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Machine learning and AI in marketing–Connecting computing power to human insights
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“Ma and Sun (2020) argue for machine learning methods for marketing research”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Ma and Sun (2020)
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Cites
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Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
“Balducci and Marinova (2018) show a data-driven approach to marketing management and research and Ma and Sun (2020) argue for machine learning methods for marketing research, while Lehmann (2020) proposes a method to blend theory and data in the evolving world of marketing research.”
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2023.03.001 · Cited by 535
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Kopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2021) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2021.11.002 · Cited by 361
How human–AI feedback loops alter human perceptual, emotional and social judgements
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“These systems clearly augment human natural capabilities in a variety of domains, such as health care 9–11 , education 12 , marketing 13 and finance 14 .”
Toward advancing theory on creativity in marketing and artificial intelligence
Ameen, N., Sharma, G. D., Tarba, S. Y., Rao, A., & Chopra, R. (2022) · Psychology and Marketing · doi:10.1002/mar.21699 · Cited by 304
Artificial intelligence focus and firm performance
Mishra, S., Ewing, M. T., & Cooper, H. B. (2022) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-022-00876-5 · Cited by 307
“These include social media mining (Kietzmann et al., 2018), real-time price optimization (Davenport et al., 2020), sentiment analysis (Ma & Sun, 2020), customer churn analysis (Loureiro et al., 2020) and talent acquisition (Makarius et al., 2020; Raisch & Krakowski, 2021).”
BackgroundResultInfluentialMogaji, E., & Nguyen, N. P. (2021) · International Journal of Bank Marketing · doi:10.1108/ijbm-09-2021-0440 · Cited by 230
Learning from Data: An Empirics-First Approach to Relevant Knowledge Generation
Golder, P. N., Dekimpe, M. G., An, J., van Heerde, H. J., Kim, D. S. U., & Alba, J. W. (2022) · Journal of Marketing · doi:10.1177/00222429221129200 · Cited by 248
Algorithmic bias in machine learning-based marketing models
Akter, S., Dwivedi, Y. K., Sajib, S., Biswas, K., Bandara, R. J., & Michael, K. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.01.083 · Cited by 242
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van Giffen, B., Herhausen, D., & Fahse, T. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.01.076 · Cited by 230
“…surprisingly, there is ample interest within the business and marketing domain to understand the opportunities presented by ML and AI (e.g., Davenport, Guha, Grewal, & Bressgott, 2020; De Bruyn, Viswanathan, Beh, Brock, & von Wangenheim, 2020; Ma & Sun, 2020; Wang, Ryoo, Bendle, & Kopalle, 2021).”
BackgroundHair, J. F., & Sarstedt, M. (2021) · The Journal of Marketing Theory and Practice · doi:10.1080/10696679.2020.1860683 · Cited by 261
Artificial Intelligence (AI): Revolutionizing Digital Marketing
van Esch, P., & Black, J. S. (2021) · Australasian Marketing Journal (AMJ) · doi:10.1177/18393349211037684 · Cited by 251
“…environments (e.g., AI comprehends data points that are labeled or classified and comprehends patterns and/or similarities that give expected output results) or unsupervised environments (e.g., AI identifies patterns in data sets where data points are not labeled or classified; Ma & Sun, 2020).”
BackgroundZuboff, S. (2022) · Organization Theory · doi:10.1177/26317877221129290 · Cited by 209
“…is thus linked to the giants’ massive-scale data that supplies “an extraordinarily rich set of information on their customers” (Jacobides et al., 2021, pp. 421, 418; see also, Agbehadji et al., 2020; Aiello et al., 2020; Gao et al., 2019; Hinds & Joinson, 2019; Iqbal et al., 2022; Ma & Sun, 2020).”
BackgroundAI Technology and Online Purchase Intention: Structural Equation Model Based on Perceived Value
Yin, J., & Qiu, X. (2021) · Sustainability · doi:10.3390/su13105671 · Cited by 264
“According to the algorithm of evolutionary behavior based on empirical data, it can accurately observe customers’ preferences and shopping needs, push personalized information for existing and potential customers, and provide more effective purchase suggestions for consumers [56].”
