Negativity bias in the diagnosticity of online review content: the effects of consumers’ prior experience and need for cognition

References

• Aggarwal, R., Gopal, R., Gupta, A., & Singh, H. (2012). Putting money where the mouths are: the relation between venture financing and electronic word-of-mouth. Information Systems Research, 23(3–part–2), 976–992. https://doi.org/10.1287/isre.1110.0402
   Web of Science ®Google Scholar
• Ahluwalia, R. (2002). How prevalent is the negativity effect in consumer environments?. Journal of Consumer Research, 29(2), 270–279. https://doi.org/10.1086/341576
   Web of Science ®Google Scholar
• Ajzen, I., & Fishbein, M. (1980). understanding attitudes and predicting social behavior. Prentice-Hall Englewood Cliffs .
   Google Scholar
• Bateson, G. (1973). Steps to an ecology of mind. London: Granada Publishing.
   Google Scholar
• Boucher, J., & Osgood, C. E. (1969). The pollyanna hypothesis. Journal of Verbal Learning and Verbal Behavior, 8(1), 1–8. https://doi.org/10.1016/S0022-5371(69)80002-2
   Google Scholar
• Boyer, K. K., & Hult, G. T. M. (2006). Customer behavioral intentions for online purchases: An examination of fulfillment method and customer experience level. Journal of Operations Management, 24(2), 124–147. https://doi.org/10.1016/j.jom.2005.04.002
   Web of Science ®Google Scholar
• Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition. Psychological Bulletin, 119(2), 197–253. https://doi.org/10.1037/0033-2909.119.2.197
   Web of Science ®Google Scholar
• Casado-Díaz, A. B., Andreu, L., Beckmann, S. C., & Miller, C. (2020). Negative online reviews and webcare strategies in social media: Effects on hotel attitude and booking intentions. Current Issues in Tourism, 23(4), 418–422. https://doi.org/10.1080/13683500.2018.1546675
   Web of Science ®Google Scholar
• Chen, Z., & Lurie, N. H. (2013). Temporal contiguity and negativity bias in the impact of online word of mouth. Journal of Marketing Research, 50(4), 463–476. https://doi.org/10.1509/jmr.12.0063
   Web of Science ®Google Scholar
• Chevalier, J. A., Dover, Y., & Mayzlin, D. (2018). Channels of impact: User reviews when quality is dynamic and managers respond. Marketing Science, 37(5), 688–709. https://doi.org/10.1287/mksc.2018.1090
   Web of Science ®Google Scholar
• Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: online book reviews. Journal of Marketing Research, 43(3), 345–354. https://doi.org/10.1509/jmkr.43.3.345
   Web of Science ®Google Scholar
• Chung, S., Animesh, A., Han, K., & Pinsonneault, A. (2020). Financial returns to firms’ communication actions on firm-initiated social media: evidence from facebook business pages. Information Systems Research, 31(1), 258–285. https://doi.org/10.1287/isre.2019.0884
   Web of Science ®Google Scholar
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (Third ed.). Erlbaum.
