Source Factors in Recommender System Credibility Evaluation


Although recommender system research in the last decade has provided significant insight into how users interact with and evaluate systems, the social role of recommender systems as advice givers has been largely neglected. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up. Specifically, research regarding the influence of source characteristics, which is abundant in the context of human-human communication, can provide an important framework for identifying potential influence factors. This chapter reviews the existing literature on source factors in the context of human-human, human-technology, and human-recommender system interactions. It also discusses system credibility evaluation in light of the increasing popularity of social technology. It concludes that many social cues that have been identified as influential in other contexts have yet to be implemented and tested with respect to recommender systems. Implications for recommender system research and design are discussed.


Recommender System Source Characteristic Social Technology Source Credibility Virtual Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Addington, D.: The effect of vocal variations on ratings of source credibility. Speech Monographs 38, 242–247 (1971)CrossRefGoogle Scholar
  2. 2.
    Aksoy, L., Bloom, P.N., Lurie, N.H., Cooil, B.: Should recommendation agents think like people? Journal of Service Research 8(4), 297–315 (2006)CrossRefGoogle Scholar
  3. 3.
    Al-Natour, S., Benbasat, I., Cenfetelli, R.T.: The role of design characteristics in shaping perceptions of similarity: The case of online shopping assistants. Journal of Association for Information Systems 7(12), 821–861 (2006)Google Scholar
  4. 4.
    Andersen, K.E., Clevenger T., J.: A summary of experimental research in ethos. Speech Monographs 30, 59–78 (1963)CrossRefGoogle Scholar
  5. 5.
    Ansari, A., Essegaier, S., Kohli, R.: Internet recommendation systems. Journal of Marketing Research 37(3), 363–375 (2000)CrossRefGoogle Scholar
  6. 6.
    Armentano, M.G., Godoy, D., Amandi, A.: Topology-based recommendation of users in micro-blogging communities. Journal of Computer Science and Technology 27(3), 624–634 (2012)CrossRefGoogle Scholar
  7. 7.
    Atkinson, D.R., Winzelberg, A., Holland, A.: Ethnicity, locus of control for family planning, and pregnancy counselor credibility. Journal of Counseling Psychology 32, 417–421 (1985)CrossRefGoogle Scholar
  8. 8.
    Bart, Y., Shankar, V., Sultan, F., Urban, G.L.: Are the drivers and role of online trust the same for all web sites and consumers?: A large scale exploratory and empirical study. Journal of Marketing 69(4), 133–152 (2005)CrossRefGoogle Scholar
  9. 9.
    Barwise, P., Elberse, A., Hammond, K.: Marketing and the internet: A research review. In: B. Weitz, R. Wensley (eds.) Handbook of Marketing, pp. 3–7. Russell Sage, New York, NY (2002)Google Scholar
  10. 10.
    Basartan, Y.: Amazon versus the shopbot: An experiment about how to improve the shopbots. Tech. rep., Carnegie Mellon University, Pittsburgh, PA (2001). Ph.D. Summer PaperGoogle Scholar
  11. 11.
    Bechwati, N.N., Xia, L.: Do computers sweat? the impact of perceived effort of online decision aids on consumers’ satisfaction with the decision process. Journal of Consumer Psychology 13(1–2), 139–148 (2003)Google Scholar
  12. 12.
    Bharti, P., Chaudhury, A.: An empirical investigation of decision-making satisfaction in web-based decision support systems. Decision Support Systems 37(2), 187–197 (2004)CrossRefGoogle Scholar
  13. 13.
    Bickman, L.: The social power of a uniform. Journal of Applied Social Psychology 4, 47–61 (1974)CrossRefGoogle Scholar
  14. 14.
    Blythe, M.A., Overbeeke, K., Monk, A., Wright, P. (eds.): Funology: From Usability to Enjoyment, Human-Computer Interaction Series, vol. 3. Kluwer (2003)Google Scholar
  15. 15.
    Bryant, J., Brown, D., Silberberg, A.R., Elliott, S.M.: Effects of humorous illustrations in college textbooks. Human Communication Research 8, 43–57 (1981)CrossRefGoogle Scholar
  16. 16.
    Buller, D.B., Burgoon, J.K.: Interpersonal deception theory. Communication Theory 6, 203–242 (1996)CrossRefGoogle Scholar
  17. 17.
    Burgoon, J.K., Birk, T., Pfau, M.: Nonverbal behaviors, persuasion, and credibility. Human Communication Research 17, 140–169 (1990)CrossRefGoogle Scholar
  18. 18.
    Burgoon, J.K., Dunbar, N.E., Segring, C.: Nonverbal influence. In: J.P. Dillard, M. Pfau (eds.) Persuasion Handbook: Developments in Theory and Practice, pp. 445–473. Sage Publications, Thousand Oaks, CA (2002)Google Scholar
  19. 19.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)MATHCrossRefGoogle Scholar
  20. 20.
