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Chatbot-aided product purchases among Generation Z: the role of personality traits

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Liu, Jinrong, Chen, Jialiang·2025·Frontiers in Psychology
DOI 10.3389/fpsyg.2025.1454197PMC PMC12425985
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Abstract

Introduction: The rapid integration of machine learning has positioned product recommendation chatbots as essential tools in the e-commerce landscape, shaping how consumers engage and make purchasing decisions. Generation Z, as a tech-savvy and AI-adept demographic, plays a central role in this transformation. While prior studies have examined chatbot-consumer interactions, limited research has explored how both personality traits and information source characteristics jointly influence purchase intentions. Methods: This study develops an integrative framework to assess how the Big Five personality traits—extraversion, agreeableness, conscientiousness, neuroticism, and openness—and key chatbot features—expertise, interactivity, trustworthiness, and customization—affect Generation Z’s willingness to purchase chatbot-recommended products. The moderating role of personal innovativeness is also examined. Data were collected from 480 Generation Z chatbot users in China through an online survey and analyzed using structural equation modeling (SEM), artificial neural networks (ANN), and necessary condition analysis (NCA). Results: Results indicate that extraversion, agreeableness, openness, expertise, interactivity, and customization significantly influence purchase intention. Moreover, personal innovativeness positively moderates the effect of extraversion on purchase intention. Discussion: These findings contribute to the literature by bridging personality psychology and human–AI interaction and offer practical insights for enhancing chatbot effectiveness in e-commerce.

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  • Purchase intention/adoption intention is mostly positive via utility/trust, but can decrease under high autonomy.

    “Mostly positive via utility/trust, but can decrease under high autonomy”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [39]

    The Results (structural equation modeling; page not provided) report positive associations between purchase intention and chatbot expertise, interactivity, and customization, supporting the functional-benefits portion of the claim. But trustworthiness had no statistically significant effect, and the passages do not report a decrease in intention under high autonomy. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • High-autonomy cues can reduce intention via powerlessness/threat-to-freedom, unless urgency/scarcity raises benefit salience and attenuates resistance.

    “High-autonomy cues can reduce intention via powerless-raises benefit salience and attenuates resistance. ness/threat-to-freedom, unless urgency/scarcity”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [39]

    The Results (Results of NCA; page unavailable) discuss conditions for purchase intention, while the conceptual background discusses chatbot information cues. Neither passage addresses high-autonomy cues, powerlessness or threats to freedom, or whether urgency or scarcity attenuates resistance; searches for those terms found no relevant passage. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • The utility chain dominates: quality/usefulness leads to attitude/satisfaction, which leads to intention.

    “Utility chain dominates: quality/usefulness → at-titude/satisfaction → intention.”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [39]

    The Results of structural equation modeling (page unavailable) report direct associations of chatbot characteristics and personality traits with purchase intention. Searches for quality/usefulness and attitude/satisfaction as mediators found no passage establishing the claimed chain or its dominance; the direct-path results do not by themselves rule it out. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 95

