The role of cognitive complexity and risk aversion in online herd behavior

Abstract

This paper investigated the role of information related, social and customer characteristics in public information adoption tendencies of online customers to result in herding in e-commerce. E-commerce platforms contains numerous online reviews about products which have the potential to influence customers. We applied structural equation modeling and a 2 × 2 scenario experiment to empirically verify the effect of a few factors in creating online herding. Two levels of cognitive complexity (simple, complex) and risk aversion (risk averse, risk taker) formed the 2 × 2 factorial design. The study's primary finding was that a person with simple cognitive structure and risk avoidance tendency may exhibit higher intention to adopt public information and engage in herding. Information specific attributes contributed maximum towards information adoption and herding. Among sociological variables, only reputation concern significantly predicted both information adoption and herding. Theoretically, the study offered a framework to explore herding intentions online and augmented the observations from the information adoption model. The quality of concise information from credible sources significantly instigates adoption of public information contained in online reviews. From the perspective of marketers, having a better understanding of herding behaviors and its mechanisms can enable the e-commerce platform to reduce herding’s erosion on the wisdom of the crowd by optimizing its information structures (i.e., public information, private information, etc.).

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. 1.

    Changchit, C., & Chuchuen, C. (2018). Cloud computing: An examination of factors impacting users’ adoption. Journal of Computer Information Systems, 58(1), 1–9.

    Article  Google Scholar 

  2. 2.

    Beyari, H., & Ghouth, A. (2018). Customer experience in social commerce websites: Toward an integrated conceptual framework. Journal of Management Research, 10(3), 52–62.

    Article  Google Scholar 

  3. 3.

    Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information systems research, 13(2), 205–223.

    Article  Google Scholar 

  4. 4.

    Dennis, C., Merrilees, B., Jayawardhena, C., & Wright, L. T. (2009). E-consumer behavior. European Journal of Marketing, 43(9), 1121–1139.

    Article  Google Scholar 

  5. 5.

    Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change1. The Journal of Psychology, 91(1), 93–114.

    Article  Google Scholar 

  6. 6.

    Bikhchandani, S., & Sharma, S. (2000). Herd behavior in financial markets. IMF Staff Papers, 47(3), 279–310.

    Google Scholar 

  7. 7.

    Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3), 797–817.

    Article  Google Scholar 

  8. 8.

    Raafat, R. M., Chater, N., & Frith, C. (2009). Herding in humans. Trends in Cognitive Sciences, 13(10), 420–428.

    Article  Google Scholar 

  9. 9.

    Baerenklau, K. A. (2005). Toward an understanding of technology adoption: Risk, learning, and neighborhood effects. Land Economics, 81(1), 1–19.

    Article  Google Scholar 

  10. 10.

    Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359–373.

    Article  Google Scholar 

  11. 11.

    Muth, J. F. (1961). Rational **expectations and the theory of price movements. Econometrica: Journal of the Econometric Society, 29(3), 315–335.

    Article  Google Scholar 

  12. 12.

    Devenow, A., & Welch, I. (1996). Rational herding in financial economics. European Economic Review, 40(3–5), 603–615.

    Article  Google Scholar 

  13. 13.

    Graham, J. R. (1999). Herding among investment newsletters: Theory and evidence. The Journal of Finance, 54(1), 237–268.

    Article  Google Scholar 

  14. 14.

    Hott, C. (2009). Herding behavior in asset markets. Journal of Financial Stability, 5(1), 35–56.

    Article  Google Scholar 

  15. 15.

    Blasco, N., Corredor, P., & Ferrer, E. (2018). Analysts herding: When does sentiment matter? Applied Economics, 50(51), 5495–5509.

    Article  Google Scholar 

  16. 16.

    Metzger, M. J. (2007). Making sense of credibility on the Web: Models for evaluating online information and recommendations for future research. Journal of the American Society for Information Science and Technology, 58(13), 2078–2091.

    Article  Google Scholar 

  17. 17.

    Adams, S. A. (2010). Revisiting the online health information reliability debate in the wake of “web 2.0”: An inter-disciplinary literature and website review. International Journal of Medical Informatics, 79(6), 391–400.

    Article  Google Scholar 

  18. 18.

    Ha, S. H., Bae, S. Y., & Son, L. K. (2015). Impact of online consumer reviews on product sales: Quantitative analysis of the source effect. Applied Mathematics and Information Sciences, 9(2L), 373–387.

