Enhancing Online Impulse Buying through Artificial Intelligence Stimuli and Social Stimuli in Generation Z: The Mediating Role of Brand Trust
Keywords:
AI recommendation accuracy, brand trust, online impulse buying, e-commerce, generation Z, subjective norms, consumer impulsiveness, Pakistan.Abstract
The purpose of this research is to investigate and explain the extent to which artificial intelligence stimuli and social behavior determinants influence online impulse buying in Pakistan’s e-retail sector positioning brand trust as central pathway mechanism among generation Z. Drawing on the integrated Stimulus-Organism-Response (SOR) framework and Technology Acceptance Model (TAM), study specified three stimuli factors AI exposure, AI accuracy perception, and recommended product, while buying impulsiveness and subjective norms were incorporated as behavioral constructs to predict impulse buying behavior mediated by the brand trust. Data were collected from generation Z and broader adult cohort via cross-sectional Google Survey yielding 508 valid responses. Partial-Least-Square, structural equation modeling (SmartPLS-4.0) was used to test the hypothesized relationship of the variables. Findings of the study revealed AI accuracy perception and subjective norms exhibited strong mediating effect through brand trust on impulse buying as compared to AI exposure, recommended product, and buying impulsiveness. Overall, the model explained 57.0% effect on brand trust and 35.8% was recorded on impulse buying with satisfactory predictive relevance and effect size. AI accuracy perception and subjective norms are strong calibration with brand trust; where effect size indicates larger trust leads to influence impulse buying significantly.
References
Abdullah Amran, G., Li, X. and Al-Bakhrani, A.A. (2026). Link prediction in social networks and E-commerce: A comprehensive review and bibliometric analysis. Expert Systems with Applications, 299, Part B, 12994. https://doi.org/10.1016/j.eswa.2025.129914
Abowitz, D.A. and Toole, T.M. (2010). Mixed Method Research: Fundamental Issues of Design, Validity, and Reliability in Construction Research. Journal of Construction Engineering and Management, 136(1), 108-116. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000026
Acharya, N., Sassenberg, A.M. and Soar, J. (2023). Consumers' Behavioural Intentions to Reuse Recommender Systems: Assessing the Effects of Trust Propensity, Trusting Beliefs and Perceived Usefulness. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 55-78. https://doi.org/10.3390/jtaer18010004
Aguirre-Urreta, M.I., Marakas, G.M. and Ellis, M.E. (2013). Measurement of composite reliability in research using partial least squares: some issues and an alternative approach. SIGMIS Database, 44(4), 11-43. https://doi.org/10.1145/2544415.2544417
Ahmad Husairi, M. and Rossi, P. (2024). Delegation of purchasing tasks to AI: The role of perceived choice and decision autonomy. Decision Support Systems, 179. 114166. https://doi.org/10.1016/j.dss.2023.114166
Ahmad, K. and Lilani, K. (2025a). From scrolling to buying: Role of beauty influencers on impulse buying a stimulus-organism-response perspective. Telematics and Informatics Reports, 19, 100239. https://doi.org/10.1016/j.teler.2025.100239
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
Akalamkam, K. and Mitra, J.K. (2018). Consumer Pre-purchase Search in Online Shopping: Role of Offline and Online Information Sources. Business Perspectives and Research, 6(1), 42-60. https://doi.org/10.1177/2278533717730448
Aljukhadar, M., Trifts, V. and Senecal, S. (2017). Consumer self-construal and trust as determinants of the reactance to a recommender advice. Psychology and Marketing, 34(7), 708-719. https://doi.org/10.1002/mar.21017
Amin, A. (2025). Artificial intelligence in social media: a catalyst for impulse buying behavior?, Young Consumers: Insight and Ideas for Responsible Marketers, 26(5), 765-785. https://doi.org/10.1108/YC-10-2024-2297
An, G.K. and Ngo, T.T.A. (2025). AI-powered personalized advertising and purchase intention in Vietnam's digital landscape: The role of trust, relevance, and usefulness. Journal of Open Innovation: Technology, Market, and Complexity, 11(3), 100580. https://doi.org/10.1016/j.joitmc.2025.100580
