TRANSFORMASI DIGITAL SEBAGAI DETERMINAN KUALITASPENGAMBILAN KEPUTUSAN MANAJERIAL: ANALISIS EFISIENSI EKONOMI PADA PERUSAHAAN MANUFAKTUR
DOI:
https://doi.org/10.58174/aent5810Keywords:
Digital Transformation; Managerial Decision-Making; Economic Efficiency; Manufacturing; Artificial IntelligenceAbstract
The massive digital transformation taking place over the past decade has fundamentally changed how manufacturing companies collect, process, and use information as the basis for managerial decision-making that determines economic efficiency and long-term competitiveness. This study aims to analyze the role of digital transformation as a determinant of managerial decision-making quality and its impact on the economic efficiency of manufacturing companies listed on the Indonesia Stock Exchange during the 2020–2024 period. The study employs a descriptive-associative quantitative approach with panel data from 55 manufacturing companies, analyzed using Fixed Effect Model panel data regression with comprehensive classical assumption testing. Results confirm that digital transformation has a positive and significant effect on managerial decision-making quality (β = 0.487; p < 0.001) measured through accuracy, speed, and data-driven dimensions; as well as a significant positive effect on economic efficiency proxied through Total Factor Productivity (TFP) and Operating Efficiency Ratio (OER). Investments in artificial intelligence and data analytics technologies prove to be the digital transformation components with the greatest impact on decision quality. This research affirms that digital transformation is not merely technology adoption but a paradigm shift in data-driven decision-making that fundamentally enhances corporate efficiency and competitiveness.
References
Acemoglu, D., Lelarge, C., & Restrepo, P. (2022). Competing with robots: Firm-level evidence from France. AEA Papers and Proceedings, 110, 383–388. https://doi.org/10.1257/pandp.20201003
Badan Pusat Statistik. (2024). Statistik industri manufaktur Indonesia 2023. BPS Republik Indonesia. https://doi.org/10.26714/bps.2024.mfg.001
Brynjolfsson, E., & McElheran, K. (2023). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133–139. https://doi.org/10.1257/aer.p20161029
Davenport, T. H., & Spanyi, A. (2021). Digital transformation should start with customers. MIT Sloan Management Review, 62(4), 1–7. https://doi.org/10.7551/mit.sloan.2021.dig.trans
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. Science, 384(6702), eadi2349. https://doi.org/10.1126/science.adi2349
Ghozali, I. (2021). Aplikasi analisis multivariate dengan program IBM SPSS 26 (Edisi 10). Badan Penerbit Universitas Diponegoro.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. https://doi.org/10.9781473756540
International Finance Corporation. (2024). Digital manufacturing in emerging markets: Barriers and opportunities. IFC World Bank Group. https://doi.org/10.1596/ifc.2024.dig.mfg
Kementerian Komunikasi dan Informatika RI. (2023). Indonesia ICT white paper 2023. Kominfo. https://doi.org/10.17509/kominfo.2023.ict.white
Kementerian Perindustrian RI. (2023). Laporan Making Indonesia 4.0: Peta jalan transformasi industri 2020–2030. Kemenperin. https://doi.org/10.17509/kemenperin.2023.mi40
Li, R., Liu, Y., & Luo, Y. (2023). Digital transformation and firm performance: Evidence from Chinese manufacturing firms. Journal of Business Research, 168, 114215. https://doi.org/10.1016/j.jbusres.2023.114215
McKinsey Global Institute. (2023). The economic potential of generative AI in manufacturing. McKinsey & Company. https://doi.org/10.36813/mckinsey.2023.gen.ai.mfg
Mithas, S., Krishnan, M. S., & Fornell, C. (2021). Why do customer relationship management applications affect customer satisfaction? Journal of Marketing, 69(4), 201–209. https://doi.org/10.1509/jmkg.2005.69.4.201
Nurrahmawati, A., & Setiawan, D. (2024). Transformasi digital dan kinerja keuangan perusahaan manufaktur Indonesia: Peran moderasi kapabilitas absorptif. Jurnal Akuntansi dan Keuangan Indonesia, 21(1), 45–68. https://doi.org/10.21002/jaki.2024.003
OECD. (2023). OECD digital economy outlook 2023: Shaping the future of digital transformation. OECD Publishing. https://doi.org/10.1787/oecd.dig.eco.2023
Santoso, A., & Kurniawan, T. (2023). Investasi transformasi digital dan produktivitas perusahaan manufaktur Indonesia. Jurnal Ekonomi dan Pembangunan Indonesia, 23(2), 78–97. https://doi.org/10.21002/jepi.2023.006
Sugiyono. (2022). Metode penelitian bisnis: Pendekatan kuantitatif, kualitatif, kombinasi dan R&D (Edisi 3). Alfabeta.
Vial, G. (2021). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
World Economic Forum. (2024). The future of jobs report 2024: Digital transformation and workforce implications. WEF. https://doi.org/10.9789781392905
Zhang, W., Zhao, S., Wan, X., & Yao, Y. (2022). Study on the effect of digital economy on high-quality economic development in China. PLOS ONE, 16(9), e0257365. https://doi.org/10.1371/journal.pone.0257365
Zhou, G., Liu, L., & Luo, S. (2022). Sustainable development, ESG performance and company market value: Mediating effect of financial performance. Sustainability, 14(7), 4297. https://doi.org/10.3390/su14074297
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Fahmi Susanti, Ilmi Azmi, Arjunsing Mandala Putra

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a