Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.30144

Artificial Intelligence Applications in Consumer Behaviour Analysis: A Systematic Review, Mapping Trends and Challenges

Adrián No-Pérez ORCID..., Sandra Castro-González ORCID...
Faculty of Business Administration, Universidade de Santiago de Compostela, Lugo, Spain

Background: The vast amounts of data generated by consumers require new forms of processing, in which artificial intelligence stands out for its ability to analyse them more quickly and deeply. However, although there is abundant literature on artificial intelligence (AI) and consumption, most of it focuses on its impact on consumer behaviour rather than its usefulness in enhancing understanding.

Objective: The aim of this study is to conduct a thorough review of the existing literature on the use of AI to understand consumer behaviour.

Methods: This study uses the PRISMA protocol for the selection of the studies. Then, it combines bibliometric methods with a TCM-ADO framework to review articles. The Scopus database was used to gather peer-reviewed articles from 2014 to 2024. VOS Viewer and R-Studio were utilised for the analysis and visualisation of data.

Results: The study provides insights into publication trends, dominant theories, methods, antecedents, decisions and results in the literature about the use of AI to understand consumer behaviour. Furthermore, it identifies potential avenues for future research to advance the development of theory and methodology.

Conclusion: Research into the use of AI to understand consumers is still in its infancy. However, everything points to the application of AI in consumer behaviour continuing to expand, and its use for analysing attitudes and behaviour becoming more sophisticated and widespread.

Keywords: AI; Consumer behaviour; Data processing; Bibliometrics; TCM-ADO; Theory context method; Antecedents decisions outcomes; Future research directions; AI applications in marketing.

Received: July 21, 2025; Revised: December 16, 2025; Accepted: January 9, 2026; Prepublished online: March 13, 2026 

Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Abdulqader, M., Namoun, A., & Alsaawy, Y. (2022). Fake Online Reviews: A Unified Detection Model Using Deception Theories. IEEE Access, 10, 128622-128655. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3227631 Go to original source...
    2. Acquila-Natale, E., & Iglesias-Pradas, S. (2021). A matter of value? Predicting channel preference and multichannel behaviors in retail. Technological Forecasting and Social Change, 162, 120401. https://doi.org/10.1016/j.techfore.2020.120401 Go to original source...
    3. Adamopoulos, P., Ghose, A., & Todri, V. (2018). The Impact of User Personality Traits on Word of Mouth: Text-Mining Social Media Platforms. Information Systems Research, 29(3), 612-640. https://doi.org/10.1287/isre.2017.0768 Go to original source...
    4. Adwan, A., & Aladwan, R. (2022). Use of artificial intelligence system to predict consumers' behaviors. International Journal of Data and Network Science, 6(4), 1223-1232. https://doi.org/10.5267/j.ijdns.2022.6.011 Go to original source...
    5. Akay, A., Dragomir, A., & Erlandsson, B.-E. (2015). A Novel Data-Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin. IEEE Journal of Biomedical and Health Informatics, 19(1), 389-396. https://doi.org/10.1109/JBHI.2013.2295834 Go to original source...
    6. Akbugday, B., Akbugday, S. P., Sadikzade, R., Akan, A., & Unal, S. (2024). Detection of Olfactory Stimulus in Electroencephalogram Signals Using Machine and Deep Learning Methods. ELECTRICA, 24(1), 175-182. https://doi.org/10.5152/electrica.2024.23111 Go to original source...
    7. Ali, M., Gomes, L. M., Azab, N., de Moraes Souza, J. G., Sorour, M. K., & Kimura, H. (2023). Panic buying and fake news in urban vs. rural England: A case study of twitter during COVID-19. Technological Forecasting and Social Change, 193, 122598. https://doi.org/10.1016/j.techfore.2023.122598 Go to original source...
    8. Aljarah, A., Ibrahim, B., & López, M. (2025). In AI, we do not trust! The nexus between awareness of falsity in AI-generated CSR ads and online brand engagement. Internet Research, 35(3), 1406-1426. https://doi.org/10.1108/INTR-12-2023-1156 Go to original source...
    9. Al-Mashraie, M., Chung, S. H., & Jeon, H. W. (2020). Customer switching behavior analysis in the telecommunication industry via push-pull-mooring framework: A machine learning approach. Computers & Industrial Engineering, 144, 106476. https://doi.org/10.1016/j.cie.2020.106476 Go to original source...
    10. Ambika, A., Shin, H., & Jain, V. (2023). Immersive technologies and consumer behavior: A systematic review of two decades of research. Australian Journal of Management, 50(1), 03128962231181429. https://doi.org/10.1177/03128962231181429 Go to original source...
    11. Andarwati Kunharyanto, S., Mayasari, R., & Oktaviana, D. (2025). Optimization in Routing and Vehicle Selection for E-commerce Last Mile Logistics: Bibliometric Analysis. Acta Informatica Pragensia, 14(1), 174-190. https://doi.org/10.18267/j.aip.257 Go to original source...
    12. Andrade, L. A. C. G., & Cunha, C. B. (2023). Disaggregated retail forecasting: A gradient boosting approach. Applied Soft Computing, 141, 110283. https://doi.org/10.1016/j.asoc.2023.110283 Go to original source...
    13. Aripin, Z., Wibowo, L. A., & Ariyanti, M. (2023). Utilization of Artificial Intelligence Systems to Predict Consumer Behavior. Journal of Jabar Economic Society Networking Forum, 1(1), 45-53.
