基於大數據、自然語言處理、雲端運算、機器學習、自然語言處理、電腦視覺、大型語言模型、機器學習及相關技術方面的進步，人工智慧（AI）的應用已愈來愈廣泛。由於AI的應用不斷滲透到消費者日常生活的各個方面，消費者對這些應用的依賴也逐漸增加。因此，無論是業界還是學術界，都需要了解促使消費者採用這些AI應用的因素。本論文則通過三個研究對美國受試者進行問卷研究來填補這些缺口。
研究一以自我決定理論為基礎，通過探討享樂動機和功利動機如何影響社會臨場感，進而影響消費者對於AI聊天機器人之使用意圖。該研究還顯示，消費者的COVID-19恐懼會加強影響社會臨場感對使用意圖的影響。
在生成式AI（GenAI）的背景下，研究二A和研究二B以社會相互依賴理論為基礎，探討GenAI的思維智能和情感智能如何透過預期成功影響消費者的使用意圖。研究二顯示，可以透過操弄消費者與GenAI關係間之社會距離來改變其對GenAI社會地位之認知。
綜上所述，本研究的發現不僅對AI相關的文獻做出貢獻，而且還能為開發AI聊天機器人和GenAI的企業提供了有價值的實務建議。

Progress in big data, natural language processing, cloud computing, computer vision, large language models, machine learning, and related technologies has driven the widespread adoption of various artificial intelligence (AI) applications. As AI applications permeate many aspects of consumers’ everyday lives, their reliance on these applications also increases. Therefore, it is imperative to identify what motivates consumers to adopt these technologies. This dissertation addresses this question through three studies using survey data from U.S. participants.
Grounded in self-determination theory, study 1 contributes to chatbot research by examining how different types of motivations, particularly intrinsic/hedonic and extrinsic/utilitarian, influence social presence, thereby shaping users’ adoption intentions. The study also reveals that fear arising from the COVID-19 pandemic heightened the influence of perceived social presence on adoption.
In generative AI (GenAI) contexts, studies 2A and study 2B draw on social interdependence theory to investigate how thinking and feeling intelligence of GenAI drive consumers’ adoption intention, with anticipated success acting as a mediator. These two studies also advance consumer research by demonstrating that by manipulating social distance in consumer-GenAI relations, researcher can alter perceptions of GenAI’s superior social status.
Together, these findings not only extend the relevant literature, but also provide practical insights for firms seeking to facilitate consumer adoption of AI-powered chatbots and GenAI.

Abernethy, A. M., & Franke, G. R. (1996). The information content of advertising: A meta-analysis. Journal of Advertising, 25(2), 1–17. https://doi.org/10.1080/00913367.1996.10673496
Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427–445. https://doi.org/10.1007/s12525-020-00414-7
Adhikari, A. (2024, May 6). The chatbot market: Trends, growth, and key players. AIM Research. https://aimresearch.co/https://aimresearch.co/market-industry/the-chatbot-market-trends-growth-and-key-players
Adler, N. E., Epel, E. S., Castellazzo, G., & Ickovics, J. R. (2000). Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy White women. Health Psychology, 19(6), 586–592. https://doi.org/10.1037/0278-6133.19.6.586
Aggarwal, P., & McGill, A. L. (2007). Is that car smiling at me? Schema congruity as a basis for evaluating anthropomorphized products. Journal of Consumer Research, 34(4), 468–479. https://doi.org/10.1086/518544
Ajzen, I. (1985). From intentions to action: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action-control: From cognition to behavior (pp. 11–39). Springer.
Ajzen, I. (1988). Attitudes, personality, and behavior. Dorsey Press.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall, Inc.
Akhtar, P., Ghouri, A. M., Ashraf, A., Lim, J. J., Khan, N. R., & Ma, S. (2024). Smart product platforming powered by AI and Generative AI: Personalization for the circular economy. International Journal of Production Economics, 273, Article 109283. https://doi.org/10.1016/j.ijpe.2024.109283
Albarracín, D., Johnson, B. T., Fishbein, M., & Muellerleile, P. A. (2001). Theories of reasoned action and planned behavior as models of condom use: A meta-analysis. Psychological Bulletin, 127(1), 142–161. https://doi.org/10.1037/0033-2909.127.1.142
Albert, D. A., & Smilek, D. (2023). Comparing attentional disengagement between Prolific and MTurk samples. Scientific Reports, 13(1), Article 20574. https://doi.org/10.1038/s41598-023-46048-5
Alessandro, G., Dimitri, O., Cristina, B., & Anna, M. (2024). The emotional impact of generative ai: Negative emotions and perception of threat. Behaviour & Information Technology. Advance online publication. https://doi.org/10.1080/0144929X.2024.2333933
Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2023). Fear from COVID-19 and technology adoption: The impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 31(3), 1293–1308. https://doi.org/10.1080/10494820.2020.1830121
Al-Nabhani, K., Wilson, A., & McLean, G. (2022). Examining consumers’ continuous usage of multichannel retailers’ mobile applications. Psychology & Marketing, 39(1), 168–195. https://doi.org/10.1002/mar.21585
Anderson, C., & Willer, R. (2014). Do status hierarchies benefit groups? A bounded functionalist account of status. In J. T. Cheng, J. L. Tracy & C. Anderson (Eds.) The psychology of social status (pp. 47–70). Springer. https://doi.org/10.1007/978-1-4939-0867-7_3
Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051
Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of other in the self scale and the structure of interpersonal closeness. Journal of Personality and Social Psychology, 63(4), 596–612. https://doi.org/10.1037/0022-3514.63.4.596
Arora, N., Chakraborty, I., & Nishimura, Y. (2024). AI-human hybrids for marketing research: Leveraging LLMs as collaborators. Journal of Marketing. Advance online publication. https://doi.org/10.1177/00222429241276529
Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644–656. https://doi.org/10.1086/209376
Baek, T. H., & Kim, M. (2023). Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence. Telematics and Informatics, 83, Article 102030. https://doi.org/10.1016/j.tele.2023.102030
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
Bagozzi, R. P., Brady, M. K., & Huang, M. H. (2022). AI service and emotion. Journal of Service Research, 25(4), 499–504. https://doi.org/10.1177/10946705221118579
Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184–206. https://doi.org/10.1177/0092070399272005
Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), Article 63. https://doi.org/10.1007/s12525-023-00680-1
Batra, R., & Ahtola, O. T. (1991). Measuring the hedonic and utilitarian sources of consumer attitudes. Marketing Letters, 2(2), 159–170. https://doi.org/10.1007/BF00436035
Battle, E. S. (1965). Motivational determinants of academic task persistence. Journal of Personality and Social Psychology, 2(2), 209–218. https://doi.org/10.1037/h0022442
Becker, S. W., & Blaloff, N. (1969). Organization structure and complex problem solving. Administrative Science Quarterly, 14(2), 45–57. https://doi.org/10.2307/2391104
Berger, J., Cohen, B., & Zelditch, M. Jr. (1972). Status characteristics and social interaction. American Sociological Review, 37, 241–255. https://doi.org/10.2307/2093465
Bertini, M., Ofek, E., & Ariely, D. (2009). The impact of add-on features on consumer product evaluations. Journal of Consumer Research, 36(1), 17–28. https://doi.org/10.1086/596717
Bhutyani, S., & Gaur, H. V. S. (2023). Use generative AI with Amazon EMR, Amazon Bedrock, and English SDK for Apache Spark to unlock insights. Amazon Web Services. https://aws.amazon.com/blogs/big-data/use-generative-ai-with-amazon-emr-amazon-bedrock-and-english-sdk-for-apache-spark-to-unlock-insights/
Biocca, F., Harms, C., & Burgoon, J. K. (2003). Toward a more robust theory and measure of social presence: Review and suggested criteria. Presence: Teleoperators & Virtual Environments, 12(5), 456–480. https://doi.org/10.1162/105474603322761270
Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632–658. https://doi.org/10.1007/s11747-020-00762-y
Bogardus, E. S. (1959). Social distance. Antioch Press.
Botti, S., & McGill, A. L. (2011). The locus of choice: Personal causality and satisfaction with hedonic and utilitarian decisions. Journal of Consumer Research, 37(6), 1065–1078. https://doi.org/10.1086/656570
Braun-LaTour, K. A., LaTour, M. S., Pickrell, J. E., & Loftus, E. F. (2004). How and when advertising can influence memory for consumer experience. Journal of Advertising, 33(4), 7–25. https://doi.org/10.1080/00913367.2004.10639171
Broekhuizen, T., Dekker, H., de Faria, P., Firk, S., Nguyen, D. K., & Sofka, W. (2023). AI for managing open innovation: Opportunities, challenges, and a research agenda. Journal of Business Research, 167, Article 114196. https://doi.org/10.1016/j.jbusres.2023.114196
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, Article 103790. https://doi.org/10.1016/j.jretconser.2024.103790
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv. https://doi.org/10.48550/arXiv.2303.12712
Byrne, D. (1971). The attraction paradigm. New York: Academic Press.
