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Published in: Journal of Business and Psychology 1/2022

15-04-2021 | Original Paper

Can You Always Catch More Flies with Honey than with Vinegar? Applying an Asymmetric Approach to Transformational Leadership Research

Authors: Cara-Lynn Scheuer, Catherine Loughlin, Arch G. Woodside

Published in: Journal of Business and Psychology | Issue 1/2022

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Abstract

This study addresses a shortcoming in the transformational leadership literature (Van Knippenberg & Sitkin The Academy of Management Annals, 7(1), 1-60, 2013) by demonstrating when and how individual transformational leadership components may predict high and low team performance, as well as how these behaviors can combine to achieve certain outcomes. The study also sheds light on how certain team leader’s demographics (i.e., leader sex, age, and tenure) and team context conditions (i.e., team size and job complexity) predict different team performance outcomes via different transformational leadership behaviors. A case-based asymmetric configurational approach and fuzzy-set qualitative comparative analysis (fsQCA) were used to examine data gathered from 59 teams in the marketing and print services sector. The results identified multiple useful configurations of transformational leadership behaviors and team leader demographics/team context conditions that accurately predict high and, separately, low team performance (i.e., achieving model prediction accuracy odds of 4 to 1 or greater). A predictive validation was also performed on a second sample whereby the highly consistent models for the study’s main sample had high predictive abilities for this second sample. From a practical standpoint, this study suggests that certain configurations of transformational sub-components may work better for some leaders than others (e.g., young female versus male leaders). It also identifies more realistic, authentic, and potentially less costly strategies for predicting high (and avoiding low) levels of team performance in different contexts. Finally, this study contributes to the literature by being a first attempt to apply an asymmetric approach to transformational leadership research.
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Footnotes
1
One reviewer noted that additional research is necessary for testing the discriminant validity of the transformational leadership dimensions for data collected at the individual level within one organization versus several organizations, and for members in the same group within versus between organizations. A future meta-analysis done separately for each of these four data categories would be helpful in confirming which transformational leadership components are more relevant for within-group (versus individual) data in dependent (versus independent) organizational settings. The present study does not address this issue but recognizes its relevancy in advancing the study of transformational leadership antecedents and impacts.
 
2
Three team leaders were responsible for managing two teams and therefore were asked to make separate ratings on each of their teams.
 
3
Data gathered for this study was part of a larger research initiative. Team size and five of the seven performance criteria from this study were used in another paper that has already been published (Scheuer & Loughlin, 2019).
 
4
Consistency occurs when a case’s level of membership in the configuration is less than or equal to its level of membership in the outcome (i.e., the configuration is a subset of the outcome; Ragin, 2008a). The fsQCA software estimates consistency (Xi ≤ Yi), by dividing the sum of the minimum of Xi or Yi by the sum of Xi. Consistency (Xi ≤ Yi) = Σ {min (Xi, Yi)}/Σ (Xi), where Xi is case i’s membership score in set X; Yi is case i’s membership score in the outcome condition, Y. This approach penalizes consistency scores for occurrences when Xi exceeds Yi in proportion to the size of the inconsistency. Calculation of consistency is definable as performing “somewhat precise outcome testing (SPOT)” (Woodside, 2017a). SPOT is an alternative, asymmetric, summary metric to the symmetric NHST (Woodside, 2017a).
 
5
To calculate coverage, the fsQCA software calculates the proportion of cases where degree of membership in the configuration is less than the membership in the outcome, but this time divides it by the total score for the outcome condition. This coverage calculation estimates the level to which the configuration (the subset) covers the outcome (the larger set; Ragin, 2008a). Coverage (Xi ≤ Yi) = Σ {min (Xi, Yi)}/Σ (Yi). Ragin (2008a) provides a full description of QCA theory and method.
 
6
Mid-level dot, “●,” indicates the presence (or high levels) of the simple antecedent condition in the model; sideways tilde, “~,” indicates the negation (or low levels) of the simple antecedent condition in the model; empty space indicates the absence of the simple antecedent condition in the model; absence indicates that the antecedent condition does not contribute or take away from the consistency of a given model.
 
7
These models represent “computing with words (CWW)” (Zadeh, 1996) in the form of screening (inclusion/exclusion) rules useful for identifying cases exhibiting success versus failure in team performance outcomes. For example, model 1 in Table 4 expressed as a “computing with words, CWW” (Zadeh, 1996) statement—readable as follows: cases having a high calibrated conjunctive membership score equal to 0.95 or higher for the complex statement that includes vision, intellectual stimulation, inspirational communication, personal recognition, and supportive leadership have team performance scores equal to 0.95 or higher. Using Boolean algebra and asymmetrical analysis, a case’s conjoined score for a complex antecedent condition is equal to the lowest score among the simple antecedent conditions making up the statement. Thus, if a case has scores above 0.95 for four of these five simple antecedent conditions but has a lower membership score (e.g., 0.22) for the additional simple condition, then the case’s conjunctive membership score for the complex antecedent condition equals this lowest score (e.g., 0.22) and the case is excluded from the analysis of model 1. Model 1 forecasts only that cases with high conjunctive scores for the complex antecedent statement will have high outcome membership scores. Cases not having high membership scores for the complex antecedent condition may still have high (or low) scores for the outcome condition, but model 1 is not relevant to predicting their outcome scores. Thus, the estimated membership scores for a complex antecedent condition can be thought of usefully as a screening algorithm for identifying the cases the researcher forecasts to have high scores on the outcome condition.
 
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Metadata
Title
Can You Always Catch More Flies with Honey than with Vinegar? Applying an Asymmetric Approach to Transformational Leadership Research
Authors
Cara-Lynn Scheuer
Catherine Loughlin
Arch G. Woodside
Publication date
15-04-2021
Publisher
Springer US
Published in
Journal of Business and Psychology / Issue 1/2022
Print ISSN: 0889-3268
Electronic ISSN: 1573-353X
DOI
https://doi.org/10.1007/s10869-021-09737-4

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