Determinan Implementasi Generative Artificial Intelligence pada Transformasi Bisnis Digital: Pendekatan Technology Organization Environment (TOE)

Muhammad Arif Zikir Risky; Rafi Septiawan Putra

doi:10.69693/ijim.v4i3.1719

Authors

Muhammad Arif Zikir Risky Institut Teknologi dan Bisnis PGRI Dewantara Jombang
Rafi Septiawan Putra UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan

DOI:

https://doi.org/10.69693/ijim.v4i3.1719

Keywords:

Generative AI, , Transformasi Bisnis Digital, TOE Framework, Adopsi Teknologi, SEM-PLS

Abstract

Akselerasi teknologi kecerdasan buatan, khususnya Generative Artificial Intelligence (Generative AI), menjadi katalisator utama restrukturisasi operasional bisnis digital. Teknologi ini menawarkan berbagai keunggulan, mulai dari peningkatan produktivitas, otomatisasi alur kerja, hingga efisiensi pemrosesan konten secara instan. Meskipun demikian, pemanfaatannya di sektor bisnis digital Indonesia belum merata akibat adanya determinan internal dan eksternal pada masing-masing pelaku usaha. Studi ini bertujuan menginvestigasi elemen pendorong adopsi Generative AI dalam ekosistem bisnis digital melalui pendekatan Technology, Organization, and Environment (TOE). Penelitian menerapkan desain kuantitatif korelasional dengan menyebarkan kuesioner berskala Likert lima poin kepada 200 pelaku usaha digital yang dipilih menggunakan teknik purposive sampling. Data dianalisis menggunakan Structural Equation Modeling Partial Least Square (SEM-PLS) berbantuan SmartPLS 4, meliputi evaluasi model pengukuran, model struktural, dan pengujian hipotesis melalui bootstrapping. Hasil penelitian mengonfirmasi bahwa dimensi teknologi, organisasi, dan lingkungan memiliki pengaruh positif dan signifikan terhadap adopsi Generative AI, dengan nilai R-Square sebesar 0,684. Dimensi teknologi terbukti sebagai faktor paling dominan karena berkaitan langsung dengan manfaat relatif, kecocokan sistem, dan kemudahan penggunaan yang dirasakan pengguna. Selain itu, komitmen manajemen puncak dan tekanan kompetisi pasar turut mengakselerasi perluasan implementasi teknologi ini. Penelitian ini memberikan kontribusi empiris berupa bukti kontekstual penerapan kerangka TOE pada bisnis digital Indonesia serta implikasi praktis bagi pelaku usaha dalam merancang strategi adopsi Generative AI yang efektif. Simpulannya, efektivitas pemanfaatan Generative AI menuntut keterpaduan antara infrastruktur teknologi, kesiapan internal, serta kondisi pasar yang suportif guna menciptakan daya saing bisnis yang berkelanjutan.

Downloads

Download data is not yet available.

References

Ain, N., Vaia, G., DeLone, W. H., & Waheed, M. (2022). Two decades of UTAUT: A review and agenda for future research. Journal of Retailing and Consumer Services, 65, 102076. https://doi.org/10.1016/j.jretconser.2021.102076

Agrawal, A. (2024). Determinants of Generative Artificial Intelligence adoption in organizations: An empirical TOE framework approach. Journal of Business Research, 178, 114682.

Al-Khatib, A. W., Alshurideh, M., Al Kurdi, R., & Al-Ansari, M. (2023). Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework. Technology in Society, 75, 102403. https://doi.org/10.1016/j.techsoc.2023.102403

Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420. https://doi.org/10.1016/j.techfore.2020.120420

Badghish, S., & Soomro, Y. A. (2024). Artificial intelligence adoption by SMEs to achieve sustainable business performance: Application of Technology–Organization–Environment framework. Sustainability, 16(5), 1864. https://doi.org/10.3390/su16051864

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012

Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2022). The adoption of artificial intelligence in manufacturing industries and firms: A systematic literature review and future research agenda. Technological Forecasting and Social Change, 174, 121122. https://doi.org/10.1016/j.techfore.2021.121122

Das, S. D., & Bala, P. K. (2024). What drives MLOps adoption? An analysis using the TOE framework. Journal of Decision Systems, 33(3), 376–412. https://doi.org/10.1080/12460125.2023.2214306

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., 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.2019.08.002

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage Publications.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

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

Mikalef, M., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2020). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 18(3), 547–578. https://doi.org/10.1007/s10257-019-00452-7

Min, S., & Kim, B. (2024). AI technology adoption in corporate IT network operations based on the TOE model. Digital, 4(4), 947–970. https://doi.org/10.3390/digital4040047

Noll, J., Wiener, M., & Benlian, A. (2025). Beyond the hype: Organisational adoption of Generative AI through the lens of the TOE framework—A mixed methods perspective. International Journal of Information Management, 82, 102982. https://doi.org/10.1016/j.ijinfomgt.2025.102982

Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002

Wamba, S. F., Queiroz, M. M., Wu, L., & Sivarajah, U. (2021). Big data analytics-enabled sensing capability and organizational outcomes: Assessing the mediating effects of business analytics culture. Information Systems Frontiers, 23(2), 311–326. https://doi.org/10.1007/s10796-019-09954-4