    Google Scholar 

  19. 19.

    Bettencourt, L. M. (2009). The rules of information aggregation and emergence of collective intelligent behavior. Topics in Cognitive Science, 1(4), 598–620.

    Article  Google Scholar 

  20. 20.

    Morris, S., & Shin, H. S. (2002). Social value of public information. American Economic Review, 92(5), 1521–1534.

    Article  Google Scholar 

  21. 21.

    Chen, Q., & Jiang, W. (2005). Analysts’ weighting of private and public information. The Review of financial studies, 19(1), 319–355.

    Article  Google Scholar 

  22. 22.

    Lorrain, F., & White, H. C. (1971). Structural equivalence of individuals in social networks. The Journal of Mathematical Sociology, 1(1), 49–80.

    Article  Google Scholar 

  23. 23.

    Corazzini, L., & Greiner, B. (2007). Herding, social preferences and (non-) conformity. Economics Letters, 97(1), 74–80.

    Article  Google Scholar 

  24. 24.

    Nelissen, R. M., & Meijers, M. H. (2011). Social benefits of luxury brands as costly signals of wealth and status. Evolution and Human Behavior, 32(5), 343–355.

    Article  Google Scholar 

  25. 25.

    Anderson, W. T., Jr., & Cunningham, W. H. (1972). The socially conscious consumer. The Journal of Marketing, 36, 23–31.

    Article  Google Scholar 

  26. 26.

    Piazza, J., & Bering, J. M. (2008). Concerns about reputation via gossip promote generous allocations in an economic game. Evolution and Human Behavior, 29(3), 172–178.

    Article  Google Scholar 

  27. 27.

    Baddeley, M., Pillas, D., Christopoulos, Y., Schultz, W., & Tobler, P. (2007). Herding and social pressure in trading tasks: A behavioral analysis. https://doi.org/10.17863/CAM.5145

  28. 28.

    Van Hiel, A., & Mervielde, I. (2003). The measurement of cognitive complexity and its relationship with political extremism. Political Psychology, 24(4), 781–801.

    Article  Google Scholar 

  29. 29.

    Shi, W., & Zantow, K. (2010). Why use internet banking? An irrational imitation model. International Journal of Banking, Accounting and Finance, 2(2), 156–175.

    Article  Google Scholar 

  30. 30.

    Teng, S., Khong, K. W., & Goh, W. W. (2014). Conceptualizing persuasive messages using ELM in social media. Journal of Internet Commerce, 13(1), 65–87.

    Article  Google Scholar 

  31. 31.

    Simon, H. A. (1972). Theories of bounded rationality. Decision and Organization, 1(1), 161–176.

    Google Scholar 

  32. 32.

    Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51(3), 629–636.

    Article  Google Scholar 

  33. 33.

    Hanson, W. A., & Putler, D. S. (1996). Hits and misses: Herd behavior and online product popularity. Marketing Letters, 7, 297–305.

    Article  Google Scholar 

  34. 34.

    Chiou, J. S., & Cheng, C. (2003). Should a company have message boards on its web sites? Journal of Interactive Marketing, 17(3), 50–61.

    Article  Google Scholar 

  35. 35.

    Huang, J. H., & Chen, Y. F. (2006). Herding in online product choice. Psychology & Marketing, 23(5), 413–428.

    Article  Google Scholar 

  36. 36.

    Weiner, B. (2000). Attributional thoughts about consumer behavior. Journal of Consumer research, 27(3), 382–387.

    Article  Google Scholar 

  37. 37.

    Shen, X. L., Zhang, K. Z., & Zhao, S. J. (2016). Herd behavior in consumers’ adoption of online reviews. Journal of the Association for Information Science and Technology, 67(11), 2754–2765.

    Article  Google Scholar 

  38. 38.

    Chamley, C. (2004). Rational herds. Cambridge University Press, Cambridge, 3(8), 20.

    Google Scholar 

  39. 39.

    Luo, B., & Lin, Z. (2013). A decision tree model for herd behavior and empirical evidence from the online P2P lending market. Information Systems and e-Business Management, 11(1), 141–160.

    Article  Google Scholar 

  40. 40.

    Sussman, S. W., & Siegal, W. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65.

    Article  Google Scholar 

  41. 41.

    Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In Communication and persuasion (pp. 1–24). New York, NY: Springer

  42. 42.

    Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752.

    Article  Google Scholar 

  43. 43.

