HomeInternational Journal of Multidisciplinary: Applied Business and Education Researchvol. 4 no. 12 (2023)

Exploring Challenges and Opportunities: Evaluating the Awareness and Readiness of Selected Government Agencies in Adopting Artificial Intelligence (AI)

 Jake C. Campued | Dorothy-May M. Papa | Armstrong C. De Castro | Bernandino P. Malang

 

Abstract:

This study undertakes a comprehensive examination of the awareness, skills, attitude, and readiness of respondents regarding the adoption of Artificial Intelligence (AI) applications in their professional settings. While the research evaluates respondents' familiarity with AI tools, proficiency levels, and overall attitude towards AI integration, it also strives to present a nuanced perspective by exploring potential challenges and reservations. The data, collected through a structured survey employing a Likert scale, captures diverse viewpoints on awareness, skills, attitude, and readiness towards AI applications. The findings reveal a generally positive outlook among respondents, emphasizing their commendable awareness of AI technologies and a strong inclination towards potential benefits. Despite varying levels of proficiency with specific AI tools, respondents express a collective willingness to embrace new technologies. The study identifies a positive attitude towards AI integration in work processes, accompanied by a proactive approach towards skill development and troubleshooting. However, it is crucial to note the potential challenges and reservations reported by some respondents, offering a balanced view of their preparedness for AI adoption. While the overall disposition towards AI technologies is favorable, the study underscores the importance of tailored training and development programs. The varying levels of proficiency reported highlight the need for targeted initiatives to address specific skill gaps. Organizations aiming to leverage AI technologies can benefit from the insights provided, emphasizing the significance of accessible training and creating a supportive environment for employees. By acknowledging challenges and reservations, this study contributes to a more comprehensive understanding of the landscape, facilitating informed strategies for successful AI integration in the workplace.



References:

