal, and product type on adoption of preference formation strategy , 1994 .

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

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

[51]  H. Kelley,et al.  Interpersonal relations: A theory of interdependence , 1978 .

[52]  Rashmi R. Sinha,et al.  The role of transparency in recommender systems , 2002, CHI Extended Abstracts.

[53]  John Riedl,et al.  Recommender systems: from algorithms to user experience , 2012, User Modeling and User-Adapted Interaction.

[54]  Sean M. McNee,et al.  Making recommendations better: an analytic model for human-recommender interaction , 2006, CHI Extended Abstracts.

[55]  Thomas Kramer.  The Effect of Measurement Task Transparency on Preference Construction and Evaluations of Personalized Recommendations , 2006 .

[56]  Clare-Marie Karat,et al.  Designing Personalized User Experiences in eCommerce , 2004, Human-Computer Interaction Series.

[57]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

[58]  N. Luhmann.  Trust and Power , 1979 .

[59]  Milena M. Head,et al.  The Impact of Infusing Social Presence in the Web Interface: An Investigation Across Product Types , 2005, Int. J. Electron. Commer..

[60]  H. Helson.  Adaptation-level theory : an experimental and systematic approach to behavior , 1964 .

[61]  E. Hirschman,et al.  Hedonic Consumption: Emerging Concepts, Methods and Propositions , 1982 .

[62]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[63]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[64]  Izak Benbasat,et al.  Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites , 2006, Inf. Syst. Res..

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

[66]  Bruce Krulwich,et al.  LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data , 1997, AI Mag..

[67]  Bernard J. Jaworski,et al.  Introduction to E-Commerce , 2001 .

[68]  R. Kohli,et al.  Internet Recommendation Systems , 2000 .

[69]  Henry C. Lucas,et al.  The Value of IT-Enabled Retailer Learning: Personalized Product Recommendations and Customer Store Loyalty in Electronic Markets , 2011, MIS Q..

[70]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[71]  Henry Lieberman,et al.  Letizia: An Agent That Assists Web Browsing , 1995, IJCAI.

[72]  D. Gefen,et al.  E-commerce: the role of familiarity and trust , 2000 .

[73]  P. Tan,et al.  WebSIFT : The Web Site Information Filter , 1999 .

[74]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[75]  Juhani Iivari,et al.  Why do individuals use computer technology? A Finnish case study , 1995, Inf. Manag..

[76]  David Meyer,et al.  The role of perceived enjoyment and social norm in the adoption of technology with network externalities , 2008, Eur. J. Inf. Syst..

[77]  LiangTing-Peng,et al.  Personalized Content Recommendation and User Satisfaction , 2007 .

[78]  Li Chen,et al.  Evaluating recommender systems from the user’s perspective: survey of the state of the art , 2012, User Modeling and User-Adapted Interaction.

[79]  David F. Larcker,et al.  Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics: , 1981 .

[80]  Marla R. Stafford,et al.  A Contingency Approach: The Effects of Spokesperson Type and Service Type on Service Advertising Perceptions , 2002 .

[81]  Katherine Gallagher,et al.  A Tale of Two Studies: Replicating ‘Advertising Effectiveness and Content Evaluation in Print and on the Web’ , 2001, Journal of Advertising Research.

[82]  J. Izhavarasi Devi.  Estimating the Helpfulness and Economic Impact of Product Reviews , 2012 .

[83]  A. Bandura.  Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychology Review.

[84]  Vincent S. Lai,et al.  Prediction of Internet and World Wide Web usage at work: a test of an extended Triandis model , 2000, Decis. Support Syst..

[85]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[86]  Hans van der Heijden,et al.  User Acceptance of Hedonic Information Systems , 2004, MIS Q..

[87]  Lawrence Feick,et al.  The Effects of Preference Heterogeneity and Source Characteristics on Ad Processing and Judgements about Endorsers , 1992 .