   Google Scholar
• Cumming, G. (2009). Inference by eye: Reading the overlap of independent confidence intervals. Statistics in Medicine, 28(2), 205–220. https://doi.org/10.1002/sim.3471
   PubMed Web of Science ®Google Scholar
• Curchod, C., Patriotta, G., Cohen, L., & Neysen, N. (2020). Working for an algorithm: power asymmetries and agency in online work settings. Administrative Science Quarterly, 65(3), 644–676. https://doi.org/10.1177/0001839219867024
   Web of Science ®Google Scholar
• D’Innocenzio, A. (2022). Fashion retailer pays $4.2M in settlement with FTC. The Associated Press. Accessed on January 26, 2022. https://apnews.com/article/technology-business-lifestyle-federal-trade-commission-75f313a380b020618ce633a90ac55d1f
   Google Scholar
• De Jong, P. F. (1999). Hierarchical regression analysis in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 6(2), 198–211. https://doi.org/10.1080/10705519909540128
   Web of Science ®Google Scholar
• Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter? – an empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016. https://doi.org/10.1016/j.dss.2008.04.001
   Web of Science ®Google Scholar
• Feldman, J. M. (1999). Four questions about human social behavior: the social cognitive approach to culture and psychology. In J. Adamopoulos & Y. Kashima (Eds.), Social psychology and cultural context (pp. 43–62). SAGE Publications, Inc. https://doi.org/10.4135/9781452220550.n4
   Google Scholar
• Forgas, J. P. (2007). When sad is better than happy: Negative affect can improve the quality and effectiveness of persuasive messages and social influence strategies. Journal of Experimental Social Psychology, 43(4), 513–528. https://doi.org/10.1016/j.jesp.2006.05.006
   Web of Science ®Google Scholar
• Gupta, P., & Harris, J. (2010). How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective. Journal of Business Research, 63(9–10), 1041–1049. https://doi.org/10.1016/j.jbusres.2009.01.015
   Web of Science ®Google Scholar
• Helson, H. (1964). Adaption-level theory: An experimental and systematic approach to behavior. Harper & Row.
   Google Scholar
• Herhausen, D., Ludwig, S., Grewal, D., Wulf, J., & Schoegel, M. (2019). Detecting, preventing, and mitigating online firestorms in brand communities. Journal of Marketing, 83(3), 1–21. https://doi.org/10.1177/0022242918822300
   Web of Science ®Google Scholar
• Ito, T., Larsen, J., Smith, N., & Cacioppo, J. (1998). Negative information weighs more heavily on the brain: The negativity bias in evaluative categorizations. Journal of Personality and Social Psychology, 75(4), 887–900. https://doi.org/10.1037/0022-3514.75.4.887
   PubMed Web of Science ®Google Scholar
• Kennedy, G. E., Judd, T. S., Churchward, A., Gray, K., & Krause, K. (2008). First year students’ experiences with technology: Are they really digital natives. Australasian Journal of Educational Technology, 24(1), 108–122. https://doi.org/10.14742/ajet.1233
   Web of Science ®Google Scholar
• Ketelaar, P. E., Willemsen, L. M., Sleven, L., & Kerkhof, P. (2015). The good, the bad, and the expert: how consumer expertise affects review valence effects on purchase intentions in online product reviews. Journal of Computer-Mediated Communication, 20(6), 649–666. https://doi.org/10.1111/jcc4.12139
   Web of Science ®Google Scholar
• Kim, J., & Gupta, P. (2012). Emotional expressions in online user reviews: How they influence consumers’ product evaluations. Journal of Business Research, 65(7), 985–992. https://doi.org/10.1016/j.jbusres.2011.04.013
   Web of Science ®Google Scholar
• Lane, J. N., Teplitskiy, M., Gray, G., Ranu, H., Menietti, M., Guinan, E., & Lakhani, K. R. (2021). Conservatism gets funded? a field experiment on the role of negative information in novel project evaluation. Management Science, Forthcoming, 1–8. https://doi.org/10.1287/mnsc.2021.4107
   Google Scholar
• Lee, A. S. (2010). Retrospect and prospect: information systems research in the last and next 25 years. Journal of Information Technology, 25(4), 336–348. https://doi.org/10.1057/jit.2010.24
   Web of Science ®Google Scholar
• Leidner, D. E. (2020). What’s in a contribution? Journal of the Association for Information Systems, 21(1), 238–245. https://doi.org/10.17705/1jais.00598
   Web of Science ®Google Scholar
• Levin, I. P., Schneider, S. L., & Gaeth, G. J. (1998). All frames are not created equal: a typology and critical analysis of framing effects. Organizational Behavior and Human Decision Processes, 76(2), 149–188. https://doi.org/10.1006/obhd.1998.2804
   PubMed Web of Science ®Google Scholar
• Lim, J., & Lee, Y. (2019). Does a mixed online consumer review lead to neutral or ambivalent evaluation? Journal of Marketing Management, 7(1), 83–93. http://jmm-net.com/journals/jmm/Vol_7_No_1_June_2019/7.pdf.
