In our paper we study the perceived innovativeness of entrepreneurs, i.e. owners and managers of start-ups in three neighboring countries-Slovenia, Croatia, and Hungary-based on the research ...framework and adult population surveys within the Global Entrepreneurship Monitor (GEM) research cycles in 2016 and 2017. Innovativeness is studied as a multidimensional process: from the perspective of technologies, product innovations, and competition. Our results show that higher innovativeness of products/services produced by early-stage entrepreneurs is associated with higher levels of technological innovativeness and with lower levels of market competition. Neither gender nor age shows a statistically significant relationship with the product/service innovativeness of early-stage entrepreneurs. The results also show that the specific institutional environment in each country does not moderate the relationships between the innovativeness of products/services on one hand, and technological and market competition viewpoints of innovativeness, on the other.
The purpose of the paper is to create a multidimensional talent management model with embedded aspects of artificial intelligence in the human resource processes to increase employees' engagement and ...performance of the enterprise. The research was implemented on a sample of 317 managers/owners in Slovenian enterprises. Multidimensional constructs of the model include several aspects of artificial intelligence implementation in the organization's activities related to human resource management in the field of talent management, especially in the process of acquiring and retaining talented employees, appropriate training and development of employees, organizational culture, leadership, and reducing the workload of employees, employee engagement and performance of the enterprise. The results show that AI supported acquiring and retaining a talented employees, AI supported appropriate training and development of employees, appropriate teams, AI supported organizational culture, AI supported leadership, reducing the workload of employees with AI have a positive effect on performance of the enterprise and employee engagement. The results will help managers or owners create a successful work environment by implementing artificial intelligence in the enterprise, leading to increased employee engagement and performance of the enterprise. Namely, our results contribute to the efficient implementation of artificial intelligence into an enterprise and give owners or top managers a broad insight into the various aspects that must be taken into account in business management in order to increase employee engagement and enterprise's competitive advantage.
Background: Our research delved into exploring various selected facets of AI-driven employee engagement, from the gender perspective, among Slovenian entrepreneurs. Methods: This research is based on ...a random sample of 326 large enterprises and SMEs in Slovenia, with an entrepreneur completing a questionnaire in each enterprise. Results: Findings suggest that there are no significant differences between male and female entrepreneurs in Slovenia regarding various aspects of AI-supported entrepreneurial management practice including the following: AI-supported entrepreneurial culture, AI-enhanced leadership, adopting AI to reduce employee workload, and incorporating AI tools into work processes. The widespread integration of AI into entrepreneurship marks a transition to a business landscape that values inclusivity and equity, measuring success through creativity, strategic technology deployment, and leadership qualities, rather than relying on gender-based advantages or limitations. Our research also focused on the identification of gender differences in path coefficients regarding the impact of the four previously mentioned aspects of AI on employee engagement. While both genders see the value in using AI to alleviate employee workload, the path coefficients indicate that female entrepreneurs report higher effectiveness in this area, suggesting differences in the implementation of AI-integrated strategies or tool selection. Male entrepreneurs, on the other hand, appear to integrate AI tools into their work processes more extensively, particularly in areas requiring predictive analytics and project scheduling. This suggests a more technical application of AI in their enterprises. Conclusions: These findings contribute to understanding gender-specific approaches to AI in enterprises and their subsequent effects on employee engagement.
The purpose of this article is to identify the differences in various aspects of the perception of artificial intelligence by students of economics and business studies at different levels of study ...and, on this basis, to formulate recommendations both to the higher education institutions themselves, which educate in the field of economic and business sciences, as well as to curriculum designers. First, we utilized descriptive statistics to analyze the responses for each construct among undergraduate and postgraduate students. In the second part, we employed the Kolmogorov-Smirnov and Shapiro-Wilk tests to assess the normality of data distribution. Finally, in the third part, we employed the non-parametric Mann-Whitney U test to identify the differences between undergraduate and postgraduate students. The results show that statistically significant differences can be identified especially in how students of both study levels see and understand the importance of AI. Although we did not identify significant differences between students of both levels in how they see their role in the future labor market, which will be (or already is) characterized by artificial intelligence, we must emphasize that students of both levels evaluate their roles modestly in this respect. Therefore, on this basis, we have made recommendations for more active development and integration of AI in the study process; the article presents important suggestions for improving education to prepare students for the business world of artificial intelligence.