Aoki, T. and Matsui, A. (2025). Does algorithmic recommendation complement or substitute advertising and influencers? Consumer attitudes toward recommendation information and the formation of purchase intentions. Computers in Human Behavior, 172, 108735. https://doi.org/10.1016/j.chb.2025.108735
Becker, J. M., Cheah, J. H., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321-346. https://doi.org/10.1108/IJCHM-04-2022-0474
Bhattacherjee, A. (2012). Social Science Research: principles, methods, and practices, Book 3. Available at: http://repository.out.ac.tz/504/1/Social_Science_Research-_Principles_Methods_and_Practices.pdf
Breidbach, C.F., Keating, B.W. and Lim, C. (2020). Fintech: research directions to explore the digital transformation of financial service systems. Journal of Service Theory and Practice, 30(1), 79-102. https://doi.org/10.1108/JSTP-08-2018-0185
Campbell, D.T. and Fiske, D.W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81-105. https://doi.org/10.1037/h0046016
Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., ... & Walker, K. (2020). Purposive sampling: complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652-661. https://doi.org/10.1177/1744987120927206
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, 114737. https://doi.org/10.1016/j.jbusres.2024.114737
Cheng, Y. and Jiang, H. (2022). Customer-brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product and Brand Management, 31(2), 252-264. https://doi.org/10.1108/JPBM-05-2020-2907
Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences, Statistical Power Analysis for the Behavioral Sciences. Available at: https://doi.org/10.4324/9780203771587
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319-339. https://doi.org/10.2307/249008
Diwanji, V.S. (2026). Should your brand hire virtual influencers? How realism and gender presentation shape trust and purchase intentions. Journal of Retailing and Consumer Services, 88, 104491. https://doi.org/10.1016/j.jretconser.2025.104491
Escobar-Farfán, M., Veas-González, I., García-Salirrosas, E. E., Veas-Salinas, K., Veas-Santibañez, V., & Zavala-González, J. (2025). From Browsing to Buying: Determinants of Impulse Buying Behavior in Mobile Commerce. Journal of Theoretical and Applied Electronic Commerce Research, 20(4). https://doi.org/10.3390/jtaer20040266
Farea, M.M. and Hussain, S. (2025). Applying the S-O-R Model to Understand Impulsive Buying Behavior Among Pakistani Online Shoppers. Social Science Review Archives, 3(1), 895-914. https://doi.org/10.70670/SRA.V3I1.383
Fornell, C. and Larcker, D.F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Franklin, C.S., Cody, P.A. and Ballan, M. (2019).Reliability and Validity in Qualitative Research. In The Handbook of Social Work Research Methods, pp. 355-374. Available at: https://doi.org/10.4135/9781544364902.n19
Gallin, S. and Portes, A. (2024). Online shopping: How can algorithm performance expectancy enhance impulse buying? Journal of Retailing and Consumer Services, 81, 103988. https://doi.org/10.1016/j.jretconser.2024.103988
Gong, M. and Liu, H. (2025). Understanding impulse buying in interest-based e-commerce: the role of content creativity. International Journal of Retail & Distribution Management, 53(2), 182-198. https://doi.org/10.1108/IJRDM-07-2023-0484
Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023). Improving PLS-SEM use for business marketing research. Industrial Marketing Management, 111, 127-142. https://doi.org/10.1016/j.indmarman.2023.03.010
Guerra-Tamez, C. R., Kraul Flores, K., Serna-Mendiburu, G. M., Chavelas Robles, D., & Ibarra Cortés, J. (2024). Decoding Gen Z: AI's influence on brand trust and purchasing behavior. Frontiers in Artificial Intelligence, 7, 32352. https://doi.org/10.3389/frai.2024.1323512
Hair, J. and Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3). https://doi.org/10.1016/j.rmal.2022.100027
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433. https://doi.org/10.1007/s11747-011-0261-6
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publication, 3rd edition. Available at: https://uk.sagepub.com/en-gb/eur/a-primer-on-partial-least-squares-structural-equation-modeling-pls-sem/book270548
Hair, J.F., Howard, M.C. and Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
He, M., Thi, T. D. P., Tran, H. M., & Duong, N. T. (2025). Consumers' intentions to use online shopping apps: A comparative analysis. Acta Psychologica, 259, 105414. https://doi.org/10.1016/j.actpsy.2025.105414
