پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 162 |
| تعداد شمارهها | 6,578 |
| تعداد مقالات | 71,072 |
| تعداد مشاهده مقاله | 125,694,562 |
| تعداد دریافت فایل اصل مقاله | 98,923,945 |
Readiness for Artificial Intelligence Adoption in Malaysian Manufacturing Companies | ||
| Journal of Information Technology Management | ||
| دوره 17، شماره 1، 2025، صفحه 1-13 اصل مقاله (2 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2025.99920 | ||
| نویسندگان | ||
| Amirah Izzaty Roszelan Izzaty Roszelan* 1؛ Melissa Shahrom2 | ||
| 1MSc. Department of Business and Management, Universiti Teknologi MARA (UiTM) Shah Alam, 40450 Selangor, Malaysia. | ||
| 2Associate Professor, Department of Business and Management, Universiti Teknologi MARA (UiTM) Shah Alam, 40450 Selangor, Malaysia. | ||
| چکیده | ||
| The advancement of artificial intelligence (AI) and its growing societal importance are reshaping decision-making processes and policy analysis roles. This study examines the readiness of manufacturing companies in Malaysia to embrace AI technology, considering its potential to enhance decision-making, productivity, quality control, job automation, and data analysis. Focusing on the Technology, Organization, and Environment (T-O-E) readiness framework, the research investigates the relationship between these dimensions and AI adoption readiness among manufacturing companies in Shah Alam, Selangor, Malaysia. AI adoption readiness serves as the dependent variable, while technological, organizational, and environmental readiness dimensions act as independent variables. The study applies the T-O-E framework to AI readiness and proposes a framework for assessing AI readiness at the manufacturing level. It identifies factors influencing readiness within the technological, organizational, and environmental dimensions, including relative advantage, compatibility, resources, competitive pressure, top management support, and government regulations. Through rigorous analysis, patterns, trends, and correlations are revealed, highlighting a significant link between the T-O-E readiness dimensions and AI adoption readiness. Notably, organizational readiness emerges as a key driver of AI adoption in Malaysian manufacturing companies. The results of this investigation have broad implications, offering suggestions to improve organizational preparedness and unlock AI’s potential benefits for businesses in the industrial sector. Additionally, the research lays the groundwork for further studies on AI readiness across various industries and international contexts. As AI becomes increasingly integrated into manufacturing processes, adaptive businesses gain competitive advantages on a global scale. These advantages include increased productivity, informed decision-making, streamlined quality control, improved customer satisfaction, and potential contributions to economic growth. The study concludes by recommending strategies to reinforce organizational readiness and emphasizes the need for future research to deepen understanding of AI adoption readiness in the manufacturing industry. The integration of AI technology offers benefits such as enhanced productivity, decision-making, quality control, and customer satisfaction, granting businesses a competitive edge in the digital landscape and increasing stakeholder interest. | ||
| کلیدواژهها | ||
| Technology Readiness؛ TOE؛ Artificial Readiness؛ Artificial Intelligence | ||
| مراجع | ||
|
Abdulkarem, A., & Hou, W. (2022). The influence of the environment on cross-border e-commerce adoption levels among SMEs in China: The mediating role of organizational context. SAGE Open, 12(2), 21582440221103855.
Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 114598.
Wahab, S.N., Olugu, E.U., Lee, W.C., and Tan, S.Y. (2018) ‘Big data analytics adoption in Malaysia warehousing industry’, Proceedings of the 32nd International Business Information Management Association Conference, IBIMA 2018 - Vision 2020: Sustainable Economic Development and Application of Innovation Management from Regional expansion to Global Growth, pp. 2349-2365, 15-16 November, Seville Spain.
Baltzan, P. (2019). Business-driven information systems.
Bernama. (2020). Malaysian companies urged to leverage on data and AI - MIDA | Malaysian Investment Development Authority.
Uzir, M.U.H., Bukari, Z., Al Halbusi, H., Lim, R., Wahab, S.N., Rasul, T. Thurasamy, R., Jerin, I. Chowdhury, M.R.K., Tarofder, A.K., Yaakop, A.Y. and Hamid, A.B.A. (2023) ‘Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context’, Heliyon, 9(8), e18666. doi: 10.1016/j.heliyon.2023.e18666.
Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2021). Does data-driven culture impact innovation and performance of a firm? An empirical examination. Annals of Operations Research, 1-26.
Dun & Bradstreet. (2022). Discover manufacturing companies in Selangor - Dun & Bradstreet.
Gao, J., Ren, L., Yang, Y., Zhang, D., & Li, L. (2022). The impact of artificial intelligence technology stimuli on smart customer experience and the moderating effect of technology readiness. International Journal of Emerging Markets.
Gartner. (2022). Gartner Data & Analytics Summit 2022 in Sydney, Australia.
Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65, 102497.
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial intelligence applications for Industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83-111.
Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262-273.
Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., & Niehaves, B. (2021). Enabling AI capabilities in government agencies: A study of determinants for European municipalities. Government Information Quarterly, 101596.
Merhi, M. I., & Harfouche, A. (2023). Enablers of artificial intelligence adoption and implementation in production systems. International Journal of Production Research, 1-15.
Nazarenko, A., Vishnevskiy, K., Meissner, D., & Daim, T. (2022). Applying digital technologies in technology roadmapping to overcome individual biased assessments. Technovation, 110, 102364.
Oxford Insights. (2019). Government AI Readiness Index 2019 — Oxford Insights.
Pizam, A., Ozturk, A. B., Balderas-Cejudo, A., Buhalis, D., Fuchs, G., Hara, T., & Chaulagain, S. (2022). Factors affecting hotel managers’ intentions to adopt robotic technologies: A global study. International Journal of Hospitality Management, 102, 103139.
Sirait, H., Rosalina, S. S., & Sari, E. (2023). The impact of digital innovation on economic growth. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(6), 12.
Yin, X., Li, J., Kadry, S. N., & Sanz-Prieto, I. (2021). Artificial intelligence assisted intelligent planning framework for environmental restoration of terrestrial ecosystems. Environmental Impact Assessment Review, 86, 106493.
Yu, X., Xu, S., & Ashton, M. (2023). Antecedents and outcomes of artificial intelligence adoption and application in the workplace: The socio-technical system theory perspective. Information Technology & People, 36(1), 454-474.
Zhou, K., Liu, T., & Zhou, L. (2015, August). Industry 4.0: Towards future industrial opportunities and challenges. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 2147-2152). IEEE.
Zhou, T., Tang, D., Zhu, H., & Zhang, Z. (2021). Multi-agent reinforcement learning for online scheduling in smart factories. Robotics and Computer-Integrated Manufacturing, 72, 102202
| ||
|
آمار تعداد مشاهده مقاله: 246 تعداد دریافت فایل اصل مقاله: 344 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب