Recommendations for Creating Better Concept Definitions in the Organizational, Behavioral, and Social Sciences

By Philip M. Podsakoff, Scott B. MacKenzie, Nathan P. Podsakoff. 2016

625 citations recorded by the source corpus.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/8830cbe5b2746004668d89dc53d78441fd062e46

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Citations in the corpus

A Review of Key Likert Scale Development Advances: 1995–2019 (2021, background): “Ensuring Concept Clarity Defining the construct one is interested in measuring is a foundational part of scale development; failing to do so properly undermines every scientific activity that follows (T. L. Thorndike, 1904; Kelley, 1927; Mackenzie, 2003; Podsakoff et al., 2016).”
“SEW” What Do We Know and Where Do We Go? A Review of Socioemotional Wealth and a Way Forward (2020, methodology): “…necessary condition analysis refers to causality statements regarding the logics of necessary and/or sufficient determinants on various outcomes, making it a useful tool for untangling the dimensionality of SEW and enabling us to augment our understanding of the construct (Podsakoff et al., 2016).”
Work motivation and public service motivation: disentangling varieties of motivation and job satisfaction (2018, background): “By identifying and distinguishing fundamental characteristics of each theory, we are able to more precisely draw conceptual boundaries, facilitate the differentiation of these concepts, and support a ‘common language’ within scientific research (Podsakoff, Mackenzie, and Podsakoff 2016).”
Envy: An Adversarial Review and Comparison of Two Competing Views (2019, background): “Fifth, studying envy as a unitary construct reduces conceptual proliferation (Podsakoff et al., 2016).”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, background): “For the proper testing of theories, it is crucial that the focal concepts are clearly defined (e.g., Podsakoff et al., 2016; Singleton & Straits, 2017) and that the construct operationalizations are appropriate such that the measures actually measure what they are supposed to measure (e.g.,…”
Meaningful Mediation Analysis: Plausible Causal Inference and Informative Communication (2017, background): “Developing and testing theories with precise definitions of mediator and outcome, and large conceptual leaps between them, increases the probability that their measures express discriminant validity (Podsakoff, MacKenzie, and Podsakoff 2016).”
An Updated Guideline for Assessing Discriminant Validity (2020, methodology): “We suggest starting by following the guidelines by J. A. Shaffer et al. (2016) and Podsakoff et al. (2016) for assessing the conceptual distinctiveness of the constructs (see also M. S. Krause, 2012).”