Understanding Consumer Behavior Through AI-Powered Recommender Systems : A Systematic Review and Bibliometric Perspective

Authors

DOI:

https://doi.org/10.17010/ijom/2025/v55/i8/175207

Keywords:

recommender system, science mapping, consumer behavior, R-package, bibliometrics.
Publication Chronology: Paper Submission Date : August 20, 2024 ; Paper sent back for Revision : March 22, 2025 ; Paper Acceptance Date : June 20, 2025 ; Paper Published Online : August 14, 2025

Abstract

Purpose : This study examined the evolving landscape of recommender systems (RSs) and their impact on consumer purchasing behavior by identifying key research themes, trends, and gaps in the current literature.

Methodology : A bibliometric analysis and systematic literature review (SLR) of 312 articles was conducted using the R package to perform performance analysis and scientific mapping, aiming to investigate the intellectual framework of the field, emerging issues, and research trends.

Findings : The study identified four prominent themes: the role of RSs and AI in consumer decision support, trust, and adoption; the consumer privacy paradox in personalized commerce; and communication strategies in web personalization. The research indicated a shift toward adaptive, emotionally intelligent, and privacy-conscious RSs.

Practical Implications : The results provided crucial insights for platform practitioners and developers to create RSs that enhanced personalization while addressing key issues related to trust, privacy, and user engagement, thereby improving the consumer experience and retention.

Originality : This paper offered a comprehensive and organized summary of the fragmented literature on RSs and consumer behavior. By integrating bibliometric analysis with an SLR presented a unique, data-driven framework for future academic exploration and responsible innovation in digital commerce.

Downloads

Download data is not yet available.

Downloads

Published

2025-08-14

How to Cite

Kumari, A., & 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

Issue

Volume 55, Issue 8, August 2025

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

1) Acharya, N., Sassenberg, A.-M., & 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

2) Akalamkam, K., & Mitra, J. K. (2017). 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

3) Aljukhadar, M., Trifts, V., & Senecal, S. (2017). Consumer self‐construal and trust as determinants of the reactance to a recommender advice. Psychology & Marketing, 34(7), 708–719. https://doi.org/10.1002/mar.21017

4) Alyari, F., & Navimipour, N. J. (2018). Recommender systems: A systematic review of the state of the art literature and suggestions for future research. Kybernetes, 47(5), 985–1017. https://doi.org/10.1108/K-06-2017-0196

5) Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

6) Aslam, W., Arif, I., Atiq, Z., & Hussain, F. (2021). Connecting mobile application features with perceived benefits in determining post-adoption behaviour. FIIB Business Review, 10(4), 454–465. https://doi.org/10.1177/23197145211035748

7) Bi, X., Yang, M., & Adomavicius, G. (2024). Consumer acquisition for recommender systems: A theoretical framework and empirical evaluations. Information Systems Research, 35(1), 339–362. https://doi.org/10.1287/isre.2023.1229

8) Broilo, P. L., Espartel, L. B., & Basso, K. (2016). Pre-purchase information search: Too many sources to choose. Journal of Research in Interactive Marketing, 10(3), 193–211. https://doi.org/10.1108/JRIM-07-2015-0048

9) Cai, H., & Mardani, A. (2023). Research on the impact of consumer privacy and intelligent personalization technology on purchase resistance. Journal of Business Research, 161, Article ID 113811. https://doi.org/10.1016/j.jbusres.2023.113811

10) Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235. https://doi.org/10.1177/053901883022002003

11) Chandra, S., Verma, S., Lim, W. M., Kumar, S., & Donthu, N. (2022). Personalization in personalized marketing: Trends and ways forward. Psychology & Marketing, 39(8), 1529–1562. https://doi.org/10.1002/mar.21670

12) Choudhary, V., & Zhang, Z. (2023). Product recommendation and consumer search. Journal of Management Information Systems, 40(3), 752–777. https://doi.org/10.1080/07421222.2023.2229123

13) Clemes, M. D., Gan, C., & Zhang, J. (2014). An empirical analysis of online shopping adoption in Beijing, China. Journal of Retailing and Consumer Services, 21(3), 364–375. https://doi.org/10.1016/j.jretconser.2013.08.003

14) Cloarec, J., Meyer‐Waarden, L., & Munzel, A. (2024). Transformative privacy calculus: Conceptualizing the personalization-privacy paradox on social media. Psychology & Marketing, 41(7), 1574–1596. https://doi.org/10.1002/mar.21998

15) Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: A review and bibliometric analysis. The TQM Journal, 32(4), 869–896. https://doi.org/10.1108/TQM-10-2019-0243

16) Doody, A. F., & Davidson, W. R. (1967). The next revolution in retailing. Harvard Business Review, 45(3), 4–16.

