The DOI matches its Crossref record; 125 of the 125 references it cites were found in a registry or an index.
Machine learning and AI in marketing–Connecting computing power to human insights
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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)
Citations
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
Cited by
Sorted by citations
Items 201–300 of 599
Ko, D., & Park, S. (2024) · Sustainability · doi:10.3390/su16114453 · Cited by 10
“Therefore, in most studies utilizing black-box models in the housing market, researchers have pursued the goal of achieving better predictive accuracy while sacrificing model interpretability [15,20,21].”
BackgroundLiu, Y., Huang, C., Yang, C. Y., & Chen, C. (2025) · Journal of Arid Land · doi:10.1007/s40333-025-0070-2 · Cited by 10
“With improvements in the computational power of machine learning, models are becoming increasingly complex, making it difficult to understand their internal mechanisms and decision-making processes (Ma and Sun, 2020).”
Artificial Intelligence Applications in the B2B Sales Funnel
Fehrenbach, D., Herrando, C., & Österle, B. (2025) · Journal of Business-to-Business Marketing · doi:10.1080/1051712x.2025.2481374 · Cited by 17
“The authors considered AI research with a primary focus on ML. Ma and Sun (2020, 481) “AI systems are information systems that act rationally given what they know (. . . ) based on the information available to them in order to solve problems.””
Towards Ethical Neuromarketing 2.0 Based on Artificial Intelligence
Attié, E. A., Bars, S. L., & Quénel, I. (2021) · Advances in business information systems and analytics book series · doi:10.4018/978-1-7998-6985-6.ch029 · Cited by 10
Feng, Y., & Chen, H. (2022) · Journal of Interactive Advertising · doi:10.1080/15252019.2022.2126337 · Cited by 10
Gross, J., & Desveaud, K. (2024) · Journal of Marketing Analytics · doi:10.1057/s41270-024-00337-8 · Cited by 10
“NLP solutions enable marketers to parse this unstructured data, extracting meaningful insights, patterns, trends, and threats (Ma and Sun 2020).”
Talebi, A., Mukherjee, S., Gera, N., Kaur, K., & Das, G. (2025) · Journal of Product Innovation Management · doi:10.1111/jpim.12774 · Cited by 10
Neelu, A., Pramod, J. P., & Lahari, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-89424-4_16 · Cited by 9
Artificial Intelligence and Electronic Marketing Outcomes: An Empirical Study
Dharmaputra, R. T., Fernando, Y., Aryshandy, G., & Ikhsan, R. B. (2021) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris52787.2021.9649533 · Cited by 9
A survey on the use of artificial intelligence (AI)-enabled techniques in digital marketing
Paul, S. P., Aggarwal, S., & Jha, S. (2022) · AIP conference proceedings · doi:10.1063/5.0108607 · Cited by 9
Ram, P. K., Pandey, N., & Persis, J. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123093 · Cited by 11
Yadav, G. N. S., & Seranmadevi, R. (2024) · Advances in web technologies and engineering book series · doi:10.4018/979-8-3693-1762-4.ch005 · Cited by 9
Efficient homomorphic encryption framework for privacy-preserving regression
Byun, J., Park, S., Choi, Y., & Lee, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04015-z · Cited by 9
“During the past few years, machine learning has shown remarkable achievements in almost every sector, including autonomous driving, marketing and finance [7, 17, 24].”
BackgroundManoharan, J. (2024) · doi:10.36227/techrxiv.170630980.05212656/v1 · Cited by 9
Omari, B. A., & Al‐Nimer, M. (2024) · Journal of Infrastructure Policy and Development · doi:10.24294/jipd.v8i8.5806 · Cited by 8
“According to research, machine learning has a set of tasks, which are as follows: Reinforcement learning (Sutton and Barto, 2018); Supervised learning and Unsupervised learning (Ma and Sun, 2020); Semi-supervised learning and transfer learning (Zhu, 2005).”
BackgroundMethodMachine Learning: The Driving Force Behind Intelligent Systems and Predictive Analytics
Virmani, D., Ghori, M. A. S., Tyagi, N., Ambilwade, R. P., Patil, P. R., & Sharma, M. (2024) · International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT) · doi:10.1109/tqcebt59414.2024.10545166 · Cited by 8
“The impressive effect of machine learning can be felt across several industries which are concerned regarding the issues which are data intensive for example consumer services where the diagnosis of the faults in its complex systems and also the controlling of planning chains [3].”
