Unveiling the drivers: engineers’ intention to adopt artificial intelligence (AI)-driven engineering tools in small and medium-sized enterprises (SMEs)
List of Authors
  • Ai Ping Teoh , S.Abirami Selvam

Keyword
  • AI-driven tools, Perceived Usefulness, Perceived Ease of Use, Perceived Risk, Performance Expectancy, Knowledge in technology

Abstract
  • The study aims to investigate the factors that influence engineers’ decisions to adopt AI-driven tools in small and medium-sized enterprises (SMEs). It will examine key drivers such as perceived usefulness (PU), perceived ease of use (PEOU), performance expectancy (PE), knowledge in technology (KT), and perceived risk (PR). These factors reflect engineers’ beliefs about the tools’ ability to enhance job performance, ease of understanding and use, expected performance improvements, familiarity with technology, and potential risks like data security concerns and job displacement. Through comprehending these factors, the study aims to offer perspectives on how to effectively encourage the adoption of AI in this setting and to pinpoint the root reasons and associations among various variables, especially those concerning cause and effect. The study will use a survey technique to analyze primary data and identify factors influencing the adoption of AI-driven engineering tools in the Malaysian SME sector. Researchers will employ an online questionnaire to examine relationships between variables and compare the data with relevant studies. The data will be analyzed using the SmartPLS 4 tool to identify the major factors influencing the decision to embrace AI. Artificial intelligence (AI) is a broad term that includes a variety of technologies, including machine translation, chatbots, and self-learning algorithms, all of which can assist engineers in better understanding their environment and acting accordingly. The study’s findings will provide insights for enhancing engineering operations, customer satisfaction, and long-term competitiveness in SMEs. By fostering innovation and effective technology adoption strategies, SMEs can harness the power of AI for sustainable growth. The study offers insights for researchers, engineers, and owners/managers of SMEs seeking to leverage AI in an industrial setting. The importance of integrating AI technology within the SME industry and the possible obstacles are explored in this study, which is among the first to uncover the key parameters driving SME adoption of AI-driven engineering tools.

Reference
  • 1. Andrews, J. E., Ward, H., & Yoon, J. W. (2021). UTAUT as a Model for Understanding Intention to Adopt AI and Related Technologies among Librarians. Journal of Academic Librarianship, 47(6). https://doi.org/10.1016/j.acalib.2021.102437

    2. Algerafi, M. A. M., Zhou, Y., Alfadda, H., & Wijaya, T. T. (2023). Understanding the Factors Influencing Higher Education Students’ Intention to Adopt Artificial Intelligence-Based Robots. IEEE Access, 11, 99752–99764. https://doi.org/10.1109/ACCESS.2023.3314499

    3. Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020). Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 142(11). https://doi.org/10.1115/1.4047855

    4. Baabdullah, A. M., Alalwan, A. A., Slade, E. L., Raman, R., & Khatatneh, K. F. (2021). SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices. Industrial Marketing Management, 98, 255–270. https://doi.org/10.1016/j.indmarman.2021.09.003

    5. Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021, April 1). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, Vol. 57. Elsevier Ltd. https://doi.org/10.1016/j.ijinfomgt.2020.102225

    6. Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201. https://doi.org/10.1016/j.techfore.2024.123247

    7. Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(JULY). https://doi.org/10.3389/fpsyg.2019.01652

    8. Chong, L. L., Ong, H. B., & Tan, S. H. (2021). Acceptability of mobile stock trading application: A study of young investors in Malaysia. Technology in Society, 64. https://doi.org/10.1016/j.techsoc.2020.101497

    9. Fearnley, M. R., & Amora, J. T. (2020). Learning Management System Adoption in Higher Education Using the Extended Technology Acceptance Model Volume 8 – Issue 2 IAFOR Journal of Education: Technology in Education Volume 8 – Issue 2 IAFOR Journal of Education: Technology in Education Volume 8 – Issue 2 IAFOR Journal of Education: Technology in Education Volume 8 – Issue 2 IAFOR Journal of Education: Technology in Education – Volume 8 – Issue 2.

