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Abstract: Umetna inteligenca (UI) je pomembna komponenta sodobnega sveta in sočasno pospeševalec digitalne transformacije. Organizacije v svoje poslovanje uvajajo tehnologije UI z namenom ohranjanja konkurenčne prednosti. Vzporedno to ustvarja tudi nove zahteve po veščinah na trgu dela in samih delovnih mestih. Starejši zaposleni (starosti 50+ let) so zaradi slabše razvitih kompetenc, potrebnih za upravljanje s tehnologijami UI, v slabšem položaju kot njihovi mlajši sodelavci, kar dodatno povečuje digitalni razkorak. Pomanjkanje ustreznih kompetenc lahko starejše zaposlene sili v zgodnjo upokojitev, nekonkurenčnost na trgu dela ali celo v brezposelnost. Raziskava temelji na sistematičnem pregledu literature v sedmih bazah podatkov, kjer smo preučevali vlogo delodajalcev pri opolnomočenju starejših zaposlenih v dobi UI. Ugotovitve poudarjajo pomen ustvarjanja kulture vseživljenjskega učenja (VŽU) v organizacijah, ki zaposlene spodbuja k nenehnemu izpolnjevanju v kontekstu hitro spreminjajočih se zahtev pri delu zaradi napredkov UI. V kontekstu starajoče se delovne sile je opolnomočenje starejših ključnega pomena, saj so zaradi omejenih digitalnih kompetenc ranljivi na trgu dela. VŽU tako služi kot ključni mehanizem za opolnomočenje starejših zaposlenih, ker jim omogoča nujno prilagajanje in uspešno delovanje na hitro spreminjajočem se trgu dela.
Keywords: management izobraževanja, opolnomočenje, starejši zaposleni, umetna inteligenca, vseživljenjsko učenje
Published in RUP: 04.12.2025; Views: 224; Downloads: 10
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Abstract: Introduction: Mental health stigma remains a major barrier to accessing psychiatric care worldwide, with pronounced effects in culturally traditional societies such as Peshawar, Pakistan. In the Pashtun cultural context, the code of Pashtunwali—an honor-based system—shapes social attitudes and behaviors, potentially influencing mental health help-seeking patterns. This study examines how cultural narratives, social norms, and stigma interact to affect help-seeking behavior in this sociocultural setting. Methods: A cross-sectional survey was conducted among a stratified random sample of 400 adults aged 19 years and above in Peshawar. Data were collected using culturally validated instruments, including the Mental Illness Stigma Scale (MISS) and a Social Norms Scale. Bivariate analyses employed simple linear regression and binary logistic regression to examine individual relationships between variables. Multivariate analyses, including multiple linear regression and Structural Equation Modeling (SEM), were used to assess combined effects and mediation pathways. Results: Cultural narratives had a positive impact on help-seeking behavior, explaining 42% of its variance. Stigma showed a significant negative association, decreasing help-seeking likelihood by 26% for each unit increase. Social norms demonstrated a positive association with help-seeking behavior and indirectly reduced stigma. Collectively, these variables accounted for 68% of the variance in help-seeking likelihood. Discussion: The findings highlight the pivotal role of culturally resonant narratives and supportive social norms rooted in Pashtunwali in improving mental health service utilization. Addressing stigma while reinforcing positive cultural frameworks can substantially enhance help-seeking behavior in Peshawar and similar sociocultural contexts.