Artificial intelligence-driven brand strategy: Impact on awareness, image, equity, and loyalty
Laksamana, P., Saripudin, S., Suharyanto, S., & Cahaya, Y. F. (2024) · Journal of Infrastructure Policy and Development · doi:10.24294/jipd10084 · Cited by 9
Borimnejad, H., & Borimnejad, V. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00705-y · Cited by 8
Effectiveness of Digital Marketing to Protect Customer's Through Artificial Intelligence
Singh, A. K., Sharma, V., Pandey, S., Gehlot, A., Gusain, A., & Das, P. (2023) · International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) · doi:10.1109/cises58720.2023.10183618 · Cited by 8
Differentially Private Auction Design for Federated Learning With non-IID Data
Ren, K., Liao, G., Ma, Q., & Chen, X. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3332102 · Cited by 8
“For example, sellers can learn customers’ preferences to provide personalized services based on extensive customer purchase data [6].”
BackgroundA Comprehensive Review on Leveraging Business Intelligence for Enhanced Marketing Analytics
Venkateswaran, P. S., Marupaka, D., Parate, S., Bhanushali, A., Reddi, L. T., & Paramasivan, P. (2023) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-2193-5.ch003 · Cited by 8
Zillow’s artificial intelligence failure and its impact on perceived trust in information systems
Susarla, P., Purnell, D. W., & Scott, K. (2024) · Journal of Information Technology Teaching Cases · doi:10.1177/20438869241279865 · Cited by 9
“AI agents, powered by machine learning algorithms, are rapidly altering various industries including real estate (Ma and Sun, 2020).”
Alarefi, M. (2024) · Engineering, Technology & Applied Science Research · doi:10.48084/etasr.7871 · Cited by 15
“In [10], both ML and AI were investigated for improving the marketing capabilities of firms in dealing with big data.”
Analyzing customer reviews with abstractive summarization and sentiment analysis: a software review
Hakimi, M., Haq, M. A. U., Ghouri, A. M., & Valette‐Florence, P. (2025) · Journal of Marketing Analytics · doi:10.1057/s41270-025-00377-8 · Cited by 8
“To address these challenges, firms are encouraged to explore automated analysis tools driven by Artificial Intelligence (AI) and Machine Learning (ML) techniques (Zhou et al. 2023; Senn-Kalb and Mehta 2023; Huang and Rust 2021; Ma and Sun 2020).”
Decision Trees for the Analysis of Digital Marketing in the Tourism Industry: Tungurahua Case Study
Armas-Arias, S., Páez-Quinde, C., Ballesteros-López, L., & López-Pérez, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96046-9_26 · Cited by 8
Post-Pandemic Reflections on Challenges and Opportunities for Marketing Research in the 21st Century
Hair, J. F., Harrison, D. E., & Risher, J. J. (2022) · Revista Inteligência Competitiva · doi:10.24883/iberoamericanic.v12i.2022.e0411 · Cited by 10
“An increase in explainable AI would not only improve machine learning adoption in practice but could also be used to extend existing theory and create and testable hypotheses which could be validated through traditional research methods (Ma & Sun 2020).”
Immersive Digital Marketing for Smart Cities Focusing Tourism
Cunha, C. R., Lopes, L., & Mendonça, V. J. D. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-9268-0_51 · Cited by 8
Catch: Collaborative Feature Set Search for Automated Feature Engineering
Lu, G., Wang, H., Yang, S., Yuan, J. B., Yang, G., Zang, C., Chen, G., & Zhao, J. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583527 · Cited by 8
“These lines of research have notably pushed several domainrelated tasks to advance, such as risk management [4], fraud detection [3], marketing technology [34], and many others.”
BackgroundIntroduction to the Special Section: Research for the New Normal
Kannan, P. K. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.08.006 · Cited by 8
“The paper “Machine learning and AI in marketing: Connecting computing power to human insights,” by (Ma and Sun, 2020) first provides a review of common machine learning tasks and methods and compares them with statistical and econometric methods.”
