Despite the rapid expansion of live streaming e-commerce, the phenomenon has received limited attention from researchers within the Information Systems (IS) field about homophily and heterophily factors, particularly in terms of its distinctive features and consumer behavior dynamics. This study aims to address this gap by investigating these underlying factors that drive impulsive buying behavior during live stream sessions.
Grounded in Latent-State Trait Theory and Parasocial Relationship Theory, this study proposes that various states, namely parasocial relationship, need-matching, and taste-matching can trigger the urge to make impulsive purchases in real-time live streaming contexts. An experimental design incorporating eight distinct scenarios was implemented, with 253 valid participant responses collected. The data were analyzed using the disjoint two-stage approach in PLS-SEM to ensure robust empirical validation.
The findings revealed that information and visibility homophily were the strongest predictors of parasocial relationships and need-matching. In turn, both parasocial relationships and need-matching, taste-matching had a significant effect on the urge to buy impulsively. The moderating effect of IT mindfulness was significant in most cases, except for the link between taste-matching and the urge to buy impulsively.

Despite the rapid expansion of live streaming e-commerce, the phenomenon has received limited attention from researchers within the Information Systems (IS) field about homophily and heterophily factors, particularly in terms of its distinctive features and consumer behavior dynamics. This study aims to address this gap by investigating these underlying factors that drive impulsive buying behavior during live stream sessions.
Grounded in Latent-State Trait Theory and Parasocial Relationship Theory, this study proposes that various states, namely parasocial relationship, need-matching, and taste-matching can trigger the urge to make impulsive purchases in real-time live streaming contexts. An experimental design incorporating eight distinct scenarios was implemented, with 253 valid participant responses collected. The data were analyzed using the disjoint two-stage approach in PLS-SEM to ensure robust empirical validation.
The findings revealed that information and visibility homophily were the strongest predictors of parasocial relationships and need-matching. In turn, both parasocial relationships and need-matching, taste-matching had a significant effect on the urge to buy impulsively. The moderating effect of IT mindfulness was significant in most cases, except for the link between taste-matching and the urge to buy impulsively.

Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 665-694.
Arghashi, V. (2025). Compulsive Social Media Use and Impulsive Buying: Social Capital in the Postpandemic Era. International Journal of Electronic Commerce, 29(1), 65-98.
Arora, A. (2021). It’s showtime! How live commerce is transforming the shopping experience. from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/its-showtime-how-live-commerce-is-transforming-the-shopping-experience
Auter, P. J., & Palmgreen, P. (2000). Development and validation of a parasocial interaction measure: The audience‐persona interaction scale. Communication Research Reports, 17(1), 79-89.
Beatty, S. E., & Ferrell, M. E. (1998). Impulse buying: Modeling its precursors. Journal of Retailing, 74(2), 169-191.
Becdach, C. (2023). Ready for prime time? The state of live commerce. from https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/ready-for-prime-time-the-state-of-live-commerce
Becker, J.-M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long range planning, 45(5-6), 359-394.
Bi, N. C., & Zhang, R. N. (2023). "I will buy what my 'friend' recommends": the effects of parasocial relationships, influencer credibility and self-esteem on purchase intentions. Journal of Research in Interactive Marketing, 17(2), 157-175.
Breves, P., Amrehn, J., Heidenreich, A., Liebers, N., & Schramm, H. (2021). Blind trust? The importance and interplay of parasocial relationships and advertising disclosures in explaining influencers’ persuasive effects on their followers. International Journal of Advertising, 40(7), 1209-1229.
Byrne, D., & Nelson, D. (1965). The effect of topic importance and attitude similarity-dissimilarity on attraction in a multistranger design. Psychonomic Science, 3(1), 449-450.
Chen, C. C., & Yao, J. Y. (2018). What drives impulse buying behaviors in a mobile auction? The perspective of the Stimulus-Organism-Response model. Telematics and Informatics, 35(5), 1249-1262.
Chen, J. V., Su, B.-c., & Widjaja, A. E. (2016). Facebook C2C social commerce: A study of online impulse buying. Decision Support Systems, 83, 57-69.
Chen, Y. H., Lu, Y. B., Wang, B., & Pan, Z. (2019). How do product recommendations affect impulse buying? An empirical study on WeChat social commerce. Information & Management, 56(2), 236-248.
D’Angelo, J. K., Dunn, L., & Valsesia, F. (2025). Is this for me? Differential responses to skin tone inclusivity initiatives by underrepresented consumers and represented consumers. Journal of Marketing, 89(2), 25-42.
Dalgic, T., & Leeuw, M. (1994). Niche marketing revisited: concept, applications and some European cases. European journal of marketing, 28(4), 39-55.
