Occupational driver safety : conceptualising a leadership-based intervention to improve safe driving performance

Occupational driving crashes are the most common cause of death and injury in the workplace. The physical and psychological outcomes following injury are also very costly to organizations. Thus, safe driving poses a managerial challenge. Some research has attempted to address this issue through modifying discrete and often simple target behaviors (e.g., driver training programs). However, current intervention approaches in the occupational driving field generally do not consider the role of organizational factors in workplace safety. This study adopts the A-B-C framework to identify the contingencies associated with an effective exchange of safety information within the occupational driving context. Utilizing a sample of occupational drivers and their supervisors, this multi-level study examines the contingencies associated with the exchange of safety information within the supervisor-driver relationship. Safety values are identified as an antecedent of the safety information exchange, and the quality of the leader-member exchange relationship and safe driving performance is identified as the behavioral consequences. We also examine the function of role overload as a factor influencing the relationship between safety values and the safety information exchange. Hierarchical Linear Modelling found that role overload moderated the relationship between supervisors’ perceptions of the value given to safety and the safety information exchange. A significant relationship was also found between the safety information exchange and the subsequent quality of the leader-member exchange relationship. Finally, the quality of the leader-member exchange relationship was found to be significantly associated with safe driving performance. Theoretical and practical implications of these results are discussed.

Service delivery in one-stop government portals : observations based on a market research study in Queensland

New government service delivery models based on a “franchise” metaphor are being proposed recently to allow more citizen-centric service delivery by decoupling the government’s internal departmental structure from the way services are presented and delivered to citizens. In order to evaluate the approach from an online channel perspective, the Queensland Government commissioned a market research study to compare their websites with the online presences of the UK Government and the South Australian Government, who both have adopted the “franchise” approach. The study aimed to inform an understanding of citizens’ preferred model for interacting in the online channel and to identify the relative strengths and weaknesses of the existing websites. In this paper, we will a) report on the findings of this third party usability study and b) position the study, in the form of a critical reflection, against the background of a more comprehensive “Transformational Government” approach using a “franchise marketplace”. The critical reflection points towards limitations of the study with regard to this bigger picture and discusses the potential benefits of service bundling that remained unconsidered in the study.

Aggregate distance based clustering using Fibonacci Series -FIBCLUS

This paper proposes an innovative instance similarity based evaluation metric that reduces the search map for clustering to be performed. An aggregate global score is calculated for each instance using the novel idea of Fibonacci series. The use of Fibonacci numbers is able to separate the instances effectively and, in hence, the intra-cluster similarity is increased and the inter-cluster similarity is decreased during clustering. The proposed FIBCLUS algorithm is able to handle datasets with numerical, categorical and a mix of both types of attributes. Results obtained with FIBCLUS are compared with the results of existing algorithms such as k-means, x-means expected maximization and hierarchical algorithms that are widely used to cluster numeric, categorical and mix data types. Empirical analysis shows that FIBCLUS is able to produce better clustering solutions in terms of entropy, purity and F-score in comparison to the above described existing algorithms.