Journal of Business Research 131 (2021) 311–326 0148-2963/© 2021 Elsevier Inc. All rights reserved. Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications Sateesh.V. Shet a, * , Tanuj Poddar b , Fosso Wamba Samuel c , Yogesh K. Dwivedi d a School of Business Management, NMIMS University, Mumbai, India b Sr.Manager- HR Transformation, eClerx, Mumbai, India c Toulose Business School, France d Swansea University, UK A R T I C L E INFO Keywords: Human resource analytics HRM analytics People analytics Adoption of HR analytics Challenges Implementation of HR analytics Big data Data analytics Framework synthesis ABSTRACT Data analytics has gained importance in human resource management (HRM) for its ability to provide insights based on data-driven decision-making processes. However, integrating an analytics-based approach in HRM is a complex process, and hence, many organizations are unable to adopt HR Analytics (HRA). Using a framework synthesis approach, we frst identify the challenges that hinder the practice of HRA and then develop a frame- work to explain the different factors that impact the adoption of HRA within organizations. This study identifes the key aspects related to the technological, organizational, environmental, data governance, and individual factors that infuence the adoption of HRA. In addition, this paper determines 23 sub-dimensions of these fve factors as the crucial aspects for successfully implementing and practicing HRA within organizations. We also discuss the implications of the framework for HR leaders, HR Managers, CEOs, IT Managers and consulting practitioners for effective adoption of HRA in organization. 1. Introduction Human resource analytics (HRA) is attracting increasing interest as an innovative practice in the domain of human resource management (HRM) (Huselid, 2018; Mclver, Lengnick-Hall & Lengnick-Hall, 2018; Boudreau & Cascio, 2017; Levenson, 2017; Rasmussen & Ulrich, 2015). The emergence of disruptive technologies such as artifcial intelligence, computational intelligence techniques, data mining, machine learning, and the Internet of Things has speeded up data-driven decision-making in HRM (Duan et al., 2019; Dwivedi et al., 2021; Tambe et al., 2019; Davenport, 2018; Brynjolfsson et al., 2011) such as candidate selection, employee mood, and sentiment analysis, and attrition prediction (Gel- bard, Ramon-Gonen, Carmeli, Bittmann & Talyansky, 2018). Such technologies have also provided enormous opportunities for advancing data-driven workforce management. Consequently, organizations are investing in analytics infrastructure, including tools, capabilities, and other resources. However, the pace of adoption of HRA has not been as expected (Angrave et al., 2016). HRA is considered as future value-driver in HRM because it enables systematic analysis of complex data that may help resolve various organizational challenges (Boudreau & Ramstad, 2007). The adoption of a scientifc approach in decision-making for HR has the potential to improve decisions concerning people much on the lines of how a sci- entifc approach helps marketers to make informed decisions about the spending strategy of their customers and fnance departments for working capital predictions (Boudreau & Ramstad, 2005). However, to reap the benefts of data-driven decision-making, HRA should be inte- grated with relevant products, services, and business-level indicators (Levenson, 2011; Angrave et al., 2016; Levenson, 2005; Lawler et al., 2004; Boudreau & Ramstad, 2007). Despite the potential benefts of HRA, it has not received adequate attention from management researchers (Marler & Boudreau, 2016). This is because very little information is available about the process through which HRA infuences organizations and their performance (Huselid, 2018; Schiemann, Seibert & Blankenship, 2017). In addition, it is also not completely clear how organizations should use HRA to ach- ieve important organizational outcomes (Mclver, Lengnick-Hall & Lengnick-Hall, 2018). Therefore, more structured solutions to these is- sues are needed before analytics can be adopted in the HRM of an organization. * Corresponding author. E-mail addresses: svshet@hotmail.com (Sateesh.V. Shet), Tanuj.Poddar@eclerx.com (T. Poddar), s.fosso-wamba@tbs-education.fr (F. Wamba Samuel), y.k. dwivedi@swansea.ac.uk (Y.K. Dwivedi). Contents lists available at ScienceDirect Journal of Business Research journal homepage: www.elsevier.com/locate/jbusres https://doi.org/10.1016/j.jbusres.2021.03.054 Received 16 August 2020; Received in revised form 25 March 2021; Accepted 26 March 2021