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Background: This article delves into an in-depth analysis of the statistically significant differences in AI support levels for project management between SMEs and large enterprises. The research was conducted based on a comprehensive survey encompassing a sample of 473 SMEs and large Slovenian enterprises. Methods: To validate the observed differences, statistical analysis, specifically the Mann–Whitney U test, was employed. Results: The results confirm the presence of statistically significant differences between SMEs and large enterprises across multiple dimensions of AI support in project management. Large enterprises exhibit on average a higher level of AI adoption across all five AI utilization dimensions. Specifically, large enterprises scored significantly higher (p < 0.05) in AI adopting strategies and in adopting AI technologies for project tasks and team creation. This study’s findings also underscored the significant differences (p < 0.05) between SMEs and large enterprises in their adoption and utilization of AI technologies for project management purposes. While large enterprises scored above 4 for several dimensions, with the highest average score assessed (mean value 4.46 on 1 to 5 scale) for the usage of predictive Analytics Tools to improve the work on the project, SMEs’ average levels, on the other hand, were all below 4. SMEs in particular may lag in incorporating AI into various project activities due to several factors such as resource constraints, limited access to AI expertise, or risk aversion. Conclusions: The results underscore the need for targeted strategies to enhance AI adoption in SMEs and leverage its benefits for successful project implementation and strengthen the company’s competitiveness.

This study investigates the adoption of artificial intelligence (AI) software in manufacturing companies in Slovenia, Slovakia and Croatia, and across six production areas. This research ad-dresses a gap in the literature regarding AI software implementation in relation to company size, technology intensity and supply chain role, and examines whether Industry 4.0 (I4.0) readiness influences AI adoption. Data from the European Manufacturing Survey 2022 were analyzed, and showed that the use of AI is still relatively low. On average only 18.4% of companies use AI software in at least one production area. Logistic regression analysis revealed that neither company size nor role in the supply chain or technology intensity are statistically significantly related to AI usage. However, a significant positive relationship was found between I4.0 readiness and AI adoption, suggesting that companies with advanced digital infrastructures and integrated cyber-physical systems are more likely to adopt AI. This finding underlines the importance of digital transformation for the integration of AI software. The study concludes that while company characteristics such as size and the role of the company in the supply chain are not statistically significantly related to the use of AI, the level of digital readiness is crucial.

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.

AbstractThe 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.

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 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 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.

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.

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 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.

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.

Nowadays, the development of Internet technology and the effects of digital transformation are leading to the need for more skillful information and communications technology (ICT) specialists in almost every sector of the economy, in order to benefit from technological innovations. That also leads to the need for every employee to be prepared for new changes in production and service delivery processes. In this study, the factors of digital skills in enterprises that significantly affect the digital entrepreneurship start-up sub-index and digital entrepreneurship scale-up sub-index in the EU countries, are identified and the models are designed to analyse the relationships between the factors. In an increasingly digital world, where production processes and service delivery are continuously evolving, EU countries, that is, policymakers and entrepreneurs should be aware of the digital transformation and, thus, pay attention to the digital skills and provide learning opportunities and supportive environment for the workers in acquiring the new skills and knowledge.

PLS-SEM has been used recently more and more often in studies researching critical factors influencing the acceptance and use of information systems, especially when the technology acceptance model (TAM) is implemented. TAM has proved to be the most promising model for researching different viewpoints regarding information technologies, tools/applications, and the acceptance and use of information systems by the employees who act as the end-users in companies. However, the use of advanced PLS-SEM techniques for testing the extended TAM research models for the acceptance of enterprise resource planning (ERP) systems is scarce. The present research aims to fill this gap and aims to show how PLS-SEM results can be enhanced by advanced techniques: artificial neural network analysis (ANN) and Importance–Performance Matrix Analysis (IPMA). ANN was used in this research study to overcome the limitations of PLS-SEM regarding the linear relationships in the model. IPMA was used in evaluating the importance and performance of factors/drivers in the SEM. From the methodological point of view, results show that the research approach with ANN artificial intelligence complements the results of PLS-SEM while allowing the capture of nonlinear relationships between the variables of the model and the determination of the relative importance of each factor studied. On other hand, IPMA enables the identification of factors with relatively low performance but relatively high importance in shaping dependent variables.