BackgroundExploring the Role of AI for Enhancement of Social Media Marketing
Aggarwal, D., Sharma, D., & Saxena, A. B. (2024) · Journal of Media Culture and Communication · doi:10.55529/jmcc.45.1.11 · Cited by 7
Exploring the Applications of Artificial Intelligence in Marketing: A Topic Modelling Analysis
NOVA Information Management School (NOVA IMS), L., Rita, P., Omran, W., University of Minho, F. O. E. A. M., Ramos, R. F., Instituto Politécnico de Coimbra, E., Costa, T., & NOVA Information Management School (NOVA IMS), L. (2025) · Tourism & Management Studies · doi:10.18089/tms.20250103 · Cited by 7
Rodrigues, G. N., Mir, M. D. N. H., Bhuiyan, M., Rafi, M., Hoque, A. S. M. M., Maua, J., & Mridha, M. F. (2025) · Data Science and Management · doi:10.1016/j.dsm.2025.09.002 · Cited by 18
A framework for AI-driven value co-creation across customer journey stages
Chandra, B., & Rahman, Z. (2026) · Service Industries Journal · doi:10.1080/02642069.2026.2612702 · Cited by 10
“For example, AI-enabled personalization has been shown to significantly influence customer engagement and satisfaction by delivering contextually relevant experiences at scale (Chandra & Rahman, 2025; Ma & Sun, 2020).”
Zhao, H., Yuan, B., Shen, X., Zhang, T., Xie, H., & Wu, S. (2026) · Service Industries Journal · doi:10.1080/02642069.2026.2620666 · Cited by 7
“This aligns with studies showing that AI can reduce employee workload and enhance trust through efficiency and precision (Frey & Osborne, 2017; Ma & Sun, 2020; Marinova et al., 2017).”
Ballouk, H., Mefteh‐Wali, S., Tabbah, G., & Jabeur, S. B. (2022) · Journal of Innovation Economics & Management · doi:10.3917/jie.pr1.0130 · Cited by 7
The use of AI in marketing: Its impact and future
Jabeen, M. (2022) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2022.16.3.1419 · Cited by 7
Chau, X. T. D., Nguyen, T. T., Tran, K. V., Quach, S., Thaichon, P., Jo, J. H., Vo, B., Tran, Q. D., & Nguyen, Q. V. H. (2023) · Australasian Marketing Journal (AMJ) · doi:10.1177/14413582221146004 · Cited by 7
“The proposed framework can act as a trigger for forthcoming studies, therefore promising to enhance knowledge in machine learning and AI theory in marketing (Ma & Sun, 2020), specialised for fake review detections.”
Scaling Up Customer Support Using Artificial Intelligence and Machine Learning Techniques
Kalinaki, K., Namuwaya, S., Mwamini, A., & Namuwaya, S. (2023) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-6684-7735-9.ch002 · Cited by 7
Artificial Intelligence in Marketing
Sığırcı, Ö. (2021) · Advances in business information systems and analytics book series · doi:10.4018/978-1-7998-6985-6.ch016 · Cited by 8
Analyzing browsing across websites by machine learning methods
Falke, A., & Hruschka, H. (2021) · Journal of Business Economics · doi:10.1007/s11573-021-01067-4 · Cited by 7
“Our paper also complies the call to investigate alternative machine learning methods especially for the analysis of clickstream data which Ma and Sun (2020) raise in their recent overview on machine learning in marketing.”
MethodFrioui, S., & Graa, A. (2024) · MANAGEMENT AND ECONOMICS REVIEW · doi:10.24818/mer/2024.01-01 · Cited by 7
Enhancing Consumer Insights Through Multimodal Artificial Intelligence and Affective Computing
César, I., Pereira, I. F., Rodrigues, F., Miguéis, V. L., Nicola, S., Madureira, A. M., Reis, J. L., dos Santos, J. P. M., Coelho, D., & de Oliveira, D. A. (2025) · IEEE Access · doi:10.1109/access.2025.3586713 · Cited by 6
“…Computing within customer interactive preferences, whether expressed explicitly, through verbal or visual evidence, or implicitly, by analyzing different captured behaviors, has proven to be critically beneficial for creating resonant marketing strategies [22], [23], [24], [30], [31], [32].”
How consumer market orientations shape algorithmic appreciation and avoidance in fashion
Geyik, P., & Weijo, H. A. (2025) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2025.07.007 · Cited by 6
Interpretable Deep Neural Network Deployment for Concrete Compressive Strength Prediction
Hamed, A. K., Alsaadawi, M. M., & Elshaarawy, M. K. (2026) · Journal of Building Pathology and Rehabilitation · doi:10.1007/s41024-025-00752-z · Cited by 6
Harnessing artificial intelligence (AI) for interactive branding
Deryl, M. D., Verma, S., Srivastava, V., & Paul, J. (2026) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-02-2025-0076 · Cited by 3
Examining Privacy and Trust Issues at the Edge of Isomorphic IoT Architectures: Case Liquid AI
Agbese, M., Mäkitalo, N., Waseem, M., Mohanani, R., Abrahamsson, P., & Mikkonen, T. (2023) · IoT · doi:10.1145/3627050.3631574 · Cited by 6
Manoharan, J. (2024) · American Journal of Computer Science and Technology · doi:10.11648/j.ajcst.20240704.13 · Cited by 17
“In marketing, AI subsets such as machine learning, deep learning, and neural networks have become crucial in solving complex challenges [22].”