    14. Barari, M., Ferm, L.-E. C., Quach, S., Thaichon, P., & Ngo, L. (2024). The dark side of artificial intelligence in marketing: Meta-analytics review. Marketing Intelligence and Planning, 42(7), 1234-1256. https://doi.org/10.1108/MIP-09-2023-0494 Go to original source...
    15. Bi, J.-W., Zhu, X.-E., & Han, T.-Y. (2024). Text Analysis in Tourism and Hospitality: A Comprehensive Review. Journal of Travel Research, 63(8), 1847-1869. https://doi.org/10.1177/00472875241247318 Go to original source...
    16. Boccia, F., & Tohidi, A. (2024). Analysis of green word-of-mouth advertising behavior of organic food consumers. Appetite, 198, 107324. https://doi.org/10.1016/j.appet.2024.107324 Go to original source...
    17. Brenncke, M. (2024). A Theory of Exploitation for Consumer Law: Online Choice Architectures, Dark Patterns, and Autonomy Violations. Journal of Consumer Policy, 47(1), 127-164. https://doi.org/10.1007/s10603-023-09554-7 Go to original source...
    18. Brzustewicz, P., & Singh, A. (2021). Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA. Energies, 14(18), 5787. https://doi.org/10.3390/en14185787 Go to original source...
    19. Cao, J., Xia, T., Li, J., Zhang, Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing, 72(7-9), 1775-1781. https://doi.org/10.1016/j.neucom.2008.06.011 Go to original source...
    20. Carbo-Valverde, S., Cuadros-Solas, P., & Rodríguez-Fernández, F. (2020). A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests. PLOS ONE, 15(10), e0240362. https://doi.org/10.1371/journal.pone.0240362 Go to original source...
    21. Chang, H. H., & Mukherjee, A. (2023). Machine Learning and Consumer Data (arXiv:2306.14118). arXiv. https://doi.org/10.48550/arXiv.2306.14118 Go to original source...
    22. Chaturvedi, A., Yadav, N., & Dasgupta, M. (2025). Tech-Driven Transformation: Unravelling the Role of Artificial Intelligence in Shaping Strategic Decision-Making. International Journal of Human-Computer Interaction, 41(19), 12305-12324. https://doi.org/10.1080/10447318.2025.2456534 Go to original source...
    23. Chaudhary, K., Alam, M., Al-Rakhami, M. S., & Gumaei, A. (2021). Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics. Journal of Big Data, 8(1), 73. https://doi.org/10.1186/s40537-021-00466-2 Go to original source...
    24. Chen, S.-S., Choubey, B., & Singh, V. (2021). A neural network based price sensitive recommender model to predict customer choices based on price effect. Journal of Retailing and Consumer Services, 61, 102573. https://doi.org/10.1016/j.jretconser.2021.102573 Go to original source...
    25. Cheung, M. L., Pires, G. D., Rosenberger, P. J., & De Oliveira, M. J. (2021). Driving COBRAs: The power of social media marketing. Marketing Intelligence and Planning, 39(3), 361-376. https://doi.org/10.1108/MIP-11-2019-0583 Go to original source...
    26. Chin, J.-H., Do, C., & Kim, M. (2022). How to Increase Sport Facility Users' Intention to Use AI Fitness Services: Based on the Technology Adoption Model. International Journal of Environmental Research and Public Health, 19(21), 14453. https://doi.org/10.3390/ijerph192114453 Go to original source...
    27. Chiu, M.-C., Tu, Y.-L., & Kao, M.-C. (2022). Applying deep learning image recognition technology to promote environmentally sustainable behavior. Sustainable Production and Consumption, 31, 736-749. https://doi.org/10.1016/j.spc.2022.03.031 Go to original source...
    28. Èufar, A., Mrhar, A., & Robnik-©ikonja, M. (2015). Assessment of surveys for the management of hospital clinical pharmacy services. Artificial intelligence in medicine, 64(2), 147-158. https://doi.org/10.1016/j.artmed.2015.04.003 Go to original source...
    29. Das, S., Nayak, J., Nayak, S., & Dey, S. (2022). Prediction of Life Insurance Premium during Pre-and Post-Covid-19: A Higher-Order Neural Network Approach. Journal of The Institution of Engineers (India): Series B, 103(5), 1747-1773. https://doi.org/10.1007/s40031-022-00771-1 Go to original source...
    30. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070 Go to original source...
    31. Duggal, H. K., Lim, W. M., Khatri, P., Thomas, A., & Shiva, A. (2024). The state of the art on self-perceived employability. Global Business and Organizational Excellence, 43(4), 88-110. https://doi.org/10.1002/joe.22245 Go to original source...
    32. Fe, H. (2023). Social networks and consumer behavior: Evidence from Yelp. Journal of Economic Behavior & Organization, 209, 1-14. https://doi.org/10.1016/j.jebo.2023.02.009 Go to original source...
    33. Fernández-Rovira, C., Álvarez Valdés, J., Molleví, G., & Nicolas-Sans, R. (2021). The digital transformation of business. Towards the datafication of the relationship with customers. Technological Forecasting and Social Change, 162, 120339. https://doi.org/10.1016/j.techfore.2020.120339 Go to original source...
    34. Flavián, C., Akdim, K., & Casaló, L. V. (2023). Effects of voice assistant recommendations on consumer behavior. Psychology & Marketing, 40(2), 328-346. https://doi.org/10.1002/mar.21765 Go to original source...
    35. Fu, X., Ouyang, T., Yang, Z., & Liu, S. (2020). A product ranking method combining the features-opinion pairs mining and interval-valued Pythagorean fuzzy sets. Applied Soft Computing, 97, 106803. https://doi.org/10.1016/j.asoc.2020.106803 Go to original source...