  1. Agarwal R. Prasad J. (1999). Are individual differences germane to the acceptance of new information technologies? Decis. Sci. 30, 361– 391. doi: 10.1111/j.1540-5915.1999.tb01614.x · DOI
  2. Ali I. Warraich N. F. (2023). Impact of personal innovativeness, perceived smartphone ease of use and mobile self-efficacy on smartphone-based personal information management practices. Electron. Libr. 41, 419– 437. doi: 10.1108/EL-12-2022-0262 · DOI
  3. Alkawsi G. Ali N. Baashar Y. (2021). The moderating role of personal innovativeness and users experience in accepting the smart meter technology. Appl. Sci. 11: 3297. doi: 10.3390/app11083297 · DOI
  4. Anbalagan S. K. Nei X. Mohan U. Kanamarlapudi V. K. Kommalapati A. Zhao X. (2025). “ Bridging UI design and Chatbot interactions: applying form-based principles to conversational agents,” in International conference on human-computer interaction. Cham, Switzerland: Springer Nature Switzerland, 223– 231.
  5. Arpaci I. Karatas K. Kusci I. Al-Emran M. (2022). Understanding the social sustainability of the Metaverse by integrating UTAUT2 and big five personality traits: a hybrid SEM-ANN approach. Technol. Soc. 71: 102120. doi: 10.1016/j.techsoc.2022.102120 · DOI
  6. Arpaci I. Kocadag Unver T. (2020). Moderating role of gender in the relationship between big five personality traits and smartphone addiction. Psychiatry Q. 91, 577– 585. doi: 10.1007/s11126-020-09718-5, PMID: 32100236 · DOI
  7. Asaoka Y. Won M. Morita T. Ishikawa E. Goto Y. (2020). Heightened negative affects associated with neurotic personality in behavioral addiction. Front. Psych. 11: 561713. doi: 10.3389/fpsyt.2020.561713, PMID: 33101082 PMC7495191 · DOI
  8. Attigeri G. Agrawal A. Kolekar S. V. (2024). Advanced NLP models for technical university information chatbots: development and comparative analysis. IEEE Access 12, 29633– 29647. doi: 10.1109/ACCESS.2024.3368382 · DOI
  9. Baumert A. Schmitt M. Perugini M. Johnson W. Blum G. Borkenau P.. (2017). Integrating personality structure, personality process, and personality development. Eur. J. Personal. 31, 503– 528. doi: 10.1002/per.2115 · DOI
  10. Boudreaux M. J. (2016). Personality-related problems and the five-factor model of personality. Personal. Disord. Theory Res. Treat. 7, 372– 383. doi: 10.1037/per0000185, PMID: 27018821 · DOI
  11. Buecker S. Maes M. Denissen J. J. A. Luhmann M. (2020). Loneliness and the big five personality traits: a meta–analysis. Eur. J. Personal. 34, 8– 28. doi: 10.1002/per.2229 · DOI
  12. Buelow M. T. Cayton C. (2020). Relationships between the big five personality characteristics and performance on behavioral decision making tasks. Pers. Individ. Differ. 160: 109931. doi: 10.1016/j.paid.2020.109931 · DOI
  13. Bunea O. I. Corboș R. A. Mișu S. I. Triculescu M. Trifu A. (2024). The next-generation shopper: a study of generation-Z perceptions of AI in online shopping. J. Theor. Appl. Electron. Commer. Res. 19, 2605– 2629. doi: 10.3390/jtaer19040125 · DOI
  14. Busseri M. A. Erb E. M. (2024). The happy personality revisited: re-examining associations between big five personality traits and subjective well-being using meta-analytic structural equation modeling. J. Pers. 92, 968– 984. doi: 10.1111/jopy.12862, PMID: 37462061 · DOI
  15. Chang Y. P. Zhu D. H. (2012). The role of perceived social capital and flow experience in building users’ continuance intention to social networking sites in China. Comput. Human Behav. 28, 995– 1001. doi: 10.1016/j.chb.2012.01.001 · DOI
  16. Chen J. Wu Y. (2024). Would you be willing to purchase virtual gifts during esports live streams? Streamer characteristics and cultural traits. Comput. Human Behav. 152: 108075. doi: 10.1016/j.chb.2023.108075 · DOI