    Hoffer, E. (1955). The passionate state of mind. New York: Harper.

    Google Scholar 

  44. 44.

    Baddeley, M. (2010). Herding, social influence and economic decision-making: Socio-psychological and neuroscientific analyses. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365(1538), 281–290.

    Article  Google Scholar 

  45. 45.

    Pech, W., & Milan, M. (2009). Behavioral economics and the economics of Keynes. The Journal of Socio-Economics, 38(6), 891–902.

    Article  Google Scholar 

  46. 46.

    Dholakia, U. M., & Soltysinski, K. (2001). Coveted or overlooked? The psychology of bidding for comparable listings in digital auctions. Marketing Letters, 12(3), 225–237.

    Article  Google Scholar 

  47. 47.

    Ouarda, M., El Bouri, A., & Bernard, O. (2012). Herding behavior under markets condition: Empirical evidence on the European financial markets. International Journal of Economics and Financial Issues, 3(1), 214–228.

    Google Scholar 

  48. 48.

    Hoitash, R., & Krishnan, M. M. (2008). Herding, momentum and investor over-reaction. Review of Quantitative Finance and Accounting, 30(1), 25–47.

    Article  Google Scholar 

  49. 49.

    Kumar, M. (2007). A journey into the bleeding city: Following the footprints of the rubble of riot and violence of earthquake in Gujarat, India. Psychology and Developing Societies, 19(1), 1–36.

    Article  Google Scholar 

  50. 50.

    Hahn, V. (2011). Sequential aggregation of verifiable information. Journal of Public Economics, 95(11–12), 1447–1454.

    Article  Google Scholar 

  51. 51.

    Dholakia, U. M., Basuroy, S., & Soltysinski, K. (2002). Auction or agent (or both)? A study of moderators of the herding bias in digital auctions. International Journal of Research in Marketing, 19(2), 115–130.

    Article  Google Scholar 

  52. 52.

    Ding, A. W., & Li, S. (2019). Herding in the consumption and purchase of digital goods and moderators of the herding bias. Journal of the Academy of Marketing Science, 47(3), 460–478.

    Article  Google Scholar 

  53. 53.

    Stafford, M. R., Kilburn, A. J., & Stern, B. B. (2006). The effects of reserve prices on bidding behavior in online auctions. International Journal of Internet Marketing and Advertising, 3(3), 240–253.

    Article  Google Scholar 

  54. 54.

    Duan, W., Gu, B., & Whinston, A. B. (2009). Informational cascades vs. network externalities: An empirical investigation of herding on software downloading. MIS Quarterly, 33(1), 23–48.

    Article  Google Scholar 

  55. 55.

    Chen, Y. F. (2008). Herd behavior in purchasing books online. Computers in Human Behavior, 24(5), 1977–1992.

    Article  Google Scholar 

  56. 56.

    Langley, D. J., Hoeve, M. C., Ortt, J. R., Pals, N., & van der Vecht, B. (2014). Patterns of herding and their occurrence in an online setting. Journal of Interactive Marketing, 28(1), 16–25.

    Article  Google Scholar 

  57. 57.

    Berkovich, E. (2011). Search and herding effects in peer-to-peer lending: Evidence from prosper. com. Annals of Finance, 7(3), 389–405.

    Article  Google Scholar 

  58. 58.

    Yoo, C. W., Kim, Y. J., Moon, J. H., & Choe, Y. C. (2008). The effect of herding behavior and perceived usefulness on intention to purchase e-learning content: Comparison analysis by purchase experience. Asia Pacific Journal of Information Systems, 18(4), 105–130.

    Google Scholar 

  59. 59.

    Munawar, M., Hassanein, K., & Head, M. (2017, June). Social commerce and herd behavior: An examination of the moderating roles of age and homophily. In 2017 12th Iberian conference on information systems and technologies (CISTI) (pp. 1–4). IEEE.

  60. 60.

    Sun, H. (2013). A longitudinal study of herd behavior in the adoption and continued use of technology. Mis Quarterly, 1013–1041.

  61. 61.

    Economou, F., Hassapis, C., & Philippas, N. (2018). Investors’ fear and herding in the stock market. Applied Economics, 50(34–35), 3654–3663.

    Article  Google Scholar 

  62. 62.

    Berger, S., Feldhaus, C., & Ockenfels, A. (2018). A shared identity promotes herding in an information cascade game. Journal of the Economic Science Association, 4(1), 63–72.