    36. Garner, B., Thornton, C., Luo Pawluk, A., Mora Cortez, R., Johnston, W., & Ayala, C. (2022). Utilizing text-mining to explore consumer happiness within tourism destinations. Journal of Business Research, 139, 1366-1377. https://doi.org/10.1016/j.jbusres.2021.08.025 Go to original source...
    37. Gauba, H., Kumar, P., Roy, P. P., Singh, P., Dogra, D. P., & Raman, B. (2017). Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Networks, 92, 77-88. https://doi.org/10.1016/j.neunet.2017.01.013 Go to original source...
    38. Gerlich, M. (2023). The Power of Virtual Influencers: Impact on Consumer Behaviour and Attitudes in the Age of AI. Administrative Sciences, 13(8), Article 8. https://doi.org/10.3390/admsci13080178 Go to original source...
    39. Ghosh, A., Pathak, D., Bhola, P., Bhattacharjee, D., & Sivarajah, U. (2023). Analysing product attributes of refurbished laptops based on customer reviews and ratings: Machine learning approach to circular consumption. Annals of Operations Research, 355, 1727-1749. https://doi.org/10.1007/s10479-023-05758-9 Go to original source...
    40. Giglio, S., Pantano, E., Bilotta, E., & Melewar, T. C. (2020). Branding luxury hotels: Evidence from the analysis of consumers' "big" visual data on TripAdvisor. Journal of Business Research, 119, 495-501. https://doi.org/10.1016/j.jbusres.2019.10.053 Go to original source...
    41. Gladstone, J. J., Matz, S. C., & Lemaire, A. (2019). Can Psychological Traits Be Inferred From Spending? Evidence From Transaction Data. Psychological Science, 30(7), 1087-1096. https://doi.org/10.1177/0956797619849435 Go to original source...
    42. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl_1), 5228-5235. https://doi.org/10.1073/pnas.0307752101 Go to original source...
    43. Guettala, M., Bourekkache, S., Kazar, O., & Harous, S. (2024). Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning. Acta Informatica Pragensia, 13(3), 460-489. https://doi.org/10.18267/j.aip.235 Go to original source...
    44. Hair, J. F., Ajjan, H., & Harrison, D. (2022). Fundamentos de Analítica de Marketing . McGraw-Hill.
    45. Hakim, A., Golan, I., Yefet, S., & Levy, D. J. (2023). DeePay: Deep learning decodes EEG to predict consumer's willingness to pay for neuromarketing. Frontiers in Human Neuroscience, 17, 1153413. https://doi.org/10.3389/fnhum.2023.1153413 Go to original source...
    46. Hasheminejad, S. M. H., & Reisjafari, Z. (2017). ATM management prediction using Artificial Intelligence techniques: A survey. Intelligent Decision Technologies, 11(3), 375-398. https://doi.org/10.3233/IDT-170302 Go to original source...
    47. Hasumoto, K., & Goto, M. (2022). Predicting customer churn for platform businesses: Using latent variables of variational autoencoder as consumers' purchasing behavior. Neural Computing and Applications, 34(21), 18525-18541. https://doi.org/10.1007/s00521-022-07418-8 Go to original source...
    48. Huang, M.-H., & Rust, R. T. (2022). A Framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98(2), 209-223. https://doi.org/10.1016/j.jretai.2021.03.001 Go to original source...
    49. Hulland, J. (2024). Bibliometric reviews-Some guidelines. Journal of the Academy of Marketing Science, 52, 935-938. https://doi.org/10.1007/s11747-024-01016-x Go to original source...
    50. Hung, C., & Chen, S.-J. (2016). Word sense disambiguation based sentiment lexicons for sentiment classification. Knowledge-Based Systems, 110, 224-232. https://doi.org/10.1016/j.knosys.2016.07.030 Go to original source...
    51. Hyun Baek, T., & Kim, M. (2023). Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence. Telematics and Informatics, 83, 102030. https://doi.org/10.1016/j.tele.2023.102030 Go to original source...
    52. Ismail, A., & Baysal, M. (2023). Dynamic Pricing Based on Demand Response Using Actor-Critic Agent Reinforcement Learning. Energies, 16(14), Article 14. https://doi.org/10.3390/en16145469 Go to original source...
    53. Jafari, S. Q., Shokouhyar, S., & Shokoohyar, S. (2022). Producer-consumer sustainability continuum: Mutual understanding to implement extended producer responsibility. Journal of Cleaner Production, 374, 133880. https://doi.org/10.1016/j.jclepro.2022.133880 Go to original source...
    54. Jain, S., Sharma, K., & Devi, S. (2024a). The dynamics of value co-creation behavior: A systematic review and future research agenda. International Journal of Consumer Studies, 48(1), e12993. https://doi.org/10.1111/ijcs.12993 Go to original source...
    55. Jain, V., Wadhwani, K., & Eastman, J. K. (2024b). Artificial intelligence consumer behavior: A hybrid review and research agenda. Journal of Consumer Behaviour, 23(2), 676-697. https://doi.org/10.1002/cb.2233 Go to original source...
    56. Katyayan, A., Bokhare, A., Gupta, R., Kumari, S., & Pardeshi, T. (2022). Analysis of Unsupervised Machine Learning Techniques for Customer Segmentation. In J. I.-Z. Chen, H. Wang, K.-L. Du, & V. Suma (Eds.), Machine Learning and Autonomous Systems (pp. 483-498). Springer Nature. https://doi.org/10.1007/978-981-16-7996-4_35 Go to original source...
