Beyond the Disclosure Dilemma: Effects of AI-Generated Brand Voice on Brand Authenticity and Attitude
Bilah Samping Artikel
Isi Artikel Utama
Abstrak
The increasing use of artificial intelligence (AI) in marketing communication has transformed how brands construct and deliver their brand voice. While AI-generated content offers efficiency and scalability, it also raises concerns regarding brand authenticity and consumer responses. This study examines the effects of AI-generated brand voice disclosure on brand authenticity and brand attitude, with brand voice authenticity positioned as a key explanatory mechanism. Using a between-subjects experimental design, data were collected through an online survey and analyzed using analysis of variance (ANOVA) and structural equation modeling (SEM). The results indicate that disclosure of AI-generated brand voice significantly reduces perceived brand authenticity compared to human-generated communication. However, AI disclosure does not directly diminish brand attitude. SEM results reveal that brand voice authenticity positively influences brand authenticity, which in turn significantly enhances brand attitude, indicating a partial mediation effect. These findings suggest that consumer responses to AI-generated brand communication are primarily shaped by authenticity perceptions rather than disclosure alone. The study contributes to branding and AI literature by highlighting authenticity as a central mechanism in AI-mediated brand communication and offers strategic insights for managers balancing transparency and brand integrity.
##plugins.themes.bootstrap3.displayStats.downloads##
Rincian Artikel
Bagian
Cara Mengutip
Referensi
Aaker, D. A. (1991). Managing brand equity: Capitalizing on the value of a brand name. Free Press.
Berger, S., Adam, M., Rühr, A., & Benlian, A. (2021). Algorithm aversion in the field: Evidence from human–algorithm interactions in consumer decision-making. Journal of Marketing, 85(4), 76–97. https://doi.org/10.1177/0022242920987012
Brown, S., Kozinets, R. V., & Sherry, J. F. (2003). Teaching old brands new tricks: Retro branding and the revival of brand meaning. Journal of Marketing, 67(3), 19–33. https://doi.org/10.1509/jmkg.67.3.19.18657
Bruhn, M., Schoenmüller, V., Schäfer, D., & Heinrich, D. (2012). Brand authenticity: Towards a conceptualization and measurement. Journal of Consumer Behaviour, 11(2), 89–102. https://doi.org/10.1002/cb.1383
Cambier, F., & Poncin, I. (2020). Inferring brand integrity from marketing communications: The effects of brand transparency signals in a consumer empowerment context. Journal of Business Research, 109, 260–270. https://doi.org/10.1016/j.jbusres.2019.11.060
Cheng, Y., & Jiang, H. (2021). How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Public Relations Research, 33(4), 211–231. https://doi.org/10.1080/1062726X.2021.1950421
Clerwall, C. (2014). Enter the robot journalist: Users’ perceptions of automated content. Journalism Practice, 8(5), 519–531. https://doi.org/10.1080/17512786.2014.883116
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Gilmore, J. H., & Pine, B. J. (2007). Authenticity: What consumers really want. Harvard Business School Press.
Graefe, A., Haim, M., Haarmann, B., & Brosius, H.-B. (2018). Readers’ perception of computer-generated news: Credibility, expertise, and readability. Journalism, 19(5), 595–610. https://doi.org/10.1177/1464884916641269
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hustvedt, G., & Kang, J. (2013). Consumer perceptions of transparency: A scale development and validation. Journal of Business Ethics, 116(1), 153–166. https://doi.org/10.1007/s10551-012-1462-0
Iwama, K., & Kano, Y. (2018). AI-driven natural language generation and its implications for marketing communication. International Journal of Information Management, 43, 123–131. https://doi.org/10.1016/j.ijinfomgt.2018.08.001
Knapp, M. (2017). Artificial intelligence and marketing communication: A conceptual overview. Journal of Marketing Analytics, 5(3–4), 73–80. https://doi.org/10.1057/s41270-017-0024-9
Kohli, C., & Yen, H. J. R. (2020). Brand voice: Conceptualization, measurement, and implications for brand personality. Journal of Product & Brand Management, 29(4), 441–456. https://doi.org/10.1108/JPBM-01-2019-2213
Lee, A. Y., & Labroo, A. A. (2004). The effect of conceptual and perceptual fluency on brand evaluation. Journal of Marketing Research, 41(2), 151–165. https://doi.org/10.1509/jmkr.41.2.151.28665
Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., & Smolander, K. (2022). What influences algorithm aversion? A systematic literature review. Information Systems Frontiers, 24(4), 1021–1044. https://doi.org/10.1007/s10796-021-10193-8
Molleda, J.-C. (2010). Authenticity and the construct’s dimensions in public relations and communication research. Journal of Communication Management, 14(3), 223–236. https://doi.org/10.1108/13632541011064508
Morhart, F., Malär, L., Guèvremont, A., Girardin, F., & Grohmann, B. (2015). Brand authenticity: An integrative framework and measurement scale. Journal of Consumer Psychology, 25(2), 200–218. https://doi.org/10.1016/j.jcps.2014.11.006
Mozafari, N., Weiger, W. H., & Hammerschmidt, M. (2021). Chatbots vs. humans: A service perspective. Journal of Service Research, 24(2), 230–247. https://doi.org/10.1177/1094670520976971
Napoli, J., Dickinson, S. J., Beverland, M. B., & Farrelly, F. (2014). Measuring consumer-based brand authenticity. Journal of Business Research, 67(6), 1090–1098. https://doi.org/10.1016/j.jbusres.2013.06.001
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Perdue, B. C., & Summers, J. O. (1986). Checking the success of manipulations in marketing experiments. Journal of Marketing Research, 23(4), 317–326. https://doi.org/10.1177/002224378602300402
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
Spears, N., & Singh, S. N. (2004). Measuring attitude toward the brand and purchase intentions. Journal of Current Issues & Research in Advertising, 26(2), 53–66. https://doi.org/10.1080/10641734.2004.10505164