Published on: 27 November, 2024
Today, we are excited to analyse the article "From Negative Emotions to Personal Growth: Failure and Re-entry into Entrepreneurship" published in the British Journal of Management (BJM) which is considered as a finest quality business journal. This journal is indexed in SSCI (Clarivate) [2023 Impact factor: 4.5], Scopus (Q1), PsycINFO, Emerald Management Reviews etc. We identify the following listed stern issues that should not have been neglected by the Editors and Reviewers of the journal.
Non-response bias is deadly for the reliability and validity of survey studies but often taken for granted. The Authors mention on page no. 1788:
"The results demonstrate that there is no evidence of late/non-response bias in the data (Armstrong and Overton, 1977; Podsakoff et al., 2003)."
On page no. 1787, the authors mention: "570 responses were received, representing a total response rate of 13.7%..."
We observe that authors made a very strong claim i.e., "no response bias in their data" which is logically and statistically wrong. In fact, this study suffers from a non-response bias of 86.3% which raises questions on representativeness of the sample (Fincham, 2008) and impact of this bias on exactitude of population parameters (Panwar, Azfar, & Tanwar, 2018). Therefore, we recommend that authors should not make "unrealistic and false claims" in their papers, and Editors and Reviewers must identify and condemn such heretical declarations.
We know that common method bias (CMB) cannot be avoided in single informant surveys (Guide & Ketokivi, 2015). Business literature is full of false claims like "CMB has no impact on our studies". To detect common method bias (CMB), Shore et al. (2024) ran Harman's single-factor test (p. 1788). Besides its frequent use in business literature to detect CMB, mainstream editors and researchers recommended to stop making false claims about absence/no impact of CMB in cross-sectional/single informant survey studies (Guide & Ketokivi, 2015). In their latest review article, Podsakoff et al. (2024) concluded and strongly endorsed the discontinuation of the use of Harman's test due to its limitations and inability to mitigate CMB. Therefore, we conclude that Shore et al. (2024) made questionable claim about the CMB based on Harman's single-factor test. Literature suggests using ex-ante techniques to reduce the impact of CMB on internal consistency of constructs (Rodríguez-Ardura & Meseguer-Artola, 2020). In addition, instead of using weak tests like Harman’s single factor test, ex-post inferential tests should be used to investigate the extent of CMB in single-informant survey studies (Guide & Ketokivi, 2015; Rodríguez-Ardura & Meseguer-Artola, 2020). We wonder why the Editors of British Journal of Management (BJM) still allow publication of articles adopting outdated techniques and publish false claims about CMB.
On page no. 1788, the authors indicated (see 1st Figure under Non-response Bias):
"To combat common source error, ‘temporal, proximal, or psychological separation between the measurement of the predictor and criterion variables’ (Podsakoff et al., 2003) was utilized to create a separation of measurement."
Temporal separation means to tap independent variables at time 1, mediator(s) at time 2, and dependent variable(s) at time 3. Podsakoff et al. (2024) inform that temporal separation of variables helps to reduce CMB. In the case of Shore at al. (2014), we find that the authors made a false claim about “temporal separation” because they clearly mention on page no. 1786 that their study is a "cross-sectional" one:
"a cross-sectional study is therefore undertaken to assess the prevalence of NER and personal growth in individuals who have re-entered entrepreneurship following failure. Whilst the cross-sectional approach has limitations in deriving causal relationships, and in presenting certain biases (Conelly, 2016), we seek to mitigate these when interpreting the associations born out of the survey."
We can see that the mention of “temporal separation” to overcome CMB was in toto false because Shore et al. (2024) adopted a clear cross-sectional design and no efforts were made to capture independent variable (failure experience), mediator (negative emotional response), and dependent variable (personal growth) at different time points (time lagged design).
Based on the above findings, we see the claim of “temporal separation” as a completely false claim which was overlooked by the Editors and Reviewers. We believe that Editors of the journals should be trained, well-informed, and vigilant enough to highlight and catch such blunders during desk review. Unfortunately, the Editors of BJM completely failed to identify and rectify this stern mistake pertaining to the “Research Design” of the study.
Surprisingly, throughout the "Methodology" section, the Authors didn't mention anything about "analysis technique" adopted to test the study hypotheses. We can find the adopted analysis technique in the “Results” section (p. 1790). However, the proper place to report the information about analysis technique is the "Methodology" section. As there is no analysis technique explained, the justification to adopt multiple regression analysis is also not provided. We wonder how could the Editors of the BJM accept an article with such a notable mistake?
By looking at the data, it is apparent that CEO observations were nested within firms, which were nested within industries. In addition, negative emotional response and personal growth were measured at individual level, whereas failure experience is a firm level variable. Therefore, it was better to adopt "Multilevel Modeling" instead of multiple regression analysis to attain better inferences. In the past, many studies adopted multilevel models to analyze the data collected from CEOs (e.g., Graffin et al. 2020; Quigley & Graffin, 2017; You et al., 2013). Also, we cannot find the type of industry CEOs belong to. This information was necessary because failure experience would vary across different industries.
The study doesn’t provide descriptive statistics for IV (1 variable), MV (1 variable), DV (1 variable), and control variables (12 variables). Descriptive statistics, especially, means and standard deviations give important insights to understand various characteristics of data. It is also important to note that the Authors made no attempt to test the data for basic regression model assumptions. Proper testing of these assumptions was necessary due to the hierarchical nature of study data.
Maybe the Editors and Reviewers took these significant issues for granted, overlooked, or left due to their nescience, we hold that it is the prime responsibility of the Editors to identify such basic problems at the level of desk review. The article shows a complete failure of the review process conducted by the British Journal of Management (BJM).
Fincham, J.E. (2008). Response rates and responsiveness for surveys, standards, and the Journal. American Journal of Pharmaceutical Education, 72(2), 43.
Graffin, S.D., Hubbard, T.D., Christensen, D.M., & Lee, E.Y. (2020). The influence of CEO risk tolerance on initial pay packages. Strategic Management Journal, 41, 788-811.
Guide, V.D.R., Jr. & Ketokivi, M. (2015), Notes from the Editors: Redefining some methodological criteria for the journal. Journal of Operations Management, 37, 5-8.
Panwar, M., Azfar, M., & Tanwar, N. (2018). Statistical treatment of nonresponse in sample surveys. In Research Trends in Mathematics & Statistics. India: AkiNik Publications.
Philip M. Podsakoff, Nathan P. Podsakoff, Larry J. Williams, Chengquan Huang, & Junhui Yang. (2024). Common Method Bias: It's Bad, It's Complex, It's Widespread, and It's Not Easy to Fix. Annual Review of Organizational Psychology and Organizational Behavior, 11, 17-61.
Quigley, T.J. & Graffin, S.D. (2017). Reaffirming the CEO effect is significant and much larger than chance: A comment on Fitza (2014). Strategic Management Journal, 38, 793-801.
Rodríguez-Ardura, I., & Meseguer-Artola, A. (2020). How to prevent, detect and control common method variance in electronic commerce research. Journal of Theoretical and Applied Electronic Commerce Research, 15(2), 1-5.
You, S., Li, Z., Jia, L., & Cai, Y. (2023). CEO narcissism and innovation ambidexterity: The moderating roles of CEO power and firm reputation. Journal of Product Innovation Management 40(2), 175-194.
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