    10. Ferri, L., Maffei, M., Spanò, R., & Zagaria, C. (2023). Uncovering risk professionals’ intentions to use artificial intelligence: empirical evidence from the Italian setting. Management Decision. https://doi.org/10.1108/MD-02-2023-0178

    11. Fred D. Davis. (1989). The Technology Acceptance Model.

    12. Huarng, K. H., Yu, T. H. K., & Lee, C. fang. (2022). Adoption model of healthcare wearable devices. Technological Forecasting and Social Change, 174. https://doi.org/10.1016/j.techfore.2021.121286

    13. Hussain, A., Rashid, S., Shabbir, S., Danish, R. Q., & Shah, M. A. (2021). Mediating Role of Productivity between Performance Expectancy and Adaptive Performance of Managers in SMEs. In Bulletin of Business and Economics (Vol. 10).

    14. Ikumoro, A. O., & Jawad, M. S. (2019). Intention to Use Intelligent Conversational Agents in e-Commerce among Malaysian SMEs: An Integrated Conceptual Framework Based on Tri-theories including Unified Theory of Acceptance, Use of Technology (UTAUT), and T-O-E. International Journal of Academic Research in Business and Social Sciences, 9(11). https://doi.org/10.6007/ijarbss/v9-i11/6544

    15. John, N., Wesseling, J. H., Worrell, E., & Hekkert, M. (2022). How key-enabling technologies’ regimes influence sociotechnical transitions: The impact of artificial intelligence on decarbonization in the steel industry. Journal of Cleaner Production, 370. https://doi.org/10.1016/j.jclepro.2022.133624

    16. Katebi, A., Homami, P., & Najmeddin, M. (2022). Acceptance model of precast concrete components in building construction based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) framework. Journal of Building Engineering, 45. https://doi.org/10.1016/j.jobe.2021.103518

    17. Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77. https://doi.org/10.1016/j.tele.2022.101925

    18. Khan, A. N., Jabeen, F., Mehmood, K., Ali Soomro, M., & Bresciani, S. (2023). Paving the way for technological innovation through adoption of artificial intelligence in conservative industries. Journal of Business Research, 165. https://doi.org/10.1016/j.jbusres.2023.114019

    19. Koh, L. Y., Xia, Z., & Yuen, K. F. (2024). Consumer acceptance of the autonomous robot in last-mile delivery: A combined perspective of resource-matching, perceived risk and value theories. Transportation Research Part A: Policy and Practice, 182. https://doi.org/10.1016/j.tra.2024.104008

    20. Kumar, M. S., & Krishnan, S. G. (2020). Perceived Usefulness (PU), Perceived Ease of Use.

    21. Liesa-Orús, M., Latorre-Cosculluela, C., Sierra-Sánchez, V., & Vázquez-Toledo, S. (2023). Links between ease of use, perceived usefulness and attitudes towards technology in older people in university: A structural equation modelling approach. Education and Information Technologies, 28(3), 2419–2436. https://doi.org/10.1007/s10639-022-11292-1

    22. Marikyan, D., & Papagiannidis, S. (2023). Unified Theory of Acceptance and Use of.

    23. Mehedi Hasan Emon Graduate Student, M., & Hassan Associate Dean, F. (2023). Predicting Adoption Intention of Artificial Intelligence- A Study on ChatGPT. In AJSE (Vol. 22).

    24. Mittal, S., Khan, M. A., Purohit, J. K., Menon, K., Romero, D., & Wuest, T. (2020). A smart manufacturing adoption framework for SMEs. International Journal of Production Research, 58(5), 1555–1573. https://doi.org/10.1080/00207543.2019.1661540

    25. Mukherjee, S., Baral, M. M., Lavanya, B. L., Nagariya, R., Singh Patel, B., & Chittipaka, V. (2023). Intentions to adopt the blockchain: investigation of the retail supply chain. Management Decision, 61(5), 1320–1351. https://doi.org/10.1108/MD-03-2022-0369

    26. Oldemeyer, L., Jede, A., & Teuteberg, F. (2024). Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges. Management Review Quarterly. https://doi.org/10.1007/s11301-024-00405-4

    27. Padovano, A., & Cardamone, M. (2024). Towards human-AI collaboration in the competency-based curriculum development process: The case of industrial engineering and management education. Computers and Education: Artificial Intelligence, 7. https://doi.org/10.1016/j.caeai.2024.100256

