Search Results (1,289)

Existing accounts of platform-mediated complementor growth rest on two limiting assumptions: that platform enablement constitutes a homogeneous environmental input and that firm growth is a unitary outcome. This double simplification obscures how distinct platform provisions generate qualitatively different forms of firm transformation. This study asks which combinations of mechanistically distinct platform enablement types and internal strategic response capabilities activate which forms of leapfrog growth among complementor firms operating under dual institutional governance. We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) on survey data from 374 complementor firms in China’s autonomous driving platform ecosystem. Five antecedent conditions are examined across two dimensions: platform enablement, comprising rule-based enablement (RE) and business platform enablement (BPE); and strategic response capabilities, comprising network linkage capability (NLC), organizational ambidexterity (OA), and policy responsiveness (PR). Three outcome variables capture three non-reducible leapfrog dimensions: technology-chain (TL), value-chain (VL), and institutional-chain (IL) transitions. A reverse-causality robustness check and a common-method-bias assessment corroborate the validity of findings. The analysis identifies equifinal configurational pathways with distinct dominant logics across the three chains. Technology-chain transitions are predominantly network-linkage-driven; value-chain transitions are policy-responsiveness-anchored; institutional-chain transitions exhibit genuine equifinality between network-linkage and policy-responsiveness pathways, both requiring dual-platform enablement as a universal structural precondition. No single enabling condition or capability suffices; leapfrog growth is irreducibly configurational and causally asymmetric. The study offers a dual-enablement, three-chain configurational framework for understanding platform-mediated firm growth under dual institutional governance. For complementor firms, findings support dimension-selective capability investment over uniform accumulation strategies. For platform orchestrators, differentiated governance design calibrated to specific complementor upgrading trajectories outperforms homogeneous resource provisioning. For policymakers, institutionalized consultative channels linking private platform governance with public regulatory processes are recommended to facilitate coordinated digital industrial transformation. Full article

This paper investigates the impact of a regional innovation-driven platform (Qinchuangyuan Innovation-driven Platform) on entrepreneurial confidence, particularly in technology-based small and medium-sized enterprises (TSMEs) during their start-up period. By analyzing data collected from 132 TSMEs, this study explores how regional innovation-driven platforms influence entrepreneurial confidence. The main findings are as follows: First, the results of ordinary least squares (OLS) regression reveal that the innovation-driven platform significantly improves entrepreneurial confidence, and the results of propensity score matching (PSM) remain still positive. Second, we conduct instrumental variable (IV) estimation as supplementary robustness evidence for potential endogeneity concerns, using whether an enterprise participates in market expansion activities and whether an enterprise uses government support services as two instrumental variables. Third, the innovation-driven platform is mediated by entrepreneurial satisfaction with the business environment and entrepreneurial satisfaction with the government, thereby enhancing entrepreneurial confidence. This paper provides a new perspective for assessing business development through entrepreneurial confidence rather than traditional performance metrics and provides a valuable reference for the development and optimization of innovation-driven platforms in similar regional contexts, especially in supporting sustained entrepreneurial activity, technology transformation, and regional economic resilience. Full article

Green labels on online travel platforms have become an important mechanism for disclosing environmental information and guiding sustainable tourism consumption. However, when a platform simultaneously provides green certification and competes with suppliers through its self-operated business, green label adoption may reshape both market competition and environmental outcomes. This study develops a game-theoretic model of a co-opetitive tourism platform supply chain consisting of a tourism service supplier (TSS) and an online travel platform (OTP). Two label adoption strategies are compared: the TSS’s self-labeling strategy and its adoption of the OTP-certified green label. The results show that, under self-labeling, the OTP can gain a competitive advantage by setting a higher price and greenness level, although this advantage weakens as consumer recognition of the TSS’s self-label increases. Under platform-certified labeling, the OTP raises the common green standard, which intensifies price competition between the two parties. In most cases, adopting the OTP’s green label improves supply chain profits; however, under certain combinations of competition intensity and platform label credibility, it may reduce the profits of both members and increase environmental damage. These findings suggest that platform-led green certification does not necessarily improve environmental performance and should be designed as a governance mechanism rather than a purely marketing instrument. Full article

