Determinasi Adopsi Generative AI Pada UKM Indonesia: Studi Empiris Chatgpt Berbasis TAM–TOE

Authors

  • Satrio Tegar Sadewo Universitas Tidar
  • Hanung Eka Atmaja Universitas Tidar

DOI:

https://doi.org/10.37385/msej.v6i6.9879

Keywords:

ChatGPT, Adopsi Generative AI, Niat Adopsi, Pemilik UKM, Technology Acceptance Model, Technology Organization Environment.

Abstract

Perkembangan teknologi kecerdasan buatan, khususnya generative AI seperti ChatGPT, membuka peluang baru bagi usaha kecil dan menengah dalam meningkatkan efisiensi, kreativitas, dan daya saing bisnis. Namun, tingkat adopsi teknologi ini pada UKM masih beragam dan dipengaruhi oleh berbagai faktor yang belum sepenuhnya dipahami secara komprehensif. Oleh karena itu, penelitian ini menjadi penting untuk menjelaskan mekanisme yang membentuk niat dan perilaku aktual pemilik UKM dalam mengadopsi ChatGPT. Penelitian ini bertujuan untuk menganalisis faktor faktor yang memengaruhi niat pemilik UKM dalam mengadopsi ChatGPT serta menjelaskan peran niat sebagai penghubung antara faktor teknologi, organisasi, dan lingkungan dengan perilaku penggunaan aktual. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis penelitian eksplanatori. Populasi penelitian adalah pemilik UKM yang beroperasi di Jawa Tengah, Jawa Timur, dan Daerah Istimewa Yogyakarta. Teknik pengambilan sampel menggunakan judgmental sampling dengan jumlah sampel sebanyak 237 responden yang memenuhi kriteria penggunaan dan pemahaman dasar teknologi digital. Pengumpulan data dilakukan melalui survei daring dalam periode Juni hingga Juli 2025. Data dianalisis menggunakan Structural Equation Modeling dengan pendekatan Partial Least Squares. Hasil penelitian menunjukkan bahwa persepsi kemudahan dan kebermanfaatan teknologi, kesiapan organisasi, serta tekanan lingkungan berperan penting dalam membentuk niat adopsi ChatGPT, yang selanjutnya mendorong perilaku penggunaan aktual. Temuan ini mengimplikasikan bahwa keberhasilan adopsi generative AI pada UKM memerlukan pendekatan yang tidak hanya berfokus pada teknologi, tetapi juga kesiapan internal dan dukungan ekosistem bisnis.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Alaskar, T., Efendioglu, A. M., & Shahzad, A. (2021). Adoption of innovative technologies in SMEs: The role of competitive pressure and organizational readiness. Journal of Small Business and Enterprise Development, 28(6), 1057–1076. https://doi.org/10.1108/JSBED-03-2020-0094

Al-Fahim, N. H., Abdulghafor, R., & Turaev, S. (2022). Determination of the TOE factors influencing the adoption of internet banking services on SMEs in Yemen: A moderated mediation approach. In Lecture Notes in Electrical Engineering (Vol. 881, pp. 371–388). Springer. https://doi.org/10.1007/978-981-19-1111-8_30

Almashawreh, A. S., Alshurideh, M. T., & Al Kurdi, B. (2024). Organizational readiness and artificial intelligence adoption in small and medium enterprises. Journal of Business Research, 170, 114303. https://doi.org/10.1016/j.jbusres.2023.114303

Alzboon, M. S., Al-shorman, H. M., Alka’awneh, S. M. N., Saatchi, S. G., Alqaraleh, M. K. S., Samara, E. I. M., Wahed, M. K. Y. A., Mohammad, S. I., & Al-Momani, A. M. (2025). The role of perceived trust in embracing artificial intelligence technologies: Insights from SMEs. In A. Hannoon & A. Mahmood (Eds.), Intelligence-driven circular economy (pp. 0–15). Springer. https://doi.org/10.1007/978-3-031-74220-0