    28. Rahman, M., Ming, T. H., Baigh, T. A., & Sarker, M. (2022). Adoption of artificial intelligence in banking services: an empirical analysis. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-06-2020-0724

    29. Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A. E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education. Computers and Education, 182. https://doi.org/10.1016/j.compedu.2022.104468

    30. Savastano, M., Amendola, C., Bellini, F., & D’Ascenzo, F. (2019). Contextual impacts on industrial processes brought by the digital transformation of manufacturing: A systematic review. Sustainability (Switzerland), 11(3). https://doi.org/10.3390/su11030891

    31. Shachak, A., Kuziemsky, C., & Petersen, C. (2019, December 1). Beyond TAM and UTAUT: Future directions for HIT implementation research. Journal of Biomedical Informatics, Vol. 100. Academic Press Inc. https://doi.org/10.1016/j.jbi.2019.103315

    32. Shanmugam, A., Thaz Savarimuthu, M., & Chai Wen, T. (2014). Factors Affecting Malaysian Behavioral Intention to Use Mobile Banking With Mediating Effects of Attitude. Academic Research International, 5(2). Retrieved from www.journals.savap.org.pk

    33. Shet, S. V., & Pereira, V. (2021). Proposed managerial competencies for Industry 4.0 – Implications for social sustainability. Technological Forecasting and Social Change, 173. https://doi.org/10.1016/j.techfore.2021.121080

    34. Sovacool, B. K., Axsen, J., & Sorrell, S. (2018, November 1). Promoting novelty, rigor, and style in energy social science: Towards codes of practice for appropriate methods and research design. Energy Research and Social Science, Vol. 45, pp. 12–42. Elsevier Ltd. https://doi.org/10.1016/j.erss.2018.07.007

    35. Tahar, A., Riyadh, H. A., Sofyani, H., & Purnomo, W. E. (2020). Perceived ease of use, perceived usefulness, perceived security and intention to use e-filing: The role of technology readiness. Journal of Asian Finance, Economics and Business, 7(9), 537–547. https://doi.org/10.13106/JAFEB.2020.VOL7.NO9.537

    36. Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies.

    37. Venkatesh, V., Smith, R. H., Morris, M. G., Davis, G. B., Davis, F. D., & Walton, S. M. (2003). Quarterly USER ACCEPTANCE OF INFORMATION TECHNOLOGY: TOWARD A UNIFIED VIEW1.

    38. Ventre, I., & Kolbe, D. (2020). The Impact of Perceived Usefulness of Online Reviews, Trust and Perceived Risk on Online Purchase Intention in Emerging Markets: A Mexican Perspective. Journal of International Consumer Marketing, 32(4), 287–299. https://doi.org/10.1080/08961530.2020.1712293

    39. Wang, J., Lu, Y., Fan, S., Hu, P., & Wang, B. (2022). How to survive in the age of artificial intelligence? Exploring the intelligent transformations of SMEs in central China. International Journal of Emerging Markets, 17(4), 1143–1162. https://doi.org/10.1108/IJOEM-06-2021-0985

    40. Wang, S., & Wang, H. (2020). Big data for small and medium-sized enterprises (SME): a knowledge management model. Journal of Knowledge Management, 24(4), 881–897. https://doi.org/10.1108/JKM-02-2020-0081

    41. Wei, R., & Pardo, C. (2022). Artificial intelligence and SMEs: How can B2B SMEs leverage AI platforms to integrate AI technologies? Industrial Marketing Management, 107, 466–483. https://doi.org/10.1016/j.indmarman.2022.10.008

    42. Zamani, S. Z. (2022). Small and Medium Enterprises (SMEs) facing an evolving technological era: a systematic literature review on the adoption of technologies in SMEs. European Journal of Innovation Management, 25(6), 735–757. https://doi.org/10.1108/EJIM-07-2021-0360

    43. Zerfass, A., Hagelstein, J., & Tench, R. (2020). Artificial intelligence in communication management: a cross-national study on adoption and knowledge, impact, challenges and risks. Journal of Communication Management, 24(4), 377–389. https://doi.org/10.1108/JCOM-10-2019-0137