The Impact of Digital Products and Artificial Intelligence on Sustainable Marketing Methods
Basit, S. A., Gharleghi, B., Rehman, A. U., Tee, P. K., Batool, K., & Nawaser, K. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-5668-5.ch001 · Cited by 6
A systematic review of low-rank and local low-rank matrix approximation in big data medical imaging
Hamlomo, S., Atemkeng, M., Brima, Y., Nunhokee, C. D., & Baxter, J. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11055-2 · Cited by 6
Zhang, Y., Fang, J., Liao, M., Han, L., Wen, C., & Addo, P. C. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115315 · Cited by 6
Applications of Machine Learning (ML) in the context of marketing: a bibliometric approach
Cardona-Acevedo, S., Agudelo-Ceballos, E., Arango-Botero, D. M., Valencia-Arías, A., Dávila, J. D. L. C. R., García, J. A. J., Goycochea, C. F., & Rojas, E. M. (2025) · F1000Research · doi:10.12688/f1000research.160010.1 · Cited by 6
INTEGRATION OF ARTIFICIAL INTELLIGENCE MARKETING TO GET BRAND RECOGNITION FOR SOCIAL BUSINESS
Shaily, S. A., & Emma, N. N. (2021) · International Review of Management and Marketing · doi:10.32479/irmm.11542 · Cited by 6
“The respondents for the questionnaire were reached through social media (Ma and Sun, 2020).”
BackgroundZhang, Z., Li, W., & Zhang, Y. (2021) · Complexity · doi:10.1155/2021/5515357 · Cited by 6
Machine Learning and Artificial Intelligence
Hossain, M. S., & Rahman, M. F. (2022) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-6684-4496-2.ch014 · Cited by 6
Cui, L. (2023) · European Integration Studies · doi:10.5755/j01.eis.1.17.33726 · Cited by 6
“ChatGPT offers numerous benefits to SMEs in the service sector, including improved efficiency, cost savings, automation, streamlined services, and enhanced product marketability, which can help SMEs gain more market share in marketing solutions and promotion (Ma & Sun, 2020).”
BackgroundA bibliometric and content analysis of marketing-finance interface research from 1986–2021
Liao, Q., Yang, L., & Lim, E. J. (2023) · Journal of Business & Finance Librarianship · doi:10.1080/08963568.2023.2263984 · Cited by 6
“Marketing researchers use computer-assisted tools (Berger et al., 2020; Humphreys & Wang, 2018; Ma & Sun, 2020; Vermeer et al., 2019) or human coding to analyze this data (Balducci & Marinova, 2018; Bayer et al., 2017).”
MethodMatosas-López, L. (2024) · Advances in business strategy and competitive advantage book series · doi:10.4018/979-8-3693-1155-4.ch012 · Cited by 6
Topic: How Artificial Intelligence and Machine Learning Can Impact Market Design
Jawaid, S. A., & Qureshi, J. (2024) · Advances in urban regional development and planning. · doi:10.20944/preprints202402.0011.v1 · Cited by 5
Marela, A. N., & Alfansi, L. (2024) · Manajemen dan Bisnis · doi:10.24123/mabis.v23i1.738 · Cited by 11
“The capability of AI to enhance service www.journalmabis.org 14 performance efficiency, manage algorithms intelligently, and accurately understand consumer behavior has become a fundamental aspect of the transformation in this industry (Javaid et al., 2022; Ma & Sun, 2020).”
BackgroundExploring Time-Based Characteristics of the E-Car Market for Effective Market Segmentation
Tripathi, S., Bachmann, N., Brunner, M., & Jodlbauer, H. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.01.007 · Cited by 6
Udayanan, A. R. (2024) · Journal of Informatics Education and Research · doi:10.52783/jier.v4i2.828 · Cited by 6
Sinap, V. (2024) · Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu dergisi · doi:10.29249/selcuksbmyd.1401822 · Cited by 6
Evolutionary Deep Learning for Sequential Data Processing in Music Education
Lin, J. (2024) · Informatica · doi:10.31449/inf.v48i8.5444 · Cited by 5
Şenyapar, H. N. D., Çolak, İ., & Bayındır, R. (2024) · doi:10.1109/icsmartgrid61824.2024.10578198 · Cited by 5
Brocato, E. D., & Davis, C. D. (2025) · Journal of Marketing Education · doi:10.1177/02734753241312350 · Cited by 8
“As Ma and Sun (2020) discuss, the potential applications of AI in marketing are far-reaching.”
