GM Foods and the Misperception of Risk Perception

Public opposition to genetically modified (GM) food and crops is widely interpreted as the result of the public's misperception of the risks. With scientific assessment pointing to no unique risks from GM crops and foods, a strategy of accurate risk communication from trusted sources has been advocated. This is based on the assumption that the benefits of GM crops and foods are self-evident. Informed by the interpretation of some qualitative interviews with lay people, we use data from the Eurobarometer survey on biotechnology to explore the hypothesis that it is not so much the perception of risks as the absence of benefits that is the basis of the widespread rejection of GM foods and crops by the European public. Some respondents perceive both risks and benefits, and may be trading off these attributes along the lines of a rational choice model. However, for others, one attribute-benefit-appears to dominate their judgments: the lexicographic heuristic. For these respondents, their perception of risk is of limited importance in the formation of attitudes toward GM food and crops. The implication is that the absence of perceived benefits from GM foods and crops calls into question the relevance of risk communication strategies for bringing about change in public opinion.     

Should I Stay or Should I Go? Explaining Turnover Intentions with Organizational Identification and Job Satisfaction*

The social identity approach is a powerful theoretical framework for the understanding of individuals' behaviour. The main argument is that individuals think and act on behalf of the group they belong to because this group membership adds to their social identity, which partly determines one's self‐esteem. In the organizational world, social identity and self‐categorization theories state that a strong organizational identification is associated with low turnover intentions. Because identification is the more general perception of shared fate between employee and organization, we propose that the relationship between identification and turnover will be mediated by job satisfaction as the more specific evaluation of one's task and working conditions. In four samples we found organizational identification feeding into job satisfaction, which in turn predicts turnover intentions.     

Strategic Shared Leadership and Organizational Dynamic Capabilities

We propose that the purposeful sharing of strategic decisions and the process of making and taking those between the dominant coalition of an organization (Strategic Shared Leadership or SSL thereafter), initiated and supported by a focal strategic leader or small team, engenders Organizational Dynamic Capabilities (ODCs) though the transfer of individually-residing DCs within the SSL team, the transformation -co-creation of novel ones and their embeddedness-institutionalization within the organization. It also enhances organizational cognition which mediates the relationship by enriching co-created ODCs and their capacity to deliver reliably change through sensing, seizing and reconfiguring. Accordingly, SSL serves as a co-creator and key predictor of the emergence of ODCs. This helps address the challenges of DCs to marry stability with change, be predictable and to be capable of predicting.     

The Production of Several Items in a Single Facility with Linearly Changing Demand Rates

In this paper we extend the ELSP model to allow for linearly changing demand rates over a fixed planning horizon. This extension of the ELSP research provides a model that can be used in coordinating the production and marketing planning activities in a firm. The model allows the user to evaluate the impact of changes in product demand on production costs and customer service. We solve the model using a standard nonlinear programming package (MINOS) and show through examples based on actual production data how the model can be used to support coordinated production and marketing planning.     

Relation of Goal Setting and Goal Sharing to Performance and Conflict for Interdependent Tasks

Despite the prevalence of goal setting in organizations, researchers have not thoroughly examined the implications of goal setting to work outcomes in interdependent work settings. A field survey (n= 442) assessed the relation of goal setting and goal sharing, the extent to which others are aware of an individual's work goal, to performance and conflict across varying levels of task interdependence. The results of moderated regression analyses demonstrate that in highly interdependent tasks, the use of goal setting resulted in lower performance levels. Moreover, goal setting and goal sharing in highly interdependent tasks resulted in individuals' reporting more somatic symptoms. The results are discussed in terms of clarifying the role of goal setting for interdependent tasks.     

Likelihood analysis for a class of beta mixed models

Beta regression is a suitable choice for modelling continuous response variables taking values on the unit interval. Data structures such as hierarchical, repeated measures and longitudinal typically induce extra variability and/or dependence and can be accounted for by the inclusion of random effects. In this sense, Statistical inference typically requires numerical methods, possibly combined with sampling algorithms. A class of Beta mixed models is adopted for the analysis of two real problems with grouped data structures. We focus on likelihood inference and describe the implemented algorithms. The first is a study on the life quality index of industry workers with data collected according to an hierarchical sampling scheme. The second is a study assessing the impact of hydroelectric power plants upon measures of water quality indexes up, downstream and at the reservoirs of the dammed rivers, with a nested and longitudinal data structure. Results from different algorithms are reported for comparison including from data-cloning, an alternative to numerical approximations which also allows assessing identifiability. Confidence intervals based on profiled likelihoods are compared with those obtained by asymptotic quadratic approximations, showing relevant differences for parameters related to the random effects. In both cases, the scientific hypothesis of interest was investigated by comparing alternative models, leading to relevant interpretations of the results within each context.     

The Gamma-count distribution in the analysis of experimental underdispersed data

Event counts are response variables with non-negative integer values representing the number of times that an event occurs within a fixed domain such as a time interval, a geographical area or a cell of a contingency table. Analysis of counts by Gaussian regression models ignores the discreteness, asymmetry and heteroscedasticity and is inefficient, providing unrealistic standard errors or possibly negative predictions of the expected number of events. The Poisson regression is the standard model for count data with underlying assumptions on the generating process which may be implausible in many applications. Statisticians have long recognized the limitation of imposing equidispersion under the Poisson regression model. A typical situation is when the conditional variance exceeds the conditional mean, in which case models allowing for overdispersion are routinely used. Less reported is the case of underdispersion with fewer modeling alternatives and assessments available in the literature. One of such alternatives, the Gamma-count model, is adopted here in the analysis of an agronomic experiment designed to investigate the effect of levels of defoliation on different phenological states upon the number of cotton bolls. Data set and code for analysis are available as online supplements. Results show improvements over the Poisson model and the semi-parametric quasi-Poisson model in capturing the observed variability in the data. Estimating rather than assuming the underlying variance process leads to important insights into the process.