The effects of big data analytics and workplace pressures on productivity

Abstract: This study set out to find out on how big data analytics, workload, work wages, and organizational structure pressures affect employee job performance and overall business performance in the Jordan banking sector in the context of that country's banking sector. 292 samples from the research study were collected, analyzed, and debated to test the study's hypotheses. Employee and company success were found to be affected by workload, compensation, and organizational structure. Workload was reflected by job completion rate, average daily issues, and work completion time, while big data was reflected by external and internal, unique application, indexing, and source correctness. Work compensation, including employee compensation, employee experience and skill, and employee incentives and rewards, is the third primary factor. Communities of practice, group work, and physical infrastructure make up the fourth and last important factor, the organization's structure. The study finds that when dealing with large amounts of data, reducing the workload can improve employee performance, increasing employee motivation through higher wages and bonuses can improve performance, and having an organizational structure that promotes teamwork and work teams can improve employees' abilities to solve problems and improve work performance, which in turn affects overall productivity. This study contributes fresh information to an emerging field that requires more investigation to fully understand the interplay between big data, workload, work wages, and organizational structure pressures. The topic of this study, the Jordan banking sector, is both innovative and highly relevant because it may help financial institutions enhance their operations and provide superior customer service.

How to cite this paper

 Ahmad, H., Kilani, Q & Alnajdawi, S. (2023). The effects of big data analytics and workplace pressures on productivity.International Journal of Data and Network Science, 7(4), 1485-1492.

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