Specification of nonverbal behaviors for clinical assessment

Nine categories of nonverbal behavior (extremity movements, self-manipulations, facial expression, posture, orienting, gestures, voice quality/tone, speech rate/pressure, and sense of timing) were tested in a standardized role play situation of social skills. Each category was judged using a new midi-level system of assessment which permitted specification of component behaviors but allowed observers to make single ratings at the ends of videotaped episodes. The midi-level measurements were as reliable and practical as more traditional global measures of social skill and social anxiety. Midis were superior to globals (i.e., single overall ratings of skill and anxiety) in terms of predicting physiological indices of social anxiety. Voice quality/tone and sense of timing appeared to be the best predictors of criterion social skill measures and self-manipulations, extremity movements, and gestures had the highest weights in predicting criterion measures of social anxiety.     

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.     

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.     

Building a Values‐Informed Mental Model for New Orleans Climate Risk Management

Individuals use values to frame their beliefs and simplify their understanding when confronted with complex and uncertain situations. The high complexity and deep uncertainty involved in climate risk management (CRM) lead to individuals’ values likely being coupled to and contributing to their understanding of specific climate risk factors and management strategies. Most mental model approaches, however, which are commonly used to inform our understanding of people's beliefs, ignore values. In response, we developed a “Values‐informed Mental Model” research approach, or ViMM, to elicit individuals’ values alongside their beliefs and determine which values people use to understand and assess specific climate risk factors and CRM strategies. Our results show that participants consistently used one of three values to frame their understanding of risk factors and CRM strategies in New Orleans: (1) fostering a healthy economy, wealth, and job creation, (2) protecting and promoting healthy ecosystems and biodiversity, and (3) preserving New Orleans’ unique culture, traditions, and historically significant neighborhoods. While the first value frame is common in analyses of CRM strategies, the latter two are often ignored, despite their mirroring commonly accepted pillars of sustainability. Other values like distributive justice and fairness were prioritized differently depending on the risk factor or strategy being discussed. These results suggest that the ViMM method could be a critical first step in CRM decision‐support processes and may encourage adoption of CRM strategies more in line with stakeholders’ values.     

Predicting the Risk of Biological Invasions Using Environmental Similarity and Transport Network Connectedness

Understanding the risk of biological invasions associated with particular transport pathways and source regions is critical for implementing effective biosecurity management. This may require both a model for physical connectedness between regions, and a measure of environmental similarity, so as to quantify the potential for a species to be transported from a given region and to survive at a destination region. We present an analysis of integrated biosecurity risk into Australia, based on flights and shipping data from each global geopolitical region, and an adaptation of the “range bagging” method to determine environmental matching between regions. Here, we describe global patterns of environmental matching and highlight those regions with many physical connections. We classify patterns of global invasion risk (high to low) into Australian states and territories. We validate our analysis by comparison with global presence data for 844 phytophagous insect pest species, and produce a list of high‐risk species not previously known to be present in Australia. We determined that, of the insect pest species used for validation, the species most likely to be present in Australia were those also present in geopolitical regions with high transport connectivity to Australia, and those regions that were geographically close, and had similar environments.