17) Farrukh, M., Raza, A., Ansari, N. Y., & Bhutta, U. S. (2022). A bibliometric reflection on the history of green human resource management research. Management Research Review, 45(6), 781–800. https://doi.org/10.1108/MRR-09-2020-0585

18) Ghoshal, A., Kumar, S., & Mookerjee, V. (2015). Impact of recommender system on competition between personalizing and non-personalizing firms. Journal of Management Information Systems, 31(4), 243–277. https://doi.org/10.1080/07421222.2014.1001276

19) Gong, T. (2025). Bridging the gap: Exploring the nexus of service robot personalization, customer identification, and citizenship behaviors. Journal of Retailing and Consumer Services, 82, Article ID 104105. https://doi.org/10.1016/j.jretconser.2024.104105

20) Guo, X., Zhang, X., & Sun, Y. (2016). The privacy–personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications, 16, 55–65. https://doi.org/10.1016/j.elerap.2015.11.001

21) Guo, Y., Deng, Y., & Meng, Q. (2023). How does the citizen's education level affect their internet rights protection willingness?—The parallel intermediary effect of digital information acquisition ability and utilization ability. Digital Government: Research and Practice, 4(4), 1–18. https://doi.org/10.1145/3624573

22) Hayes, J. L., Brinson, N. H., Bott, G. J., & Moeller, C. M. (2022). The influence of consumer–brand relationship on the personalized advertising privacy calculus in social media. Journal of Interactive Marketing, 55(1), 16–30. https://doi.org/10.1016/j.intmar.2021.01.001

23) Ho, S. Y., & Bodoff, D. (2014). The effects of web personalization on user attitude and behavior. MIS Quarterly, 38(2), 497–520. https://www.jstor.org/stable/26634936

24) Khodabandehlou, S., Golpayegani, S. A., & Rahman, M. Z. (2021). An effective recommender system based on personality traits, demographics and behavior of customers in time context. Data Technologies and Applications, 55(1), 149–174. https://doi.org/10.1108/DTA-04-2020-0094

25) Kim, J., Kang, S., & Bae, J. (2022). The effects of customer consumption goals on artificial intelligence driven recommendation agents: Evidence from stitch fix. International Journal of Advertising, 41(6), 997–1016. https://doi.org/10.1080/02650487.2021.1963098

26) Kumar, N., & Singh, V. (2022). Shopping with voice assistant: Understanding consumer intention and the mediating role of trust. Indian Journal of Marketing, 52(8), 51–65. https://doi.org/10.17010/ijom/2022/v52/i8/171224

27) Kumar, S., Spais, G. S., Kumar, D., & Sureka, R. (2020). A bibliometric history of the Journal of Promotion Management (1992–2019). Journal of Promotion Management, 26(1), 97–120. https://doi.org/10.1080/10496491.2019.1685622

28) Lee, D., & Hosanagar, K. (2021). How do product attributes and reviews moderate the impact of recommender systems through purchase stages? Management Science, 67(1), 524–546. https://doi.org/10.1287/mnsc.2019.3546

29) Long, F., Bhuiyan, M. A., Rahman, M. K., & Aziz, N. A. (2023). How CSR influences young Chinese consumers' purchase decisions towards Western brands: The moderating effect of consumer ethnocentrism. Kybernetes, 52(11), 5269–5283. https://doi.org/10.1108/K-04-2022-0484

30) Luo, D., Ni, X., Aw, E., & Tan, G. (2025). Willingness to disclose information in mobile banking apps: How to tackle privacy concerns? International Journal of Bank Marketing, 43(3), 665–682. https://doi.org/10.1108/IJBM-01-2024-0001

