Cilt 14 Sayı 3 (2026): Business & Management Studies: An International Journal
Makaleler

İnsan ve Yapay Zekâ Influencer'larda hata üstlenme biçiminin tüketici tepkilerine etkisi

Selçuk Yasin Yıldız
Doç. Dr., Sivas Cumhuriyet Üniversitesi, Sivas, Türkiye

Yayınlanmış 25.09.2026

Anahtar Kelimeler

  • AI Influencers, Error Acknowledgment, Parasocial Interaction
  • Yapay Zekâ İnfluencer'lar, Hata Üstlenme, Parasosyal Etkileşim

Nasıl Atıf Yapılır

İnsan ve Yapay Zekâ Influencer’larda hata üstlenme biçiminin tüketici tepkilerine etkisi. (2026). Business & Management Studies: An International Journal, 14(3), 1874-1891. https://doi.org/10.15295/bmij.v14i3.2844

Nasıl Atıf Yapılır

İnsan ve Yapay Zekâ Influencer’larda hata üstlenme biçiminin tüketici tepkilerine etkisi. (2026). Business & Management Studies: An International Journal, 14(3), 1874-1891. https://doi.org/10.15295/bmij.v14i3.2844

Öz

İnsan ve yapay zekâ (AI) influencer'lar ürün tavsiyelerini giderek daha yaygın biçimde paylaşmaktadır; ancak bu aktörlerin bir bilgi hatasına verdikleri yanıt karşısında tüketicilerin nasıl tepki verdiği belirsizliğini korumaktadır. Bu çalışma, hatanın sorumluluğunu üstlenmenin, hatayı reddetmeye kıyasla, insan ve yapay zekâ influencer'lar için farklı tüketici tepkileri üretip üretmediğini incelemektedir. 496 sosyal medya kullanıcısıyla 2 (influencer tipi: insan vs. AI) × 2 (hata yanıtı: üstlenme vs. reddetme) gruplar arası senaryo deneyi yürütülmüştür. Veriler MANOVA, ANOVA ve 5.000 bootstrap örneklemli düzenlenmiş seri aracılık analizleriyle çözümlenmiştir. Hata üstlenme her iki influencer tipinde de algılanan samimiyeti, parasosyal etkileşimi, güveni ve satın alma niyetini artırmış; ancak etkiler insan influencer'larda belirgin biçimde daha güçlü çıkmıştır. Algılanan yetkinlikte çaprazlama etkileşim gözlenmiştir: Üstlenme insan influencer'ın yetkinliğini artırırken yapay zekâ influencer'ın yetkinliğini düşürmüştür. Güven ve satın alma niyeti üzerindeki dolaylı etkiler ağırlıklı olarak samimiyet üzerinden işlerken, yetkinlik cezası davranışsal niyetlere yalnızca parasosyal etkileşim aracılığıyla seri biçimde ulaşmıştır. Bulgular, pratfall etkisinin sınır koşullarını belirlemekte ve yapay zekâ şeffaflığının yetkinlik maliyetini ortaya koymaktadır.

