Awesome Machine Learning & Management Research
July 27, 2021 · View on GitHub
Work-in-Progress
A curated list of machine learning (ML) research in academic management journals.
The purpose of this repository is intended to give an overview and cover the interesting topics in Information Systems and Marketing to serve as a short and non-exhaustive review on the intersection of ML and management research. In particular, I explored these two fields since they have historically served as the technical interface for conventional business school disciplines. Specifically, I mainly covered the following academic journals:
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Information Systems
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Marketing
Table of Contents
Papers in top journals often make contributions across many topics so I classified them according to their key contributions.
Note. If you want to contribute to this list, please send me a pull request or email me quantic.jh@gmail.com
- Review Papers
- Content Engineering
- Consumer Profiling and Market Structure
- Prediction
- Causal Inference
- Explainable Artificial Intelligence (XAI)
- Fairness, Accountability, and Transparency (FAT) in Machine Learning
Review Papers
| Title | Author | Year | Journal | Link | Selected Keywords (Up to five) |
|---|---|---|---|---|---|
| Deep Learning for Information Systems Research | Samtani et al. | 2020 | WP | Link | - |
| Predicting the Future: Machine Learning and Marketing | Wang et al. | 2019 | WP | Link | - |
| How can machine learning aid behavioral marketing research? | Hagen et al. | 2020 | Marketing Letters | Link | - |
| Unstructured data in marketing | Balducci and Marinova | 2018 | Journal of the Academy of Marketing Science | Link | - |
| Soul and machine (learning) | Proserpio et al. | 2020 | Marketing Letters | Link | - |
Content Engineering
| Title | Author | Year | Journal | Link | Selected Keywords (Up to five) |
|---|---|---|---|---|---|
| How Much is an Image Worth? The Impact of Professional versus Amateur Airbnb Property Images on Property Demand | Zhang et al. | 2020 | WP | Link | economic impact of images, computer vision, deep learning, image quality classification, image attribute analysis |
| Serving with a Smile on Airbnb: Analyzing the Economic Returns and Behavioral Underpinnings of the Host’s Smile | Zhang et al. | 2020 | WP | Link | Smile Effect, Gender Bias, Facial Attributes, Airbnb Demand, Controlled Experiment |
| A Deep Learning Approach for Recognizing Activity of Daily Living (ADL) for Senior Care: Exploiting Interaction Dependency and Temporal Patterns | Zhu et al. | Forthcoming | MIS Quarterly | Link | activity recognition, human-object interaction, CNN, Seq2Seq, design science |
| Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach | Shin et al. | Forthcoming | MIS Quarterly | - | semantic content analysis, customer engagement, topic modeling, representation learning, Tumblr |
| A Comprehensive Analysis of Triggers and Risk Factors for Asthma Based on Machine Learning and Large Heterogeneous Data Sources | Zhang and Ram | 2020 | MIS Quarterly | Link | Chronic disease management, asthma triggers/risk factors, CNN, sequential pattern mining, geometric inference |
| Go to You Tube and Call Me in the Morning: Use of Social Media for Chronic Conditions | Liu et al. | 2020 | MIS Quarterly | Link | healthcare informatics, digital therapeutics, BLSTM, CNN, collective engagement |
| The Impact of User Personality Traits on Word of Mouth: Text-Mining Social Media Platforms | Adamopoulos et al. | 2018 | Information Systems Research | Link | - |
| Large Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning | Liu et al. | 2019 | Journal of Marketing Research | Link | consumer purchase journey, economic impact of text, product reviews, NLP, regression discontinuity in time |
| Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages | Ordenes et al. | 2019 | Journal of Consumer Research | Link | consumer sharing, speech act theory, image acts, text mining, message dynamics |
| Visual listening in: Extracting brand image portrayed on social media | Liu et al. | 2020 | Marketing Science | Link | - |
| Uniting the tribes: Using text for marketing insight | Berger et al. | 2019 | Journal of Marketing | Link | - |
| A Video-Based Automated Recommender (VAR) System for Garments | Lu et al. | 2016 | Marketing Science | Link | - |
