PERAN LINGKUNGAN DALAM NIAI ADOPSI AI PADA BISNIS RITEL (PERSPEKTIF BERBAGAI TEORI)
DOI:
https://doi.org/10.26486/jpsb.v12i2.3937Keywords:
Environment, Intention to Adopt AI, TAM, TEO Framework, UTAUTAbstract
Tujuan penelitian untuk menganalisis faktor-faktor nilai adopsi Artificial Intelligence (AI) dan peran lingkungan dalam faktor-faktor niat adopsi AI dengan pendekatan berbagai teori. Perkembangan teknologi yang pesat menjadikan adopsi AI urgensi bagi bisnis ritel untuk meningkatkan efisiensi operasional dan pengalaman pelanggan. Metode penelitian yang digunakan adalah kuantitatif dengan pendekatan survei, melibatkan 135 pelaku bisnis ritel yang dipilih secara acak. Data dikumpulkan melalui kuesioner dengan skala Likert 5 poin dan dianalisis menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM). Hasil penelitian menunjukkan bahwa Perceived Useful, Effort Expectacy dan Environment mempunyai pengaruh positif dan signifikan terhadap Intention to Adopt AI, sedangkan Perceived Easy of Use, Performance Expectacy tidak berpengaruh signifikan terhadap Intention to Adopt AI. Environment memoderasi pengaruh pengaruh Perceived Usefull terhadap Intention to Adopt AI, tetapi tidak memoderasi pengaruh Perceived Easy to Use, Performance Expectacy dan Effort Expectacy terhadap Intention to Adopt AI. Penelitian ini hanya menganalisis aspek niat dalam perilaku adopsi AI pada bidang bisnis ritel dengan berdasar pada teori TAM, UTAUT, dan TEO Framework, sehingga bagi peneliti yang akan datang dapat menganalisis faktor demografi dalam aspek perilaku yang lain seperti sikap atau kekonsistenan dalam adopsi Ai, menggunakan subyek dari berbagai bidang bisnis, menggunakan perspektif teori seperti teori TPB, DIF dan sebagainya dalam menganalisis faktor kunci pada adopsi AI.
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
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in M-learning context: A systematic review. Computers & Education, 125, 389–412.
Al-Maroof, R. S., & Al-Emran, M. (2018). Factors influencing the adoption of social media as a learning tool in higher education. International Journal of Emerging Technologies in Learning (IJET), 13(5), 188–199.
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2018). Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management, 31(1), 2–18.
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2019). Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management, 31(1), 2–18.
Badrinarayanan, V., Ramachandran, I., & Madhavaram, S. (2021). Artificial intelligence in retailing: The AI-enabled shopper experience. Journal of Retailing and Consumer Services, 59(102393).
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
Buhalis, D., & Sinarta, Y. (2019). Real-time co-creation and nowness service: Lessons from tourism and hospitality. Journal of Tourism Futures, 5(1), 7–23.
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
Canhoto, A. I., Keegan, B. J., & Ryzhikh, M. (2023). Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment. Information Systems Frontiers, 26(3), 1005–1024. https://doi.org/10.1007/s10796-023-10369-7
Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P., & Sharma, A. (2023). Assessing Organizational Users’ Intentions and Behavior to AI Integrated CRM Systems: a Meta-UTAUT Approach. Information Systems Frontiers, 25(4), 1299–1313. https://doi.org/10.1007/s10796-021-10181-1
Cichosz, M., Wallenburg, C. M., & Knemeyer, A. M. (2020). Digital transformation at logistics service providers: Barriers, success factors and leading practices. International Journal of Logistics Management, 31(2), 209–238.
Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122., 273–290.
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.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers`, 21(3), 719–734.
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
Grant, J., Kraus, S., & Wooldridge, M. (2010). Intentions in Equilibrium. Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010, 786–791. https://doi.org/10.1609/aaai.v24i1.7632
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.
Holdsworth, L. M., Park, C., Asch, S. M., & Lin, S. (2021). Technology-enabled and artificial intelligence support for pre-visit planning in ambulatory care: Findings from an environmental scan. Annals of Family Medicine, 19(5), 419–426. https://doi.org/10.1370/AFM.2716
Horani, O. M. (2023). The critical determinants impacting artificial intelligence adoption at the organizational level. Information Development. https://doi.org/10.1177/02666669231166889
Huang, T. (2023). Psychological factors affecting potential users’ intention to use autonomous vehicles. PLoS ONE, 18(3 March), 1–22. https://doi.org/10.1371/journal.pone.0282915
Ifinedo, P. (2018). Roles of perceived fit and perceived individual learning support in employees’ technology acceptance: A case of an ERP. Journal of Workplace Learning, 30(4), 256–270.
