1. Kompetence prihodnosti v športni industrijiKlemen Širok, Elizabeta Zirnstein, Ajda Fošner, Suzana Sedmak, Barbara Švagan, Ana Grdović Gnip, Igor Stubelj, Suzana Laporšek, 2025, pregledni znanstveni članek Opis: Šport kot gospodarska panoga doživlja hitro preobrazbo, ki jo poganjajo digitalizacija, avtomatizacija in širjenje umetne inteligence. Ti premiki preoblikujejo zahteve na trgu dela in odpirajo vprašanja o ustreznosti veljavnih kompetenčnih mo-delov. Članek uporablja metodologijo predvidevanja potreb po znanju in veščinah (angl. skills anticipation) ter jo prilaga-ja posebnostim športne industrije. Analiza temelji na pregle-du sive literature, kot so poročila o strateškem predvideva-nju in svetovalna poročila, ter izsledke primerja z evropskim ogrodjem znanja, veščin, kompetenc in poklicev (ESCO) ter nedavnim sistematičnim pregledom kompetenc na področju športnega menedžmenta. Rezultati kažejo na dopolnjevanje tradicionalnih kompetenc, kot so vodenje, komunikacija, prila-godljivost in etična zavezanost, z novimi zahtevami po digital-nih, podatkovnih in povezanih kompetencah. Hkrati pa se ka-žejo pomembne vrzeli: kibernetska varnost, digitalna varnost in napredne digitalne veščine (npr. imerzivne tehnologije, upravljanje umetne inteligence). Ugotavljamo, da morajo izo-braževalni programi in usposabljanja v športu združevati teh-nične in k človeku usmerjene kompetence. Prilagodljive oblike izobraževanja in usposabljanja, kot so mikrodokazila, pa naj se dodatno osredotočijo na potrebne specialne veščine in kom-petence, ki so bila prepoznane kot nezadostno zastopane.
Ključne besede: veščine, kompetence, prihodnost, napovedovanje kompetence, tehnološke spremembe, športna panoga
Objavljeno v RUP: 29.12.2025; Ogledov: 167; Prenosov: 0
 Celotno besedilo (16,98 MB)
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3. AI automation and technologies within industries across Europe : current size and expected growthAna Grdović Gnip, 2025, objavljeni povzetek znanstvenega prispevka na konferenci Opis: 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.
Ključne besede: automation, AI technologies, productivity, Europe
Objavljeno v RUP: 28.08.2025; Ogledov: 612; Prenosov: 8
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4. Making sense of the algorithm : perceptions of algorithmic management in location-based platform workBarbara Švagan, Suzana Laporšek, Ajda Fošner, Suzana Sedmak, Elizabeta Zirnstein, Ana Grdović Gnip, Klemen Širok, Igor Stubelj, 2025, objavljeni povzetek znanstvenega prispevka na konferenci Opis: 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.
Ključne besede: platform work, algorithmic management, job quality
Objavljeno v RUP: 01.07.2025; Ogledov: 840; Prenosov: 20
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