Platforms, Volume 3, Issue 3 (September 2025) – 6 articles

The increasing complexity of managing maintenance activities across geographically dispersed hospitality facilities necessitates advanced digital solutions capable of effectively balancing operational costs and service quality. This study addresses this challenge by designing and validating an intelligent Prescriptive Maintenance module, leveraging advanced Reinforcement Learning (RL) techniques within a Digital Twin (DT) infrastructure, specifically tailored for luxury hospitality networks characterized by high standards and demanding operational constraints. The proposed framework is based on an RL agent trained through Proximal Policy Optimization (PPO), which allows the system to dynamically prescribe preventive and corrective maintenance interventions. By adopting such an AI-driven approach, platforms are the enablers to minimize service disruptions, optimize operational efficiency, and proactively manage resources in dynamic and extended operational contexts. Experimental validation highlights the potential of the developed solution to significantly enhance resource allocation strategies and operational planning compared to traditional preventive approaches, particularly under varying resource availability conditions. By providing a comprehensive and generalizable representation model of maintenance management, this study delivers valuable insights for both researchers and industry practitioners aiming to leverage digital transformation and AI for sustainable and resilient hospitality operations. Full article

Since the term “Industry 4.0” was coined in 2011, machine data retrieval, storage and processing has been one of the major drivers for process optimization and factory management. Data platforms have been introduced as a key resource to process, align and enhance data from machines, sensors and other sources. At the same time, different use cases and applications vary greatly in their technical demands towards data amounts, formats, retrieval rate, scalability, latency and many more. Thus, holistic data platforms are often a compromise between these requirements. This contribution thus looks into the requirements and needs of different use cases and applications for data usage in manufacturing—from factory management, to process control and auxiliaries monitoring. From these use cases, specific requirements will be deducted to propose architectures that reflect the needs of these distinctive applications. From a structured literature analysis with more than 100 scientific publications, archetypes for data management platforms in manufacturing will be condensed and explained in detail. Overall, eight distinctive archetypes have been identified and clustered, with the major distinguishing feature being their reliance on extensive models or digital twin. This contribution closes with application examples for some of these archetypes. Full article

The rapid evolution of Industry 4.0 technologies has resulted in a scenario in which collaborative networks are essential to overcome the challenges related to their implementation. However, the frameworks to guide such collaborations remain underexplored. This study addresses this gap by proposing Business Rules and Goals Models to operationalize Industry 4.0 solutions through enterprise collaboration. Using the For Enterprise Modeling (4EM) method, the research integrates qualitative insights from expert opinions, including interviews with 12 professionals (academics, industry professionals, and consultants) from Brazilian manufacturing sectors. The Goals Model identifies five main objectives—competitiveness, efficiency, flexibility, interoperability, and real-time collaboration—while the Business Rules Model outlines 18 actionable recommendations, such as investing in digital infrastructure, upskilling employees, and standardizing information technology systems. The results reveal that cultural resistance, limited resources, and knowledge gaps are critical barriers, while interoperability and stakeholder integration emerge as enablers of digital transformation. The study concludes that successfully adopting Industry 4.0 requires technological investments, organizational alignment, structured governance, and collaborative ecosystems. These models provide a practical roadmap for companies navigating the complexities of Industry 4.0, emphasizing adaptability and cross-functional synergy. The research contributes to the literature on collaborative networks by connecting theoretical frameworks with actionable enterprise-level strategies. Full article

This paper concentrates on the valuation of big data assets within the digital transformation of logistics enterprises. As data evolve into a core production factor in the logistics industry, their valuation is essential, not only for enterprises’ resource allocation decisions, but also as a key indicator for measuring the effectiveness of digital transformation. This paper combines the multiperiod excess earnings model with the analytic hierarchy process (AHP), creating an evaluation system through a comprehensive weighting method. Initially, the multiperiod excess earnings model is used to calculate the excess earnings of off-balance-sheet intangible assets. The AHP is subsequently applied to construct a hierarchical structural model of the enterprise, identifying the core factors that influence the excess earnings of off-balance-sheet intangible assets. This allows for precise segmentation and determination of the distribution rate of the value of data assets. The evaluation model fully accounts for the diversity, dynamics, and potential value of big data assets, effectively identifying and quantifying factors that are not easily observable directly. The findings not only provide a novel evaluation tool for data asset management in logistics enterprises but also offer theoretical support and practical guidance for enhancing the industry’s data asset valuation system and facilitating the realization of data asset value. Full article

This paper provides a comparative review of digital and human (face-to-face) innovation platforms and their roles in promoting innovation and sustainability. These platforms are particularly significant in advancing sustainability objectives as outlined in Sustainable Development Goal 17, (SDG17) which emphasizes the importance of knowledge and technology partnerships to address sustainability challenges, foster innovation, and enhance scientific collaboration. Through a systematic literature review of organizational and management research over the past decade, the study identifies key features, benefits, and limitations of each platform type. Digital platforms offer scalability, asynchronous collaboration, and data-driven innovation, yet face challenges such as trust deficits, cybersecurity risks, and digital inequality. In contrast, human (face-to-face) platforms facilitate trust, emotional communication, and spontaneous idea generation, but are limited in scalability and resource efficiency. By categorizing insights into thematic tables and evaluating implications for organizations, the paper highlights how the integration of both platform types can optimize innovation outcomes. The authors argue that hybrid models—combining the scalability and efficiency of digital platforms with the relational depth of human (face-to-face) platforms—offer a promising path toward sustainable innovation ecosystems. The paper concludes with a call for future empirical research on platform integration strategies and sector-specific applications. Full article

Social media influencers (SMIs) have become pivotal stakeholders in digital marketing. This study examines how SMIs influence consumer decision-making and investigates the role of trust in this process. Drawing on the theory of planned behavior (TPB), we developed a research model with testable hypotheses. Using partial least squares structural equation modeling (PLS-SEM), we analyzed survey data from 232 social media users in Greater London, UK. Our results indicated that SMIs significantly enhance purchase intentions, yet these intentions exhibited only a weak conversion into actual purchasing behavior. Contrary to expectations, trust in SMIs demonstrated a significant negative relationship with purchase intention, suggesting that higher trust may paradoxically diminish purchase likelihood. This counterintuitive finding underscores the complexity of trust dynamics in influencer marketing, where perceived commercialization or consumer skepticism may counteract its positive effects. Furthermore, while SMIs strongly foster trust, our analysis reveals that trust does not mediate the relationship between SMIs and actual purchases. These findings contribute to literature by elucidating the nuanced role of trust and highlighting the intention–behavior gap in influencer marketing. Future research could explore contextual and psychological moderators to deepen our understanding of trust dynamics. Full article