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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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Research works printed but not linked: 6
- Lipton, Z.C., (2016), “The mythos of model interpretability,” arXiv preprint arXiv:1606.03490 · arXiv
- Malik, N., P.V. Singh, D.K. Lee, and K. Srinivasan, (2019), “A Dynamic Analysis of Beauty Premium,” working paper, SSRN: https://ssrn.com/abstract=3208162
- Mitchell, T., (1997), “Machine learning,” McGraw Hill, p. 2, ISBN 978-0071154673.
- Sutton, R.S. and A.G. Barto, (2018), “Reinforcement learning: An introduction,” MIT press.
- Watkins, 1992, Q-learning, Machine Learning, 8, 279
- Zhang, M. and L. Luo, (2019), “Can User-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp,” working paper, SSRN: https://ssrn.com/abstract=3108288
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Items 101–200 of 599
Artificial Intelligence and the Value Co-Creation Process in Higher Education Institutions
Robayo-Pinzón, O., Berrío, S. P. R., Rincón-Novoa, J. L., & Ramirez-Barrera, A. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2259722 · Cited by 45
“AI can be seen as the attri-bution of human-like intelligence to machines (Ma & Sun, 2020).”
Generative AI in Supply Chain Management
Shekhar, E. A. A. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i9.9786 · Cited by 36
Malik, A., Kumar, S., Basu, S., & Bebenroth, R. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.113828 · Cited by 36
Toward Artificial Empathy for Human-Centered Design
Zhu, Q., & Luo, J. (2023) · Journal of Mechanical Design · doi:10.1115/1.4064161 · Cited by 37
Machines vs. humans: The evolving role of artificial intelligence in livestreaming e-commerce
Yuan, H., Lü, K., & Fang, W. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.115077 · Cited by 41
“AI can be seen as the affordance of human intelligence to machines (Ma and Sun 2020).”
Lopes, J. M., Gomes, S., Nogueira, E., & Trancoso, T. (2024) · Cogent Business & Management · doi:10.1080/23311975.2024.2440628 · Cited by 41
“…concerns the use of machines to perform tasks that normally require human intelligence, such as learning, problem-solving, and decision-making (Ma & sun, 2020; schweidel et al., 2023; Vincent, 2021) ai technologies are transforming the way online retail stores operate, allowing companies to…”
A theory of predictive sales analytics adoption
Habel, J., Alavi, S. H., & Heinitz, N. (2023) · AMS Review · doi:10.1007/s13162-022-00252-0 · Cited by 47
“Therefore, non-parametric models hold the potential to increase the predictive validity (Ma & Sun, 2020; Nygård & Mezei, 2020).”
BackgroundAnalysis of green word-of-mouth advertising behavior of organic food consumers
Boccia, F., & Tohidi, A. (2024) · Appetite · doi:10.1016/j.appet.2024.107324 · Cited by 35
“Machine learning methods have a flexible structure and strong performance in modeling consumer behavior by connecting computing power to human insight and marketing theories (Ma & Sun, 2020).”
Artificial intelligence in the context of digital marketing communication
Bormane, S., & Blaus, E. (2024) · Frontiers in Communication · doi:10.3389/fcomm.2024.1411226 · Cited by 83
Organic food market segmentation based on the neobehavioristic theory of consumer behavior
Tohidi, A., Mousavi, S. M., Dourandish, A., & Alizadeh, P. (2022) · British Food Journal · doi:10.1108/bfj-12-2021-1269 · Cited by 20
Islam, T., Meade, N., Carson, R. T., Louviere, J. J., & Wang, J. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.07.004 · Cited by 38
“The marketing datasets in these applications are typically numeric, but the use of nonnumeric datasets is increasing (Sheth & Kellstadt, 2021; for a review, see Ma & Sun, 2020).”
BackgroundArtificial Intelligence Impact on Digital Content Marketing Research
Hsu, S.-F., & Liou, S. (2021) · International Conference on Orange Technology (ICOT) · doi:10.1109/icot54518.2021.9680666 · Cited by 30
Matosas-López, L. (2024) · Journal of Marketing Analytics · doi:10.1057/s41270-023-00278-8 · Cited by 43
“These implications can be of a strategic nature, for example, when it comes to the company's decision to use the technology rather than human beings to handle its interactions with consumers (Ma and Sun 2020).”