Caliskan, F., Idug, Y., Uvet, H., Gligor, N., & Kayaalp, A. (2024). Social comparison theory: A review and future directions. Psychology & Marketing, 41(11), 2823–2840. https://doi.org/10.1002/mar.22087
Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, Article 123247. https://doi.org/10.1016/j.techfore.2024.123247
Canevello, A., & Crocker, J. (2010). Creating good relationships: Responsiveness, relationship quality, and interpersonal goals. Journal of Personality and Social Psychology, 99(1), 78–106. https://doi.org/10.1037/a0018186
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A comprehensive survey of ai-generated content (AIGC): A history of generative AI from GAN to ChatGPT. arXiv. https://doi.org/10.48550/arXiv.2303.04226
Caruelle, D., Shams, P., Gustafsson, A., & Lervik-Olsen, L. (2022). Affective computing in marketing: practical implications and research opportunities afforded by emotionally intelligent machines. Marketing Letters, 33(1), 163–169. https://doi.org/10.1007/s11002-021-09609-0
Castilla, E. J., & Benard, S. (2010). The paradox of meritocracy in organizations. Administrative Science Quarterly, 55(4), 543–676. https://doi.org/10.2189/asqu.2010.55.4.543
Celiktutan, B., Klesse, A. K., & Tuk, M. A. (2024). Acceptability lies in the eye of the beholder: Self-other biases in GenAI collaborations. International Journal of Research in Marketing. Advance online publication. https://doi.org/10.1016/j.ijresmar.2024.05.006
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
Chakraborty, D., Kar, A. K., Patre, S., & Gupta, S. (2024). Enhancing trust in online grocery shopping through generative AI chatbots. Journal of Business Research, 180, Article 114737. https://doi.org/10.1016/j.jbusres.2024.114737
Chang, W., & Park, J. (2024). A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set. Journal of Retailing and Consumer Services, 78, Article 103743. https://doi.org/10.1016/j.jretconser.2024.103743
Chaturvedi, R., Verma, S., & Srivastava, V. (2024). Empowering AI companions for enhanced relationship marketing. California Management Review, 66(2), 65–90. https://doi.org/10.1177/00081256231215838
Chaves, A. P., & Gerosa, M. A. (2021). How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human–Computer Interaction, 37(8), 729–758.
Chen, K., Tsai, C.-F., Hu, Y.-H., & Hu, C.-W. (2024a). The effect of review visibility and diagnosticity on review helpfulness—An accessibility-diagnosticity theory perspective. Decision Support Systems, 178, Article 114145. https://doi.org/10.1016/j.dss.2023.114145
Chen, S., Kou, S., & Lv, L. (2024b). Stand out or fit in: Understanding consumer minimalism from a social comparison perspective. Journal of Business Research, 170, Article 114307. https://doi.org/10.1016/j.jbusres.2023.114307
Chen, S., Li, X., Liu, K., & Wang, X. (2023). Chatbot or human? The impact of online customer service on consumers' purchase intentions. Psychology & Marketing, 40(11), 2186–2200. https://doi.org/10.1002/mar.21862
Cheng, J., & Tracy, J. L. (2014). Toward a unified science of hierarchy: Dominance and prestige are two fundamental pathways to human social rank. In J. T. Cheng, J. L. Tracy, & C. Anderson (Eds.), The psychology of social status (pp. 3–27). Springer.
Cheng, S. L., Lu, L., Xie, K., & Vongkulluksn, V. W. (2020). Understanding teacher technology integration from expectancy-value perspectives. Teaching and Teacher Education, 91, Article 103062. https://doi.org/10.1016/j.tate.2020.103062
Cheng, Y., & Jiang, H. (2020). How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting & Electronic Media, 64(4), 592–614. https://doi.org/10.1080/08838151.2020.1834296
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
Choi, W. J., & Winterich, K. P. (2013). Can brands move in from the outside? How moral identity enhances out-group brand attitudes. Journal of Marketing, 77(2), 96–111. https://doi.org/10.1509/jm.11.0544
Chong, T., Yu, T., Keeling, D. I., & de Ruyter, K. (2021). AI-chatbots on the services frontline addressing the challenges and opportunities of agency. Journal of Retailing and Consumer Services, 63. Advance online publications. https://doi.org/10.1016/j.jretconser.2021.102735
Christensen, J., Hansen, J. M., & Wilson, P. (2024). Understanding the role and impact of Generative Artificial Intelligence (AI) hallucination within consumers’ tourism decision-making processes. Current Issues in Tourism. Advance online publication. https://doi.org/10.1080/13683500.2023.2300032
Claeys, C., Swinnen, A., & Abeele, P. V. (1995). Consumer’s means-end chains for “think” and “feel” products. International Journal of Research in Marketing, 12(3), 193–208. https://doi.org/10.1016/0167-8116(95)00021-S
Claffey, E., & Brady, M. (2017). Examining consumers’ motivations to engage in firm‐hosted virtual communities. Psychology & Marketing, 34(4), 356–375. https://doi.org/10.1002/mar.20994
Cobb, S. (1976). Social support as a moderator of life stress. Psychosomatic Medicine, 38(5), 300–314. https://doi.org/10.1097/00006842-197609000-00003
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates, Inc.
Cohen, J., Cohen, P., West, S.G., and Aiken, L.S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Earlbaum Associates.
Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310–357. https://doi.org/10.1037/0033-2909.98.2.310
Cohen, S., Underwood, L. G., & Gottlieb, B. H. (Eds.) (2000). Social support measurement and intervention: A guide for health and social scientists. Oxford University Press. https://doi.org/10.1093/med:psych/9780195126709.001.0001
Cong-Lem, N., Soyoof, A., & Tsering, D. (2024). A systematic review of the limitations and associated opportunities of ChatGPT. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2024.2344142
Connors, S., Khamitov, M., Thomson, M., & Perkins, A. (2021). They’re just not that into you: How to leverage existing consumer–brand relationships through social psychological distance. Journal of Marketing, 85(5), 92–108. https://doi.org/10.1177/0022242920984492
Cook, S., Hagiu, A., & Wright, J. (2024). Turn generative AI from an existential threat into a competitive advantage. Harvard Business Review, 102(1), 118–125.
Croissant, C. (2021). Why participants get banned. Prolific. https://www.prolific.com/resources/why-participants-get-banned
Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T. (2022). Blame the bot: Anthropomorphism and anger in customer–chatbot interactions. Journal of Marketing, 86(1), 132–148. https://doi.org/10.1177/00222429211045687
Cuddy, A. J. C., Fiske, S. T., Kwan, V. S. Y., Glick, P., Demoulin, S., Leyens, J.-P., Bond, M. H., Croizet, J.-C., Ellemers, N., Sleebos, E., Htun, T. T., Kim, H.-J., Maio, G., Perry, J., Petkova, K., Todorov, V., Rodríguez-Bailón, R., Morales, E., Moya, M., ... Ziegler, R. (2009). Stereotype content model across cultures: Towards universal similarities and some differences. British Journal of Social Psychology, 48(1), 1–33. https://doi.org/10.1348/014466608X314935
D’Angelo, J. K., Diehl, K., & Cavanaugh, L. A. (2019). Lead by example? Custom-made examples created by close others lead consumers to make dissimilar choices. Journal of Consumer Research, 46(4), 750–773. https://doi.org/10.1093/jcr/ucz019
Dang, J., & Liu, L. (2022). Implicit theories of the human mind predict competitive and cooperative responses to AI robots. Computers in Human Behavior, 134, Article 107300. https://doi.org/10.1016/j.chb.2022.107300
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://psycnet.apa.org/doi/10.1111/j.1559-1816.1992.tb00945.x
De Freitas, J., Uğuralp, A. K., Oğuz‐Uğuralp, Z., & Puntoni, S. (2023). Chatbots and mental health: Insights into the safety of generative AI. Journal of Consumer Psychology, 34(3), 481–491. https://doi.org/10.1002/jcpy.1393
De Kwaadsteniet, E. W., & Van Dijk, E. (2010). Social status as a cue for tacit coordination. Journal of Experimental Social Psychology, 46(3), 515–524. https://doi.org/10.1016/j.jesp.2010.01.005
Debrot, A., Schoebi, D., Perrez, M., & Horn, A. B. (2013). Touch as an interpersonal emotion regulation process in couples’ daily lives: The mediating role of psychological intimacy. Personality and Social Psychology Bulletin, 39(10), 1373–1385. https://doi.org/10.1177/0146167213497592
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum.
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Deci, E. L., & Ryan, R. M. (2014). Autonomy and need satisfaction in close relationships: Relationships motivation theory. In N. Weinstein (Ed.), Human motivation and interpersonal relationships: Theory, research, and applications (pp. 53–73). Springer Science + Business Media. https://doi.org/10.1007/978-94-017-8542-6_3
Dellande, S., Gilly, M. C., & Graham, J. L. (2004). Gaining compliance and losing weight: The role of the service provider in health care services. Journal of Marketing, 68(3), 78–91. https://doi.org/10.1509/jmkg.68.3.78.34764
Deloitte (2024). Now decides next: Getting real about Generative AI (Deloitte’s state of generative ai in the enterprise quarter two report). https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-state-of-gen-ai-report-q2.pdf
Deutsch, M. (1948). The effects of cooperation and competition upon group process (Doctoral dissertation, Massachusetts Institute of Technology). DSpace@MIT Home.
Deutsch, M. (1949a). A theory of co-operation and competition. Human Relations, 2(2), 129–152. https://doi.org/10.1177/001872674900200204
Deutsch, M. (1949b). An experimental study of the effects of co-operation and competition upon group process. Human Relations, 2(3), 199–231. https://doi.org/10.1177/001872674900200301
Deutsch, M. (1958). Trust and suspicion. Journal of Conflict Resolution, 2(4), 265–279. https://doi.org/10.1177/002200275800200401
Deutsch, M. (1960). The effect of motivational orientation upon trust and suspicion. Human Relations, 13(2), 123–139. https://doi.org/10.1177/001872676001300202
Deutsch, M. (1962). Cooperation and trust: Some theoretical notes. In M. R. Jones (Ed.), Nebraska Symposium on Motivation, 1962 (pp. 275–320). University of Nebraska Press.
Deutsch, M. (1973). The resolution of conflict: Constructive and destructive processes. Yale University Press.
Deutsch, M. (1985). Distributive justice: A social-psychological perspective. Yale University Press.
Deutsch, M. (2000). Cooperation and competition. In M. Deutsch & P. Coleman (Eds.), Handbook of conflict resolution: Theory and practice (pp. 21–40). Jossey-Bass.
Deutsch, M. (2001). Cooperation and conflict resolution: Implications for consulting psychology. Consulting Psychology Journal: Practice and Research, 53(2), 76–81. https://doi.org/10.1037/1061-4087.53.2.76
Deutsch, M. (2011). Interdependence and psychological orientation. In P. T. Coleman (Ed.), Conflict, interdependence, and justice: The intellectual legacy of Morton Deutsch (pp. 247–271). Springer Science + Business Media. https://doi.org/10.1007/978-1-4419-9994-8_11
Deutsch, M. (2012). A theory of cooperation—Competition and beyond. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (pp. 275–294). Sage Publications Ltd.
