Daniel J. Svyantek

A Complex-Systems Approach to Organizations

The physical sciences have developed new theories of nonlinear behavior of complex systems. Defining characteristics of complex systems include (a) being composed of many variables that interact strongly to determine system behavior, (b) sensitivity to initial conditions, and (c) stability across time. Two complex-system concepts, phase spaces and attractors, provide insight into the evolution of system behavior and make prediction of future behavior possible. It is proposed that complex-systems research has application to the study of organizations and social behavior. Organizational attractors exist and seem to be both sensitive to initial conditions and stable. The discussion of concepts from complex systems, and their application to organizations, provides insight into how organizational research should be conducted. If organizations are assumed to exhibit nonlinear behavior, more historical, longitudinal, and qualitative research methods should be used to provide context-specific descriptions of organizational behavior.

The illusion of certainty: A catastrophe model of decision framing

Tversky and Kahneman (1981) have proposed that decision frames act to bias the processing of decision-relevant information by decision makers. Decision frames act as illusions to which most decision makers are susceptible. We believe that catastrophe theory provides a unique framework by which the effects of decision framing can be studied. Catastrophe theory has been proposed as a way to explain the sudden shifts in preference in perceptual experiments (Stewart & Peregoy, 1983). Tversky and Kahneman’s (1981) decision-framing concept, in decision situations, is proposed to be analogous to the cognitive, organizing centers underlying catastrophic changes in response in perceptual experiments. The results of this study were interpreted as demonstrating the heuristic value of catastrophe theory in describing the decision-framing phenomenon as a cognitive illusion.

The Impossibility of Using Random Strategies to Study the Organization Development Process

The organization development (OD) process seeks collaborative, diagnosis-based organizational change. This article discusses how the use of random strategies of a traditional experimental design to study the OD process fundamentally changes that process to something other than OD. Following a literature search, the authors conclude that random processes and the OD process are fundamentally incompatible and thus cannot be used simultaneously. Researchers may use random strategies to study OD techniques, but not the OD process itself It is never appropriate to attempt to use random selection or random assignment to study the OD process. The authors call for the development of alternative standards and methods that are both rigorous and relevant to OD research for no true experiment studying the OD process can ever take place.

The Relationship Between Organizational Characteristics and Team Building Success

The present paper has three goals. First, an approach to meta-analysis which combines meta-analytic procedures with a more complete description of the organizational setting is presented. Second, a meta-analysis of the influence of team building on workgroup effectiveness is conducted. The results of this meta-analysis support the contention that team building impacts positively on workgroup productivity. Finally, a discussion of the role of meta-analysis in providing information which both scientists and practitioners may use to understand the relationship between organizational characteristics and the effects of team building on productivity measures is given.

Applications of Bayesian Methods to OD Evaluation and Decision Making

This paper provides an introductory discussion of Bayesian statistics and their applicability to OD evaluation. Bayesian statistics provide an alternative to traditional statistics in the evaluation of OD interventions. Bayesian statistics allow for the explicit quantification of prior probabilities and give management and consultants control over the determination of the practical significance of an intervention. This makes Bayesian evaluation methods a more flexible tool for investigating dynamic interventions in systems. A discussion of the role of subjectivity in Bayesian statistical analysis and examples of how Bayesian evaluation strategies may be used in OD settings are provided.

“Make haste slowly”: Augustus Caesar transforms the Roman world

The Roman Empire illustrates how change occurs in complex social systems. An analysis of: the effects of transformational leadership and transactional leadership styles in complex social systems; and the relationship between leadership style and the social context is conducted. Julius Caesar is shown to have failed to create a new method of governing Rome. Augustus Caesar, however, created the basis for the Roman Empire. Their careers show that change which is incremental and does not violate the core culture of a system is more likely to transform a social system than more radical, transformational methods.

Job insecurity and compensation evaluations: the role of overall justice

Purpose – The purpose of this paper is to examine the relations of job insecurity with pay and incentive satisfaction and the role of overall justice in these relationships. Design/methodology/approach – The authors surveyed employees of an industrial equipment sales firm located in the Southeastern USA. Surveys were completed by 151 employees using instruments assessing job insecurity, overall justice, pay satisfaction, and incentive satisfaction. Findings – The study results indicated job insecurity is negatively related to both pay and incentive satisfaction. Further, the study found that overall justice mediated the job insecurity to pay satisfaction relationship, but not the job insecurity to incentive satisfaction relationship. Research limitations/implications – Because overall justice only explained the job insecurity-pay satisfaction relationship, future research should examine other potential mediators to better understand these disparate effects when compared with incentive satisfaction. Future...

The Robustness of ME/I Evaluations to Among-Group Dependence

This study evaluates the robustness of multisample confirmatory factor analysis approaches to measurement equivalence/invariance (ME/I) evaluations under a specific model misspecification: when independence among the groups is assumed, but groups are dependent. Monte Carlo simulation is used to investigate the robustness of several ME/I evaluation procedures including the likelihood ratio test ( ), ΔCFI, ΔSRMR, and ΔRMSEA. The effect of this model misspecification on the factor loadings and their standard errors is also considered. Assuming the groups are independent when they are not is shown to have no practical effect on the results of the , ΔCFI, ΔSRMR, or ΔRMSEA procedures because the effect of model misspecification is canceled out through differencing. Similarly, the estimated factor loadings and standard errors are not significantly affected by incorrectly assuming among-group dependence. These results are used to develop recommendations for researchers who conduct multigroup analyses using structural equation models with samples in which independence is unclear.

A comparison of methods for the evaluation of significance: Evidence from organizational change interventions

Current statistical methods are complemented by Bayesian statistical methods when evaluating change in applied settings. Bayesian methods are a flexible tool for investigating the effects of change interventions. Bayesian statistics may be used to simultaneously test the statistical and practical significance of change in organizations. A description of the evaluation of a three-year study in six manufacturing plants of a large, United States corporation is provided. The results of traditional statistical methods are compared to Bayesian statistics for this field study. A discussion of (1) the results of this evaluation and (2) the applicability of the Bayesian approach for the evaluation of organizational change is presented.