Zuo, Z., Luo, Y., Yan, S., & Jiang, L. (2025) · Systems · doi:10.3390/systems13080664 · Cited by 29
Yoganathan, V., & Osburg, V. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114573 · Cited by 29
The Future of Marketing: The Transformative Power of Artificial Intelligence
Şenyapar, H. N. D. (2024) · International Journal of Management and Administration · doi:10.29064/ijma.1412272 · Cited by 29
Artificial Intelligence in Marketing: Two Decades Review
Jain, R., & Kumar, A. (2024) · NMIMS Management Review · doi:10.1177/09711023241272308 · Cited by 45
“Because of this abundant data, marketers started making huge investments in AI and machine learning to strengthen their performance and capabilities (Ma & Sun, 2020).”
A bibliometric approach to the evolution of artificial intelligence in digital marketing
Nalbant, K. G., & Aydın, S. (2025) · International Marketing Review · doi:10.1108/imr-04-2024-0132 · Cited by 40
Dekhili, S., & Ertz, M. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.139987 · Cited by 30
Liu, C.-H. S., Horng, J., Chou, S.-F., Yu, T., Lee, M.-T., & Lapuz, M. C. B. (2022) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.103213 · Cited by 30
Yu, F., Isleem, H. F., Almoghayer, W. J. K., Shahin, R. I., Yehia, S. A., Khishe, M., & Elshaarawy, M. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-97258-y · Cited by 28
“ML lays the foundation with predictive algorithms, while DL adds the necessary sophistication for capturing structural data’s complex, non-linear relationships 101 .”
Deep Learning in ChatGPT - A Survey
Hashana, A. M. J., Brundha, P., Ayoobkhan, M. U. A., & S, F. (2023) · 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei56765.2023.10125852 · Cited by 20
Revolutionizing Supply Chains Using Power of Generative AI
Yandrapalli, V. K. (2023) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.4.1223.123417 · Cited by 35
Application of Decision Tree‐Based Classification Algorithm on Content Marketing
Liu, Y., & Yang, S. (2022) · Journal of Mathematics · doi:10.1155/2022/6469054 · Cited by 27
“Due to the fast development of machine learning, the current content marketing strategies tend to be digital-driven, meaning that companies put more attention to the consumer concentration on the Internet or historical habitats [6].”
BackgroundAI-Enhanced Teaching Materials for Education: A Shift Towards Digitalization
Syahrizal, S., Yasmi, F., & Mary, T. (2024) · International Journal of Religion · doi:10.61707/j6sa1w36 · Cited by 57
Chou, J.-R. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102689 · Cited by 27
Artificial Intelligence and the UK Construction Industry – Empirical Study
Jallow, H., Renukappa, S., Suresh, S., & Rahimian, F. P. (2022) · Engineering Management Journal · doi:10.1080/10429247.2022.2147381 · Cited by 26
“…E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Ma and Sun (2020) argue that machine learning AI has proven its effectiveness for processing masses of data which are not well structured in…”
BackgroundEmerging of artificial intelligence and technology in pharmaceuticals: review
Sultana, A., Maseera, R., Rahamanulla, A., & Misiriya, A. (2023) · Future Journal of Pharmaceutical Sciences · doi:10.1186/s43094-023-00517-w · Cited by 30
“Without relying on human experience, it recognizes patterns and examines historical data to deduce the significance of these data points and arrive at a potential conclusion [20].”
BackgroundFirm AI Adoption Intensity and Marketing Performance
Chen, J., Zhou, W., & Frankwick, G. L. (2023) · Journal of Computer Information Systems · doi:10.1080/08874417.2023.2277751 · Cited by 41
The need for more informative defect prediction: A systematic literature review
Grattan, N., da Costa, D. A., & Stanger, N. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107456 · Cited by 32
Navigating the Digital Odyssey: AI-Driven Business Models in Industry 4.0
Ji, F. Q., Zhou, Y., Zhang, H., Cheng, G., & Luo, Q. (2024) · Journal of the Knowledge Economy · doi:10.1007/s13132-024-02096-4 · Cited by 30
“Ma and Sun (2020) argue that utilizing the extensive data of interconnected devices can offer organizations valuable insights, allowing them to optimize their business strategies and promote innovation.”
Lee, Y., & Trim, P. R. J. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6030078 · Cited by 35
“, blogs, documents, video, and images) [27,40].”
BackgroundMethodInfluentialAlgorithms in Digital Marketing: Does Smart Personalization Promote a Privacy Paradox?
Saura, J. R. (2024) · FIIB Business Review · doi:10.1177/23197145241276898 · Cited by 23
“This concept indicates that machines can learn independently, either supervised by humans or unsupervised (Ma & Sun, 2020).”
