The integration of generative AI into creative industries is reshaping how content is produced, evaluated, and distributed. While recent advancements offer new opportunities for automation and innovation, they also raise questions about authorship, authenticity, and professional identity. This study examines public discourse on generative AI in creative domains through a text-mining analysis of nearly 4000 Reddit posts and comments. Drawing on six relevant subreddits from 2022 to 2025, the research investigates the structure of user engagement, interaction dynamics, and language patterns. It identifies dominant terms and phrases related to AI creativity, explores thematic clusters, and compares discussion styles across key tools such as Midjourney, ChatGPT, Stable Diffusion, and DALL·E. Additionally, it provides a sentiment overview based on automated classification and narrative interpretation. The findings show that Reddit users engage with generative AI not only as a set of technical tools but as a source of cultural, ethical, and creative negotiation. This study contributes to a deeper understanding of how digital transformation in creative industries is shaped by public perception, platform discourse, and evolving community norms.

Generative artificial intelligence (GenAI) is increasingly transforming creative industries through its ability to generate high-quality content, raising critical questions about authorship, ownership, and the future of creative labor. This paper addresses these challenges by conducting a systematic bibliometric review of 119 peer-reviewed articles on GenAI in the creative sectors, published between 2023 and 2025. The study applies PRISMA 2020 guidelines and keyword co-occurrence analysis using VOSviewer to identify thematic clusters and map research trends. The central research question is how the academic literature conceptualizes the role and impact of GenAI within creative industries and how this has evolved over time. Findings reveal nine major thematic areas, ranging from technical implementations to ethical, economic, and institutional perspectives. The analysis shows that recent research emphasizes not only the technological capacities of GenAI, but also its implications for value creation, creative agency, and industry structures. The main contribution of the paper lies in offering a structured overview of current research trajectories, clarifying conceptual ambiguities, and highlighting understudied areas—particularly regarding the intersection of GenAI, platform economies, and labor dynamics. The review also identifies a methodological gap in comparative empirical studies and proposes directions for future research. By mapping the evolving discourse on GenAI in creative industries, this study contributes to both scholarly understanding and policy development. It provides a foundation for interdisciplinary inquiry and a forward-looking agenda for critically assessing GenAI’s role in reshaping creative work.

BACKGROUND: In the last 15 years, theoretical discussions, and empirical research on the domain of business models have grown significantly. Different authors understand and focus on either constituencies of business models or narratives. The question remains what the predictive value of business model is. OBJECTIVES: The purpose of this interdisciplinary conceptual paper is to investigate and discuss business models as scientific models. METHODS: This article is conceptual, although previous studies on business models enrich this discussion. RESULTS: A narrative perspective on business models shows that they also have a predictive value that is expressed through language not in numbers, maps, and diagrams. The process of reflexivity that is contextualised and bounded in the form of a narrative lies at the centre of designing and changing the business model. The article provides substantial discussion on business models as scientific models, and their predictive power. These concepts have not been deeply discussed yet. CONCLUSIONS: An argument is provided to support the proposition that business models can be considered as scientific models that is, however, embedded in specific organizational, cultural, and technological contexts. Future research needs to be directed towards the empirical analysis of narratives in companies.

This study develops the Information Systems–Organizational Fit Alignment Model (ISOFAM) to evaluate information systems (IS) alignment during the pre-combination phase of mergers and acquisitions (M&A)—a critical yet underexplored stage in integration planning. Through constructivist grounded theory and the reanalysis of qualitative data from two anonymized M&A cases—one domestic (Slovenian) and one cross-border (European)—this study identifies four diagnostic dimensions: Technical Compatibility, Functional Complementarity, Cultural and Governance Fit, and Planning Maturity. ISOFAM is operationalized through visual tools, including the Risk–Opportunity Diagnostic Matrix, IS Misalignment Escalation Flowchart, and Temporal Integration Framework, which facilitate early alignment and strategic foresight. These contributions position IS as a strategic pre-combination priority, enhancing both theoretical and practical outcomes in digital M&A.

Hitri razvoj umetne inteligence prinaša številne priložnosti za izboljšanje učinkovitosti in inovativnosti v managementu, hkrati pa odpira tudi kompleksna etična vprašanja. Ta prispevek raziskuje etične dileme, povezane z uvedbo umetne inteligence v poslovne procese, ter išče ravnotežje med izkoriščanjem tehnologije in zagotavljanjem etičnih standardov. Z analizo literature in primerov dobre prakse identificiramo ključna vprašanja, kot so pristranskost v algoritmih, transparentnost odločitev ter vpliv uporabe umetne inteligence na zaposlene in delovno okolje. Rezultati študije kažejo, da kljub potencialnim koristim uporabe umetne inteligence managerji pogosto naletijo na izzive pri zagotavljanju etične uporabe teh orodij. Pomembna je ozaveščenost o problematiki zaščite podatkov, pravic delavcev in diskriminacije, ki lahko izhajajo iz neomejenega zaupanja v tehnične rešitve. Prispevek predlaga razvoj etičnih okvirov in smernic za uporabo umetne inteligence v managementu ter priporočila za usposabljanje vodstvenih kadrov, da se zavedajo teh vprašanj in lahko sprejemajo odgovorne odločitve.

Uporaba umetne inteligence (UI) bistveno preoblikuje procese odločanja v managementu. Prispevek preučuje uporabo sodobnih UI tehnologij v managerskih procesih in kako orodja UI lahko dopolnjujejo človeško inteligenco za izboljšanje kakovosti in učinkovitosti odločitev. S pregledom literature in analizo teme smo identificirali ključna področja in izzive pri uporabi UI v procesih managerskega odločanja. Rezultati študije kažejo, da so orodja UI managerjem v pomoč in lahko pomagajo pri zbiranju relevantnih in pravočasnih odločitvenih informacij. Vendar pa uporaba UI prinaša tudi izzive, vključno z etičnimi in pravnimi vidiki uporabe UI, predvsem glede zasebnosti podatkov ter potrebo, da managerji razvijejo nove veščine za učinkovito uporabo UI orodij. Študija predlaga tudi uvajanje celovitih programov usposabljanja, da bo management lahko izkoristil potencial UI ob hkratnem zmanjševanju tveganj. Ta prispevek prispeva k tekoči razpravi o vlogi UI v managementu, saj nudi uporabne vpoglede za raziskovalce in praktike, ki želijo izkoristiti UI v svojih procesih odločanja.