Key Factors Affecting AI Adoption Rate in Retail Business (Multiple Theory Perspectives)
Abstract
This study aims to identify and analyze the key factors that influence the adoption intention of Artificial Intelligence (AI) in retail businesses in Surakarta City. With the rapid development of technology, AI adoption has become an urgency for retail businesses to improve operational efficiency and customer experience. The research method used was quantitative with a survey approach, involving 135 randomly selected retail businesses. Data were collected through a questionnaire with a 5-point Likert scale and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results showed thatperceived ease of use,environmental factors andeffort expectancy had a positive and significant influence on AI adoption intention, while perceivedusefulness andperformanceexpectancy showed no significant influence. The limitations of this study lie in the limited geographical scope and small sample size. Future research is expected to expand geographic coverage and use a larger sample to increase the generalizability of the findings. In addition, future research can explore other factors that may influence AI adoption in different contexts.
References
Agrawal, A., Gans, J. S., & Goldfarb, A. (2024). Artificial intelligence adoption and system-wide change. Journal of Economics and Management Strategy, 33(2), 327–337. https://doi.org/10.1111/jems.12521
Akgül, E., Üstündağ, M. T., & Tanriverdi, M. (2018). No TitBusiness Intelligence Application For Campaign Magagement In The Retail Sectorle. Ai & Society (Gazi Publishing), 1, 8–25. https://doi.org/DOI: https://doi.org/10.30855/AIS.2018.01.01.02
Alboqami, H. (2023). Factors Affecting Consumers Adoption of AI-Based Chatbots: The Role of Anthropomorphism. American Journal of Industrial and Business Management, 13(04), 195–214. https://doi.org/10.4236/ajibm.2023.134014
Bolesnikov, M., Popović Stijačić, M., Keswani, A. B., & Brkljač, N. (2022). Perception of Innovative Usage of AI in Optimizing Customer Purchasing Experience within the Sustainable Fashion Industry. Sustainability (Switzerland), 14(16), 1–18. https://doi.org/10.3390/su141610082
Bunod, R., Augstburger, E., Brasnu, E., Labbe, A., & Baudouin, C. (2022). Intelligence artificielle et glaucome : une revue de la littérature. Journal Français d’Ophtalmologie, 45(2), 216–232. https://doi.org/10.1016/j.jfo.2021.11.002
Chin, J. H., Do, C., & Kim, M. (2022). How to Increase Sport Facility Users’ Intention to Use AI Fitness Services: Based on the Technology Adoption Model. International Journal of Environmental Research and Public Health, 19(21). https://doi.org/10.3390/ijerph192114453
Dabbous, A., & Barakat, Karine Aoun Sayegh, M. M. (2021). Enabling organizational use of artificial intelligence: an employee perspective. Journal of Asia Business Studies (Emerald Publishing Limited).
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340.
García de Blanes Sebastián, M., Sarmiento Guede, J. R., & Antonovica, A. (2022). Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.993935
Giroux, M., Kim, J., Lee, J. C., & Park, J. (2022). Artificial Intelligence and Declined Guilt: Retailing Morality Comparison Between Human and AI. Journal of Business Ethics, 178(4), 1027–1041. https://doi.org/10.1007/s10551-022-05056-7
Goti, A., Querejeta-Lomas, L., Almeida, A., de la Puerta, J. G., & López-de-Ipiña, D. (2023). Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review. Mathematics, 11(13). https://doi.org/10.3390/math11132943
Gu, X., Firdousi, S. F., Obrenovic, B., Afzal, A., Amir, B., & Wu, T. (2023). The influence of green finance availability to retailers on purchase intention: a consumer perspective with the moderating role of consciousness. Environmental Science and Pollution Research, 30(27), 71209–71225. https://doi.org/10.1007/s11356-023-27355-w
Hair, Joe F., Sarstedt, M., Hopkins, L., and Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121.
Ho, Y. H., Alam, S. S., Masukujjaman, M., Lin, C. Y., Susmit, S., & Susmit, S. (2022). Intention to Adopt AI-Powered Online Service Among Tourism and Hospitality Companies. International Journal of Technology and Human Interaction, 18(1), 1–19. https://doi.org/10.4018/IJTHI.299357
Horani, O. M. (2023). The critical determinants impacting artificial intelligence adoption at the organizational level. Information Development. https://doi.org/10.1177/02666669231166889
Hossain, M. A., Akter, S., Yanamandram, V., & Gunasekaran, A. (2022). Operationalizing Artificial Intelligence-Enabled Customer Analytics Capability in Retailing. Journal of Global Information Management, 30(8), 1–23. https://doi.org/10.4018/JGIM.298992
Keegan, B. J., Dennehy, D., & Naudé, P. (2022a). Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective. Information Systems Frontiers, 26(3), 1025–1039. https://doi.org/10.1007/s10796-022-10294-1
Keegan, B. J., Dennehy, D., & Naudé, P. (2022b). Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective. Information Systems Frontiers, 2018. https://doi.org/10.1007/s10796-022-10294-1
Lee, J. C., Tang, Y., & Jiang, S. Q. (2023). Understanding continuance intention of artificial intelligence (AI)-enabled mobile banking applications: an extension of AI characteristics to an expectation confirmation model. Humanities and Social Sciences Communications, 10(1), 1–12. https://doi.org/10.1057/s41599-023-01845-1
Li, K. (2023). Determinants of College Students’ Actual Use of AI-Based Systems: An Extension of the Technology Acceptance Model. Sustainability (Switzerland) , 15(6). https://doi.org/10.3390/su15065221
Liang, Y., Hu, Y., Luo, D., Zhu, Q., Chen, Q., & Wang, C. (2023). Distributed Dynamic Pricing Strategy Based on Deep Reinforcement Learning Approach in a Presale Mechanism. Sustainability (Switzerland), 15(13). https://doi.org/10.3390/su151310480
Maheswari, S. (2023). The Transformative Power of AI in Marketing FMCG. International Journal For Multidisciplinary Research, 5(3), 1–8. https://doi.org/10.36948/ijfmr.2023.v05i03.3760
Mehta, R., Verghese, J., Mahajan, S., Barykin, S., Bozhuk, S., Kozlova, N., Vasilievna Kapustina, I., Mikhaylov, A., Naumova, E., & Dedyukhina, N. (2022). Consumers’ behavior in conversational commerce marketing based on messenger chatbots. F1000Research, 11, 647. https://doi.org/10.12688/f1000research.122037.1
Na, S., Heo, S., Choi, W., Han, S., & Kim, C. (2023). Firm Size and Artificial Intelligence (AI)-Based Technology Adoption: The Role of Corporate Size in South Korean Construction Companies. Buildings, 13(4). https://doi.org/10.3390/buildings13041066
Nadhir, M. F., & Karismariyanti, M. (2015). Adopsi Sistem ERP Pada Aplikasi Pengadaan Aset Tetap Pada Sebuah Perusahaan Media Cetak. Seminar Nasional Teknologi Informasi Dan Komunikasi Terapan (SEMANTIK ) 2015, 457–462.
