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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 reviews empirical studies into the relationship between management control, strategy and sustainability.

The review explores the theoretical frameworks and models used in previous empirical research as well as the research questions and methods applied to empirically explore this emerging research area.

Even if a growing body of empirical research has emerged over the last decade, our knowledge of how companies design or use management control to support sustainability strategy appears to be limited, providing considerable scope for further research.

This review structures the state of our empirical knowledge in the area of management control, strategy and sustainability and makes suggestions for future research paths.

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.

This chapter outlines a method for developing simulation code from case-based data using narrative sequence analysis. This analytical method allows researchers to systematically specify the ‘real-world’ behaviours and causal mechanisms that describe the research problem and translate this mechanism into simulation code. An illustrative example of the process used for code development from case-based data is detailed using a well-documented case of photovoltaic innovation. Narrative sequence analysis is used to analyse case data. Micro-sequences are identified and simplified. Each micro-sequence is presented first in pseudo-code and then in simulation code. This chapter demonstrates the coding process using Netlogo code. Narrative sequence analysis provides a rigorous and systematic approach to identifying the underlying mechanisms to be described when building simulation models. This analytical technique also provides necessary and sufficient information to write simulation code. This chapter addresses a current gap in the methodology literature by including case data within agent-based model building processes. It benefits B2B marketing researchers by outlining guiding processes and principles in the use of case-based data to build simulation models.

This chapter identifies research advances in theory and analytics that contribute successfully to the primary need to be filled to achieve scientific legitimacy: configurations that include accurate explanation, description, and prediction – prediction here refers to predicting future outcomes and outcomes of cases in samples separate from the samples of cases used to construct models. The MAJOR PARADOX: can the researcher construct models that achieve accurate prediction of outcomes for individual cases that also are generalizable across all the cases in the sample? This chapter presents a way forward for solving the major paradox. The solution here includes philosophical, theoretical, and operational shifts away from variable-based modeling and null hypothesis statistical testing (NHST) to case-based modeling and somewhat precise outcome testing (SPOT). These shifts are now occurring in the scholarly business-to-business literature.

Consumer behavior in tourism (CBT) is an interdisciplinary field of study encompassing the basic behavioral and economic sciences (e.g., psychology, sociology, and economics) and applied fields of study (e.g., management, marketing, tourism, and hospitality) focusing on all aspects of discretionary travel. This chapter describes major issues and findings in the literature relating to CBT. The chapter directs the reader’s attention to some of the highly-cited studies in this literature – these studies provide a foundation of knowledge on the central topics, issues, methods, findings, and theoretical/practical contributions in research on CBT. Research studies in CBT focus on one-to-all five core theoretical issues in basic and applied fields of study: describe who is doing what, when, where, how, and the consequences of the activities; explain the meanings of activities and motivations of the actors; predict (model) what actions and outcomes will occur and the impacts of influence attempts before, during, and after engaging in tourist actions; control (influence) the beliefs, attitudes, behaviors, and assessments of tourists, local community members, and additional stakeholders; evaluate tourism service/product delivery, tourism management performance, and customer satisfaction. Survey research using verbal (written) responses to questions is pervasive and the most frequent method for data collection in CBT. Additional research genres in CBT include direct observations of tourism behavior with or without some oral questioning (unobtrusive studies, the long interview method (McCracken, 1988), use of “consumer culture theory”), participant observation including semester abroad and unpaid internships away from home, formal field experiments, and the study of secondary sources (e.g., photographs and writings in blogs and social media (e.g., TripAdvisor) reviews).

Despite a large and growing literature on the subject, little is understood about the phenomenon of small business growth. Specifically, the small business growth literature has often emphasized “why” opposed to “how” firms grow. This chapter sheds light on this black box of growth by investigating the phases of planning and implementation processes separately to explore the choice of strategic expansion modes. It examines a much under-researched firm category: declining small firms. Employing a three-year longitudinal study using a multi-case study method, we find that while growth approaches are typically contextually (industry) derived, formalized planning greatly affects implementation. Further, resources are the key mediating variable between formal planning and implementation – firms with slack resources will typically implement their contextually influenced planned growth course, and firms with inadequate resources will typically implement through interactive learning, which causes them to downscale the growth plans or exit the market (merger or sale).

The present research examines the effect of incentivizing both outcome and driver measures of SPMS on middle managers' proactivity in influencing the strategy formulation process. A case-based experiment was conducted among 74 full-time employees. The results suggest that when incentives are linked to both outcome and driver measures of SPMS, compared with when they are outcome-based and not linked to the SPMS, managers are more proactive in communicating strategy-related issues to top management. In addition, this effect of SPMS-based incentives on middle managers' proactivity is mediated by their autonomous extrinsic motivation to achieve strategic goals. The results are in general consistent with postulates of the self-determination theory of motivation. This chapter also has practical implication. Specifically, recent evidence suggests that most SPMS adopters fail to validate causal business models underlying their formulated strategies (Ittner, 2008; Ittner & Larcker, 2003, 2005). Middle managers' proactive strategic behavior may be one means to prompt top management to inspect formulated strategies and their underlying business models.

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.