Deutsch, M., & Krauss, R. M. (1962). Studies of interpersonal bargaining. Journal of Conflict Resolution, 6(1), 52–76. https://doi.org/10.1177/002200276200600107
Dinh, C. M., & Park, S. (2024). How to increase consumer intention to use Chatbots? An empirical analysis of hedonic and utilitarian motivations on social presence and the moderating effects of fear across generations. Electronic Commerce Research, 24(4), 2427–2467. https://doi.org/10.1007/s10660-022-09662-5
Do, K. T., Gip, H., Guchait, P., Wang, C. Y., & Baaklini, E. S. (2023). Empathetic creativity for frontline employees in the age of service robots: conceptualization and scale development. Journal of Service Management, 34(3), 433–466. https://doi.org/10.1108/JOSM-09-2021-0352
Dogerlioglu-Demir, K., Akpinar, E., & Ceylan, M. (2022). Combating the fear of COVID‐19 through shared accommodations: Does perceived human presence create a sense of social connectedness? Journal of Consumer Behaviour, 21(2), 400–413. https://doi.org/10.1002/cb.2015
Douglas, B. D., Ewell, P. J., & Brauer, M. (2023). Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA. Plos One, 18(3), Article e0279720. https://doi.org/10.1371/journal.pone.0279720
Driskell, J. E., & Mullen, B. (1990). Status, expectations, and behavior: A meta-analytic review and test of the theory. Personality and Social Psychology Bulletin, 16(3), 541–553. https://doi.org/10.1177/0146167290163012
Duong, C. D., Ngo, T. V. N., Khuc, T. A., Tran, N. M., & Nguyen, T. P. T. (2024a). Unraveling the dark side of ChatGPT: A moderated mediation model of technology anxiety and technostress. Information Technology & People. Advance online publication. https://doi.org/10.1108/ITP-11-2023-1151
Duong, C. D., Nguyen, T. H., Ngo, T. V. N., Pham, T. T. P., Vu, A. T., & Dang, N. S. (2024b). Using generative artificial intelligence (ChatGPT) for travel purposes: parasocial interaction and tourists’ continuance intention. Tourism Review. Advance online publication. https://doi.org/10.1108/TR-01-2024-0027
Elemo, A. S., Satici, S. A., & Griffiths, M. D. (2023). The fear of COVID-19 scale: psychometric properties of the Ethiopian Amharic version. International Journal of Mental Health and Addiction, 21(2), 878–889. https://doi.org/10.1007/s11469-020-00448-0
Epstein, Z., Hertzmann, A., & Investigators of Human Creativity (2023). Art and the science of generative AI. Science, 380(6650), 1110–1111. https://doi.org/10.1126/science.adh4451
Escoe, B., Martin, N. S., & Salerno, A. (2024). That’s so cringeworthy! Understanding what cringe is and why we want to share it. Journal of Marketing Research. Advance online publication. https://doi.org/10.1177/00222437241305104
Esmaeilzadeh, H., & Vaezi, R. (2022). Conscious empathic AI in service. Journal of Service Research, 25(4), 549–564. https://doi.org/10.1177/10946705221103531
Feeney, B. C., & Collins, N. L. (2015). A new look at social support: A theoretical perspective on thriving through relationships. Personality and Social Psychology Review, 19(2), 113–147. https://doi.org/10.1177/1088868314544222
Feldman, J. M., & Lynch, J. G. (1988). Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. Journal of Applied Psychology, 73(3), 421–435.
Festinger, L. (1954a). A theory of social comparison processes. Human Relations, 7(2), 117–140. https://doi.org/10.1177/001872675400700202
Festinger, L. (1954b). Motivation leading to social behavior. In M. R. Jones (Ed.), Nebraska symposium on motivation (Vol. 2, pp. 191–218). University of Nebraska Press.
Fibel, B., & Hale, W. D. (1978). The Generalized Expectancy for Success Scale: A new measure. Journal of Consulting and Clinical Psychology, 46(5), 924–931. https://doi.org/10.1037/0022-006X.46.5.924
Fishbein, M. (1980). A theory of reasoned action: Some applications and implications. In H. Howe & M. Page (Eds.), Nebraska symposium on motivation (Vol. 27, pp. 65–116). University of NebraskaPress.
Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology Press.
Fiske, S. T., Cuddy, A. J. C., Glick, P., & Xu, J. (2002). A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. Journal of Personality and Social Psychology, 82(6), 878–902. https://doi.org/10.1037/0022-3514.82.6.878
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
Flavián, C., Akdim, K., & Casaló, L. V. (2023). Effects of voice assistant recommendations on consumer behavior. Psychology & Marketing, 40(2), 328–346. https://doi.org/10.1002/mar.21765
Fleming, P., Watson, S. J., Patouris, E., Bartholomew, K. J., & Zizzo, D. J. (2017). Why do people file share unlawfully? A systematic review, meta-analysis and panel study. Computers in Human Behavior, 72, 535–548. https://doi.org/10.1016/j.chb.2017.02.014
Forbes Advisor (2024). Top AI statistics and trends. https://www.forbes.com/advisor/in/business/ai-statistics/
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/10.2307/3151312
Fragale, A. R., Overbeck, J. R., & Neale, M. A. (2011). Resources versus respect: Social judgments based on targets’ power and status positions. Journal of Experimental Social Psychology, 47(4), 767–775. https://doi.org/10.1016/j.jesp.2011.03.006
Gagné, M.-H., Piché, G., Clément, M.-È., & Villatte, A. (2021). Families in confinement: A pre–post COVID-19 study. Couple and Family Psychology: Research and Practice, 10(4), 260–270. https://doi.org/10.1037/cfp0000179
Galea, S., Merchant, R. M., & Lurie, N. (2020). The mental health consequences of COVID-19 and physical distancing: The need for prevention and early intervention. JAMA Internal Medicine, 180(6), 817–818. https://doi.org/10.1001/jamainternmed.2020.1562
Gao, H., Winterich, K. P., & Zhang, Y. (2016). All that glitters is not gold: How others’ status influences the effect of power distance belief on status consumption. Journal of Consumer Research, 43(2), 265–281. https://doi.org/10.1093/jcr/ucw015
Gardner, H. E. (1983). Frames of mind: The theory of multiple intelligences. Basic books.
Gaskin, J., James, M., & Lim, J. (2019a). HTMT Plugin for AMOS. Gaskination’s StatWiki. https://statwiki.gaskination.com/index.php/Plugins
Gefen, D., & Straub, D. W. (2004). Consumer trust in B2C e-commerce and the importance of social presence: Experiments in e-products and e-services. Omega, 32(6), 407–424. https://doi.org/10.1016/j.omega.2004.01.006
Geiger, A., Horbel, C., & Germelmann, C. C. (2018). “Give and take”: How notions of sharing and context determine free peer-to-peer accommodation decisions. Journal of Travel & Tourism Marketing, 35(1), 5–15. https://doi.org/10.1080/10548408.2016.1231101
Gerber, J. P., Wheeler, L., & Suls, J. (2018). A social comparison theory meta-analysis 60+ years on. Psychological Bulletin, 144(2), 177–197. https://doi.org/10.1037/bul0000127
Giebelhausen, M., & Poehlman, T. A. (2024). Permissibility vs. feasibility: AI in service from a CX perspective. Journal of Services Marketing, 38(1), 87–102. https://doi.org/10.1108/JSM-06-2023-0210
Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316. https://doi.org/10.1016/j.chb.2019.01.020
Goertzel, B. (2023). Generative AI vs. AGI: The cognitive strengths and weaknesses of modern LLMs. arXiv. https://doi.org/10.48550/arXiv.2309.10371
Goleman, D. (1995). Emotional intelligence. Bantam Books.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, &K. Q. Weinberger (Eds), Advances in Neural Information Processing Systems 27: 28th Annual Conference on Neural Information Processing Systems 2014 (pp. 2672–2680). Neural Information Processing Systems Foundation, Inc. (NeurIPS).
Gordon, A. (2024). How to improve data quality in online studies. Prolific. https://www.prolific.com/resources/how-to-improve-data-quality-in-online-studies
Guo, B., & Wang, D. (2024). Will online shopping lead to more brand loyalty than offline shopping? The role of uncertainty avoidance. Journal of Marketing Research, 61(1), 92–109. https://doi.org/10.1177/00222437231153075
Haley, E., & Pittman, M. (2022). Remembering the FCB grid: Thinking, feeling, and involvement in the age of social media. Journal of Advertising, 51(3), 323–335. https://doi.org/10.1080/00913367.2022.2026845
Han, H., Kim, S., Hailu, T. B., Al-Ansi, A., Lee, J., & Kim, J. J. (2024a). Effects of cognitive, affective and normative drivers of artificial intelligence ChatGPT on continuous use intention. Journal of Hospitality and Tourism Technology, 15(4), 629–647. https://doi.org/10.1108/JHTT-11-2023-0363
Han, H., Kim, S., Hailu, T. B., Al-Ansi, A., Loureiro, S. M. C., & Kim, J. J. (2024b). Determinants of approach behavior for ChatGPT and their configurational influence in the hospitality and tourism sector: A cumulative prospect theory. International Journal of Contemporary Hospitality Management. Advance online publication. https://doi.org/10.1108/IJCHM-07-2023-1072
Harris, L. T., & Fiske, S. T. (2008). Diminishing vertical distance: Power and social status as barriers to intergroup reconciliation. In A. Nadler, T. E. Malloy, & J. D. Fisher, (Eds.), The social psychology of intergroup reconciliation (pp. 301–318). Oxford University Press.
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.
