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README

        # Awesome Machine Learning & Management Research

*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:

- **Information Systems**

    - [Information Systems Research](https://pubsonline.informs.org/journal/isre)

    - [MIS Quarterly](https://www.misq.org/)

    - [Management Science](https://pubsonline.informs.org/journal/mnsc)

- **Marketing**

    - [Marketing Science](https://pubsonline.informs.org/journal/mksc)

    - [Journal of Marketing](https://journals.sagepub.com/home/jmx)

    - [Journal of Marketing Research](https://journals.sagepub.com/home/mrj)

    - [Journal of Consumer Research](https://academic.oup.com/jcr)

    - [Journal of the Academy of Marketing Science](https://www.springer.com/journal/11747)

## 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 [email protected]*

- [Review Papers](#review_papers)

- [Content Engineering](#content_engineering)

- [Consumer Profiling and Market Structure](#customer_preference)

- [Prediction](#prediction)

- [Causal Inference](#causal_inference)

- [Explainable Artificial Intelligence (XAI)](#xai)

- [Fairness, Accountability, and Transparency (FAT) in Machine Learning](#fat)



### Review Papers

| Title  | Author | Year | Journal | Link | Selected Keywords (Up to five) |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| Deep Learning for Information Systems Research | Samtani et al. | 2020 | WP | [Link](https://arxiv.org/abs/2010.05774) | - |

| Predicting the Future: Machine Learning and Marketing | Wang et al. | 2019 | WP | [Link](https://sites.insead.edu/facultyresearch/research/file.cfm?fid=64810) | - | 

| How can machine learning aid behavioral marketing research? | Hagen et al. | 2020 | Marketing Letters | [Link](https://link.springer.com/article/10.1007/s11002-020-09535-7)| - |

| Unstructured data in marketing | Balducci and Marinova | 2018 | Journal of the Academy of Marketing Science | [Link](https://link.springer.com/article/10.1007/s11747-018-0581-x)| - |

| Soul and machine (learning) | Proserpio et al.  | 2020 | Marketing Letters | [Link](https://link.springer.com/article/10.1007/s11002-020-09538-4)  | - |



### 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](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2976021)  | 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](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3692623) | 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](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3595738)  | 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](https://misq.org/a-comprehensive-analysis-of-triggers-and-risk-factors-for-asthma-based-on-machine-learning-and-large-heterogeneous-data-sources.html)  | 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](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3061149)  | 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](https://pubsonline.informs.org/doi/10.1287/isre.2017.0768)  | -  |

| Large Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning | Liu et al.  | 2019  | Journal of Marketing Research  | [Link](https://doi.org/10.1177%2F0022243719866690)  | 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](https://academic.oup.com/jcr/article/45/5/988/4964963) | 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](https://pubsonline.informs.org/doi/10.1287/mksc.2020.1226)| - |

| Uniting the tribes: Using text for marketing insight | Berger et al. | 2019 | Journal of Marketing | [Link](https://journals.sagepub.com/doi/10.1177/0022242919873106) |- |

| A Video-Based Automated Recommender (VAR) System for Garments | Lu et al. | 2016 | Marketing Science | [Link](https://pubsonline.informs.org/doi/10.1287/mksc.2016.0984) | - |

| Just the faces: Exploring the effects of facial features in print advertising | Li et al. | 2014 | Marketing Science | [Link](https://pubsonline.informs.org/doi/10.1287/mksc.2013.0837) | - |



### 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](https://academic.oup.com/jcr/article-abstract/44/6/1274/4283031?redirectedFrom=fulltext) | - |

| A semantic approach for estimating consumer content preferences from online search queries | Liu et al. | 2018 | Marketing Science | [Link](https://pubsonline.informs.org/doi/10.1287/mksc.2018.1112) | - |

| Identifying customer needs from usergenerated content | Timoshenko et al. | 2018 | Marketing Science | [Link](https://pubsonline.informs.org/doi/10.1287/mksc.2018.1123) | - |

| Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowd-Sourced | Ghose et al. | 2012 | Marketing Science| [Link](http://www.andrew.cmu.edu/user/beibeili/Publications/BeibeiLi_Ranking_MktSci.2012.pdf) | 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](https://pubsonline.informs.org/doi/10.1287/mksc.1120.0713) | - | 



### 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](https://pubsonline.informs.org/doi/10.1287/mksc.2015.0946) | - |

| Retention Futility: Targeting High-Risk Customers Might Be Ineffective | Ascarza, E.| 2018 | Journal of Marketing Research | [Link](https://journals.sagepub.com/doi/abs/10.1509/jmr.16.0163?journalCode=mrja) | - |

| Copycats vs. original mobile apps: A machine learning copycat-detection method and empirical analysis  | Wang et al.  | 2018  | Information Systems Research | [Link](https://pubsonline.informs.org/doi/abs/10.1287/isre.2017.0735?journalCode=isre)  | -  |

| Empirical Asset Pricing via Machine Learning | Gu et al. | 2020 | The Review of Financial Studies | [Link](https://academic.oup.com/rfs/article/33/5/2223/5758276) | - |

| Autoencoder Asset Pricing Models | Gu et al. | 2020 | Journal of Econometrics | [Link](https://www.sciencedirect.com/science/article/abs/pii/S0304407620301998) | 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](https://pubsonline.informs.org/doi/10.1287/isre.2017.0727) | - |

| Observational vs Experimental Data When Making Automated Decisions Using Machine Learning | Fernández-Loría and Provost | 2020 | WP | [Link](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3444678) | - |

| Machine Learning Instrument Variables for Causal Inference | Singh et al. | 2020 | ACM Conference on Economics and Computation | [Link](https://dl.acm.org/doi/10.1145/3391403.3399466) | - |

| The impact of machine learning on economics | Athey, S. | 2018 | The Economics of Artificial Intelligence: An Agenda | [Link](https://www.nber.org/system/files/chapters/c14009/c14009.pdf) | - | 

| A Measure of Robustness to Misspecification | Athey and Imbens | 2015 | American Economic Review | [Link](https://www.aeaweb.org/articles?id=10.1257/aer.p20151020) | - |

| Efficient Inference of Average Treatment Effects in High Dimensions via Approximate Residual Balancing | Athey et al. | 2016 | WP | [Link](https://ideas.repec.org/p/ecl/stabus/3408.html) | - |

| The state of applied econometrics: Causality and policy evaluation| Athey and Imbens | 2017 | Journal of Economic Perspectives | [Link](https://www.aeaweb.org/articles?id=10.1257/jep.31.2.3) | - |

| 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](https://www.pnas.org/content/pnas/113/27/7353.full.pdf) | - |



### 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](https://arxiv.org/abs/2001.07417) | - |

| Explainable AI: from black box to glass box  | Arun Rai  | 2019 | Journal of the Academy of Marketing Science | [Link](https://misq.org/misq/downloads/download/editorial/673/)  | -  |

| Good Explanation for Algorithmic Transparency  | Lu et al.  | 2020 | WP | [Link](https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3503603)  | explainable AI, interpretable AI, lab experiments  |

| Focused Concept Miner (FCM): Interpretable Deep Learning for Text Exploration  | Lee et al.  | 2020  | WP |  [Link](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3304756) | interpretable machine learning, text mining, automatic concept extraction, augmented hypothesis development  | 

| Predicting Returns with Text Data | Ke et al. | 2020 | WP | [Link](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3389884) | 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  |