The construction (AEC) industry has consolidated Building Information Modelling (BIM) as the standard for producing and managing project information, yet its analytical exploitation in Business Intelligence (BI) environments remains manual, ad hoc and dependent on proprietary platforms. Existing literature addresses partial aspects of the problem—IFC extraction, dashboards, semantic approaches, and data quality—without articulating a coherent architecture that integrates dimensional modelling, open-standard-based ETL, granular lineage and pre-ingestion validation. This work proposes BIM2BI, a BIM-BI integration architecture organised into four functional layers (data sources, transformation and orchestration, analytical storage and exploitation) that formalises a separation of responsibilities, explicit data contracts between layers and an extensibility-without-redesign principle. The architecture is grounded in the openBIM standards IFC, IDS and BCF, adopts the IfcGlobalId as a technical key for end-to-end lineage and uses IDS as a pre-ETL quality gate with a staged three-level validation strategy. The proposal is validated empirically through an open-source reference implementation (MIT licence) applied to ten representative real-world use cases from projects in Spain and Chile, comprising 69 IFC files and approximately 4.5 GB of input data grouped in three complexity profiles, with end-to-end execution times ranging from under a minute for single-discipline deliveries to under fifteen minutes for the most demanding infrastructure case. The results demonstrate the viability of the architecture in terms of data quality, traceability, reproducibility and scalability, and document empirical findings on the real behaviour of openBIM standards within automated analytical workflows. The proposal targets BIM Managers, AEC consultants and contractors, and Data Engineers seeking auditable, vendor-independent BIM analytics aligned with ISO 19650. Full article

Hybrid monetization is increasingly common in digital platforms, yet we know little about how sponsors of open-source ecosystems combine freeness, community participation and value capture under decentralised data constraints. This paper examines how PrestaShop, a large open-source e-commerce platform, reconfigures its business model to assemble a hybrid monetization architecture on top of a free, self-hosted core. Drawing on an abductive, qualitative single case study, we analyse semi-structured interviews with senior and middle managers, internal documents and performance dashboards, and participant observation in strategic and product meetings. Our process analysis traces three dynamics: a shift from community-led freeness and loosely governed marketplace revenues to intentional monetization; the construction of data and artificial intelligence (AI) capabilities as monetization infrastructure that makes the ecosystem legible and segmentable; and the layering of transactional, infrastructural and curated subscription revenues around the open-source core. We show how hybrid monetization emerges through sequential, overlapping moves rather than a single pivot, and how each new revenue mechanism entails adjustments in control points, partner relationships and data governance. The study contributes to research on commercial open source, hybrid multi-sided platforms and AI-enabled business models by conceptualising hybrid monetization as a staged reconfiguration under structural constraints. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. About 98 percent of large businesses will use cloud computing services in 2025 to enable remote working. The highly distributed structures of cloud computing are prone to attacks starting from weakened access control to data breaches. The sources making cloud systems vulnerable to attacks are public accessibility, auto scaling, and shared form of network architecture. Distributed Denial of Service (DDoS) is one of the most serious forms of attacks where multiple botnets get created simultaneously and flood massive requests for the cloud services. If the DDoS attack is not identified early it leads to the unavailability of cloud services, increased cost of migration, exhaustion of resources, and frequent violations of Service Level Agreements (SLAs). Hence, there is a need to detect DDoS at an early stage. Traditional machine learning models demand high computational power and larger memory capacity which make it unsuitable for a real-time cloud environment. This limitation is overcome by presenting a novel Quantum-Enhanced Knowledge Distillation framework (QKD) to detect DDoS attacks in cloud systems. QKD is a highly potential form of architecture which uses quantum computing to enhance the knowledge transfer between teacher and student models. The knowledge is extracted from the teacher model and quantum encoding of knowledge is performed. The complex correlation between the features of the traffic is extracted by applying the entanglement gates. The student model is trained considering the distillation loss and optimized until convergence. The simulation of the QKD is performed using DynamicCloudSim 3.0.3 simulator considering benchmark dataset CIC-DDoS2019and the performance is further validated using expected value analysis methodology. The performance of QKD is found to be promising toward performance metrics such as packet loss rate, attack detection time, attack recovery ratio, bandwidth utilization, and response time. Full article