Amoah, J., Bruce, E., Shurong, Z., Bankuoru Egala, S., & Kwarteng, K. (2023). Social media adoption in SMEs sustainability: Evidence from an emerging economy. Cogent Business & Management, 10(1). https://doi.org/10.1080/23311975.2023.2183573

Ayinaddis, S. G. (2025). Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis. Journal of Innovation & Knowledge, 10(3), 100682. https://doi.org/10.1016/j.jik.2025.100682

Badghish, S., & Soomro, Y. A. (2024). Drivers and barriers of artificial intelligence adoption in SMEs: Evidence from emerging economies. Technological Forecasting and Social Change, 194, 122734. https://doi.org/10.1016/j.techfore.2023.122734

Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Kizgin, H., & Patil, P. (2021). Consumer use of artificial intelligence-based chatbots in digital marketing. International Journal of Information Management, 57, 102250. https://doi.org/10.1016/j.ijinfomgt.2020.102250

Barnes, S. J., Goel, S., & Dutta, S. (2019). Blockchain-enabled systems in supply chains: The role of external support and trust. International Journal of Information Management, 49, 132–146. https://doi.org/10.1016/j.ijinfomgt.2019.03.007

Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research. Frontiers in Public Health, 6, 149. https://doi.org/10.3389/fpubh.2018.00149

Biloš, A., & Budimir, B. (2024). Understanding the adoption dynamics of ChatGPT among Generation Z: Insights from a modified UTAUT2 model. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 863–879. https://doi.org/10.3390/jtaer19020045

Brown, S., Dennis, A., & Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9–54. https://doi.org/10.2753/MIS0742-1222270201

Bryant, F. B., & Yarnold, P. R. (1995). Principal-components analysis and exploratory and confirmatory factor analysis. [Sumber/jurnal tidak jelas pada data Anda]. (Mohon lengkapi nama buku atau jurnal jika ingin dibuat 100% rapi).

Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). Adoption of artificial intelligence in organizations: A systematic literature review. Journal of Enterprise Information Management, 34(5), 1474–1496. https://doi.org/10.1108/JEIM-01-2020-0031

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage.

Cruz-Jesus, F., Oliveira, T., Bacao, F., & Irani, Z. (2019). Assessing the pattern between economic and digital development of countries. Information Systems Frontiers, 21(2), 417–432. https://doi.org/10.1007/s10796-018-9874-1

Davis, F. D. (1987). User acceptance of information systems: The technology acceptance model (TAM). Management Science, 35(8), 982–1003.

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

Dinh, T. L., Vu, M., & Tran, G. T. C. (2025). Artificial intelligence in SMEs: Enhancing business functions through technologies and applications. Information, 16(415), 1–22.

Dube, T., Van Eck, R., & Zuva, T. (2020). Review of technology adoption models and theories to measure readiness and acceptable use of technology in a business organization. Journal of Information Technology and Digital World, 2(4), 207–212.

Duong, C. D. (2024). Modeling the determinants of HEI students’ continuance intention to use ChatGPT for learning: A stimulus–organism–response approach. Journal of Research in Innovative Teaching and Learning, 17(2), 391–407. https://doi.org/10.1108/JRIT-01-2024-0006

Enshassi, A., El-Ghandour, M., & Abu Mosa, J. (2025). Barriers to artificial intelligence adoption in small firms. Journal of Small Business and Enterprise Development, 32(1), 77–95. https://doi.org/10.1108/JSBED-07-2023-0311

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.

FakhrHosseini, S. M., Ringle, C. M., Sarstedt, M., & Hair, J. F. (2024). Measurement and structural model assessment in PLS-SEM. European Journal of Marketing, 58(3), 589–620. https://doi.org/10.1108/EJM-11-2022-0903

Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.