    57. Kaur, J., Mogaji, E., Paliwal, M., Jha, S., Agarwal, S., & Mogaji, S. A. (2024). Consumer behavior in the metaverse. Journal of Consumer Behaviour, 23(4), 1720-1738. https://doi.org/10.1002/cb.2298 Go to original source...
    58. Keat, E. Y., Sharef, N. M., Yaakob, R., Kasmiran, K. A., Marlisah, E., Mustapha, N., & Zolkepli, M. (2022). Multiobjective Deep Reinforcement Learning for Recommendation Systems. IEEE Access, 10, 65011-65027. https://doi.org/10.1109/ACCESS.2022.3181164 Go to original source...
    59. Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925 Go to original source...
    60. Khatri, J., Marín-Morales, J., Moghaddasi, M., Guixeres, J., Giglioli, I. A. C., & Alcañiz, M. (2022). Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store. Frontiers in Psychology, 13, 752073. https://doi.org/10.3389/fpsyg.2022.752073 Go to original source...
    61. Kita, P., Szczyrba, Z., Fiedor, D., & Letal, A. (2018). Recognition of business risks when purchasing goods on the Internet using GIS: Experience from Slovakia. Electronic Commerce Research, 18(3), 647-663. https://doi.org/10.1007/s10660-017-9276-5 Go to original source...
    62. Ko, Y. J., Kwak, D. H., Jang, E. W., Lee, J. S., Asada, A., Chang, Y., Kim, D., Pradhan, S., & Yilmaz, S. (2023). Using Experiments in Sport Consumer Behavior Research: A Review and Directions for Future Research. Sport Marketing Quarterly, 32(1), 33-46. https://doi.org/10.32731/SMQ.321.032023.03 Go to original source...
    63. Koç, U., & Sevgi̇Li̇, T. (2020). Consumer loans' first payment default detection: A predictive model. Turkish Journal of Electrical Engineering & Computer Sciences, 28(1), 167-181. https://doi.org/10.3906/elk-1809-190 Go to original source...
    64. Kopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2022). Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 39(2), 522-540. https://doi.org/10.1016/j.ijresmar.2021.11.002 Go to original source...
    65. Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802-5805. https://doi.org/10.1073/pnas.1218772110 Go to original source...
    66. Kumar, R., Mukherjee, S., & Rana, N. P. (2024). Exploring Latent Characteristics of Fake Reviews and Their Intermediary Role in Persuading Buying Decisions. Information Systems Frontiers, 26(3), 1091-1108. https://doi.org/10.1007/s10796-023-10401-w Go to original source...
    67. Kuo, C.-N., Lin, Y.-D., Nguyen, D.-M., & Cheng, Y.-H. (2023). Based on Decision Tree Model to Analyze the Influencing Factors of Customer's Insurance Transactions. Journal of Information Science and Engineering, 39(4), 797-807. https://doi.org/10.6688/JISE.202307_39(4).0006 Go to original source...
    68. Langen, H., & Huber, M. (2023). How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign. PLOS ONE, 18(1), e0278937. https://doi.org/10.1371/journal.pone.0278937 Go to original source...
    69. Lee, S. M., & Lee, D. (2020). "Untact": A new customer service strategy in the digital age. Service Business, 14(1), 1-22. https://doi.org/10.1007/s11628-019-00408-2 Go to original source...
    70. Li, B., Yao, R., & Nan, Y. (2024). How does anthropomorphism promote consumer responses to social chatbots: Mind perception perspective. Internet Research, 35(6), 2471-2495. https://doi.org/10.1108/INTR-04-2024-0583 Go to original source...
    71. Li, F., Katsumata, S., Lee, C.-H., Ye, Q., Dahana, W. D., Tu, R., & Li, X. (2020). Autoencoder-Enabled Potential Buyer Identification and Purchase Intention Model of Vacation Homes. IEEE Access, 8, 212383-212395. https://doi.org/10.1109/ACCESS.2020.3037920 Go to original source...
    72. Lin, X. M., Ho, C. H., Xia, L. T., & Zhao, R. Y. (2021). Sentiment analysis of low-carbon travel APP user comments based on deep learning. Sustainable Energy Technologies and Assessments, 44, 101014. https://doi.org/10.1016/j.seta.2021.101014 Go to original source...
    73. Liberos, E., Ahumada, S., & Sánchez, M. (2024). Inteligencia Artificial para el Marketing: Como la tecnología revolucionará tu estrategia. ESIC Editorial.
    74. Liu, X. (2023). Dynamic Coupon Targeting Using Batch Deep Reinforcement Learning: An Application to Livestream Shopping. Marketing Science, 42(4), 637-658. https://doi.org/10.1287/mksc.2022.1403 Go to original source...
    75. Liu, X., & Zhao, H. (2021). Dairy brand loyalty measurement model based on machine learning clustering algorithm. Journal of Intelligent & Fuzzy Systems, 40(4), 7601-7612. https://doi.org/10.3233/JIFS-189580 Go to original source...
    76. Liu, X., Zhong, M., Li, B., Su, Y., Tan, J., Gharibzahedi, S. M. T., Guo, Y., & Liu, J. (2019). Identifying Worldwide Interests in Organic Foods by Google Search Engine Data. IEEE Access, 7, 147771-147781. https://doi.org/10.1109/ACCESS.2019.2945105 Go to original source...