    Burke, R., Hammond, K., Young, B.: The findme approach to assisted browsing. IEEE Expert 4(12), 32–40 (1997)CrossRefGoogle Scholar
  21. 21.
    Byrne, D.: The attraction paradigm. Academic Press, New York (1971)Google Scholar
  22. 22.
    Byrne, D., Rhamey, R.: Magnitude of positive and negative reinforcements as a determinant of attraction. Journal of Personality and Social Psychology 2, 884–889 (1965)CrossRefGoogle Scholar
  23. 23.
    Carli, L.L., Ganley, R., Pierce-Otay, A.: Similarity and satisfaction in roommate relationships. Personality and Social Psychology Bulletin 17(4), 419–426 (1991)CrossRefGoogle Scholar
  24. 24.
    Chang, K.J., Gruner, C.R.: Audience reaction to self-disparaging humor. Southern Speech Communication Journal 46, 419–426 (1981)CrossRefGoogle Scholar
  25. 25.
    Cialdini, R.B.: Interpersonal influence. In: S. Shavitt, T.C. Brock (eds.) Persuasion: Psychological Insights and Perspective, pp. 195–217. Allyn and Bacon, Needhan Heights, Massachusetts (1994)Google Scholar
  26. 26.
    Cooke, A.D.J., Sujan, H., Sujan, M., Weitz, B.A.: Marketing the unfamiliar: The role of context and item-specific information in electronic agent recommendations. Journal of Marketing Research 39(4), 488–497 (2002)CrossRefGoogle Scholar
  27. 27.
    Cooper, J., Bennett, E.A., Sukel, H.L.: Complex scientific testimony: How do jurors make decisions? Law and Human Behavior 20 (1996)Google Scholar
  28. 28.
    Cosley, D., Lam, S.K., Albert, I., Konstan, J., Riedl, J.: Is seeing believing? how recommender systems influence users’ opinions. In: Proceedings of ACM CHI: Human Factors in Computing Systems, pp. 585–592 (2003)Google Scholar
  29. 29.
    Cowell, A.J., Stanney, K.M.: Manipulation of non-verbal interaction style and demographic embodiment to increase anthropomorphic computer character credibility. International Journal of Human-Computer Studies 62, 281–306 (2005)CrossRefGoogle Scholar
  30. 30.
    Delgado-Ballester, E.: Applicability of a brand trust scale across product categories: A multigroup invariance analysis. European Journal of Marketing 38(5–6), 573–592 (2004)CrossRefGoogle Scholar
  31. 31.
    Delia, J.G.: Regional dialect, message acceptance, and perceptions of the speaker. Central States Speech Journal 26, 188–194 (1975)CrossRefGoogle Scholar
  32. 32.
    Dijkstra, J.J., Liebrand, W.B.G., Timminga, E.: Persuasiveness of expert systems. Behaviour & Information Technology 17(3), 155–163 (1998)CrossRefGoogle Scholar
  33. 33.
    Eagly, A.H., Ashmore, R.D., Makhijani, M.G., Longo, L.C.: What is beautiful is good, but …: A meta-analytic review of research on the physical attractiveness stereotype. Psychological Bulletin 110, 109–128 (1991)CrossRefGoogle Scholar
  34. 34.
    Eagly, A.H., Chaiken, S.: An attribution analysis of the effect of communicator characteristics on opinion change: The case of communicator attractiveness. Journal of Personality and Social Psychology 32(1), 136–144 (1975)CrossRefGoogle Scholar
  35. 35.
    Eagly, A.H., Wood, W., Chaiken, S.: Causal inferences about communicators and their effect on opinion change. Journal of Personality and Social Psychology 36, 424–435 (1978)CrossRefGoogle Scholar
  36. 36.
    Engstrom, E.: Effects of nonfluencies on speakers’ credibility in newscast settings. Perceptual and Motor Skills 78, 739–743 (1994)CrossRefGoogle Scholar
  37. 37.
    Fasolo, B., McClelland, G.H., Lange, K.A.: The effect of site design and interattribute correlations on interactive web-based decisions. In: C.P. Haughvedt, K. Machleit, R. Yalch (eds.) Online Consumer Psychology: Understanding and Influencing Behavior in the Virtual World, pp. 325–344. Lawrence Erlbaum Associates, Mahwah, NJ (2005)Google Scholar
  38. 38.
    Felix, D., Niederberger, C., Steiger, P., Stolze, M.: Feature-oriented versus needs-oriented product access for non-expert online shoppers. In: B. Schmid, K. Stanoevska-Slabeva, V. Tschammer-Zurich (eds.) Towards the E-Society: E-Commerce, E-Business, and E-Government, pp. 399–406. Springer, New York (2001)Google Scholar
  39. 39.
