dc.contributor.author |
Singh, Rupali |
|
dc.contributor.author |
Sharma, Pooja |
|
dc.contributor.author |
Foropon, Cyril |
|
dc.contributor.author |
Belal, H.M |
|
dc.date.accessioned |
2024-12-03T14:55:00Z |
|
dc.date.available |
2024-12-03T14:55:00Z |
|
dc.date.issued |
2021 |
|
dc.identifier.citation |
Singh,Rupali [et..al]..(2021),The role of big data and predictive analytics in the employee retention: a resource-based view, International Journal of Manpower,1-37, 0143-7720 |
en_US |
dc.identifier.issn |
0143-7720 |
|
dc.identifier.uri |
http://10.9.150.37:8080/dspace//handle/atmiyauni/2106 |
|
dc.description.abstract |
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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
International Journal of Manpower |
en_US |
dc.subject |
Big data |
en_US |
dc.subject |
Big data and predictive analytics (BDPA |
en_US |
dc.subject |
Resource-based view (RBV |
en_US |
dc.subject |
Human resource management |
en_US |
dc.subject |
Employee retention |
en_US |
dc.title |
The role of big data and predictive analytics in the employee retention |
en_US |
dc.title.alternative |
a resource-based view |
en_US |
dc.type |
Article |
en_US |