  17. Chen Q. Yin C. Gong Y. (2025). Would an AI chatbot persuade you: an empirical answer from the elaboration likelihood model. Inf. Technol. People 38, 937– 962. doi: 10.1108/ITP-10-2021-0764 · DOI
  18. Cuartero N. Tur A. M. (2021). Emotional intelligence, resilience and personality traits neuroticism and extraversion: predictive capacity in perceived academic efficacy. Nurse Educ. Today 102: 104933. doi: 10.1016/j.nedt.2021.104933, PMID: 33957394 · DOI
  19. Dawes J. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10-point scales. Int. J. Market. Res. 50, 61– 104. doi: 10.1177/147078530805000106 · DOI
  20. Denoeux T. Lengellé R. (1993). Initializing backpropagation networks with prototypes. Neural Netw. 6, 351– 363. doi: 10.1016/0893-6080(93)90003-F · DOI
  21. Ding N. Chen M. Hu L. (2025). Examining the impact of big five personality traits on generation Z designers’ subscription to paid AI drawing tools using SEM and FsQCA. Sci. Rep. 15: 17587. doi: 10.1038/s41598-025-02926-8, PMID: 40399399 PMC12095695 · DOI
  22. Dong W. Wang Y. Qin J. (2023). An empirical study on impulse consumption intention of livestreaming e-commerce: the mediating effect of flow experience and the moderating effect of time pressure. Front. Psychol. 13: 1019024. doi: 10.3389/fpsyg.2022.1019024, PMID: 36726500 PMC9885107 · DOI
  23. Dul J. Van der Laan E. Kuik R. (2020). A statistical significance test for necessary condition analysis. Organ. Res. Methods 23, 385– 395. doi: 10.1177/1094428118795272 · DOI
  24. Duong C. D. (2021). Big five personality traits and green consumption: bridging the attitude-intention-behavior gap. Asia Pac. J. Mark. Logist. 34, 1123– 1144. doi: 10.1108/APJML-04-2021-0276 · DOI
  25. Eid M. Riemann R. Angleitner A. Borkenau P. (2003). Sociability and positive emotionality: genetic and environmental contributions to the covariation between different facets of extraversion. J. Pers. 71, 319– 346. doi: 10.1111/1467-6494.7103003, PMID: 12762418 · DOI
  26. Erdogan B. Z. (1999). Celebrity endorsement: a literature review. J. Mark. Manag. 15, 291– 314. doi: 10.1362/026725799784870379 · DOI · Paper page
  27. Fajkowska M. (2017). Personality traits: hierarchically organized systems. J. Pers. 86, 36– 54. doi: 10.1111/jopy.12314, PMID: 28295309 · DOI
  28. Fazli-Salehi R. Torres I. M. Madadi R. Zúñiga M. Á. (2021). The impact of interpersonal traits (extraversion and agreeableness) on consumers’ self-brand connection and communal-brand connection with anthropomorphized brands. J. Brand Manag. 29, 13– 34. doi: 10.1057/s41262-021-00251-9 · DOI
  29. Fleischmann E. Dalkner N. Fellendorf F. T. Bengesser S. A. Lenger M. Birner A.. (2023). The big five as predictors of cognitive function in individuals with bipolar disorder. Brain Sci. 13: 773. doi: 10.3390/brainsci13050773, PMID: 37239245 PMC10216477 · DOI
  30. Gan C. (2016). An empirical analysis of factors influencing continuance intention of mobile instant messaging in China. Inf. Dev. 32, 1109– 1119. doi: 10.1177/0266666915591298 · DOI
  31. Gil de Zúñiga H. Diehl T. Huber B. Liu J. (2017). Personality traits and social media use in 20 countries: how personality relates to frequency of social media use, social media news use, and social media use for social interaction. Cyberpsychol. Behav. Soc. Netw. 20, 540– 552. doi: 10.1089/cyber.2017.0295, PMID: 28922034 · DOI
  32. Gorham J. (1988). The relationship between verbal teacher immediacy behaviors and student learning. Commun. Educ. 37, 40– 53. doi: 10.1080/03634528809378702 · DOI
  33. Han M. C. (2021). The impact of anthropomorphism on consumers’ purchase decision in chatbot commerce. J. Internet Commer. 20, 46– 65. doi: 10.1080/15332861.2020.1863022 · DOI · Paper page
  34. Haslam N. Bastian B. Bissett M. (2004). Essentialist beliefs about personality and their implications. Personal. Soc. Psychol. Bull. 30, 1661– 1673. doi: 10.1177/0146167204271182, PMID: 15536247 · DOI