    Flanagin, A. J., Metzger, M. J.:Perceptions of Internet Information credibility. Journalism & Mass Communication Quarterly, 77(3), 515–540 (2000)Google Scholar
  40. 40.
    Flanagin, A.J., Metzger, M.J.: The role of site features, user attributes, and information verification behaviors on the perceived credibility of web-based information. New Media & Society 9, 319–342 (2007)CrossRefGoogle Scholar
  41. 41.
    Fleshler, H., Ilardo, J., Demoretcky, J.: The influence of field dependence, speaker credibility set, and message documentation on evaluations of speaker and message credibility. Southern Speech Communication Journal 39, 389–402 (1974)CrossRefGoogle Scholar
  42. 42.
    Fogg, B.J.: Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann, San Francisco (2003)Google Scholar
  43. 43.
    Fogg, B.J., Lee, E., Marshall, J.: Interactive technology and persuasion: Developments in theory and practice. In: P. Dillard, M. Pfau (eds.) Persuasion handbook, pp. 765–797. Sage, London, United Kingdom (2002)Google Scholar
  44. 44.
    Fogg, B.J., Nass, C.: Silicon sycophants: Effects of computers that flatter. International Journal of Human-Computer Studies 46(5), 551–561 (1997)CrossRefGoogle Scholar
  45. 45.
    Friedrich, G., Zanker, M.: A taxonomy for generating explanations in recommender systems. AI Magazine 32(3), 90–98 (2011)Google Scholar
  46. 46.
    Gatignon, H., Robertson, T.S.: Innovative Decision Processes. Prentice Hall, Englewood Cliffs, NJ (1991)Google Scholar
  47. 47.
    Giffen, K., Ehrlich, L.: Attitudinal effects of a group discussion on a proposed change in company policy. Speech Monographs 30, 377–379 (1963)CrossRefGoogle Scholar
  48. 48.
    Giles, H., Coupland, N.: Language: Contexts and Consequences. Brooks/Cole, Pacific Grove, CA (1991)Google Scholar
  49. 49.
    Gilly, M.C., Graham, J.L., Wolfinbarger, M.F., Yale, L.J.: A dyadic study of personal information search. Journal of the Academy of Marketing Science 26(2), 83–100 (1998)CrossRefGoogle Scholar
  50. 50.
    Grasch, P., Felfernig, A., Reinfrank, F.: Recomment: Towards critiquing-based recommendation with speech interaction. In: Proceedings of the 7th ACM Conference on Recommender Systems (RecSys), pp. 157–164 (2013)Google Scholar
  51. 51.
    Gretzel, U., Fesenmaier, D.R.: Persuasion in recommender systems. International Journal of Electronic Commerce 11(2), 81–100 (2007)CrossRefGoogle Scholar
  52. 52.
    Gruner, C.R., Lampton, W.E.: Effects of including humorous material in a persuasive sermon. Southern Speech Communication Journal 38, 188–196 (1972)CrossRefGoogle Scholar
  53. 53.
    Gundersen, D.F., Hopper, R.: Relationships between speech delivery and speech effectiveness. Communication Monographs 43, 158–165 (1976)CrossRefGoogle Scholar
  54. 54.
    Guy, I., Carmel, D.: Social recommender systems. In: Proceedings of the World-Wide-Web Conference (WWW), pp. 283–284 (2011)Google Scholar
  55. 55.
    Guy, I., Zwerdling, N., Ronen, I., Carmel, D., Uziel, E.: Social media recommendation based on people and tags. In: Proceedings of the ACM SIGIR Conference, pp. 194–201 (2010)Google Scholar
  56. 56.
    Harkins, S.G., Petty, R.E.: The multiple source effect in persuasion: The effects of distraction. Personality and Social Psychology Bulletin 4, 627–635 (1981)CrossRefGoogle Scholar
  57. 57.
    Harkins, S.G., Petty, R.E.: Information utility and the multiple source effect. Journal of Personality and Social Psychology 52, 260–268 (1987)CrossRefGoogle Scholar
  58. 58.
    Harmon, R.R., Coney, K.A.: The persuasive effects of source credibility in buy and lease situations. Journal of Marketing Research 19(2), 255–260 (1982)CrossRefGoogle Scholar
  59. 59.
    Häubl, G., Murray, K.: Preference construction and persistence in digital marketplaces: The role of electronic recommendation agents. Journal of Consumer Psychology 13(1–2), 75–91 (2003)CrossRefGoogle Scholar
  60. 60.
    Herlocker, J., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 241–250. Philadelphia, PA (2000)Google Scholar
  61. 61.
