PurposeThe purpose of this article is to examine various antecedents to establish their effect on public service motivation (PSM) and its four dimensions.Design/methodology/approachFive categories of antecedents were examined these included: personal attributes, role states, job characteristics, employee‐leader relations, and employee perception of the organisation. Results were obtained through: structural equation modelling for the examination of multiple relationships between PSM and its dimensions, and the antecedents; and ANOVA for testing the individual hypotheses.FindingsThis study provides some evidence to show that the PSM of public employees is mainly the result of the organisational environment surrounding them. The motivational context variables particularly those related to the organisational setting are the most dominant predictors of the PSM dimensions.Research limitations/implicationsThe empirical results presented in this study should be viewed as preliminary that necessitate further extensive empirical research.Practical implicationsThe findings suggest that public sector management has the task of creating a proper and appropriate climate for its employees. Furthermore, PSM has generated particular interest because it is perceived or assumed to have a positive impact on the job behaviour of individuals in particular, job satisfaction and fulfilment, and their respective level of performance. It is therefore important that public sector organisations find ways of encouraging PSM amongst its employees.Originality/valueThis paper contributes to the literature regarding PSM by examining the relationship between dominant antecedents and the dimensions of PSM, and presents the findings as a model to show the dynamics in these relationships.

Many European public service organisations are undergoing major reforms related to their European Union membership. Therefore, the development of a model to explore and understand the dynamics of the relationships between dominant antecedents, organisational commitment and public service motivation is perceived to be essential for the successful implementation of the change management process. Five categories of antecedents were considered in examining their relationship with the dimensions of organisational commitment and public service motivation. Structural equation modelling outcomes indicate that organisational commitment strengthens public service motivation, and that affective organisational commitment has a direct effect on all the dimensions of public service motivation. Furthermore, the employee perception of the organisation antecedent is the best predictor for affective and normative organisational commitment; whilst alternative job opportunity is a better predictor for continuance organisational commitment. Moreover, family life cycle status has a direct relationship with most public service motivation dimensions but not with the organisational commitment dimensions. Finally, the cross model validation has provided satisfactory results particularly for the public service motivation dimensions.

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The chapter illustrates how data mining and knowledge management concepts may be applied in a project oriented environment for both the private and public sectors. It identifies the project environment success roadmap that consists of four levels leading to project corporate success. Processes that control the dataflow for generating the projects data warehouse are identified and the projects data warehouse contents are defined. The rest of the chapter shows how data mining may be utilised at each project success level to increase the chances of delivering profitable projects that will have the intended impact on the corporate business strategy. The general conclusion is that there is a need to structure and prioritise information for specific end-user problems and to address a number of organizational issues that may facilitate the application of data mining and knowledge management in a project oriented environment. Finally, the chapter concludes by identifying the issues that need to be addressed by private and public sector organizations so that data mining may be utilised successfully in their decision making process.

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