Referanslar

  1. Aaker, J., Vohs, K. D., & Mogilner, C. (2010). Nonprofits are seen as warm and for-profits as competent: Firm stereotypes matter. Journal of Consumer Research, 37(2), 224–237. https://doi.org/10.1086/651566
  2. Abele, A. E., Ellemers, N., Fiske, S. T., Koch, A., & Yzerbyt, V. (2021). Navigating the social world: Toward an integrated framework for evaluating self, individuals, and groups. Psychological Review, 128(2), 290–314. https://doi.org/10.1037/rev0000262
  3. Allal-Chérif, O., Puertas, R., & Carracedo, P. (2024). Intelligent influencer marketing: How AI-powered virtual influencers outperform human influencers. Technological Forecasting and Social Change, 200, 123113. https://doi.org/10.1016/j.techfore.2023.123113
  4. Ao, L., Bansal, R., Pruthi, N., & Khaskheli, M. B. (2023). Impact of social media influencers on customer engagement and purchase intention: A meta-analysis. Sustainability, 15(3), 2744. https://doi.org/10.3390/su15032744
  5. Ashraf, A., Hameed, I., & Saeed, S. A. (2023). How do social media influencers inspire consumers' purchase decisions? The mediating role of parasocial relationships. International Journal of Consumer Studies, 47(4), 1416–1433. https://doi.org/10.1111/ijcs.12917
  6. Auter, P. J. (1992). Psychometric: TV that talks back: An experimental validation of a parasocial interaction scale. Journal of Broadcasting & Electronic Media, 36(2), 173–181. https://doi.org/10.1080/08838159209364165
  7. Baek, T. H., Kim, J., & Kim, J. H. (2026). Effect of disclosing AI-generated content on prosocial advertising evaluation. International Journal of Advertising, 45(1), 171–192. https://doi.org/10.1080/02650487.2024.2401319
  8. Belanche, D., Casaló, L. V., & Flavián, M. (2024). Human versus virtual influences, a comparative study. Journal of Business Research, 173, 114493. https://doi.org/10.1016/j.jbusres.2023.114493
  9. Berger, B., Adam, M., Rühr, A., & Benlian, A. (2021). Watch me improve-algorithm aversion and demonstrating the ability to learn. Business & Information Systems Engineering, 63(1), 55–68. https://doi.org/10.1007/s12599-020-00678-5
  10. Bernritter, S. F., Verlegh, P. W., & Smit, E. G. (2016). Why nonprofits are easier to endorse on social media: The roles of warmth and brand symbolism. Journal of Interactive Marketing, 33(1), 27–42. https://doi.org/10.1016/j.intmar.2015.10.002
  11. Brambilla, M., Sacchi, S., Rusconi, P., Cherubini, P., & Yzerbyt, V. Y. (2012). You want to give a good impression? Be honest! Moral traits dominate group impression formation. British Journal of Social Psychology, 51(1), 149–166. https://doi.org/10.1111/j.2044-8309.2010.02011.x
  12. Brüns, J. D., & Meißner, M. (2024). Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity. Journal of Retailing and Consumer Services, 79, 103790. https://doi.org/10.1016/j.jretconser.2024.103790
  13. Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116–131. https://doi.org/10.1037/0022-3514.42.1.116
  14. Cacioppo, J. T., Petty, R. E., & Kao, C. F. (1984). The efficient assessment of need for cognition. Journal of Personality Assessment, 48(3), 306–307. https://doi.org/10.1207/s15327752jpa4803_13
  15. Cacioppo, J. T., Petty, R. E., & Morris, K. J. (1983). Effects of need for cognition on message evaluation, recall, and persuasion. Journal of Personality and Social Psychology, 45(4), 805–818. https://doi.org/10.1037/0022-3514.45.4.805
  16. Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788
  17. 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), 752–766. https://doi.org/10.1037/0022-3514.39.5.752
  18. Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022). Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing & Management, 59(3), 102940. https://doi.org/10.1016/j.ipm.2022.102940
  19. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5
  20. Choi, S., Mattila, A. S., & Bolton, L. E. (2021). To err is human(-oid): How do consumers react to robot service failure and recovery?. Journal of Service Research, 24(3), 354–371. https://doi.org/10.1177/1094670520978798