Qian, Y., Jiang, Y., Shang, J. S., Chai, Y., & Liu, Y. (2023) · Decision Support Systems · doi:10.1016/j.dss.2023.113956 · Cited by 23
Yawised, K., Apasrawirote, D., Chatrangsan, M., & Muneesawang, P. (2022) · Journal of Entrepreneurship in Emerging Economies · doi:10.1108/jeee-06-2022-0169 · Cited by 32
Review of Machine Learning Techniques in the Supply Chain Management of Indian Industry
Panigrahi, R. R., Dash, M., Shaikh, Z. H., & Irfan, M. (2023) · Advances in finance, accounting, and economics book series · doi:10.4018/978-1-6684-4483-2.ch013 · Cited by 22
Sharipov, K., Abdurashidova, N., Valieva, A., qizi, T. V. F., Kholmatova, M., & Khozhimuratovna, M. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30258-9_33 · Cited by 22
Deng, C., Wang, Y., Mi, W., Xie, X., Sun, X., & Ghorbani, H. (2025) · Natural Resources Research · doi:10.1007/s11053-025-10490-1 · Cited by 21
“Recent studies have explored the use of ML to uncover deeper insights into these interrelationships (Ma & Sun, 2020).”
Unleashing the predators: Autonomous predation and manipulation through algorithms
Abada, I., & Lambin, X. (2026) · Journal of Retailing · doi:10.1016/j.jretai.2026.02.002 · Cited by 21
(Re)-discovering simulation as a critical element of OM/SCM research: call for research
Melnyk, S. A., Thürer, M., Blome, C., Schoenherr, T., & Gold, S. (2023) · International Journal of Operations & Production Management · doi:10.1108/ijopm-08-2023-0665 · Cited by 21
CUNHA, M. N., CIAC - Centro de Investigação em Artes e Comunicação, L., Pereira, M. S., Cardoso, A., Figueiredo, J., & Oliveira, I. (2024) · GeoJournal of Tourism and Geosites · doi:10.30892/gtg.53214-1226 · Cited by 20
Singh, S., Singh, M., & Attri, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127243 · Cited by 23
Foltean, F. S., & van Bruggen, G. H. (2022) · Springer eBooks · doi:10.1007/978-3-030-98183-9_1 · Cited by 23
Arachchige, J. A., Quach, S., Roca, E., Liu, B., Liew, A. W., & Earl, G. (2022) · Journal of Consumer Behaviour · doi:10.1002/cb.2055 · Cited by 22
Agbese, M., Alanen, H.-K., Antikainen, J., Halme, E., Isomaki, H., Jantunen, M., Kemell, K., Rousi, R. A., Vainio-Pekka, H., & Vakkur, V. (2022) · e-Informatica Software Engineering Journal · doi:10.37190/e-inf230101 · Cited by 21
Robots make no effort! Service evaluation and consumer mindset
Nie, X., & Huang, J. (2023) · Journal of Consumer Behaviour · doi:10.1002/cb.2138 · Cited by 20
Gull, A., Ashfaq, J., & Aslam, M. (2023) · International Journal of Business and Economic Affairs · doi:10.24088/ijbea-2023-84007 · Cited by 24
“Artificial intelligence’s (AI) development has been marked by notable breakthroughs in neural networks, natural language processing, and machine learning—all of which were initially proposed by researchers including Ma and Sun (2020).”
Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study
Castner, N., Arsiwala-Scheppach, L. T., Mertens, S., Krois, J., Thaqi, E., Kasneci, E., Wahl, S., & Schwendicke, F. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01192-8 · Cited by 21
“There was a higher number of fi xations when they had the AI turned off (23.41 [15.98, 74.43]) compared to when they had the AI turned on (11.56 [4.94, 20.45]: p = 0.001), but this behavior can be attributed to them havingtheAIturnedonforanaverageof24.”
BackgroundInfluentialYawised, K., Apasrawirote, D., Chatrangsan, M., & Muneesawang, P. (2022) · Emerging Science Journal · doi:10.28991/esj-2021-sp1-014 · Cited by 19
“- Deep/Machine learning [48] - Big data assimilation [49] - Business intelligence [38] - Internet of things (IOTs) [26, 27, 28] - Bibliometric network analysis [50, 51] -Blockchain technology in digital era.”