Roy, S. K., Arafin, M., & Ahmed, J. (2025) · Telematics and Informatics Reports · doi:10.1016/j.teler.2025.100219 · Cited by 5
Karasenko, A., & Baier, D. (2025) · Journal of Business Economics · doi:10.1007/s11573-025-01232-z · Cited by 9
“Machine learning has revolutionized how marketing research can leverage unstructured text data (Ma & Sun 2020).”
Diversity in Interactive Advertising
Eisend, M. (2025) · Journal of Interactive Advertising · doi:10.1080/15252019.2025.2513236 · Cited by 8
“Machine learning can explore rich, complex, large-scale data from various modalities (Ma and Sun 2020), allowing for the analysis of diversity beyond just visual cues (e.g., ethnic diversity indicated by skin color) to include, for instance, vocal characteristics (e.g., cultural diversity indicated…”
Small Businesses Can Enhance Strategic Insights by Text Mining Organizational Writings
Jackson, R. A., & Heath, B. L. (2023) · American Journal of Industrial and Business Management · doi:10.4236/ajibm.2023.138046 · Cited by 5
Byun, J., Lee, J., Lee, H. W., & Son, B. (2024) · IEEE Access · doi:10.1109/access.2024.3523967 · Cited by 5
Automatic inverse design of second-order differential metasurfaces based on reinforcement learning
Li, Z., Yang, S., & Liu, D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110812 · Cited by 5
Kaperonis, S. (2025) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-3799-8.ch013 · Cited by 6
Krupa, T. J., & Mullaney, M. (2026) · Review of Accounting Studies · doi:10.1007/s11142-026-09940-9 · Cited by 8
“It can provide managers with better insights into nontax considerations, such as the supply chain and demand forecasting (Labro et al. 2023; Ma and Sun 2020; Purdy and Williams 2023) as well as outcomes from previous tax strategies (Milner and Berg 2017; Mueller 2020).”
Artificial Intelligence in Marketing and Sales
Chan, L., Hogaboam, L., & Cao, R. (2022) · Applied innovation and technology management · doi:10.1007/978-3-031-05740-3_5 · Cited by 5
Gümrük Beyannamesi Sürecinde Öğrenmeye Dayalı Algoritmaların Etkinliğinin İncelenmesi
Günerkan, M., Şahinaslan, E., & Şahınaslan, Ö. (2022) · Acta Infologica · doi:10.26650/acin.1057060 · Cited by 5
How AI is automating writing: The rise of robot writers
Dargham, M., Hachimi, H., & Boutalline, M. (2022) · International Conference on Optimization and Applications (ICOA) · doi:10.1109/icoa55659.2022.9934723 · Cited by 5
“It needn't be limited to things that already exist as data, though developments in machine learning [3]will make this more common.”
BackgroundImpact of Machine Learning in Digital Marketing Applications
Gürsoy, G., Varol, A., & Varol, S. (2022) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec56263.2022.9998211 · Cited by 5
Ko, K.-C., Liu, S.-K., Wei, C., Hsieh, J.-S., & Yang, R.-H. (2023) · International Journal of Market Research · doi:10.1177/14707853231158605 · Cited by 7
“…needs to take several factors into account, including the type of target task, the characteristics of data samples involved in the target task, the size of training data available, demand for interpretability, the balance between accuracy and efficiency, etc. (Lee & Shin, 2020; Ma & Sun, 2020).”
BackgroundFMCG Market Analysis for Wholesalers and Retailers Using Machine Learning
Kumari, C. S., Deepu, B. S. V. V., Dheeraj, G., & Surampudi, V. K. S. (2023) · International Conference on Pervasive Computing and Social Networking (ICPCSN) · doi:10.1109/icpcsn58827.2023.00059 · Cited by 5
“Ma et al [12] discussed the potential of machine learning and artificial intelligence (AI) in marketing, particularly in connecting computing power to human insights.”
