Behavioral strategies for reducing disease transmission in the workplace

The coronavirus pandemic highlighted that workplaces may serve as a hub of disease transmission if proper precautions are not enacted. The Centers for Disease Control recommends several strategies for decreasing the spread of illnesses in the workplace, including a) promoting proper hand hygiene, b) cleaning and sanitizing the work area, c) encouraging sick employees to stay home, d) personal protective equipment, and, e) social distancing. Research suggests that instructions are often not sufficient to change work behaviors, and behavioral interventions may be needed. Thus, the present paper reviews existing research that informs the implementation of behavioral strategies to reduce the spread of disease in the workplace, and makes recommendations for organizations to protect employees, clients, and customers. Intervention components such as training, prompts, the reduction of response effort, clear workplace policies, feedback, and consequences are discussed, and practical recommendations and suggestions for future research are provided.     

Make‐or‐Break: Chasing Risky Goals or Settling for Safe Rewards?

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality.     

心理学在社会服务体系中作用的思考——以复原力建设为例

在社会心理服务体系建设中, 心理学到底起着什么样的作用?怎样才能更好地构建全面系统的多主体社会心理服务体系?在社会心理服务体系建设中如何推动心理学自身的发展?这些问题一直受到广大心理学者的关注。以复原力为例, 通过对个体、团队以及社区心理复原力建设路径的理论总结, 加上实践案例, 以期为社会心理服务体系建设提供知识借鉴与参考, 并反思心理学在此过程中的作用与角色。心理学专业研究团队可以通过整合其在社会支持体系中的应用, 推动心理学在社会服务体系中的应用。     

Jealousy at work: The role of rivals’ characteristics

The present study examined rival characteristics that may evoke jealousy in the workplace, differences between men and women in this regard, and the relationship between jealousy responses and intrasexual competitiveness and social comparison orientation. Participants were 426 male and female employees. By means of a questionnaire, participants were presented with a jealousy‐evoking scenario after which jealousy responses to 24 rival characteristics were assessed. Findings showed that a rival's social communal attributes evoked highest levels of jealousy, and that, compared to men, women reported more jealousy in response to a rival's physical attractiveness. Overall, as individuals had higher scores on intrasexual competitiveness and social comparison orientation, they also experienced more jealousy in response to their rival, regardless of his or her characteristics. These findings suggest that those characteristics that are highly valued in employees may backfire when employees perceive co‐workers as rivals.     

Model-based recursive partitioning of extended redundancy analysis with an application to nicotine dependence among US adults

Extended redundancy analysis (ERA) is used to reduce multiple sets of predictors to a smaller number of components and examine the effects of these components on a response variable. In various social and behavioural studies, auxiliary covariates (e.g., gender, ethnicity) can often lead to heterogeneous subgroups of observations, each of which involves distinctive relationships between predictor and response variables. ERA is currently unable to consider such covariate-dependent heterogeneity to examine whether the model parameters vary across subgroups differentiated by covariates. To address this issue, we combine ERA with model-based recursive partitioning in a single framework. This combined method, MOB-ERA, aims to partition observations into heterogeneous subgroups recursively based on a set of covariates while fitting a specified ERA model to data. Upon the completion of the partitioning procedure, one can easily examine the difference in the estimated ERA parameters across covariate-dependent subgroups. Moreover, it produces a tree diagram that aids in visualizing a hierarchy of partitioning covariates, as well as interpreting their interactions. In the analysis of public data concerning nicotine dependence among US adults, the method uncovered heterogeneous subgroups characterized by several sociodemographic covariates, each of which yielded different directional relationships between three predictor sets and nicotine dependence.     

Representing decision-makers using styles of behavior: An approach designed for group decision support systems

Supporting decision-making processes when the elements of a group are geographically dispersed and on a tight schedule is a complex task. Aiming to support decision-makers anytime and anywhere, Web-based group decision support systems have been studied. However, the limitations in the decision-makers’ interactions associated to this scenario bring new challenges. In this work, we propose a set of behavioral styles from which decision-makers’ intentions can be modelled into agents. The goal is that, besides having agents represent typical preferences of the decision-makers (towards alternatives and criteria), they can also represent their intentions. To do so, we conducted a survey with 64 participants in order to find homogeneous operating values so as to numerically define the proposed behavioral styles in four dimensions. In addition, we also propose a communication model that simulates the dialogues made by decision-makers in face-to-face meetings. We developed a prototype to simulate decision scenarios and found that agents are capable of acting according to the decision-makers’ intentions and fundamentally benefit from different possible behavioral styles, just as a face-to-face meeting benefits from the heterogeneity of its participants.