The Mechanistic Structure Shift and Strategic Reorientation in Declining Firms Attempting Turnarounds

Past researchers have observed that decliningorganizations often experience mechanistic structuralchanges that centralize authority, increase reliance onformalized procedures, and reduce the amount of information flowing to top managers. Many haveproposed that this “mechanistic shift” indeclining organizations reduces their adaptivecapability by making innovative organizational change in response to decline less likely. However,despite much research on declining firms and theirturnaround attempts, many questions about mechanisticshifts remain, such as: (1) To what extent do declining firms become systematically mechanistic whentrying to recover from decline? (2) What aspects ofdeclining firms' situations make mechanistic shifts morelikely? (3) Do mechanistic shifts reduce the likelihood of large-scale strategic reorientation asdeclining firms attempt to recover? We sought answers tothese questions through an analysis of data fromin-depth interviews with top managers (mostly CEOs) at 29 U.S. firms attempting turnarounds fromdecline. Our primary finding was that mechanisticstructure shifts did restrict firms' abilities to changetheir strategic orientations in response to decline. Highlighting the important role of the contextof turnaround attempts, we further found the averagedeclining firm attempting a turnaround did not becomemore mechanistic, but that turnaround attempts launched from financial crises were significantly morelikely to lead to mechanistic shifts. Also, we foundthat the common practice of replacing the firm's CEOduring turnaround attempts had conflicting andparadoxical effects on firms' abilities to enact strategicreorientations.     

When is a curve an outlier? An account of a tricky problem

ABSTRACT The analysis of a set of data consisting of N short (≤20 observations each) multivariate time series, where the observations are irregularly spaced and where observations for the different components of each multivariate series are observed at different times, is discussed. With the increased use of automatic recording devices in many fields, data such as these, which are of course samples from smooth response curves, are becoming more common. In this application, which was a clinical trial comparing two cements for use in hip replacement surgery, the key to the analysis was in recognizing that the interest lay in the degree to which the five curves representing a patient's vital signs deviated from baseline (i.e., normal for that patient) during surgery. This enabled the statisticians to define appropriate response variables. The analysis included Rosseeuw's (1984) technique for the identification of multivariate outliers and logistic regressions to identify any effects on the process producing the outliers due to treatment or covariates.     

Managing Potential Health Risks from Electric Powerlines: A Decision Analysis Caught in Controversy

Over the past 20 years, several epidemiological studies have found an association between exposure to electromagnetic fields (EMFs) and health effects, including childhood leukemia and adult brain cancer. However, experts strongly disagree about whether this association is causal and, if so, how strong it is. In this article, we examine several alternatives to reduce EMFs from sources of the California power grid, including undergrounding distribution and transmission lines and reconfiguring or rephasing lines. The alternatives were evaluated in terms of the potential health risk reduction, cost, impacts on service reliability, property values, and many other consequences. Because of the uncertainty about an EMF-health link, the main effort was to determine the sensitivity of the decisions to the probability and seriousness of an EMF hazard. User-friendly computer models were developed to allow stakeholders to change the model assumptions and parameters to analyze the impacts of their own assumptions and estimates on the decision. The analysis clearly demonstrated that only four of the many concerns raised by the stakeholders could make a difference in the decision: health risks, costs, service reliability, and property values. Whether undergrounding, moderate alternatives for EMF reduction, or no change was the best decision depended on a few key factors, including the probability that EMF exposure is a hazard, the severity of this hazard, how the EMF reduction measures are financed, and the impacts on property values. While the analysis did not resolve the EMF issues, it showed that even in the most controversial settings, a little analysis goes a long way to clarifying the issues and to focus the debate.     

Altruism and social solidarity: Envisioning a field of specialization

Conclusion This article has presented a vision of what a field of altruism and social solidarity could potentially involve. An additional perspective on the nature of this field and how it could contribute to the discipline and to society is provided by the science of psychology. In the last decade a fundamental new orientation has developed in this discipline, growing from the pioneering work of Martin E.P. Seligman. Generally identified as “positive psychology” it represents a shift from a focus on trying to understand and find solutions to mental illness and various pathologies of thought and behavior in a “disease model” to a focus on human strengths, virtues, and other positive characteristics. This shift in focus is regarded as making a direct contribution to understanding what is best in human emotions and traits and how society can support the psychological flourishing of individuals. This new perspective in turn provides increased awareness of how the problems studied in the disease model can more effectively be prevented (Seligman, 2003, 2005). The importance of this focus on the positive for sociology and a call to action is stated by Seligman (2003): The third pillar of positive psychology is the study of positive institutions and positive communities.     

THE GREAT OVERESTIMATION: TAX DATA AND INEQUALITY MEASUREMENTS IN THE UNITED STATES, 1913–1943

Historical measures of income inequality in the United States must grapple with the challenge of data quality. We examine one such problem affecting the well-known estimates of income inequality produced by Piketty and Saez (2003) using the records of the Internal Revenue Service (IRS). Prior to 1943, incomes were self-reported. Combined with lax enforcement on the part of the IRS, self-reporting of incomes could provide a misleading portrait of the income distribution. To test the accuracy of IRS records, we compare them to independently tabulated state income tax returns between 1919 and 1945 from states with more comprehensive and rigorously enforced tax collection procedures. State income tax records show lower overall levels of income inequality than IRS records. However, we still find that top income concentrations declined across the period between 1929 and World War II. These findings attest to the sensitivity of distributional estimation to the reporting selectivity and economic quality of underlying tax data, suggesting that the existing IRS-derived series systematically overstates top-income concentration in the interwar period. (JEL H2, N32, D31, E01)     

Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

In this article, a classification model based on the majority rule sorting (MR‐Sort) method is employed to evaluate the vulnerability of safety‐critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited‐size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the classification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different app oaches are here considered to this aim: (i) a model–retrieval‐based approach, (ii) the bootstrap method, and (iii) the leave‐one‐out cross‐validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.