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/10.2307/3151312

Fu, J., Ahmad, N., & Chen, Z. (2024). Human capital and digital technology adoption in small enterprises. Journal of Small Business Management, 62(2), 327–349. https://doi.org/10.1080/00472778.2023.2182154

Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065

Ghobakhloo, M., Arias-Aranda, D., & Benitez-Amado, J. (2011). Adoption of e-commerce applications in SMEs. Industrial Management & Data Systems, 111(8), 1238–1269. https://doi.org/10.1108/02635571111170785

Ghobakhloo, M., & Ching, N. T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, 100107. https://doi.org/10.1016/j.jii.2019.100107

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson.

Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.

Haq, F., Suki, N. M., Zaigham, H., Masood, A., & Rajput, A. (2025). Exploring AI adoption and SME performance in resource-constrained environments: A TOE–RBV perspective with mediation and moderation effects. Journal of Digital Economy. Advance online publication. https://doi.org/10.1016/j.jdec.2025.07.002

Ifinedo, P. (2011). Internet/e-business technologies acceptance in Canada’s SMEs: An exploratory investigation. Internet Research, 21(3). https://doi.org/10.1108/10662241111139309

Ingalagi, L., Ngari, J., & Gichira, R. (2021). Competitive pressure and digital innovation in SMEs. International Journal of Innovation Science, 13(4), 497–515. https://doi.org/10.1108/IJIS-09-2020-0159

Israel, M., & Hay, I. (2006). Research ethics for social scientists. Sage.

Ketchen, D. J. (2013). A primer on partial least squares structural equation modeling. Long Range Planning, 46(1–2), 184–185. https://doi.org/10.1016/j.lrp.2013.01.002

Kim, S., Lee, J., & Park, S. (2024). Behavioral intention and actual use of artificial intelligence tools in small firms. Information & Management, 61(3), 103756. https://doi.org/10.1016/j.im.2023.103756

Kim, Y., Blazquez, V., & Oh, T. (2024). Determinants of generative AI system adoption and usage behavior in Korean companies: Applying the UTAUT model. Behavioral Sciences, 14(11). https://doi.org/10.3390/bs14111035

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). Guilford Press.

Lada, S., Chekima, B., Karim, M. R. A., Fabeil, N. F., Ayub, M. S., Amirul, S. M., Ansar, R., Bouteraa, M., Fook, L. M., & Zaki, H. O. (2023). Determining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100144. https://doi.org/10.1016/j.joitmc.2023.100144

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4

Lyu, Y., Zhou, Y., & Xu, J. (2024). Understanding user resistance and acceptance of AI-based systems. Computers in Human Behavior, 146, 107797. https://doi.org/10.1016/j.chb.2023.107797

Malik, A., Dhir, A., & Talwar, S. (2021). Emerging role of blockchain technology in operations management. Annals of Operations Research, 308(1–2), 317–346. https://doi.org/10.1007/s10479-020-03615-6

Mäntymäki, M., Islam, A. K. M. N., & Benbasat, I. (2020). What drives subscribing to premium in freemium services? A consumer value-based view of differences between upgrading to and staying with premium. Information Systems Journal, 30(2), 295–333. https://doi.org/10.1111/isj.12262

Marei, M., AlSharif, A., & Qasem, Y. (2023). Competitive pressure and fintech adoption in SMEs. Journal of Innovation & Knowledge, 8(2), 100371. https://doi.org/10.1016/j.jik.2023.100371

Maroufkhani, P., Wan Ismail, W. K., & Ghobakhloo, M. (2020). Big data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management, 11(4), 483–513. https://doi.org/10.1108/JSTPM-02-2020-0018

Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2023). Determinants of big data analytics adoption in small and medium-sized enterprises (SMEs). Industrial Management & Data Systems, 123(1), 278–301. https://doi.org/10.1108/IMDS-11-2021-0695