   Google Scholar
• McKinney, J., E, H., Yoos, I. I., & C, J. (2010). Information about information: a taxonomy of views. MIS Quarterly, 34(2), 329–344. https://doi.org/10.2307/20721430
   Web of Science ®Google Scholar
• Misra, R., & McKean, M. (2000). College students’ academic stress and its relation to their anxiety, time management, and leisure satisfaction. American Journal of Health Studies, 16(1), 41–51. https://web.s.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=2&sid=f432b719-03e4-470b-9d5d-4dc1bf574d19%40redis.
   Google Scholar
• Montazemi, A. R., & Qahri-Saremi, H. (2014). The effectiveness of electronic word of mouth on consumers’ perceptions of adopting products/services – a literature review. 10th international conference on web information systems and technologies (webist 2014), Barcelona, Spain. doi:10.5220/0004850603240331.
   Google Scholar
• Montazemi, A. R., & Qahri-Saremi, H. (2015). Factors Affecting Adoption of Online Banking: A Meta-Analytic Structural Equation Modeling Study. Information & Management, 52(2), 210–226. https://doi.org/10.1016/j.im.2014.11.002
   Web of Science ®Google Scholar
• Murphy, R. (2020). Local consumer review survey 2020. https://www.brightlocal.com/research/local-consumer-review-survey-2020/.
   Google Scholar
• Nazlan, N. H., Tanford, S., & Montgomery, R. (2018). The effect of availability heuristics in online consumer reviews. Journal of Consumer Behaviour, 17(5), 449–460. https://doi.org/10.1002/cb.1731
   Web of Science ®Google Scholar
• Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115–143. https://doi.org/10.2307/25148720
   Web of Science ®Google Scholar
• Peeters, G., & Czapinski, J. (1990). Positive-negative asymmetry in evaluations: the distinction between affective and informational negativity effects. European Review of Social Psychology, 1(1), 33–60. https://doi.org/10.1080/14792779108401856
   Google Scholar
• Peracchio, L. A., & Tybout, A. M. (1996). The moderating role of prior knowledge in schema-based product evaluation. Journal of Consumer Research, 23(3), 177–192. https://doi.org/10.1086/209476
   Web of Science ®Google Scholar
• Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19(1), 123–205. https://doi.org/10.1016/S0065-2601(08)60214-2.
   Google Scholar
• Petty, R. E., & Wegener, D. T. (1998). Attitude change: Multiple roles for persuasion variables. In D. Gilbert, S. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (Vol. 1, 4th ed., pp. 323–390). McGraw-Hill.
   Google Scholar
• Pfeffer, J., Zorbach, T., & Carley, K. (2014). Understanding online firestorms: Negative word-of-mouth dynamics in social media networks. Journal of Marketing Communications, 20(1–2), 117–128. https://doi.org/10.1080/13527266.2013.797778
   Google Scholar
• Qahri-Saremi, H., & Montazemi, A. (2016). On the Effectiveness of eWOM Communications: the Moderating Effect of Consumers' Prior Experience. Americas' Conference on Information Systems, San Diego, CA, The Association for Information Systems
   Google Scholar
• Qahri-Saremi, H., & Montazemi, A. R. (2019). Factors affecting the adoption of an electronic word of mouth message: A meta-analysis. Journal of Management Information Systems, 36(3), 969–1001. https://doi.org/10.1080/07421222.2019.1628936
   Web of Science ®Google Scholar
• Qahri-Saremi, H., & Montazemi, A. R. (2020). Negativity Bias in the Effects of eWoM Reviews: An Elaboration Likelihood Perspective in Online Service Adoption Context. European Conference on Information Systems, A Virtual AIS Conference; The Association for Information Systems.
