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.

Purpose – To work out an innovative organisation model for tourism destinations in rural areas. Design/methodology/approach – By outlining the problematic of tourism development in rural areas and implementing the concept of network management and the systems theory, we developed an innovative approach to destination management for non-urban areas. Findings – Network management means a new lesson for destination management. Our model has been conceived to systematically deal with different subsystems and both systemic and non-systemic entities in a rural destination. Research limitations/implications – The focus is limited to destination management in rural areas only. Although several methods suit different types of destinations, some seem to be less adequate for rural areas. The model was intended to search an optimal organisational model only. Originality/value – The model introduces an original approach to the destination management and allows its application in a wide range of real situations. It can help achieving a better organisation and thus an improved competitiveness of a rural destination.

In addressing the nuanced interplay between consumer attitudes and Artificial Intelligence (AI) use readiness in physical retail stores, the main objective of this study is to test the impacts of prior experience, as well as perceived risks with AI technologies, self-assessment of consumers’ ability to manage AI technologies, and the moderator role of gender in this relationship. Using a quantitative cross-sectional survey, data from 243 consumers familiar with AI technologies were analyzed using structural equation modeling (SEM) methods to explore these dynamics in the context of physical retail stores. Additionally, the moderating impacts were tested after the invariance analysis across both gender groups. Key findings indicate that positive prior experience with AI technologies positively influences AI use readiness in physical retail stores, while perceived risks with AI technologies serve as a deterrent. Gender differences significantly moderate these effects, with perceived risks with AI technologies more negatively impacting women’s AI use readiness and self-assessment of the ability to manage AI technologies showing a stronger positive impact on men’s AI use readiness. The study concludes that retailers must consider these gender-specific perceptions and attitudes toward AI to develop more effective strategies for technology integration. Our research also highlights the need to address gender-specific barriers and biases when adopting AI technology.

The topic of health-promoting leadership has often been investigated on its impact on health outcomes. However, it is still unclear if healthpromoting leadership has an impact on other well-being parameters at work besides health. Another leadership behavior, the leaders’ listening skills, can benefit well-being parameters at the workplace, such as job satisfaction, which in turn lowers turnover intention. In the present study, we investigate the relationship between health-promoting leadership, the leaders’ listening skills, job satisfaction, and turnover intention to get a clearer picture about the effects of different leadership behaviors on the employees’ well-being. The results of an online-study with 354 Austrian and Slovenian workers showed that both types of leadership behaviors had a direct effect on the employees’ job satisfaction and an indirect effect on turnover intention through job satisfaction. Listening was found to have a stronger direct impact on job satisfaction. The findings indicate that both leadership behaviors are able to support well-being at the workplace.