    77. Long, H. V., Son, L. H., Khari, M., Arora, K., Chopra, S., Kumar, R., Le, T., & Baik, S. W. (2019). A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis. Computational Intelligence and Neuroscience, 2019(1), 9252837. https://doi.org/10.1155/2019/9252837 Go to original source...
    78. Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911-926. https://doi.org/10.1016/j.jbusres.2020.11.001 Go to original source...
    79. Luo, C. (2021). Analyzing the impact of social networks and social behavior on electronic business during COVID-19 pandemic. Information Processing & Management, 58(5), 102667. https://doi.org/10.1016/j.ipm.2021.102667 Go to original source...
    80. Lussange, J., Vrizzi, S., Bourgeois-Gironde, S., Palminteri, S., & Gutkin, B. (2023). Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model. Computational Economics, 61(4), 1523-1544. https://doi.org/10.1007/s10614-022-10249-3 Go to original source...
    81. Ma, L., Pahlevan Sharif, S., Ray, A., & Khong, K. W. (2023). Investigating the relationships between MOOC consumers' perceived quality, emotional experiences, and intention to recommend: An NLP-based approach. Online Information Review, 47(3), 582-603. https://doi.org/10.1108/OIR-09-2021-0482 Go to original source...
    82. Ma, X., Li, Y., & Asif, M. (2024). E-Commerce Review Sentiment Analysis and Purchase Intention Prediction Based on Deep Learning Technology: Journal of Organizational and End User Computing, 36(1), 1-29. https://doi.org/10.4018/JOEUC.335122 Go to original source...
    83. Maddalena, S. (2024). Digital 2024. We Are Social Spain. https://wearesocial.com/es/blog/2024/01/digital-2024/
    84. Mahfuza, R., Islam, N., Toyeb, M., Emon, M. A. F., Chowdhury, S. A., & Alam, M. G. R. (2022). LRFMV: An efficient customer segmentation model for superstores. Plos One, 17(12), e0279262. https://doi.org/10.1371/journal.pone.0279262 Go to original source...
    85. Makarchev, N., Xiao, C., Yao, B., Zhang, Y., Tao, X., & Le, D. A. (2022). Plastic consumption in urban municipalities: Characteristics and policy implications of Vietnamese consumers' plastic bag use. Environmental Science & Policy, 136, 665-674. https://doi.org/10.1016/j.envsci.2022.07.015 Go to original source...
    86. Malinowski, M. R. B., & Povinelli, R. J. (2022). Using Smart Meters to Learn Water Customer Behavior. IEEE Transactions on Engineering Management, 69(3), 729-741. https://doi.org/10.1109/TEM.2020.2995529 Go to original source...
    87. Manandhar, P., Rafiq, H., Rodriguez-Ubinas, E., Barbosa, J. D., Qureshi, O. A., Tarek, M., & Sgouridis, S. (2023). Understanding Energy Behavioral Changes Due to COVID-19 in the Residents of Dubai Using Electricity Consumption Data and Their Impacts. Energies, 16(1), Article 1. https://doi.org/10.3390/en16010285 Go to original source...
    88. Mariani, M. M., Perez-Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755-776. https://doi.org/10.1002/mar.21619 Go to original source...
    89. Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135-155. https://doi.org/10.1007/s11747-016-0495-4 Go to original source...
    90. Meizhi, T., & Zhongzheng, W. (2024). Marketing Strategy of AI E-commerce Platform Based on User Profile. In 2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB), (pp. 527-529). IEEE. https://doi.org/10.1109/ICEIB61477.2024.10602682 Go to original source...
    91. Moghaddasi, M., Marín-Morales, J., Khatri, J., Guixeres, J., Chicchi Giglioli, I. A., & Alcañiz, M. (2021). Recognition of Customers' Impulsivity from Behavioral Patterns in Virtual Reality. Applied Sciences, 11(10), 4399. https://doi.org/10.3390/app11104399 Go to original source...
    92. Morán, J. M., Santillán-García, A., Herrera-Peco, I., Morán, J. M., Santillán-García, A., & Herrera-Peco, I. (2022). SCRUTATIOm: Cómo detectar literatura retractada incluida en revisiones sistemáticas y metaanálisis usando SCOPUS© y ZOTERO©. Gaceta Sanitaria, 36(1), 64-66. https://doi.org/10.1016/j.gaceta.2020.06.012 Go to original source...
    93. Mou, Y., Xu, T., & Hu, Y. (2023). Uniqueness neglect on consumer resistance to AI. Marketing Intelligence & Planning, 41(6), 669-689. https://doi.org/10.1108/MIP-11-2022-0505 Go to original source...
    94. Mukherjee, D., Lim, W. M., Kumar, S., & Donthu, N. (2022). Guidelines for advancing theory and practice through bibliometric research. Journal of Business Research, 148, 101-115. https://doi.org/10.1016/j.jbusres.2022.04.042 Go to original source...
    95. Mustafa, S., Zhang, W., Anwar, S., Jamil, K., & Rana, S. (2022). An integrated model of UTAUT2 to understand consumers' 5G technology acceptance using SEM-ANN approach. Scientific Reports, 12(1), 20056. https://doi.org/10.1038/s41598-022-24532-8 Go to original source...
    96. Mysaka, H., & Derun, I. (2024). Bibliometric Panorama of Accounting Information System Research Evolution. Acta Informatica Pragensia, 13(1), 134-164. https://doi.org/10.18267/j.aip.232 Go to original source...
    97. Namys³owska, M. (2025). The Silent Death of EU Consumer Law and Its Resilient Revival: Reinventing Consumer Protection Against Unfair Digital Commercial Practices. Journal of Consumer Policy, 48, 317-336. https://doi.org/10.1007/s10603-025-09590-5 Go to original source...
