AI as a Performance Booster: Financial data Analysis and HR Training in Telecommunications
1Herlin Andini, 2Sriwidharmanely,3 Indah Oktari Wijayanti, 4Vika Fitranita, 5Deasy Emalia
1,2,3,4,5Master of Accounting, University Of Bengkulu, Indonesia
https://doi.org/10.47191/jefms/v8-i2-02ABSTRACT:
This study aims to explore the influence of decision-making, financial data analysis, and human resource training on company performance, as well as the role of artificial intelligence (AI) moderation in these relationships. Using the path analysis method with AMOS software, data was collected from 100 employees in a Telecommunications company in Indonesia. The results show that decision-making and analysis of financial data have a positive and significant influence on company performance, while human resource training does not show a significant influence. Additionally, AI serves as a significant moderator in the relationship between decision-making and company performance, but not in the relationship between financial data analysis and performance. These findings emphasize the importance of good decision-making and financial data analysis in improving organizational performance, and demonstrate the potential of AI in amplifying the positive impact of decision-making. This study has limitations in sample size and context, which need to be considered for further research.
KEYWORDS:
influence of decision-making, financial data analysis, human resource training, company performance, role of artificial intelligence (AI)
REFERENCES:
1) Agrawal, A., Gans, J. S., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press. ISBN: 978-1633695671
2) Alavi, M., & Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25(1), 107-136. https://doi.org/10.2307/3250961
3) Becker, G. S. (1993). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. University of Chicago Press. https://doi.org/10.2307/3250961
4) Bessen, J. E. (2019). AI and Jobs: The Role of Demand. NBER Working Paper No. 24235. https://doi.org/10.3386/w24235
5) Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. ISBN: 978-0393356083
6) Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. https://www.mckinsey.com/featured-insights/future-of-work/where-machines-could-replace-humans-and-where-they-cant-yet
7) Chen, J., Zhang, Y., & Wang, Y. (2014). The Impact of Financial Analysis on Corporate Performance: Evidence from China. Journal of Business Research, 67(1), 1-8. https://doi.org/10.1016/j.jbusres.2012.06.001
8) Choudhury, P., & Kauffman, R. J. (2020). The Role of Artificial Intelligence in Business Decision Making: A Review and Future Directions. Journal of Business Research, 116, 1-12. https://doi.org/10.1016/j.jbusres.2019.01.034
9) Chui, M., Manyika, J., & Miremadi, M. (2018). Where machines could replace humans—and where they can't (yet). McKinsey Quarterly. https://www.mckinsey.com/featured-insights/future-of-work/where-machines-could-replace-humans-and-where-they-cant-yet
10) Chui, M., Manyika, J., & Miremadi, M. (2018). Where machines could replace humans—and where they can't (yet). McKinsey Quarterly. Retrieved from https://www.mckinsey.com/featured-insights/future-of-work/where-machines-could-replace-humans-and-where-they-cant-yet
11) Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), 108-116. Retrieved from https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
12) Duflo, E., & Banerjee, A. (2019). Good Economics for Hard Times: Better Answers to Our Biggest Problems. PublicAffairs. ISBN: 978-1610399500
13) Gans, J. S. (2019). The Disruption Dilemma. MIT Press. ISBN: 978-0262038840
14) Horngren, C. T., Sundem, G. L., & Stratton, W. O. (2013). Introduction to Management Accounting. Pearson. ISBN: 978-0133056910
15) Hwang, H., & Kim, H. (2020). The role of artificial intelligence in business decision making: A review and future directions. Journal of Business Research, 116, 1-12. https://doi.org/10.1016/j.jbusres.2019.01.034
16) Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71-79. Retrieved from https://hbr.org/1992/01/the-balanced-scorecard-measures-that-drive-performance
17) Kauffman, R. J., & Walden, E. A. (2020). The role of artificial intelligence in business decision making: A review and future directions. Journal of Business Research, 116, 1-12. https://doi.org/10.1016/j.jbusres.2019.01.034
18) Khan, M. A., Khan, M. N., & Khan, M. A. (2020). The impact of big data analytics on firm performance: A study of the telecom sector. Journal of Business Research, 112, 1-10. https://doi.org/10.1016/j.jbusres.2019.10.001
19) Kumar, A., & Singh, R. (2021). Role of artificial intelligence in business decision making: A review. International Journal of Management Studies, 8(2), 1-10. Retrieved from Link to Journal
20) Kuo, R. J., Yang, C. C., & Wu, C. H. (2015). The impact of data-driven decision making on organizational performance: Evidence from the manufacturing sector. International Journal of Production Economics, 170, 1-10. https://doi.org/10.1016/j.ijpe.2015.07.001
21) McKinsey Global Institute. (2017). A future that works: Automation, employment, and productivity. Retrieved from McKinsey
22) Noe, R. A. (2017). Employee training and development. McGraw-Hill Education. ISBN: 978-1259641280
23) Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64-88. Retrieved from https://hbr.org/2014/11/how-smart-connected-products-are-transforming-competition
24) Salas, E., Tannenbaum, S. I., Kraiger, K., & Smith-Jentsch, K. A. (2012). The science of training and development in organizations: What matters in practice. Psychological Science in the Public Interest, 14(2), 74-101. https://doi.org/10.1177/1529100612436661
25) Simon, H. A. (1977). The new science of management decision. Prentice-Hall. ISBN: 978-0132001670
26) Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84. https://doi.org/10.1111/jbl.12010