  35. Haugeland I. K. F. Følstad A. Taylor C. Bjørkli C. A. (2022). Understanding the user experience of customer service chatbots: an experimental study of chatbot interaction design. Int. J. Hum.-Comput. Stud. 161: 102788. doi: 10.1016/j.ijhcs.2022.102788 · DOI · Paper page
  36. He Y. Li W. Xue J. (2022). What and how driving consumer engagement and purchase intention in officer live streaming? A two-factor theory perspective. Electron. Commerce Res. Appl. 56: 101223. doi: 10.1016/j.elerap.2022.101223 · DOI
  37. Hopwood C. J. (2025). Personality functioning, problems in living, and personality traits. J. Pers. Assess. 107, 143– 158. doi: 10.1080/00223891.2024.2345880, PMID: 38700238 · DOI
  38. Hörz-Sagstetter S. Ohse L. Kampe L. (2021). Three dimensional approaches to personality disorders: a review on personality functioning, personality structure, and personality organization. Curr. Psychiatry Rep. 23: 45. doi: 10.1007/s11920-021-01250-y, PMID: 34181116 PMC8238706 · DOI
  39. Huang S. C. Silalahi A. D. K. Eunike I. J. Riantama D. (2024). Understanding impulse buying in E-commerce: the big five traits perspective and moderating effect of time pressure and emotions. Telemat. Inform. Rep. 15: 100157. doi: 10.1016/j.teler.2024.100157 · DOI
  40. Huete-Alcocer N. Martinez-Ruiz M. P. López-Ruiz V. R. Izquiedo-Yusta A. (2019). Archeological tourist destination image formation: influence of information sources on the cognitive, affective and unique image. Front. Psychol. 10: 2382. doi: 10.3389/fpsyg.2019.02382, PMID: 31695658 PMC6817941 · DOI
  41. Jackson J. D. Yi M. Y. Park J. S. (2013). An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology. Inf. Manag. 50, 154– 161. doi: 10.1016/j.im.2013.02.006 · DOI
  42. Jiang Y. Yang X. Zheng T. (2023). Make chatbots more adaptive: dual pathways linking human-like cues and tailored response to trust in interactions with chatbots. Comput. Human Behav. 138: 107485. doi: 10.1016/j.chb.2022.107485 · DOI
  43. Jin E. Eastin M. S. (2023). Birds of a feather flock together: matched personality effects of product recommendation chatbots and users. J. Res. Interact. Mark. 17, 416– 433. doi: 10.1108/JRIM-03-2022-0089 · DOI
  44. Kelman H. C. (2017). “ Processes of opinion change” in Attitude change (London, United Kingdom: Routledge), 205– 233.
  45. Kovbasiuk A. Triantoro T. Przegalińska A. Sowa K. Ciechanowski L. Gloor P. (2025). The personality profile of early generative AI adopters: a big five perspective. Cent. Eur. Manag. J. 33, 252– 264. doi: 10.1108/CEMJ-02-2024-0067 · DOI
  46. Kumar A. Shankar A. Nayal P. (2024). Metaverse is not my cup of tea! An investigation into how personality traits shape metaverse usage intentions. J. Retail. Consum. Serv. 77: 103639. doi: 10.1016/j.jretconser.2023.103639 · DOI
  47. Lajante M. Tojib D. Ho T. I. (2023). When interacting with a service robot is (not) satisfying: the role of customers’ need for social sharing of emotion. Comput. Hum. Behav. 146: 107792. doi: 10.1016/j.chb.2023.107792 · DOI
  48. Lau A. J. Tan G. W. H. Loh X. M. Leong L.-Y. Lee V.-H. Ooi K.-B. (2021). On the way: hailing a taxi with a smartphone? A hybrid SEM-neural network approach. Mach. Learn. Appl. 4: 100034. doi: 10.1016/j.mlwa.2021.100034 · DOI
  49. Lee S. A. Bottomley J. S. (2021). Personality and grief-related panic attacks: analysis of neuroticism, worry, and anxiety sensitivity. OMEGA – J. Death. Dying 86, 849– 861. doi: 10.1177/0030222821992192, PMID: 33554746 · DOI
  50. Lew S. Tan G. W. H. Loh X. M. Hew J. J. Ooi K. B. (2020). The disruptive mobile wallet in the hospitality industry: an extended mobile technology acceptance model. Technol. Soc. 63: 101430. doi: 10.1016/j.techsoc.2020.101430, PMID: 33071395 PMC7550096 · DOI