| Just the faces: Exploring the effects of facial features in print advertising | Li et al. | 2014 | Marketing Science | Link | - |
Consumer Profiling and Market Structure
| Title | Author | Year | Journal | Link | Selected Keywords (Up to five) |
|---|---|---|---|---|---|
| Automated text analysis for consumer research | Humphreys et al. | 2017 | Journal of Consumer Research | Link | - |
| A semantic approach for estimating consumer content preferences from online search queries | Liu et al. | 2018 | Marketing Science | Link | - |
| Identifying customer needs from usergenerated content | Timoshenko et al. | 2018 | Marketing Science | Link | - |
| Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowd-Sourced | Ghose et al. | 2012 | Marketing Science | Link | user-generated content, search engines; hotels, structural models, text mining |
| Mine your own business: Market-structure surveillance through text mining | Netzer et al. | 2012 | Marketing Science | Link | - |
Prediction
| Title | Author | Year | Journal | Link | Selected Keywords (Up to five) |
|---|---|---|---|---|---|
| Consumer preference elicitation of complex products using fuzzy support vector machine active learning | Huang et al. | 2016 | Marketing Science | Link | - |
| Retention Futility: Targeting High-Risk Customers Might Be Ineffective | Ascarza, E. | 2018 | Journal of Marketing Research | Link | - |
| Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis | Wang et al. | 2018 | Information Systems Research | Link | - |
| Empirical Asset Pricing via Machine Learning | Gu et al. | 2020 | The Review of Financial Studies | Link | - |
| Autoencoder Asset Pricing Models | Gu et al. | 2020 | Journal of Econometrics | Link | stock returns, conditional asset pricing model, autoencoder |
Causal Inference
| Title | Author | Year | Journal | Link | Selected Keywords (Up to five) |
|---|---|---|---|---|---|
| Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining | Yang et al. | 2018 | Information Systems Research | Link | - |
| Observational vs Experimental Data When Making Automated Decisions Using Machine Learning | Fernández-Loría and Provost | 2020 | WP | Link | - |
| Machine Learning Instrument Variables for Causal Inference | Singh et al. | 2020 | ACM Conference on Economics and Computation | Link | - |
| The impact of machine learning on economics | Athey, S. | 2018 | The Economics of Artificial Intelligence: An Agenda | Link | - |
| A Measure of Robustness to Misspecification | Athey and Imbens | 2015 | American Economic Review | Link | - |
| Efficient Inference of Average Treatment Effects in High Dimensions via Approximate Residual Balancing | Athey et al. | 2016 | WP | Link | - |
| The state of applied econometrics: Causality and policy evaluation | Athey and Imbens | 2017 | Journal of Economic Perspectives | Link | - |
| Recursive partitioning for heterogeneous causal effects | Athey and Imbens | 2016 | Proceedings of the National Academy of Sciences of the United States of America (PNAS) | Link | - |
Explainable Artificial Intelligence (XAI)
| Title | Author | Year | Journal | Link | Selected Keywords (Up to five) |
|---|---|---|---|---|---|
| Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach | Fernández-Loría et al. | 2020 | WP | Link | - |
| Explainable AI: from black box to glass box | Arun Rai | 2019 | Journal of the Academy of Marketing Science | Link | - |
| Good Explanation for Algorithmic Transparency | Lu et al. | 2020 | WP | Link | explainable AI, interpretable AI, lab experiments |
| Focused Concept Miner (FCM): Interpretable Deep Learning for Text Exploration | Lee et al. | 2020 | WP | Link | interpretable machine learning, text mining, automatic concept extraction, augmented hypothesis development |
| Predicting Returns with Text Data | Ke et al. | 2020 | WP | Link | supervised topic modeling, sentiment analysis, return predictability |
Fairness, Accountability, and Transparency (FAT) in Machine Learning
| Title | Author | Year | Journal | Link | Selected Keywords (Up to five) |
|---|---|---|---|---|---|
| Hidden Is Safe? Location Protection against Machine-Learning Prediction Attacks in Social Networks | Han et al. | Forthcoming | MIS Quarterly | - | private information protection, personal exposure risk, machine-learning, location prediction attack |