This paper aims to develop a multidimensional model of AI-supported employee workload reduction to increase company performance in today’s VUCA environment. Multidimensional constructs of the model include several aspects of artificial intelligence related to human resource management: AI-supported organizational culture, AI-supported leadership, AI-supported appropriate training and development of employees, employees’ perceived reduction of their workload by AI, employee engagement, and company’s performance. The main survey involved 317 medium-sized and large Slovenian companies. Structural equation modeling was used to test the hypotheses. The results show that three multidimensional constructs (AI-supported organizational culture, AI-supported leadership, and AI-supported appropriate training and development of employees) have a statistically significant positive effect on employees’ perceived reduction of their workload by AI. In addition, employees’ perceived reduced workload by AI has a statistically significant positive effect on employee engagement. The results show that employee engagement has a statistically significant positive effect on company performance. The concept of engagement is based on the fact that the development and growth of the company cannot be achieved by increasing the number of employees or by adding capital; the added value comes primarily from increased productivity, which is a result of the innovative ability of employees and their work engagement, which improve the company’s performance. The results will significantly contribute to creating new views in the field of artificial intelligence and adopting important decisions in creating working conditions for employees in today’s rapidly changing work environment.

Organizations thrive on attaining and sustaining competitiveness because of the turbulent, rapidly changing business environment. The research shows that it is possible to improve organizations’ competitiveness by considering the multidimensional model viewpoints, which include the strategic talent management ecosystem viewpoints and the strategic agile management ecosystem viewpoints. The multidimensional model includes the essential constructs for developing strategic talent management, i.e., acquiring talented employees, target development of talented employees, designing talent teams, and talent leadership, as well as the following essential constructs for developing strategic agile management, i.e., designing agile teams, agile leadership, and support agile management culture. The research was implemented on 532 owners/managers in organizations. Structural equation modeling was used to test the hypotheses. Results show that developing a strategic talent management ecosystem positively affects developing a strategic agile management ecosystem and leads to higher organization competitiveness. A developed multidimensional model will help owners and managers comprehensively embrace and implement agility in their organization to increase competitiveness.

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.

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.

Background and Purpose: Age diverse employees are faced with different types of occupational stress and different symptoms of burnout when carrying out their work. Therefore, the role of age in job design and implementation of work should not be ignored. The main aim of this paper is to investigate the age differences of occupational stress and symptoms of burnout of employees and to analyze the impact of occupational stress, symptoms of burnout and age on employees’ work satisfaction. Methodology : The paper is based on research that includes a survey of employees in two age groups: younger employees under 50 years of age and older employees between 50 to 65 years of age. Factor analysis was used to form the constructs of occupational stress, symptoms of burnout and employees’ work satisfaction. The non-parametric Mann-Whitney U test was used to test differences in the obtained constructs of occupational stress and symptoms of burnout in the workplace between two independent groups. The multiple regression analysis was used to examine the impact of occupational stress, symptoms of burnout and age on employees’ work satisfaction. Results : The results show there are statistically significant differences in occupational stress and symptoms of burnout in the workplace between older and younger employees. On average, younger employees perceived higher levels of occupational stress and burnout as compared to the older group. Research results also show that occupational stress, behavioral symptoms and emotional symptoms of burnout, as well as age, have a significant impact on employees’ work satisfaction. Conclusion: Managing diversity requires a strategic approach to managing people at work. Therefore, this research adds to the body of knowledge by pointing out the relationship between age and occupational stress and burnout symptoms. Because the role of age in job design has largely been ignored, these relationships have not been studied enough.

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.