The relationship between transformational leadership and innovative behaviour in a healthcare context:a team learning versus a cohesion perspective

The purpose of this study was to explore whether the relationship between transformational leadership and innovative behaviour is explained via the mediating role of team learning, or whether instead team cohesion mediates this relationship. Using survey data from 158 professionals within 21 teams in the Dutch healthcare context, we tested by means of hierarchical regression analyses: (a) the relationship between transformational leadership and innovative behaviour; (b) whether team learning or cohesion mediates this relationship; and (c) the relationship between team learning and cohesion, in relation to transformational leadership. Results showed that transformational leadership is positively related to innovative behaviour and that both cohesion and team learning mediate this relationship, with team learning being the strongest mediator. Addressing a neglected area, our study provides evidence to show that managers who enhance team learning are likely to maximise employees' scope for engaging in innovative behaviours. © 2012 Inderscience Enterprises Ltd.

Predicting binge-drinking behaviour using an extended TPB:examining the impact of anticipated regret and descriptive norms

Aims: To investigate the utility of an extended Theory of Planned Behaviour (TPB), including descriptive norms and anticipated regret, in predicting binge-drinking intentions and behaviour. Methods: A total of178 undergraduates completed a questionnaire containing measures of TPB variables, descriptive norms, anticipated regret, and previous binge-drinking behaviour. One week later, 104 students completed a measure of binge-drinking behaviour. Results: Hierarchical regression demonstrated that attitudes (beta = 0.30, P < 0.001) and anticipated regret (beta = 0.47, P < 0.001) were significant predictors of intentions, with the final equation accounting for 58% of the variance. Hierarchial regression found that intentions (beta = -0.21, P < 0.05) and previous binge-drinking behaviour (beta = 0.36, P < 0.01) predicted current drinking behaviour, accounting for 33% of the variance. Conclusions: The study suggests that modifying attitudes and inducing regret may be effective strategies for reducing binge-drinking intentions among undergraduates, which should reduce subsequent binge-drinking behaviour. © The Author 2006. Published by Oxford University Press on behalf of the Medical Council on Alcohol. All rights reserved.

Understanding data collection behaviour of mental health practitioners

Failure to detect patients at risk of attempting suicide can result in tragic consequences. Identifying risks earlier and more accurately helps prevent serious incidents occurring and is the objective of the GRiST clinical decision support system (CDSS). One of the problems it faces is high variability in the type and quantity of data submitted for patients, who are assessed in multiple contexts along the care pathway. Although GRiST identifies up to 138 patient cues to collect, only about half of them are relevant for any one patient and their roles may not be for risk evaluation but more for risk management. This paper explores the data collection behaviour of clinicians using GRiST to see whether it can elucidate which variables are important for risk evaluations and when. The GRiST CDSS is based on a cognitive model of human expertise manifested by a sophisticated hierarchical knowledge structure or tree. This structure is used by the GRiST interface to provide top-down controlled access to the patient data. Our research explores relationships between the answers given to these higher-level 'branch' questions to see whether they can help direct assessors to the most important data, depending on the patient profile and assessment context. The outcome is a model for dynamic data collection driven by the knowledge hierarchy. It has potential for improving other clinical decision support systems operating in domains with high dimensional data that are only partially collected and in a variety of combinations.