Battle of the voices: the effects of inconsistent sentiment valence between video content and danmaku
DOI:
https://doi.org/10.47989/ir31iConf64270Keywords:
Sentiment valence, Danmaku, Gender, Social mediaAbstract
Introduction. This paper aims to examine the effects of inconsistent sentiment valence between video content and danmaku (positive video content with negative danmaku (PVND) versus negative video content with positive danmaku (NVPD)) and the gender of users (male versus female) on decision-making.
Method. An online experiment was conducted. Participants were randomly assigned to one of two experimental conditions (PVND versus NVPD).
Analysis. Three hypotheses were tested using two-way ANOVA within SPSS software.
Results. Empirical results (N = 224) show significant difference in decision-making between users exposed to PVND and NVPD, but no gender difference is found. In addition, the interaction between inconsistent sentiment valence and gender affects their decision-making.
Conclusion(s). This study enriches the literature by not only shedding light on the effects of inconsistent sentiment valence in the dynamic video-based reviews but also adding to the scholarly understanding of gender differences in users’ responses to online reviews.
References
Abdul-Ghani, E., Kim, J., Kwon, J., Hyde, K. F., & Cui, Y. (Gina). (2022). Love or like: Gender effects in emotional expression in online reviews. European Journal of Marketing, 56(12), 3592–3616. https://doi.org/10.1108/EJM-01-2021-0064
Agrawal, S. R., & Mittal, D. (2022). Optimising customer engagement content strategy in retail and E-tail: Available on online product review videos. Journal of Retailing and Consumer Services, 67, 102966. https://doi.org/10.1016/j.jretconser.2022.102966
Alexander, M. G., & Wood, W. (2000). Women, men, and positive emotions: A social role interpretation. In A. H. Fischer (Ed.), Gender and Emotion: Social Psychological Perspectives (pp. 189–210). Cambridge University Press. https://doi.org/10.1017/CBO9780511628191.010
Bessi, A., Zollo, F., Del Vicario, M., Puliga, M., Scala, A., Caldarelli, G., Uzzi, B., & Quattrociocchi, W. (2016). Users polarisation on Facebook and Youtube. PloS One, 11(8). https://doi.org/10.1371/journal.pone.0159641
Bi, N. C., Zhang, R., & Ha, L. (2018). Does valence of product review matter? The mediating role of self-effect and third-person effect in sharing YouTube word-of-mouth (vWOM). Journal of Research in Interactive Marketing, 13(1), 79–95. https://doi.org/10.1108/JRIM-04-2018-0049
Blanchette, I., & Richards, A. (2010). The influence of affect on higher level cognition: A review of research on interpretation, judgement, decision making and reasoning. Cognition and Emotion, 24(4), 561–595. https://doi.org/10.1080/02699930903132496
Blau, B. M., DeLisle, J. R., & Price, S. M. (2015). Do sophisticated investors interpret earnings conference call tone differently than investors at large? Evidence from short sales. Journal of Corporate Finance, 31, 203–219. https://doi.org/10.1016/j.jcorpfin.2015.02.003
Bolls, P., Muehling, D., & Yoon, K. (2003). The effects of television commercial pacing on viewers’ attention and memory. Journal of Marketing Communications, 9, 17–28. https://doi.org/10.1080/1352726032000068032
Byun, K., Ma, M., Kim, K., & Kang, T. (2021). Buying a New Product with Inconsistent Product Reviews from Multiple Sources: The Role of Information Diagnosticity and Advertising. Journal of Interactive Marketing, 55(1), 81–103. https://doi.org/10.1016/j.intmar.2021.01.003
Chaplin, T. M. (2015). Gender and Emotion Expression: A Developmental Contextual Perspective. Emotion Review: Journal of the International Society for Research on Emotion, 7(1), 14–21. https://doi.org/10.1177/1754073914544408
Chen, T., Samaranayake, P., Cen, X., Qi, M., & Lan, Y. (2022). The Impact of Online Reviews on Consumers’ Purchasing Decisions: Evidence from an Eye-Tracking Study. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.865702
Chen, Y., Chang, C., & Yeh, C. (2017). Emotion classification of YouTube videos. Decision Support Systems, 101(C), 40–50. https://doi.org/10.1016/j.dss.2017.05.014
Courtenay, W. H. (2000). Constructions of masculinity and their influence on men’s well-being: A theory of gender and health. Social Science & Medicine (1982), 50(10), 1385–1401. https://doi.org/10.1016/s0277-9536(99)00390-1
Crutcher, H. L. (1975). A Note on the Possible Misuse of the Kolmogorov-Smirnov Test. Journal of Applied Meteorology (1962-1982), 14(8), 1600–1603.
Darley, W. K., & Smith, R. E. (1995). Gender Differences in Information Processing Strategies: An Empirical Test of the Selectivity Model in Advertising Response. Journal of Advertising, 24(1), 41–56.