  51. Lim E. Gallagher E. A. Bui C. N. Beaulieu C. Simpson E. Driscoll L.. (2019). What does my personality says about my friendships in later life? Innov. Aging 3: S164. doi: 10.1093/geroni/igz038.586 · DOI
  52. Li M. Wang R. (2023). Chatbots in e-commerce: the effect of chatbot language style on customers’ continuance usage intention and attitude toward brand. J. Retail. Consum. Serv. 71: 103209. doi: 10.1016/j.jretconser.2022.103209 · DOI
  53. Lin Y.-T. Hung T.-W. Huang L. T.-L. (2020). Engineering equity: how AI can help reduce the harm of implicit bias. Philos. Technol. 34, 65– 90. doi: 10.1007/s13347-020-00406-7 · DOI
  54. Liu A. Y. Botella Carrubi M. D. Blanco González-Tejero C. (2024). Community startup businesses: the impact of big five personality traits and social media technology acceptance on group buying leaders. Int. J. Entrep. Behav. Res. 30, 955– 978. doi: 10.1108/IJEBR-07-2023-0685 · DOI
  55. Liu D. Campbell W. K. (2017). The big five personality traits, big two metatraits and social media: a meta-analysis. J. Res. Pers. 70, 229– 240. doi: 10.1016/j.jrp.2017.08.004 · DOI
  56. Liu X. Wang D. Gu M. Yang J. (2023). Research on the influence mechanism of anchors' professionalism on consumers' impulse buying intention in the livestream shopping scenario. Enterp. Inf. Syst. 17: 2065457. doi: 10.1080/17517575.2022.2065457 · DOI
  57. Li Y. Shin H. (2023). Should a luxury brand's chatbot use emoticons? Impact on brand status. J. Consum. Behav. 22, 569– 581. doi: 10.1002/cb.2104 · DOI
  58. Lo P. S. Dwivedi Y. K. Tan W. H. Ooi K.-B. Aw E. C.-X. Metri B. (2022). Why do consumers buy impulsively during live streaming? A deep learning-based dual-stage SEM-ANN analysis. J. Bus. Res. 147, 325– 337. doi: 10.1016/j.jbusres.2022.04.013 · DOI
  59. Louwen C. Reidlinger D. Milne N. (2023). Profiling health professionals’ personality traits, behaviour styles and emotional intelligence: a systematic review. BMC Med. Educ. 23: 120. doi: 10.1186/s12909-023-04003-y, PMID: 36803372 PMC9938999 · DOI
  60. Lu Z. Min Q. Jiang L. Chen Q. (2024). The effect of the anthropomorphic design of chatbots on customer switching intention when the chatbot service fails: an expectation perspective. Int. J. Inf. Manag. 76: 102767. doi: 10.1016/j.ijinfomgt.2024.102767 · DOI
  61. Lynn M. (2021). Effects of the big five personality traits on tipping attitudes, motives, and behaviors. Int. J. Hospit. Manag. 92: 102722. doi: 10.1016/j.ijhm.2020.102722 · DOI
  62. Marengo D. Sindermann C. Elhai J. D. Montag C. (2020). One social media company to rule them all: associations between use of Facebook-owned social media platforms, sociodemographic characteristics, and the big five personality traits. Front. Psychol. 11: 936. doi: 10.3389/fpsyg.2020.00936, PMID: 32547442 PMC7273309 · DOI
  63. Meier M. Maier C. Thatcher J. B. Weitzel T. (2024). Chatbot interactions: how consumption values and disruptive situations influence customers’ willingness to interact. Inf. Syst. J. 34, 1579– 1625. doi: 10.1111/isj.12507 · DOI
  64. Moisescu O. I. Gică O. A. Herle F. A. Dan I. Sarstedt M. (2025). Does one size fit all? The role of extraversion in generating electronic word-of-mouth through social media brand page engagement. Psychol. Mark. 42, 1827– 1847. doi: 10.1002/mar.22205 · DOI
  65. Nordheim C. B. Følstad A. Bjørkli C. A. (2019). An initial model of trust in chatbots for customer service—findings from a questionnaire study. Interact. Comput. 31, 317– 335. doi: 10.1093/iwc/iwz022 · DOI · Paper page
  66. Orden-Mejía M. Carvache-Franco M. Huertas A. Carvache-Franco O. Carvache-Franco W. (2023). Modeling users’ satisfaction and visit intention using AI-based chatbots In A. Luqman (Ed.),. PLoS One 18: e0286427. doi: 10.1371/journal.pone.0286427, PMID: 37682931 PMC10490898 · DOI
