Platforms, Volume 4, Issue 3 (September 2026) – 10 articles

On-site search has become a critical component of e-commerce platforms, shaping product discovery, navigation, and purchasing decisions. While prior research has extensively examined search algorithms and information retrieval, less attention has been given to on-site search as a navigation environment in which interface design, visual guidance, and user search behavior jointly relate to search effectiveness and commercial outcomes. This study addresses this gap through three observational analyses using real-world behavioral data from a large Greek e-commerce retailer. Studies 1 and 2 employ before-and-after comparisons to examine changes associated with Search Results Page enrichment and the introduction of visual promotional guidance, while Study 3 descriptively examines generic query use and broad-to-narrow search behavior. The observed patterns show that Search Results Page enrichment coincided with lower refinement rates and improved commercial metrics, while visual guidance coincided with higher engagement and conversion-related outcomes. The behavioral analysis further indicates a strong prevalence of short, generic queries and patterns consistent with broad-to-narrow navigation. Overall, the findings suggest that on-site search performance is associated with both interface configuration and user search behavior. The study contributes real-world evidence on search as a navigation process and offers practical implications for retailers seeking to improve search-interface performance, while acknowledging the limits of observational before-and-after data for causal inference. Full article

Technological advancements have transformed the ways in which individuals interact with digital platforms, giving rise to data-driven ecosystems in which interface design plays a central role. Within these environments, dark patterns and manipulative and deceptive design techniques shape user behaviour in ways that undermine privacy, autonomy, and informed consent. This paper develops a harm-based analytical framework for privacy dark patterns by deriving a typology of privacy-relevant design mechanisms through a structured consolidation of four independently produced taxonomies. It then constructs a harm typology by combining an injury-based framework with the harm categories developed by regulatory authorities and maps each design mechanism to the harms it is structurally apt to produce. Finally, it identifies the provisions of EU law that each mechanism engages, across the GDPR, consumer protection law, the Digital Services Act, the Digital Markets Act, the Data Act and the AI Act. The analysis finds that the GDPR engages every mechanism identified, that coverage by post-GDPR instruments is uneven, and that one mechanism—the exploitation of relational and third-party data—engages no post-GDPR instrument squarely. The deficiency in the EU framework therefore lies not in the substance of its prohibitions but in the coordination of enforcement and in the evidentiary architecture through which non-material harm must be established. Full article

Social research now depends on environments that commercial platforms own and continually redesign. This article argues that platforms operate not only as objects of inquiry and infrastructures for data production but also as epistemic devices that shape what can be observed, classified, measured, and anticipated in social research. Drawing on science and technology studies, digital sociology, and critical data studies, it identifies four operations through which this shaping occurs (selection, classification, quantification, and temporalisation) and integrates them into a five-level framework that traces how social activity becomes scientific knowledge. The account differs from existing treatments of method as co-production by specifying what makes the platform a historically distinctive device: it is proprietary, strategically self-interested, and able to withdraw the visibility on which research depends and to reorganise itself once it has been studied. The closure of the research API provides the clearest expression of this condition, while Article 40 of the Digital Services Act marks an emerging attempt to subject epistemic access to public regulation. Offered as a reflexive instrument for research design rather than a procedure, the framework grounds a case for methodological pluralism and is set out together with its limits and possible empirical extensions. Full article

