Review Article

Influence of AI technologies on the psychology of sustainable consumption: Scoping review

Özgün Arda Kuş 1 *
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1 Uskudar University, Istanbul, TURKEY* Corresponding Author
Online Journal of Communication and Media Technologies, 16(2), April 2026, e202622, https://doi.org/10.30935/ojcmt/18275
Published: 23 April 2026
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This article belongs to the special issue "Interdisciplinary Perspectives on Communication, Education, and Ethics in the Digital Age"

ABSTRACT

This scoping review maps the emerging evidence on how artificial intelligence (AI) technologies shape the psychology of sustainable consumption. Guided by Arksey and O’Malley’s (2005) framework and PRISMA-ScR guidelines, a comprehensive search was conducted on June 25, 2025, using Scopus and Web of Science, combining the keywords artificial intelligence, psychology, and sustainability. There were 1,561 publications when the corpus was limited to English-language papers published between 2020 and 2025. After removing duplicates, screening titles and abstracts, and reviewing full texts, 19 research satisfied the requirements for inclusion. Using descriptive charts and inductive thematic analysis, four main things were found: (1) anthropomorphic chatbots are the most popular AI touch-point and consistently increase people’s desire to buy green products; (2) AI effects are mediated by psychological states, such as social presence, hedonic motivation, perceived usefulness, and green satisfaction, rather than by technology alone; (3) algorithmic advice can be less effective than human guidance when moral or reputational stakes are high; and (4) theory building is heavily biased toward technology-acceptance models, leaving value-based and affective mechanisms under-explored. These findings highlight both the promise and the boundary conditions of AI-enabled persuasion and chart a research agenda that integrates richer motivational theories, hybrid human–AI designs, and longitudinal real-world evaluations.
Keywords: artificial intelligence, sustainable consumption, consumer psychology, anthropomorphic chatbots, pro-environmental behavior, psychological mediation, scoping review

CITATION (APA)

Kuş, Ö. A. (2026). Influence of AI technologies on the psychology of sustainable consumption: Scoping review. Online Journal of Communication and Media Technologies, 16(2), e202622. https://doi.org/10.30935/ojcmt/18275

REFERENCES

  1. 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
  2. Al Mamun, A., Hayat, N., Masud, M. M., Makhbul, Z. K. M., Jannat, T., & Salleh, M. F. M. (2022). Modelling the significance of value-belief-norm theory in predicting solid waste management intention and behavior. Frontiers in Environmental Science, 10, Article 906002. https://doi.org/10.3389/fenvs.2022.906002
  3. Al-Emran, M., & Griffy-Brown, C. (2023). The role of technology adoption in sustainable development: Overview, opportunities, challenges, and future research agendas. Technology in Society, 73, Article 102240. https://doi.org/10.1016/j.techsoc.2023.102240
  4. Allcott, H., & Rogers, T. (2014). The short-run and long-run effects of behavioral interventions: Experimental evidence from energy conservation. American Economic Review, 104(10), 3003-3037. https://doi.org/10.1257/aer.104.10.3003
  5. Alshammari, S. H., & Alkhwaldi, A. F. (2025). An integrated approach using social support theory and technology acceptance model to investigate the sustainable use of digital learning technologies. Scientific Reports, 15, Article 342. https://doi.org/10.1038/s41598-024-83450-z
  6. Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, Article 106548. https://doi.org/10.1016/j.chb.2020.106548
  7. 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
  8. Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19-32. https://doi.org/10.1080/1364557032000119616
  9. Bag, S., Dhamija, P., Singh, R. K., Rahman, M. S., & Sreedharan, V. R. (2023). Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study. Journal of Business Research, 154, Article 113315. https://doi.org/10.
