Human Centered Artificial Intelligence Adoption and Financial Decision Making Quality for Sustainable Corporate Performance
DOI:
https://doi.org/10.59890/ijmbi.v4i4.31Keywords:
Human-Centered Artificial Intelligence, Financial Decision Making Quality, Sustainable Corporate Performance, Artificial Intelligence Adoption, Financial ManagementAbstract
The increasing adoption of artificial intelligence (AI) in financial management requires a human-centered approach to enhance decision quality and ensure sustainable corporate performance. This study examines the effect of Human-Centered Artificial Intelligence Adoption on Financial Decision Making Quality and its implications for Sustainable Corporate Performance. A quantitative cross-sectional survey was conducted involving 120 financial managers and executives from Indonesian companies using AI-based financial systems. Data were collected through structured questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Human-Centered AI Adoption significantly improves Financial Decision Making Quality, which subsequently enhances Sustainable Corporate Performance. These findings highlight the strategic role of human-centered AI in supporting high-quality financial decisions and promoting long-term organizational sustainability.
References
Alaminos, D., Salas, M. B., & Callejón-Gil, Á. M. (2024). Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks. Quantitative Finance and Economics, 8(1), 153–209. https://doi.org/10.3934/QFE.2024007
Altepost, A., Elaroussi, F., Hansen-Ampah, A., Harlacher, M., & Merx, W. (2024). Assessing organizational framework conditions for the successful, human-centered introduction of AI applications. Zeitschrift für Arbeitswissenschaft, 78, 335–348. https://doi.org/10.1007/s41449-024-00440-7
Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management, 92, 178–189.
Boston Consulting Group. (2024, August 14). Harnessing the power of (Gen)AI in Indonesian financial services. Boston Consulting Group.
Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: Complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652–661. https://doi.org/10.1177/1744987120927206
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE Publications.
Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283–314. https://doi.org/10.1016/j.jbusres.2020.08.019
Elkington, J. (2020). Green swans: The coming boom in regenerative capitalism. Fast Company Press.
Fatima, T., & Elbanna, S. (2023). Corporate social responsibility implementation: A review and a research agenda towards an integrative framework. Journal of Business Ethics, 183(1), 105–121.
Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. In S. Carta (Ed.), Machine learning and the city: Applications in architecture and urban design (pp. 535–545). John Wiley & Sons.
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer. https://doi.org/10.1007/978-3-030-80519-7
Henseler, J. (2021). Composite-based structural equation modeling: Analyzing latent and emergent variables. Guilford Press.
Herrmann, T., & Pfeiffer, S. (2023). Keeping the organization in the loop: A socio-technical extension of human-centered artificial intelligence. AI & Society, 38, 1523–1542. https://doi.org/10.1007/s00146-022-01391-5
Kock, N., & Hadaya, P. (2021). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
Kraus, S., Durst, S., Ferreira, J. J., Veiga, P., Kailer, N., & Weinmann, A. (2022). Digital transformation in business and management research: An overview of the current status quo. International Journal of Information Management, 63, Article 102466. https://doi.org/10.1016/j.ijinfomgt.2021.102466
Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, Article 102623. https://doi.org/10.1016/j.technovation.2022.102623
Mhlanga, D. (2023). FinTech and artificial intelligence for sustainable development: The role of smart technologies in achieving development goals. Springer. https://doi.org/10.1007/978-3-031-37776-1
Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, Article 102104.
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2021.0072
Ramayah, T., Cheah, J.-H., Chuah, F., Ting, H., & Memon, M. A. (2021). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated guide and practical guide to statistical analysis (2nd ed.). Pearson.
Rane, N. L., Choudhary, S. P., & Rane, J. (2024). Acceptance of artificial intelligence: Key factors, challenges, and implementation strategies. Journal of Applied Artificial Intelligence, 5(2), 50–70. https://doi.org/10.48185/jaai.v5i2.1017
Saunders, M., Lewis, P., & Thornhill, A. (2023). Research methods for business students (9th ed.). Pearson.
Sekaran, U., & Bougie, R. (2020). Research methods for business: A skill-building approach (8th ed.). Wiley.
Sele, D., & Chugunova, M. (2024). Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making. PLOS ONE, 19(2), Article e0298037.
Shneiderman, B. (2022). Human-centered AI. Oxford University Press.
Stahl, B. C., & Wright, D. (2024). Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Security & Privacy, 16(3), 26–33. https://doi.org/10.1109/MSP.2024.2701164
Taherdoost, H. (2022). What are different research approaches? Comprehensive review of qualitative, quantitative, and mixed method research, their applications, types, and limitations. Journal of Management Science & Engineering Research, 5(1), 53–63.
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11, Article 233.
Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, Article 102544. https://doi.org/10.1016/j.ijinfomgt.2022.102544