Deng, F., Lin, Y., & Jiang, X. (2023). Influence mechanism of consumers’ characteristics on impulsive purchase in E-commerce livestream marketing. Computers in human behavior, 148, 107894.
Ding, R., Chen, X., Wei, S., & Wang, J. (2025). What drives trust building in live streaming E-commerce? From an elaboration likelihood model perspective. Industrial Management & Data Systems, 125(3), 969-999.
Farivar, S., Wang, F., & Yuan, Y. (2021). Opinion leadership vs. para-social relationship: Key factors in influencer marketing. Journal of Retailing and Consumer Services, 59, 102371.
Fu, J. R., & Hsu, C. W. (2023). Live-streaming shopping: the impacts of para-social interaction and local presence on impulse buying through shopping value. Industrial Management & Data Systems, 123(7), 1861-1886.
Gao, X., Xu, X.-Y., Tayyab, S. M. U., & Li, Q. (2021). How the live streaming commerce viewers process the persuasive message: An ELM perspective and the moderating effect of mindfulness. Electronic Commerce Research and Applications, 49, 101087.
Gong, W. Q., & Li, X. G. (2017). Engaging fans on microblog: the synthetic influence of parasocial interaction and source characteristics on celebrity endorsement. Psychology & Marketing, 34(7), 720-732.
Grant, A. E., Guthrie, K. K., & Ballrokeach, S. J. (1991). Television Shopping - a Media System Dependency Perspective. Communication Research, 18(6), 773-798.
Gu, Y., Cheng, X. S., & Shen, J. (2023). Design shopping as an experience: Exploring the effect of the live-streaming shopping characteristics on consumers' participation intention and memorable experience. Information & Management, 60(5).
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.
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook: Springer Nature.
Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123.
He, W., & Jin, C. Y. (2024). A study on the influence of the characteristics of key opinion leaders on consumers' purchase intention in live streaming commerce: based on dual-systems theory. Electronic Commerce Research, 24(2), 1235-1265.
Heidenreich, S., Wittkowski, K., Handrich, M., & Falk, T. (2015). The dark side of customer co-creation: exploring the consequences of failed co-created services. Journal of the academy of marketing science, 43(3), 279-296.
Hildebrandt, F., Brendel, A. B., Dennis, A. R., & Sachdeva, A. (2023). New bots-the influence of a conversational agent’s rookie personality on users’ satisfaction.
Ho, S. Y., & Lim, K. H. (2018). Nudging moods to induce unplanned purchases in imperfect mobile personalization contexts. Mis Quarterly, 42(3), 757-A713.
Horton, D., & Richard Wohl, R. (1956). Mass communication and para-social interaction: Observations on intimacy at a distance. psychiatry, 19(3), 215-229.
Hsiao, C. C., & Chiou, J. S. (2012). The impact of online community position on online game continuance intention: Do game knowledge and community size matter? Information & Management, 49(6), 292-300.
Hsu, C. L. (2020). How vloggers embrace their viewers: Focusing on the roles of para-social interactions and flow experience. Telematics and Informatics, 49.
Hu, M., & Chaudhry, S. S. (2020). Enhancing consumer engagement in e-commerce live streaming via relational bonds. Internet Research, 30(3), 1019-1041.
Hu, M., Zhang, M., & Wang, Y. (2017). Why do audiences choose to keep watching on live video streaming platforms? An explanation of dual identification framework. Computers in human behavior, 75, 594-606.
Hu, Q., Pan, Z., Lu, Y., & Wang, B. (2023). Heterophily or homophily of social media influencers: The role of dual parasocial relationships in impulsive buying. International Journal of Electronic Commerce, 27(4), 558-589.
Hu, X., Chen, X., & Davison, R. M. (2019). Social support, source credibility, social influence, and impulsive purchase behavior in social commerce. International Journal of Electronic Commerce, 23(3), 297-327.
Hung, K., Tse, D. K., & Chan, T. H. (2022). E-Commerce influencers in China: Dual-route model on likes, shares, and sales. Journal of Advertising, 51(4), 486-501.
Kerlinger, F. N. (1966). Foundations of behavioral research. Holt, Rinehart and Winston: New York.
Kim, Y., & McFarland, R. G. (2024). Are you looking for something specific or just looking around? Adaptive selling on the basis of customer shopping goals in retail sales. Journal of the Academy of Marketing Science, 52(6), 1780-1804.
Köhler, C. F., Breugelmans, E., & Dellaert, B. G. (2011). Consumer acceptance of recommendations by interactive decision aids: The joint role of temporal distance and concrete versus abstract communications. Journal of Management Information Systems, 27(4), 231-260.
