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Machine learning and AI in marketing–Connecting computing power to human insights

Ma, Liye, Sun, Baohong·2020·International Journal of Research in Marketing

DOI 10.1016/j.ijresmar.2020.04.005

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Method
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Pasha, F. M. (2022) · Research Square · doi:10.21203/rs.3.rs-1578985/v1 · Cited by 1
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Gokce, A. (2022) · doi:10.23889/suthesis.61502 · Cited by 1
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Horton, A. (2024) · Preprints.org · doi:10.20944/preprints202408.1344.v1
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Framework of EcomTDMA for Transactional Data Mining Using Frequent Item Set for E-Commerce Application
Ambavane, P., Zaware, S. N., & Zaware, N. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-2937-2_21
Разработка подходов к оценке эффективности маркетинговых акций для участников программ лояльности в розничной торговле (на примере аптечной сети)
Степанов, А. С., & Чемоданов, А. А. (2022) · Экономика Информатика · doi:10.52575/2687-0932-2022-49-1-92-102
The Role of Virtual Communities on Consumer Purchase Decisions with Brand Equity as Mediating Variable
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“(4) Make repeat purchases (Ancillai et al., 2019; Ma & Sun, 2020).”
Background
Applications of Machine Learning to Form-giving in Industrial Design
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Increasing Generalizability: Naïve Bayes Vs K-Nearest Neighbors
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“Studies that check the accuracy of predictive models using a test dataset are uncommon within the Marketing discipline, although such studies are present in other disciplines [7,8].”
Method
Essays on causal inference
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Chatbot em realidade aumentada para atendimento ao público da Assembleia Legislativa do Amazonas
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“In psychology, it can provide researchers with a novel tool to study the relationship between facial features and personality traits on a large scale [7].”
Background
When Should They Buy? Surviving Machine Learning Models for Purchase Timing
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“Computers are no longer used as a calculating tool, more than, computers are expected to be empowered to do everything that humans can do (Ma & Sun, 2020) that can be done by humans.”
Background
Quantile Normalized Neighbor Combinatorial Machine Learning Based Recommendation in Digital Marketing
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Marketing faced with the organizational challenge of AI explainability
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Challenge of Criminal Imputation for Negligence Crime Involving AI to the Traditional Criminal Imputation Theory
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High-Performance Computing Web Search System Based on Computer Big Data
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An Empirical Analysis Of Critical Factors For E Commerce Application In Banking Industry
Gupta, P. R. S. D. S. K., & Deshmukh, T. T. D. V. P. (2024) · Journal of Informatics Education and Research · doi:10.52783/jier.v4i2.872
Modular Mathematical Modeling Based on BP Neural Network and Cluster Analysis
Wang, Q. (2023) · IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) · doi:10.1109/aeeca59734.2023.00167
“Considering that the process of clustering is the optimization process of objective function [15], this paper tries to apply the optimization model to cluster analysis and proposes a density clustering algorithm based on discrete Morse optimization model.”
Method
User Repeat Purchase Behavior Prediction Based on Accuracy-Weighted Stacking Algorithm
Huang, Y., & Zheng, S. (2024) · Journal of Applied Mathematics and Computation · doi:10.26855/jamc.2024.03.008
Affinity-Based Resource and Task Allocation in Edge Computing Systems
Zou, W., Liu, X., Hou, S., Zhang, Y., Miao, L., Gong, Y., & Li, N. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00162
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Background
Artificial Intelligence and the Tourism Film
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Nayak, B. S., & Walton, N. (2024) · Springer eBooks · doi:10.1007/978-3-031-62308-0_4
When Ai Takes Control:Exploring Trust, Wellbeing, and Usage Intentions in High Automation EnvironmentsPreprint
Pavone, G., Cloarec, J., Kuhn, M. M., Meyer‐Waarden, L., Huber, M., & Munzel, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4907281
Comparative Analysis of Naïve Bayes and K-Nearest Neighbors Algorithms in Predicting Public Interest in Electric Motorcycles
Syahputra, A., Harahap, S. Z., & Nasution, M. (2024) · International Journal Of Science Technology & Management · doi:10.46729/ijstm.v5i4.1134
“…technique to analyze a large number of customer data, including response surveys, online reviews, and other sources, to gain a deeper insight into customer preferences, purchasing intentions, and the factors that influence the adoption of new technologies such as electric bikes [18], [19].”
Background
An Application Evaluation for Differentially Private Database Release Methods
Nicho, M., Walia, M., & Khan, S. N. (2024) · International Conference on Cryptography, Security and Privacy (CSP) · doi:10.1109/csp62567.2024.00013
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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)
DOI: 10.1016/j.ijresmar.2020.04.005
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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