Memon, M. A., Ramayah, T., Cheah, J. H., Ting, H., Chuah, F., & Cham, T. H. (2021). PLS-SEM statistical programs: A review. Journal of Applied Structural Equation Modeling, 5(1), i–xiv. https://doi.org/10.47263/JASEM.5(1)06

Millers, M., & Gaile-Sarkane, E. (2021). Towards new typology of the owners-managers of the small and medium enterprises. In Selected Papers of the International Scientific Conference “Contemporary Issues in Business, Management and Economics Engineering 2021”. https://doi.org/10.3846/cibmee.2021.603

N’Dri, A. B., & Su, Z. (2024). Successful configurations of technology–organization–environment factors in digital transformation: Evidence from exporting small and medium-sized enterprises in the manufacturing industry. Information & Management, 61(7), 104030. https://doi.org/10.1016/j.im.2024.104030

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.

O’Rourke, N., & Hatcher, L. (2013). A step-by-step approach to using SAS for factor analysis and structural equation modeling (2nd ed.). SAS Institute.

Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. The Electronic Journal Information Systems Evaluation, 14(1), 110–121.

Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259

Popa, S., Dabija, D. C., & Pelau, C. (2025). Generative artificial intelligence adoption in small businesses. Business Horizons, 68(1), 45–56. https://doi.org/10.1016/j.bushor.2024.09.002

Priambodo, I. T., Sasmoko, S., Abdinagoro, S. B., & Bandur, A. (2021). E-commerce readiness of creative industry during the COVID-19 pandemic in Indonesia. Journal of Asian Finance, Economics and Business, 8(3), 865–873. https://doi.org/10.13106/jafeb.2021.vol8.no3.0865

Qu, C., & Kim, E. (2025). Investigating AI adoption, knowledge absorptive capacity, and open innovation in Chinese apparel MSMEs: An extended TAM-TOE model with PLS-SEM analysis. Sustainability, 17(5), 1–31. https://doi.org/10.3390/su17051873

Ramdani, B., Chevers, D., & Williams, D. A. (2013). SMEs’ adoption of enterprise applications: A technology-organisation-environment model. Journal of Small Business and Enterprise Development, 20(4), 735–753. https://doi.org/10.1108/JSBED-12-2011-0035

Ritz, W., Wolf, M., & McQuitty, S. (2019). Digital marketing adoption and success for small businesses: The application of the do-it-yourself and technology acceptance models. Journal of Research in Interactive Marketing, 13(2), 179–203. https://doi.org/10.1108/JRIM-04-2018-0062

Rizzuto, T. E., Schwarz, A., & Schwarz, C. (2014). Toward a deeper understanding of IT adoption: A multilevel analysis. Information & Management, 51(4), 479–487. https://doi.org/10.1016/j.im.2014.02.005

Sánchez, E., Calderón, R., & Herrera, F. (2025). Artificial intelligence adoption in SMEs: A TOE-based empirical investigation. Applied Sciences, 15(12), 6465. https://doi.org/10.3390/app15126465

Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. (2025). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management, 63(3), 1297–1331. https://doi.org/10.1080/00472778.2024.2379999

Shahadat, M. M. H., Nekmahmud, M., Ebrahimi, P., & Fekete-Farkas, M. (2023). Digital technology adoption in SMEs: What technological, environmental and organizational factors influence in emerging countries? Global Business Review. https://doi.org/10.1177/09721509221137199

Sekaran, U., & Bougie, R. (2020). Research methods for business: A skill-building approach (8th ed.). Wiley.