31) Luo, Y., Yang, L., Ye, Q., & Liao, Q. (2023). Effects of customization and personalization affordances on perceived value and continuance intention of smartwatch use. Technological Forecasting and Social Change, 194, Article ID 122752. https://doi.org/10.1016/j.techfore.2023.122752

32) Lyndyuk, A., Havrylyuk, I., Tomashevskii, Y., Khirivskyi, R., & Kohut, M. (2024). The impact of artificial intelligence on marketing communications: New business opportunities and challenges. Economics of Development, 23(4), 60–71. https://doi.org/10.57111/econ/4.2024.60

33) Mahadevan, K., & Joshi, S. (2021). Trends in electronic word of mouth research: A bibliometric review and analysis. Indian Journal of Marketing, 51(4), 8–26. https://doi.org/10.17010/ijom/2021/v51/i4/158468

34) Mahajan, Y., & Gadekar, A. (2021). A bibliometric analysis of buzz marketing: Research areas, concerns, and suggestions for advancement. Indian Journal of Marketing, 51(2), 43–59. https://doi.org/10.17010/ijom/2021/v51/i2/157550

35) Mican, D., & Sitar-Taut, D.-A. (2023). The effect of perceived usefulness of recommender systems and information sources on purchase intention. Kybernetes, 53(7), 2301–2321. https://doi.org/10.1108/K-08-2022-1145

36) Mulay, P., Joshi, R., & Chaudhari, A. (2020). Distributed incremental clustering algorithms: A bibliometric and word-cloud review analysis. Science and Technology Libraries, 39(3), 289–306. https://doi.org/10.1080/0194262X.2020.1775163

37) Necula, S.-C., & Păvăloaia, V.-D. (2023). AI-Driven recommendations: A systematic review of the state of the art in e-commerce. Applied Sciences, 13(9), 5531. https://doi.org/10.3390/app13095531

38) Nizette, F., Hammedi, W., van Riel, A. C., & Steils, N. (2025). Why should I trust you? Influence of explanation design on consumer behavior in AI-based services. Journal of Service Management, 36(1), 50–74. https://doi.org/10.1108/JOSM-05-2024-0223

39) Onjewu, A.-K., Sadraei, R., & Jafari-Sadeghi, V. (2024). A bibliometric analysis of obesity in marketing research. EuroMed Journal of Business, 19(3), 518–543. https://doi.org/10.1108/EMJB-03-2022-0051

40) Pahari, S., Polisetty, A., Sharma, S., Jha, R., & Chakraborty, D. (2023). Adoption of AI in the banking industry: A case study on Indian banks. Indian Journal of Marketing, 53(3), 26–41. https://doi.org/10.17010/ijom/2023/v53/i3/172654

41) Paul, J., & Criado, A. R. (2020). The art of writing literature review: What do we know and what do we need to know? International Business Review, 29(4), Article ID 101717. https://doi.org/10.1016/j.ibusrev.2020.101717

42) Radhakrishnan, S., Erbis, S., Isaacs, J. A., & Kamarthi, S. (2017). Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PloS ONE, 12(3), Article ID e0172778. https://doi.org/10.1371/journal.pone.0172778

43) Rao, K. S., & Rao, B. (2019). Examining eWOM credibility-Consumer purchase intention relationship in Facebook: A mediation analysis. Indian Journal of Marketing, 49(8), 7–22. https://doi.org/10.17010/ijom/2019/v49/i8/146169

44) Rauf, A., Muhammad, N., Mahmood, H., & Aftab, M. (2025). Healthcare service quality: A systematic review based on PRISMA guidelines. International Journal of Quality & Reliability Management, 42(3), 837–850. https://doi.org/10.1108/IJQRM-12-2023-0403

45) Rojas-Sánchez, M. A., Palos-Sánchez, P. R., & Folgado-Fernández, J. A. (2023). Systematic literature review and bibliometric analysis on virtual reality and education. Education and Information Technologies, 28(1), 155–192. https://doi.org/10.1007/s10639-022-11167-5

46) Roscoe, R. D., Grebitus, C., O'Brian, J., Johnson, A. C., & Kula, I. (2016). Online information search and decision making: Effects of web search stance. Computers in Human Behavior, 56, 103–118. https://doi.org/10.1016/j.chb.2015.11.028