Henseler, J., Ringle, C.M. and Sarstedt, M. (2014). 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
Henseler, J., Ringle, C.M. and Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405-431. https://doi.org/10.1108/IMR-09-2014-0304
Huynh, P.H. (2021). Enabling circular business models in the fashion industry: The role of digital innovation. International Journal of Productivity and Performance Management, 71(3), 870-895. https://doi.org/10.1108/IJPPM-12-2020-0683
Ingram, R. (1998). Power analysis and sample size estimation. Journal of Research in Nursing, 3(2), 132-139. https://doi.org/10.1177/174498719800300210
Jin, S.V. and Ryu, E. (2025). Unraveling the dynamics of digital equality and trust in AI-empowered metaverses and AI-VR-convergence. Technological Forecasting and Social Change, 210,123877. https://doi.org/10.1016/j.techfore.2024.123877
Kathuria, A. and Bakshi, A. (2025). Unveiling the dynamics that shape online impulse buying behavior. Journal of Research in Interactive Marketing, 19(5), 770-786. https://doi.org/10.1108/JRIM-03-2024-0147
Ketchen, D.J. (2013). A Primer on Partial Least Squares Structural Equation Modeling. Long Range Planning, 46(1-2), 184-185. https://doi.org/10.1016/j.lrp.2013.01.002
Kumari, A. and Laheri, V.K. (2025). Understanding Consumer Behavior Through AI-Powered Recommender Systems: A Systematic Review and Bibliometric Perspective. Indian Journal of Marketing, 55(8), 9-32. https://doi.org/10.17010/ijom/2025/v55/i8/175207
Li, J. and Kang, J. (2025). Less stress, fewer delays: The role of sophisticated AI in mitigating decision fatigue and purchase postponement in luxury retail. Journal of Retailing and Consumer Services, 85, 104268. https://doi.org/10.1016/j.jretconser.2025.104268
Li, X., Zhou, T., Hu, C., & Liu, H. (2025). Availability Bias in AI Recommenders: Impacts on Impulse Buying. Journal of Computer Information Systems, 2547173. https://doi.org/10.1080/08874417.2025.2547173
Lo, L.Y.S., Lin, S.W. and Hsu, L.Y. (2016). Motivation for online impulse buying: A two-factor theory perspective. International Journal of Information Management, 36(5), 759-772. https://doi.org/10.1016/j.ijinfomgt.2016.04.012
Longoni, C., Fradkin, A., Cian, L., & Pennycook, G. (2022). News from Generative Artificial Intelligence Is Believed Less. In ACM International Conference Proceeding Series. Association for Computing Machinery, pp. 97-106. Available at: https://doi.org/10.1145/3531146.3533077
Longoni, C. and Cian, L. (2022). Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The "Word-of-Machine" Effect. Journal of Marketing, 86(1), 91-108. https://doi.org/10.1177/0022242920957347
Luo, Y., Kumar, N. and Yazdanmehr, A. (2026). AI nudging and decision quality: Evidence from randomized experiments in online recommendation setting. Decision Support Systems, 200, 114565. https://doi.org/10.1016/j.dss.2025.114565
Jyothsna, M., & Kryvinska, N. (2024). Exploring the Chatbot usage intention-A mediating role of Chatbot initial trust. Heliyon, 10(12). https://doi.org/10.1016/j.heliyon.2024.e33028
Malka, S.C., MacLennan, H. and Tiell, R.H. (2025). Organizational Decline by Micromanagement: The Implications of Over-Controlling Remote and Hybrid Employees. Workplace Transformations in a Post Pandemic Era, 75-88. https://doi.org/10.1108/978-1-80592-439-520251004
Mani, S., Tiwari, P., Ramchandani, S., Acharya, P. S., & Irudayaraj, V. D. (2025). From Clicks to Conversions: How AI Shapes Consumer Trust, Experience, and Online Buying Behaviour. Advances in Consumer Research, 4, 5028-5035. Available at: https://acr-journal.com/article/from-clicks-to-conversions-how-ai-shapes-consumer-trust-experience-and-online-buying-behaviour-1612/
Mehrabian, A. and Russell, J.A. (1974). An Approach to Environmental Psychology. Available at: https://psycnet.apa.org/record/1974-22049-000?ref=nepopularna.org
Munuera-Aleman, J.L., Delgado-Ballester, E. and Yague-Guillen, M.J. (2003). Development and Validation of a Brand Trust Scale. International Journal of Market Research, 45(1), 1-18. https://doi.org/10.1177/147078530304500103
Ngo, T. T. A., Nguyen, H. L. T., Nguyen, H. P., Mai, H. T. A., Mai, T. H. T., & Hoang, P. L. (2024a). A comprehensive study on factors influencing online impulse buying behavior: Evidence from Shopee video platform. Heliyon, 10(15), e35743. https://doi.org/10.1016/j.heliyon.2024.e35743