Shahzad, A., Chin, H. K., Altaf, M., & Bajwa, F. A. (2024). User acceptance of AI-driven systems. Journal of Enterprise Information Management, 37(1), 78–96. https://doi.org/10.1108/JEIM-02-2023-0089

Sharma, R., Mithas, S., & Kankanhalli, A. (2024). Human capability and AI-enabled transformation in small enterprises. MIS Quarterly, 48(1), 231–259. https://doi.org/10.25300/MISQ/2024/17892

Sharma, A., & Venkatraman, S. (2023). Towards a standard framework for organizational readiness for technology adoption. In Advances in digital manufacturing systems: Technologies, business models, and adoption (pp. 197–219). Springer. https://doi.org/10.1007/978-981-19-7071-9_10

Sharma, S., Singh, G., Islam, N., & Dhir, A. (2024). Why do SMEs adopt artificial intelligence-based chatbots? IEEE Transactions on Engineering Management, 71, 1773–1786. https://doi.org/10.1109/TEM.2022.3203469

Shiau, W. L., Liu, C., Zhou, M., & Yuan, Y. (2023). Insights into customers’ psychological mechanism in facial recognition payment in offline contactless services: Integrating belief–attitude–intention and TOE–I frameworks. Internet Research, 33(1), 344–387. https://doi.org/10.1108/INTR-08-2021-0629

Sison, A. J. G., Daza, M. T., Gozalo-Brizuela, R., & Garrido-Merchán, E. C. (2023). ChatGPT: More than a “weapon of mass deception” ethical challenges and responses from the human-centered artificial intelligence (HCAI) perspective. International Journal of Human-Computer Interaction, 1–31. https://doi.org/10.1080/10447318.2023.2225931

Song, J., Kim, Y., & Park, H. (2025). Understanding generative AI adoption. Technological Forecasting and Social Change, 200, 123211. https://doi.org/10.1016/j.techfore.2024.123211

Sison, L., Nguyen, T., & Lee, J. (2023). Conversational AI systems and user engagement. Journal of Business Research, 157, 113587. https://doi.org/10.1016/j.jbusres.2022.113587

Soomro, R. B., Al-rahmi, W. M., Dahri, N. A., Almuqren, L., Al-mogren, A. S., & Aldaijy, A. (2025). A SEM–ANN analysis to examine impact of artificial intelligence technologies on sustainable performance of SMEs. Scientific Reports, 15, 5438.

Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.

Toros, A., Aydin, G., & Gursoy, D. (2024). Ease of use and trust in AI-powered decision support systems. Computers & Industrial Engineering, 187, 109774. https://doi.org/10.1016/j.cie.2023.109774

Tummalapenta, S. R., Pasupuleti, R. S., Chebolu, R. M., Banala, T. V., & Thiyyagura, D. (2024). Factors driving ChatGPT continuance intention among higher education students: Integrating motivation, social dynamics, and technology adoption. Journal of Computers in Education. Advance online publication. https://doi.org/10.1007/s40692-024-00343-w

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

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

Wang, C., Ahmad, S. F., Ayassrah, A. Y. A. B. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-razgan, M., Khan, Y., & Han, H. (2023). An empirical evaluation of technology acceptance model for artificial intelligence in e-commerce. Heliyon, 9, e18349. https://doi.org/10.1016/j.heliyon.2023.e18349

Wang, Y., Li, X., & Zhang, H. (2023). Behavioral intention and post-adoption behavior of AI technologies. Information Systems Journal, 33(5), 1082–1104. https://doi.org/10.1111/isj.12425

Yang, J., Blount, Y., & Amrollahi, A. (2024). Artificial intelligence adoption in a professional service industry: A multiple case study. Technological Forecasting and Social Change, 201, 123251. https://doi.org/10.1016/j.techfore.2024.123251

Zhao, F., Wallis, J., & Singh, M. (2022). Institutional pressure and digital innovation adoption. Technology in Society, 70, 102047. https://doi.org/10.1016/j.techsoc.2022.102047

Downloads

Published

2025-12-15

How to Cite

Sadewo, S. T., & Hanung Eka Atmaja. (2025). Determinasi Adopsi Generative AI Pada UKM Indonesia: Studi Empiris Chatgpt Berbasis TAM–TOE. Management Studies and Entrepreneurship Journal (MSEJ), 6(6), 1176–1192. https://doi.org/10.37385/msej.v6i6.9879