IpKin Anthony Wong, Keng Fong Cheng Chau, H. U. C. (2023). An empirical study on customers’ gambling intention in AI-supported casinos. Journal of Hospitality and Tourism Technology, 14(2), 121–136. https://doi.org/10.1108/JHTT-08-2021-0225
Keegan, B. J., Dennehy, D., & Naudé, P. (2022). 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
Lu, Y., & Deng, Y. (2022). What Drives Construction Practitioners’ Acceptance of Intelligent Surveillance Systems? An Extended Technology Acceptance Model. Buildings, 12(2). https://doi.org/10.3390/buildings12020104
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
Milicevic, N., Djokic, N., Mirovic, V., Djokic, I., & Kalas, B. (2023). Banking Support for Energy Security: The Customer Aspect. Sustainability (Switzerland), 15(1). https://doi.org/10.3390/su15010112
Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework. Buildings, 12(2). https://doi.org/10.3390/buildings12020090
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
Neural, A., & Approach, N. (2020). Predicting Sustainable Farm Performance — Using Hybrid Structural Equation Modelling with an. 1–37.
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.
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2018). Mobile payment adoption: A new model with moderating effects of individual and technological characteristics. Journal of Business Research, 91, 101–116.
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2020). Mobile payment adoption: A new model with moderating effects of individual and technological characteristics. Journal of Business Research, 101–116(91).
Omar Mohammed Horani, Ahmad Samed Al-Adwan, Husam Yaseen, Hazar Hmoud, Waleed Mugahed Al-Rahmi, and A. A. (2023). The critical determinants impacting artificial intelligence adoption at the organizational level. Sage Journals, 6(4). https://doi.org/https://doi.org/10.1177/02666669231166889
Park, D. Y., & Kim, H. (2023). Determinants of Intentions to Use Digital Mental Healthcare Content among University Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot. Sustainability (Switzerland), 15(1). https://doi.org/10.3390/su15010872
Pillai, R., Sivathanu, B., Mariani, M. M., Rana, N. P., & Yang, Bai Dwivedi, Y. K. (2022). Adoption of AI-empowered industrial robots in auto component manufacturing companies. Journals Production Planning & Control , The Operational Management (Taylor & Francis), 3(16), 1–17. https://doi.org/10.1080/09537287.2021.1882689
Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2020). Artificial Intelligence in Business Gets Real. MIT Sloan Management Review, 61(4), 1–19.
Rauschnabel, P. A., He, J., & Ro, Y. K. (2018). Antecedents to the adoption of augmented reality smart glasses: A closer look at privacy risks. Journal of Business Research, 92, 374–384.
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.
Tarhini, A., Hone, K., Liu, X. (2015). A cross-cultural examination of the impact of social, organizational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46(4), 739–755.
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.
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.
Venkatesh, V., Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
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.
Venkatesh, V., Thong, J. Y., & Xu, X. (2020). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 21(1), 328–376.
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901.
Verma, S., Bhattacharyya, S. S., & Kumar, S. (2020). An extension of the technology acceptance model in content management system adoption: an empirical study of cognitive factors. International Journal of Information Management, 51, 102056.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2019). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.
Wang, M. (2022). Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance : An Empirical Analysis on an Emerging Market.
Wang, W., Chen, L., Xiong, M., & Wang, Y. (2023). Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care. Information Systems Frontiers, 25(6), 2239–2256. https://doi.org/10.1007/s10796-021-10154-4
Wang, X., Ji, K., & Xie, T. (2023). AI Carbon Footprint Management with Multi-Agent Participation: A Tripartite Evolutionary Game Analysis Based on a Case in China. Sustainability (Switzerland), 15(11). https://doi.org/10.3390/su15119013
Yigitcanlar, T., Mehmood, R., & Corchado, J. M. (2021). Green artificial intelligence: towards an efficient, sustainable and equitable technology for smart cities and futures. Sustainability (Switzerland), 13(16), 1–14. https://doi.org/10.3390/su13168952
Yin, J., & Qiu, X. (2021). AI Technology and Online Purchase Intention : Structural Equation Model Based on Perceived Value Artificial Intelligence Winning Consumers : Enabling Human Intelligence ”. According to. Sustainability (Switzerland), 13, 1–16.
Zeng, L. (2022). Analysis of the Stage Performance Effect of Environmental Protection Music and Dance Drama Based on Artificial Intelligence Technology. Journal of Environmental and Public Health, 2022. https://doi.org/10.1155/2022/2891993
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