    Hess, T., J. Fuller, M.A., Mathew, J.: Involvement and decision-making performance with a decision aid: The influence of social multimedia, gender, and playfulness. Journal of Management Information Systems 22(3), 15–54 (2005)Google Scholar
  62. 62.
    Hewgill, M.A., Miller, G.R.: Source credibility and response to fear-arousing communications. Speech Monographs 32, 95–101 (1965)CrossRefGoogle Scholar
  63. 63.
    Hofling, C.K., Brotzman, E., Dalrymple, S., Graves, N., Pierce, C.M.: An experimental study of nurse-physician relationships. Journal of Nervous and Mental Disease 143, 171–180 (1966)CrossRefGoogle Scholar
  64. 64.
    Hogg, M.A., CooperShaw, L., Holzworth, D.W.: Group prototypically and depersonalized attraction in small interactive groups. Personality and Social Psychology Bulletin 19(4), 452–465 (1993)CrossRefGoogle Scholar
  65. 65.
    Holzwarth, M., Janiszewski, C., Neumann, M.M.: The influence of avatars on online cosumer shopping behavior. Journal of Marketing 70, 19–36 (2006)CrossRefGoogle Scholar
  66. 66.
    Horai, J., Naccari, N., Fatoullah, E.: The effects of expertise and physical attractiveness upon opinion agreement and liking. Sociometry 37, 601–606 (1974)CrossRefGoogle Scholar
  67. 67.
    Jackson, J.M.: Theories of group behavior, chap. Social impact theory: A social forces model of influence, pp. 111–124. Springer, New York (1987)Google Scholar
  68. 68.
    Jannach, D., Zanker, M., Ge, M., Groening, M.: Recommender systems in computer science and information systems - a landscape of research. In: Proceedings of the 13th International Conference on Electronic Commerce and Web Technologies (EC-Web), pp. 76–87 (2012)Google Scholar
  69. 69.
    Jiang, J.J., Klein, G., Vedder, R.G.: Persuasive expert systems: The influence of confidence and discrepancy. Computers in Human Behavior 16, 99–109 (2000)MATHCrossRefGoogle Scholar
  70. 70.
    Jiang, Z., Benbasat, I.: Virtual product experience: Effects of visual and functional control of products on perceived diagnosticity and flow in electronic shopping. Journal of Management Information Systems 21(3), 111–148 (2005)Google Scholar
  71. 71.
    Khooshabeh, J., McCall, P., Gratch, C., Blascovich, J., Gandhe, S.: Does it mater if a computer jokes? In: Proceedings of ACM CHI: Human Factors in Computing Systems, pp. 77–86. Vancouver, Canada (2011)Google Scholar
  72. 72.
    Kiesler, S., Sproull, L., Waters, K.: A prisoner’s dilemma experiment on cooperation with people and human-like computers. Journal of Personality and Social Psychology 70(1), 47–65 (1996)CrossRefGoogle Scholar
  73. 73.
    Kim, B.D., Kim, S.O.: A new recommender system to combine content-based and collaborative filtering systems. Journal of Database Marketing 8(3), 244–252 (2001)CrossRefGoogle Scholar
  74. 74.
    Koda, T.: Agents with faces: A study on the effects of personification of software agents. Master’s thesis, Massachusetts Institute of Technology, Boston, MA, USA (1996)Google Scholar
  75. 75.
    Komiak, S.X., Benbasat, I.: Understanding customer trust in agent-mediated electronic commerce, web-mediated electronic commerce and traditional commerce. Information Technology and Management 5(1–2), 181–207 (2004)CrossRefGoogle Scholar
  76. 76.
    Komiak, S.Y.X., Wang, W., Benbasat, I.: Trust building in virtual salespersons versus in human salespersons: Similarities and differences. e-Service Journal 3(3), 49–63 (2005)Google Scholar
  77. 77.
    Konstan, J.A., Riedl, J.: Designing Information Spaces: The Social Navigation Approach, chap. Collaborative Filtering: Supporting Social Navigation in Large, Crowded Infospaces, pp. 43–82. Springer, London (2003)Google Scholar
  78. 78.
    Lascu, D.N., Bearden, W.O., Rose, R.L.: Norm extremity and personal influence on consumer conformity. Journal of Business Research 32, 201–213 (1995)CrossRefGoogle Scholar
  79. 79.
    Latané, B.: The psychology of social impact. American Psychologist 36, 343–356 (1981)CrossRefGoogle Scholar
  80. 80.
    Lautman, M.R., Dean, K.J.: Time compression of television advertising. In: L. Percy, A.G. Woodside (eds.) Advertising and consumer psychology, pp. 219–236. Lexington Books, Lexington, Ma (1983)Google Scholar
  81. 81.