    Article  Google Scholar 

  63. 63.

    Alhaj-Yaseen, Y. S., & Rao, X. (2019). Does asymmetric information drive herding? An empirical analysis. Journal of Behavioral Finance, 20(4), 451–470.

    Article  Google Scholar 

  64. 64.

    Kang, I., He, X., & Shin, M. M. (2020). Chinese consumers’ herd consumption behavior related to Korean luxury cosmetics: The mediating role of fear of missing out. Frontiers in Psychology, 11, 121.

    Article  Google Scholar 

  65. 65.

    Sunder, S., Kim, K. H., & Yorkston, E. A. (2019). What drives herding behavior in online ratings? The role of rater experience, product portfolio, and diverging opinions. Journal of Marketing, 83(6), 93–112.

    Article  Google Scholar 

  66. 66.

    Wang, W., Guo, L., & Sun, R. (2019). Rational herd behavior in online learning: Insights from MOOC. Computers in Human Behavior, 92, 660–669.

    Article  Google Scholar 

  67. 67.

    Li, X., & Wu, L. (2018). Herding and social media word-of-mouth: Evidence from groupon. MIS Quarterly, 42(4), 1331–1351.

    Google Scholar 

  68. 68.

    Liu, Y., & Yang, Y. (2018). Empirical examination of users’ adoption of the sharing economy in china using an expanded technology acceptance model. Sustainability, 10(4), 1262.

    Article  Google Scholar 

  69. 69.

    Liu, Y., Feng, J., & Liao, X. (2017). When online reviews meet sales volume information: Is more or accurate information always better? Information Systems Research, 28(4), 723–743.

    Article  Google Scholar 

  70. 70.

    Tseng, S. L., Lu, S., Grover, V., & Weathers, D. (2017). The effect of herding behavior on online review voting participation. https://pdfs.semanticscholar.org/17a5/0a7c6323521a13f3c34b823e5216cf6d89ac.pdf. Accessed 12 Sept 2019.

  71. 71.

    Xu, X., Li, Q., Peng, L., Hsia, T. L., Huang, C. J., & Wu, J. H. (2017). The impact of informational incentives and social influence on consumer behavior during Alibaba’s online shopping carnival. Computers in Human Behavior, 76, 245–254.

    Article  Google Scholar 

  72. 72.

    Liu, Q., Huang, S., & Zhang, L. (2016). The influence of information cascades on online purchase behaviors of search and experience products. Electronic Commerce Research, 16(4), 553–580.

    Article  Google Scholar 

  73. 73.

    Lee, Y. J., Hosanagar, K., & Tan, Y. (2015). Do I follow my friends or the crowd? Information cascades in online movie ratings. Management Science, 61(9), 2241–2258.

    Article  Google Scholar 

  74. 74.

    Cheung, C. M., Xiao, B. S., & Liu, I. L. (2014). Do actions speak louder than voices? The signaling role of social information cues in influencing consumer purchase decisions. Decision Support Systems, 65, 50–58.

    Article  Google Scholar 

  75. 75.

    Shang, R. A., Chen, Y. C., & Chen, C. J. (2013). The social and objective value of information in virtual investment communities. Online Information Review, 37(4), 498–517.

    Article  Google Scholar 

  76. 76.

    Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026.

    Article  Google Scholar 

  77. 77.

    Tajfel, H. (1982). Social psychology of intergroup relations. Annual Review of Psychology, 33(1), 1–39.

    Article  Google Scholar 

  78. 78.

    Latané, B., & Wolf, S. (1981). The social impact of majorities and minorities. Psychological Review, 88(5), 438.

    Article  Google Scholar 

  79. 79.

    Burt, R. S., & Talmud, I. (1993). Market niche. Social Networks, 15(2), 133–149.

    Article  Google Scholar 

  80. 80.

    McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.

    Article  Google Scholar 

  81. 81.

    Jones, E. E. (1984). Social stigma: The psychology of marked relationships. WH Freeman

  82. 82.

    Rook, L. (2006). An economic psychological approach to herd behavior. Journal of Economic Issues, 40(1), 75–95.

    Article  Google Scholar 

  83. 83.

    Burnkrant, R. E., & Cousineau, A. (1975). Informational and normative social influence in buyer behavior. Journal of Consumer research, 2(3), 206–215.

    Article  Google Scholar 

  84. 84.

