The EFQM excellence model, the knowledge management process and the corresponding results: an explanatory and predictive study

被引:0
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作者
José Bocoya-Maline
Manuel Rey-Moreno
Arturo Calvo-Mora
机构
[1] Universidad de Cadiz,Department of Business Organization, Faculty of Economics and Business Studies
[2] Universidad de Sevilla,Department of Business Administration and Marketing, Faculty of Tourism and Finance
[3] Universidad de Sevilla,Department of Business Administration and Marketing, Faculty of Economics and Business Studies
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关键词
EFQM model; Knowledge management; Organisational results; PLS-SEM; Predictive modelling; Out-of-sample prediction; M10; M16;
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摘要
This study aims to analyse the relationships among the EFQM model, the knowledge management (KM) process and the corresponding results. It also seeks to analyse the predictive power of the phases of the KM process with regard to organisational results. The sample under study is composed of 113 Spanish organisations that feature some kind of Excellence Recognition System granted by the European Foundation for Quality Management (EFQM). This paper uses partial least squares (PLS) path modelling to test and validate the research model and the proposed hypotheses. In addition, thorough analyses are conducted to assess the model’s predictive performance. The results show that organisations that use the management framework proposed by the EFQM model implement the phases of the KM process efficiently. Moreover, the synergies resulting from the simultaneous implementation of the EFQM model and the KM process contribute to improving the corresponding results. Also, the predictive power of the phases of the KM process is confirmed in terms of their ability to anticipate the results that the organisation will be able to achieve with respect to customers, people, society and key business factors. Finally, this study provides empirical evidence of the direct and indirect relationships among the EFQM model, the KM process and the corresponding results. In addition, the paper identifies out-of-sample prediction as an integral element of the evaluation of the model using PLS-SEM and as a way to evaluate its practical relevance, since it allows us to predict results.
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页码:1281 / 1315
页数:34
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