  10. 1016/j.jbusres.2022.113315
  11. Barnes, A. J., Zhang, Y., & Valenzuela, A. (2024). AI and culture: Culturally dependent responses to AI systems. Current Opinion in Psychology, 58, Article 101838. https://doi.org/10.1016/j.copsyc.2024.101838
  12. Batool, N., Wani, M. D., Shah, S. A., & Dada, Z. A. (2024). Theory of planned behavior and value-belief norm theory as antecedents of pro-environmental behaviour: Evidence from the local community. Journal of Human Behavior in the Social Environment, 34(5), 693-709. https://doi.org/10.1080/10911359.2023.2205912
  13. Bednar, P. M., & Welch, C. (2020). Socio-technical perspectives on smart working: Creating meaningful and sustainable systems. Information Systems Frontiers, 22(2), 281-298. https://doi.org/10.1007/s10796-019-09921-1
  14. Blut, M., Wang, C., Wünderlich, N. V., Brock, C., 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, 632-658. https://doi.org/10.1007/s11747-020-00762-y
  15. Bozdog, L.-S., Naghi, R. I., Preda, G., & Prada, S. I. (2025). The influence of AI chatbots on the purchase intention of sustainable products. Transformations in Business & Economics, 24(1), 238-261. https://doi.org/10.15388/Tibe.2025.24.1.11
  16. Cai, C. W. (2019). Nudging the financial market? A review of the nudge theory. Accounting & Finance, 60(4), 3341-3365. https://doi.org/10.1111/acfi.12471
  17. Cao, P., & Liu, S. (2023). The impact of artificial intelligence technology stimuli on sustainable consumption behavior: Evidence from ant forest users in China. Behavioral Sciences, 13(7), Article 604. https://doi.org/10.3390/bs13070604
  18. Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809-825. https://doi.org/10.1177/0022243719851788
  19. Chekima, B., Chekima, S., Syed Khalid Wafa, S. A. W., Igau, O. A., & Sondoh Jr, S. L. (2015). Sustainable consumption: The effects of knowledge, cultural values, environmental advertising, and demographics. International Journal of Sustainable Development & World Ecology, 23(2), 210-220. https://doi.org/10.1080/
  20. 13504509.2015.1114043
  21. Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022). Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing & Management, 59(3), Article 102940. https://doi.org/10.1016/j.ipm.2022.102940
  22. Chi, N. T. K., & Chi, N. T. K. (2024). The effect of AI chatbots on pro-environment attitude and willingness to pay for environment protection. SAGE Open, 14(1). https://doi.org/10.1177/21582440231226001
  23. Choi, D., & Johnson, K. K. P. (2019). Influences of environmental and hedonic motivations on intention to purchase green products: An extension of the theory of planned behavior. Sustainable Production and Consumption, 18, 145-155. https://doi.org/10.1016/j.spc.2019.02.001
  24. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [PhD thesis, Massachusetts Institute of Technology].
  25. Deck, C., & Jahedi, S. (2015). The effect of cognitive load on economic decision making: A survey and new experiments. European Economic Review, 78, 97-119. https://doi.org/10.1016/j.euroecorev.2015.05.004
  26. Fenwick, A., Molnar, G., & Frangos, P. (2024). The critical role of HRM in AI-driven digital transformation: A paradigm shift to enable firms to move from AI implementation to human-centric adoption. Discover Artificial Intelligence, 4, Article 34. https://doi.org/10.1007/s44163-024-00125-4
  27. Filiz, I., Judek, J. R., Lorenz, M., & Spiwoks, M. (2023). The extent of algorithm aversion in decision-making situations with varying gravity. PLoS ONE, 18(2), Article e0278751. https://doi.org/10.1371/journal.pone.0278751
  28. Flavián, C., Belk, R. W., Belanche, D., & Casaló, L. V. (2024). Automated social presence in AI: Avoiding consumer psychological tensions to improve service value. Journal of Business Research, 175, Article 114545. https://doi.org/10.1016/j.jbusres.2024.114545