Dong, L., Hua, Z., Huang, L., Ji, T., Jiang, F., Tan, G., & Zhang, J. (2024). The impacts of live chat on service–product purchase: Evidence from a large online outsourcing platform. Information & Management, 61(3), 103931. https://doi.org/10.1016/j.im.2024.103931
Eslami, S. P., & Ghasemaghaei, M. (2018). Effects of online review positiveness and review score inconsistency on sales: A comparison by product involvement. Journal of Retailing and Consumer Services, 45, 74–80. https://doi.org/10.1016/j.jretconser.2018.08.003
He, C., He, L., Lu, T., & Li, B. (2021). Beyond Entertainment: Unpacking Danmaku and Comments’ Role of Information Sharing and Sentiment Expression in Online Crisis Videos. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1–27. https://doi.org/10.1145/3479555
He, M., Ge, Y., Chen, E., Liu, Q., & Wang, X. (2017). Exploring the Emerging Type of Comment for Online Videos: DanMu. ACM Transactions on the Web, 12(1), 1:1-1:33. https://doi.org/10.1145/3098885
Kanwal, M., Burki, U., Ali, R., & Dahlstrom, R. (2022). Systematic Review of Gender Differences and Similarities in Online Consumers’ Shopping Behavior. Journal of Consumer Marketing, 39, 29–43. https://doi.org/10.1108/JCM-01-2021-4356
Khan, M. L. (2017). Social media engagement: What motivates user participation and consumption on YouTube? Computers in Human Behavior, 66, 236–247. https://doi.org/10.1016/j.chb.2016.09.024
Kim, W. G., Lim, H., & Brymer, R. A. (2015). The effectiveness of managing social media on hotel performance. International Journal of Hospitality Management, 44, 165–171. https://doi.org/10.1016/j.ijhm.2014.10.014
Li, S., Zhu, H., Qian, Y., Ren, S., & Fang, B. (2022). Classification and Quantification of Danmaku Interactions in Online Video Lectures: An Exploratory Study. Wireless Communications and Mobile Computing, 2022, e5656669. https://doi.org/10.1155/2022/5656669
Lim, T.-S., & Loh, W.-Y. (1996). A comparison of tests of equality of variances. Computational Statistics & Data Analysis, 22(3), 287–301. https://doi.org/10.1016/0167-9473(95)00054-2
Markovits, H., Benenson, J., & White, S. (2006). Gender and priming differences in speed of processing of information relating to social structure. Journal of Experimental Social Psychology, 42(5), 662–667. https://doi.org/10.1016/j.jesp.2005.09.003
Meyers-Levy, J., & Sternthal, B. (1991). Gender Differences in the Use of Message Cues and Judgments. Journal of Marketing Research, 28(1), 84–96. https://doi.org/10.1177/002224379102800107
Nunnally, J. C. (1978). Psychometric Theory. McGraw-Hill.
Oh, H., Goh, K.-Y., & Phan, T. Q. (2023). Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing. Information Systems Research, 34(1), 111–136. https://doi.org/10.1287/isre.2022.1112
Pradhana, F., & Sastiono, P. (2019). Gender Differences in Online Shopping: Are Men More Shopaholic Online? https://doi.org/10.2991/icbmr-18.2019.21
Richard, M.-O., Chebat, J.-C., Yang, Z., & Putrevu, S. (2010). A proposed model of online consumer behavior: Assessing the role of gender. Journal of Business Research, 63(9), 926–934. https://doi.org/10.1016/j.jbusres.2009.02.027
Ruiz-Mafe, C., Chatzipanagiotou, K., & Curras-Perez, R. (2018). The role of emotions and conflicting online reviews on consumers’ purchase intentions. Journal of Business Research, 89, 336–344. https://doi.org/10.1016/j.jbusres.2018.01.027
Saed, H. A., Haider, A. S., Al-Salman, S., & Hussein, R. F. (2021). The use of YouTube in developing the speaking skills of Jordanian EFL university students. Heliyon, 7(7), e07543. https://doi.org/10.1016/j.heliyon.2021.e07543
Salvendy, G., & St, C. (2022). Handbook of human factors and ergonomics fourth edition.
Svenson, O. (1979). Process Descriptions of Decision Making. Organisational Behavior and Human Performance, 23, 86–112. https://doi.org/10.1016/0030-5073(79)90048-5
Tzeng, S.-Y., He, L., & Huang, K. (2023). Danmaku’s effects on viewing experience and destination food image in food-themed documentaries. Journal of Hospitality and Tourism Management, 55, 29–39. https://doi.org/10.1016/j.jhtm.2023.02.010
Vaidyanathan, R., & Aggarwal, P. (2020). Does MSRP impact women differently? Exploring gender-based differences in the effectiveness of retailer-provided reference prices. Journal of Retailing and Consumer Services, 54, 102049. https://doi.org/10.1016/j.jretconser.2020.102049
Wendt, L. M., Griesbaum, J., & Kölle, R. (2016). Product advertising and viral stealth marketing in online videos: A description and comparison of comments on YouTube. Aslib Journal of Information Management, 68(3), 250–264. https://doi.org/10.1108/AJIM-11-2015-0174
Wilson, S. R. (2002). Seeking and Resisting Compliance: Why People Say What They Do When Trying to Influence Others. SAGE Publications.
Xi, D., Xu, W., Chen, R., Zhou, Y., & Yang, Z. (2021). Sending or not? A multimodal framework for Danmaku comment prediction. Information Processing & Management, 58(6), 102687. https://doi.org/10.1016/j.ipm.2021.102687
Zhai, L., Yin, P., Li, C., Wang, J., & Yang, M. (2022). Investigating the Effects of Video-Based E-Word-of-Mouth on Consumers’ Purchase Intention: The Moderating Role of Involvement. Sustainability, 14(15), 9522. https://doi.org/10.3390/su14159522
Zhang, K. Z. K., Cheung, C. M. K., & Lee, M. K. O. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. International Journal of Information Management, 34(2), 89–98. https://doi.org/10.1016/j.ijinfomgt.2013.12.001
Zhang, L., Yan, Q., & Zhang, L. (2020). A text analytics framework for understanding the relationships among host self-description, trust perception and purchase behavior on Airbnb. Decision Support Systems, 133, 113288. https://doi.org/10.1016/j.dss.2020.113288
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