  21. Chung, S., & Lee, S. (2021). Crisis management and corporate apology: The effects of causal attribution and apology type on publics' cognitive and affective responses. International Journal of Business Communication, 58(1), 125–144. https://doi.org/10.1177/2329488417735646
  22. Cocker, H., Mardon, R., & Daunt, K. L. (2021). Social media influencers and transgressive celebrity endorsement in consumption community contexts. European Journal of Marketing, 55(7), 1841–1872. https://doi.org/10.1108/EJM-07-2019-0567
  23. Cuddy, A. J., Fiske, S. T., & Glick, P. (2008). Warmth and competence as universal dimensions of social perception: The stereotype content model and the BIAS map. Advances in Experimental Social Psychology, 40, 61–149. https://doi.org/10.1016/S0065-2601(07)00002-0
  24. Dabiran, E., Farivar, S., Wang, F., & Grant, G. (2024). Virtually human: Anthropomorphism in virtual influencer marketing. Journal of Retailing and Consumer Services, 79, 103797. https://doi.org/10.1016/j.jretconser.2024.103797
  25. Dedeoğlu, B. B., Bilgihan, A., Ye, B. H., Wang, Y., & Okumus, F. (2021). The role of elaboration likelihood routes in relationships between user-generated content and willingness to pay more. Tourism Review, 76(3), 614–638. https://doi.org/10.1108/TR-01-2019-0013
  26. Diamantopoulos, A., Szőcs, I., Florack, A., Kolbl, Ž., & Egger, M. (2021). The bond between country and brand stereotypes: Insights on the role of brand typicality and utilitarian/hedonic nature in enhancing stereotype content transfer. International Marketing Review, 38(6), 1143–1165. https://doi.org/10.1108/IMR-09-2020-0209
  27. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
  28. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers' product evaluations. Journal of Marketing Research, 28(3), 307–319. https://doi.org/10.1177/002224379102800305
  29. El Hedhli, K., Zourrig, H., Al Khateeb, A., & Alnawas, I. (2023). Stereotyping human-like virtual influencers in retailing: Does warmth prevail over competence?. Journal of Retailing and Consumer Services, 75, 103459. https://doi.org/10.1016/j.jretconser.2023.103459
  30. Eriksson, M. (2018). Lessons for crisis communication on social media: A systematic review of what research tells the practice. International Journal of Strategic Communication, 12(5), 526–551. https://doi.org/10.1080/1553118X.2018.1510405
  31. Fiske, S. T. (2018). Stereotype content: Warmth and competence endure. Current Directions in Psychological Science, 27(2), 67–73. https://doi.org/10.1177/0963721417738825
  32. Fiske, S. T., Cuddy, A. J., & Glick, P. (2007). Universal dimensions of social cognition: Warmth and competence. Trends in Cognitive Sciences, 11(2), 77–83. https://doi.org/10.1016/j.tics.2006.11.005
  33. Folkvord, F., Roes, E., & Bevelander, K. (2020). Promoting healthy foods in the new digital era on Instagram: An experimental study on the effect of a popular real versus fictitious fit influencer on brand attitude and purchase intentions. BMC Public Health, 20(1), 1677. https://doi.org/10.1186/s12889-020-09779-y
  34. 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
  35. Franke, C., Groeppel-Klein, A., & Müller, K. (2023). Consumers' responses to virtual influencers as advertising endorsers: Novel and effective or uncanny and deceiving?. Journal of Advertising, 52(4), 523–539. https://doi.org/10.1080/00913367.2022.2154721
  36. Ge, H., Wang, W., Wang, Y., & Tan, R. (2025). How does original equipment manufacturing brand disclosure affect purchase intention? The mediating role of brand competence and brand warmth. Asia Pacific Journal of Marketing and Logistics, 37(8), 2205–2227. https://doi.org/10.1108/APJML-07-2024-0866
  37. Gidaković, P., & Žabkar, V. (2022). The formation of consumers' warmth and competence impressions of corporate brands: The role of corporate associations. European Management Review, 19(4), 639–653. https://doi.org/10.1111/emre.12509