MethodMarketing with ChatGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology
Rivas, P., & Zhao, L. (2023) · AI · doi:10.3390/ai4020019 · Cited by 191
AI-driven personalization: Unraveling consumer perceptions in social media engagement
Teepapal, T. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108549 · Cited by 179
Harnessing the power of AI in qualitative research: Exploring, using and redesigning ChatGPT
Zhang, H., Wu, C., Xie, J., Lyu, Y., Cai, J., & Carroll, J. M. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100144 · Cited by 357
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Volkmar, G. V., Fischer, P. M., & Reinecke, S. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.04.007 · Cited by 227
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“In the 1980s, the focus of AI moved to knowledge and rule-based expert systems; it was essentially considered a form of “advanced computing” (Ma & Sun, 2020).”
Qiu, H., Li, M., Bai, B., Wang, N., & Li, Y. (2022) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-08-2021-0960 · Cited by 154
Artificial intelligence in interactive marketing: a conceptual framework and research agenda
Peltier, J. W., Dahl, A. J., & Schibrowsky, J. A. (2023) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-01-2023-0030 · Cited by 191
The Impact of Artificial Intelligence on Branding
Varsha, P. S., Akter, S., Kumar, A., Gochhait, S., & Patagundi, B. (2021) · Journal of Global Information Management · doi:10.4018/jgim.20210701.oa10 · Cited by 163
“By implementing the prediction analytics in the firms able to create marketing trends like interactive bots, recommendations, real-time optimizations to create a brand value in the global market (Ma & Sun, 2020).”
BackgroundMachine learning in marketing: A literature review, conceptual framework, and research agenda
Ngai, E. W. T., & Wu, Y. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.02.049 · Cited by 165
Botifoll, M., Pinto‐Huguet, I., & Arbiol, J. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00377e · Cited by 147
AI-based chatbots in conversational commerce and their effects on product and price perceptions
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“AI-based chat-bots typically capture users’ historical data, predict their current needs, and thus support more complex consumer decision-making processes (Ma & Sun, 2020).”
BackgroundConsumer reactions to AI design: Exploring consumer willingness to pay for AI‐designed products
Zhang, H., Bai, X., & Ma, Z. (2022) · Psychology and Marketing · doi:10.1002/mar.21721 · Cited by 140
Artificial Intelligence in Business Communication: The Changing Landscape of Research and Teaching
Getchell, K. M., Carradını, S., Cardon, P. W., Fleischmann, C., Ma, H., Aritz, J., & Stapp, J. (2022) · Business and Professional Communication Quarterly · doi:10.1177/23294906221074311 · Cited by 163
“In the past 3 years alone, research in business disciplines such as marketing (Huang & Rust, 2021; Ma & Sun, 2020; Overgoor et al., 2019), management and organizational studies (Baldegger et al., 2020; Shrestha et al., 2021; van Esch et al., 2019), and information systems (Abbass, 2019; Bawack et…”
BackgroundAI and digitalization in relationship management: Impact of adopting AI-embedded CRM system
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Ethical implications of text generation in the age of artificial intelligence
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A retrospective review of the first 35 years of the International Journal of Research in Marketing
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Dwivedi, R., Nerur, S., & Balijepally, V. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2023.100185 · Cited by 126
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A meta‐analysis of the elaboration likelihood model in the electronic word of mouth literature
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Machine learning and artificial intelligence use in marketing: a general taxonomy
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“Machine Learning applications may support such efforts, by enabling techniques useful to explore data to derive correlations, patterns, and therefore predictive models, useful for interesting marketing applications (Ma & Sun, 2020).”
BackgroundMethodInfluentialAdvancing algorithmic bias management capabilities in AI-driven marketing analytics research
Akter, S., Sultana, S., Mariani, M. M., Wamba, S. F., Spanaki, K., & Dwivedi, Y. K. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.08.013 · Cited by 127
Singh, V. K., Nanavati, B., Kar, A. K., & Gupta, A. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10314-0 · Cited by 100
“9 Advertisement selection with time stamp using UCB personalization and targeting, real time and automation, and better understanding of journeys (Ma & Sun, 2020).”
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“The majority of studies employing black-box models for a housing market pursued the goal of achieving better predictive accuracy, completely abandoning models’ interpretability (Abidoye & Chan, 2018; Hong et al., 2020; Ma & Sun, 2020; Neloy et al., 2019).”