BackgroundBhatt, V. (2021) · IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) · doi:10.1109/ictmod52902.2021.9739228 · Cited by 19
The future of platforms, big data and new forms of capital accumulation
Nayak, B. S., & Walton, N. (2023) · Information Technology and People · doi:10.1108/itp-05-2022-0409 · Cited by 19
Baier, D., Decker, R., & Asenova, Y. (2025) · Schmalenbach Journal of Business Research · doi:10.1007/s41471-025-00208-7 · Cited by 31
“For this purpose, the following data analysis and machine learning methods for unsupervised and supervised learning are widespread (Wedel and Kannan 2016; Ma and Sun 2020; Ngai and Wu 2022; Herhausen et al. 2024; Duarte et al. 2022).”
Empowering Machine Learning With Scalable Feature Engineering and Interpretable AutoML
Eldeeb, H., & Shawi, R. E. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3400752 · Cited by 21
“Machine learning (ML) has effectively harnessed this abundance of data across various application domains, risk management [2], fraud detection [3], marketing technology [4], and many others.”
A Systematic Review on Responsible Multimodal Sentiment Analysis in Marketing Applications
César, I., Pereira, I. F., Rodrigues, F., Miguéis, V. L., Nicola, S., Madureira, A. M., Reis, J. L., dos Santos, J. P. M., & de Oliveira, D. A. (2024) · IEEE Access · doi:10.1109/access.2024.3441514 · Cited by 18
“Technology-enhanced systems guarantee a place of great importance in other fields of business, demonstrating the flexibility and importance of their action in understanding and personalizing the user experience [17], [21].”
Backgroundbridging ai skills gaps in marketing education: prompt engineering as a key competency
Torkestani, M. S., Dose, D. B., & Mansouri, T. (2025) · Marketing Education Review · doi:10.1080/10528008.2025.2501788 · Cited by 20
“Well-designed prompts enable AI systems to generate outputs that are more accurate, contextually relevant, and creatively aligned with marketing objectives, attributes critical for tasks such as content creation, customer engagement, and data analysis (Liu et al., 2023; Ma & Sun, 2020).”
Digitalization and its Impact on the Development of Contemporary Marketing Strategies
Кобець, Д. Л. (2024) · Economic Affairs · doi:10.46852/0424-2513.3.2024.26 · Cited by 18
Shin, E., & Yang, H. (2024) · Journal of Fashion Marketing and Management · doi:10.1108/jfmm-08-2023-0186 · Cited by 21
Sales and marketing automation in the post-Covid-19 scenario: value drivers in B2B relationships
Corsaro, D., Maggioni, I., & Olivieri, M. (2021) · Italian Journal of Marketing · doi:10.1007/s43039-021-00024-x · Cited by 18
Artificial Intelligence and Machine Learning‐Based Manufacturing and Drug Product Marketing
Baviskar, K. P., Bedse, A. P., Raut, S., & Darapaneni, N. (2023) · Wiley eBooks · doi:10.1002/9781119865728.ch10 · Cited by 18
Social media mining in drug development—Fundamentals and use cases
Koss, J., Rheinlaender, A., Truebel, H., & Bohnet-Joschko, S. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.08.012 · Cited by 17
Bendig, D., & Bräunche, A. (2024) · Management Review Quarterly · doi:10.1007/s11301-024-00451-y · Cited by 22
“Lastly, we include reinforcement learning (Ma and Sun 2020) and (large) language models (Vaswani et al. 2017).”
Ooi, K., Koohang, A. A., Aw, E. C., Cham, T., Çobanoğlu, C., Dennis, C., Dwivedi, Y. K., Hew, J.-J., Kelly, H. L., Hughes, L., et al. (2024) · Industrial Management & Data Systems · doi:10.1108/imds-10-2023-0703 · Cited by 21
Haidong, X. (2022) · International Journal of Speech Technology · doi:10.1007/s10772-022-09958-9 · Cited by 16
Ahmad, S. N., & Laroche, M. (2023) · Journal of Marketing Analytics · doi:10.1057/s41270-023-00218-6 · Cited by 17
“In this light, it is very important that marketers build their tools using machine learning techniques as well as the other soft skills, especially Marketing-specific knowledge and skill to get most out of the data (Ma and Sun 2020).”
MethodHybrid Quantum Variational Autoencoders for Representation Learning
Rivas, P., Liang, Z., & Orduz, J. (2021) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci54926.2021.00085 · Cited by 18
“Autoencoder is beginning to be used to generate meaningful descriptions from complex data in marketing contexts such as consumer social networks or consumer-product networks [10].”
