The Implementation of Self Congruency Theory on Online Impulse Buying Intentions Mediated by Consumer Confidence in Online Shopping Towards Gen-Z

Authors

  • Linda Anadya Tastya Universitas Trisakti
  • Yolanda Masnita Siagian Universitas Trisakti
  • Husna Leila Yusran Universitas Trisakti

DOI:

https://doi.org/10.47747/ijmhrr.v7i2.3311

Keywords:

Social Media Influencer, Consumer Confidence, Social Media InfluenOnline Impulse Buying Intentions

Abstract

This research focuses on Online Impulse Buying Intentions, defined as the motivation to purchase items without planning while browsing online sites, often triggered by product recommendations. The study employs the Self-Congruency Theory framework, which explains how the alignment between consumer identity and perceptions of influencers or products facilitates impulsive behavior. A contradiction exists in the literature regarding the relationship between Influencer–Product Congruence and Online Impulse Buying Intentions: some studies find a direct influence, while others do not. To address this, a research model was developed to test the influence of Consumer – Influencer Congruence, Consumer – Product Congruence, and Influencer – Product Congruence on Online Impulse Buying Intentions. This model integrates Wishful Identification as a moderating variable and Consumer Confidence in Online Shopping as a mediating variable. The main contribution of this study is to expand the theoretical model by incorporating Consumer Confidence in Online Shopping as a mediating variable. This addition aims to provide comprehensive insights into the psychological mechanisms underlying impulsive behavior in online shopping. These findings suggest that direct congruence between the consumer and the influencer, and between the consumer and the product, is a strong driver of Online Impulse Buying Intentions. The recommendation is that marketers should focus on creating strong congruence between consumer values and the influencer’s image, as well as product suitability with the consumer's lifestyle, as these factors directly trigger Online Impulse Buying Intentions

Author Biographies

Yolanda Masnita Siagian, Universitas Trisakti

Yolanda Masnita Siagian is a lecturer at the Faculty of Economics and Business, Universitas Trisakti.

Husna Leila Yusran, Universitas Trisakti

Husna Leila Yusran is a lecturer at the Faculty of Economics and Business, Universitas Trisakti.

References

Anggraini, R., Siagian, Y. M., & Yusran, H. L. (2023). Influencing factors in enhancing innovation performance in rural tourism in Indonesia. European Journal of Business and Management Research, 8(3). https://doi.org/10.24018/ejbmr.2023.8.3.1939

Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.

Bastrygina, T., & Lim, W. M. (2023). Wishful identification in influencer marketing: A self-congruence perspective. Journal of Interactive Advertising, 23(3), 234-252.

Bastrygina, T., Lim, W. M., Jopp, R., & Weissmann, M. A. (2024). Unraveling the power of social media influencers: Qualitative insights into the intersection of influencer marketing and tourism. Journal of Vacation Marketing, 30(1), 134-150. https://doi.org/10.1177/13567667231205347

Beatty, S. E., & Ferrell, M. E. (1998). Impulse buying: Modeling its precursors. Journal of Retailing, 74(2), 169-191. https://doi.org/10.1016/S0022-4359(98)90009-4

Belanche, D., Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2020). Understanding influencer marketing: The role of congruence between influencers, products and consumers. Journal of Business Research, 132, 186-195. https://doi.org/10.1016/j.jbusres.2021.03.067

Belanche, D., Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2021). Building influencers' credibility on Instagram: Effects on followers' attitudes and behavioral responses toward the influencer. Journal of Retailing and Consumer Services, 61, 102554. https://doi.org/10.1016/j.jretconser.2021.102554

Chan, T. K. H. (2018). Impulse buying in live streaming commerce. In Proceedings of the Pacific Asia Conference on Information Systems (PACIS) (pp. 1-15).

Chan, T. K. H., Cheung, C. M. K., & Lee, Z. W. Y. (2017). The state of online impulse-buying research: A literature analysis. Information & Management, 54(2), 204-217. https://doi.org/10.1016/j.im.2016.06.001

Cheah, J. H., Lim, X. J., Ting, H., Liu, Y., & Quach, S. (2024). Are privacy concerns still a barrier to online shopping? An empirical study from a developing country. Journal of Retailing and Consumer Services, 70, 103178. https://doi.org/10.1016/j.jretconser.2023.103178

Chen, J. V., Su, B. C., & Widjaja, A. E. (2019). Facebook C2C social commerce: A study of online impulse buying. Decision Support Systems, 83, 57-69. https://doi.org/10.1016/j.dss.2015.12.008

Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 655-690). Springer. https://doi.org/10.1007/978-3-540-32827-8_29

Gamage, T. C., & Ashill, N. J. (2023). #Sponsored-influencer marketing: Effects of the commercial orientation of influencer-created content on followers' willingness to search for information. Journal of Product & Brand Management, 32(2), 316-329. https://doi.org/10.1108/JPBM-10-2021-3701

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.