  38. Grover, S. L., Abid-Dupont, M. A., Manville, C., & Hasel, M. C. (2019). Repairing broken trust between leaders and followers: How violation characteristics temper apologies. Journal of Business Ethics, 155(3), 853–870. https://doi.org/10.1007/s10551-017-3509-3
  39. Han, J., & Ko, D. (2025). Trust formation, error impact, and repair in human-AI financial advisory: A dynamic behavioral analysis. Behavioral Sciences, 15(10), 1370. https://doi.org/10.3390/bs15101370
  40. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  41. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
  42. Hu, Y., Min, H., & Su, N. (2021). How sincere is an apology? Recovery satisfaction in a robot service failure context. Journal of Hospitality & Tourism Research, 45(6), 1022–1043. https://doi.org/10.1177/10963480211011533
  43. Inman, J. J., McAlister, L., & Hoyer, W. D. (1990). Promotion signal: Proxy for a price cut?. Journal of Consumer Research, 17(1), 74–81. https://doi.org/10.1086/208538
  44. Joel-Edgar, S., Chowdhury, S., Nagy, P., & Ren, S. (2025). Virtual influencers in social media versus the metaverse: Mind perception, blame judgements and brand trust. Journal of Business Research, 189, 115139. https://doi.org/10.1016/j.jbusres.2024.115139
  45. Khalfallah, D., & Keller, V. (2025). Authenticity, ethics, and transparency in virtual influencer marketing: A cross-cultural analysis of consumer trust and engagement: A systematic literature review. Acta Psychologica, 260, 105573. https://doi.org/10.1016/j.actpsy.2025.105573
  46. Kolbl, Ž., Arslanagic-Kalajdzic, M., & Diamantopoulos, A. (2019). Stereotyping global brands: Is warmth more important than competence?. Journal of Business Research, 104, 614–621. https://doi.org/10.1016/j.jbusres.2018.12.060
  47. Lombard, M., & Xu, K. (2021). Social responses to media technologies in the 21st century: The media are social actors paradigm. Human-Machine Communication, 2, 29–55. https://doi.org/10.30658/hmc.2.2
  48. Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58–73. https://doi.org/10.1080/15252019.2018.1533501
  49. Lou, C., Kiew, S. T. J., Chen, T., Lee, T. Y. M., Ong, J. E. C., & Phua, Z. (2023). Authentically fake? How consumers respond to the influence of virtual influencers. Journal of Advertising, 52(4), 540–557. https://doi.org/10.1080/00913367.2022.2149641
  50. Mahmood, A., & Huang, C. M. (2024). Gender biases in error mitigation by voice assistants. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1), 1–27. https://doi.org/10.1145/3637337
  51. Masuda, H., Han, S. H., & Lee, J. (2022). Impacts of influencer attributes on purchase intentions in social media influencer marketing: Mediating roles of characterizations. Technological Forecasting and Social Change, 174, 121246. https://doi.org/10.1016/j.techfore.2021.121246
  52. Molina, M. D., & Sundar, S. S. (2024). Does distrust in humans predict greater trust in AI? Role of individual differences in user responses to content moderation. New Media & Society, 26(6), 3638–3656. https://doi.org/10.1177/14614448221103534
  53. Mrad, M., Ramadan, Z., Tóth, Z., Nasr, L., & Karimi, S. (2025). Virtual influencers versus real connections: Exploring the phenomenon of virtual influencers. Journal of Advertising, 54(1), 1–19. https://doi.org/10.1080/00913367.2024.2393711
  54. Muniz, F., Stewart, K., & Magalhães, L. (2024). Are they humans or are they robots? The effect of virtual influencer disclosure on brand trust. Journal of Consumer Behaviour, 23(3), 1234–1250. https://doi.org/10.1002/cb.2271
  55. Nielsen, Y. A., Pfattheicher, S., & Keijsers, M. (2022). Prosocial behavior toward machines. Current Opinion in Psychology, 43, 260–265. https://doi.org/10.1016/j.copsyc.2021.08.004
  56. Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205. https://doi.org/10.1016/S0065-2601(08)60214-2