47) Sab, M. C., Kappi, M., Bagalkoti, V., & Biradar, B. S. (2020). Indian Journal of Marketing: A bibliometric analysis. Indian Journal of Marketing, 50(4), 55–65. https://doi.org/10.17010/ijom/2020/v50/i4/151574

48) Shao, Z., Zhang, J., Zhang, L., & Benitez, J. (2025). Uncovering post-adoption usage of AI-based voice assistants: A technology affordance lens using a mixed-methods approach. European Journal of Information Systems, 34(3), 475–501. https://doi.org/10.1080/0960085X.2024.2363322

49) Sharma, P., Gupta, S., & Kapoor, D. (2020). Digital marketing and consumer millennials: A comparative study of men, women, and transgender consumers' buying behaviour in Punjab. Indian Journal of Marketing, 50(3), 47–57. https://doi.org/10.17010/ijom/2020/v50/i3/151029

50) Shen, H., Wu, L., Yi, S., & Xue, L. (2020). The effect of online interaction and trust on consumers' value co-creation behavior in the online travel community. Journal of Travel & Tourism Marketing, 37(4), 418–428. https://doi.org/10.1080/10548408.2018.1553749

51) Si, H., Shi, J. G., Tang, D., Wen, S., Miao, W., & Duan, K. (2019). Application of the theory of planned behavior in environmental science: A comprehensive bibliometric analysis. International Journal of Environmental Research and Public Health, 16(15), 1–26. https://doi.org/10.3390/ijerph16152788

52) Sima, V., Gheorghe, I. G., Subić, J., & Nancu, D. (2020). Influences of the industry 4.0 revolution on the human capital development and consumer behavior: A systematic review. Sustainability, 12(10), 4035. https://doi.org/10.3390/su12104035

53) Srivastava, A., Bala, P. K., & Kumar, B. (2020). New perspectives on gray sheep behavior in e-commerce recommendations. Journal of Retailing and Consumer Services, 53, Article ID 101764. https://doi.org/10.1016/j.jretconser.2019.02.018

54) Srivastava, M., Sivaramakrishnan, S., & Saini, G. K. (2020). The relationship between electronic word-of-mouth and consumer engagement: An exploratory study. IIM Kozhikode Society & Management Review, 10(1), 66–81. https://doi.org/10.1177/2277975220965075

55) Strycharz, J., van Noort, G., Helberger, N., & Smit, E. (2019). Contrasting perspectives–practitioner's viewpoint on personalised marketing communication. European Journal of Marketing, 53(4), 635–660. https://doi.org/10.1108/EJM-11-2017-0896

56) Tanwar, A. S., Chaudhry, H., & Srivastava, M. K. (2021). Influencer marketing as a tool of digital consumer engagement: A systematic literature review. Indian Journal of Marketing, 51(10), 27–42. https://doi.org/10.17010/ijom/2021/v51/i10/166439

57) Tatham, P., Spens, K., & Kovács, G. (2017). The humanitarian common logistic operating picture: A solution to the inter‐agency coordination challenge. Disasters, 41(1), 77–100. https://doi.org/10.1111/disa.12193

58) Valencia-Arias, A., Uribe-Bedoya, H., González-Ruiz, J. D., Santos, G. S., Ramírez, E. C., & Rojas, E. M. (2024). Artificial intelligence and recommender systems in e-commerce. Trends and research agenda. Intelligent Systems with Applications, 24, Article ID 200435. https://doi.org/10.1016/j.iswa.2024.200435

59) Venkatesh, V., Speier-Pero, C., & Schuetz, S. (2022). Why do people shop online? A comprehensive framework of consumers' online shopping intentions and behaviors. Information Technology & People, 35(5), 1590–1620. https://doi.org/10.1108/ITP-12-2020-0867

60) Yahelska, K., Vasylyshyna, L., & Shkurov, Y. (2023). Development of information and communication technologies to study consumer behavior in the process of brand management. Eastern-European Journal of Enterprise Technologies, 3(13(123)), 34–41. https://doi.org/10.15587/1729-4061.2023.279615