The "Positivity Bias" inEvaluations of Public Figures:Evidence Against Instrument Artifacts

The "positivity bias" is a term used to describe the consistentfavorable evaluation of public figures found in surveys overthe past 40 years. This paper explored several possible artifactualexplanations for this bias,focusing on the survey instrumentitself. Two experiments varied the labeling and ordering ofscale endpoints, the affective value of the initial contextevaluated, and the presence or absence of a prestigious jobtitle associated with the nameof the public figure. None ofthe variations produced significantly different levels of positivitythan the standard control condition used in each experiment.RichardR. Lau is a graduate student in social psychology at the Universityof California, Los Angeles. David O. Sears is Professor of Psychologyand Political Science at UCLA. Richard Centers is Professorof Psychology at UCLA. This research was supported in part byGrant #SOC73-09153 A03 from the National Science Foundationto David O. Sears. The authors wish to expess their thanks toMark Williams, who did most of the coding and checking involvedin the survey.     

Estimation from Zero-Failure Data

When performing quantitative (or probabilistic) risk assessments, it is often the case that data for many of the potential events in question are sparse or nonexistent. Some of these events may be well-represented by the binomial probability distribution. In this paper, a model for predicting the binomial failure probability, P , from data that include no failures is examined. A review of the literature indicates that the use of this model is currently limited to risk analysis of energetic initiation in the explosives testing field. The basis for the model is discussed, and the behavior of the model relative to other models developed for the same purpose is investigated. It is found that the qualitative behavior of the model is very similar to that of the other models, and for larger values of n (the number of trials), the predicted P values varied by a factor of about eight among the five models examined. Analysis revealed that the estimator is nearly identical to the median of a Bayesian posterior distribution, derived using a uniform prior. An explanation of the application of the estimator in explosives testing is provided, and comments are offered regarding the use of the estimator versus other possible techniques.     

Strategic learning with scenarios

Since the oil shocks upset the business world in the 1970s, the use of multiple scenario analysis has been increasingly propagated as an approach to deal effectively with the many long-run uncertainties that surround business organisations. Since its introduction, the scenario approach has undergone some considerable changes and it is now claimed fulfils a diverse range of functions. Newly-added functions include the stretching of managers' mental models and the triggering and acceleration of processes of organisational learning. Although these functions currently get most of the attention in academic and management journals in recent years, a satisfying explanation of how scenarios fulfil these functions is still missing in the scenario literature. The scenario methodology seems to tell only part of the story suggesting that construing and using scenarios ‘simply’ consists of sequentially completing several distinct phases. If multiple scenario analysis really is able to fulfil the wide range of functions ascribed to it another, more dynamic process has to be hidden behind the rather static phase model. The scenario literature does not give any insight into this latter process. This article aims to increase the understanding of multiple scenario analysis by unravelling some of the mysteries surrounding it. For this purpose, the role of scenarios in strategic management is studied from a cognitive perspective. It appears that scenarios can deal effectively with several bottlenecks that potentially hinder organisational learning on a strategic level in organisations.     

Data relaying with constraints in hierarchical sensor networks

In this paper we present and discuss several optimisation problems that arise in the management of data flow in wireless sensor networks (WSNets). We consider a hierarchical architecture for WSNets composed of sensors, relays, and relay gateways. Sensors send data they generate at a known average bit rate to relays in one hop. The relay nodes use a multi-hop mechanism to reach a set of assigned gateways which then forward the data directly to the base station. We are concerned with finding an assignment of relay gateways to relays so that certain constraints are satisfied. We define a unified model in which constraints such as lifetime, data delay, and data flow splitting are formulated in terms of four optimisation problem in graphs.     

Strategic Alliance Outcomes: a Transaction‐Cost Economics Perspective

Empirical research on strategic alliances has been limited because previous studies examined alliance outcomes, and the factors associated with them, from a single partner in a manufacturing alliance. Furthermore, many of these studies have been done from a transaction cost perspective and researchers have inferred opportunistic behavior, rather than directly measuring it and observing its actual relationship with alliance performance. Building on previous transaction cost theory and research, this study seeks to address these gaps by analyzing factors associated with both opportunistic behavior and alliance performance within a major service sector, namely the US healthcare industry. After controlling for asset specificity and alliance age, we found that partner trustworthiness and contractual safeguards were negatively related to opportunistic behavior. Furthermore, opportunistic behavior was negatively related to alliance performance, as hypothesized. Interestingly, mutual equity investments were found to be unrelated to opportunistic behavior, counter to transaction‐cost logic. These findings refine and extend the transaction‐cost economics perspective regarding our understanding of strategic alliance behavior and outcomes, and offer executives in service‐based industries some practical ideas for assuring favorable strategic alliance outcomes.     

Social Loafing: A Field Investigation

Social loafing was investigated by testing a multilevel model among 23 intact work groups comprised of 168 employees representing two organizations. Results demonstrated that as hypothesized at the individual level, increases in task interdependence and decreases in task visibility and distributive justice were associated with greater occurrence of social loafing. At the group level, increased group size and decreased cohesiveness were related to increased levels of social loafing. Of particular interest was the finding that group member perceptions of perceived coworker loafing was associated with reduced social loafing, opposite of our predictions. We suggested that this unexpected finding may provide evidence of a social compensation effect.