Li, M., Jin, Y., Zhang, J., & Liu, R. (2022). Does shape in backgrounds matter? Effects of shape–taste congruence on product evaluations. Journal of Retailing and Consumer Services, 67, 102990.
Li, X. X., Wang, Q., Yao, X. L., Yan, X. B., & Li, R. H. (2025). How do influencers' impression management tactics affect purchase intention in live commerce? - Trust transfer and gender differences. Information & Management, 62(2).
Liu, F., Lim, E. T., Li, H., Tan, C.-W., & Cyr, D. (2020). Disentangling utilitarian and hedonic consumption behavior in online shopping: An expectation disconfirmation perspective. Information & Management, 57(3), 103199.
Liu, P., He, J., & Li, A. (2019). Upward social comparison on social network sites and impulse buying: A moderated mediation model of negative affect and rumination. Computers in Human Behavior, 96, 133-140.
Liu, Z., Wang, X., Min, Q., & Li, W. (2019). The effect of role conflict on self‐disclosure in social network sites: An integrated perspective of boundary regulation and dual process model. Information Systems Journal, 29(2), 279-316.
Lo, P. S., Dwivedi, Y. K., Tan, G. W. H., Ooi, K. B., Aw, E. C. X., & Metri, B. (2022). Why do consumers buy impulsively during live streaming? A deep learning-based dual-stage SEM-ANN analysis. Journal of Business Research, 147, 325-337.
Lu, B., & Chen, Z. (2021). Live streaming commerce and consumers’ purchase intention: An uncertainty reduction perspective. Information & Management, 58(7), 103509.
Lukas, B. A., Whitwell, G. J., & Heide, J. B. (2013). Why do customers get more than they need? How organizational culture shapes product capability decisions. Journal of Marketing, 77(1), 1-12.
Luo, X., Cheah, J.-H., Lim, X.-J., Ramayah, T., & Dwivedi, Y. K. (2024). Inducing shoppers’ impulsive buying tendency in live-streaming: integrating signaling theory with social exchange theory. Internet Research.
Ma, Y. (2025). E-commerce in Taiwan - statistics & facts. from https://www.statista.com/topics/6758/e-commerce-in-taiwan/
Maier, C., Laumer, S., Wirth, J., & Weitzel, T. (2019). Technostress and the hierarchical levels of personality: a two-wave study with multiple data samples. European Journal of Information Systems, 28(5), 496-522.
Min, D. J., & Cunha, M. (2019). The influence of horizontal and vertical product attribute information on decision making under risk: The role of perceived competence. Journal of Business Research, 97, 174-183.
Pan, M., Blut, M., Ghiassaleh, A., & Lee, Z. W. (2025). Influencer marketing effectiveness: A meta-analytic review. Journal of the Academy of Marketing Science, 53(1), 52-78.
Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009). The Influence of Website Characteristics on a Consumer's Urge to Buy Impulsively. Information Systems Research, 20(1), 60-78.
Park, E. J., Kim, E. Y., Funches, V. M., & Foxx, W. (2012). Apparel product attributes, web browsing, and e-impulse buying on shopping websites. Journal of Business Research, 65(11), 1583-1589.
Perse, E. M. (1990). Media Involvement and Local News Effects. Journal of Broadcasting & Electronic Media, 34(1), 17-36.
Piron, F. (1991). Defining impulse purchasing. Advances in consumer research, 18(1).
Pounders, K. R., Babin, B. J., & Close, A. G. (2015). All the same to me: outcomes of aesthetic labor performed by frontline service providers. Journal of the Academy of Marketing Science, 43(6), 670-693.
Qiu, L. F., Chhikara, A., & Vakharia, A. (2021). Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence. Information Systems Research, 32(3), 876-894.
Reinkemeier, F., Gnewuch, U., & Toporowski, W. (2022). Can humanizing voice assistants unleash the potential of voice commerce?
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor's comments: a critical look at the use of PLS-SEM in" MIS Quarterly". MIS quarterly, iii-xiv.
Rogers, E. M., & Bhowmik, D. K. (1970). Homophily-heterophily: Relational concepts for communication research. Public opinion quarterly, 34(4), 523-538.
Rook, D. W. (1987). The Buying Impulse. Journal of Consumer Research, 14(2), 189-199.
Rubin, R. B., & Mchugh, M. P. (1987). Development of Parasocial Interaction Relationships. Journal of Broadcasting & Electronic Media, 31(3), 279-292.
Sarstedt, M., Hair Jr, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian marketing journal, 27(3), 197-211.
Shi, Z., & Raghu, T. S. (2020). An Economic Analysis of Product Recommendation in the Presence of Quality and Taste-Match Heterogeneity. Information Systems Research, 31(2), 399-411.
