1. Kompetence prihodnosti v športni industrijiKlemen Širok, Elizabeta Zirnstein, Ajda Fošner, Suzana Sedmak, Barbara Švagan, Ana Grdović Gnip, Igor Stubelj, Suzana Laporšek, 2025, review article Abstract: 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.
Keywords: veščine, kompetence, prihodnost, napovedovanje kompetence, tehnološke spremembe, športna panoga
Published in RUP: 29.12.2025; Views: 219; Downloads: 1
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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, published scientific conference contribution abstract 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: 903; Downloads: 21
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