  29. Folwarczny, M., Otterbring, T., & Ares, G. (2023). Sustainable food choices as an impression management strategy. Current Opinion in Food Science, 49, Article 100969. https://doi.org/10.1016/j.cofs.2022.100969
  30. Foroughi, B., Naghmeh-Abbaspour, B., Wen, J., Ghobakhloo, M., Al-Emran, M., & Al-Sharafi, M. A. (2025). Determinants of generative AI in promoting green purchasing behavior: A hybrid partial least squares-artificial neural network approach. Business Strategy and the Environment, 34(4), 4072-4094. https://doi.org/10.1002/bse.4186
  31. Frezza, M. (2024). Spillover of sustainable routines from work to private life: Application of the identity and practice interdependence framework. Frontiers in Psychology, 15, Article 1420701. https://doi.org/10.3389/fpsyg.2024.1420701
  32. Geels, F. W. (2005). Technological transitions and system innovations: A co-evolutionary and socio-technical analysis. Edward Elgar. https://doi.org/10.4337/9781845424596
  33. Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), Article 6. https://doi.org/10.3390/soc15010006
  34. Guan, B., Li, X., Luo, Z., & Liu, P. (2024). Can (A)I arouse you? The impact of AI services on consumer pro-environmental behavior. Journal of Hospitality & Tourism Research, 49(5), 932-945. https://doi.org/10.1177/10963480241256602
  35. Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
  36. Hameed, I., Waris, I., & Amin Ul Haq, M. (2019). Predicting eco-conscious consumer behavior using theory of planned behavior in Pakistan. Environmental Science and Pollution Research, 26, 15535-15547. https://doi.org/10.1007/s11356-019-04967-9
  37. Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.
  38. 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
  39. Huong, N. T. T., Khanh, C. T., Hoa, H. T., Sam, P. T., Chi, N. L., An, L. M., Anh, V. P., & Minh, P. N. (2025). Impact of artificial intelligence on the purchase intention of smart fashion products. Ianna Journal of Interdisciplinary Studies, 7(2), 594-607. https://doi.org/10.5281/zenodo.15501866
  40. Jha, R., Jha, R., & Islam, M. (2025). Forecasting US data center CO2 emissions using AI models: Emissions reduction strategies and policy recommendations. Frontiers in Sustainability, 5, Article 1507030. https://doi.org/10.3389/frsus.2024.1507030
  41. Jin, M., Yang, Z., Freling, T. L., & Janakiraman, N. (2025). The human superiority effect in advice taking: A multimethod exploration and implications for policy makers and governmental organizations. Journal of Public Policy & Marketing, 44(3), 350-369. https://doi.org/10.1177/07439156251320314
  42. Kim, B.-J., & Kim, M.-J. (2025). The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning. Journal of Environmental Management, 380, Article 125102. https://doi.org/10.1016/j.jenvman.2025.125102
  43. Kim, B.-J., Kim, M.-J., & Lee, J. (2024). Code green: Ethical leadership’s role in reconciling AI-induced job insecurity with pro-environmental behavior in the digital workplace. Humanities and Social Sciences Communications, 11, Article 1627. https://doi.org/10.1057/s41599-024-04139-2
  44. Kim, J., & Im, I. (2023). Anthropomorphic response: Understanding interactions between humans and artificial intelligence agents. Computers in Human Behavior, 139, Article 107512. https://doi.org/10.1016/j.chb.2022.107512
  45. Klein, K., & Martinez, L. F. (2023). The impact of anthropomorphism on customer satisfaction in chatbot commerce: An experimental study in the food sector. Electronic Commerce Research, 23, 2789-2825. https://doi.org/10.1007/s10660-022-09562-8
  46. Lata, S., & Rana, K. (2025). AI’s influence on young consumer behavior: Fostering sustainable consumption. Young Consumers: Insight and Ideas for Responsible Marketers, 26(5), 848-864. https://doi.org/10.1108/yc-05-2024-2081
  47. Lee, C.-C., Pan, C., & Song, Y. (2025). How live marketing affects green purchase in the age of artificial intelligence? Emerging Markets Finance and Trade, 61(1), 1-20. https://doi.org/10.1080/1540496x.2023.2300654
