How Cognitive Biases Influence Problematic Research Methods Practices

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DOI:

https://doi.org/10.34190/ejbrm.22.1.3212

Keywords:

Cognitive biases, Citation, Research methods, Artificial intelligence (AI), ChatGPT

Abstract

A growing body of academic research addresses issues related to questionable choices and errors in the use of research methods in published business research. These problematic research method practices (PRMPs) may be purposeful or unconscious, but they reduce the rigor of academic research and can harm the accumulation of scientific knowledge. Yet, absent from much of this literature is a theoretically grounded approach to understanding why these problematic practices occur. Prior scholars have summarized specific types of PRMPs, but attributions about their causes are primarily limited to research lack of motivation or poor doctoral education. While these may certainly be at play, the current manuscript proposes that the deeper psychological phenomenon of cognitive bias is a likely explanation. Cognitive biases occur when human cognition produces an outcome that is systematically distorted from objective reality (Haselton, Nettle, and Murray, 2016). More colloquially, cognitive biases are systematic errors that humans make when they are faced with perceiving, remembering, and understanding information. These unintentional biases are particularly likely when that information is voluminous and ambiguous. Cognitive biases are explained by two theories—heuristic theory and fuzzy trace theory. Heuristic theory suggests that humans default to using mental shortcuts as a means to make decisions more efficiently (Chaiken and Ledgerwood, 2012). Further, fuzzy trace theory explains how memory and reasoning can be flawed (Reyna and Brainerd, 1995). Because of the limitations of the human mind, heuristic theory and fuzzy trace theory act to create unintentional cognitive biases. The current manuscript argues that the cognitive biases of source confusion, gist memory, repetition effects, bandwagon effects, and confirmation bias are mostly subconscious means by which researchers make errors in research methods use. We argue that these biases are not a useful part of the didactic approach to research, but are rather mental shortcuts that can limit researcher effectiveness. Next, specific PRMPs are addressed: reliance on methodological myths and urban legends, errors in citations, use of questionable research practices, and inappropriate use of artificial intelligence (AI) tools and technology in research. Finally, there are a number of insights and recommendations derived from research on cognitive biases to assist scholars in promoting research methods best practices. In particular, researchers can combat cognitive biases by recognizing what they are and by providing more transparency about research methods use in their articles. Incentives for authors and reviewers may reduce the impact of cognitive biases on PRMPs. Editors should create and share clear guidelines on the use of AI in research. In summary, this manuscript addresses those critical issues, fills a gap in current research regarding why PRMPs occur, and provides researchers with key insights to effectively combat cognitive biases.

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Published

19 Feb 2024

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