Ngo, T.T.A., Le, T.N.T. and Phan, T.Y.N. (2026). How knowledge influences the purchase intention of generation Z toward genetically modified foods. Journal of Agriculture and Food Research, 26, 102694. https://doi.org/10.1016/j.jafr.2026.102694
Nguyen, T. D. X., & Nguyen, K. M. (2026). Demystifying how emotional and cognitive resonance to AI-driven content consistency sparks customer engagement and impulse buying in short-video E-commerce. Journal of Retailing and Consumer Services, 89, 104646. https://doi.org/10.1016/j.jretconser.2025.104646
Niros, A., Niros, M. I., Baltas, G., Giovanis, A., & Painesis, G. (2025). Chatbot marketing efforts in the era of artificial intelligence: The moderating role of individualism. International Marketing Review, 42(4), 788-816. https://doi.org/10.1108/IMR-10-2024-0443
Nong, Z. and Wu, J. (2025). Understanding the knowledge sharing behaviors in social Commerce: Affordances, coactive vicarious Learning, and need for cognitive closure. Electronic Commerce Research and Applications, 74, 101565. https://doi.org/10.1016/j.elerap.2025.101565
Nyrhinen, J., Sirola, A., Koskelainen, T., Munnukka, J., & Wilska, T. A. (2024). Online antecedents for young consumers' impulse buying behavior. Computers in Human Behavior, 153, 108129. https://doi.org/10.1016/j.chb.2023.108129
Ozturk, A. B., Çağlayan, B., Kapmaz, M., Çalık, I., Tekin, S., İliaz, S., & Fırat, P. (2023). Hypersensitivity reactions to COVID-19 vaccines: a case of Eosinophilic pneumonia following Sinovac/CoronaVac vaccination. European Annals of Allergy and Clinical Immunology, 55(1), 41-45. Available at: https://www.eurannallergyimm.com/wp-content/uploads/2024/04/complete-issue-5144allp1.pdf#page=43
Pathak, Y., Kumar, B., Makhijani, J., & Niranjan, M. K. (2025). The Role of AI-Generated Real-Time Product Recommendations in Impulse Buying Among Centennials. Volume Title: Proceedings of the International Conference on Artificial Intelligence in Management for Business and Industrial Growth (AIMBIG 2025), pp. 303-323. https://www.atlantis-press.com/proceedings/aimbig-25/126018203
Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6), e04284. https://doi.org/10.1016/j.heliyon.2020.e04284
Rook, D.W. and Fisher, R.J. (1995) 'Normative Influences on Impulsive Buying Behavior', Journal of Consumer Research, 22(3), p. 305. Available at: https://doi.org/10.1086/209452
Roopak, R. and Chakrabarti, S. (2025). Framing knowledge structure of customer engagement: a multimethod review. VINE Journal of Information and Knowledge Management Systems, 55(4), 902-950. https://doi.org/10.1108/VJIKMS-12-2022-0364
Ruiz-Viñals, C., Pretel-Jiménez, M., Del Olmo Arriaga, J. L., & Miró Pérez, A. (2024). The Influence of Artificial Intelligence on Generation Z's Online Fashion Purchase Intention. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 2813-2827. https://doi.org/10.3390/jtaer19040136
Russell, J.A. and Mehrabian, A. (1977). Evidence for a three-factor theory of emotions. Journal of Research in Personality, 11(3), 273-294. https://doi.org/10.1016/0092-6566(77)90037-X
Xilai, Q. I. U., Bashir, T., Naseem, A., Gul, R. F., & Sadiq, B. A. (2026). A dual-process psychological model of impulse buying in digital commerce: evidence from livestream and marketplace contexts. Frontiers in Psychology, 17, 1774474. https://doi.org/10.3389/fpsyg.2026.1774474
Samoggia, A., Rossi, G., Fantini, A., Mouchtaropoulou, E., & Argiriou, N. (2025). What drives consumers' intention towards fairness-oriented products purchasing? An emotion-extended model of theory of planned behavior. Heliyon, 11(1), e41285. https://doi.org/10.1016/j.heliyon.2024.e41285
Sarstedt, M., Ringle, C.M. and Hair, J.F. (2021). Partial Least Squares Structural Equation Modeling', in Handbook of Market Research. Cham: Springer International Publishing (Classroom Companion: Business), pp. 587-632. Available at: https://doi.org/10.1007/978-3-319-57413-4_15
Saunders, M. N., Lewis, P., Thornhill, A., & Bristow, A. (2015). Understanding research philosophy and approaches to theory development. In: Saunders, Mark N. K.; Lewis, Philip and Thornhill, Adrian eds. Research Methods for Business Students. Harlow: Pearson Education, pp. 122–161. Available at: https://oro.open.ac.uk/53393/