GIS is often used in companies where employees already use enterprise information systems/enterprise resource planning (ERP) systems and where both systems are used simultaneously. This article is a research study of GIS acceptance by users using GIS, not as a stand-alone tool, but as an integrated tool into the ERP system. The research was conducted using the Technology Acceptance Model (TAM), one of the most often used research models for researching behavioral intention and actual use of information systems. For this research study, TAM has been modified and extended with external factors. The main research objective is to identify determinants of GIS acceptance and the attitude of individuals toward using GIS integrated with ERP systems. The structural equation model with partial least squares (PLS) method was used to analyze collected survey data. The study researched 12 external factors in the TAM model, out of which 10 were found significant. The most important factors that impact behavioral intention to use ERP-GIS systems are perceived usefulness of ERP-GIS systems and attitude toward using GIS, followed by education and training about ERP-GIS systems and perceived enjoyment using ERP-GIS systems.

This paper focuses on the diversity of financial resources for Slovenian early-stage entrepreneurs. In the empirical study, we examined whether early-stage entrepreneurs with different characteristics regarding innovativeness also differ regarding the mode of obtaining financial resources, where the average number of used financial resource types and the average share of owned financial resources provided were analysed. Demographic characteristics (age and gender) of early-stage entrepreneurs were also taken into account. As results suggest, there are differences in the diversity of financial resources used between groups of early-stage entrepreneurs in relation to various aspects of their innovativeness. However, a statistically significant difference occurred only when analysing innovativeness in terms of technology. Results also indicate that there are statistically significant differences between groups of entrepreneurs according to their age, in both the number of financial resources used and the share of own financial resources provided. On the other hand, results don’t indicate any statistically significant gender differences, either in the number of financial resources used or in the share of own financial resources provided. This paper is based on Global Entrepreneurship Monitor (GEM) data for Slovenia; the large number of countries participating in the world-wide GEM research enables the international comparability of the topic analysed. As such, this research provides important insights into early-stage entrepreneurs’ behaviour in a country context.

Despite growing literature on the different aspects of airline service quality in relation to behavioural intentions, less attention has been paid to some specific aspects of in-flight services. The focus of the present research is, therefore, on a multiple conceptual model of the quality of in-flight services in relation to passengers’ perception of value, followed by recommendations (word of mouth - WOM) of airlines, as well as the quality and comfort of airline seats. The study is performed using two databases of reviewers’/passengers’ opinions regarding the quality of in-flight airline services and airline seat comfort. Our research results reveal that the perceived comfort of the airplane seat is the most important factor of passengers’ perceived quality of in-flight airline services, which also considerably affects the passengers’ perception of value, and consequently moderates behavioural intentions (in our research, expressed through positive WOM). The analysis of the relative importance of the components of perceived airline seats’ comfort shows that seat width is the most significant factor that contributes to the overall perceived comfort of the airline seat.

Enterprise Resource Planning (ERP) solutions are the most frequently used software tool in companies in all industries. Therefore, the labour market requires the knowledge and skills for usage of ERP solutions from graduates – future employees. The main objective of our paper is therefore the identification of important factors that contribute to the acceptance of ERP solutions by students in economics and business and that shape their intentions to use this knowledge in the future. The model of our research is based on the Technology Acceptance Model (TAM), extended by identified important multidimensional external factors that refer to (1) students’ personal characteristics and information literacy, (2) perceived system and technological characteristics of ERP solutions and (3) perceived support within the study process. Research results revealed that several dimensions of the three multidimensonal factors play an important role in shaping the attitudes towards acceptance of ERP solutions by students.