Haynie, J. J., Richardson, H. A., Fuller, B., Martin, C. L., & Bush, J. (2024). Coworker injustices and their delegated authority: Developing an indirect actor model of supervisor justice. Journal of Management, 50(5), 1714–1742. https://doi.org/10.1177/01492063231155826
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
Hermann, E., & Puntoni, S. (2024). Artificial intelligence and consumer behavior: From predictive to generative AI. Journal of Business Research, 180, Article 114720. https://doi.org/10.1016/j.jbusres.2024.114720
Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92–101. https://doi.org/10.2307/1251707
Hofstede, G. (1991). Cultures and organizations: Software of the mind. McGraw-Hill.
Hollebeek, L. D., Menidjel, C., Sarstedt, M., Jansson, J., & Urbonavicius, S. (2024). Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research. Psychology & Marketing, 41(4), 880–898. https://doi.org/10.1002/mar.21957
Hollebeek, L. D., Sprott, D. E., & Brady, M. K. (2021). Rise of the machines? Customer engagement in automated service interactions. Journal of Service Research, 24(1), 3–8. https://doi.org/10.1177/1094670520975110
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
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
Huang, M. H., & Rust, R. T. (2021a). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9
Huang, M. H., & Rust, R. T. (2021b). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41. https://doi.org/10.1177/1094670520902266
Huang, M. H., & Rust, R. T. (2022a). A framework for collaborative artificial intelligence in marketing. Journal of Retailing, 98(2), 209–223. https://doi.org/10.1016/j.jretai.2021.03.001
Huang, M. H., & Rust, R. T. (2022b). AI as customer. Journal of Service Management, 33(2), 210–220. https://doi.org/10.1108/JOSM-11-2021-0425
Huang, M. H., & Rust, R. T. (2024). The caring machine: Feeling AI for customer care. Journal of Marketing, 88(5), 1–23. https://doi.org/10.1177/00222429231224748
Huang, M. H., Rust, R., & Maksimovic, V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review, 61(4), 43–65. https://doi.org/10.1177/0008125619863436
Huang, S. Y., & Lee, C. J. (2022). Predicting continuance intention to fintech chatbot. Computers in Human Behavior, 129, Article 107027. https://doi.org/10.1016/j.chb.2021.107027
Huang, X. (I.), Li, X., & Zhang, M. (2013). “Seeing” the social roles of brands: How physical positioning influences brand evaluation. Journal of Consumer Psychology, 23(4), 509–514. https://doi.org/10.1016/j.jcps.2013.03.001
Huh, J., Nelson, M. R., & Russell, C. A. (2023). ChatGPT, AI advertising, and advertising research and education. Journal of Advertising, 52(4), 477–482. https://doi.org/10.1080/00913367.2023.2227013
Hyman, H. H. (1942). The psychology of status. Archives of Psychology (Columbia University).
Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management, 48(1), 1–8. https://doi.org/10.1016/j.im.2010.09.001
Im, S., Bayus, B. L., & Mason, C. H. (2003). An empirical study of innate consumer innovativeness, personal characteristics, and new-product adoption behavior. Journal of the Academy of Marketing Science, 31(1), 61–73. https://doi.org/10.1177/0092070302238602
Insider Intelligence (2021, February). Chatbot market in 2021: Stats, trends, and companies in the growing AI chatbot industry. Insider Inc. https://www.businessinsider.com/chatbot-market-stats-trends
Jiang, K., Qin, M., & Li, S. (2022). Chatbots in retail: How do they affect the continued use and purchase intentions of Chinese consumers? Journal of Consumer Behaviour, 21(4), 756–772. https://doi.org/10.1002/cb.2034
Jiménez-Barreto, J., Rubio, N., & Molinillo, S. (2021). “Find a flight for me, Oscar!” Motivational customer experiences with chatbots. International Journal of Contemporary Hospitality Management, 33(11), 3860–3882. https://doi.org/10.1108/IJCHM-10-2020-1244
Jobst, L. J., Bader, M., & Moshagen, M. (2023). A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods, 28(1), 207–221. https://doi.org/10.1037/met0000423
Johnson, D. W. (2003). Social interdependence: Interrelationships among theory, research, and practice. American Psychologist, 58(11), 934–945. https://doi.org/10.1037/0003-066X.58.11.934
Johnson, D. W., & Johnson, R. T. (1974). Instructional goal structure: Cooperative, competitive, or individualistic. Review of Educational Research, 44(2), 213–240. https://doi.org/10.2307/1170165
Johnson, D. W., & Johnson, R. T. (1989). Cooperation and competition: Theory and research. Interaction Book Company.
Johnson, D. W., & Johnson, R. T. (1994). Learning together and alone: Cooperative, competitive, and individualistic learning (4th ed.). Allyn and Bacon.
Johnson, D. W., & Johnson, R. T. (1999). Learning together and alone: Cooperative, competitive, and individualistic learning (5th ed.). Allyn and Bacon.
Johnson, D. W., & Johnson, R. T. (2003). Student motivation in co-operative groups: Social interdependence theory. In R. M. Gillies & A. F. Ashman (Eds.), Co-operative learning: The social and intellectual outcomes of learning in groups (pp. 136–176). Routledge.
Johnson, D. W., & Johnson, R. T. (2004). Cooperation and the use of technology. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed., pp. 785–811). Lawrence Erlbaum Associates Publishers.
Johnson, D. W., & Johnson, R. T. (2005). New developments in social interdependence theory. Genetic, Social, and General Psychology Monographs, 131(4), 285–358. https://doi.org/10.3200/MONO.131.4.285-358
Johnson, D. W., & Johnson, R. T. (2008). Social interdependence theory and cooperative learning: The teacher’s role. In R. M. Gillies, A. Ashman, & J. Terwel (Eds.), The teacher’s role in implementing cooperative learning in the classroom (pp. 9–37). Springer. https://doi.org/10.1007/978-0-387-70892-8_1
Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365–379. https://doi.org/10.3102/0013189X09339057
Johnson, D. W., & Johnson, R. T. (2011). Intellectual legacy: Cooperation and competition. In P. T. Coleman (Ed.), Conflict, interdependence, and justice: The intellectual legacy of Morton Deutsch (pp. 41–63). Springer Science + Business Media. https://doi.org/10.1007/978-1-4419-9994-8_3
Johnson, D. W., & Johnson, R. T. (2012). Social interdependence theory. In D. J. Christie (Ed.), Encyclopedia of peace psychology. Wiley-Blackwell. https://doi.org/10.1002/9780470672532.wbepp257
Johnson, D. W., & Johnson, R. T. (2015). Theoretical approaches to cooperative learning. In R. Gillies (Ed.), Collaborative learning: Developments in research and practice (pp. 17–46). Nova.
Johnson, D. W., Johnson, R. T., Tjosvold, D., & Roseth, C. J. (2019). Morton Deutsch: Celebrating his theorizing and research. Negotiation and Conflict Management Research, 12(1), 89–102. https://doi.org/10.1111/ncmr.12122
Johnson, D. W., Skon, L., & Johnson, R. (1980). Effects of cooperative, competitive, and individualistic conditions on children’s problem-solving performance. American Educational Research Journal, 17(1), 83–93. https://doi.org/10.2307/1162510
Johnson, R. T., & Johnson, D. W. (1979). Type of task and student achievement and attitudes in interpersonal cooperation, competition, and individualization. The Journal of Social Psychology, 108(1), 37–48. https://doi.org/10.1080/00224545.1979.9711959
Johnson, R. T., Johnson, D. W., & Bryant, B. (1973). Cooperation and competition in the classroom. The Elementary School Journal, 74(3), 172–181. https://doi.org/10.1086/460817
Jones, J. T., Pelham, B. W., Carvallo, M., & Mirenberg, M. C. (2004). How do I love thee? Let me count the Js: Implicit egotism and interpersonal attraction. Journal of Personality and Social Psychology, 87 (5), 665–683. https://doi.org/10.1037/0022-3514.87.5.665
Jung, T., Bae, S., Moorhouse, N., & Kwon, O. (2024). The effects of experience-technology fit (ETF) on consumption behavior: Extended reality (XR) visitor experience. Information Technology & People, 37(5), 2006–2034. https://doi.org/10.1108/ITP-01-2023-0100
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
Kakkar, H., & Sivanathan, N. (2022). The impact of leader dominance on employees’ zero-sum mindset and helping behavior. Journal of Applied Psychology, 107(10), 1706–1724. https://doi.org/10.1037/apl0000980
Kalimo, R., Taris, T. W., & Schaufeli, W. B. (2003). The effects of past and anticipated future downsizing on survivor well-being: An equity perspective. Journal of Occupational Health Psychology, 8(2), 91–109. https://doi.org/10.1037/1076-8998.8.2.91
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
Kasser, T., & Ryan, R. M. (1996). Further examining the American dream: Differential correlates of intrinsic and extrinsic goals. Personality and Social Psychology Bulletin, 22(3), 280–287. https://doi.org/10.1177/0146167296223006
Kayis, A. R., Satici, B., Deniz, M. E., Satici, S. A., & Griffiths, M. D. (2021). Fear of Covid-19, loneliness, smartphone addiction, and mental wellbeing among the Turkish general population: A serial mediation model. Behaviour & Information Technology, 41(11), 2484–2496. https://doi.org/10.1080/0144929X.2021.1933181
Khan, H., Sararueangpong, P., Mathmann, F., & Wang, D. (2024). Consumers’ promotion focus mitigates the negative effects of chatbots on purchase likelihood. Journal of Consumer Behaviour, 23(3), 1528–1539. https://doi.org/10.1002/cb.2289
Kim, J. H., Kim, J., Kim, C., & Kim, S. (2023a). Do you trust ChatGPTs? Effects of the ethical and quality issues of generative AI on travel decisions. Journal of Travel & Tourism Marketing, 40(9), 779–801. https://doi.org/10.1080/10548408.2023.2293006
Kim, J., Kim, J. H., Kim, C., & Park, J. (2023b). Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations. Journal of Retailing and Consumer Services, 75, Article 103494. https://doi.org/10.1016/j.jretconser.2023.103494
Kim, K., Zhang, M., & Li, X. (2008). Effects of temporal and social distance on consumer evaluations. Journal of Consumer Research, 35(4), 706–713. https://doi.org/10.1086/592131
Kirshner, S. N. (2024). GPT and CLT: The impact of ChatGPT’s level of abstraction on consumer recommendations. Journal of Retailing and Consumer Services, 76, Article 103580. https://doi.org/10.1016/j.jretconser.2023.103580
Koukova, N. T., Wang, R. J. H., & Isaac, M. S. (2023). “If you loved our product”: Do conditional review requests harm retailer loyalty? Journal of Retailing, 99(1), 85–101. https://doi.org/10.1016/j.jretai.2022.09.002
Kovács, B. (2024). The Turing test of online reviews: Can we tell the difference between human-written and GPT-4-written online reviews? Marketing Letters, 35, 651–666. https://doi.org/10.1007/s11002-024-09729-3
LaBar, K. S. (2016). Fear and anxiety. In L. Feldman Barrett, M. Lewis & J. M. Haviland-Jones (Eds.), Handbook of emotions (4th ed., pp. 751–773). Guilford Publications.
Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Magazine, 38(4), 13–26. https://doi.org/10.1609/aimag.v38i4.2744
Larsen, R. J. (2000). Toward a science of mood regulation. Psychological Inquiry, 11(3), 129–141. https://doi.org/10.1207/S15327965PLI1103_01
Laumann, E. O., & Senter, R. (1976). Subjective social distance, occupational stratification, and forms of status and class consciousness: A cross-national replication and extension. American Journal of Sociology, 81(6), 1304–1338. https://doi.org/10.1086/226225
Le, K. B. Q., Sajtos, L., Kunz, W. H., & Fernandez, K. V. (2024). The future of work: Understanding the effectiveness of collaboration between human and digital employees in service. Journal of Service Research. Advance online publication. https://doi.org/10.1177/10946705241229419
Leary, M. R., Jongman-Sereno, K. P., & Diebels, K. J. (2014). The pursuit of status: A self-presentational perspective on the quest for social value. In J. T. Cheng, J. L. Tracy, & C. Anderson (Eds.), The psychology of social status (pp. 159–178). Springer.
Lee, J. K., Hansen, S. S., & Lee, S. Y. (2020). The effect of brand personality self-congruity on brand engagement and purchase intention: The moderating role of self-esteem in Facebook. Current Psychology, 39(6), 2116–2128. https://doi.org/10.1007/s12144-018-9898-3
Lee, K. M., & Nass, C. (2004). The multiple source effect and synthesized speech: Doubly-disembodied language as a conceptual framework. Human Communication Research, 30(2), 182–207. https://doi.org/10.1093/hcr/30.2.182
Lee, K. W., & Li, C. Y. (2023). It is not merely a chat: Transforming chatbot affordances into dual identification and loyalty. Journal of Retailing and Consumer Services, 74, Article 103447. https://doi.org/10.1016/j.jretconser.2023.103447
Lee, M., & Park, J. S. (2022). Do parasocial relationships and the quality of communication with AI shopping chatbots determine middle‐aged women consumers’ continuance usage intentions? Journal of Consumer Behaviour. 21(4), 842–854. https://doi.org/10.1002/cb.2043
Lee, S. G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption. Journal of World Business, 48(1), 20–29. https://doi.org/10.1016/j.jwb.2012.06.003
Lei, X., & Rau, P. L. P. (2023). Effects of task structure and attribution target on responsibility attributions in human–robot interaction. International Journal of Human-Computer Studies, 176, Article 103038. https://doi.org/10.1016/j.ijhcs.2023.103038
Leong, L. Y., Hew, T. S., Ooi, K. B., Metri, B., & Dwivedi, Y. K. (2023). Extending the theory of planned behavior in the social commerce context: A meta-analytic SEM (MASEM) approach. Information Systems Frontiers, 25(5), 1847–1879. https://doi.org/10.1007/s10796-022-10337-7
Leung, E., Paolacci, G., & Puntoni, S. (2018). Man versus machine: Resisting automation in identity-based consumer behavior. Journal of Marketing Research, 55(6), 818–831. https://doi.org/10.1177/0022243718818423
Levina, N., & Vaast, E. (2008). Innovating or doing as told? Status differences and overlapping boundaries in offshore collaboration. MIS Quarterly, 32(2), 307–332. https://doi.org/10.2307/25148842
Li, M., & Mao, J. (2015). Hedonic or utilitarian? Exploring the impact of communication style alignment on user’s perception of virtual health advisory services. International Journal of Information Management, 35(2), 229–243.
Li, M., & Wang, R. (2023). Chatbots in e-commerce: The effect of chatbot language style on customers’ continuance usage intention and attitude toward brand. Journal of Retailing and Consumer Services, 71, Article 103209.
Li, S., Han, R., Fu, T., Chen, M., & Zhang, Y. (2024). Tourists’ behavioural intentions to use ChatGPT for tour route planning: an extended TAM model including rational and emotional factors. Current Issues in Tourism. Advance online publication. https://doi.org/10.1080/13683500.2024.2355563
Li, X., & Sung, Y. (2021). Anthropomorphism brings us closer: The mediating role of psychological distance in user–AI assistant interactions. Computers in Human Behavior, 118, Article 106680.
Li, Y., & Lee, S. O. (2024). Navigating the generative AI travel landscape: the influence of ChatGPT on the evolution from new users to loyal adopters. International Journal of Contemporary Hospitality Management. Advance online publication. https://doi.org/10.1108/IJCHM-11-2023-1767
Li, Y., & Shin, H. (2023). Should a luxury brand’s chatbot use emoticons? Impact on brand status. Journal of Consumer Behaviour, 22(3), 569–581.
Li, Y., Gan, Z., & Zheng, B. (2023). How do artificial intelligence chatbots affect customer purchase? Uncovering the dual pathways of anthropomorphism on service evaluation. Information systems frontiers. Advance online publication. https://doi.org/10.1007/s10796-023-10438-x
Lin, H. (2020). Probing two-way moderation effects: A review of software to easily plot Johnson-Neyman figures. Structural Equation Modeling: A Multidisciplinary Journal, 27(3), 494–502. https://doi.org/10.1080/10705511.2020.1732826
Ling, E. C., Tussyadiah, I., Tuomi, A., Stienmetz, J., & Ioannou, A. (2021). Factors influencing users’ adoption and use of conversational agents: A systematic review. Psychology & Marketing, 38(7), 1031–1051. https://doi.org/10.1002/mar.21491
Liu, H., Liu, W., Yoganathan, V., & Osburg, V. S. (2021). COVID-19 information overload and generation Z's social media discontinuance intention during the pandemic lockdown. Technological Forecasting and Social Change, 166, Article 120600. https://doi.org/10.1016/j.techfore.2021.120600
Liu, X. S., Wan, L. C., & Yi, X. S. (2022). Humanoid versus non-humanoid robots: How mortality salience shapes preference for robot services under the COVID-19 pandemic? Annals of Tourism Research, 94, Article 103383.
Liu-Thompkins, Y., Okazaki, S., & Li, H. (2022). Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience. Journal of the Academy of Marketing Science, 50(6), 1198–1218. https://doi.org/10.1007/s11747-022-00892-5
Liviatan, I., Trope, Y., & Liberman, N. (2008). Interpersonal similarity as a social distance dimension: Implications for perception of others’ actions. Journal of Experimental Social Psychology, 44(5), 1256–1269. https://doi.org/10.1016/j.jesp.2008.04.007
Locke, C. C., & Anderson, C. (2015). The downside of looking like a leader: Power, nonverbal confidence, and participative decision-making. Journal of Experimental Social Psychology, 58, 42–47. https://doi.org/10.1016/j.jesp.2014.12.004
Lotsof, E. J. (1958). Expectancy for success and decision-time. The American Journal of Psychology, 71(2), 416–419. https://doi.org/10.2307/1420090
Lotsof, E. J. (1959). Expectancy for success and certainty of response. The American Journal of Psychology, 72(4), 600–602. https://doi.org/10.2307/1419507
Luan, J., Filieri, R., Xiao, J., & Sun, Y. (2023). Consumer–brand relationships and social distance: A construal level theory perspective. Psychology & Marketing, 40(7), 1299–1315. https://doi.org/10.1002/mar.21818
Lutz, R. J. (1991). The role of attitude theory in marketing. In H. H. Kassarjian & T. S. Robertson (Eds.), Perspectives in consumer behavior (4th ed., pp. 317–339). Prentice Hall.
Lynch, J. G., Marmorstein, H., & Weigold, M. F. (1988). Choices from sets including remembered brands: Use of recalled attributes and prior overall evaluations. Journal of Consumer Research, 15(2), 169–184. https://doi.org/10.1086/209155
Lysyakov, M., & Viswanathan, S. (2023). Threatened by AI: Analyzing users’ responses to the introduction of AI in a crowd-sourcing platform. Information Systems Research, 34(3), 1191–1210. https://doi.org/10.1287/isre.2022.1184
Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, Article 102362. https://doi.org/10.1016/j.techsoc.2023.102362
MacInnis, D. J., Moorman, C., & Jaworski, B. J. (1991). Enhancing and measuring consumers’ motivation, opportunity, and ability to process brand information from ads. Journal of Marketing, 55(4), 32–53. https://doi.org/10.2307/1251955
MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555. https://doi.org/10.1016/j.jretai.2012.08.001
MacKenzie, S. B., & Spreng, R. A. (1992). How does motivation moderate the impact of central and peripheral processing on brand attitudes and intentions? Journal of Consumer Research, 18(4), 519–529. https://doi.org/10.1086/209278
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334. https://doi.org/10.2307/23044045
Marks, I. M. (1987). Fears, phobias, and rituals: Panic, anxiety, and their disorders. Oxford University Press.