    98. Netsiri, P. (2023). Application of Natural Language Processing to Extract Consumer Behaviors from Product Reviews. Thesis. Prague University of Economics and Business.
    99. Nilashi, M., Abumalloh, R. A., Alghamdi, A., Minaei-Bidgoli, B., Alsulami, A. A., Thanoon, M., Asadi, S., & Samad, S. (2021). What is the impact of service quality on customers' satisfaction during COVID-19 outbreak? New findings from online reviews analysis. Telematics and Informatics, 64, 101693. https://doi.org/10.1016/j.tele.2021.101693 Go to original source...
    100. Olan, F., Suklan, J., Arakpogun, E. O., & Robson, A. (2024). Advancing Consumer Behavior: The Role of Artificial Intelligence Technologies and Knowledge Sharing. IEEE Transactions on Engineering Management, 71, 13227-13239. https://doi.org/10.1109/TEM.2021.3083536 Go to original source...
    101. Oncioiu, I. (2023). Predicting the Use of Chatbots for Consumer Channel Selection in Multichannel Environments: An Exploratory Study. Systems, 11(10), 522. https://doi.org/10.3390/systems11100522 Go to original source...
    102. Panda, D., Chakladar, D. D., Rana, S., & Shamsudin, M. N. (2024). Spatial Attention-Enhanced EEG Analysis for Profiling Consumer Choices. IEEE Access, 12, 13477-13487. https://doi.org/10.1109/ACCESS.2024.3355977 Go to original source...
    103. Pantano, E. (2020). Non-verbal evaluation of retail service encounters through consumers' facial expressions. Computers in Human Behavior, 111, 106448. https://doi.org/10.1016/j.chb.2020.106448 Go to original source...
    104. Pantano, E., & Dennis, C. (2019). Store buildings as tourist attractions: Mining retail meaning of store building pictures through a machine learning approach. Journal of Retailing and Consumer Services, 51, 304-310. https://doi.org/10.1016/j.jretconser.2019.06.018 Go to original source...
    105. Pantano, E., Dennis, C., & De Pietro, M. (2021). Shopping centers revisited: The interplay between consumers' spontaneous online communications and retail planning. Journal of Retailing and Consumer Services, 61, 102576. https://doi.org/10.1016/j.jretconser.2021.102576 Go to original source...
    106. Paul, J., Ueno, A., & Dennis, C. (2023). ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda. International Journal of Consumer Studies, 47(4), 1213-1225. https://doi.org/10.1111/ijcs.12928 Go to original source...
    107. Peltier, J. W., Dahl, A. J., & Schibrowsky, J. A. (2024). Artificial intelligence in interactive marketing: A conceptual framework and research agenda. Journal of Research in Interactive Marketing, 18(1), 54-90. https://doi.org/10.1108/JRIM-01-2023-0030 Go to original source...
    108. Pilone, V., Di Pasquale, A., & Stasi, A. (2023). Consumer Preferences for Craft Beer by Means of Artificial Intelligence: Are Italian Producers Doing Well? Beverages, 9(1), 26. https://doi.org/10.3390/beverages9010026 Go to original source...
    109. Puspitasari, I., Rusydi, F., Nuzulita, N., & Hsiao, C.-S. (2023). Investigating the role of utilitarian and hedonic goals in characterizing customer loyalty in E-marketplaces. Heliyon, 9(8), e19193. https://doi.org/10.1016/j.heliyon.2023.e19193 Go to original source...
    110. Pyate, P. G., & Srinivasan, B. B. (2024). Social Media Platforms: Investigate Sentiment Analysis For Transforming Business Decisions In Car Segments. Feedback International Journal of Communication, 1(2), Article 2. https://doi.org/10.62569/fijc.v1i2.24 Go to original source...
    111. Radu, V. (2023). What is Consumer Behavior: Types & Examples. Omniconvert. Omniconvert Ecommerce Growth Blog. https://www.omniconvert.com/blog/consumer-behavior-in-marketing-patterns-types-segmentation/
    112. Rajasekaran, V., & Tamilselvan, D. L. (2023). A Novel Approach to Predict Consumers Behaviour using Implicit Product Properties in E-Commerce using Deep Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 290-297.
    113. Rasul, T., Lim, W. M., Dowling, M., Kumar, S., & Rather, R. A. (2022). Advertising expenditure and stock performance: A bibliometric analysis. Finance Research Letters, 50, 103283. https://doi.org/10.1016/j.frl.2022.103283 Go to original source...
    114. Ribeiro, M. N., Carvalho, I. A., Fonseca, G. A., Lago, R. C., Rocha, L. C., Ferreira, D. D., Vilas Boas, E. V., & Pinheiro, A. C. (2021). Quality control of fresh strawberries by a random forest model. Journal of the Science of Food and Agriculture, 101(11), 4514-4522. https://doi.org/10.1002/jsfa.11092 Go to original source...
    115. Sabbaghi, M., Esmaeilian, B., Raihanian Mashhadi, A., Behdad, S., & Cade, W. (2015). An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior. Waste Management, 36, 305-315. https://doi.org/10.1016/j.wasman.2014.11.024 Go to original source...
    116. Saha, L., Tripathy, H. K., Masmoudi, F., & Gaber, T. (2022). A Machine Learning Model for Personalized Tariff Plan based on Customer's Behavior in the Telecom Industry. International Journal of Advanced Computer Science and Applications, 13(10), 171-184. https://doi.org/10.14569/IJACSA.2022.0131023 Go to original source...