    Lazarsfeld, P., Merton., R.K.: Friendship as a social process: A substantive and methodological analysis. In: M. Berger, T. Abel, C.H. Page (eds.) Freedom and Control in Modern Society, pp. 18–66. Van Nostrand, New York (1954)Google Scholar
  82. 82.
    Levine, R.V.: Whom do we trust? experts, honesty, and likability. In: R.V. Levine (ed.) The Power of Persuasion, pp. 29–63. John Wiley & Sons, Hoboken, NJ (2003)Google Scholar
  83. 83.
    MacLachlan, J.: Listener perception of time-compressed spokespersons. Journal of Advertising Research 22(2), 47–51 (1982)Google Scholar
  84. 84.
    Maes, P., Guttman, R.H., Moukas, A.G.: Agents that buy and sell. Communications of the ACM 42(3), 81–91 (1999)CrossRefGoogle Scholar
  85. 85.
    Mayer, R.C., Davis, J.H., Schoorman, F.D.: An integrative model of organizational trust. Academy of Management Review 20, 709–734 (1995)Google Scholar
  86. 86.
    Mayer, R.E., Johnson, W.L., Shaw, E., Sandhu, S.: Constructing computer-based tutors that are socially sensitive: Politeness in educational software. International Journal of Human-Computer Studies 64(1), 36–42 (2006)CrossRefGoogle Scholar
  87. 87.
    McCroskey, J.C.: The effects of evidence as an inhibitor of counter-persuasion. Speech Monographs 37, 188–194 (1970)CrossRefGoogle Scholar
  88. 88.
    McCroskey, J.C., Mehrley, R.S.: The effects of disorganization and nonfluency on attitude change and source credibility. Speech Monographs 36, 13–21 (1969)CrossRefGoogle Scholar
  89. 89.
    McGinty, L., B., S.: Deep dialogue vs casual conversation in recommender systems. In: F. Ricci, B. Smyth (eds.) Proceedings of the Workshop on Personalization in eCommerce at the Second International Conference on Adaptive Hypermedia and Web-Based Systems (AH), pp. 80–89. Springer, Universidad de Malaga, Malaga, Spain (2002)Google Scholar
  90. 90.
    McGuire, W.J.: The nature of attitudes and attitude change. In: G. Lindzey, E. Aronson (eds.) Handbook of Social Psychology. Addison-Wesley, Reading, MA (1968)Google Scholar
  91. 91.
    McNee, S.M., Lam, S.K., Konstan, J.A., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: User Modeling, LNCS 2702, pp. 178–187. Springer (2003)Google Scholar
  92. 92.
    Metzger, M.J., Flanagin, A.J., Medders, R.B.: Social and heuristic approaches to credibility evaluation online. Journal of Communication 60, 413–439 (2010)CrossRefGoogle Scholar
  93. 93.
    Mills, J., Kimble, C.E.: Opinion change as a function of perceived similarity of the communicator and subjectivity of the issue. Bulletin of the psychonomic society 2, 35–36 (1973)CrossRefGoogle Scholar
  94. 94.
    Mohr, L.A., Bitner, M.J.: The role of employee effort in satisfaction with service transactions. Journal of Business Research 32(3), 239–252 (1995)CrossRefGoogle Scholar
  95. 95.
    Moon, Y.: Personalization and personality: Some effects of customizing message style based on consumer personality. Journal of Consumer Psychology 12(4), 313–326 (2002)CrossRefGoogle Scholar
  96. 96.
    Moon, Y., Nass, C.: How “real” are computer personalities? psychological responses to personality types in human-computer interaction. Communication Research 23(6), 651–674 (1996)CrossRefGoogle Scholar
  97. 97.
    Moreno, R., Mayer, R.E., Spires, H.A., Lester, J.C.: The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction 19(2), 177–213 (2001)CrossRefGoogle Scholar
  98. 98.
    Morkes, J., Kernal, H.K., Nass, C.: Effects of humor in task-oriented human-computer interaction and computer-mediated communication: A direct test of srct theory. Human-Computer Interaction 14(4), 395–435 (1999)CrossRefGoogle Scholar
  99. 99.
    Moundridou, M., Virvou, M.: Evaluation the persona effect of an interface agent in a tutoring system. Journal of Computer Assisted Learning 18(3), 253–261 (2002)CrossRefGoogle Scholar
  100. 100.
    Munn, W.C., Gruner, C.R.: “sick” jokes, speaker sex, and informative speech. Southern Speech Communication Journal 46, 411–418 (1981)CrossRefGoogle Scholar
  101. 101.
    Murano, P.: Anthropomorphic vs. non-anthropomorphic software interface feedback for online factual delivery. In: Proceedings of the Seventh International Conference on Information Visualization (2003)CrossRefGoogle Scholar
  102. 102.