BackgroundUnderstanding customer satisfaction via deep learning and natural language processing
Aldunate, Á., Maldonado, S., Vairetti, C., & Armelini, G. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118309 · Cited by 100
Machine learning in marketing: Recent progress and future research directions
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How to build a competitive advantage for your brand using generative AI
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Ghosh, S., Yadav, S., Devi, A., & Thomas, T. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112412 · Cited by 87
Investigating the impact of artificial intelligence on consumer’s purchase intention in e-retailing
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Digital marketing capability: the mystery of business capabilities
Apasrawirote, D., Yawised, K., & Muneesawang, P. (2022) · Marketing Intelligence & Planning · doi:10.1108/mip-11-2021-0399 · Cited by 148
Thakur, J., & Kushwaha, B. P. (2023) · Global Business and Organizational Excellence · doi:10.1002/joe.22233 · Cited by 103
Addressing Algorithmic Bias in AI-Driven Customer Management
Akter, S., Dwivedi, Y. K., Biswas, K., Michael, K., Bandara, R. J., & Sajib, S. (2021) · Journal of Global Information Management · doi:10.4018/jgim.20211101.oa3 · Cited by 98
“Ma and Sun (2020), Rust (2020) Explore how AI and related systems ensure the quality of life and well-being of consumers (e.g., ensure fairness and eliminate social biases, and safety-related concerns).”
BackgroundAI AND PRODUCT MANAGEMENT: A THEORETICAL OVERVIEW FROM IDEA TO MARKET
Ogundipe, D. O., Babatunde, S. O., & Abaku, E. A. (2024) · International Journal of Management & Entrepreneurship Research · doi:10.51594/ijmer.v6i3.965 · Cited by 75
Artificial intelligence marketing (AIM): connecting-the-dots using bibliometrics
Anayat, S., & Rasool, G. (2022) · The Journal of Marketing Theory and Practice · doi:10.1080/10696679.2022.2103435 · Cited by 96
“The future of marketing will be technology-driven due to the expansion of AI-based systems into every business area (Ma & Sun, 2019).”
BackgroundNeuromarketing algorithms’ consumer privacy and ethical considerations: challenges and opportunities
Goncalves, M. V., Hu, Y., Aliagas, I., & Suárez, L. M. C. (2024) · Cogent Business & Management · doi:10.1080/23311975.2024.2333063 · Cited by 92
“…from segmentation through direct marketing and targeting campaigns to relying on ai shopping assistants and chatbots. however, Ml algorithms are mainly applied in the phases of segmentation and targeting due to their predictive and optimization abilities (Ma & sun, 2020; chang & Fan, 2023).”
InfluentialAI in sales: Laying the foundations for future research
McClure, C. E., Epler, R. T., Schmitt, L., & Rangarajan, D. (2024) · Journal of Personal Selling and Sales Management · doi:10.1080/08853134.2024.2329905 · Cited by 83
“ML is often used to target customers with personalization efforts, segment and focus on aspects of the customer journey (Ma and Sun 2020), which provides a unique opportunity for the potential of ML in sales (Glackin and Adivar 2023; Nguyen et al. 2023; Syam and Sharma 2018).”
BackgroundLee, J., Jung, O., Lee, Y., Kim, O., & Park, C. H. (2021) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer16050083 · Cited by 78
“In the marketing literature, studies using machine learning have been reported (for a review, see Ma and Sun [41]).”
BackgroundThe role of machine learning analytics and metrics in retailing research
Wang, X., Ryoo, J. H., Bendle, N. T., & Kopalle, P. K. (2020) · Journal of Retailing · doi:10.1016/j.jretai.2020.12.001 · Cited by 75
Machine learning–assisted triboelectric nanogenerator technology for intelligent sports
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Exploring online customer experience: A systematic literature review and research agenda
Kacprzak, A., & Hensel, P. G. (2023) · International Journal of Consumer Studies · doi:10.1111/ijcs.12966 · Cited by 72
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“…[69], [70] Efficiency improvements, accuracy improvements, better decision making, customer relationship improvements, sales increases, cost reductions, and risk reductions [5], [34], [71] Predict customer behavior, one-to-one digital marketing, enhance sales, and increase customer satisfaction.”
BackgroundThe impact of Covid-19 on customer journeys: implications for interactive marketing
Kannan, P. K., & Kulkarni, G. (2021) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-03-2021-0078 · Cited by 63
Alshmrany, S. (2021) · Cluster Computing · doi:10.1007/s10586-021-03403-3 · Cited by 58
Yang, F. X., Li, Y., Li, X., & Yuan, J. (2022) · Tourism Management · doi:10.1016/j.tourman.2022.104575 · Cited by 67
Information Technology Adoption on Digital Marketing: A Literature Review
Figueiredo, F., Gonçalves, M. J., & Teixeira, S. (2021) · Informatics · doi:10.3390/informatics8040074 · Cited by 68
“[68] Machine learning and AI in marketing— Connecting computing power to human insights Briefly discuss common machine learning methods and processes and their implication for business.”