  1. Adoption of Artificial Intelligence (AI) in Local Governments: An Exploratory Study on the Attitudes and Perceptions of Offi-cial - University Knowledge Digital Re-pository - UPLB, by John Paul M. Medina, et al.
  2. Al-Jumeily, D., Hussain, A., Randles, M., & Al-Jumaily, M. (2017). A framework for an AI decision support system for per-sonnel planning. Neural Computing and Applications, 28(12), 3619-3628.
  3. Arntz, M., Gregory, T., & Zierahn, U. (2016). The Risk of Automation for Jobs in OECD Countries: A Comparative Analy-sis. OECD Social, Employment and Mi-gration Working Papers, No. 189, OECD Publishing, Paris. https://doi.org/10.1787/5jlz9h56dvq7-en
  4. Artificial Intelligence and Government: Oppor-tunities and Challenges, by Organiza-tion for Economic Co-operation and Development (OECD).
  5. Awareness and Readiness of Nigerian Poly-technic Students towards Adopting Ar-tificial Intelligence in Libraries - Re-searchGate, by K. A. Owolabi, et al.
  6. Bala, R. (2019). Public understanding of artifi-cial intelligence: A review. Journal of Artificial Intelligence and Soft Compu-ting Research, 9(3), 177-188.
  7. Bala, R., & Venkatesh, V. (2020). How do indi-vidual technology users influence in-formation systems that shape their work? A theory of technology appro-priation. Information Systems Re-search, 31(4), 1017-1037.
  8. Barney, J. B. (1991). Firm Resources and Sus-tained Competitive Advantage. Journal of Management, 17(1), 99-120.
  9. Brown, A., & Jones, B. (2018). Age and atti-tudes towards technology in the work-place. Journal of Organizational Behav-ior, 39(2), 170-189.
  10. Brynjolfsson, E., & McAfee, A. (2014). The Sec-ond Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Tech-nologies. W. W. Norton & Company.
  11. Chui, M., Manyika, J., & Mehra, S. (2018). Where AI can and can't replace hu-mans. McKinsey Quarterly. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/where-machines-can-replace-humans-and-where-they-cant-yet#
  12. Cohen, D., Lindvall, M., & Costa, P. (2019). AI for dummies. John Wiley & Sons.
  13. Columbus, L. (2018). How AI is enabling edge computing and IoT adoption. Forbes. https://www.forbes.com/sites/louiscolumbus/2018/12/19/how-ai-is-enabling-edge-computing-and-iot-adoption/#7aa57b337d6d
  14. Columbus, L. (2019). 10 ways machine learn-ing is revolutionizing manufacturing. Forbes. https://www.forbes.com/sites/louiscolumbus/2019/11/23/10-ways-machine-learning-is-revolutionizing-manufacturing/#5f6d15067e0b
  15. Davis, F. D. (1989). Perceived Usefulness, Per-ceived Ease of Use, and User Ac-ceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
  16. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theo-retical models. Management Science, 35(8), 982-1003.
  17. Davison, R. M., & Martinsons, M. G. (2016). Legacy system transition strategies: A case study of handling operations in the aftermath of system failure. Infor-mation Systems Journal, 26(2), 109-139.
  18. Dignum, V. (2018). Responsible artificial intel-ligence: How to develop and use AI in a responsible way. AI & Society, 33(2), 159-165.
  19. Eisenhardt, K. M., & Tabrizi, B. N. (1995). Ac-celerating adaptive processes: Product innovation in the global computer in-dustry. Administrative Science Quar-terly, 40(1), 84-110.
  20. Eisenhardt, K. M., & Tabrizi, B. N. (1995). Ac-celerating Adaptive Processes: Product Innovation in the Global Computer In-dustry. Administrative Science Quar-terly, 40(1), 84-110.
  21. Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and Tensor-Flow. O'Reilly Media, Inc.
  22. Hultin, M., & Szabó-Morvai, Á. (2020). Gender diversity, team decision quality, and innovation in organizations: A new framework. Organizational Psychology Review, 10(2), 109-129.
  23. IBM Global AI Adoption Index 2022, by IBM.
  24. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guide-lines. Nature Machine Intelligence, 1(9), 389-399.
  25. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
  26. Kasi, V., Ramanathan, A., & Ismail, I. (2020). Artificial intelligence in healthcare: A critical review of the state-of-the-art. Health Information Science and Sys-tems, 8(1), 1-10.
  27. Kasi, V., Ramanathan, A., & Ismail, I. (2020). Artificial intelligence in healthcare: A critical review of the state-of-the-art. Health Information Science and Sys-tems, 8(1), 1-10.
  28. Kudyba, S., & Diwan, R. (2016). Analytics in healthcare: A practical introduction. CRC Press.
  29. Kudyba, S., & Hoptroff, R. (2020). A frame-work for understanding employee re-sistance and acceptance towards artifi-cial intelligence in the workplace. Ex-pert Systems with Applications, 144, 113061.
  30. Lacity, M. C., Willcocks, L. P., & Craig, A. (2017). Robotic process automation at Telefónica O2. MIS Quarterly Execu-tive, 16(2).
  31. Larson, S. D. (2010). Media and technology usage and attitudes among college stu-dents: A 10-year comparison. TechTrends, 54(2), 19-26.
  32. Li, X., & Karahanna, E. (2015). Online recom-mendation systems in a B2C e-commerce context: A review and fu-ture directions. Journal of the Associa-tion for Information Systems, 16(2), 72-107.
  33. Liao, Q. V., Liu, Y., Marley, A. A. J., & Lu, J. (2019). How do humans understand and trust automation that makes deci-sions? A review of the empirical litera-ture. Ergonomics, 62(2), 155-172.
  34. Nguyen, H., Pham, T., Nguyen, T., & Hluchy, L. (2019). Are you afraid of AI? An empir-ical study of the effect of self-efficacy on AI adoption. In 2019 IEEE 11th In-ternational Conference on Knowledge and Systems Engineering (KSE) (pp. 1-6). IEEE.
  35. Nguyen, T. H., Newby, M., & Macaulay, M. J. (2020). Information technology adop-tion in small businesses: Confirmation of a proposed framework. Journal of Small Business Management, 58(1), 28-59.
  36. Oliveira, T., & Martins, M. F. (2019). Under-standing e-government services adop-tion: A unified theory of acceptance and use of technology and perceived risk application. Government Infor-mation Quarterly, 36(3), 501-515.
  37. PwC. (2022). PwC AI Readiness Index 2022. Retrieved from https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html
  38. Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
  39. Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
  40. Tajpour, A., & Neshat, M. (2019). Artificial in-telligence in medicine: A systematic review of its applications and risks. Health Information Management Jour-nal, 48(2), 62-75.
  41. The AI Playbook for Government, by World Economic Forum.
  42. The Future of Government Services: Artificial Intelligence and Beyond, by Deloitte Insights.
  43. Van den Brink, M., & Benschop, Y. (2014). Gender and leadership: A feminist per-spective. Routledge.
  44. Venkatesh, V., Morris, M. G., Davis, G. B., & Da-vis, F. D. (2003). User acceptance of in-formation technology: Toward a uni-fied view. MIS Quarterly, 27(3), 425-478.
  45. Venkatesh, V., Morris, M. G., Davis, G. B., & Da-vis, F. D. (2012). User acceptance of in-formation technology: Toward a uni-fied view. MIS Quarterly, 27(3), 425-478.
  46. Wang, D., Xing, Y., Cao, Y., & Zhang, Y. (2020). Examining the factors influencing em-ployees' adoption of business analyt-ics: Evidence from China. Industrial Management & Data Systems, 120(9), 1756-1775.
  47. Wong, Y. D., Raman, M., & Sriratanaviriyakul, N. (2018). Cultural determinants of adoption intention towards technolog-ical innovation: A study on emerging mobile payment services in Malaysia. Information Development, 34(4), 284-299.
  48. World Economic Forum. (2023, January 18). The Global Gender Gap Report 2023. Retrieved from https://www3.weforum.org/docs/WEF_GGGR_2023.pdf
  49. Zhang, Y., Cao, Q., Zhang, S., Zhang, L., & Guo, L. (2019). Big data driven marketing: Applying neural network analysis of social media and purchase behaviors. IEEE Transactions on Industrial Infor-matics, 15(6), 3676-3684.