    Nass, C., Brave, S.: Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship. MIT Press, Cambridge, MA (2005)Google Scholar
  103. 103.
    Nass, C., Fogg, B.J., Moon, Y.: Can computers be teammates? International Journal of Human-Computer Studies 45(6), 669–678 (1996)CrossRefGoogle Scholar
  104. 104.
    Nass, C., Isbister, K., Lee, E.J.: Truth is beauty: Researching embodied conversational agents. In: J. Cassell, J. Sullivan, S. Prevost, E. Churchill (eds.) Embodied conversational agents, pp. 374–402. MIT Pres, Cambridge, MA (2000)Google Scholar
  105. 105.
    Nass, C., Moon, Y.: Machines and mindlessness: Social responses to computers. Journal of Social Issues 56(1), 81–103 (2000)CrossRefGoogle Scholar
  106. 106.
    Nass, C., Moon, Y., Carney, P.: Are respondents polite to computers? social desirability and direct responses to computers. Journal of Applied Social Psychology 29(5), 1093–1110 (1999)CrossRefGoogle Scholar
  107. 107.
    Nass, C., Moon, Y., Green, N.: Are computers gender-neutral? gender stereotypic responses to computers. Journal of Applied Social Psychology 27(10), 864–876 (1997)CrossRefGoogle Scholar
  108. 108.
    Nguyen, H., Masthoff, J., P., E.: Persuasive effects of embodied conversational agent teams. In: Proceedings of 12th International Conference on Human-Computer Interaction, pp. 176–185. Springer-Verlag, Berlin, Beijing, China (2007)Google Scholar
  109. 109.
    Nowak, K.: The influence of anthropomorphism and agency on social judgment in virtual environments. Journal of Computer-Mediated Communication 9(2) (2004)Google Scholar
  110. 110.
    Nowak, K., Rauh, C.: The influence of the avatar on online perceptions of anthropomorphism, androgyny, credibility, homophily, and attraction. Journal of Computer-Mediated Communication 11(1) (2005)Google Scholar
  111. 111.
    Nowak, K.L., Biocca, F.: The effect of the agency and anthropomorphism on user’s sense of telepresence, copresence, and social presence in virtual environments. Presence: Teleperators and Virtual Environments 12(5), 481–494 (2003)CrossRefGoogle Scholar
  112. 112.
    O’Keefe, D.J.: Justification explicitness and persuasive effect: A meta-analytic review of the effects of varying support articulation in persuasive messages. Argumentation and advocacy 35, 61–75 (1998)Google Scholar
  113. 113.
    O’Keefe, D.J.: Persuasion: Theory & Research. Sage Publications, Thousand Oaks, CA (2002)Google Scholar
  114. 114.
    P., B., A., S.M.: I thought it was terrible and everyone else loved it - a new perspective for effective recommender system design. In: Proceedings of the 19th British HCI Group Annual Conference, pp. 251–261. Napier University, Edinburgh, UK (2005)Google Scholar
  115. 115.
    Parise, S., Kiesler, S., Sproull, L., Waters, K.: Cooperating with life-like interface agents. Computers in Human Behavior 15, 123–142 (1999)CrossRefGoogle Scholar
  116. 116.
    Pazzani, M.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13, 393–408 (1999)CrossRefGoogle Scholar
  117. 117.
    Pereira, R.E.: Optimizing human-computer interaction for the electronic commerce environment. Journal of Electronic Commerce Research 1(1), 23–44 (2000)Google Scholar
  118. 118.
    Perloff, R.M.: The Dynamics of Persuasion, 2nd edition. Lawrence Erlbaum Associates, Mahwah, NJ (2003)Google Scholar
  119. 119.
    Petty, R.E., Cacioppo, J.T.: Attitudes and Persuasion: Classic And Contemporary Approaches. William C. Brown, Dubuque, IA (1981)Google Scholar
  120. 120.
    Pittam, J.: Voice in Social Interaction: An Interdisciplinary Approach. Sage, Thousand Oaks, CA (1994)CrossRefGoogle Scholar
  121. 121.
    Pu, P., Chen, L.: Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems 20, 542–556 (2007)MathSciNetCrossRefGoogle Scholar
  122. 122.
    Qiu, L.: Designing social interaction with animated avatars and speech output for product recommendation agents in electronic commerce. Ph.D. thesis, University of British Columbia, Vancouver (2006)Google Scholar
  123. 123.
    Quintanar, L.R., Crowell, C.R., Pryor, J.B., Adamopoulos, J.: Human-computer interaction: A preliminary social psychological analysis. Behavior Research Methods & Instrumentation 14(2), 210–220 (1982)CrossRefGoogle Scholar
  124. 124.
