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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).

This chapter proposes moving beyond relying on the dominant logic of multiple regression analysis (MRA) toward thinking and using algorithms in advancing and testing theory in accounting, consumer research, finance, management, and marketing. The chapter includes an example of testing an MRA model for fit and predictive validity. The same data used for the MRA is used to conduct a fuzzy-set qualitative comparative analysis (fsQCA). The chapter reviews a number of insights by prominent scholars including Gerd Gigerenzer’s treatise that “Scientists’ tools are not neutral.” Tools impact thinking and theory crafting as well theory testing. The discussion may be helpful for early career scholars unfamiliar with David C. McClelland’s brilliance in data analysis and in introducing business research scholars to fsQCA as an alternative tool for theory development and data analysis.

This chapter describes tenets of complexity theory including the precept that within the same set of data X relates to Y positively, negatively, and not at all. A consequence to this first precept is that reporting how X relates positively to Y with and without additional terms in multiple regression models ignores important information available in a data set. Performing contrarian case analysis indicates that cases having low X with high Y and high X with low Y occur even when the relationship between X and Y is positive and the effect size of the relationship is large. Findings from contrarian case analysis support the necessity of modeling multiple realities using complex antecedent configurations. Complex antecedent configurations (i.e., 2–7 features per recipe) can show that high X is an indicator of high Y when high X combines with certain additional antecedent conditions (e.g., high A, high B, and low C) – and low X is an indicator of high Y as well when low X combines in other recipes (e.g., high A, low R, and high S), where A, B, C, R, and S are additional antecedent conditions. Thus, modeling multiple realities – configural analysis – is necessary, to learn the configurations of multiple indicators for high Y outcomes and the negation of high Y. For a number of X antecedent conditions, a high X may be necessary for high Y to occur but high X alone is almost never sufficient for a high Y outcome.

This paper reports the results of a three-year-long research on business relationships, relying on qualitative data gathered through multiple-case study research of four focal companies operating in Australia. The industry settings are as follows: steel construction, vegetable oils trading, aluminum and steel can manufacture, and imaging solutions. The research analyzes two main aspects of relationships: structure and process. This paper deals with structure describing it by the most desired features of intercompany relationships for each focal company. The primary research data have been coded drawing on extant research into business relationships. The main outcome of this part of the research is a five construct model composed by trust, commitment, bonds, distance, and information sharing that accounts for all informants’ utterances about relationship structure.

This chapter describes the complementary benefits of model-building and data analysis using algorithm and statistical modeling methods in the context of unobtrusive marketing field experiments and in transforming findings into isomorphic-management models. Relevant for marketing performance measurement, case-based configural analysis is a relatively new paradigm in crafting and testing theory. Statistical testing of hypotheses to learn net effects of individual terms in MRA equations is the current dominant logic. Isomorphic modeling might best communicate what executives should decide using the findings from algorithm and statistical models. Data testing these propositions here uses data from an unobtrusive field experiment in a retailing context and includes two levels of expertise, four price points, and presence versus absence of a friend (“pal” condition) during the customer-salesperson interactions (n = 240 store customers). The analyses support the conclusion that all three approaches to modeling provide useful complementary information substantially above the use of one or the other alone and that transforming findings from such models into isomorphic-management models is possible.

This chapter examines the problem of teacher education as it unfolds in the Indian context. It focuses on the historical and cultural context in which teachers’ attitudes and identities develop. Attention is particularly paid to contextual factors that frame teachers’ actions beyond individual intentions. Possibilities for breaking these frames and engaging in new alternatives for action are imagined. An historical approach is employed to understand teachers’ current pedagogical beliefs and action, and its future orientation.

The chapter provides the reader with a critical, conceptual framework for further independent exploration of actor-network theory (ANT) when applied to higher education reform. First, it introduces briefly the potentials of ANT as a means of questioning, and eventually escaping, the formal policy level as the “natural” point of departure for studying policy reform. Second, by pointing to my experiences from an on-going study on a Danish subset of the European Bologna process, in which I invited relevant actors to participate in formulating the research questions, it concretizes – and critically reviews – how ANT may feed new insights as well as challenges into the research process.

Balance Theory has accumulated an impressive record of empirical confirmation at both the micro- and macro-levels. Yet, it is unclear why humans consistently prefer balanced relations when imbalance offers the opportunity to reap material rewards. We argue that balance is preferred because it functions as a “compression heuristic,” allowing networks to be more easily encoded in, and recalled from, memory.

We present the results of a novel randomized laboratory experiment using nearly 300 subjects. We evaluate the independent and joint effects of degree of balance/imbalance and presence/absence of kin compression heuristics on network recall.

We find that memory for relationship valence is more accurate for balanced, rather than imbalanced, networks and that relationship existence and relationship valence are separable cognitive elements. We also use comparisons between kin and non-kin networks to suggest that humans are implicitly aware of the conditions under which imbalanced networks will be most durable.

We show that the tension/strain postulated to generate mental and behavioral responses to increase balance likely stems from cognitive limitations. More broadly, this connects balance theory to models of human cognition and evolution and suggests that human general processing ability may have evolved in response to social, rather than physical, challenges.