Positivism

Big data

Philosophy

Multilevel model

Biology

Statistics

Data mining

Computer science

Structural equation modeling

Human resource management

Computer network

Empirical research

Paleontology

Test (biology)

Confirmatory factor analysis

Psychology

Mathematics

Statistical hypothesis testing

Applied psychology

Epistemology

Data science

Predictive analytics

Knowledge management

Machine learning

Resource (disambiguation)

International Journal of Manpower

Purpose The authors have attempted to understand how big data and predictive analytics (BDPA) can help retain employees in the organization. Design/methodology/approach This study is grounded in the positivism philosophy. The authors have used a resource-based view (RBV) to develop their research hypotheses. The authors tested their research hypotheses using primary data gathered using a single-informant questionnaire. The authors obtained 254 usable responses. The authors performed the assumptions test, performed confirmatory factor analysis (CFA) to test the validity of the proposed theoretical model, and further tested their research hypotheses using hierarchical regression analysis. Findings The 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 The authors have grounded their study in the positivism philosophy. Moreover, the authors tested their hypotheses using single-informant cross-sectional data. Hence, the authors cannot ignore the effects of the common method bias on their research findings. Moreover, the research findings are based on a particular setting. Thus, the authors caution the readers that their findings must be examined in the light of their study limitations. Practical implications The study provided empirical findings based on survey data. Hence, the authors 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 helps 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 This 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 in 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. This study’s findings further open the new avenues of research that need to be tackled.

The role of big data and predictive analytics in the employee retention: a resource-based view