  48. Lee, K., & Joshi, K. (2020). Understanding the role of cultural context and user interaction in artificial intelligence based systems. Journal of Global Information Technology Management, 23(3), 171-175. https://doi.org/10.1080/1097198x.2020.1794131
  49. Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5, Article 69. https://doi.org/10.1186/1748-5908-5-69
  50. Li, T.-G., Zhang, C.-B., Chang, Y., & Zheng, W. (2024). The impact of AI identity disclosure on consumer unethical behavior: A social judgment perspective. Journal of Retailing and Consumer Services, 76, Article 103606. https://doi.org/10.1016/j.jretconser.2023.103606
  51. Li, Y., Zhou, X., Jiang, X., Fan, F., & Song, B. (2024). How service robots’ human-like appearance impacts consumer trust: A study across diverse cultures and service settings. International Journal of Contemporary Hospitality Management, 36(9), 3151-3167. https://doi.org/10.1108/ijchm-06-2023-0845
  52. Liao, C.-H. (2025). AI product factors and pro-environmental behavior: An integrated model with hybrid analytical approaches. Systems, 13(3), Article 144. https://doi.org/10.3390/systems13030144
  53. Lin, J., Zeng, Y., Wu, S., & Luo, X. (2024). How does artificial intelligence affect the environmental performance of organizations? The role of green innovation and green culture. Information & Management, 61(2), Article 103924. https://doi.org/10.1016/j.im.2024.103924
  54. Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650. https://doi.org/10.1093/jcr/ucz013
  55. Low, M. P., Rahim, F. A., & Wut, T. M. (2025). Leveraging artificial intelligence to foster pro-environmental and green behavior in organizations: Insights from PLS-SEM and necessary condition analysis. Sustainable Futures, 9, Article 100786. https://doi.org/10.1016/j.sftr.2025.100786
  56. Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36-51. https://doi.org/10.1016/j.ijhm.2019.01.005
  57. Ma, N., Khynevych, R., Hao, Y., & Wang, Y. (2025). Effect of anthropomorphism and perceived intelligence in chatbot avatars of visual design on user experience: Accounting for perceived empathy and trust. Frontiers in Computer Science, 7, Article 1531976. https://doi.org/10.3389/fcomp.2025.1531976
  58. Majid, G. M., Tussyadiah, I., & Kim, Y. R. (2024). Exploring the potential of chatbots in extending tourists’ sustainable travel practices. Journal of Travel Research, 64(6), 1292-1317. https://doi.org/10.1177/00472875241247316
  59. Majid, G. M., Tussyadiah, I., Kim, Y. R., & Chen, J. L. (2025). Promoting pro-environmental behaviour spillover through chatbots. Journal of Sustainable Tourism, 33(11), 2440-2458. https://doi.org/10.1080/09669582.2024.2393256
  60. Marvi, R., Foroudi, P., & Cuomo, M. T. (2025). Past, present and future of AI in marketing and knowledge management. Journal of Knowledge Management, 29(11), 1-31. https://doi.org/10.1108/jkm-07-2023-0634
  61. Mehrabian, A., & Russell, J. A. (1974). A verbal measure of information rate for studies in environmental psychology. Environment and Behavior, 6(2), Article 233. https://doi.org/10.1177/001391657400600205
  62. Mhlanga, D. (2025). AI in hospital administration: Revolutionizing healthcare. CRC Press. https://doi.org/10.1201/9781003475804
  63. Mills, S., Costa, S., & Sunstein, C. R. (2023). AI, behavioural science, and consumer welfare. Journal of Consumer Policy, 46(3), 387-400. https://doi.org/10.1007/s10603-023-09547-6
  64. Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389-404. https://doi.org/10.1016/j.jbusres.2020.10.044
  65. Nguyen, M. T., Thach, K. T. D., Nguyen, C. N. L., Nguyen, A. C., & Doan, H. K. (2026). The influence of AI chatbots on green satisfaction and loyalty: Evidence from sustainability-driven consumer behavior. Journal of Global Marketing, 39(1), 103-132. https://doi.org/10.1080/08911762.2025.2503495