Seth, A., Jiang, Y., Gyamfi, S. A., Emmanuel, D., & Amankwa, E. (2022). Development of Measurement Scale For Personalized Recommended Product Acceptance (PRPA-SCALE). Malaysian E Commerce Journal, 6(2), 76-85. https://doi.org/10.26480/mecj.02.2022.76.85
Shamim, K. and Misra, S.C. (2025). Leveraging AI to drive online impulse buying: a SOBC perspective. International Journal of Retail & Distribution Management, 53(10-11), 1040-1056. https://doi.org/10.1108/IJRDM-07-2024-0334
Snijkers, G., Bavdaž, M., Bender, S., Jones, J., Macfeely, S., Sakshaug, J. W., ... & van Delden, A. (2023). Advances in Business Statistics, Methods and Data Collection: Introduction. Advances in Business Statistics, Methods and Data Collection, pp. 3-22. https://doi.org/10.1002/9781119672333.ch1
Sreejesh, S. and Singha, S. (2026). Reconciling consumer intuition and machine: Algorithmic intuition conflict and the design of Consumer-AI collaboration. Journal of Retailing and Consumer Services, 90. https://doi.org/10.1016/j.jretconser.2025.104660
Suen, L. J. W., Huang, H. M., & Lee, H. H. (2014). A comparison of convenience sampling and purposive sampling.. Journal of Nursing, 61(3), 105-111. https://doi.org/10.6224/JN.61.3.105
Sung, E. C., Han, D. I. D., Bae, S., & Kwon, O. (2022). What drives technology-enhanced storytelling immersion? The role of digital humans. Computers in Human Behavior, 132, 107246. https://doi.org/10.1016/j.chb.2022.107246
Szymkowiak, A., Melović, B., Dabić, M., Jeganathan, K., & Kundi, G. S. (2021). Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people. Technology in Society, 65(1). https://doi.org/10.1016/j.techsoc.2021.101565
Tam, F.Y. and Lung, J. (2025). Digital marketing strategies for luxury fashion brands: A systematic literature review. International Journal of Information Management Data Insights, 5(1). https://doi.org/10.1016/j.jjimei.2024.100309
Tariq, M., Munir, A.F., Elahi, A.R., Zainab, Z., Naqash, M. & Fatima, A. (2025). Artificial Intelligence in E-Commerce: A Comparative Study of AI-Driven Innovations in Amazon and Daraz within the Pakistani Market. Journal for Social Science Archives, 3(2), 606-620. https://doi.org/10.59075/jssa.v3i2.265
Vasiliki, M., Serdaris, P., Antoniadis, I., & Spinthiropoulos, K. (2025). Ethical Consumer Attitudes and Trust in Artificial Intelligence in the Digital Marketplace: An Empirical Analysis of Behavioral and Value-Driven Determinants. Digital, 6(1), 1. https://doi.org/10.3390/digital6010001
De Vaus, D. and de Vaus, D. (2013) Surveys In Social Research, 6th Edition, Routledge, London. https://doi.org/10.4324/9780203519196
Verhagen, T. and Van Dolen, W. (2011) 'The influence of online store beliefs on consumer online impulse buying: A model and empirical application', Information and Management, 48(8), pp. 320-327. Available at: https://doi.org/10.1016/j.im.2011.08.001
Wallace, E., Torres, P., Augusto, M., & Stefuryn, M. (2022). Do brand relationships on social media motivate young consumers' value co-creation and willingness to pay? The role of brand love. Journal of Product and Brand Management, 31(2), 189-205. https://doi.org/10.1108/JPBM-06-2020-2937
Wellington, J. and Szczerbinski, M. (2007) Research Methods for the Social Sciences, Research Methods for the Social Sciences. Available at: https://books.google.com/books?hl=en&lr=&id=y23UAwAAQBAJ&oi=fnd&pg=PP1&dq=Research+Methods+in+Social+Sciences&ots=kjL3gLcDpi&sig=B8UrHXGZW67wZKG-28PRHIO9tEg
Wongkitrungrueng, A. and Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117, 543-556. https://doi.org/10.1016/j.jbusres.2018.08.032
Yin, J., Qiu, X., & Wang, Y. (2025). The Impact of AI-Personalized Recommendations on Clicking Intentions: Evidence from Chinese E-Commerce. Journal of Theoretical and Applied Electronic Commerce Research, 20(1), 21. https://doi.org/10.3390/jtaer20010021
Yu, X., Xu, D.J. and Li, K. (2026). Effects of AI reviews on consumers' purchase intention: Influence of product category, review breadth, and consumer review volume. Decision Support Systems, 201, 114590. https://doi.org/10.1016/j.dss.2025.114590
Zhang, X., Fan, J. and Zhang, R. (2024). The impact of social exclusion on impulsive buying behaviour of consumers on online platforms: Samples from China. Heliyon, 10(1), e23319. https://doi.org/10.1016/j.heliyon.2023.e23319