Marr, B. (2024). 5 generative AI chatbots everyone should know about. Forbes. https://www.forbes.com/sites/bernardmarr/2024/02/05/five-generative-ai-chatbots-everyone-should-know-about/
Mathur, A. (1998). Examining trying as a mediator and control as a moderator of intention–behavior relationship. Psychology & Marketing, 15(3), 241–259. https://doi.org/10.1002/(SICI)1520-6793(199805)15:3<241::AID-MAR3>3.0.CO;2-A
McKinsey & Company (2024). The state of AI in early 2024: Gen AI adoption spikes and starts to generate value. https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai/2024/the-state-of-ai-in-early-2024-final.pdf
McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa… examine the variables influencing the use of artificial intelligent in-home voice assistants. Computers in Human Behavior, 99, 28–37. https://doi.org/10.1016/j.chb.2019.05.009
McLean, G., Al-Nabhani, K., & Marriott, H. (2021). ‘Regrettable‐escapism’ the negative effects of mobile app use: A retail perspective. Psychology & Marketing, 39(1), 150–167. https://doi.org/10.1002/mar.21584
McLean, G., Al-Nabhani, K., & Wilson, A. (2018). Developing a mobile applications customer experience model (MACE)-implications for retailers. Journal of Business Research, 85, 325–336. https://doi.org/10.1016/j.jbusres.2018.01.018
McLeod, P. L., Baron, R. S., Marti, M. W., & Yoon, K. (1997). The eyes have it: Minority influence in face-to-face and computer-mediated group discussion. Journal of Applied Psychology, 82(5), 706–718. https://doi.org/10.1037/0021-9010.82.5.706
Mehdi, Y. (2023). Reinventing search with a new AI-powered Microsoft Bing and Edge, your copilot for the web. The Official Microsoft Blog. https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-ai-powered-microsoft-bing-and-edge-your-copilot-for-the-web/
Mende, M., Scott, M. L., Ubal, V. O., Hassler, C. M. K., Harmeling, C. M., & Palmatier, R. W. (2024). Personalized communication as a platform for service inclusion? Initial insights into interpersonal and AI-based personalization for stigmatized consumers. Journal of Service Research, 27(1), 28–48. https://doi.org/10.1177/10946705231188676
Merdin-Uygur, E., & Ozturkcan, S. (2023). Consumers and service robots: Power relationships amid COVID-19 pandemic. Journal of Retailing and Consumer Services, 70, Article 103174. https://doi.org/10.1016/j.jretconser.2022.103174
Metiu, A. (2006). Owning the code: Status closure in distributed groups. Organization Science, 17(4), 418–435. https://doi.org/10.1287/orsc.1060.0195
Miller, B. K., & Simmering, M. J. (2023). Attitude toward the color blue: An ideal marker variable. Organizational Research Methods, 26(3), 409–440. https://doi.org/10.1177/10944281221075361
Mimoun, M. S. B., & Poncin, I. (2015). A valued agent: How ECAs affect website customers’ satisfaction and behaviors. Journal of Retailing and Consumer Services, 26, 70–82. https://doi.org/10.1016/j.jretconser.2015.05.008
Min, L., Liu, P. J., & Anderson, C. L. (2024). The visual complexity = higher production cost lay belief. Journal of Consumer Research. Advance online publication. https://doi.org/10.1093/jcr/ucae044
Mishra, A., Shukla, A., & Sharma, S. K. (2022). Psychological determinants of users’ adoption and word-of-mouth recommendations of smart voice assistants. International Journal of Information Management, 67, Article 102413. https://doi.org/10.1016/j.ijinfomgt.2021.102413
Morrison, D. G. (1979). Purchase intentions and purchase behavior. Journal of Marketing, 43(2), 65–74. https://doi.org/10.1177/002224297904300207
Mureșan, G. M., Văidean, V. L., Mare, C., & Achim, M. V. (2022). Were we happy and we didn’t know it? A subjective dynamic and financial assessment pre-, during and post-COVID-19. The European Journal of Health Economics, 24, 749–768. https://doi.org/10.1007/s10198-022-01506-1
Nan, D., Sun, S., Zhang, S., Zhao, X., & Kim, J. H. (2024). Analyzing behavioral intentions toward generative artificial intelligence: The case of ChatGPT. Universal Access in the Information Society. Advance online publication. https://doi.org/10.1007/s10209-024-01116-z
Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103. https://doi.org/10.1111/0022-4537.00153
Nass, C., Steuer, J., Tauber, E., & Reeder, H. (1993). Anthropomorphism, agency, and ethopoeia: Computers as social actors. In S. Ashlund, K. Mullet, A. Henderson, E. Hollnagel, & T. White (Eds.), Proceedings of the INTERCHI93: Conference on Human Factors in Computing (pp. 111–112). Association for Computing Machinery. https://doi.org/10.1145/259964.260137
Nicomedes, C. J. C., & Avila, R. M. A. (2020). An analysis on the panic during COVID-19 pandemic through an online form. Journal of Affective Disorders, 276, 14–22. https://doi.org/10.1016/j.jad.2020.06.046
Niu, B., & Mvondo, G. F. N. (2024). I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT. Journal of Retailing and Consumer Services, 76, Article 103562. https://doi.org/10.1016/j.jretconser.2023.103562
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: An integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148
Öhman, A. (2008). Fear and anxiety: Overlaps and dissociations. In M. Lewis, J. M. Haviland-Jones, & L. F. Barrett (Eds.), Handbook of emotions (pp. 709–728). The Guilford Press.
Öhman, A., & Mineka, S. (2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108(3), 483–522. https://doi.org/10.1037/0033-295X.108.3.483
OpenAI (2022). Introducing ChatGPT. https://openai.com/index/chatgpt/
OpenAI (2023). ChatGPT can now see, hear, and speak. https://openai.com/index/chatgpt-can-now-see-hear-and-speak/
OpenAI (2024a). Hello GPT-4o. https://openai.com/index/hello-gpt-4o/
OpenAI (2024b). Introducing ChatGPT Pro. https://openai.com/index/introducing-chatgpt-pro/
Park, R. E. (1924). The concept of social distance: As applied to the study of racial relations. Journal of Applied Sociology, 8(6), 339–344.
Park, S. S., Tung, C. D., & Lee, H. (2021). The adoption of AI service robots: A comparison between credence and experience service settings. Psychology & Marketing, 38(4), 691–703. https://doi.org/10.1002/mar.21468
Parrott, W. G. (1993). Beyond hedonism: Motives for inhibiting good moods and for maintaining bad moods. In D. M. Wegner & J. W. Pennebaker (Eds.), Handbook of mental control (pp. 278–305). Prentice-Hall, Inc.
Peer, E., Rothschild, D., Gordon, A., Evernden, Z., & Damer, E. (2022). Data quality of platforms and panels for online behavioral research. Behavior Research Methods, 54(4), 1643–1662. https://doi.org/10.3758/s13428-021-01694-3
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269–275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Perrucci, R. (1963). Social distance strategies and intra-organizational stratification: A study of the status system on a psychiatric ward. American Sociological Review, 28(6), 951–962. https://doi.org/10.2307/2090314
Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10(2), 135–146. https://doi.org/10.1086/208954
Pham, H. C., Duong, C. D., & Nguyen, G. K. H. (2024). What drives tourists’ continuance intention to use ChatGPT for travel services? A stimulus-organism-response perspective. Journal of Retailing and Consumer Services, 78, Article 103758. https://doi.org/10.1016/j.jretconser.2024.103758
Picard, R. W. (2000). Affective computing. MIT press.
Pichai, S. (2023). An important next step on our AI journey. Google Blog. https://blog.google/technology/ai/bard-google-ai-search-updates/
Pichai, S., & Hassabis, D. (2024). Our next-generation model: Gemini 1.5. Google Blog. https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/#sundar-note
Picot-Coupey, K., Krey, N., Huré, E., & Ackermann, C. L. (2021). Still work and/or fun? Corroboration of the hedonic and utilitarian shopping value scale. Journal of Business Research, 126, 578–590. https://doi.org/10.1016/j.jbusres.2019.12.018
Pitardi, V., & Marriott, H. R. (2021). Alexa, she’s not human but… Unveiling the drivers of consumers’ trust in voice‐based artificial intelligence. Psychology & Marketing, 38(4), 626–642. https://doi.org/10.1002/mar.21457
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. https://doi.org/10.1146/annurev-psych-120710-100452
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
Podsakoff, P. M., Podsakoff, N. P., Williams, L. J., Huang, C., & Yang, J. (2024). Common method bias: It’s bad, it’s complex, it’s widespread, and it’s not easy to fix. Annual Review of Organizational Psychology and Organizational Behavior, 11, 17–61. https://doi.org/10.1146/annurev-orgpsych-110721-040030
Polman, E., Effron, D. A., & Thomas, M. R. (2018). Other people’s money: Money’s perceived purchasing power is smaller for others than for the self. Journal of Consumer Research, 45(1), 109–125. https://doi.org/10.1093/jcr/ucx119
Poushneh, A., Vasquez-Parraga, A., & Gearhart, R. S. (2024). The effect of empathetic response and consumers’ narcissism in voice-based artificial intelligence. Journal of Retailing and Consumer Services, 79, Article 103871. https://doi.org/10.1016/j.jretconser.2024.103871
Pratto, F., Sidanius, J., Stallworth, L. M., & Malle, B. F. (1994). Social dominance orientation: A personality variable predicting social and political attitudes. Journal of Personality and Social Psychology, 67(4), 741–763. https://doi.org/10.1037/0022-3514.67.4.741
Prolific (2024). Who are the participants on Prolific? https://researcher-help.prolific.com/en/article/2bb2f4
Puccetti, R. (1967). On thinking machines and feeling machines. British Journal for the Philosophy of Science, 18(1), 39–51. https://doi.org/10.1093/bjps/18.1.39
Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and Artificial Intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151. https://doi.org/10.1177/0022242920953847
Putwain, D. W., Nicholson, L. J., Pekrun, R., Becker, S., & Symes, W. (2019). Expectancy of success, attainment value, engagement, and achievement: A moderated mediation analysis. Learning and Instruction, 60, 117–125. https://doi.org/10.1016/j.learninstruc.2018.11.005
Rachman, S. (2004). Fear of contamination. Behaviour Research and Therapy, 42, 1227–1255.