BackgroundMethodAbdar, M., Khosravi, A., Islam, S. M. S., Acharya, U. R., & Vasilakos, A. V. (2022) · IEEE Systems, Man, and Cybernetics Magazine · doi:10.1109/msmc.2022.3150144 · Cited by 55
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Wang, Q., Qi, Y., & Li, R. (2026) · Sustainable Development · doi:10.1002/sd.70200 · Cited by 55
Liu, Y., Liu, X., Wang, X., & Jiang, H. (2025) · Biosensors · doi:10.3390/bios15080487 · Cited by 60
“Machine learning (ML), as the core of AI [38], addresses the computational problem of automatic improvement through experience.”
Unlocking Marketing Creativity Using Artificial Intelligence
Pagani, M., & Wind, Y. (2024) · Journal of Interactive Marketing · doi:10.1177/10949968241265855 · Cited by 72
Hadi, M. U., Al-Tashi, Q., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Hassan, S. Z., et al. (2023) · doi:10.36227/techrxiv.23589741.v3 · Cited by 71
Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS
Alsamhi, S. H., Almalki, F. A., Al-Dois, H., Soufiene, B. O., Hassan, J. A., Hawbani, A., Sahal, R., Lee, B., & Saleh, H. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/6805151 · Cited by 61
“Therefore, the combination of IoTand ML is growing in the market with such robust understanding and capability for performing complex works [90].”
BackgroundOrdenes, F. J. V., & Silipo, R. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.08.036 · Cited by 51
Suraña‐Sánchez, C., & Aramendia-Muneta, M. E. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.13027 · Cited by 67
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Serious Games and AI: Challenges and Opportunities for Computational Social Science
Pérez, J., Castro, M., & López, G. (2023) · IEEE Access · doi:10.1109/access.2023.3286695 · Cited by 61
“personal data is for marketing purposes [123].”
Experiential Narratives in Marketing: A Comparison of Generative AI and Human Content
Wen, Y., & Laporte, S. (2024) · Journal of Public Policy & Marketing · doi:10.1177/07439156241297973 · Cited by 62
“…15, 2024 Ever since the growth of arti fi cial intelligence (AI) during the 1990s, the marketing fi eld has incorporated AI technology into various areas such as services, management of customer relationships, personalization, and targeting (Huang and Rust 2018; Libai et al. 2020; Ma and Sun 2020).”
The role of consumer data in marketing: A research agenda
Blasco‐Arcas, L., Lee, H.-H. M., Kastanakis, M. N., Alcañíz, M., & Reyes-Menéndez, A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.054 · Cited by 64
Machine Learning and Marketing: A Systematic Literature Review
Duarte, V., Zúñiga-Jara, S., & Contreras, S. A. (2022) · IEEE Access · doi:10.1109/access.2022.3202896 · Cited by 77
Huang, M., & Rust, R. T. (2022) · Journal of service management · doi:10.1108/josm-11-2021-0425 · Cited by 53
IDS for Industrial Applications: A Federated Learning Approach with Active Personalization
Kelli, V., Argyriou, V., Λάγκας, Θ., Fragulis, G. F., Grigoriou, E., & Sarigiannidis, P. G. (2021) · Sensors · doi:10.3390/s21206743 · Cited by 46
“Specifically, machine learning helps to extract insights and knowledge from IoT data, attributes which would have been extremely difficult to obtain with other means [4].”
BackgroundDetecting Pain Points from User-Generated Social Media Posts Using Machine Learning
Salminen, J. O., Mustak, M., Corporan, J., Jung, S., & Jansen, B. J. J. (2022) · Journal of Interactive Marketing · doi:10.1177/10949968221095556 · Cited by 50
“Despite its potential, the application of ML is still, in many regards, in the early stages in marketing research (Ma and Sun 2020; Salminen et al. 2019); although, as noted in the literature review, some work has been done in this area.”
BackgroundMethodResultInfluentialŞenyapar, H. N. D. (2024) · Technium Social Sciences Journal · doi:10.47577/tssj.v55i1.10651 · Cited by 44
Blomster, M., & Koivumäki, T. (2021) · Information Systems and e-Business Management · doi:10.1007/s10257-021-00547-y · Cited by 50
“When using ML methods, marketing organizations should use human insights for guidance to understand the actual fit and the quality of the results of the ML in their existing marketing operations (Ma and Sun 2020).”