    Reeves, B., Nass, C.: The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. CSLI, New York, NY (1996)Google Scholar
  125. 125.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proceedings of ACM Conference on Computer Supported Cooperative Work, pp. 175–186 (1994)Google Scholar
  126. 126.
    Rhoads, K.V., Cialdini, R.B.: The business of influence. In: J.P. Dillard, M. Pfau (eds.) Persuasion handbook: Developments in theory and practice, pp. 513–542. Sage, London, United Kingdom (2002)CrossRefGoogle Scholar
  127. 127.
    Rosen, D.J.: Driver education for the information super-highway: How adult learners and practitioners use the internet. Literacy Leader Fellowship Program Reports 2(2) (1998)Google Scholar
  128. 128.
    Sampson, E.E., Insko, C.A.: Cognitive consistency and performance in the autokinetic situation. Journal of Abnormal and Social Psychology 68, 184–192 (1964)CrossRefGoogle Scholar
  129. 129.
    Schafer, J.B.: Dynamiclens: A dynamic user-interface for a meta-recommendation system. In: Proceedings of the Workshop: Beyond Personalization at IUI’05. San Diego, CA (2005)Google Scholar
  130. 130.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: The Adaptive Web, LNCS 4321, pp. 291–324 (2007)Google Scholar
  131. 131.
    Schafer, J.B., Knostan, J.A., Riedl, J.: Meta-recommendation systems: User-controlled integration of diverse recommendations. In: Proceedings of the 11th international Conference on Information and Knowledge Management. McLean, VA (2002)Google Scholar
  132. 132.
    Schafer, J.B., Konstan, J.A., Riedl, J.: View through metalens: Usage patterns for a meta-recommendation system. IEE Proceedings-Software 151 (2004)Google Scholar
  133. 133.
    Schliesser, H.F.: Information transmission and ethos of a speaker using normal and defective speech. Central States Speech Journal 19, 169–174 (1968)CrossRefGoogle Scholar
  134. 134.
    Scholz-Crane, A.: Evaluating the future: A preliminary study of the process of how undergraduate students evaluate Web sources, Reference Services Review 26(3–4): 53–60, (1998)CrossRefGoogle Scholar
  135. 135.
    Self, C.S.: Credibility. In: D.W. Stacks, M.B. Salwen (eds.) An integrated approach to communication theory and research, pp. 421–441. Lawrence Erlbaum, Mahwah, NJ (1996)Google Scholar
  136. 136.
    Sénécal, S., Nantel, J.: Online influence of relevant others: A framework. In: Proceedings of the Sixth International Conference on Electronic Commerce Research (ICECR-6). Dallas, Texas (2003)Google Scholar
  137. 137.
    Sénécal, S., Nantel, J.: The influence of online product recommendations on consumers’ online choices. Journal of Retailing 80(2), 159–169 (2004)CrossRefGoogle Scholar
  138. 138.
    Shani, G., Rokach, L., Shapira, B., Hadash, S., Tangi, M.: Investigating confidence displays for top-n recommendations. Journal of the American Society for Information Science and Technology 64(12), 2548–2563 (2013)CrossRefGoogle Scholar
  139. 139.
    Shavitt, S., Brock, T.C.: Persuasion: Psychological Insights and Perspectives. Allyn and Bacon, Needham Heights, MA (1994)Google Scholar
  140. 140.
    Shimazu, H.: Expertclerk: A conversational case-based reasoning tool for salesclerk agents in e-commerce webshops. Artificial Intelligence Review 18(3–4), 223–244 (2002)CrossRefGoogle Scholar
  141. 141.
    Sinha, R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: Proceedings of the 2nd DELOS Network of Excellence Workshop on Personalization and Recommender Systems in Digital Libraries, pp. 18–20. Dublin, Ireland (2001)Google Scholar
  142. 142.
    Smith, A.G.: Testing the surf: Criteria for evaluation internet information resources. Public-Access Computer System Review 8(3), 5–23 (1997)Google Scholar
  143. 143.
    Smith, R.E., Hunt, S.D.: Attributional processers and effects in promotional situations. Journal of Consumer Research 5, 149–158 (1978)CrossRefGoogle Scholar
  144. 144.
    Snyder, M., Rothbart, M.: Communicator attractiveness and opinion change. Canadian Journal of Behavioural Science 3, 377–387 (1971)CrossRefGoogle Scholar
  145. 145.
    Sproull, L., Subramani, M., Kiesler, S., Walker, J.H., Waters, K.: When the interface is a face. Human-Computer Interaction 11(1), 97–124 (1996)CrossRefGoogle Scholar
  146. 146.
