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Business Intelligence for Human Capital Management

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  • Maria José Sousa

    (ISCTE - Instituto Universitário de Lisboa, Lisbon, Portugal)

  • Ivo Dias

    (Universidade Europeia, Lisbon, Portugal)

Abstract

This article presents the results of an exploratory study of the use of business intelligence (BI) tools to help to make decisions about human resources management in Portuguese organizations. The purpose of this article is to analyze the effective use of BI tools in integrating reports, analytics, dashboards, and metrics, which impacts on the decision making the process of human resource managers. The methodology approach was quantitative based on the results of a survey to 43 human resource managers and technicians. The data analysis technique was correlation coefficient and regression analysis performed by IBM SPSS software. It was also applied qualitative analysis based on a focus group to identify the impacts of business intelligence on the human resources strategies of Portuguese companies. The findings of this study are that: business intelligence is positively associated with HRM decision-making, and business intelligence will significantly predict HRM decision making. The research also examines the process of the information gathered with BI tools from the human resources information system on the decisions of the human resources managers and that impacts the performance of the organizations. The study also gives indications about the practices and gaps, both in terms of human resources management and in processes related to business intelligence (BI) tools. It points out the different factors that must work together to facilitate effective decision-making. The article is structured as follows: a literature review concerning the use of the business intelligence concept and tools and the link between BI and human resources management, methodology, and the main findings and conclusions.

Suggested Citation

  • Maria José Sousa & Ivo Dias, 2020. "Business Intelligence for Human Capital Management," International Journal of Business Intelligence Research (IJBIR), IGI Global, vol. 11(1), pages 38-49, January.
  • Handle: RePEc:igg:jbir00:v:11:y:2020:i:1:p:38-49
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