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Belal, HM, Singh, R, Sharma, P and Foropon, C (2022) The Role of Big Data & Predictive Analytics in the Employee Retention: A Resource Based View. International Journal of Manpower. ISSN 0143-7720
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The Role of Big Data ^0 Predictive Analytics in the Employee Retention A Resource Based View.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (1MB) | Preview |
Abstract
Purpose – We have attempted to understand how big data & predictive analytics (BDPA) can help retain employees in the organization. Design/methodology/approach – Our study is grounded in the positivism philosophy. We have used a resource-based view (RBV) to develop our research hypotheses. We tested our research hypotheses using primary data gathered using a single-informant questionnaire. We obtained 254 usable responses. We performed the assumptions test, performed confirmatory factor analysis (CFA) to test the validity of the proposed theoretical model, and further tested our research hypotheses using hierarchical regression analysis. Findings – Our statistical result suggests that the various human resource management strategies play a significant role in improving retention, under the mediating effect of the BDPA. Research limitations/implications – We have grounded our study in the positivism philosophy. Moreover, we tested our hypotheses using single-informant cross-sectional data. Hence, we cannot ignore the effects of the common method bias on our research findings. Moreover, the research findings are based on a particular setting. Thus we caution the readers that our findings must be examined in the light of our study limitations. Practical implications – The study provided empirical findings based on survey data. Hence, we provide numerous guidelines to the practitioners that how the organization can invest in creating BDPA that helps analyze complex data to extract meaningful and relevant information. This information related to employee turnaround may guide top management to reduce the dissatisfaction level among the employees working in high-stress environments resulting from a high degree of uncertainty. Social implications – The study help understand the complex factors that affect the morale of the employee. In the high-paced environment, the employees are often exposed to various negative forces that affect their morale which further affect their productivity. Due to lack of awareness and adequate information, most of the employees and their issues are not dealt with effectively and efficiently by their line managers. Thus the BDPA can help tackle the most complex problem of society in a significant way. Originality/value – Our study offers some useful contributions to the literature which attempts to unfold the complex nexus between human resource management, information management, and strategy. The study contributes to the BDPA literature and how it helps the retention of employees is one of the areas which still remains elusive to the academic community. Moreover, the managers are still skeptical about the application of BDPA in understanding human-related issues due to a lack of understanding of how and to what extent the employee-related information can be stored and processed. Our study findings further open the new avenues of research that need to be tackled.
| Item Type: | Article |
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| Additional Information: | This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com |
| Uncontrolled Keywords: | 1402 Applied Economics, 1503 Business and Management |
| Subjects: | H Social Sciences > HF Commerce > HF5001 Business |
| Divisions: | Business & Management (from Sep 19) |
| Publisher: | Emerald |
| Related URLs: | |
| Date Deposited: | 02 Dec 2021 10:32 |
| Last Modified: | 11 Feb 2022 12:15 |
| DOI or ID number: | 10.1108/IJM-03-2021-0197 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/15649 |
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