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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 = 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).

The introductory chapter includes how to design-in good practices in theory, data collection procedures, analysis, and interpretations to avoid these bad practices. Given that bad practices in research are ingrained in the career training of scholars in sub-disciplines of business/management (e.g., through reading articles exhibiting bad practices usually without discussions of the severe weaknesses in these studies and by research courses stressing the use of regression analysis and structural equation modeling), this editorial is likely to have little impact. However, scholars and executives supporting good practices should not lose hope. The relevant literature includes a few brilliant contributions that can serve as beacons for eliminating the current pervasive bad practices and for performing highly competent research.

The traditional and still dominant logic among nearly all empirical positivist researchers in schools of management is to write symmetric (two-directional) variable hypotheses (SVH) even though the same researchers formulate their behavioral theories at the case (typology) identification level. Cyert and March’s (1963), Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall), Howard and Sheth’s (1969, Howard, J. A., & Sheth, J. N. (1969). The theory of buyer behavior. New York, NY: Wiley), and Miles, R. E., & Snow, C. C.’s (1978, Miles, R. E., & Snow, C. C. (1978). Organizational strategy, structure, and process. [A. D. Meyer, collaborator; H. J. Coleman Jr., contributor]. New York, NY: McGraw Hill) typologies of organizations’ strategy configurations (e.g., “Prospectors, Analyzers, and Defenders”) are iconic examples of formulating theory at the case identification level. When testing such theories, most researchers automatically, nonconsciously, switch from building theory of beliefs, attitudes, and behavior at the case identification level to empirically testing of two-directional relationships and additive net-effect influences of variables. Formulating theory focusing on creating case identification hypotheses (CIH) to describe, explain, and predict behavior and then empirically testing at SVH is a mismatch and results in shallow data analysis and frequently inaccurate contributions to theory. This chapter describes the mismatch and resulting unattractive outcomes as well as the pervasive practice of examining only fit validity in empirical studies using symmetric tests. The chapter reviews studies in the literature showing how matching both case-based theory and empirical positivist research of CIH is possible and produces findings that advance useful theory and critical thinking by executives and researchers.

Traditionally, the main goal of empirical research has been to test theories. Yet, theory-testing is problematical in the social sciences. Findings from empirical studies have proven hard to replicate and there is a lack of progress in creating a coherent and cumulative knowledge base. There are both practical and epistemological issues that prevent effective empirical tests. It is difficult to operationalize constructs and design decisive tests of theories. The laws and regularities posited in theories in the natural sciences are independent of human actors, while theories in the social sciences describe systems and structures that are created and maintained by human actors. Nonetheless, human actors are sometimes guided by theories. They may change their behavior or make different decisions based on academically produced knowledge. This relationship is usually mediated by the use of tools of various sorts (i.e., design principles, diagrams, or stories). I discuss why scholars should conduct empirical research to test the pragmatic validity of tools that are derived from theories rather than testing the scientific validity of the theories themselves.

This chapter elaborates on the usefulness of embracing complexity theory, modeling outcomes rather than directionality, and modeling complex rather than simple outcomes in strategic management. Complexity theory includes the tenet that most antecedent conditions are neither sufficient nor necessary for the occurrence of a specific outcome. Identifying a firm by individual antecedents (i.e., noninnovative vs. highly innovative, small vs. large size in sales or number of employees, or serving local vs. international markets) provides shallow information in modeling specific outcomes (e.g., high sales growth or high profitability) – even if directional analyses (e.g., regression analysis, including structural equation modeling) indicate that the independent (main) effects of the individual antecedents relate to outcomes directionally – because firm (case) anomalies almost always occur to main effects. Examples: a number of highly innovative firms have low sales while others have high sales and a number of noninnovative firms have low sales while others have high sales. Breaking-away from the current dominant logic of directionality testing – null hypothesis significance testing (NHST) – to embrace somewhat precise outcome testing (SPOT) is necessary for extracting highly useful information about the causes of anomalies – associations opposite to expected and “statistically significant” main effects. The study of anomalies extends to identifying the occurrences of four-corner strategy outcomes: firms doing well in favorable circumstances, firms doing badly in favorable circumstances, firms doing well in unfavorable circumstances, and firms doing badly in unfavorable circumstances. Models of four-corner strategy outcomes advance strategic management beyond the current dominant logic of directional modeling of single outcomes.

The purpose of this chapter is to provide researchers a summary of some of the latest developments in item response theory (IRT), and to help these groups realize that psychometric tools can now be used for theory testing in addition to the traditional role of improving construct measurement. The author first reviews some of the fundamental tenets of classical test theory to contrast with IRT. He then describes recent advances in goodness-of-fit tests that have helped turn IRT into a model-testing tool. Finally, the author reviews several new test models that provide new flexibilities, summarizing summarize several examples of research that has used these new models in organizational research. At the end of this review, the author provides suggestions to help researchers better use these new IRT tools. Although there have been significant advances in IRT in the past decade, there has not been a systematic review of these developments. This review places those developments in context to provide readers a real appreciation of these breakthroughs.

A valuable, although little-used, case data analysis technique, degrees-of-freedom analysis (DFA), is the subject of Chapter 12. Given the richness of case data and its prevalence in business marketing research, DFA has the potential to become an important addition to one's “research workbench.” Donald Campbell (1975) first proposed this theory testing.

This chapter presents three business-to-business marketing applications; the first two involve use of the technique to compare the extent to which four theories of group decision making are manifested in organizations. The third application illustrates how the technique is useful for theory development in the context of manufacturer–distributor relationships. The contribution is in demonstrating how researchers can link “traditional” (i.e., logical positivistic) hypothesis testing procedures to examine theoretical propositions in case study research. This approach is one way of achieving a critical test (Carlsmith, Ellsworth, & Aronson, 1976), that is, testing the relative empirical strengths of competing theories. The chapter highlights the value of generalizing case data to theory versus the inappropriate attempt to generalize such data to a population (Yin, 1994). The explication and demonstration of this technique is not available elsewhere to the degree that Chapter 12 provides.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).