Mohammed, M., Oleiwi, J. K., Mohammed, A. M., Osman, A. F., Adam, T., Betar, B. O., & Gopinath, S. C. B. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110933 · Cited by 15
Customer Loyalty in the Covid-19 Pandemic: The Application of Machine Learning in Survey Data
Khoa, B. T., Oanh, N. T. T., Uyen, V. T. T., & DUNG, Đ. C. H. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-2877-1_38 · Cited by 15
Nogueira, E., Lopes, J. M., & Gomes, S. (2025) · Management Review Quarterly · doi:10.1007/s11301-025-00531-7 · Cited by 28
“Researchers have explored the implications of AI and machine learning in neuromarketing, focusing on issues of consumer privacy, data security, and the potential for manipulation in marketing practices (Ma and Sun 2020; Ullah et al. 2022).”
Recent trends in the digitalization of finance and accounting
Breuer, W., & Knetsch, A. (2023) · Journal of Business Economics · doi:10.1007/s11573-023-01181-5 · Cited by 14
“…to take different transformed forms, including binned form, high orders, and interactions, all as an input at the same time, hence ML approaches are specialized in using complex, nonlinear, noisy data which results in relatively accurate predictions (e.g., Chen and Hao 2017 ; Ma and Sun 2020 ).”
BackgroundTodorova, A., & Antonova, D. (2023) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit58785.2023.10304899 · Cited by 14
“…search, social media interaction, tracking and engagement, and online shopping will be increasingly driven by intelligent algorithms, the logic of which few people would understand, but the effect of the extracted data will inevitably lead to exponential growth of certain business areas [8].”
BackgroundInfluentialEthical Implications of Generative AI in Art and the Media
Vyas, B. (2022) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2022.v04i04.9392 · Cited by 16
Enrooting Artificial Intelligence Advantageously in Marketing
Zamil, A. M. A., Areiqat, A. Y., Dabaghıa, M. N., & Joudeh, J. M. M. (2024) · Technical and vocational education and training · doi:10.1007/978-981-99-6909-8_43 · Cited by 14
Muth, M., Lingenfelder, M., & Nufer, G. (2024) · Management Review Quarterly · doi:10.1007/s11301-024-00447-8 · Cited by 15
“In addition, further learning styles can be found in the research literature (see further Ma and Sun 2020; Zhang 2020).”
InfluentialPlečko, S., Tominc, P., & Širec, K. (2023) · Sustainability · doi:10.3390/su152316150 · Cited by 13
Generative AI in Hotel Marketing
Almeida, S., & Ivanov, S. H. (2024) · Tourism · doi:10.37741/t.72.3.10 · Cited by 14
Harnessing AI for Entrepreneurial Idea Generation
Zreık, M. (2024) · Advances in business strategy and competitive advantage book series · doi:10.4018/979-8-3693-3498-0.ch001 · Cited by 13
Wang, Z., Yuan, R., & Li, B. (2024) · Journal of Consumer Behaviour · doi:10.1002/cb.2408 · Cited by 16
Artificial Intelligence and Consumer’s Perception: A Research on Environmentally Conscious Consumer
Bhatnagar, A., & Sharma, M. (2024) · Journal of Metaverse · doi:10.57019/jmv.1488022 · Cited by 22
Dynamic Marketing Resource Allocation with Two-Stage Decisions
Zhang, S., Liao, P., Ye, H.-Q., & Zhou, Z. (2022) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer17010017 · Cited by 13
“Ma and Sun [35] provided a systematical overview of marketing research with machine learning methods.”
BackgroundShort-form Video Content (SVC) Engagement and Marketing Capabilities
Apasrawirote, D., Yawised, K., Chatrangsan, M., & Muneesawang, P. (2022) · Asian Journal of Business and Accounting · doi:10.22452/ajba.vol15no2.8 · Cited by 13
Xavier, J., & Picoto, W. (2023) · International Journal of Law and Management · doi:10.1108/ijlma-02-2023-0022 · Cited by 11
AI Transforming Business and Everyday Life
Bialkova, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-56471-0_9 · Cited by 12
Analyzing sales proposal rejections via machine learning
Nguyen, P., Friend, S. B., Chase, K. S., & Johnson, J. S. (2022) · Journal of Personal Selling and Sales Management · doi:10.1080/08853134.2022.2067554 · Cited by 12
“Furthermore, while a general weakness of ML is that relationships are typically correlational rather than causal (Ma and Sun 2020), a time-series approach would likewise help counter such methodological limitations.”