  67. Pavlou P. A. (2003). Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. Int. J. Electron. Commer. 7, 101– 134. doi: 10.1080/10864415.2003.11044275, PMID: 40766041 · DOI · Paper page
  68. Peng X. Zhou L. Tang Q. Liu Y. (2023). “ Effects of the conversation and recommendation mechanism on Chatbots’ recommendation effectiveness” in International conference on human-computer interaction (Cham: Springer Nature Switzerland), 493– 501.
  69. Periaiya S. Nandukrishna A. T. (2023). What drives user stickiness and satisfaction in OTT video streaming platforms? A mixed-method exploration. Int. J. Hum.-Comput. Interact. 40, 2326– 2342. doi: 10.1080/10447318.2022.2160224 · DOI
  70. Preston C. C. Colman A. M. (2000). Optimal number of response categories in rating scales: reliability, validity, discriminating power, and respondent preferences. Acta Psychol. 104, 1– 15. doi: 10.1016/S0001-6918(99)00050-5, PMID: 10769936 · DOI · Paper page
  71. Rahevar M. Darji S. (2024). The adoption of AI-driven chatbots into a recommendation for e-commerce systems to targeted customer in the selection of product. Int. J. Manag. Econ. Commer. 1, 128– 137. doi: 10.62737/m1vpdq75 · DOI
  72. Reinikainen H. Kari J. T. Luoma-Aho V. (2020). Generation Z and organizational listening on social media. Media Commun. 8, 185– 196. doi: 10.17645/mac.v8i2.2772 · DOI
  73. Richter N. F. Schubring S. Hauff S. Ringle C. M. Sarstedt M. (2020). When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA. Ind. Manag. Data Syst. 120, 2243– 2267. doi: 10.1108/IMDS-11-2019-0638 · DOI
  74. Riedl R. (2022). Is trust in artificial intelligence systems related to user personality? Review of empirical evidence and future research directions. Electron. Mark. 32, 2021– 2051. doi: 10.1007/s12525-022-00594-4 · DOI
  75. Sadeq N. A. Valdes E. G. Harrison Bush A. L. Andel R. (2018). The role of personality in a regular cognitive monitoring program. Alzheimer Dis. Assoc. Disord. 32, 226– 231. doi: 10.1097/WAD.0000000000000236, PMID: 29461270 · DOI
  76. Salmony F. U. Kanbach D. K. (2022). Personality trait differences across types of entrepreneurs: a systematic literature review. Rev. Manag. Sci. 16, 713– 749. doi: 10.1007/s11846-021-00466-9 · DOI
  77. Sardar S. Tata S. V. Sarkar S. (2024). Examining the influence of source factors and content characteristics of influencers' post on consumer engagement and purchase intention: a moderated analysis. J. Retail. Consum. Serv. 79: 103888. doi: 10.1016/j.jretconser.2024.103888 · DOI
  78. Seyfi S. Kim M. J. Nazifi A. Murdy S. Vo-Thanh T. (2025). Understanding tourist barriers and personality influences in embracing generative AI for travel planning and decision-making. Int. J. Hospit. Manag. 126: 104105. doi: 10.1016/j.ijhm.2025.104105 · DOI
  79. Shin H. Bunosso I. Levine L. R. (2023). The influence of chatbot humour on consumer evaluations of services. Int. J. Consum. Stud. 47, 545– 562. doi: 10.1111/ijcs.12849 · DOI
  80. Shin H.-R. Choi J.-G. (2021). The moderating effect of ‘generation’ on the relations between source credibility of social media contents, hotel brand image, and purchase intention. Sustainability 13: 9471. doi: 10.3390/su13169471 · DOI
  81. Sowmya D. Debbata K. Vignesh G. S. Sagar D. S. N. Bavanthula A. R. Pallavi L. (2023). “ Emerging role of healthcare chatbots in improving medical assistance,” in 2023 3rd Asian Conference on Innovation in Technology (ASIANCON). IEEE. p. 1– 6.
  82. Stavrova O. Evans A. M. van Beest I. (2022). The effects of partner extraversion and agreeableness on trust. Personal. Soc. Psychol. Bull. 49, 1028– 1042. doi: 10.1177/01461672221086768, PMID: 35481439 PMC10302358 · DOI