As data volumes grow and business requirements become increasingly complex, Hybrid Transactional/Analytical Processing (HTAP) technologies, capable of handling both Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads on a single platform, have gained prominence. HTAP databases typically maintain dual data storage formats and dual query engines: one row-oriented for OLTP, and another column-oriented for OLAP. Query plans, known as hybrid plans, can be segmented and pushed down to execute on these different formats. However, existing HTAP solutions still face challenges in optimizing these hybrid plans, struggling to explore the vast space of potential execution strategies effectively. To address these issues, this study introduces a learning-based query optimizer for row–column dual store HTAP database systems, which automatically generates multiple high-quality query optimizer hints (HINTs) to derive candidate plans. To balance plan generation efficiency with plan quality, a lightweight, learning-based algorithm using Monte Carlo Tree Search (MCTS) for generating hybrid access HINTs is proposed. Moreover, a Transformer-based neural network model coupled with a hybrid plan feature representation method is developed to select the candidate execution plan with the lowest predicted execution time. This work focuses on latency-oriented hybrid-plan selection for analytical queries in a row–column dual-store HTAP architecture; the current evaluation does not cover full mixed OLTP/OLAP workload scheduling, transactional interference, or concurrency control, which are left as future work. Experimental results on AlloyDB Omni, a recent row–column dual-store HTAP database, using the real-world IMDB dataset and JOB benchmark demonstrate that our system reduces execution time by 75.02% compared to the Cost-Based Optimizer (CBO) and by 62.23% compared to the state-of-the-art row-store-based learning query optimizer in this evaluated analytical-query setting. Full article

Misinformation has become a persistent feature of contemporary digital information environments. Platform designs and business models often privilege attention, engagement, and repeated exposure over epistemic quality. However, misinformation does not diffuse uniformly across platform structures. This study examines how contested claims in a South Korean social policy controversy circulate on YouTube. The analysis focuses on unfounded allegations regarding permanent employment offers to part-time workers at Incheon International Airport across two analytic levels: (1) a videoclip network, in which video-to-video ties are formed through shared commenters over time, and (2) a channel network, in which channel-to-channel ties are formed through shared commenters over time. Drawing on YouTube Data API records, we employ a mixed computational approach that integrates social network analysis, speech-to-text transcription, natural language processing, semantic network analysis, and automated content classification. Videos are classified as misinformation or non-misinformation based on the presence of demonstrably incorrect claims or corrective content. We compare network structure, diffusion patterns, and engagement dynamics across these two layers. The results reveal pronounced layer-specific differences. Misinformation diffuses more extensively within the channel network, which exhibits higher density and stronger cross-channel interconnectedness, suggesting that creator-level infrastructures function as stabilising conduits for the circulation of false claims. By contrast, diffusion pathways at the videoclip level show comparatively weaker differentiation between misinformation and non-misinformation content. Engagement patterns also diverge misinformation videos attract significantly more likes, while message format and channel attributes are less consistently distinguishing. From a theoretical standpoint, this study advances a multi-layer diffusion perspective on platform-mediated misinformation by demonstrating how platform architectures shape the visibility, persistence, and amplification of false claims. The findings highlight the importance of intervention strategies that move beyond individual content moderation toward creator- and network-level governance mechanisms, with implications for the design of platform features, recommendation systems, and misinformation mitigation tools. Full article

The objectives of this study are to examine the direct relationships among perceived ethics, perceived sustainability, customer trust, customer engagement, and customer loyalty; and to investigate the mediating roles of customer trust and customer engagement in explaining the relationship between ethical and sustainability perceptions and customer loyalty. Using the Stimulus–Organism–Response (SOR) framework and the Theory of Planned Behavior (TPB) as theoretical foundations, this research examines how ethical and sustainability perceptions within social commerce environments influence customers’ psychological states and behavioral responses. A quantitative approach was used, involving data collection from 360 Thai consumers who had previously bought sustainable fashion items through social media. The proposed model was then evaluated using partial least squares structural equation modeling (PLS-SEM). The results suggest that consumers’ evaluations of seller ethics significantly enhance their perceptions of product sustainability, customer trust, and engagement. Furthermore, perceived sustainability of fashion products affects both trust and engagement. Customer trust subsequently promotes both engagement and loyalty; however, customer engagement exhibits the most substantial direct effect on customer loyalty. Mediation analysis confirms the essential functions of trust and engagement in mediating the impacts of ethical and sustainability perceptions on loyalty. These findings highlight the importance of ethical transparency and proactive customer engagement in fostering trust and long-term customer loyalty within social media-based sustainable fashion commerce. Therefore. This study provides both theoretical and practical insights for sustainable fashion enterprises functioning within digital contexts. Full article