The purpose of the paper is to develop a multidimensional model of the new work environment in the digital age to increase a company's performance and competitiveness in VUCA (volatility, ...uncertainty, complexity, and ambiguity) business environment. The multidimensional model covers the implementation of an agile work environment through the prism of using artificial intelligence technology to increase company's performance and competitiveness. Researched determined multidimensional aspects for successful implementation of work environment in the digital age are, therefore 1) drivers for shifting towards agility, 2) implementation of agile leadership, 3) implementation of an agile work environment, 4) implementation of AI technology in work environment, 5) company's performance, 6) competitiveness. The main survey involved randomly selected 473 medium-sized and large companies in Slovenia. Structural equation modelling was used for statistical data analysis. The results show that drivers for shifting towards agility have a positive effect on implementation of agile leadership. Also, results show that implementation of agile leadership and implementation of AI technology in work environment have a positive effect on implementation of an agile work environment. Moreover, results show that implementation of an agile work environment has positive effect on company's performance and competitiveness. The paper highlights the important multidimensional aspects of the successful implementation of an agile work environment to increase the company's performance and competitiveness. Also, our results will contribute to the proper implementation of the work environment in the digital age and give owners or top managers a broad insight into the various aspects that must be considered in their business governance in today's rapidly changing business environment.
This paper aims to measure the level of artificial intelligence (AI) support to project management (PM) in selected service sector activities. The exploratory factor analysis was employed based on ...the extensive survey on AI in Slovenian companies and the multi-criteria measurement with an emphasis on value functions and pairwise comparisons in the analytic hierarchy process. The synthesis and performance sensitivity analysis results show that in the service sector, concerning all criteria, PM is with the level 0.276 best supported with AI in services of professional, scientific, and technical activities, which also stand out concerning the first-level goals in using AI solutions in a project with the value 0.284, and in successful project implementation using AI with the value 0.301. Although the lowest level of AI support to PM, which is 0.220, is in services of wholesale and retail trade and repair of motor vehicles and motorcycles, these services excel in adopting AI technologies in a project with a value of 0.277. Services of financial and insurance activities, with the level 0.257 second-ranked concerning all criteria, have the highest value of 0.269 concerning the first-level goal of improving the work of project leaders using AI. The paper, therefore, contributes to the comparison of AI support to PM in service sector activities. The results can help AI development policymakers determine which activities need to be supported and which should be set as an example. The presented methodological frame can serve to perform measurements and benchmarking in various research fields.
The paper's main aim is to analyze five constructs of organizational culture, AI-supported leadership, AI-supported appropriate training of employees, teams' effective performance, and employee ...engagement, and their relationship through the prism of artificial intelligence on a sample of large and medium-sized Slovenian companies. The second aim of the paper is to test the proposed model with two different statistical techniques in the scope of structural equation modeling (SEM) that enable us to assess linear (PLS-SEM) and non-linear relationships (CB-SEM) among the constructs. The empirical research included 437 medium-sized and large Slovenian companies. From each company, a CEO or owner participated in our research. The findings of the research with both techniques show that organizational culture had no impact on AI-supported appropriate training of employees and was not significant as well as that organizational culture had an impact on AI-supported leadership. The impact of AI-supported leadership on AI-supported appropriate training of employees were supported only for the PLS-SEM model. The impact of AI-supported leadership for employees on teams was positive. Contrary to that, the impact of AI-supported leadership for business solutions on teams was non-significant. In both cases, AI-supported appropriate training of employees' impact on teams was strong and positive. Also, employee engagement impact on teams was positive and statistically significant with PLS-SEM and CB-SEM methods. The research yields important implications for companies seeking to integrate artificial intelligence effectively in their operations. It emphasizes the critical role of AI-supported leadership in driving positive outcomes, such as improved employee training and enhanced team effectiveness. Companies should focus on developing leaders who can leverage AI tools to foster a skilled and engaged workforce. By adopting data-driven decision-making processes and incorporating insights from structural equation modeling, organizations can develop effective AI integration strategies. These provide valuable guidance for enhancing human resource management practices and achieving successful AI adoption across companies. The findings contribute to the formation of new views in the field of artificial intelligence implementation in the companies and show companies a broader picture of which aspects of human resource management need to be improved.