  66. Ni, B., Wu, F., & Huang, Q. (2023). When artificial intelligence voices human concerns: The paradoxical effects of AI voice on climate risk perception and pro-environmental behavioral intention. International Journal of Environmental Research and Public Health, 20(4), Article 3772. https://doi.org/10.3390/ijerph20043772
  67. Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, Artcile 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
  68. Otterbring, T., Gasiorowska, A., & Folwarczny, M. (2023). Editorial: Impression management strategies and environmental cues as focal factors in food research. Frontiers in Nutrition, 10, Article 1254856. https://doi.org/10.3389/fnut.2023.1254856
  69. Papagiannidis, S., & Marikyan, D. (2022). Environmental sustainability: A technology acceptance perspective. International Journal of Information Management, 63, Article 102445. https://doi.org/10.1016/j.ijinfomgt.2021.102445
  70. Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18(10), 2119-2126. https://doi.org/10.11124/JBIES-20-00167
  71. 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
  72. 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
  73. Pycha, A., & Zellou, G. (2024). The influence of accent and device usage on perceived credibility during interactions with voice-AI assistants. Frontiers in Computer Science, 6, Article 1411414. https://doi.org/10.3389/fcomp.2024.1411414
  74. Raman, R., Pattnaik, D., Lathabai, H. H., Kumar, C., Govindan, K., & Nedungadi, P. (2024). Green and sustainable AI research: An integrated thematic and topic modeling analysis. Journal of Big Data, 11, Article 55. https://doi.org/10.1186/s40537-024-00920-x
  75. Raza, M. H., Rind, Y. M., Javed, I., Zubair, M., Mehmood, M. Q., & Massoud, Y. (2023). Smart meters for smart energy: A review of business intelligence applications. IEEE Access, 11, 120001-120022. https://doi.org/10.1109/access.2023.3326724
  76. Roy, R., & Naidoo, V. (2021). Enhancing chatbot effectiveness: The role of anthropomorphic conversational styles and time orientation. Journal of Business Research, 126, 23-34. https://doi.org/10.1016/j.jbusres.2020.12.051
  77. 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
  78. Savaget, P., Geissdoerfer, M., Kharrazi, A., & Evans, S. (2019). The theoretical foundations of sociotechnical systems change for sustainability: A systematic literature review. Journal of Cleaner Production, 206, 878-892. https://doi.org/10.1016/j.jclepro.2018.09.208
  79. Selvakumar, P., & Manjunath, T. C. (2025). Food technology innovation. In Z. Hussain, A. Albattat, F. Fakir, & Z. Yi (Eds.), Innovative trends shaping food marketing and consumption (pp. 215-242). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8542-5.ch009
  80. Sharma, A. K., & Sharma, R. (2024). Assessing the influence of artificial intelligence on sustainable consumption behavior and lifestyle choices. Young Consumers: Insight and Ideas for Responsible Marketers, 26(5), 702-727. https://doi.org/10.1108/yc-09-2024-2214
  81. Shi, H., Shangguan, L., Dong, L., Li, M., & Zhang, Y. (2024). Voluntary vs. compulsory: Examining the consequences of two forms of employee green behaviors. Current Psychology, 43(26), 22297-22306. https://doi.org/10.1007/s12144-024-05885-x
  82. Silalahi, A. D. K. (2025). Can generative artificial intelligence drive sustainable behavior? A consumer-adoption model for AI-driven sustainability recommendations. Technology in Society, 83, Article 102995. https://doi.org/10.1016/j.techsoc.2025.102995
  83. Singh, D., & Kunja, S. R. (2025). Engaging guests for a greener tomorrow: Examining the role of hotel chatbots in encouraging pro-environmental behavior. Tourism and Hospitality Research. https://doi.org/10.1177/14673584241313339
  84. Sohaib, O., Alshemeili, A., & Bhatti, T. (2025). Exploring AI-enabled green marketing and green intention: An integrated PLS-SEM and NCA approach. Cleaner and Responsible Consumption, 17, Article 100269. https://doi.org/10.1016/j.clrc.2025.100269
  85. 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