BackgroundDigital Marketing Data Classification by Using Machine Learning Algorithms
Abd, N. S., Atiyah, O. S., Ahmed, M. T., & Bakhit, A. H. (2024) · Iraqi Journal for Electrical And Electronic Engineering · doi:10.37917/ijeee.20.1.23 · Cited by 6
Generative AI-Enabled IoT Applications for Smart Cities
Ghai, A. S., Ghai, K., & Kurubacak, G. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-2373-1.ch011 · Cited by 5
Bai, S., Huang, X., Han, C., Yang, M., Yu, D., & Bi, X. (2024) · Current Psychology · doi:10.1007/s12144-024-05973-y · Cited by 5
“AI techniques based on deep learning algorithms are developing rapidly, especially in processing large numbers of pictures, and are being used extensively for pattern recognition and classification in photo research (Ma & Sun, 2020).”
Introduction to Chatbot AI Applications
Bialkova, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-56471-0_1 · Cited by 5
Winarto, L., & Wisesa, A. (2024) · European Journal of Business Management and Research · doi:10.24018/ejbmr.2024.9.5.2241 · Cited by 5
“The following hypotheses are put forward: behavior algorithm based on empirical data (Ma & Sun, 2020).”
MethodAnagnostopoulos, C., Rahman, S. A., Rizvi, S. W. F., & Mamo, Y. (2025) · International Journal of Sports Marketing and Sponsorship · doi:10.1108/ijsms-09-2024-0261 · Cited by 6
Khoa, B. T., ANH, H. N., Ly, N. M., & Truong, N. X. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2641-8_48 · Cited by 5
Gong, S., Lyu, Q., & Wang, W. (2022) · Social Behavior and Personality An International Journal · doi:10.2224/sbp.11174 · Cited by 5
Differentially Private Auction for Federated Learning with Non-IID Data
Ren, K. (2022) · International Conference on Service Science (ICSS) · doi:10.1109/icss55994.2022.00054 · Cited by 5
“Nowadays, machine learning has become an important technology to learn valuable information from diverse kinds of data in various fields, such as healthcare [1], education [2], marketing [3], and games [4].”
BackgroundKlasifikasi Loyalitas Pengguna Sistem E-Learning Menggunakan Net Promoter Score dan Machine Learning
Supriyadi, D., Safitri, S. T., Amriza, R. N. S., & Kristiyanto, D. Y. (2022) · Jurnal Edukasi dan Penelitian Informatika (JEPIN) · doi:10.26418/jp.v8i1.49300 · Cited by 5
“Perkembangan AI maupun machine learning telah banyak memberikan perubahan yang signifikan dalam berbagai bidang termasuk bidang biologi, Pendidikan maupun pemasaran [11].”
BackgroundAlenezi, A. M., Alsmairat, M. A. K., & Ljepava, N. (2024) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr61006.2024.10533067 · Cited by 5
“[37] Furthermore, predictive analytics models can accurately identify consumer preferences and add a cognitive component to traditionally human-powered and automated tasks.”
BackgroundLiu, W., & Lu, B. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/9884629 · Cited by 5
Vana, P., Kopalle, P. K., Pachigolla, P., & Carlson, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4692101 · Cited by 5
Mukherjee, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4739488 · Cited by 19
Digital Marketing and Artificial Intelligence
Romic, J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13701-3.00539-9 · Cited by 5
Co-Creation with AI in B2B Markets: A Systematic Literature Review
Fehrenbach, D., Herrando, C., & Hoyos, M. J. M. (2024) · Sustainability · doi:10.3390/su16188009 · Cited by 11
Danó, G., Kovács, S., & Surman, V. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.102985 · Cited by 6
Optimizing Online Marketing Strategies With Machine Learning and Deep Learning Innovations
Naga, S. B. V., Thangavel, S., Kuchoor, S. K., Narukulla, N., & Yenduri, L. K. (2025) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-9783-1.ch018 · Cited by 4
Do AI traits shape value-in-use across the retail customer journey?
Lee, Y.-H., & Park, C. H. (2026) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-05-2025-0398 · Cited by 4
Bengoechea, G. G., & Jung, J. (2024) · Journal of Information Policy · doi:10.5325/jinfopoli.14.2024.0015 · Cited by 6
[Retracted] Corporate Marketing Strategy Analysis with Machine Learning Algorithms
Nan, H., & Hu, M. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/9450020 · Cited by 4
“audience, so that consumers can feel the sincerity of the enterprise in the process of comprehensive experience of online and offline quality services [18].”
BackgroundRetracted
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
