ABSTRACT The phenomenon of hype is a central element of the contemporary computer game market, yet its role as an independent factor mediating consumer decision-making remains under-researched. This article aims to identify the importance of hype in consumers’ purchasing decisions and to assess whether hype can be regarded as a commercialization tool in the computer game industry. The study is based on an integrative literature review and a multiple case study of four AAA titles: Red Dead Redemption II, Cyberpunk 2077, Grand Theft Auto VI and The Witcher 4. Gartner’s Hype Cycle is adapted to the computer game market and combined with marketing indicators (e.g., trailer views, social media engagement, pre-orders, sales dynamics, user and critic ratings) to propose an operationalization of hype across pre-release and post-release phases. The analysis shows that hype is not a spontaneous phenomenon but the outcome of deliberate and capital-intensive promotional strategies that can significantly shape consumer expectations, emotions and fear of missing out, and thereby influence purchasing decisions. At the same time, mismanaged hype may lead to disappointment, reputational damage and regulatory scrutiny, as illustrated by the case of Cyberpunk 2077. The article concludes that hype functions as a powerful, albeit risky, commercialization tool.

ABSTRACT This article examines whether Polish and European Union legal frameworks, supported by institutional oversight, provide consumers with sufficient protection against the adverse consequences of decisions made by artificial intelligence (AI) systems, and whether legal gaps persist in this area. The study aims to identify and assess these gaps and to formulate recommendations for strengthening consumer safeguards in the age of algorithmic decision-making. A qualitative descriptive analysis was applied to selected Polish and international legal acts and scholarly literature, including the Act on Competition and Consumer Protection of 16 February 2007 and Regulation (EU) 2024/1689 of the European Parliament and of the Council (the AI Act), along with expert opinions from Polish legal scholars. The findings indicate that while existing Polish and EU provisions, reinforced by institutional supervision, afford consumers a degree of protection, this coverage does not extend to all potential risks associated with AI use in consumer markets. Significant legal gaps remain, and the development of new laws that keep pace with the rapid evolution of AI poses a substantial legislative challenge. As a result, fully eliminating these gaps in the near future may prove difficult, if not impossible.

ABSTRACT Objective This article clarifies how the terms university “spin-off” and “spin-out” are used across scholarship, institutional reporting, and policy, and proposes operational definitions to distinguish the two notions that enhance measurement comparability while accommodating contextual diversity. Methodology We conduct a narrative review with emphasis on post-2020 work, synthesize institutional standards (OECD/Eurostat, European Commission, AUTM), and run an exploratory co-occurrence mapping of Scopus-indexed publications (2015–2025) using VOSviewer with a harmonizing thesaurus. Findings Two complementary lenses – IP-centric and knowledge-/founder-linked – structure the conceptual landscape, while institutional regimes create distinct “measurement windows.” Bibliometric analysis reveals five clusters (technology transfer, ecosystems, finance/policy, definitional core, HEI entrepreneurship). Overlay results show growing emphasis on ecosystems and finance. We propose operational definitions of academic spin-off and spin-out, supported by a minimal descriptor set for comparability. Practical implications Dual reporting using standardized descriptors enables universities, TTOs, and policymakers to better capture both IP-intensive and software/data-driven pathways, including those common in CEE systems with indirect commercialization routes. Originality/value The framework aligns scholarly lenses with the EU’s shift from “intellectual property” to “intellectual assets,” offering operationally useful definitions that support cross-study and cross-country comparability. Research limitations/implications: Results reflect Scopus coverage and keyword indexing. Further work should integrate transaction-level data and mixed-method evidence from founders and TTOs.

ABSTRACT Worries and concerns are long recognized as barriers to technology adoption in organizations (Bedué & Fritzsche, 2022; Mariani et al., 2022). Artificial intelligence (AI) exemplifies this tension: While it promises significant benefits (Wilson & Daugherty, 2018), individuals and organizations also fear its potential harms, from job loss (Kelly, 2023) to biased or opaque decision making (Rainie et al., 2021). Such concerns can shape both individual behavior (Shell & Buell, 2022) and organizational adoption strategies, particularly for generative AI (e.g., ChatGPT) (IBM Institute for Business Value, 2024). This study analyzes survey data from over 10,000 U.S. adults (Pew Research Center, 2021) to investigate whether worries about AI and related technologies are expressed uniformly or cluster into distinct attitudinal patterns. Using cluster analysis and ANOVA, we identify three groups with differing levels of concern and show that these groups vary systematically across specific technologies, following a consistent directional order. Our findings contribute to research on technology acceptance by highlighting attitudinal heterogeneity and by linking individual-level dispositions to organizational challenges in AI adoption. They also provide practical insights for managers seeking to address concerns that may otherwise slow the integration of AI into organizational practice.

ABSTRACT Systematic literature reviews (SLR) are essential for synthesizing research across disciplines, yet their manual execution is time-consuming and increasingly challenging due to the rapid proliferation of academic publications. This study examines the role of artificial intelligence (AI) in the SLR process within management sciences. Drawing on established SLR methodologies, particularly from health sciences where automation is widely applied, this research identifies AI’s role in contributing to key review stages, including literature identification, selection, data extraction, synthesis, and reporting. This study itself applied a systematic literature review methodology, querying Scopus and Web of Science, complemented by AI-based tools (Elicit and SciSpace) to extend coverage. Backward and forward citation searches were also conducted, resulting in a final sample of 93 publications. The findings from this sample suggest that AI enables researchers to shift roles from literature examiners to managers of the review process, overseeing AI tools executing repetitive and time-consuming tasks. However, despite the benefits of using AI in generating SLRs, its application in management research presents challenges, particularly in handling context-dependent and interpretative analyses. The study highlights both the transformative potential and the critical need for human oversight in AI-assisted reviews. Limitations include the reliance on existing automation techniques developed for health sciences and the exclusion of certain literature sources. Future research should explore AI’s effectiveness in managing SLRs, ethical considerations, and hybrid human–AI collaboration models. AI’s growing role in academic research entails the need to balance automation with scholarly rigor and methodological integrity.