Nagy, S., & Hajdú, N. (2021). Consumer Acceptance of the Use of Artificial Intelligence in Online Shopping: Evidence From Hungary. Amfiteatru Economic, 23(56), 1–1. https://doi.org/10.24818/EA/2021/56/155
Neumann, O., Guirguis, K., & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114–141. https://doi.org/10.1080/14719037.2022.2048685
Nguyen, T. H., Le, X. C., & Vu, T. H. L. (2022). An Extended Technology-Organization-Environment (TOE) Framework for Online Retailing Utilization in Digital Transformation: Empirical Evidence from Vietnam. Journal of Open Innovation: Technology, Market, and Complexity, 8(4). https://doi.org/10.3390/joitmc8040200
Nicholas R. J. Frick. (2021). Maneuvering through the stormy seas of digital transformation: the impact of empowering leadership on the AI readiness of enterprises. Journal of Decision Systems, 30, 235–258.
Pranoto, G. T. (2022). Pengembangan Strategi Is/It Dalam Berbagai Bidang Bisnis: State of the Art. Jurnal Sistem Informasi Dan Sains Teknologi, 4(2), 1–8. https://doi.org/10.31326/sistek.v4i2.1275
R., M. Devi, A. J. (2022). Amazon’s Artificial Intelligence in Retail Novelty - Case Study. International Journal of Case Studies in Business, 2(1), 787–804.
R., M., & Devi, A. J. (2022). Amazon’s Artificial Intelligence in Retail Novelty - Case Study. International Journal of Case Studies in Business, IT, and Education, ISSN: 2581.
Santos, M. A. R. dos, Santos, C. A. F. dos, Serique, N. P., & Lima, R. R. (2020). Estado da arte: aspectos históricos e fundamentos teórico-metodológicos. Revista Pesquisa Qualitativa, 8(17), 202–220. https://doi.org/10.33361/rpq.2020.v.8.n.17.215
Senarath, S., Pathirana, P., Meedeniya, D., & Jayarathna, S. (2022). Customer Gaze Estimation in Retail Using Deep Learning. IEEE Access, 10, 64904–64919. https://doi.org/10.1109/ACCESS.2022.3183357
Sharma, A. (2023). Analyzing the Role of Artificial Intelligence in Predicting Customer Behavior and Personalizing the Shopping Experience in Ecommerce. Interantional Journal of Scientific Research in Engineering and Management, 07(02), 1–8. https://doi.org/10.55041/ijsrem17839
Shavneet Sharma, Nazrul Islam, Gurmeet Singh, A. D. (2018). Why do retail customers adopt artificial intelligence (AI)-based autonomous decision-making systems? Journal of Cleaner Production, 0–48.
Shekhawat, S. (2023). Making Retail Smarter with Digital Twins. The Magazine Fo The IT Professional.
Sudaryanto, M. R., Hendrawan, M. A., & Andrian, T. (2023). The Effect of Technology Readiness, Digital Competence, Perceived Usefulness, and Ease of Use on Accounting Students Artificial Intelligence Technology Adoption. E3S Web of Conferences, 388. https://doi.org/10.1051/e3sconf/202338804055
Tjondronegoro, D., Yuwono, E., Richards, B., & Green, D. (2022). Responsible AI Implementation: A Human-centered Framework for Accelerating the Innovation Process. Arxiv.Org. https://arxiv.org/abs/2209.07076
Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.
Tunfadilah. (2023). Optimalisasi Pemasaran Produk Ritel Melalui Ojek (Online, Jejaring Sosial, E-Commerce Dan Komunitas) Pada Siswa Kelas Xi Bisnis Daring Dan Pemasaran 1 Di Smk Negeri 1 Pemalang. TEACHING : Jurnal Inovasi Keguruan Dan Ilmu Pendidikan, 3(1), 83–93. https://doi.org/10.51878/teaching.v3i1.2167
Upadhyay, Nitin, Upadhyay, S., Al-Debei, M. M., Baabdullah, A. M., Dwivedi, Y. K., & of Digital Marketing, P. (2022). Influence of Digital Entrepreneurship and Entrepreneurial Orientation on Intention of Family Businesses to Adopt Artificial Intelligence: Examining Mediating Role of Business Innovativeness. 1–65.
