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This study applies asymmetric rather than conventional symmetric analysis to advance theory in occupational psychology. The study applies systematic case-based analyses to model complex relations among conditions (i.e., configurations of high and low scores for variables) in terms of set memberships of managers. The study uses Boolean algebra to identify configurations (i.e., recipes) reflecting complex conditions sufficient for the occurrence of outcomes of interest (e.g., high versus low financial job stress, job strain, and job satisfaction). The study applies complexity theory tenets to offer a nuanced perspective concerning the occurrence of contrarian cases for example, in identifying different cases (e.g., managers) with high membership scores in a variable (e.g., core self-evaluation) who have low job satisfaction scores and when different cases with low membership scores in the same variable have high job satisfaction. In a large-scale empirical study of managers (n ¼ 928) in four (contextual) segments of the farm industry in New Zealand, this study tests the fit and predictive validities of set membership configurations for simple and complex antecedent conditions that indicate high/low core self-evaluations, job stress, and high/low job satisfaction. The findings support the conclusion that complexity theory in combination with configural analysis offers useful insights for explaining nuances in the causes and outcomes to high stress as well as low stress among farm managers. Some findings support and some are contrary to symmetric relationship findings (i.e., highly significant correlations that support main effect hypotheses).
Adopting a broad view for a moment, the present study contributes by its additional evidence from a national survey of farm managers of CSE’s association with job satisfaction. The evidence here supports Judge and Bono’s (2001) findings in a meta-analysis that all four CSE sub-traits display statistically significant positive correlations with job satisfaction. The present study further contributes by using complexity theory to model the conditions when low scores for CSE indicate high job satisfaction (i.e., Table 13a findings) and high scores for CSE indicate low job satisfaction (i.e., Table 13b findings). Thus, the theory and empirical findings in the present study do more than complement and extend Judge and Bono’s (2001) conclusion from their meta-analysis that CSE and CSE sub-traits have nonzero correlations of similar magnitude with job satisfaction. Complexity theory, asymmetric configurational analysis using Boolean algebra to identify specific outcomes are the bases for case-based modeling and analysis in the present study rather than the currently pervasive use of linear model of independent terms in regression models using matrix algebra. Fiss (2007) correctly observes that independent terms in regression models pose variables as rivals in attempting to account for their individual influences on a dependent variable. Given that the independent terms in a regression model are rarely independent as the positive correlations among the four CSE sub-traits bear witness the attempt to measure the independent contribution of each term in a linear regression model is an attempt to answer a bad question. Relevant here is Cohen’s (1997, p. 1000) conclusion, “‘Discovering’ in the population that a difference between two means is not precisely zero, or that a correlation between two variables is not precisely zero, are trivial findings.