In this article, we investigate how entrepreneurial intentions of individuals in the eight countries of the Danube region are shaped by different components of individuals’ personal attitudes, the subjective norm and personal behavioral control. We analyze the internal structure of these components as well as some demographic and human capital factors. Cultural and developmental differences influencing variation in causal effects among variables in the model are analyzed. Structural equation modeling is used for data obtained by adult population surveys within the GEM research. Results of our study show that the entrepreneurial intention model is applicable across countries and that the internal effects among components of motivational antecedents exist, although not all hypothesized relationships are confirmed. Our study suggests that the process from perception to intention is similarly shaped across the eight countries of the Danube region, although there are several differences in the magnitude of causal effects as well as differences regarding influential factors.

Cruise tourism generates different types of cruise consumption and related indirect, direct and induced expenditure effects, in homeports as well as in ports of call. Cruise passengers’ expenditures produce positive economic effects for destinations, from increasing the incomes and employment, to tax incomes, duties, etc. Therefore, it is no doubt that cruise stakeholders and local economies can benefit from increased cruise passenger consumption. To stimulate higher consumption and passengers’ satisfaction, it is necessary to design the supportive policy framework and build appropriate quality of products and services. Identifying influential variables of cruise passengers’ expenditures in this sense enables the design of appropriate policies and measures. In the current research, based on a survey of 357 cruise passengers, several variables included in a new theoretical model of the expenditures determinants, such as gender, nationality, frequency of cruising and frequency of visits, were found to be statistically significantly associated with cruise passengers’ expenditures. Several conclusions and suggestions to stimulate cruise passenger expenditures based on research findings are provided.

This paper uses the technology acceptance model (TAM) framework for the research of economic and geography students’ attitudes towards interdisciplinary knowledge. Based on the SmartPLS Structural equation modelling SEM variance-based method, research results were gained through analysis of survey data of economic and geography students. They participated in the Spationomy project in the period of 2017–2019. Online questionnaires were fulfilled before and after students’ participation in the project and their future behavioural intention to use interdisciplinary knowledge was analysed. Based on the research, we can confirm that the Spationomy project has achieved its purpose, as both groups of students (economic and geography students) have acquired interdisciplinary knowledge and students intend to use it also in the future. Therefore, we can argue that the students included in the project in practice gained recognition of systems thinking about the importance of mutual interdisciplinary cooperation towards achieving synergies. The results also show that TAM can be successfully implemented to analyse how students of economics and geography accept the use of interdisciplinary knowledge in the learning process, which is an important implication for management and education as well as from the theoretical implications viewpoint. While effective analysis using TAM has been used successfully and relatively frequently in economics and business field, we have not found relevant examples of its implementation in the broader field of geography. However, the acceptance of geographic information system (GIS) or other information technologies/information software (IT/IS) tool-based approaches of analysis in the geography field may be of most importance. Therefore, also, this represents an important implication for the research area.

The purpose of this paper is to contribute to the understanding of the importance of different determinants and their impact on the severity of injuries of individuals in road traffic accidents, so that measures that are supposed to prevent or reduce severe injury consequences can be developed. In this paper three research models have been built. Model I was built to study the impact of demographic factors (gender and age) on the individual’s likelihood to wear a seat-belt while Model II to study the impact of demographic factors (gender and age) and the impact of wearing a seat belt on the likelihood that fatal injuries of individuals in road traffic accidents occur. Model III was formed to study the impact of several environmental factors on the likelihood that the accident involves severe or fatal injuries of road traffic accident participants. Altogether our study revealed that middle-aged individuals (over 25 years and up to 65 years old) are less likely to wear a seat belt and at the same time more likely to suffer fatal injuries in road traffic accidents. This is the result that implies that the targeted policy measures to the population between 25 and 65 years of age are needed to reduce the fatal injuries occurrence in Slovenia.