    Lascu, D. N., & Zinkhan, G. (1999). Consumer conformity: Review and applications for marketing theory and practice. Journal of Marketing Theory and Practice, 7(3), 1–12.

    Article  Google Scholar 

  85. 85.

    Keynes, J. M. (1937). The general theory of employment. The Quarterly Journal of Economics, 51(2), 209–223.

    Article  Google Scholar 

  86. 86.

    Scharfstein, D. S., & Stein, J. C. (1990). Herd behavior and investment. The American Economic Review, 80(3), 465–479.

    Google Scholar 

  87. 87.

    Hogg, M. A. (2000). Subjective uncertainty reduction through self-categorization: A motivational theory of social identity processes. European Review of Social Psychology, 11(1), 223–255.

    Article  Google Scholar 

  88. 88.

    Miller, H., & Bieri, J. (1965). Cognitive complexity as a function of the significance of the stimulus objects being judged. Psychological Reports, 16(3_suppl), 1203–1204.

    Article  Google Scholar 

  89. 89.

    Hendrick, H. W. (1996). Cognitive complexity, conceptual systems, and behavior. Journal of the Washington Academy of Sciences, 84(2), 53–67.

    Google Scholar 

  90. 90.

    March, J. G., & Shapira, Z. (1987). Managerial perspectives on risk and risk taking. Management Science, 33(11), 1404–1418.

    Article  Google Scholar 

  91. 91.

    Sjöberg, L. (2003). Distal factors in risk perception. Journal of Risk Research, 6(3), 187–211.

    Article  Google Scholar 

  92. 92.

    Zambrano-Cruz, R., Cuartas-Montoya, G. P., Meda-Lara, R. M., Palomera-Chávez, A., & Tamayo-Agudelo, W. (2018). Perception of risk as a mediator between personality and perception of health: Test of a model. Psychology Research and Behavior Management, 11, 417.

    Article  Google Scholar 

  93. 93.

    Moon, Y. (2000). Intimate exchanges: Using computers to elicit self-disclosure from consumers. Journal of Consumer Research, 26(4), 323–339.

    Article  Google Scholar 

  94. 94.

    Bollig, M., & Göbel, B. (1997). Risk, uncertainty and pastoralism: An introduction. Nomadic Peoples, 1(1), 5–21.

    Article  Google Scholar 

  95. 95.

    Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73.

    Article  Google Scholar 

  96. 96.

    Bieri, J. (1955). Cognitive complexity-simplicity and predictive behavior. Journal of Abnormal and Social Psychology, 51, 263–268.

    Article  Google Scholar 

  97. 97.

    Zhang, M., Xin, Z., & Lin, C. (2012). Measures of cognitive complexity and its development in Chinese adolescents. Journal of Constructivist Psychology, 25(2), 91–111.

    Article  Google Scholar 

  98. 98.

    O’keefe, D. J., & Sypher, H. E. . (1981). Cognitive complexity measures and the relationship of cognitive complexity to communication. Human Communication Research, 8(1), 72–92.

    Article  Google Scholar 

  99. 99.

    Allen, M., Mabry, E. A., Banski, M., Stoneman, M., & Carter, P. (1990). A thoughtful appraisal of measuring cognition using the role category questionnaire. Communication Reports, 3(2), 49–57.

    Article  Google Scholar 

  100. 100.

    Burleson, B. R., Applegate, J. L., & Delia, J. G. (1991). On validly assessing the validity of the role category questionnaire: A reply to Allen et al. Communication Reports, 4(2), 113–119.

    Article  Google Scholar 

  101. 101.

    Bitner, M. J. (1990). Evaluating service encounters: The effects of physical surroundings and employee responses. Journal of Marketing, 54(2), 69–82.

    Article  Google Scholar 

  102. 102.

    Gupta, S., Yun, H., Xu, H., & Kim, H. W. (2017). An exploratory study on mobile banking adoption in Indian metropolitan and urban areas: A scenario-based experiment. Information Technology for Development, 23(1), 127–152.

    Article  Google Scholar 

  103. 103.

    Kim, J. H., & Jang, S. S. (2014). A scenario-based experiment and a field study: A comparative examination for service failure and recovery. International Journal of Hospitality Management, 41, 125–132.

    Article  Google Scholar 

  104. 104.

    Cooper, D. R., & Schindler, P. S. (2011). Qualitative research. Business Research Methods, 4(1), 160–182.

    Google Scholar 

  105. 105.

