On doing better science: From thrill of discovery to policy implications

By John Antonakis. 2017

400 citations recorded by the source corpus.

Subfields: Information Systems and Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cfe52a4c5dcfa3c8ae843f4b933cc3b7523833a7

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In Information Systems and Management

In Statistics and Probability

Citations in the corpus

An Identity Perspective on Ethical Leadership to Explain Organizational Citizenship Behavior: The Interplay of Follower Moral Identity and Leader Group Prototypicality (2019, background): “Another approach may lie in the identification of suitable instrumental variables to counter endogeneity in the analyses and corroborate causality (Antonakis 2017).”
On doing relevant and rigorous experiments: Review and recommendations (2018, background): “Such financial incentives motivate participants to be attentive and focused on the experiment (Antonakis, 2017; Hertwig & Ortmann, 2001) and clarify the decision situation in which participants find themselves (Camerer & Hogarth, 1999; Hertwig & Ortmann, 2001; Smith & Walker, 1993).”
Benevolence and authority as WEIRDly unfamiliar: A multi-language meta-analysis of paternalistic leadership behaviors from 152 studies (2019, background): “Estimating the relationships of PL and its dimensions to establish leadership constructs is critical in order to understand the nomological network of a construct and as part of establishing its unique and/or redundant nature (Antonakis, 2017).”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, background): “Although using the same or a very similar variable across different studies based on the same dataset can be problematic (e.g., leading to “salami sliced” publications; Antonakis, 2017)—particularly when it concerns the dependent variables—this practice can also be a requirement when prior publicati”
Servant Leadership: A systematic review and call for future research (2019, methodology): “endogeneity bias, measurement error, and common method bias (Antonakis, Bendahan, Jacquart, & Lalive, 2010)) and the small number of studies coupled with relatively low sample sizes that were used in the meta-analyses (Hoch et al., 2018). Thus, we present a brief overview of the theoretical and empi”
Common method bias in applied settings: The dilemma of researching in organizations (2020, background): “unique data collected for each variable in your model (Antonakis, 2017).”
A review of the methodological misconceptions and guidelines related to the application of structural equation modeling: A Malaysian scenario (2017, background): “, 2017a; Hair, Black, Babin, & Anderson, 2010). In other words, a total of eight items (maximum) can be excluded if the original instrument consists of 40 items. It is recommended that the postgraduate students should explicitly report how many items are discarded in the process, and justify such ex”