Artificial intelligence has become increasingly embedded in digital marketing, reshaping how firms collect consumer data, personalize content, automate persuasion, and evaluate campaign performance. While these developments offer clear strategic benefits, they also raise important ethical questions concerning privacy, transparency, fairness, manipulation, and accountability within platform-mediated marketing environments. This article presents a structured thematic review of recent research on the ethics of AI-driven digital marketing. The review focuses on English-language peer-reviewed journal articles and high-quality conference proceedings published between January 2023 and September 2025. Searches were conducted across Scopus, ScienceDirect, SpringerLink, ACM Digital Library, IEEE Xplore, MDPI, PubMed, and selected academic repositories. After deduplication, screening, and full-text assessment, 91 studies were included in the final synthesis. Methodological quality was appraised using the Mixed Methods Appraisal Tool and Joanna Briggs Institute critical appraisal criteria, while the findings were examined through thematic synthesis. The review identifies five recurring ethical domains in the literature: data privacy and GDPR compliance, algorithmic transparency and explainable AI, algorithmic fairness in targeting and automated decision-making, dark patterns and deceptive interface design, and influencer or virtual influencer disclosure. The evidence suggests that privacy, consent, deceptive design, and transparency are the most extensively discussed areas, whereas the practical effectiveness of fairness interventions, explainability tools, and disclosure mechanisms remains more mixed and context-dependent. Across these themes, ethical risks appear not only as the result of individual corporate decisions but also as outcomes shaped by platform infrastructures, ranking systems, data access arrangements, and performance-oriented advertising metrics. The article contributes by organizing recent scholarship into a coherent thematic framework and by situating AI-driven digital marketing ethics within a broader platform-governance perspective. It argues that responsible implementation requires clearer distribution of accountability among businesses, platforms, regulators, and researchers. The review is limited by its focus on the 2023–2025 period, its English-language scope, and the methodological heterogeneity of the included studies, which prevents statistical meta-analysis. Full article

Online toxicity has become a defining feature of contemporary digital discourse, permeating all forms of online discussion, from news websites and discussion forums to social media platforms. While prior research has examined toxic behaviour across these domains, less attention has been paid to the interplay among broader socio-political polarization, platform architectures, and psychological mechanisms that jointly sustain this discursive paradigm. This literature review proposes a multi-level integrative framework to conceptualize the toxicity lifecycle. At the macro level, affective polarization fosters societal antagonism, reinforcing entrenched in-group and out-group dynamics. However, the intensity and expression of these societal cleavages are shaped by distinct media–political system configurations, including varying degrees of political parallelism, and journalistic professionalization. At the meso level, platform affordances such as algorithmic amplification and engagement-based metrics promote emotionally charged, conflict-driven content. At the micro level, disinhibition lowers normative constraints on expression, often leading to verbal aggression that undermines the principles of healthy public deliberation. Addressing these challenges requires a comprehensive response that brings together users, platforms, and policymakers to safeguard the normative democratic values of constructive public deliberation. Full article

This paper studies platform environments in which participation incentives change discontinuously when valuation crosses salient thresholds. Empirical research on digital and tokenised platforms documents valuation plateaus, abrupt participation shifts, and weak short-run links between price stability and usage dynamics, patterns that are difficult to reconcile with smooth adjustment models. We capture these features structurally by modelling adoption and valuation as a coupled equilibrium system with regime-dependent incentives, formulated as a Filippov differential inclusion. Token prices are determined endogenously through market clearing between usage-driven demand and regime-dependent speculative demand. We establish global well-posedness and identify conditions under which valuation thresholds become attracting manifolds. In these regimes, prices remain anchored at the threshold while adoption continues to evolve, generating persistent valuation plateaus and path dependence. When threshold crossings are regular and the induced dynamics are uniformly dissipative, endogenous boom–bust cycles are ruled out. The framework yields design-relevant insights for digital platforms. Valuation anchoring and volatility depend on governance primitives such as effective circulating supply and regime-dependent speculative depth. A diagnostic numerical analysis links time-at-anchor and sliding intensity to adoption volatility and platform risk, providing empirically interpretable indicators of stability in tokenised platforms. Full article

In the complex landscape of Industry 5.0, traditional management systems for smart manufacturing struggle to harmonize high-speed production with the rapid integration of AI and digital technologies. Crucially, these legacy frameworks often fail to capture tacit human knowledge or ensure trustworthy AI and trusted sharing of sensitive industrial data. This paper proposes a novel Decision Support Framework (DSF) that addresses these challenges through a multi-layered approach. At its core, the framework utilizes Data Spaces to enable secure, sovereign data sharing, ensuring that organizations maintain control over their assets. To handle the inherent ambiguity of industrial data, the system employs fuzzy logic and DAG-based root-cause-oriented investigation to provide robust recommendations, helping users distinguish descriptive correlations from plausible structural dependencies that require expert validation. Furthermore, the framework integrates eXplainable AI (XAI) services and AI-driven visual analytics, transforming complex algorithmic outputs into transparent, intuitive insights. By synthesizing data sovereignty with interpretable machine intelligence, this framework empowers trusted data sharing and human-centric decision-making, providing an advanced platform for achieving operational excellence within the Industry 5.0 vision. The proposed DSF is validated in three different pilot cases with end-users to be a milk industry, an automotive supplier and a machine manufacturer. Full article