   Google Scholar
• Ren, J., & Nickerson, J. V. (2019). Arousal, valence, and volume: How the influence of online review characteristics differs with respect to utilitarian and hedonic products. European Journal of Information Systems, 28(3), 272–290. https://doi.org/10.1080/0960085X.2018.1524419
   Web of Science ®Google Scholar
• Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5(4), 296–320. https://doi.org/10.1207/S15327957PSPR0504_2
   Web of Science ®Google Scholar
• Skowronski, J. J., & Carlston, D. E. (1987). Social judgment and social memory: The role of cue diagnosticity in negativity, positivity, and extremity biases. Journal of Personality and Social Psychology, 52(4), 689–699. https://doi.org/10.1037/0022-3514.52.4.689
   Web of Science ®Google Scholar
• Skowronski, J. J., & Carlston, D. E. (1989). Negativity and extremity biases in impression formation: A review of explanations. Psychological Bulletin, 105(1), 131–142. https://doi.org/10.1037/0033-2909.105.1.131
   Web of Science ®Google Scholar
• Srivastava, K., & Sharma, N. K. (2012). Consumer attitude towards brand-extension incongruity: The moderating role of need for cognition and need for change. Journal of Marketing Management, 28(5–6), 652–675. https://doi.org/10.1080/0267257X.2011.558383
   Google Scholar
• Sujan, M. (1985). Consumer knowledge: Effects on evaluation strategies mediating consumer judgments. Journal of Consumer Research, 12(1), 31–46. https://doi.org/10.1086/209033
   Web of Science ®Google Scholar
• Sundaram, D., & Webster, C. (1999). The role of brand familiarity on the impact of word-of-mouth communication on brand evaluations. Advances in Consumer Research, 26(1), 664–670. https://www.acrwebsite.org/volumes/8342/volumes/v26/NA-26/full.
   Google Scholar
• Taylor, S. E. (1991). Asymmetrical effects of positive and negative events: the mobilization-minimization hypothesis. Psychological Bulletin, 110(1), 67. https://doi.org/10.1037/0033-2909.110.1.67
   PubMed Web of Science ®Google Scholar
• Thomas, J. B., Peters, C. O., Howell, E. G., & Robbins, K. (2012). Social media and negative word of mouth: strategies for handing unexpecting comments. Atlantic Marketing Journal, 1(2), 87–108. https://digitalcommons.kennesaw.edu/amj/vol1/iss2/7.
   Google Scholar
• Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science, 31(2), 198–215. https://doi.org/10.1287/mksc.1110.0682
   Web of Science ®Google Scholar
• Wei, W., Miao, L., & Huang, Z. (2013). Customer engagement behaviors and hotel responses. International Journal of Hospitality Management, 33(June 2013), 316–330. https://doi.org/10.1016/j.ijhm.2012.10.002
   Google Scholar
• Wood, S. L., & Swait, J. (2002). Psychological indicators of innovation adoption: Cross-classification based need for cognition and need for change. Journal of Consumer Psychology, 12(1), 1–13. https://doi.org/10.1207/S15327663JCP1201_01
   Web of Science ®Google Scholar
• Wu, P. F. (2013). In search of negativity bias: An empirical study of perceived helpfulness of online reviews. Psychology & Marketing, 30(11), 971–984. https://doi.org/10.1002/mar.20660
   Web of Science ®Google Scholar
• Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209. https://doi.org/10.2307/25148784
   Web of Science ®Google Scholar
• Yin, D., Bond, S., & Zhang, H. (2014). Anxious or angry? effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2), 539–560. https://doi.org/10.25300/MISQ/2014/38.2.10
   Web of Science ®Google Scholar
• Yin, D., Mitra, S., & Zhang, H. (2012). Mechanisms of negativity bias: an empirical exploration of app reviews in apple’s app store. Thirty third international conference on information systems, Orlando, FL, USA, The Association for Information Systems (AIS). https://aisel.aisnet.org/icis2012/proceedings/HumanComputerInteractions/5/.
   Google Scholar
• Yu, I. Y., Wan, L. C., & Yi, X. S. (2021). Managerial response to negative online reviews in the service industry: a tactic-based and culture-based model. Journal of Global Marketing, 34(3), 238–248. https://doi.org/10.1080/08911762.2021.1888393
   Google Scholar