Rafaeli, A., Altman, D., Gremler, D. D., Huang, M. H., Grewal, D., Iyer, B., Parasuraman, A., & de Ruyter, K. (2017). The future of frontline research: Invited commentaries. Journal of Service Research, 20(1), 91–99. https://doi.org/10.1177/1094670516679275
Raffo, D. (2024). Gemini vs. ChatGPT: What’s the difference? Informa TechTarget. https://www.techtarget.com/searchenterpriseai/tip/Gemini-vs-ChatGPT-Whats-the-difference
Rajaobelina, L., Prom Tep, S., Arcand, M., & Ricard, L. (2021). Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot. Psychology & Marketing, 38(12), 2339–2356. https://doi.org/10.1002/mar.21548
Reeves, B., & Nass, C. I. (1996). The media equation: How people treat computers, television, and new media like real people and places. Center for the Study of Language and Information; Cambridge University Press.
Rese, A., Ganster, L., & Baier, D. (2020). Chatbots in retailers’ customer communication: How to measure their acceptance? Journal of Retailing and Consumer Services, 56, Article 102176. https://doi.org/10.1016/j.jretconser.2020.102176
Rice, R. E. (1993). Media appropriateness: Using social presence theory to compare traditional and new organizational media. Human Communication Research, 19(4), 451–484. https://doi.org/10.1111/j.1468-2958.1993.tb00309.x
Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives: Examining post HOC statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12(4), 762–800. https://doi.org/10.1177/1094428109332834
Ridgeway, C. L. (2014). Why status matters for inequality. American Sociological Review, 79(1), 1–16. https://doi.org/10.1177/0003122413515997
Ridgeway, C. L., Boyle, E. H., Kuipers, K. J., & Robinson, D. T. (1998). How do status beliefs develop? The role of resources and interactional experience. American Sociological Review, 63(3), 331–350. https://doi.org/10.2307/2657553
Rosso, I. M., Young, A. D., Femia, L. A., & Yurgelun‐Todd, D. A. (2004). Cognitive and emotional components of frontal lobe functioning in childhood and adolescence. Annals of the New York Academy of Sciences, 1021(1), 355–362. https://doi.org/10.1196/annals.1308.045
Rothan, H. A., & Byrareddy, S. N. (2020). The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. Journal of autoimmunity, 109, Article 102433. https://doi.org/10.1016/j.jaut.2020.102433
Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15–26. https://doi.org/10.1016/j.ijresmar.2019.08.002
Rust, R. T., & Huang, M. H. (2021). The feeling economy. Palgrave Macmillan Cham. https://doi.org/10.1007/978-3-030-52977-2
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications.
Ryan, R. M., & La Guardia, J. G. (2000). What is being optimized?: Self-determination theory and basic psychological needs. In S. H. Qualls & N. Abeles (Eds.), Psychology and the aging revolution: How we adapt to longer life (pp. 145–172). American Psychological Association. https://doi.org/10.1037/10363-008
Rychalski, A., & Hudson, S. (2017). Asymmetric effects of customer emotions on satisfaction and loyalty in a utilitarian service context. Journal of Business Research, 71, 84–91. https://doi.org/10.1016/j.jbusres.2016.10.014
Sadiq, M. W., Akhtar, M. W., Huo, C., & Zulfiqar, S. (2024). ChatGPT-powered chatbot as a green evangelist: an innovative path toward sustainable consumerism in E-commerce. The Service Industries Journal, 44(3-4), 173–217. https://doi.org/10.1080/02642069.2023.2278463
Salah, M., Alhalbusi, H., Ismail, M. M., & Abdelfattah, F. (2024). Chatting with ChatGPT: Decoding the mind of chatbot users and unveiling the intricate connections between user perception, trust and stereotype perception on self-esteem and psychological well-being. Current Psychology, 43(9), 7843–7858. https://doi.org/10.1007/s12144-023-04989-0
Salesforce (2020). State of the Connected Customer (4th edition). Salesforce. https://www.salesforce.com/content/dam/web/en_us/www/documents/research/salesforce-state-of-the-connected-customer-4th-ed.pdf
Scarpi, D. (2024). Strangers or friends? Examining chatbot adoption in tourism through psychological ownership. Tourism Management, 102, Article 104873. https://doi.org/10.1016/j.tourman.2023.104873
Schepers, J., Belanche, D., Casaló, L. V., & Flavián, C. (2022). How smart should a service robot be? Journal of Service Research, 25(4), 565–582. https://doi.org/10.1177/10946705221107704
Schubert, T., Süssenbach, P., Schäfer, S. J., & Euteneuer, F. (2016). The effect of subjective social status on depressive thinking: An experimental examination. Psychiatry Research, 241, 22–25. https://doi.org/10.1016/j.psychres.2016.04.081
Scott, S. E., Rozin, P., & Small, D. A. (2020). Consumers prefer “natural” more for preventatives than for curatives. Journal of Consumer Research, 47(3), 454-471. https://doi.org/10.1093/jcr/ucaa034
Shakespeare, W. (1981). The complete works of William Shakespeare (P. Alexander, Ed.). Collins, London & Glasgow. (Original work published 1594)
Shaw, M. E. (1964). Communication networks. In L. Berkowitz (Ed.), Advances in experimental social psychology (pp. 111–147). New York: Academic.
Shi, J., Lee, M., Girish, V. G., Xiao, G., & Lee, C. K. (2024). Embracing the ChatGPT revolution: Unlocking new horizons for tourism. Journal of Hospitality and Tourism Technology, 15(3), 433–448. https://doi.org/10.1108/JHTT-07-2023-0203
Shin, D., Koerber, A., & Lim, J. S. (2024). Impact of misinformation from generative AI on user information processing: How people understand misinformation from generative AI. New Media & Society. Advance online publication. https://doi.org/10.1177/14614448241234040
Shin, S., Kim, J., Lee, E., Yhee, Y., & Koo, C. (2023). ChatGPT for trip planning: The effect of narrowing down options. Journal of Travel Research. Advance online publication. https://doi.org/10.1177/00472875231214196
Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications. Wiley.
Simmering, M. J., Fuller, C. M., Richardson, H. A., Ocal, Y., & Atinc, G. M. (2015). Marker variable choice, reporting, and interpretation in the detection of common method variance: A review and demonstration. Organizational Research Methods, 18(3), 473–511. https://doi.org/10.1177/1094428114560023
Şimşir, Z., Koç, H., Seki, T., & Griffiths, M. D. (2022). The relationship between fear of COVID-19 and mental health problems: A meta-analysis. Death Studies, 46(3), 515–523. https://doi.org/10.1080/07481187.2021.1889097
Smith, R. E. (1993). Integrating information from advertising and trial: Processes and effects on consumer response to product information. Journal of Marketing Research, 30(2), 204–219. https://doi.org/10.1177/002224379303000206
SOA Policy Team (2024). SoA survey reveals a third of translators and quarter of illustrators losing work to AI. The Society of Authors. https://www2.societyofauthors.org/2024/04/11/soa-survey-reveals-a-third-of-translators-and-quarter-of-illustrators-losing-work-to-ai/
Sohn, K., Sung, C. E., Koo, G., & Kwon, O. (2020). Artificial intelligence in the fashion industry: Consumer responses to generative adversarial network (GAN) technology. International Journal of Retail & Distribution Management, 49(1), 61–80. https://doi.org/10.1108/IJRDM-03-2020-0091
Song, S. W., & Shin, M. (2024). Uncanny valley effects on chatbot trust, purchase intention, and adoption intention in the context of e-commerce: The moderating role of avatar familiarity. International Journal of Human–Computer Interaction, 40(2), 441–456. https://doi.org/10.1080/10447318.2022.2121038
Soper, D. S. (2023). A-priori sample size calculator for structural equation models [Software]. https://www.danielsoper.com/statcalc
Spataro, J. (2024). New agent capabilities in Microsoft Copilot unlock business value. Microsoft. https://www.microsoft.com/en-us/microsoft-365/blog/2024/05/21/new-agent-capabilities-in-microsoft-copilot-unlock-business-value/
Srivastava S., & Anderson C. (2011). Accurate when it counts: Perceiving power and status in social groups. In Smith J. L., Ickes W., Hall J., Hodges S. D., Gardner W. (Eds.), Managing interpersonal sensitivity: Knowing when—and when not—to understand others (pp. 41–58). Nova Science.
Steenkamp, J.-B. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25(1), 78–90. https://doi.org/10.1086/209528
Stephan, E., Liberman, N., & Trope, Y. (2010). Politeness and psychological distance: a construal level perspective. Journal of Personality and Social Psychology, 98(2), 268–280. https://doi.org/10.1037/a0016960
Sternberg, R. J. (1981). A componential theory of intellectual giftedness. Gifted Child Quarterly, 25(2), 86–93. https://doi.org/10.1177/001698628102500208
Sternberg, R. J. (1999). The theory of successful intelligence. Review of General Psychology, 3(4), 292–316. https://doi.org/10.1037/1089-2680.3.4.292
Straub, D. W. (1994). The effect of culture on it diffusion: E-mail and fax in Japan and the US. Information Systems Research, 5(1), 23–47. https://doi.org/10.1287/isre.5.1.23
Strich, F., Mayer, A. S., & Fiedler, M. (2021). What do I do in a world of artificial intelligence? Investigating the impact of substitutive decision-making AI systems on employees’ professional role identity. Journal of the Association for Information Systems, 22(2), 304–324. https://doi.org/10.17705/1jais.00663
Su, Y., Rao, W., Li, M., Caron, G., D’Arcy, C., & Meng, X. (2022). Prevalence of loneliness and social isolation among older adults during the COVID-19 pandemic: A systematic review and meta-analysis. International Psychogeriatrics, 35(5), 229–241. https://doi.org/10.1017/S1041610222000199
Sui, J., Shen, H., & Zhou, X. (2024). Impact of cultural tightness on consumers’ preference for anthropomorphic AI services. Psychology & Marketing, 41(11), 2841–2853. https://doi.org/10.1002/mar.22086
Suls, J. M. (1977). Social comparison theory and research: An overview from 1954. In J. M. Suls & R. L. Miller (Eds.), Social comparison processes: Theoretical and empirical perspectives (pp. 1–19). Hemisphere.