Objective: This paper investigates how Generation Z students perceive the quality, accessibility, and capacity of higher education in the Czech Republic, with relevance for the wider Visegrád (V4) region and for the design of educational platforms. Methodology: The study draws on a questionnaire survey of 819 students and analyses 38 Likert-type statements aggregated into five domains: digitalisation and technology, innovation in teaching, practical orientation and mobility, support and well-being, and capacity constraints and infrastructure. The analysis combines descriptive statistics, reliability and dimensionality checks, Pearson correlation analysis with normality and robustness diagnostics, and subgroup comparisons by gender, level of study, field of study, institution type, and age group. Results: Students report the strongest agreement with digitalisation and technology (M = 3.90) and practical orientation and mobility (M = 3.89), while support and well-being (M = 3.40) and capacity constraints and infrastructure (M = 3.36) remain weaker. Perceived adequacy of the student–teacher ratio is positively associated with perceived teacher availability (r = 0.37) and emotional support (r = 0.34), while teacher availability is positively associated with emotional support (r = 0.44). Subgroup tests indicate limited but meaningful differences, particularly by institution type, field of study, and age group. Conclusion: The findings suggest that Generation Z students value digitally enabled, practice-oriented and innovative educational platforms, but sustainable quality improvement also requires investment in human capacity, teacher availability, and student-facing support systems. Full article

Technological and social development is desirable and even indispensable, which necessarily involves the restriction of new life situations within legal frameworks. European legislation has been visibly struggling with this problem in recent years, but the established/ongoing regulation may be an obstacle to development. Among other things, this includes the issue of regulating platform work. The emergence and spread of platform work has numerous advantages from an economic point of view, but from a legal point of view, the cautious regulation of this relatively new employment construction is not acceptable to the majority dealing with labour law. In our opinion, the relevant EU legislation is fundamentally flawed, as it basically seeks to answer the question of whether a given legal relationship is an employment relationship or not. The current binary classification might not be sufficient. Thus, the present study examines why platform work can be considered special and what are the labour law guarantees that are justified to be extended—at least as a rule—in this regard. To further investigate the practical risks of the current rules, a recent and relevant judgement of the Hungarian Supreme Court is also analysed in order to illustrate the uncertainties in litigation. The ruling demonstrates that traditional employment tests fail to recognise algorithmic control—including GPS surveillance, scheduling penalties, and unilateral remuneration determination—as indicators of subordination, while placing an insurmountable burden of proof on workers. This case empirically confirms the practical difficulties of the current binary classification. Our aim is to examine whether it is necessary to develop a minimum guarantee system that allows for easier transparency, greater legal certainty and a more uniform application of the law, unlike the current regulation. Full article

Digital platforms increasingly mediate economic coordination, labor allocation, and decision-making. As artificial intelligence becomes embedded within these platform ecosystems, automation no longer targets only manual labor. Instead, algorithmic systems are displacing routine tasks across both low-wage entry-level work and middle-management functions. This paper argues that the emerging phase of platform-mediated automation risks hollowing out labor structures from both directions, from below through the erosion of repetitive, junior roles, and from above through the automation of supervisory coordination functions. Drawing on institutional economics, platform governance literature, and recent research on AI-enhanced learning and workforce development, the paper examines how this dual displacement creates structural vulnerability. Entry-level roles have historically functioned as apprenticeships in which workers acquire tacit knowledge and critical judgment. At the same time, experienced workers are aging out of the workforce. If platforms curtail formative occupational layers, organizations may face a shortage of workers capable of exercising contextual reasoning required to manage complex systems. The paper situates these developments within broader debates about technological unemployment, platform labor, and the political economy of capitalism. It argues that the challenge is not merely job quantity, but institutional continuity, how societies reproduce practical competence when platforms optimize for efficiency rather than formation. This study proposes a framework for evaluating platform ecosystems by their long-term effects on human capital formation and institutional resilience. Full article