  83. Svendsen G. B. Johnsen J.-A. K. Almås-Sørensen L. Vittersø J. (2013). Personality and technology acceptance: the influence of personality factors on the core constructs of the technology acceptance model. Behav. Inf. Technol. 32, 323– 334. doi: 10.1080/0144929X.2011.553740 · DOI
  84. Tucaković L. Nedeljković B. (2022). Personality and affective correlates of openness to experience from big five and HEXACO personality models: the dual nature of big five openness. J. Pers. Assess. 105, 544– 554. doi: 10.1080/00223891.2022.2117047, PMID: 36121901 · DOI
  85. Venkatesh V. JYL T. Xu X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36: 157. doi: 10.2307/41410412 · DOI · Paper page
  86. Walley K. Goodall S. Humphries A. Huntington J. White D. Asson T. (2017). Key dimensions of personal innovativeness. Int. J. Bus. Innov. Res. 12: 259. doi: 10.1504/IJBIR.2017.081408 · DOI
  87. Wang C. L. (2023). “ Interactive marketing is the new normal” in The Palgrave handbook of interactive marketing (Cham: Springer International Publishing), 1– 12.
  88. Wang L. Li X. Zhu H. Zhao Y. (2023). Influencing factors of livestream selling of fresh food based on a push-pull model: a two-stage approach combining structural equation modeling (SEM) and artificial neural network (ANN). Expert Syst. Appl. 212: 118799. doi: 10.1016/j.eswa.2022.118799 · DOI
  89. Wang X. Luo R. Liu Y. Chen P. Tao Y. He Y. (2023). Revealing the complexity of users’ intention to adopt healthcare chatbots: a mixed-method analysis of antecedent condition configurations. Inf. Process. Manag. 60: 103444. doi: 10.1016/j.ipm.2023.103444 · DOI
  90. Wilmot M. P. Ones D. S. (2022). Agreeableness and its consequences: a quantitative review of meta-analytic findings. Personal. Soc. Psychol. Rev. 26, 242– 280. doi: 10.1177/10888683211073007, PMID: 35220818 · DOI · Paper page
  91. Yang X. Liu Y. Dong J. Li S. (2023). Impact of streamers’ characteristics on sales performance of search and experience products: evidence from Douyin. J. Retail. Consum. Serv. 70: 103155. doi: 10.1016/j.jretconser.2022.103155 · DOI
  92. Yin J. Qiu X. (2021). “ Influence mechanism research on the AI Marketing Technology of Online Shopping Platform on consumers’ purchase intention” in 6th Annual International Conference on Social Science and Contemporary Humanity Development (SSCHD 2020) (Dordrecht, The Netherlands: Atlantis Press), 833– 838.
  93. Zhong H. Han Z. (2023). A systematic review of information source preference research. J. Doc. 80, 255– 274. doi: 10.1108/JD-03-2023-0059 · DOI
  94. Zhu Y. P. Xin L. Wang H. Park H. W. (2025). Effects of AI virtual anchors on brand image and loyalty: insights from perceived value theory and SEM-ANN analysis. Systems 13: 79. doi: 10.3390/systems13020079 · DOI
  95. Zweig D. Webster J. (2003). Personality as a moderator of monitoring acceptance. Comput. Human Behav. 19, 479– 493. doi: 10.1016/S0747-5632(02)00075-4 · DOI

Cited by

Sorted by citations

Authors
Liu, J., & Chen, J.
Affiliations
  • University of Shanghai for Science and Technology (CN)
  • Shanghai University of Sport (CN)
Corresponding author
Chen, Jialiang
ORCID
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2025-08-29
Volume
16
Pages
1454197-1454197
ISSN
1664-1078 (ISSN-L)
DOI
10.3389/fpsyg.2025.1454197
PMC
PMC12425985
OpenAlex
W4413803900
Cited by
10
References
8
Citations by year
2026: 9 · 2025: 1
FWCI
4.01
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Psychology (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
Keywords
Generation Z, purchase intention, Big Five personality traits, extraversion, agreeableness, conscientiousness, neuroticism, openness to experience, personal innovativeness, human-AI interaction, e-commerce, necessary condition analysis

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