Artificial Intelligence Utilization and Financial Management Optimization in Kendari Startups and Innovative Ventures

Ridha Taurisma Lajaria; Rastiani Parabang; Khafiza Olivia; Rasty Rasty

doi:10.61255/jeemba.v4i5.1860

Authors

Ridha Taurisma Lajaria Universitas Sulawesi Tenggara, Indonesia
Rastiani Parabang Universitas Sulawesi Tenggara, Indonesia
Khafiza Olivia Universitas Sulawesi Tenggara, Indonesia
Rasty Universitas Sulawesi Tenggara, Indonesia

DOI:

https://doi.org/10.61255/jeemba.v4i5.1860

Keywords:

Artificial Intelligence, Financial Management, Startups, Innovative Ventures, Kendari

Abstract

Purpose – This study examines whether Artificial Intelligence (AI) utilization is positively associated with financial management optimization among startups and innovative ventures in Kendari City. It addresses the limited local empirical evidence on how AI utilization capability relates to operational financial outcomes in emerging entrepreneurial ecosystems.

Design/methodology/approach – A quantitative explanatory survey was conducted with 50 startup owners, founders, and financial managers selected through purposive sampling. Data were collected using a structured four-point Likert questionnaire and analyzed with descriptive statistics, Pearson correlation, and simple linear regression.

Finding/Results – AI utilization capability demonstrated a positive and significant association with financial management optimization (r = 0.681, p < 0.001). Regression analysis confirms that AI utilization capability is positively associated with financial management optimization (B = 0.646; standardized β = 0.681; p < 0.001; R² = 0.464). Startups with higher AI utilization report better operational efficiency, planning and reporting accuracy, cash-flow control, and financial decision quality.
Originality/Value – The study extends the AI-finance literature by providing context-specific evidence from Kendari and by linking AI utilization to four managerial outcome areas of startup financial management. The findings highlight predictive analytics and decision-support functions as especially relevant for financially constrained and uncertainty-prone ventures.

Abstract views: 25 ,

PDF downloads: 34

Downloads

Download data is not yet available.

References

Bhimani, A., & Willcocks, L. (2021). Digitisation, big data and the transformation of accounting information. Accounting and Business Research, 51(3), 261–278. https://doi.org/10.1080/00014788.2020.1862812

Bouwman, H., Nikou, S., & de Reuver, M. (2021). Digitalization, business models, and SMEs: How digital transformation affects business model innovation. Telecommunications Policy, 45(7), 102168. https://doi.org/10.1016/j.telpol.2019.101828

Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. No DOI assigned (Harvard Business Review article)

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Artificial intelligence for decision making in the era of big data – Evolution, challenges and research agenda. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2020.101994

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J. S., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., ... Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2020.101994

Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1–20. https://doi.org/10.2308/jeta-10511

Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes—An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63, 5–20. https://doi.org/10.1007/s12599-020-00676-7

Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://doi.org/10.2308/jeta-51730

Kraus, S., Jones, P., Kailer, N., Weinmann, A., Chaparro-Banegas, N., & Roig-Tierno, N. (2021). Digital transformation: An overview of the current state of the art of research. SAGE Open, 11(3), 1–15. https://doi.org/10.1177/21582440211047576

Kurniawan, D., & Wijaya, R. (2021). Analisis manajemen keuangan pada startup digital di Indonesia (Analysis of financial management in Indonesian digital startups). Jurnal Manajemen dan Inovasi, 12(2), 45–60.

Lajaria, R.T., Parabang, R., Olivia, K., & Rasty. (2026). The Impact of Artificial Intelligence Utilization on Financial Management Optimization in Startups and Innovative Ventures in Kendari City. Proceedings of the 4th APSKI International Conference on Association of Indonesian Entrepreneurship Study Programs, Paper ID 148. ISSN 3064-3635.

Lehner, O. M., Leitner-Hanetseder, S., & Eisl, C. (2022). Artificial intelligence in accounting: Opportunities and challenges. Journal of Applied Accounting Research, 23(4), 790–805.

Li, L., Su, F., Zhang, W., & Mao, J. Y. (2018). Digital transformation by SME entrepreneurs: A capability perspective. Information Systems Journal, 28(6), 1129–1157. https://doi.org/10.1111/isj.12153

Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434

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.2018.0072

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Srisathan, W. A., Ketkaew, C., & Naruetharadhol, P. (2022). The intervention of organizational capabilities in the relationship between digital transformation and sustainable business performance. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 24. https://doi.org/10.3390/joitmc8010024

Sutton, S. G., & Holt, M. (2016). “The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting. International Journal of Accounting Information Systems, 22, 60–73. https://doi.org/10.1016/j.accinf.2016.07.005

Suryani, L. (2022). Pemanfaatan artificial intelligence untuk optimalisasi keuangan usaha mikro dan startup [Utilization of artificial intelligence for optimizing the finances of microenterprises and startups]. Jurnal Teknologi dan Bisnis Digital, 5(1), 23–35.

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

Warner, K. S. R., & Wäger, M. (2021). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 54(2), 102110. https://doi.org/10.1016/j.lrp.2020.101994