The Role of Big Data Analytics in Enhancing Decision-Making Processes in Business Accounting
Abstract
In the digital era, Big Data Analytics (BDA) has become an important tool in business decision-making, including in the fields of accounting and finance. The implementation of BDA allows companies to manage data at scale, improving efficiency, accuracy, and transparency in financial statements and audit processes. This study aims to analyze the role of BDA in improving the decision-making process in business accounting, identify its benefits in the efficiency and transparency of financial statements, and explore the key challenges companies face in adopting this technology. The method used in this study is quantitative with a survey approach, where data is collected from 22 respondents consisting of accountants, financial managers, CFOs, financial analysts, and IT/data engineers. The analysis techniques used included descriptive statistics and simple regression. The results show that the use of BDA has a significant influence on the quality of business accounting decision-making (β1 = 0.76, p = 0.000), as well as contributing to improving the efficiency, accuracy, and transparency of financial statements and audits. The main challenges in BDA adoption include lack of HR skills (Mean = 3.5), implementation costs (Mean = 3.7), and system integration (Mean = 3.5). Therefore, companies need to implement HR training strategies, use cloud solutions, and develop API-based integration systems to overcome these obstacles.
Keywords: Big data analytics (BDA), accounting decision making, financial efficiency and transparency.
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AJAH IA, and NWEKE HF. Big data and business analytics: Trends, platforms, success factors and applications. Big data and Cognitive Computing, 2019, 3(2): 32. https://doi.org/10.3390/bdcc3020032
HATAMLAH H, ALLAHHAM M, ABU-ALSONDOS IA, AL-JUNAIDI A, AL-ANATI GM, and AL-SHAIKH M. The role of business intelligence adoption as a mediator of big data analytics in the management of outsourced reverse supply chain operations. Applied Mathematics and Information Sciences, 2023, 17( 5): 897–903. https://dx.doi.org/10.18576/amis/170516
OYEWO B, OBANOR A, and IWUANYANWU C. Determinants of the adoption of big data analytics in business consulting service: a survey of multinational and indigenous consulting firms. Transnational Corporations Journal, 2023, 15(2): 1–20. https://doi.org/10.1080/19186444.2022.2044737
WANG J, XU C, ZHANG J, and ZHONG R. Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems, 2022, 62: 738–752. https://doi.org/10.1016/j.jmsy.2021.03.005
BHAT SA, and HUANG N-F. Big data and AI revolution in precision agriculture: Survey and challenges. IEEE Access, 2021, 9: 110209–110222. https://doi.org/10.1109/ACCESS.2021.3102227
KARATAS M, ERISKIN L, DEVECI M, PAMUCAR D, and GARG H. Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives. Expert Systems with Applications, 2022, 200: 116912. https://doi.org/10.1016/j.eswa.2022.116912
DUAN Y, EDWARDS JS, and DWIVEDI YK. Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 2019, 48: 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
JAGATHEESAPERUMAL SK, RAHOUTI M, AHMAD K, AL-FUQAHA A, and GUIZANI M. The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions. IEEE Internet Things Journal, 2021, 9(15): 12861–12885. https://doi.org/10.1109/JIOT.2021.3139827
GULIN D, HLADIKA M, and VALENTA I. Digitalization and the Challenges for the Accounting Profession. ENTRENOVA Conference Proceedings, 2019, 5(1): 428–437. https://dx.doi.org/10.2139/ssrn.3492237
HAMILTON RH, and SODEMAN WA. The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 2020, 63(1): 85–95. https://doi.org/10.1016/j.bushor.2019.10.001