  57. 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), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  58. Rizzo, C., Baima, G., Janovská, K., & Bresciani, S. (2025). Navigating the uncertainty path of virtual influencers: Empirical evidence through a cultural lens. Technological Forecasting and Social Change, 210, 123896. https://doi.org/10.1016/j.techfore.2024.123896
  59. Roy, R., & Naidoo, V. (2021). Enhancing chatbot effectiveness: The role of anthropomorphic conversational styles and time orientation. Journal of Business Research, 126, 23–34. https://doi.org/10.1016/j.jbusres.2020.12.051
  60. Sands, S., Campbell, C. L., Plangger, K., & Ferraro, C. (2022). Unreal influence: Leveraging AI in influencer marketing. European Journal of Marketing, 56(6), 1721–1747. https://doi.org/10.1108/EJM-12-2019-0949
  61. Shi, J., & Jiang, Z. (2023). Competence or warmth: Why do consumers pay for green advertising?. Asia Pacific Journal of Marketing and Logistics, 35(11), 2834–2857. https://doi.org/10.1108/APJML-01-2023-0002
  62. Sokolova, K., & Kefi, H. (2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. Journal of Retailing and Consumer Services, 53, 101742. https://doi.org/10.1016/j.jretconser.2019.01.011
  63. Song, M., Zhang, H., Xing, X., & Duan, Y. (2023). Appreciation vs. apology: Research on the influence mechanism of chatbot service recovery based on politeness theory. Journal of Retailing and Consumer Services, 73, 103323. https://doi.org/10.1016/j.jretconser.2023.103323
  64. Srinivasan, R., & Sarial-Abi, G. (2021). When algorithms fail: Consumers' responses to brand harm crises caused by algorithm errors. Journal of Marketing, 85(5), 74–91. https://doi.org/10.1177/0022242921997082
  65. Stein, J.-P., Breves, P. L., & Anders, N. (2024). Parasocial interactions with real and virtual influencers: The role of perceived similarity and human-likeness. New Media & Society, 26(6), 3433–3453. https://doi.org/10.1177/14614448221102900
  66. Sung, B., Im, H., & Duong, V. C. (2023). Task type's effect on attitudes towards voice assistants. International Journal of Consumer Studies, 47(5), 1772–1790. https://doi.org/10.1111/ijcs.12946
  67. Thomas, V. L., & Fowler, K. (2021). Close encounters of the AI kind: Use of AI influencers as brand endorsers. Journal of Advertising, 50(1), 11–25. https://doi.org/10.1080/00913367.2020.1810595
  68. Toader, D. C., Boca, G., Toader, R., Măcelaru, M., Toader, C., Ighian, D., & Rădulescu, A. T. (2020). The effect of social presence and chatbot errors on trust. Sustainability, 12(1), 256. https://doi.org/10.3390/su12010256
  69. Xu, Y., & Ling, I. L. (2026). Restoring trust: Gratitude vs. apology in healthcare service recovery. Journal of Service Theory and Practice, 36(7), 46–75. https://doi.org/10.1108/JSTP-05-2025-0176
  70. Yang, M. R., Pathak, S., & Huang, S. Z. (2025). Warmth and competence: An empirical investigation of the dual impact of corporate apologies on repairing brand trust. Journal of Human, Earth, and Future, 6(1), 95–114. https://doi.org/10.28991/HEF-2025-06-01-07
  71. Yılmazdoğan, O. C., Doğan, R. Ş., & Altıntaş, E. (2021). The impact of the source credibility of Instagram influencers on travel intention: The mediating role of parasocial interaction. Journal of Vacation Marketing, 27(3), 299–313. https://doi.org/10.1177/1356766721995973
  72. Yuan, S., & Lou, C. (2020). How social media influencers foster relationships with followers: The roles of source credibility and fairness in parasocial relationship and product interest. Journal of Interactive Advertising, 20(2), 133–147. https://doi.org/10.1080/15252019.2020.1769514
  73. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302
  74. Zerilli, J., Bhatt, U., & Weller, A. (2022). How transparency modulates trust in artificial intelligence. Patterns, 3(4), 100455. https://doi.org/10.1016/j.patter.2022.100455
  75. Zhang, Q., & Abdullah, F. (2026). Virtual vs. human influencers: AI-mediated trust transfer and brand attachment among female consumers. Journal of Theoretical and Applied Electronic Commerce Research, 21(7), 209. https://doi.org/10.3390/jtaer21070209