    Sutcliffe, A.G., Ennis, M., Hu, J.: Evaluating the effectiveness of visual user interfaces for information retrieval. International Journal of Human-Computer Studies 53, 741–763 (2000)MATHCrossRefGoogle Scholar
  147. 147.
    Swartz, T.A.: Relationship between source expertise and source similarity in an advertising context. Journal of Advertising 13(2), 49–55 (1984)MathSciNetCrossRefGoogle Scholar
  148. 148.
    Swearingen, K., Sinha, R.: Beyond algorithms:an hci perspective on recommender systems. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems. New Orleans, Louisiana (2001)Google Scholar
  149. 149.
    Tamborini, R., Zillmann, D.: College students’ perceptions of lecturers using humor. Perceptual and Motor Skills 52, 427–432 (1981)CrossRefGoogle Scholar
  150. 150.
    Taylor, P.M.: An experimental study of humor and ethos. Southern Speech Communication Journal 39, 359–366 (1974)CrossRefGoogle Scholar
  151. 151.
    Tintarev, N., Masthoff, J.: Effective explanations of recommendations: User-centered design. In: Proceedings of the ACM Conference on Recommender Systems (RecSys), pp. 153–156. Minneapolis, USA (2007)Google Scholar
  152. 152.
    Tzeng, J.Y.: Toward a more civilized design: Studying the effects of computers that apologize. International Journal of Human-Computer Studies 61(3), 319–345 (2004)CrossRefGoogle Scholar
  153. 153.
    Wang, W.: Design of trustworthy online recommendation agents: Explanation facilities and decision strategy support. Ph.D. thesis, University of British Columbia, Vancouver (2005)Google Scholar
  154. 154.
    Wang, W., Benbasat, I.: Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems 6(3), 72–101 (2005)Google Scholar
  155. 155.
    Wang, W., Benbasat, I.: Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems 23(4), 217–246 (2007)CrossRefGoogle Scholar
  156. 156.
    Wang, Y.D., Emurian, H.H.: An overview of online trust: Concepts, elements and implications. Computers in Human Behavior 21(1), 105–125 (2005)CrossRefGoogle Scholar
  157. 157.
    West, P.M., Ariely, D., Bellman, S., Bradlow, E., Huber, J., Johnson, E., Kahn, B., Little, J., Schkade, D.: Agents to the rescue? Marketing Letters 10(3), 285–300 (1999)CrossRefGoogle Scholar
  158. 158.
    Wolf, S., Bugaj, A.M.: The social impact of courtroom witnesses. Social Behaviour 5(1), 1–13 (1990)Google Scholar
  159. 159.
    Xiao, B., Benbasat, I.: E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly 31(1), 137–209 (2007)Google Scholar
  160. 160.
    Xiao, B., Benbasat, I.: Handbook of Strategic e-Business Management, chap. Research on the Use, Characteristics, and Impact of e-Commerce Product Recommendation Agents: A Review and Update for 2007–2012, pp. 403–431. Springer, Berlin Heidelberg (2014)Google Scholar
  161. 161.
    Yoo, K.H.: Creating more credible and likable recommender systems. Ph.D. thesis, Texas A&M University, College Station, USA (2010)Google Scholar
  162. 162.
    Yoo, K.H., Gretzel, U.: The influence of perceived credibility on preferences for recommender systems as sources of advice. Information Technology & Tourism 10(2), 133–146 (2008)CrossRefGoogle Scholar
  163. 163.
    Yoo, K.H., Gretzel, U.: The influence of virtual representatives on recommender system evaluation. In: Proceedings of the 15th Americas Conference on Information Systems. San Francisco, California (2009)Google Scholar
  164. 164.
    Yoon, S.N., Lee, Z.: The impact of the web-based product recommendation systems from previous buyers on consumers’ purchasing behavior. In: 10th Americas Conference on Information Systems. New York, New York (2004)Google Scholar
  165. 165.
    Zanker, M., Bricman, M., Gordea, S., Jannach, D., Jessenitschnig, M.: Persuasive online-selling in quality and taste domains. In: E-Commerce and Web Technologies, pp. 51–60. Springer (2006)Google Scholar
  166. 166.
    Zanker, M., Jessenitschnig, M.: Case-studies on exploiting explicit customer requirements in recommender systems. User Modeling and User-Adapted Interaction 19(1–2), 133–166 (2009)CrossRefGoogle Scholar
  167. 167.
    Zhou, X., Xu, Y., Li, Y., Josang, A., Cox, C.: The state-of-the-art in personalized recommender systems for social networking. Artificial Intelligence Review 37, 119–132 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.William Paterson UniversityWayneUSA
  2. 2.University of QueenslandBrisbaneAustralia
  3. 3.Alpen-Adria-Universitaet KlagenfurtUniversitaetsstrasse 65KlagenfurtAustria

Personalised recommendations