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

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. https://doi.org/10.1108/IMDS-09-2015-0382

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118

Johar, J. S., & Sirgy, M. J. (1991). Value-expressive versus utilitarian advertising appeals: When and when not to use them. Journal of Advertising, 20(3), 23-33. https://doi.org/10.1080/00913367.1991.10673213

Joshi, Y., Lim, W. M., Jagani, K., & Kumar, S. (2024). Social media influencer marketing: Foundations, trends, and ways forward. Electronic Commerce Research, 24(1), 101-158. https://doi.org/10.1007/s10660-023-09719-z

Koay, K. Y., & Lim, W. M. (2025). Congruence effects in social media influencer marketing: The moderating role of wishful identification in online impulse buying intentions. Journal of Research in Interactive Marketing. https://doi.org/10.1108/JRIM-03-2024-0097

Koay, K. Y., Lim, W. M., Kaur, S., Soh, K., & Poon, W. C. (2023). Building trust in social commerce: The role of trust transference, self-congruence, and digital nativity. Journal of Electronic Commerce Research, 24(2), 90-106.

Leung, F. F., Gu, F. F., & Palmatier, R. W. (2022). Online influencer marketing. Journal of the Academy of Marketing Science, 50(2), 226-251. https://doi.org/10.1007/s11747-021-00829-4

Lohmöller, J. B. (1989). Latent variable path modeling with partial least squares. Physica-Verlag. https://doi.org/10.1007/978-3-642-52512-4

Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press.

Min, K., Kim, J., & Lee, J. (2019). Consumer-product congruence and consumer involvement in social media brand communities. Computers in Human Behavior, 97, 19-27. https://doi.org/10.1016/j.chb.2019.02.030

Osgood, C. E., & Tannenbaum, P. H. (1955). The principle of congruity in the prediction of attitude change. Psychological Review, 62(1), 42-55. https://doi.org/10.1037/h0048153

Qalati, S. A., Vela, E. G., Li, W., Dakhan, S. A., Hong Thuy, T. T., & Merani, S. H. (2021). Effects of perceived service quality, website quality, and reputation on purchase intention: The mediating and moderating roles of trust and perceived risk in online shopping. Asia Pacific Management Review, 26(4), 201-210. https://doi.org/10.1016/j.apmrv.2021.03.005

Raja, A. S., Abdul, Z., Zia, A., & Adnan, M. (2024). Investigating the determinants of consumer confidence and online impulse buying intentions: An experimental study. Asia Pacific Journal of Business Administration. https://doi.org/10.1108/APJBA-10-2023-0501

Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0. Pearson.

Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Marketing ZFP, 39(3), 4-16. https://doi.org/10.15358/0344-1369-2017-3-4

Rook, D. W., & Fisher, R. J. (1995). Normative influences on impulsive buying behavior. Journal of Consumer Research, 22(3), 305-313. https://doi.org/10.1086/209452

Shan, Y., Chen, K. J., & Lin, J. S. (2020). When social media influencers endorse brands: The effects of self-influencer congruence, parasocial identification, and perceived endorser motive. International Journal of Advertising, 39(5), 590-610. https://doi.org/10.1080/02650487.2019.1678322

Sintia, L., Siagian, Y. M., & Kurniawati, K. (2023). The determinants of purchase intention in social commerce. Jurnal Manajemen Bisnis, 14(1), 214–237. https://doi.org/10.18196/mb.v14i1.15754

Siqueira, J. R., Peña, N. G., ter Horst, E., & Molina, G. (2019). Spreading the word: How customer experience in a traditional retail setting influences consumer traditional and electronic word-of-mouth intention. Electronic Commerce Research and Applications, 37, 100870. https://doi.org/10.1016/j.elerap.2019.100870

Sirgy, M. J. (1982). Self-concept in consumer behavior: A critical review. Journal of Consumer Research, 9(3), 287-300. https://doi.org/10.1086/208924

Sun, T., & Wu, G. (2011). Traits, predictors, and consequences of Facebook self-presentation. Social Science Computer Review, 30(4), 419-433. https://doi.org/10.1177/0894439311426258

Tafheem, N., Elgammal, I., & Zafar, A. U. (2022). Modeling the impact of online fashion retail service quality on customer satisfaction and loyalty. International Journal of Online Marketing, 12(1), 1-18. https://doi.org/10.4018/IJOM.303117

Tran, G. A. (2022). Consumer impulse buying behavior: The role of confidence as moderating effect. Heliyon, 8(6), e09672. https://doi.org/10.1016/j.heliyon.2022.e09672

Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2022). Understanding the antecedents of consumer product knowledge and its role in online shopping behavior. Decision Support Systems, 157, 113760. https://doi.org/10.1016/j.dss.2022.113760

Zhao, Q., Chen, C. D., Cheng, H. W., & Wang, J. L. (2021). Determinants of live streamers' continuance broadcasting intentions on Twitch: A self-determination theory perspective. Telematics and Informatics, 56, 101478. https://doi.org/10.1016/j.tele.2020.101478

Downloads

Published

2026-04-21

How to Cite

Tastya, L. A., Siagian, Y. M. ., & Yusran, H. L. (2026). The Implementation of Self Congruency Theory on Online Impulse Buying Intentions Mediated by Consumer Confidence in Online Shopping Towards Gen-Z. International Journal of Marketing & Human Resource Research, 7(2), 759 - 773. https://doi.org/10.47747/ijmhrr.v7i2.3311