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Productivity Analysis of Public Services: An Application of Data Mining

Productivity Analysis of Public Services: An Application of Data Mining

Aki Jääskeläinen, Paula Kujansivu, Jaani Väisänen
Copyright: © 2010 |Pages: 23
ISBN13: 9781605669069|ISBN10: 1605669067|ISBN13 Softcover: 9781616923075|EISBN13: 9781605669076
DOI: 10.4018/978-1-60566-906-9.ch005
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MLA

Jääskeläinen, Aki, et al. "Productivity Analysis of Public Services: An Application of Data Mining." Data Mining in Public and Private Sectors: Organizational and Government Applications, edited by Antti Syvajarvi and Jari Stenvall, IGI Global, 2010, pp. 83-105. https://doi.org/10.4018/978-1-60566-906-9.ch005

APA

Jääskeläinen, A., Kujansivu, P., & Väisänen, J. (2010). Productivity Analysis of Public Services: An Application of Data Mining. In A. Syvajarvi & J. Stenvall (Eds.), Data Mining in Public and Private Sectors: Organizational and Government Applications (pp. 83-105). IGI Global. https://doi.org/10.4018/978-1-60566-906-9.ch005

Chicago

Jääskeläinen, Aki, Paula Kujansivu, and Jaani Väisänen. "Productivity Analysis of Public Services: An Application of Data Mining." In Data Mining in Public and Private Sectors: Organizational and Government Applications, edited by Antti Syvajarvi and Jari Stenvall, 83-105. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-906-9.ch005

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Abstract

Productivity is a key success factor in any organization. In order to improve productivity, it is necessary to understand how various factors affect it. The previous research has mainly focused on productivity analysis at macro level (e.g. nations) or in private companies. Instead, there is a lack of knowledge about productivity drivers in public service organizations. This study aims to scrutinize the role of various operational (micro level) factors in improving public service productivity. In particular, this study focuses on child day care services. First, the drivers of productivity are identified in light of the existing literature and of the results of workshop discussions. Second, the drivers most conducive to high productivity and the specific driver combinations associated with high productivity are defined by applying methods of data mining. The empirical data includes information on 239 day care centers of the City of Helsinki, Finland. According to the data mining results, the factors most conducive to high productivity are the following: proper use of employee resources, efficient utilization of premises, high employee competence, large size of day care centers, and customers with little need for additional support.

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