Abstract This article explores the role of Facebook posts as a tool for building consumer engagement with the travel agency community. A quantitative content analysis of the posts of Poland’s largest travel agencies reveals that while promotional posts received the most likes, interactive posts generated greater engagement in the form of more comments. Smaller agencies in some cases outperformed larger competitors in terms of comment frequency, highlighting the importance of content strategy. Analysis of the sentiment expressed in the comments showed the prevalence of mostly positive or neutral comments, with companies effectively managing negative comments. The findings suggest that interactive posts foster deeper consumer engagement, highlighting the need for travel agencies to diversify their content strategy and actively engage their audiences. The study recommends that travel agencies strive to optimize social media engagement by balancing promotional posts with interactive posts that encourage discussion, such as questions, surveys and other posts not related to advertising the products on offer. Moreover, analyzing sentiment in such comments can help refine a brand’s communication strategy, improving consumer relationships and brand perception.

Abstract This study examines the prevalence and dynamics of innovation-related collaboration among Polish industrial and service enterprises. Despite the well-documented benefits of collaboration in fostering innovation, empirical data indicate that such collaboration remains relatively uncommon and inconsistent across different Polish business sectors. Using a combination of critical literature analysis, statistical evaluation, and comparative methods, this study identifies key trends in enterprise innovation activity and assesses the role of managerial decision-making in shaping cooperative initiatives. The findings suggest that innovation-related collaboration is often incidental rather than strategically managed, with a significant gap between theoretical models of innovation management and their practical implementation. Large enterprises are more likely to engage in cooperative innovation, whereas small and medium-sized enterprises demonstrate lower participation rates. Furthermore, collaboration primarily occurs with domestic partners, with limited engagement in international networks. The study underscores the need for a more structured approach to innovation management, including enhanced knowledge integration, increased managerial awareness of cooperation benefits, and the development of policies that incentivize strategic partnerships. Addressing these challenges could improve Poland’s innovation ecosystem, aligning it more closely with international best practices.

Abstract Marketing plays a key role in the operation of contemporary universities, especially in the context of the increasing commercialisation of education. Like business, universities need to present themselves favourably to students, but also to employers and the broader public. Higher education marketing is a specialized branch of service marketing, which has evolved with increasing competition and the commercialisation of education. The unique nature of marketing in higher education stems from its dual purpose: attracting students while upholding the university’s broader mission of education, research, and human capital development. Marketing strategies must be coherent with the institution’s values and cater to multiple audiences. As such, the themes of ongoing research in higher education marketing can be expected to reflect these challenges and transformations. This study aims to analyse the evolution of research topics in this field over the period 1999–2024, highlighting key themes, influential authors, and significant publication venues. To achieve this, the study employs bibliometric analysis, using both performance analysis and science mapping (bibliometric mapping) to examine how research areas, disciplines, and key contributions are interconnected. A Web of Science search was conducted based on identified keywords, resulting in a dataset of 494 publications. Using author-assigned keywords, the study identifies major research directions and their shifts over time.

Abstract This article discusses the use of Stevens’ measurement scales in marketing research, contributing to a broader discussion, underway for over 70 years, as to whether researchers can ignore the Likert scale’s limitations as an ordinal scale. The central question explored is whether the use of various statistical methods and techniques in marketing research has gone too far, limiting researchers’ horizon of thought, leading erroneous conclusions to be drawn, and diverting attention from trying to explain the non-quantitative attitudes of consumers (who are people, not machines or AIs). Stevens’ measurement scales are still widely used in data analysis across social sciences, including marketing research. Although they were revolutionary, they had certain flaws which have fueled an ongoing debate about the acceptability or permissibility of using different tests and statistical techniques at different scales and levels of measurement. The Likert scale, one of the scales most frequently used to measure customer attitudes, was intended to overcome the limitations of simple scales, having the advantage of being multi-item. However, historically, two competing views have evolved independently of each other, in the related literature and in the practice of empirical research: one emphasizing the ordinal nature of Likert scales, the other interpreting them as having interval-scale properties. This debate has significant consequences for the permissible scope of statistical analysis of empirical data. The problem discussed here is likely to become even more complex with the development of artificial intelligence (AI), machine learning, data science and big data, as data scientists perform computational analysis but are not often involved in data collection or deciding about how data is represented.

Abstract Knowledge valorisation, the process of transforming research outputs into societal, economic, and environmental value, remains a significant challenge for many research institutions, particularly in achieving measurable social impact. While knowledge commercialisation has gained prominence, the broader valorisation process is hindered by barriers that are often poorly understood. This study explores these barriers through partially standardized, in-depth interviews conducted in 2023 with representatives from 13 research units across five countries. Key findings reveal that researchers face constraints such as insufficient time, lack of awareness, and limited engagement, compounded by inadequate financial support for valorisation projects. A novel barrier identified is the absence of robust metrics to assess social impact – a critical gap not observed in knowledge commercialisation. These insights provide actionable recommendations for researchers, managers, and university leaders to enhance the knowledge valorisation process and its societal contributions. The study underscores the need for systemic changes to address these challenges and foster impactful collaborations between academia and broader society.