Sprotles, G. B., & Kendall, E. L. (1986). A methodology for profiling consumers' decision‐making styles. Journal of Consumer Affairs, 20(2), 267-279.
Steyer, R., Mayer, A., Geiser, C., & Cole, D. A. (2015). A theory of states and traits—Revised. Annual review of clinical psychology, 11(1), 71-98.
Steyer, R., Schmitt, M., & Eid, M. (1999). Latent state–trait theory and research in personality and individual differences. European Journal of Personality, 13(5), 389-408.
Steyer, R., & Schmitt, M. J. (1990). Latent state-trait models in attitude research. Quality and Quantity, 24(4), 427-445.
Sun, H., Fang, Y., & Zou, H. M. (2016). Choosing a fit technology: Understanding mindfulness in technology adoption and continuance. Journal of the Association for Information Systems, 17(6), 2.
Tam, K. Y., & Ho, S. Y. (2005). Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Information systems research, 16(3), 271-291.
Thatcher, J. B., Wright, R. T., Sun, H., Zagenczyk, T. J., & Klein, R. (2018). Mindfulness in information technology use. MIS quarterly, 42(3), 831-A814.
Venciute, D., Mackeviciene, I., Kuslys, M., & Correia, R. F. (2023). The role of influencer-follower congruence in the relationship between influencer marketing and purchase behaviour. Journal of Retailing and Consumer Services, 75.
Wang, D. Y., Luo, X., Hua, Y., & Benitez, J. (2022). Big arena, small potatoes: A mixed-methods investigation of atmospheric cues in live-streaming e-commerce. Decision Support Systems, 158.
Wang, L., Lowry, P. B., Luo, X., & Li, H. (2023). Moving consumers from free to fee in platform-based markets: an empirical study of multiplayer online battle arena games. Information Systems Research, 34(1), 275-296.
Wang, L., Zhu, H., Li, X., & Zhao, Y. (2024). Formation mechanism of user stickiness in live e-commerce: the hybrid PLS-SEM and ANN approach. Industrial Management & Data Systems, 124(3), 1234-1262.
Wang, Q., Li, X., & Yan, X. (2025). When the mindful ones experience flow: a moderated-mediation model of purchase intention in live commerce. Information Technology & People.
Wells, J. D., Parboteeah, V., & Valacich, J. S. (2011). Online impulse buying: understanding the interplay between consumer impulsiveness and website quality. Journal of the Association for Information Systems, 12(1), 3.
Wongkitrungrueng, A., & 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.
Wu, B., Liu, C. Z., & Zhu, H. (2025). Online creators’ strategic cooperation in two-sided synchronous UGC market: Empirical evidence from a livestreaming platform. Information & Management, 62(2), 104095.
Xiang, L., Zheng, X., Lee, M. K., & Zhao, D. (2016). Exploring consumers’ impulse buying behavior on social commerce platform: The role of parasocial interaction. International journal of information management, 36(3), 333-347.
YahooFinance. (2025). Global Livestream E-Commerce Market Poised to Reach US$ 6079.8 Bn by 2035, Driven by AI Integration and Enhanced Consumer Engagement | Latest Report by Transparency Market Research. from https://finance.yahoo.com/news/global-livestream-e-commerce-market-152700348.html
Zafar, A. U., Qiu, J. N., & Shahzad, M. (2020). Do digital celebrities' relationships and social climate matter? Impulse buying in f-commerce. Internet Research, 30(6), 1731-1762.
Zhang, K. Z. K., Xu, H. Q., Zhao, S., & Yu, Y. G. (2018). Online reviews and impulse buying behavior: the role of browsing and impulsiveness. Internet Research, 28(3), 522-543.
Zhang, X., & Zhang, S. (2024). Investigating impulse purchases in live streaming e-commerce: A perspective of match-ups. Technological Forecasting and Social Change, 205, 123513.
Zhang, X. P., Cheng, X. S., & Huang, X. W. (2023). "Oh, My God, Buy It!" Investigating Impulse Buying Behavior in Live Streaming Commerce. International Journal of Human-Computer Interaction, 39(12), 2436-2449.
Zheng, X. B., Men, J. Q., Yang, F., & Gong, X. Y. (2019). Understanding impulse buying in mobile commerce: An investigation into hedonic and utilitarian browsing. International Journal of Information Management, 48, 151-160.
Zierau, N., Flock, K., Janson, A., Söllner, M., & Leimeister, J. M. (2021). The influence of AI-based chatbots and their design on users’ trust and information sharing in online loan applications. Paper presented at the Hawaii International Conference on System Sciences (HICSS).-Koloa (Hawaii), USA.