Keywords: cultural narratives, mental health stigma, social norms, psychiatric help, structural equation modeling (SEM)
Published in RUP: 28.08.2025; Views: 647; Downloads: 7
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Abstract: This paper examines the current landscape of artificial intelligence (AI) automation and technologies across various industries in Europe, utilizing descriptive statistics derived from Eurostat data. By analyzing the proportions of AI adoption within key sectors, the aim is to provide a comprehensive overview of the present state of AI integration in European industries. This is relevant from the perspective of its possible economic effects. The existing litareture that focuses on the effects of automation technologies on employment is rather inconsistent and inconclusive (Filippi et al, 2023). According to Bowles (2014) 54 % of European workers are at risk of substitution (by applying the occupation-based approach), while according to Pouliakas (2018) only 13.9 % of workers will face a risk higher than 70 % (by applying the task-based approach). Moreover, McGuinness et al 2021, show how 16 % of adult European workers have recently experienced a skills-displacing technological change, i.e., some changes in the use of technologies (e.g., machinery and ICT systems) in the last five years and thus concludes that several of their skills will become outdated in the next five years. Josten and Lordan (2020) predicted that by 47% of European jobs will be automatable (of which 35% are fully automatable), while 40% of them are not expected to be automated. Anyhow, the probability of automation varies considerably across industries. The service sector is generally less threatened by automation (Pajarinen et al, 2015), no matter the fact that wholesale and retail trade have a high probability of automation (Nedeloska and Quintini, 2018). Several studies conclude that (besides services) industries with a low probability of automation (lower than 40 %) include: education, health and social work, arts, sport and entertainment, management, business and finance, public administration and public utility services (Illessy et al, 2021, Yamashita nad Cummins, 2021; among others). Furthermore, this paper explores projected growth trajectories for AI usage, highlighting anticipated advancements and the potential for increased efficiency and innovation. Basically, an independent survey conducted in 2024 by the German Reichelt elektronics on the current status and potential of technologies (such as AI, ML, big data, robotics and IoT) and their use in European industrial companies shows that many European companies in the industrial sector (60%) believe that production will be fully automated in five years’ time. In addition, more than two thirds of the European industry (68%) consider automation to be essential in order to remain competitive. Therefore this paper also discusses the challenges and hurdles that may impede the widespread adoption of AI technologies, including regulatory, ethical, and infrastructural considerations, while seeking to understand the dynamics of AI implementation in Europe and its implications for future economic development.
Keywords: automation, AI technologies, productivity, Europe
Published in RUP: 28.08.2025; Views: 611; Downloads: 8
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Abstract: Agri-environmental schemes (AECS) are integral components of the European Union’s Common Agricultural Policy, designed to promote environmentally sustainable farming practices and mitigate the adverse impacts of agriculture on ecosystems by providing financial incentives to farmers (Unay-Gailhard and Bojnec, 2016; Ait Sidhoum, Canessa, and Sauer, 2023). This study analyses the impact of AES participation on variable input costs for energy consumption, fertilizer use, and crop protection in Slovenia. Findings indicate that AES participation significantly reduces fertilizer, pesticide, and energy costs. While AES lower input costs, they may also lead to short-term yield reductions, potentially affecting farm profitability and farm efficiency (Baráth, Fertő, and Bojnec, 2020). These results highlight the need for complementary policies that enhance sustainable yield improvements and farm resilience. By promoting resource-efficient practices, AES contributes to reducing environmental externalities such as soil and water pollution and improving population wellbeing (Fukuyama, Hashimoto and Weber, 2020). The study underscores AES as essential for the transition to sustainable agriculture while emphasizing the challenge of balancing environmental, economic, and other sustainability objectives (Fertő and Bojnec, 2024, 2025). Policymakers should consider strategies that support both ecological benefits and farm income stability, ensuring long-term agricultural sustainability and resilience in the face of environmental and economic challenges.
Keywords: agri-environmental schemes, costs, energy, fertilizer, crop protection, Slovenia
Published in RUP: 18.08.2025; Views: 443; Downloads: 3
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Abstract: This study explores how on-location platform workers perceive and interpret algorithmic management within digitally mediated labor environments. While platforms often present their algorithmic systems as neutral, objective, and designed for efficiency, workers frequently encounter them as opaque, inconsistent, and lacking in transparency. In the absence of clear communication about how decisions, such as task allocation, performance ratings, and disciplinary measures, are made, workers engage in a process of sensemaking and develop informal theories to explain how the system functions. These interpretations play a key role in shaping workers’ daily strategies and behaviors. Perceptions about what actions are rewarded or punished, such as logging in at certain times, consistently accepting tasks, or demonstrating specific behavioral patterns, influence how workers organize their availability, pace their work, and try to align with platform expectations. Even when these assumptions are speculative, they nonetheless produce real behavioral effects. In this way, algorithmic ambiguity becomes a powerful mechanism of indirect control. Workers begin to self-discipline based on presumed algorithmic preferences, even in the absence of formal rules or explicit feedback. This dynamic reinforces power asymmetries, as the platform retains control over decision making while minimizing accountability and managerial visibility. Drawing on existing empirical literature, this study argues that the power of algorithmic management lies not only in its ability to automate coordination and oversight, but also in the psychological and behavioral effects it produces through uncertainty. By centering the interpretations and practices of workers, the paper offers insight into the lived experience of platform work and the subtle, often invisible mechanisms through which control is exercised. This perspective contributes to a more nuanced understanding of algorithmic governance in the platform economy, emphasizing that platform power operates as much through worker perception as it does through code. Our study also highlights the need for further research into how such systems shape worker agency, motivation, and well-being.
Keywords: platform work, algorithmic management, job quality
Published in RUP: 01.07.2025; Views: 839; Downloads: 20
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