    Hulland, J., Baumgartner, H., & Smith, K. M. (2018). Marketing survey research best practices: Evidence and recommendations from a review of JAMS articles. Journal of the Academy of Marketing Science, 46(1), 92–108.

    Article  Google Scholar 

  106. 106.

    Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate date analysis with readings. Englewood Cliff, NJ: Prentce.

    Google Scholar 

  107. 107.

    Fisher, R. J. (1993). Social desirability bias and the validity of indirect questioning. Journal of Consumer Research, 20(2), 303–315.

    Article  Google Scholar 

  108. 108.

    Dabholkar, P. A. (1994). Incorporating choice into an attitudinal framework: Analyzing models of mental comparison processes. Journal of Consumer Research, 21(1), 100–118.

    Article  Google Scholar 

  109. 109.

    Zinko, R., Ferris, G. R., Humphrey, S. E., Meyer, C. J., & Aime, F. (2012). Personal reputation in organizations: Two-study constructive replication and extension of antecedents and consequences. Journal of Occupational and Organizational Psychology, 85(1), 156–180.

    Article  Google Scholar 

  110. 110.

    Goldsmith, R. E., Clark, R. A., & Goldsmith, E. B. (2015). The desire for unique consumer products, innovativeness, and conformity. In Proceedings of the 2007 academy of marketing science (AMS) annual conference (pp. 206–210). Cham: Springer

  111. 111.

    Walker, G., Kogut, B., & Shan, W. (1997). Social capital, structural holes and the formation of an industry network. Organization Science, 8(2), 109–125.

    Article  Google Scholar 

  112. 112.

    Burt, R. S. (1987). Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology, 92(6), 1287–1335.

    Article  Google Scholar 

  113. 113.

    Salancik, G. R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 23(2), 224–253.

    Article  Google Scholar 

  114. 114.

    Choi, S. M., & Rifon, N. J. (2002). Antecedents and consequences of web advertising credibility: A study of consumer response to banner ads. Journal of Interactive Advertising, 3(1), 12–24.

    Article  Google Scholar 

  115. 115.

    Sun, H. (2013). A longitudinal study of herd behavior in the adoption and continued use of technology. MIS Quarterly, 37(4), 1013–1041.

    Article  Google Scholar 

  116. 116.

    Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. Thousand Oaks: Sage Publications.

    Google Scholar 

  117. 117.

    Kline, R. B. (2015). Principles and practice of structural equation modeling. New York: Guilford publications.

    Google Scholar 

  118. 118.

    Muthén, B., & Kaplan, D. (1985). A comparison of methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38(1), 171–189.

    Article  Google Scholar 

  119. 119.

    Gao, S., Mokhtarian, P. L., & Johnston, R. A. (2008). Nonnormality of data in structural equation models. Transportation Research Record, 2082(1), 116–124.

    Article  Google Scholar 

  120. 120.

    Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78–117.

    Article  Google Scholar 

  121. 121.

    Dufour, J. M., & Dagenais, M. G. (1985). Durbin–Watson tests for serial correlation in regressions with missing observations. Journal of Econometrics, 27(3), 371–381.

    Article  Google Scholar 

  122. 122.

    Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263–282.

    Article  Google Scholar 

  123. 123.

    Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.

    Article  Google Scholar 

  124. 124.

    Barnes, J., Cote, J., Cudeck, R., & Malthouse, E. (2001). Checking assumptions of normality before conducting factor analyses. Journal of Consumer Psychology, 10(1/2), 79–81.

    Google Scholar 

  125. 125.

    Bollen, K. A. (2014). Structural equations with latent variables (Vol. 210). Hoboken: Wiley.

    Google Scholar 

  126. 126.

    Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods & Research, 21(2), 205–229.

    Article  Google Scholar 

  127. 127.

    Byrne, B. M. (2010). Multivariate applications series. Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York, NY, US: Routledge/Taylor & Francis Group.

    Google Scholar 

  128. 128.

    Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388.

  129. 129.

    Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206.

    Article  Google Scholar 

  130. 130.

    Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal (AMJ), 25(1), 76–81.

    Article  Google Scholar 

  131. 131.

    Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and non-experimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422.

    Article  Google Scholar 

  132. 132.

    Friedman, M. (1988). Models of consumer choice behavior. In Handbook of economic psychology (pp. 332–357). Dordrecht: Springer

  133. 133.

    Rejikumar, G., & Asokan, A. A. (2017). Information seeking behavior causing satisfaction modification intentions—An empirical study to address emerging challenges in a service context. Journal of Indian Business Research, 9(4), 304–328.

