THE INFLUENCE OF BIG DATA MANAGEMENT ON ORGANIZATIONAL PERFORMANCE IN ORGANIZATIONS: THE ROLE OF ELECTRONIC RECORDS MANAGEMENT SYSTEM POTENTIALITY

Abstract

Aim/Purpose The use of digital technology, such as an electronic records management system (ERMS), has prompted widespread changes across organizations. The organiza-tion needs to support its operations with an automation system to improve pro-duction performance. This study investigates ERMS's potentiality to enhance organizational performance in the oil and gas industry. Background Oil and gas organizations generate enormous electronic records that lead to dif-ficulties in managing them without any system or digitalization procedure. The need to use a system to manage big data and records affects information secu-rity and creates several problems. This study supports decision-makers in oil and gas organizations to use ERMS to enhance organizational performance. Methodology We used a quantitative method by integrating the typical partial least squares (SEM-PLS) approach, including measurement items, respondents' de-mographics, sampling and collection of data, and data analysis. The SEM-PLS approach uses a measurement and structural model assessment to analyze data. Contribution This study contributes significantly to theory and practice by providing ad-vancements in identity theory in the context of big data management and elec-tronic records management. This study is a foundation for further research on the role of ERMS in operations performance and Big Data Management (BDM). This research makes a theoretical contribution by studying a theory-driven framework that may serve as an essential lens to evaluate the role of ERMS in performance and increase its potentiality in the future. This research also evaluated the combined impacts of general technology acceptance theory elements and identity theory in the context of ERMS to support data manage-ment. Findings This study provides an empirically tested model that helps organizations to adopt ERMS based on the influence of big data management. The current study's findings looked at the concerns of oil and gas organizations about inte-grating new technologies to support organizational performance. The results demonstrated that individual characteristics of users in oil and gas organiza-tions, in conjunction with administrative features, are robust predictors of ERMS. The results show that ERMS potentiality significantly influences the or-ganizational performance of oil and gas organizations. The research results fit the big ideas about how big data management and ERMS affect respondents to adopt new technologies. Recommendations for Practitioners This study contributes significantly to the theory and practice of ERMS poten-tiality and BDM by developing and validating a new framework for adopting ERMS to support the performance and production of oil and gas organiza-tions. The current study adds a new framework to identity theory in the context of ERMS and BDM. It increases the perceived benefits of using ERMS in pro-tecting the credibility and authenticity of electronic records in oil and gas organ-izations. Recommendations for Researchers This study serves as a foundation for future research into the function and in-fluence of big data management on ERMS that support the organizational per-formance. Researchers can examine the framework of this study in other na-tions in the future, and they will be able to analyze this research framework to compare various results in other countries and expand ERMS generalizability and efficacy. Impact on Society ERMS and its impact on BDM is still a developing field, and readers of this ar-ticle can assist in gaining a better understanding of the literature's dissemination of ERMS adoption in the oil and gas industry. This study presents an experi-mentally validated model of ERMS adoption with the effect of BDM in the oil and gas industry. Future Research In the future, researchers may be able to examine the impact of BDM and user technology fit as critical factors in adopting ERMS by using different theories or locations. Furthermore, researchers may include the moderating impact of de-mographical parameters such as age, gender, wealth, and experience into this study model to make it even more robust and comprehensive. In addition, fu-ture research may examine the significant direct correlations between human traits, organizational features, and individual perceptions of BDM that are di-rectly related to ERMS potentiality and operational performance in the future.