    117. Sánchez-Núñez, P., Cobo, M. J., Heras-Pedrosa, C. D. L., Peláez, J. I., & Herrera-Viedma, E. (2020). Opinion Mining, Sentiment Analysis and Emotion Understanding in Advertising: A Bibliometric Analysis. IEEE Access, 8, 134563-134576. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3009482 Go to original source...
    118. Senarath, S., Pathirana, P., Meedeniya, D., & Jayarathna, S. (2022). Customer Gaze Estimation in Retail Using Deep Learning. IEEE Access, 10, 64904-64919. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3183357 Go to original source...
    119. Seo, D., & Yoo, Y. (2023). Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance. IEEE Access, 11, 7924-7932. https://doi.org/10.1109/ACCESS.2023.3239425 Go to original source...
    120. Shah, S. M. A., Usman, S. M., Khalid, S., Rehman, I. U., Anwar, A., Hussain, S., Ullah, S. S., Elmannai, H., Algarni, A. D., & Manzoor, W. (2022). An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications. Sensors, 22(24), 9744. https://doi.org/10.3390/s22249744 Go to original source...
    121. Shen, Y., Hamm, J. A., Gao, F., Ryser, E. T., & Zhang, W. (2021). Assessing Consumer Buy and Pay Preferences for Labeled Food Products with Statistical and Machine Learning Methods. Journal of Food Protection, 84(9), 1560-1566. https://doi.org/10.4315/JFP-20-486 Go to original source...
    122. Silva, W. D. O., Morais, D. C., Da Silva, K. G., & Carmona Marques, P. (2023). Exploring Influential Factors with Structural Equation Modeling-Artificial Neural Network to Involve Medicine Users in Home Medicine Waste Management and Preventing Pharmacopollution. Sustainability, 15(14), 10898. https://doi.org/10.3390/su151410898 Go to original source...
    123. Singh, C., Dash, M. K., Sahu, R., & Kumar, A. (2023). Artificial intelligence in customer retention: A bibliometric analysis and future research framework. Kybernetes, 53(11), 4863-4888. https://doi.org/10.1108/K-02-2023-0245 Go to original source...
    124. Singh, J., & Goyal, G. (2019). Anticipating movie success through crowdsourced social media videos. Computers in Human Behavior, 101, 484-494. https://doi.org/10.1016/j.chb.2018.08.050 Go to original source...
    125. Singh, U. S., Singh, N., Gulati, K., Kumar Bhasin, N., Kumar, H., & Sreejith, P. M. (2022). A study on the revolution of consumer relationships as a combination of human interactions and digital transformations. Materials Today: Proceedings, 51, 460-464. https://doi.org/10.1016/j.matpr.2021.05.578 Go to original source...
    126. Smith, A. (2020). Consumer behaviour and analytics. Routledge, Taylor & Francis Group.
    127. Sukumaran, L., & Majhi, R. (2024). Uncorking the delights: Deciphering Indian wine consumers' tastes with a multi-method approach and consumer insights. International Journal of Wine Business Research, 37(2), 315-332. https://doi.org/10.1108/IJWBR-10-2023-0057 Go to original source...
    128. Sun, H., Zafar, M. Z., & Hasan, N. (2022). Employing Natural Language Processing as Artificial Intelligence for Analyzing Consumer Opinion Toward Advertisement. Frontiers in Psychology, 13, 856663. https://doi.org/10.3389/fpsyg.2022.856663 Go to original source...
    129. Swenson, E. R., Bastian, N. D., & Nembhard, H. B. (2016). Data analytics in health promotion: Health market segmentation and classification of total joint replacement surgery patients. Expert Systems with Applications, 60, 118-129. https://doi.org/10.1016/j.eswa.2016.05.006 Go to original source...
    130. Swetha, P., & Dayananda, R. B. (2020). Improvised_XgBoost Machine learning Algorithm for Customer Churn Prediction. EAI Endorsed Transactions on Energy Web, 7(30), Article 30. https://doi.org/10.4108/eai.13-7-2018.164854 Go to original source...
    131. Taboada Villamarín, A. (2024). Big data en ciencias sociales. Una introducción a la automatización de análisis de datos de texto mediante procesamiento de lenguaje natural y aprendizaje automático. Revista CENTRA de Ciencias Sociales, 3(1), 51-75. https://doi.org/10.54790/rccs.51 Go to original source...
    132. Taghikhah, F., Voinov, A., Shukla, N., & Filatova, T. (2021). Shifts in consumer behavior towards organic products: Theory-driven data analytics. Journal of Retailing and Consumer Services, 61, 102516. https://doi.org/10.1016/j.jretconser.2021.102516 Go to original source...
    133. Tian, J., Zhang, Y., & Zhang, C. (2018). Predicting consumer variety-seeking through weather data analytics. Electronic Commerce Research and Applications, 28, 194-207. https://doi.org/10.1016/j.elerap.2018.02.001 Go to original source...
    134. Tohidi, A., Mousavi, S., Dourandish, A., & Alizadeh, P. (2023). Organic food market segmentation based on the neobehavioristic theory of consumer behavior. British Food Journal, 125(3), 810-831. https://doi.org/10.1108/BFJ-12-2021-1269 Go to original source...
    135. Tran, L.-T., Brewster, P., Chidambaram, V., & Hurdle, J. (2017). An Innovative Method for Monitoring Food Quality and the Healthfulness of Consumers' Grocery Purchases. Nutrients, 9(5), 457. https://doi.org/10.3390/nu9050457 Go to original source...