    Article  Google Scholar 

  134. 134.

    Jacoby, J. (1984). Perspectives on information overload. Journal of Consumer Research, 10(4), 432–435.

    Article  Google Scholar 

  135. 135.

    Chan, Y. Y., & Ngai, E. W. (2011). Conceptualising electronic word of mouth activity: An input-process-output perspective. Marketing Intelligence & Planning, 29(5), 488–516.

    Article  Google Scholar 

  136. 136.

    Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825.

    Article  Google Scholar 

  137. 137.

    Grimalda, G., Pondorfer, A., & Tracer, D. P. (2016). Social image concerns promote cooperation more than altruistic punishment. Nature Communications, 7, 12288.

    Article  Google Scholar 

  138. 138.

    Lacetera, N., & Macis, M. (2010). Social image concerns and prosocial behavior: Field evidence from a nonlinear incentive scheme. Journal of Economic Behavior & Organization, 76(2), 225–237.

    Article  Google Scholar 

  139. 139.

    Tajfel, H. (1974). Social identity and intergroup behavior. Information (International Social Science Council), 13(2), 65–93.

    Article  Google Scholar 

  140. 140.

    Mazar, N., Amir, O., & Ariely, D. (2008). The dishonesty of honest people: A theory of self-concept maintenance. Journal of Marketing Research, 45(6), 633–644.

    Article  Google Scholar 

  141. 141.

    Quan-Haase, A., & Young, A. L. (2010). Uses and gratifications of social media: A comparison of Facebook and instant messaging. Bulletin of Science, Technology & Society, 30(5), 350–361.

    Article  Google Scholar 

  142. 142.

    Cheng, X., Fu, S., Sun, J., Bilgihan, A., & Okumus, F. (2019). An investigation on online reviews in sharing economy driven hospitality platforms: A viewpoint of trust. Tourism Management, 71, 366–377.

    Article  Google Scholar 

  143. 143.

    Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44(9), 1175.

    Article  Google Scholar 

  144. 144.

    Berger, J., & Wagner, D. G. (2007). Expectation states theory. The Blackwell Encyclopedia of Sociology. https://doi.org/10.1002/9781405165518.wbeose084.pub2.

  145. 145.

    Emerson, R. M. (1976). Social exchange theory. Annual Review of Sociology, 2(1), 335–362.

    Article  Google Scholar 

  146. 146.

    Ridgeway, C. L., & Erickson, K. G. (2000). Creating and spreading status beliefs. American Journal of Sociology, 106(3), 579–615.

    Article  Google Scholar 

  147. 147.

    Ridgeway, C. L. (2014). Why status matters for inequality. American Sociological Review, 79(1), 1–16.

    Article  Google Scholar 

  148. 148.

    Mattila, A. S., & Wirtz, J. (2001). Congruency of scent and music as a driver of in-store evaluations and behavior. Journal of Retailing, 77(2), 273–289.

    Article  Google Scholar 

  149. 149.

    Murray, K. B., & Schlacter, J. L. (1990). The impact of services versus goods on consumers’ assessment of perceived risk and variability. Journal of the Academy of Marketing Science, 18(1), 51–65.

    Article  Google Scholar 

  150. 150.

    Bilgihan, A., Okumus, F., Nusair, K., & Bujisic, M. (2014). Online experiences: Flow theory, measuring online customer experience in e-commerce and managerial implications for the lodging industry. Information Technology & Tourism, 14(1), 49–71.

    Article  Google Scholar 

  151. 151.

    Walsh, G., & Mitchell, V. W. (2010). The effect of consumer confusion proneness on word of mouth, trust, and customer satisfaction. European Journal of Marketing, 44(6), 838–859.

    Article  Google Scholar 

  152. 152.

    Diehl, K., & Poynor, C. (2010). Great expectations?! Assortment size, expectations, and satisfaction. Journal of Marketing Research, 47(2), 312–322.

    Article  Google Scholar 

  153. 153.

    Spassova, G., & Isen, A. M. (2013). Positive affect moderates the impact of assortment size on choice satisfaction. Journal of Retailing, 89(4), 397–408.

    Article  Google Scholar 

  154. 154.

    Yi, C., Jiang, Z., & Benbasat, I. (2015). Enticing and engaging consumers via online product presentations: The effects of restricted interaction design. Journal of Management Information Systems, 31(4), 213–242.

