False claims in a widely-cited paper. No corrections. No consequences. Welcome to the Business School.

2026-03-24 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Research Methodology & Innovation, Social Sciences & Behavioral Studies · Depth: Intermediate, medium

Summary

A highly cited 2014 Management Science paper by Eccles, Ioannou, and Serafeim, claiming "High Sustainability companies significantly outperform their counterparts," is revealed to be fatally flawed. The authors acknowledged in September 2025 that the method described in the paper was not the one actually used, but they have refused to issue a corrigendum. This paper, cited approximately 2,000 times annually, has profoundly influenced investment practices and public policy, partly due to its appealing message across the political spectrum. Efforts by collaborator Andy King to correct the record through Management Science and various Research Integrity Offices (RIOs) have been unsuccessful, as journals only allow authors to request corrections, and RIOs from London Business School, Harvard Business School, Oxford, and the UK have either disclaimed responsibility or declined to act.

Key takeaway

For AI Scientists evaluating foundational research or seeking to apply academic findings, you should exercise extreme caution with highly cited papers, especially those with broad appeal. The persistence of uncorrected, flawed research like the Eccles et al. (2014) paper highlights a systemic lack of accountability in academic publishing and integrity offices. Always attempt to independently verify methodologies and data, and be skeptical of claims that align too perfectly with prevailing narratives, as institutional mechanisms for correction are often ineffective.

Key insights

Institutional failures enable influential, flawed research to persist uncorrected, undermining scientific integrity.

Principles

• Journals often restrict correction requests to original authors.
• Research Integrity Offices frequently lack enforcement power.
• Flawed research can gain traction via appealing narratives.

In practice

• Verify research methods against stated claims.
• Scrutinize studies promoting "feel-good" or politically convenient messages.

Topics

• Research Integrity
• Academic Misconduct
• Publication Ethics
• Corporate Sustainability
• Management Science

Best for: AI Scientist, Research Scientist, AI Ethicist, Policy Maker

Related on AIssential

• Program misleading high school students into paying to perform academic misconduct in ML Research [D] — Paid programs exploit high school students' college aspirations by facilitating low-quality, AI-assisted research for workshop publications.
• Fraud and the false optimism of AI for science — The ethical status of AI-generated scientific papers hinges on human oversight, intent, and the preservation of scientific integrity.
• Concerns ‘AI slop’ used by University of Sydney-based institute to lobby for $20m gambling education funding — A research institute's funding request was undermined by an evidence review containing significant inaccuracies, potentially due to AI-generated content.
• We Asked the ‘Future of Truth’ Author to Explain How He Used AI. It Didn’t Go Well — The irony of a book on AI and truth containing AI-generated falsehoods highlights critical authorial responsibility.
• AI might jeopardize the uncertainty required in science — AI's impact on scientific communication extends to altering how uncertainty is expressed and interpreted.
• Attributing and situating knowledge cannot be left to language models — LLMs cannot reliably handle scholarly attribution, risking miscitation and undermining research standards.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.