Reference health
The Perks and Perils of Machine Learning in Business and Economic Research
58/58 checkable references clean · checked 2026-08-04
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
35 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 58 checked references that resolve
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Scientific teams and institutional collaborations: Evidence from U.S. universities, 1981–1999
resolves10.1136/bmj.n2281
Risk of bias in studies on prediction models developed using supervised
machine learning techniques: systematic review
resolves10.1111/ecin.13222
Is economics self‐correcting? Replications in the <i>American Economic Review</i>
resolves10.1016/j.neuroimage.2016.02.079
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
resolves10.1007/s12525-022-00537-z
Artificial intelligence in E-Commerce: a bibliometric study and literature review
resolves10.1016/j.respol.2022.104604
Artificial intelligence in science: An emerging general method of invention
resolves10.1007/s11192-013-0996-6
Are academics who publish more also more cited? Individual determinants of publication and citation records
resolves10.1257/aer.20190687
Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics
resolves10.1111/1475-679X.12429
Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data
resolves10.1002/joom.1228
Supervised machine learning for theory building and testing: Opportunities in operations management
resolves10.1257/jel.20171350
Transparency, Reproducibility, and the Credibility of Economics Research
resolves10.1257/pandp.20201058
Technology and Big Data Are Changing Economics: Mining Text to Track Methods
resolves10.1016/j.respol.2023.104875
Weeding out or picking winners in open innovation? Factors driving multi-stage crowd selection on LEGO ideas
resolves10.1109/MSP.2017.2762725
Artificial Intelligence in the Rising Wave of Deep Learning: The Historical Path and Future Outlook [Perspectives]
resolves10.1016/j.joi.2010.06.003
The effect of scholar collaboration on impact and quality of academic papers
resolves10.1002/smj.582
The use of logit and probit models in strategic management research: Critical issues
resolves10.1145/3514094.3534196
The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning
resolves10.1016/j.patter.2023.100804
Leakage and the reproducibility crisis in machine-learning-based science
resolves10.1016/j.ejor.2019.09.018
Deep learning in business analytics and operations research: Models, applications and managerial implications
resolves10.5465/amr.2019.0247
Ghost in the Machine: On Organizational Theory in the Age of Machine Learning
resolves10.1177/1042258720927369
Pursuing Impactful Entrepreneurship Research Using Artificial Intelligence
resolves10.1002/jcpy.1092
Predicting the Personal Appeal of Marketing Images Using Computational Methods
resolves10.1038/s41586-024-07146-0
Artificial intelligence and illusions of understanding in scientific research
resolves10.1080/0960085X.2025.2506875
Responsible AI starts with the artifact: Challenging the concept of responsible AI in IS research
resolves10.1057/ejis.2016.2
Utilizing big data analytics for information systems research: challenges, promises and guidelines
resolves10.1038/s42256-021-00307-0
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
resolves10.1038/s41467-024-46150-w
Data leakage inflates prediction performance in connectome-based machine learning models
resolves10.1111/joes.12004
QUALITATIVE AND MIXED‐METHODS RESEARCH IN ECONOMICS: SURPRISING GROWTH, PROMISING FUTURE
resolves10.1109/MSPEC.2021.9563954
Deep Learning's Diminishing Returns: The Cost of Improvement is Becoming Unsustainable
resolves10.1016/j.artmed.2020.101987
Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling
resolves10.1007/s10551-022-05120-2
Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework
The 35 references without a DOI — listed, not checked
no DOI — not checkedThe Impact of Machine Learning on Economics
no DOI — not checkedref6
no DOI — not checkedEconomic Restrictions: Evidence from Stock Return Predictability
no DOI — not checkedChallenges to the Reproducibility of Machine Learning Models in Health Care
no DOI — not checkedref11
no DOI — not checkedIt Takes Two to Tango: Economic Theory and Model Uncertainty for Equity Premium Prediction
no DOI — not checkedref14
no DOI — not checkedThe Economics of Generative AI
no DOI — not checkedDesign choices, machine learning, and the cross-section of stock returns
no DOI — not checkedref27
no DOI — not checkedref28
no DOI — not checkedref32
no DOI — not checkedref33
no DOI — not checkedref34
no DOI — not checkedState of the Art: Reproducibility in Artificial Intelligence
no DOI — not checkedAre our predictions healthy? A comparative metaanalysis of machine learning studies in predictive healthcare
no DOI — not checkedCausal Machine Learning and Business Decision Making
no DOI — not checkedREFORMS: Consensus-based Recommendations for Machine-learningbased Science
no DOI — not checkedref50
no DOI — not checkedChallenging the Performance-Interpretability Trade-Off: An Evaluation of Interpretable Machine Learning Models
no DOI — not checkedref55
no DOI — not checkedref59
no DOI — not checkedMachine learning from a Universe of signals: The role of feature engineering
no DOI — not checkedref63
no DOI — not checkedReproducibility in machine learning for health research: Still a ways to go
no DOI — not checkedref70
no DOI — not checkedref74
no DOI — not checkedref75
no DOI — not checkedref76
no DOI — not checkedref77
no DOI — not checkedBusiness and management research: Low instances of replication studies and a lack of author independence in replications
no DOI — not checkedref82
no DOI — not checkedNonreplicable publications are cited more than replicable ones
no DOI — not checkedTradeoffs in the Choice Between Logit and OLS for Accounting Choice Studies
no DOI — not checkedref91
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