The purpose of the paper is to present a model of factors affecting the successful project implementation by introducing agility and artificial intelligence to increase the company’s competitiveness. ...In the model, the multidimensional constructs describing the implementation of an agile work environment and artificial intelligence technologies and tools were developed. These multidimensional constructs are agile work environment, agile leadership, agile team skills and capabilities, improving the work of the leader in the project, adopting AI technologies in the project, and using AI solutions in a project. Their impact on successful project implementation and on the company competitiveness was tested. The fundamental reason for conducting this research and developing the model is to enhance the understanding of factors that contribute to the successful implementation of projects and to increase a company’s competitiveness. Our developed model encompasses multidimensional constructs that describe the agile work environment and the utilization of AI technologies. By examining the impact of these constructs on both successful project implementation and company competitiveness, we aimed to establish a comprehensive framework that captures the relationship between agility, AI, and successful project implementation. This model serves as a valuable tool for companies seeking to improve their project implementation processes and gain a competitive edge in the market. The research was based on a sample of 473 managers/owners in medium-sized and large companies. Structural equation modeling was used to test the hypotheses. In today’s turbulent environment, the results will help develop guidelines for a successful combination of agile business practices and artificial intelligence to achieve successful project implementation, increasing a company’s competitiveness.
The purpose of this article is to present the relationships among older employee stress, motivation, satisfaction, and relationships in the workplace using two different approaches and different ...sample sizes. Research was implemented on an initial sample of 1013 older employees. In the next step, six smaller samples were calculated using the random selection of cases, namely samples with 25, 50, 100, 250, 400, and 500 older employees. This way the possible impact of sample size on relationships between latent variables using the covariance-based structural equation modeling (CB-SEM) and the partial least squares structural equation modeling (PLS-SEM) methods was assessed. The results on the larger samples have proved to be quite robust since they were confirmed with both approaches. They indicate that stress has a strong and negative impact on employee relationships and also a negative impact on employee satisfaction. Furthermore, employee relationships have a strong and positive impact on satisfaction and a positive impact on employee motivation. In addition, satisfaction has a strong and positive impact on employee motivation. The present paper helps readers to better understand the difference between the CB-SEM and the PLS-SEM methods. Researchers should be encouraged to use both techniques, even though CB-SEM methods have had a long tradition in management and marketing research since both fields heavily rely on psychometric measurement. From the organizational point of view, conclusions highlight the importance of the impact the variables of older employee stress, motivation, satisfaction and employee relations have on each other in the workplace.
This study examines the impact of project-management practices on high-growth small and medium-sized enterprises (HG SMEs) from a systems perspective, utilizing structural equation modelling (SEM) ...and data from a diverse SME sample. It investigates the intricate relationships among several factors: project management system support, project-management processes, stakeholder involvement, project management success, project success, and HG SME growth. Our findings highlight the substantial positive influence of project-management processes and stakeholder engagement on project management success. These factors subsequently contribute significantly to both project success and the overall growth of HG SMEs. Notably, project management system support does not exhibit a substantial influence on these success factors. Furthermore, our research uncovers important indirect effects. Project-management processes indirectly impact both project success and HG SME growth, underscoring their central role. Similarly, stakeholder involvement indirectly influences HG SME growth through its impact on project success, emphasizing its significance. This study contributes to the existing body of knowledge by emphasizing the critical roles of project-management processes, stakeholder engagement, and project success as drivers of SME growth. These insights have valuable implications for SME managers, project leaders, and policymakers, highlighting the essential nature of effective project management in shaping the growth trajectory of SMEs.
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