MOLL J, and YIGITBASIOGLU O. The role of internet-related technologies in shaping the work of accountants: New directions for accounting research. British Accounting Review, 2019, 51(6): 100833. https://doi.org/10.1016/j.bar.2019.04.002
QASIM A, and KHARBAT FF. Blockchain technology, business data analytics, and artificial intelligence: Use in the accounting profession and ideas for inclusion into the accounting curriculum. Journal of Emerging Technologies in Accounting, 2020, 17(1): 107–117. https://doi.org/10.2308/jeta-52649
SALIJENI G, SAMSONOVA-TADDEI A, and TURLEY S. Big Data and changes in audit technology: contemplating a research agenda. Accounting and Business Research, 2019, 49(1): 95–119. https://doi.org/10.1080/00014788.2018.1459458
HASAN AR. Artificial Intelligence (AI) in accounting & auditing: A Literature review. Open Journal of Business and Management, 2021, 10(1): 440–465. https://doi.org/10.4236/ojbm.2022.101026
DAGILIENĖ L. and KLOVIENĖ L. Motivation to use big data and big data analytics in external auditing. Managerial Auditing Journal, 2019, 34(7): 750–782. https://doi.org/10.1108/MAJ-01-2018-1773
MÖLLER K, SCHÄFFER U, and VERBEETEN F. Digitalization in management accounting and control: an editorial. Journal of Management Control, 2020, 31(1): 1–8. https://doi.org/10.1007/s00187-020-00300-5
KHANRA S, DHIR A, and MÄNTYMÄKI M. Big data analytics and enterprises: a bibliometric synthesis of the literature. Enterprise Information Systems, 2020, 14(6): 737–768. https://doi.org/10.1080/17517575.2020.1734241
HILTON RW, and PLATT DE. Managerial accounting: creating value in a dynamic business environment. McGraw-Hill, 2020.
TIWARI K, and KHAN MS. Sustainability accounting and reporting in the industry 4.0. Journal of Cleaner Production, 2020, 258: 120783.
ZHANG Y, XIONG F, XIE Y, FAN X, and GU H. The impact of artificial intelligence and blockchain on the accounting profession. IEEE Access, 2020, 8: 110461–110477. https://doi.org/10.1109/ACCESS.2020.3000505
SUGIYONO. Metode Penelitian Kuantitatif, Kualitatif, R&D. Bandung: IKAPI, 2016.
BEAVER WH. Financial reporting: an accounting revolution, 3rd ed. Prentice Hall, 1998.
MCAFEE A, BRYNJOLFSSON E, DAVENPORT TH, PATIL DJ, and BARTON D. Big data: the management revolution. Harvard Business Review, 2012, 90(10): 60–68. https://ailab-ua.github.io/courses/MIS510/big_data_-_the_management_revolution_0.pdf
GALBRAITH JR. Organization design: An information processing view. Interfaces (Providence), 1974, 4(3): 28–36. https://doi.org/10.1287/inte.4.3.28
BARNEY JB. Firm resources and sustained competitive advantage. In Economics Meets Sociology in Strategic Management (pp. 203-227). Emerald Group Publishing Limited, 2000. https://doi.org/10.1016/S0742-3322(00)17018-4.
FAMA EF. Efficient capital markets. Journal of Finance, 1991, 46(5): 1575–1617. https://doi.org/10.1111/j.1540-6261.1991.tb04636.x
DAVENPORT TH, and DYCHÉ J. Big data in big companies. International Institure for Analitics, 2013, 1–31. https://www.iqpc.com/media/7863/11710.pdf
CRESSEY DR. Other people’s money; a study of the social psychology of embezzlement. Wadsworth Publishing Company, 1971.
TORNATZKY LG, FLEISCHER M, and CHAKRABARTI AK. The processes of technological innovation. Lexington Books, 1990.
BUGHIN J, SEONG J, MANYIKA J, CHUI M, and JOSHI R. Notes from the AI frontier: Modeling the impact of AI on the world economy. Discussion Paper, McKinsey Global Institute, 2018. https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/notes%20from%20the%20frontier%20modeling%20the%20impact%20of%20ai%20on%20the%20world%20economy/mgi-notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy-september-2018.ashx
CRENSHAW KW. On Intersectionality: Essential Writings. Faculty Books, 2017, 255. https://scholarship.law.columbia.edu/books/255
WALBY S. Theorizing Patriarchy. John Willey and Sons, 1991.
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