Making things happen : a model of proactive motivation

Being proactive is about making things happen, anticipating and preventing problems, and seizing opportunities. It involves self-initiated efforts to bring about change in the work environment and/or oneself to achieve a different future. The authors develop existing perspectives on this topic by identifying proactivity as a goal-driven process involving both the setting of a proactive goal (proactive goal generation) and striving to achieve that proactive goal (proactive goal striving). The authors identify a range of proactive goals that individuals can pursue in organizations. These vary on two dimensions: the future they aim to bring about (achieving a better personal fit within one’s work environment, improving the organization’s internal functioning, or enhancing the organization’s strategic fit with its environment) and whether the self or situation is being changed. The authors then identify “can do,” “reason to,” and “energized to” motivational states that prompt proactive goal generation and sustain goal striving. Can do motivation arises from perceptions of self-efficacy, control, and (low) cost. Reason to motivation relates to why someone is proactive, including reasons flowing from intrinsic, integrated, and identified motivation. Energized to motivation refers to activated positive affective states that prompt proactive goal processes. The authors suggest more distal antecedents, including individual differences (e.g., personality, values, knowledge and ability) as well as contextual variations in leadership, work design, and interpersonal climate, that influence the proactive motivational states and thereby boost or inhibit proactive goal processes. Finally, the authors summarize priorities for future researc

A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

Can a Supportive Workplace Impact Employee Resilience in a High Pressure Performance Environment? An Investigation of the Chinese Banking Industry

Resilience is one of the positive emotions that will enhance employees’ ability to cope in adverse conditions, such as work intensification, organisational change, and work stress. Despite growing research interest in the employee resilience area, there is limited knowledge of the process through which critical social support at workplaces, such as supportive leadership and co‐worker support, affects employees’ ability to cope in challenging situations. This study, underpinned by the theory of conservation of resources and social cognitive theory, examines the role of supportive leadership and co‐worker support in employee resilience, and how this may be moderated by work pressures in the context of the Chinese banking industry. Using a sample of 2,025 Chinese banking workers, we tested four hypotheses. Our findings demonstrate that supportive leadership and co‐worker support are positively associated with employee resilience. High work pressure moderates the relationship between both supportive leadership and co‐worker support and employee resilience, such that the relationship between these conditions is stronger when perceived performance pressure is high. Our study raises important implications for both the theoretical development of employee resilience and for management practices with respect to fostering employee resilience in organisations.No Full Tex