Suls, J., Collins, R. L., & Wheeler, L. (Eds.) (2019). Social comparison, judgment, and behavior. Oxford University Press.
Susarla, A., Gopal, R., Thatcher, J. B., & Sarker, S. (2023). The Janus effect of generative AI: Charting the path for responsible conduct of scholarly activities in information systems. Information Systems Research, 34(2), 399–408. https://doi.org/10.1287/isre.2023.ed.v34.n2
Tarde, G. (1903). The laws of imitation. Henry Holt and Company.
Taylor, S. (2019). The psychology of pandemics: Preparing for the next global outbreak of infectious disease. Cambridge Scholars Publishing.
Taylor, S. E., & Lobel, M. (1989). Social comparison activity under threat: Downward evaluation and upward contacts. Psychological Review, 96(4), 569–575. https://doi.org/10.1037/0033-295X.96.4.569
Taylor, S., Landry, C. A., Paluszek, M. M., Fergus, T. A., McKay, D., & Asmundson, G. J. (2020). Development and initial validation of the COVID Stress Scales. Journal of Anxiety Disorders, 72, Article 102232. https://doi.org/10.1016/j.janxdis.2020.102232
Tesser, A., & Campbell, J. (1980). Self-definition: The impact of the relative performance and similarity of others. Social Psychology Quarterly, 43(3), 341–347. https://doi.org/10.2307/3033737
Teubner, T., Flath, C. M., Weinhardt, C., van der Aalst, W., & Hinz, O. (2023). Welcome to the era of ChatGPT et al.: The prospects of large language models. Business & Information Systems Engineering, 65(2), 95–101. https://doi.org/10.1007/s12599-023-00795-x
To, C., Kilduff, G. J., & Rosikiewicz, B. L. (2020). When interpersonal competition helps and when it harms: An integration via challenge and threat. Academy of Management Annals, 14(2), 908–934. https://doi.org/10.5465/annals.2016.0145
Triandis, H. C. (1980). Values, attitudes, and interpersonal behavior. In H. E. Howe, Jr. & M. Page (Eds.), Nebraska symposium of motivation (Vol. 27, pp. 195–259). University of Nebraska Press.
Triandis, H. C., & Triandis, L. M. (1962). A cross-cultural study of social distance. Psychological Monographs: General and Applied, 76(21), 1–21. https://doi.org/10.1037/h0093836
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460. https://doi.org/10.1093/mind/LIX.236.433
Ülkümen, G., & Cheema, A. (2011). Framing goals to influence personal savings: The role of specificity and construal level. Journal of Marketing Research, 48(6), 958–969. https://doi.org/10.1509/jmr.09.0516
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. arXiv. https://doi.org/10.48550/arXiv.1706.03762.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021). ‘Okay google, what about my privacy?’: User's privacy perceptions and acceptance of voice based digital assistants. Computers in Human Behavior, 120, Article106763. https://doi.org/10.1016/j.chb.2021.106763
Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134. https://doi.org/10.1007/s11747-015-0455-4
Vorobeva, D., El Fassi, Y., Costa Pinto, D., Hildebrand, D., Herter, M. M., & Mattila, A. S. (2022). Thinking skills don’t protect service workers from replacement by artificial intelligence. Journal of Service Research, 25(4), 601–613. https://doi.org/10.1177/10946705221104312
Wang, H. (2024). Decoding herding dynamics in the generative AI investment amid key technological advancements: A timeline perspective. Finance Research Letters, 64, Article 105432. https://doi.org/10.1016/j.frl.2024.105432
Wang, J., Novemsky, N., & Dhar, R. (2009). Anticipating adaptation to products. Journal of Consumer Research, 36(2), 149–159. https://doi.org/10.1086/597050
Wang, S., Chen, L., Ran, H., Che, Y., Fang, D., Sun, H., Peng, J., Liang, X., & Xiao, Y. (2022a). Depression and anxiety among children and adolescents pre and post COVID-19: A comparative meta-analysis. Frontiers in Psychiatry, Article 1752. https://doi.org/10.3389/fpsyt.2022.917552
Wang, X., Lin, X., & Shao, B. (2022b). How does artificial intelligence create business agility? Evidence from chatbots. International Journal of Information Management, 66, Article 102535. https://doi.org/10.1016/j.ijinfomgt.2022.102535
Wang, Y. (2006). Cognitive informatics: Towards future generation computers that think and feel. In Y. Yao, Z. Shi, Y. Wang, and W. Kinsner (Eds.), Proceedings of the firth IEEE international conference on cognitive informatics: ICCI 2006 (Vol. 1/2, pp. 3–7). IEEE Computer Society Order. https://doi.org/10.1109/COGINF.2006.365666
Wang, Y., Song, M., Guo, R., & Duan, Y. (2023). How about non-human tour guides? The influence of AI tour guides’ dress and conversation style on the intention of consumers to continue using them. Journal of Travel & Tourism Marketing, 40(9), 849–862. https://doi.org/10.1080/10548408.2023.2296638
Wang, Z., Mao, H., Li, Y. J., & Liu, F. (2017). Smile big or not? Effects of smile intensity on perceptions of warmth and competence. Journal of Consumer Research, 43(5), 787–805. https://doi.org/10.1093/jcr/ucw062
Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132(2), 249–268. https://doi.org/10.1037/0033-2909.132.2.249
Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https://doi.org/10.1016/j.elerap.2010.07.003
Wheeler, L. (1991). A brief history of social comparison theory. In J. Suls & T. A. Wills (Eds.), Social comparison: Contemporary theory and research (pp. 3–21). Lawrence Erlbaum Associates, Inc.
Whitley, S. C., Trudel, R., & Kurt, D. (2018). The influence of purchase motivation on perceived preference uniqueness and assortment size choice. Journal of Consumer Research, 45(4), 710–724. https://doi.org/10.1093/jcr/ucy031
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1999.1015
Williams, L. J., & O’Boyle, E. H. (2015). Ideal, nonideal, and no-marker variables: The confirmatory factor analysis (CFA) marker technique works when it matters. Journal of Applied Psychology, 100(5), 1579–1602. https://doi.org/10.1037/a0038855
Williams, L. J., Hartman, N., & Cavazotte, F. (2010). Method variance and marker variables: A review and comprehensive CFA marker technique. Organizational Research Methods, 13(3), 477–514. https://doi.org/10.1177/1094428110366036
Witte, K. (1994). Fear control and danger control: A test of the extended parallel process model (EPPM). Communication Monographs, 61(2), 113–134. https://doi.org/10.1080/03637759409376328
Wolf, T., Jahn, S., Hammerschmidt, M., & Weiger, W. H. (2021). Competition versus cooperation: How technology-facilitated social interdependence initiates the self-improvement chain. International Journal of Research in Marketing, 38(2), 472–491. https://doi.org/10.1016/j.ijresmar.2020.06.001
Wood, J. V. (1989). Theory and research concerning social comparisons of personal attributes. Psychological Bulletin, 106(2), 231–248. https://doi.org/10.1037/0033-2909.106.2.231
Wright, O., & Blackburn, E. (2020, April 28). How COVID-19 will permanently change consumer behavior. Accenture. https://www.accenture.com/_acnmedia/pdf-123/accenture-covid19-pulse-survey-research-pov.pdf
Xu, X. A., Wen, N., & Liu, J. (2024a). Empathic accuracy in artificial intelligence service recovery. Tourism Review, 79(5), 1058–1075. https://doi.org/10.1108/TR-06-2023-0394
Xu, Y. W., Cai, R. R., & Gursoy, D. (2024b). When disclosing the artificial intelligence (AI) technology integration into service delivery backfires: Roles of fear of AI, identity threat and existential threat. International Journal of Hospitality Management, 122, Article 103829. https://doi.org/10.1016/j.ijhm.2024.103829
Yalçın, Ö. N., & DiPaola, S. (2020). Modeling empathy: Building a link between affective and cognitive processes. Artificial Intelligence Review, 53(4), 2983–3006. https://doi.org/10.1007/s10462-019-09753-0
Yim, A., Cui, A. P., & Walsh, M. (2024). The role of cuteness on consumer attachment to artificial intelligence agents. Journal of Research in Interactive Marketing, 18(1), 127–141. https://doi.org/10.1108/JRIM-02-2023-0046
Yim, M. Y. C., Yoo, S. C., Sauer, P. L., & Seo, J. H. (2014). Hedonic shopping motivation and co-shopper influence on utilitarian grocery shopping in superstores. Journal of the Academy of Marketing Science, 42(5), 528–544. https://doi.org/10.1007/s11747-013-0357-2
Young, М. (1958). The Rise of the Meritocracy, 1870-2033: An essay on education and inequality. Thames & Hudson.
Zheng, Y., & Alba, J. W. (2023). Origin versus substance: Competing determinants of disruption in duplication technologies. Journal of Consumer Research, 49(6), 944-966. https://doi.org/10.1093/jcr/ucac031
Zhou, T., & Wu, X. (2024). Examining generative AI user disclosure intention: An ELM perspective. Universal Access in the Information Society. Advance online publication. https://doi.org/10.1007/s10209-024-01130-1
Zumstein, D., & Hundertmark, S. (2017). Chatbots: An interactive technology for personalized communication, transactions, and services. IADIS International Journal On WWW/Internet, 15(1), 96–109.