References
- Ajgaonkar, S., Neelam, N.G., & Wiemann, J. (2021). Drivers of workforce agility: a dynamic capability perspective. International Journal of Organizational Analysis, 30(4), 951-982. DOI: 10.1108/IJOA-11-2020-2507
- Attar, M., & Abdul-Kareem, A. (2020). The Role of Agile Leadership in Organisational Agility, Akkaya, B. (Ed.) Agile Business Leadership Methods for Industry 4.0, Emerald Publishing Limited, Leeds, pp. 171-191. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/978-1-80043-380-920201011
- Babber, G., & Mittal, A. (2023). Achieving sustainability through the integration of lean, agile, and innovative systems: implications for Indian micro small medium enterprises (MSMEs). Journal of Science and Technology Policy Management, Vol. ahead-of-print No. ahead-of-print. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/JSTPM-05-2023-0087
- Bhalerao, K., Kumar. A., & Pujari, P. (2022). A study of Barriers and Benefits of Artificial Intelligence Adoption in small and medium enterporise. Academy of Marketing Studies Journal, 26(S1), 1-6.
- Bresciani, S., Ferraris, A., Romano, M. & Santoro, G. (2021). Agility for Successful Digital Transformation”, Digital Transformation Management for Agile Organizations: A Compass to Sail the Digital World, Emerald Publishing Limited, Leeds, pp. 167-187. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/978-1-80043-171-320211010
- Dabbous, A., Aoun Barakat, K., & Merhej Sayegh, M., (2022). Enabling organizational use of artificial intelligence: an employee perspective. Journal of Asia Business Studies, 16(2), 245–266. DOI: https://doi.org/10.1108/JABS-09-2020-0372
- Denning, S. (2016). How to make the whole organization “Agile”. Strategy & Leadership, 44(4), 10–17. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/SL-06-2016-0043
- EESC (European Economic and Social Committee) (2021). Boosting the use of Artificial Intelligence in Europe’s micro, small and medium-sized Enterprises. Retrieved from https://www.eesc.europa.eu/en/our-work/publications-other-work/publications/boosting-use-artificial-intelligence-europes-micro-small-and-medium-sized-enterprises
- English, L. (2023). The Impact Of AI On Company Culture And How To Prepare Now. Retrieved from https://www.forbes.com/sites/larryenglish/2023/05/25/the-impact-of-ai-on-company-culture-and-how-to-prepare-now/?sh=511060df5f15
- Hansen, E. B., & Bøgh, S. (2021). Artifcial Intelligence and Internet of Things in Small and Medium-Sized Enterprises: A Survey. Journal of Manufacturing Systems, 58(2), 362–372. DOI: https://doi.org/10.1016/j.jmsy.2020.08.009
- Hollander, M., Wolfe, D. A., & Chicken, E. (2013). Nonparametric Statistical Methods. USA: Wiley
- Hughes, C., Robert, I., Frady, K., & Arroyos, A. (2019). Artificial Intelligence. Employee Engagement, Fairness, and Job Outcomes, Managing Technology and Middle-and Low-skilled Employees (The Changing Context of Managing People), Emerald Publishing Limited, 61-68. DOI: https://doi.org/10.1108/978-1-78973-077-720191005
- Isensee, C., Griese, K. M., & Teuteberg, F. (2021). Sustainable artificial intelligence: A corporate culture perspective. Nachhaltigkeits Management Forum 29, 217–230. DOI: https://doi.org/10.1007/s00550-021-00524-6
- Kanade, V. (2022). What Is Machine Learning? Retrieved from https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-ml/
- Klein, V. B., & Todesco, J. L. (2021). COVID-19 Crisis and SMEs Responses: The Role of Digital Transformation. Knowledge and Progress Management, 28(2), 117–133. DOI: https://doi.org/10.1002/kpm.1660
- Kureljusic, M., & Metz, J. (2023). The applicability of machine learning algorithms in accounts receivables management. Journal of Applied Accounting Research, 24(4), pp. 769-786. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/JAAR-05-2022-0116
- Lantz, B. (2021). Overview of Machine Learning Tools, Einhorn, M., Löffler, M., de Bellis, E., Herrmann, A. and Burghartz, P. (Ed.) The Machine Age of Customer Insight. Emerald Publishing Limited, Leeds, pp. 79-90. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/978-1-83909-694-520211008
- Liu, Y., Chung, H.F.L., Zhang, Z., & Wu, M. (2023). When and how digital platforms empower professional services firms: an agility perspective. Journal of Service Theory and Practice, 33(2), 149–168. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/JSTP-04-2022-0092
- Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2022). Impact of artificial intelligence on employees working in industry 4.0 led organizations. International Journal of Manpower, 43(2), 334–354. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/IJM-03-2021-0173
- Mani, S., & Mishra, M. (2020). Characteristics and ingredients of an agile work force – a strategy framework. Strategic HR Review, 19(5), 227–230. DOI: https://doi.org/10.1108/SHR-02-2020-0013
- Martinez-Sanchez, A., & Vicente-Oliva, S. (2023). Supporting agile innovation and knowledge by managing human resource flexibility. International Journal of Innovation Science, 15(3), 558–578. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/IJIS-11-2021-0200
- McKinsey Global Institute. (2023). AI could increase corporate profits by $4.4 trillion a year, according to new research. Retrieved from https://www.mckinsey.com/mgi/overview/in-the-news/ai-could-increase-corporate-profits-by-4-trillion-a-year-according-to-new-research
- Mer, A. (2023). Artificial Intelligence in Human Resource Management: Recent Trends and Research Agenda. In Grima, S., Thalassinos, E., Noja, G.G., Stamataopoulos, T.V., Vasiljeva, T. and Volkova, T. (Ed.) Digital Transformation, Strategic Resilience, Cyber Security and Risk Management (Contemporary Studies in Economic and Financial Analysis, 111B. Leeds: Emerald Publishing Limited, pp. 31-56. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/S1569-37592023000111B003
- Papadopoulos, T., Baltas, K. N., & Baltac, M. E. (2020). The Use of Digital Technologies by Small and Medium Enterprises During COVID-19: Implications for Theory and Practice. International Journal of Information Management, 55, 102192. DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102192
- Peeters, T., Van De Voorde, K., & Paauwe, J. (2022). The effects of working agile on team performance and engagement. Team Performance Management, 28(1/2), 61–78. DOI: https://doi.org/10.1108/TPM-07-2021-0049
- Petermann, M.K.H., & Zacher, H. (2021). Development of a behavioral taxonomy of agility in the workplace. International Journal of Managing Projects in Business, 14(6), 1383–1405. DOI: https://doi.org/10.1108/IJMPB-02-2021-0051
- Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599-2629. DOI: https://doi.org/10.1108/BIJ-04-2020-0186
- Prentice, C., Wong, A.I., & Lin, Z. C.J. (2023). Artificial intelligence as a boundary-crossing object for employee engagement and performance, Journal of Retailing and Consumer Services, 73, 103376. DOI: https://doi.org/10.1016/j.jretconser.2023.103376.
- Qiu, H., Li, M., Bai, B., Wang, N., & Li, Y. (2022). The impact of AI-enabled service attributes on service hospitableness: the role of employee physical and psychological workload. International Journal of Hospitality Management, 34(4), 1–24. DOI: https://doi.org/10.1108/IJCHM-08-2021-0960
- Ransbotham, S., Candelon, F., Kiron, D., LaFountain, B., & Khodabandeh, S. (2021). The Cultural Benefits of Artificial Intelligence in the Enterprise. Retrieved from https://sloanreview.mit.edu/projects/the-cultural-benefits-of-artificial-intelligence-in-the-enterprise/
- Ribeiro, J., Lima, R., Eckhardt, T., & Paiva, S., (2021). Robotic Process Automation and Artificial Intelligence in Industry 4.0 – A Literature Review. Procedia Computer Science 181(1), 51-58. DOI: https://doi.org/10.1016/j.procs.2021.01.104
- Solheim, M.C.W., Aadland, T., Eide, A.E., & Haneberg, D.H. (2023). Drivers for agility in times of crisis. European Business Review, 35(1), 57–73. DOI: https://doi-org.ezproxy.lib.ukm.si/10.1108/EBR-01-2022-0014
- Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54–70 DOI: https://doi.org/10.1016/j.cogr.2023.04.001
- Statistical Office of the Republic of Slovenia. (2021). Digital entrepreneurship. Retrieved from https://www.stat.si/StatWeb/en/News/Index/9885
- Statistical Office of the Republic of Slovenia. (2022). Digital entrepreneurship. Retrieved from https://www.stat.si/StatWeb/News/Index/10766
- 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, 102544. DOI: https://doi.org/10.1016/j.ijinfomgt.2022.102544.
- Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E., (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. DOI: https://doi.org/10.1108/BPMJ-10-2019-0411
- Wijayati, D. T., Rahman, Z., Fahrullah, A., Rahman, M. F. W., Arifah, I. D. C., & 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), 486–512. DOI: https://doi.org/10.1108/IJM-07-2021-0423
- ZGD-1. (2006). Companies Act (ZGD-1). Retrieved from http://www.pisrs.si/Pis.web/pregledPredpisa?id=ZAKO4291#