    Article  Google Scholar 

  155. 155.

    Kelly, G. A. (2003). A brief introduction to personal construct theory. In F. Fransella (Ed.), International handbook of personal construct psychology (pp. 3–20), Wiley.

  156. 156.

    Woznyj, H. M., Banks, G. C., Dunn, A. M., Berka, G., & Woehr, D. (2020). Re-introducing cognitive complexity: A meta-analysis and agenda for future research. Human Performance, 33(1), 1–33.

    Article  Google Scholar 

  157. 157.

    Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. Boston: The MIT Press.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to G. Rejikumar.

Ethics declarations

Conflict of interest

No conflict of interest exists.

Appendices

Appendix 1

Scenario-1 (cognitive Simple vs risk averse)

“You spend time on social media and other online platforms to gather information from reviews to make an online purchase decision. Mostly, you find reviews helpful and accept such information to make decisions without much evaluations about correctness and avoid the risk of committing mistakes by taking decisions against the majority”.

Scenario-2 (cognitive simple vs risk taking)

“You spend time on social media and other online platforms to gather information from reviews to make an online purchase decision. Mostly, you find reviews helpful and accept such information to make decisions without much evaluations about correctness but prefer to make decisions based on own judgments.

Scenario-3 (cognitive complex vs risk averse)

“You spend time on social media and other online platforms to gather information from reviews to make an online purchase decision. Mostly, you find reviews helpful but search for more private information for detailed evaluations but ultimately avoid the risk of committing mistakes by taking decisions against the majority”.

Scenario-4 (cognitive complex vs risk taking)

“You spend time on social media and other online platforms to gather information from reviews to make an online purchase decision. Mostly, you find reviews helpful but search for more private information for detailed evaluations and will prefer to make decision based on own judgments.

Appendix 2 (survey instrument)

Dear Respondent,

The scenario provided below narrates an online buying decision-making process. You may kindly visualize yourself in the scenario and cast your position on following questions on a scale varying from “strongly disagree” to “strongly agree.” (Tick in the appropriate box).

“You spend time on social media and other online platforms to gather information from reviews to make an online purchase decision. Mostly, you find reviews helpful and accept such information to make decisions without much evaluations about correctness and avoid the risk of committing mistakes by taking decisions against the majority”.

No. Statements Strongly disagree Disagree Neutral Agree Strongly agree
1 I feel online information that imparts knowledge are credible      
2 I feel online information shared out of expertise on the matter are credible      
3 I feel that to adopt online information, its contents should be trustworthy      
4 I think online Information is credible if many others share the same feeling      
5 I feel online information should be complete to consider adopting it      
6 I feel online information should meet the objective of information search      
7 I feel online information should be believable to consider adopting it      
8 I feel online information should be complete to consider adopting it      
9 Others will not respect me if I commit a mistake      
10 My colleagues will not trust me if I commit mistakes      
11 Others will not consider me an expert in quality decisions if I commit mistakes      
12 others will challenge my integrity if I commit mistakes      
13 I will be contributing to society by accepting the majority opinion      
14 I will enjoy equal social status by accepting views of majority      
15 My importance in society will increase by accepting majority views      
16 I can influence others by accepting their opinions      
17 I feel everyone will agree to my decisions if I follow majority      
18 I am flexible to adopt other’s views in my decisions      
19 If I go with the majority, chances of complaints are less      
20 I feel more confidence by accommodating other’s views      
21 I consider other’s views in my decisions      
22 I will be motivated to share information that I find useful      
23 I generally trust information if many people share it      
24 I like to use popular online reviews in my decision-making      
25 I will follow the majority in my decisions      
26 I feel that accepting views of the majority is riskless      
27 I feel that accepting views of the majority is safe      
28 I feel that accepting views of the majority is beneficial      
29 I felt the situation described in scenario as realistic      
30 I had no difficulty imagining myself in this situation described in the scenario      
31 I prefer to make decisions by trusting public information available online      
32 I prefer to avoid risk by accepting majority decision rather than going independently      

Name:

Gender:

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rejikumar, G., Asokan-Ajitha, A., Dinesh, S. et al. The role of cognitive complexity and risk aversion in online herd behavior. Electron Commer Res (2021). https://doi.org/10.1007/s10660-020-09451-y

Download citation

Keywords

  • Information adoption
  • Herding
  • Cognitive complexity
  • Risk aversion
  • Scenario-based experiment