The Impact of Artificial Intelligence on the Performance of Virtual Teams: A Field Study on Al-Omair Construction Company
DOI:
https://doi.org/10.61856/thh8kz78Keywords:
الذكاء الاصطناعي، الفرق الافتراضية، تحسين الأداء، شركة الأومير للمقاولاتAbstract
This study aimed to examine the impact of artificial intelligence (AI) in its various dimensions expert systems, neural networks, genetic algorithms, and intelligent agents on the performance of virtual teams. A descriptive analytical approach was employed, utilizing a questionnaire to collect data from a random sample of 69 employees at Al-Omair Construction Company in the Kingdom of Saudi Arabia. The results revealed variability in the degree of AI technology implementation at the company. However, the perceptions of respondents were generally positive, with an average score of 4.02. The actual performance of the virtual teams was also high, with a mean score of 4.15. The study showed a statistically significant impact of the combined AI dimensions on virtual team performance, with a correlation coefficient of 0.965 and a coefficient of determination of 0.931. This indicates that AI explains 93.1% of the variance in virtual team performance. The study recommends that Al-Omair Construction Company adopt AI as a tool for administrative development to gain a competitive advantage. Furthermore, the company should enhance virtual team management by focusing on improving communication, building trust among team members, fostering a culture of achievement, and establishing an organizational culture that supports smart applications to further improve virtual team performance
References
Budzik, J. and Hammond, K. (2086), User Interaction With Everyday Applications as Context For Just-In-Time Information’s Access, Proceedings of the 2000 International Conference on Intelligent User Interfaces, 44-51. DOI:10.1145/325737.325776
Castellano, S., Chandavimol, K., Khelladi, I., & Orhan, M. A. (2021). Impact of self-leadership and shared leadership on the performance of virtual R&D teams. Journal of Business Research, 128, 578–586. https://doi.org/10.1016/j.jbusres.2020.12.030
Forrest, E. & Hoanca, B. (2015). Artificial Intelligence: Marketing's Game Changer. In T. Tsiakis (Ed.), Trends and Innovations in Marketing Information Systems (pp. 45-64). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-4666-8459-1.ch003
Golab-Andrzejak, E. (2022). Enhancing Customer Engagement in Social Media with AI - a Higher Education case study. Procedia Computer Science, 207. https://doi.org/10.1016/j.procs.2022.09.361
Gupta, P., Lakhera, G., & Sharma, M. (2024). Examining the impact of artificial intelligence on employee performance in the digital era: An analysis and future research direction. The Journal of High Technology Management Research, 35(2), . https://doi.org/10.1016/j.hitech.2024.100520
Hariguna, T., & Ruangkanjanases, A. (2024). Assessing the impact of artificial intelligence on customer performance: A quantitative study using partial least squares methodology. Data Science and Management, 3. 155-163 . https://doi.org/10.1016/j.dsm.2024.01.001.
Klonnek, F., & Parker, S. K. (2021). Designing smart teamwork: How work design can boost performance in virtual teams. Organizational Dynamics, 50(1), 100843. https://doi.org/10.1016/j.orgdyn.2021.100841
Kwaye, A. S. (2018). Effective strategies for building trust in virtual teams (Doctoral dissertation, Walden University). Walden Dissertations and Doctoral Studies, 5740. Retrieved from https://scholarworks.waldenu.edu/dissertations/5740
Prentice, Catherine, Wong, IpKin Anthony & Lin, Zhiwei. (2023). Artificial intelligence as a boundary-crossing object for employee engagement and performance. Journal of Retailing and Consumer Services. (73). https://doi.org/10.1016/j.jretconser.2023.103376.
Ramachandran, K, Mary, A. Apsara, Hawladar, Shibani, Asokk, D, Bhaskar, Bandi & Pitroda, Dr. Jayeshkumar. (2021). Machine learning and role of artificial intelligence in optimizing work performance and employee behavior. Materials Today: Proceedings. 51(8). https://doi.org/10.1016/j.matpr.2021.11.544 .
Rosa, W & Bechler, C. (2024). Unveiling the Adverse Effects of Artificial Intelligence on Financial Decisions Via the AI-IMPACT Model. Current Opinion in Psychology. 58. https://doi.org/10.1016/j.copsyc.2024.101843.
Saafein, O & Shaykhian, G. (2014). Factors affecting virtual team performance in telecommunication support environment. Telematics and Informatics. 31(3). https://doi.org/10.1016/j.tele.2013.10.004.
Tolun, M.R., Sahin, S. and Oztoprak, K. (2016) Expert Systems. In: Ley, C., Ed., Kirk-Othmer Encyclopedia of Chemical Engineering, John Wiley & Sons, New York, 1-12. https://doi.org/10.1002/0471238961.0524160518011305.a01.pub2
Varma, Arup, Pereira, Vijay & Patel, Parth. (2024). Artificial intelligence and performance management. Organizational Dynamics. 53(1). https://doi.org/10.1016/j.orgdyn.2024.101037
Verma, S. and Sharma, A. (2019). “Artificial intelligence: Employment and society, “International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(752). 239-242. http://creativecommons.org/licenses/by-nc-nd/4.0/
Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility, International Journal of Information Management. (67). https://doi.org/10.1016/j.ijinfomgt.2022.102544
Wang, C., Luo, D., Deng, Q., & Yang, G. (2024). Dynamics analysis and FPGA implementation of discrete memristive cellular neural network with heterogeneous activation functions. Chaos, Solitons & Fractals, 177, 113270. https://doi.org/10.1016/j.chaos.2024.113270
Wijayati, D. T, Rahman, Z, Fahrullah, A, Wahyudi R, Muhammad, F, Arifah, I & Kautsar, A. (2022). A study of artificial intelligence on employee performance and work engagement: the moderating role of change leadership. International Journal of Manpower. 43(2). DOI: 10.1108/IJM-07-2021-0423.
Yin, J., Ngiam, K. Y., & Teo, H. H. (2021). Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. Journal of medical Internet research, 23(4), e25759. https://doi.org/10.2196/25759
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