    136. Urbancokova, V., Kompan, M., Trebulova, Z., & Bielikova, M. (2020). Behavior-Based Customer Demography Prediction in E-Commerce. Journal of Electronic Commerce Research, 21(2), 96-112.
    137. Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002 Go to original source...
    138. Wang, C., Han, D., Liu, Q., & Luo, S. (2019). A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM. IEEE Access, 7, 2161-2168. https://doi.org/10.1109/ACCESS.2018.2887138 Go to original source...
    139. Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., Anandkumar, A., Bergen, K., Gomes, C. P., Ho, S., Kohli, P., Lasenby, J., Leskovec, J., Liu, T.-Y., Manrai, A., … Zitnik, M. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620(7972), 47-60. https://doi.org/10.1038/s41586-023-06221-2 Go to original source...
    140. Wang, N., Yang, J., Kong, X., & Gao, Y. (2022). A fake review identification framework considering the suspicion degree of reviews with time burst characteristics. Expert Systems with Applications, 190, 116207. https://doi.org/10.1016/j.eswa.2021.116207 Go to original source...
    141. Wang, Q., Zhu, X., Wang, M., Zhou, F., & Cheng, S. (2023). A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis. PLOS ONE, 18(5), e0286034. https://doi.org/10.1371/journal.pone.0286034 Go to original source...
    142. Wassouf, W. N., Alkhatib, R., Salloum, K., & Balloul, S. (2020). Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study. Journal of Big Data, 7(1), 29. https://doi.org/10.1186/s40537-020-00290-0 Go to original source...
    143. Weiß, T., & Pfeiffer, J. (2024). Consumer decisions in virtual commerce: Predict good help-timing based on cognitive load. Journal of Neuroscience, Psychology, and Economics, 17(2), 119. https://doi.org/10.1037/npe0000191 Go to original source...
    144. Wong, L.-W., Tan, G. W.-H., Ooi, K.-B., & Dwivedi, Y. (2023). The role of institutional and self in the formation of trust in artificial intelligence technologies. Internet Research, 34(2), 343-370. https://doi.org/10.1108/INTR-07-2021-0446 Go to original source...
    145. Wong, M., Farooq, B., & Bilodeau, G.-A. (2018). Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling. Journal of Choice Modelling, 29, 152-168. https://doi.org/10.1016/j.jocm.2017.11.003 Go to original source...
    146. Xue, Z., Li, Q., & Zeng, X. (2023). Social media user behavior analysis applied to the fashion and apparel industry in the big data era. Journal of Retailing and Consumer Services, 72, 103299. https://doi.org/10.1016/j.jretconser.2023.103299 Go to original source...
    147. Yadav, J., Misra, M., Rana, N. P., Singh, K., & Goundar, S. (2022). Netizens' behavior towards a blockchain-based esports framework: A TPB and machine learning integrated approach. International Journal of Sports Marketing and Sponsorship, 23(4), 665-683. https://doi.org/10.1108/IJSMS-06-2021-0130 Go to original source...
    148. Yang, X., Yang, G., Wu, J., Dang, Y., & Fan, W. (2021). Modeling relationships between retail prices and consumer reviews: A machine discovery approach and comprehensive evaluations. Decision Support Systems, 145, 113536. https://doi.org/10.1016/j.dss.2021.113536 Go to original source...
    149. Ye, Z., & Huang, X. (2022). Adoption of a deep learning-based neural network model in the psychological behavior analysis of resident tourism consumption. Frontiers in Public Health, 10, 995828. https://doi.org/10.3389/fpubh.2022.995828 Go to original source...
    150. Zhang, M. (2022). Research on Precision Marketing Based on Consumer Portrait from the Perspective of Machine Learning. Wireless Communications and Mobile Computing, 2022, 1-10. https://doi.org/10.1155/2022/9408690 Go to original source...
    151. Zhang, Q., Abdullah, A. R., Chong, C. W., & Ali, M. H. (2022). E-Commerce Information System Management Based on Data Mining and Neural Network Algorithms. Computational Intelligence and Neuroscience, 2022, 1-11. https://doi.org/10.1155/2022/1499801 Go to original source...
    152. Zhang, S., Lu, Y., & Lu, B. (2023). Shared Accommodation Services in the Sharing Economy: Understanding the Effects of Psychological Distance on Booking Behavior. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), Article 1. https://doi.org/10.3390/jtaer18010017 Go to original source...
    153. Zhang, Y., & Wang, S. (2023). The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types. Journal of Retailing and Consumer Services, 74, 103432. https://doi.org/10.1016/j.jretconser.2023.103432 Go to original source...
    154. Zhao, X., Gao, L., & Huang, Z. (2023). Customer satisfaction evaluation for drugs: A research based on online reviews and PROMETHEE-Ⅱ method. Plos One, 18(6), e0283340. https://doi.org/10.1371/journal.pone.0283340 Go to original source...
    155. Zheng, Q., & Ding, Q. (2022). Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment. Plos One, 17(5), e0268007. https://doi.org/10.1371/journal.pone.0306470 Go to original source...
    156. Zhou, F., Jiang, Y., Qian, Y., Liu, Y., & Chai, Y. (2024). Product consumptions meet reviews: Inferring consumer preferences by an explainable machine learning approach. Decision Support Systems, 177, 114088. https://doi.org/10.1016/j.dss.2023.114088 Go to original source...
    157. Ziakis, C., & Vlachopoulou, M. (2023). Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review. Information, 14(12), Article 12. https://doi.org/10.3390/info14120664 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return