Data-driven innovation and human resource management: proposing a human resource analytics adoption framework

Document Type : Original Article

Authors

1 Management Department, Faculty of Economics & Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

2 Professor of the Department of Management, Faculty of Administrative and Economic Sciences; Ferdowsi University of Mashhad; Mashhad, Iran.

3 Assistant Professor, Department of Management, Faculty of Administrative and Economic Sciences, Ferdowsi University of Mashhad

4 Management Department, Faculty of Economics & Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

10.22080/shrm.2024.5101

Abstract

Data analytics has become important in human resource management due to its ability to provide insight based on data-based decision making. However, integrating an analytics-based approach into human resource management is a complex process.Therefore, many organizations are unable to adopt human resource analytics. Using the framework synthesis approach, the present study has identified and analyzed important themes about the role of data-driven innovation (human resource analytics) in human resource management and finally proposed an integrated framework. Based on the analysis and synthesis of the reviewed articles, this study makes an initial attempt to integrate data-driven innovation adoption and human resource management by developing a diffusion of innovation model with two main processes (pre-adoption and post-adoption) in which 29 sub-components were identified which can be classified into three categories: initiation/driver, diffusion/adoption and effects. Finally, using the snowball method, the data extracted from the synthesis of the framework was provided to experts from the field of human resource management and data science, and the validity of the data was analyzed using the Lawshe validation method. As a result, based on the acceptable values of Lawshe's coefficients based on the number of experts, the validity of the proposed model was approved by the experts.

Keywords

Journal of Sustainable Human Resource Management
Volume 6, Issue 11
October 2024

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