BackgroundMethodCarlson, K., Kopalle, P. K., Riddell, A. B., Rockmore, D. N., & Vana, P. (2022) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2022.02.004 · Cited by 51
Marti, C. L., Liu, H., Kour, G., Bilgihan, A., & Xu, Y. (2024) · Journal of service management · doi:10.1108/josm-10-2023-0443 · Cited by 49
Sodiya, E. O., Amoo, O. O., Umoga, U. J., & Atadoga, A. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.2.0502 · Cited by 39
“This orchestration is made possible by Artificial Intelligence, which acts as the conductor, interpreting user behavior, predicting preferences, and dynamically shaping content delivery (Ma and Sun, 2020, Gao et al., 2023).”
BackgroundChatGPT and marketing: Analyzing public discourse in early Twitter posts
Zhou, W., Zhang, C., Wu, L., & Shashidhar, M. (2023) · Journal of Marketing Analytics · doi:10.1057/s41270-023-00250-6 · Cited by 42
“The conventional literature predominantly revolves around the application of AI in marketing, with a particular emphasis on machine learning’s capacity to handle vast amounts of unstructured data (Ma and Sun 2020).”
BackgroundInfluentialAbrokwah-Larbi, K. (2023) · Industrial Artificial Intelligence · doi:10.1007/s44244-023-00012-4 · Cited by 38
Is there a glitch in the matrix? Artificial intelligence and management education
Krammer, S. M. S. (2023) · Management Learning · doi:10.1177/13505076231217667 · Cited by 39
“The core of ChatGPT’s commercial advantage resides in business applications such as customer service (e.g. help chatbots) or marketing (e.g. content analysis, product descriptions and generation of marketing content) (Ma and Sun, 2020).”
Advances in artificial intelligence for olfaction and gustation: a comprehensive review
Hao, Z., Li, H., Guo, J., & Xu, Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11309-4 · Cited by 41
“For instance, Ma and Sun ( 2020 ) utilized machine learning to identify correlations between chemical constituents and consumer preferences, enhancing product formulation and aligning with market demands.”
Trim, P. R. J., & Lee, Y. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040110 · Cited by 44
Langen, H., & Huber, M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0278937 · Cited by 37
Marketing Applications of Emerging Technologies
Aarabe, M., Khizzou, N. B., Alla, L., & Benjelloun, A. S. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-3172-9.ch002 · Cited by 37
The Future of Marketing: Artificial Intelligence, Virtual Reality, and Neuromarketing
Adeola, O., Evans, O., Edeh, J. N., & Adisa, I. (2022) · Palgrave studies of marketing in emerging economies · doi:10.1007/978-3-030-88678-3_12 · Cited by 40
How Does Competition Affect Exploration vs. Exploitation? A Tale of Two Recommendation Algorithms
Cao, H. H., Ma, L., Ning, Z. E., & Sun, B. (2023) · Management Science · doi:10.1287/mnsc.2023.4722 · Cited by 36
Self-citationPedersen, C. L., & Ritter, T. (2024) · Industrial Marketing Management · doi:10.1016/j.indmarman.2024.10.005 · Cited by 64
Exploring AI-enabled green marketing and green intention: An integrated PLS-SEM and NCA approach
Sohaib, O., Alshemeili, A., & Bhatti, T. (2025) · Cleaner and Responsible Consumption · doi:10.1016/j.clrc.2025.100269 · Cited by 47
Basu, R., Aktar, M. N., & Kumar, S. (2025) · Journal of Marketing Analytics · doi:10.1057/s41270-024-00355-6 · Cited by 35
Metadata
- Authors
- Ma, L., & Sun, B.
- Affiliations
- University of Maryland, College Park (US)
- Cheung Kong Graduate School of Business (CN)
- Corresponding author
- Ma, Liye
- Venue
- International Journal of Research in Marketing
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 37
- Issue
- 3
- Pages
- 481-504
- ISSN
- 0167-8116 (ISSN-L)
- OpenAlex
- W3081095512
- Cited by
- 602
- References
- 124
- Citations by year
- 2026: 83 · 2025: 111 · 2024: 156 · 2023: 108 · 2022: 99 · 2021: 39 · 2020: 6
- FWCI
- 24.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Research in Marketing (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- machine learning in marketing, AI agents, marketing research methodology, model interpretability, unstructured data, large-scale data, tracking data, network data, decision support, causal inference, prescriptive analytics, marketing theory, research agenda
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