  86. Stern, P. C., Dietz, T., Abel, T., Guagnano, G. A., & Kalof, L. (1999). A value-belief-norm theory of support for social movements: The case of environmentalism. Human Ecology Review, 6(2), 81-97. https://www.humanecologyreview.org/pastissues/her62/62sternetal.pdf
  87. Thomas, A. (2024). Digitally transforming the organization through knowledge management: A socio-technical system (STS) perspective. European Journal of Innovation Management, 27(9), 437-460. https://doi.org/10.1108/ejim-02-2024-0114
  88. Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … & Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467-473. https://doi.org/10.7326/M18-0850
  89. Tsai, W.-H. S., Liu, Y., & Chuan, C.-H. (2021). How chatbots’ social presence communication enhances consumer engagement: The mediating role of parasocial interaction and dialogue. Journal of Research in Interactive Marketing, 15(3), 460-482. https://doi.org/10.1108/jrim-12-2019-0200
  90. Tussyadiah, I. P., Zach, F. J., & Wang, J. (2020). Do travelers trust intelligent service robots? Annals of Tourism Research, 81, Article 102886. https://doi.org/10.1016/j.annals.2020.102886
  91. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  92. 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
  93. Vitezić, V., & Perić, M. (2021). Artificial intelligence acceptance in services: Connecting with generation Z. The Service Industries Journal, 41(13-14), 926-946. https://doi.org/10.1080/02642069.2021.1974406
  94. Wang, K., Lu, L., Fang, J., Xing, Y., Tong, Z., & Wang, L. (2023). The downside of artificial intelligence (AI) in green choices: How AI recommender systems decrease green consumption. Managerial and Decision Economics, 44(6), 3346-3353. https://doi.org/10.1002/mde.3882
  95. Wien, A. H., & Peluso, A. M. (2021). Influence of human versus AI recommenders: The roles of product type and cognitive processes. Journal of Business Research, 137, 13-27. https://doi.org/10.1016/j.jbusres.2021.08.016
  96. Yamawaki, M., Ueda, K., Ishii, H., Shimoda, H., Ito, K., Sato, H., Fujioka, T., Sun, Q., Asa, Y., & Numata, T. (2023). Effects of virtual agent interactivity on pro-environmental behavior promotion. Journal of Environmental Psychology, 88, Article 101999. https://doi.org/10.1016/j.jenvp.2023.101999
  97. Yang, Y., Li, C., & Qu, Z. (2025). An AI-driven approach to sustainability: The effect of AI accent on tourists’ pro-environmental behavioral intentions. Journal of Hospitality and Tourism Management, 63, 478-487. https://doi.org/10.1016/j.jhtm.2025.05.012
  98. Yin, Y., Wang, H., & Deng, X. (2024). Real-time logistics transport emission monitoring-Integrating artificial intelligence and internet of things. Transportation Research Part D: Transport and Environment, 136, Article 104426. https://doi.org/10.1016/j.trd.2024.104426
  99. Zhang, J., & Cao, A. (2025). The psychological mechanisms of education for sustainable development: Environmental attitudes, self-efficacy, and social norms as mediators of pro-environmental behavior among university students. Sustainability, 17(3), Article 933. https://doi.org/10.3390/su17030933
  100. Zhang, J., Zhang, Y., Lu, L., & Zhang, L. (2022). Proactive responses to job insecurity: Why and when job-insecure employees engage in political behaviors. Management Decision, 60(12), 3188-3208. https://doi.org/10.1108/md-06-2021-0766
  101. Zhao, X., Sun, Y., Liu, W., & Wong, C.-W. (2025). Tailoring generative AI chatbots for multiethnic communities in disaster preparedness communication: Extending the CASA paradigm. Journal of Computer-Mediated Communication, 30(1), Article zmae022. https://doi.org/10.1093/jcmc/zmae022
  102. Zhong, B., Niu, N., Li, J., Wu, Y., & Fan, W. (2024). Social observation modulates the influence of socioeconomic status on pro-environmental behavior: An event-related potential study. Frontiers in Neuroscience, 18, Article 1428659. https://doi.org/10.3389/fnins.2024.1428659