A Resource-Based View Assessment of Big Data Analysis and Its Impact on Strategic Human Resources Quality Management Systems

Main Article Content

Dr P Hameem Khan, Hari Prasad M S
Nithish.S, Bhageshwari. M, Rushyendhar M. K

Abstract

Introduction: The study here aimed to assess the impact of big data on Strategic HR quality management from a resource-based view. In order to develop better knowledge regarding the topic, objectives have formed and questions have also been set in research.


Literature Review: In this chapter, authenticated articles and journals are gathered as the crucial resources to complete the information. In addition, to enhance the quality of research, relevant literature can find the best possible way to understand the research matter. The selected theoretical framework is associated with the analysis of big data and strategic human resource quality systems.


Methodology: In order to collect meaningful information in this study, a primary quantitative method was selected where a survey was conducted with 65 participants.


Findings and analysis: Collected information was analysed through the SPSS tool that helped in generating regression and descriptive values along with correlation values effectively. Each variable failed to share a strong interconnection with each other as the values are lower than the Pearson Correlation value of 0.8.


Discussion: A strategic administration paradigm known as "the Resource-Based View" or "RBV" contends that a company's competitive advantage originates from its valuable, rare, unique, and not replaceable (VRIN) materials.


Conclusion: It is evident from this that the study that follows is warranted given that using big data has become necessary for modern businesses to increase productivity.

Article Details

How to Cite
Hari Prasad M S, D. P. H. K., & Rushyendhar M. K, N. B. M. (2024). A Resource-Based View Assessment of Big Data Analysis and Its Impact on Strategic Human Resources Quality Management Systems. European Economic Letters (EEL), 14(2), 332–344. https://doi.org/10.52783/eel.v14i2.1329
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