BackgroundInfluentialApplication of fairness to healthcare, organizational justice, and finance: A survey
Birzhandi, P., & Cho, Y.-S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119465 · Cited by 16
Nalluri, V., Mayopu, R. G., & Chen, L. (2023) · Journal of Modelling in Management · doi:10.1108/jm2-02-2023-0022 · Cited by 12
Whittaker, L., Mulcahy, R. F., Russell‐Bennett, R., Letheren, K., & Kietzmann, J. (2025) · Journal of Advertising Research · doi:10.1080/00218499.2025.2498830 · Cited by 19
Pepper, just show me the way! How robotic shopping assistants should look and act
Fukawa, N., Huang, Y., & Babin, B. J. (2023) · Journal of Consumer Behaviour · doi:10.1002/cb.2273 · Cited by 11
Simplifying Sentiment Analysis on Social Media: A Step-by-Step Approach
Chau, X. T. D., Nguyen, T. T., Jo, J. H., Quach, S., Ngo, L. V., Hien, P. T., & Thaichon, P. (2023) · Australasian Marketing Journal (AMJ) · doi:10.1177/14413582231217126 · Cited by 11
“As a result, using supervised machine learning improves not only efficiency but also reproducibility (Feng et al., 2021; Ma & Sun, 2020).”
Artificial Intelligence in Digital Marketing: A Bibliometric Analysis and Future Research Directions
Khandelwal, Y., Malhotra, S., Sharma, R. K., & Sarin, G. (2024) · Abhigyan · doi:10.1177/09702385241277358 · Cited by 16
“Connecting computing power to human insights, machine learning and AI are being utilized in marketing to gain deeper understanding of customer behaviour and preferences’ (Ma & Sun, 2020).”
InfluentialAutomated machine learning in insurance
Dong, P., & Quan, Z. (2024) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2024.10.002 · Cited by 11
8‐T Framework for Artificial Intelligence‐Driven Branding: A Strategic Typology
Deryl, M. D., Verma, S., & Srivastava, V. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.70002 · Cited by 22
EXPLORING PERCEPTIONS OF AI IN THE MARKETING CLASSROOM: A STIMULI-ORGANISM-RESPONSE FRAMEWORK
Slepchuk, A. N., & John, J. (2025) · Marketing Education Review · doi:10.1080/10528008.2025.2478869 · Cited by 13
“AI systems use machine learning to make predictions, recommendations, and decisions (U.S. Department of State, 2020) and are now heavily integrated into businesses (Ma & Sun, 2020).”
Fukukawa, K., & Trivedi, R. H. (2025) · Psychology and Marketing · doi:10.1002/mar.22235 · Cited by 13
“…‐ abled customers (Chang et al. 2018), adapt controversial products such as sex robots (Ma et al. 2022), generate marketing insights and sales strategy (Ma and Sun 2020), transform big data into valuable consumer insights (Hermann 2022; Kietzmann et al. 2018), reduce customer defection (Ng and Liu…”
A fair classifier chain for multi‐label bank marketing strategy classification
Radovanović, S., Petrović, A., Delibašić, B., & Suknović, M. (2021) · International Transactions in Operational Research · doi:10.1111/itor.13059 · Cited by 12
Artificial Intelligence in Staffing
Nguyen, L. A., & Park, M. (2022) · Vision The Journal of Business Perspective · doi:10.1177/09722629221096803 · Cited by 14
“Machine learning appears to be the most popular (Ma & Sun, 2020).”
A deep learning approach for estimation of price determinants
Ansari, O. B., & Binninger, F.-M. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100101 · Cited by 11
The Change of Digital Marketing with Artificial Intelligence
Efendi̇oğlu, İ. H. (2023) · The Proceedings of the International Conference on Applied Research in Management, Economics and Accounting. · doi:10.33422/7th.iarmea.2023.07.101 · Cited by 25
“It is followed by Leng (2015) and Ma (2020), each with 166 citations.”
Artificial Intelligence Applied to Digital Marketing
Aleixo, J. E., Reis, J. L., Teixeira, S., & de Lima, A. P. (2023) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-6684-9324-3.ch002 · Cited by 11
Supriyadi, D., Purwanto, P., & Warsito, B. (2022) · AIP conference proceedings · doi:10.1063/5.0106201 · Cited by 11
Augmenting machine learning with human insights: the model development for B2B personalization
Yaghtin, S., & Mero, J. (2023) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-02-2023-0073 · Cited by 10
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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