Physical manufacturing and research infrastructures are essential for advanced innovation but often remain inaccessible to SMEs, start-ups, and research institutions that cannot justify ownership of capital-intensive assets. This study examines whether platform-mediated infrastructure sharing can function as a sustainable open-innovation mechanism in advanced manufacturing. Using the SCIP/SYNPRO platform developed in the SYNERGY and IDEATION projects, an exploratory case-study design combines descriptive analysis of a registry of 290 infrastructure items across 11 countries with qualitative analysis of 23 documented access requests, interaction records, and pilot reports. The results show that the Provider–Taker model facilitates observable access-enabling interactions, including infrastructure publication, request submission, provider–taker communication, negotiation, and selected documented use, although it does not measure population-wide access outcomes. Sharing potential is uneven: modular and emerging technologies, especially VR/AR infrastructures, attract higher request intensity than production-integrated assets. Users and providers favour negotiated access, flexible pricing, operator support, and contractual clarification rather than standardised rental models. Qualitative evidence shows that value is created through access to otherwise unavailable equipment, postponed investment, experimentation, technology familiarisation, student training, capability development, and new inter-organisational research links. The findings indicate that infrastructure sharing can support more resource-efficient innovation but depends on discoverability, governance, trust, and support mechanisms to scale. Full article

Digital transformation (DT) is becoming of strategic importance for Small, Medium and Micro Enterprises (SMMEs), especially in the retail sector, where a significant portion of customer engagement, operational efficiency, and market competitiveness is shaped by digital technologies. Even though there is a growing availability of smartphones, mobile payment systems, and social media platforms, many South African retail SMMEs struggle to achieve a sustained and meaningful DT. Existing studies offer limited insights into the dynamic interactions between technological, organisational, and human agency factors that enable digital uptake over time. This study investigates the sociotechnical dynamics of DT among retail SMMEs in the Eastern and Western Cape provinces of South Africa. The research integrates Adaptive Structuration Theory (AST) with the Limits to Success Archetype (LSA) to conceptualise DT as an evolving process shaped by the interplay of technology, organisational structures (formal arrangement of roles, responsibilities, authority, and communication patterns within an organisation), and human agency. Using an exploratory qualitative research design, purposively sampled semi-structured interviews were conducted with 23 retail owners, directors and managers. The interviews were transcribed, and the data were analysed thematically using the Braun and Clarke six-step thematic analysis framework on Atlas.ti 25. Findings indicate that DT in retail SMMEs is enabled by pragmatic, tool-level digital adoption, training, education, ongoing skill development, alignment with business capacity, regulatory clarity, operational realities, addressing scams, fraud, data security, a user-friendly interface, and the availability of native language digital tools, structural interventions that reduce inequality, and DT ecosystem support. The study contributes to DT scholarship by integrating sociotechnical and systems-thinking perspectives to explain the trajectories of DT in retail SMMEs. It also provides practical insights for policymakers, support institutions, and digital ecosystem actors seeking to democratise DT in emerging-market retail contexts. Full article

European football has become a massive business. Keeping football clubs financially viable depends on accurate player valuations, which underpin balancing incoming and outgoing transfers, contract negotiations, and other expenses. Players’ market values are generally available on public platforms. Still, clubs and analysts increasingly rely on data-driven approaches to enable consistent valuation across leagues, to assess the main drivers of players’ market value, and to early identify the most promising players. This study attempts to predict and interpret football players’ market value in five major European football leagues (England, Spain, Italy, Germany, and France) using match-derived performance statistics and players’ general information. The dataset analyzed comprises about 14,000 player–season observations available through the worldfootballR package, which aggregates data from FBref and Transfermarkt. Five regression algorithms were evaluated within a unified machine learning framework. Model performance was assessed on a test set using RMSE and R² metrics. Results show that non-linear machine learning models outperform the linear ones. Gradient boosting and neural networks recorded the best predictive performance. Model interpretation techniques reveal playing-time exposure and player age as the main determinants of predicted market value, highlighting the importance of match involvement and career stage in the valuation of football players. Full article

The transition toward a circular economy has made industrial symbiosis an important approach for improving resource efficiency and reducing environmental impact, especially for small- and medium-sized enterprises (SMEs). However, the extent to which SMEs can adopt these practices differs across countries. This study aims to explore the readiness of SMEs for industrial symbiosis in Türkiye, Jordan, Portugal, and Croatia, and to propose a digital model that can support this transition. The research is based on a qualitative, literature-driven comparative analysis examining institutional structures, technological capacity, sectoral characteristics, and collaboration networks in each country. The findings indicate that, despite contextual differences, all four countries face similar challenges, such as limited data sharing, insufficient digital infrastructure, and weak inter-firm cooperation. While EU member states demonstrate more developed policy frameworks, implementation gaps remain evident across cases. Building on these insights, the study introduces the Digital Recycling and Material Network (DREAM) model, a digital platform that connects waste-generating firms, recycling companies, and businesses that use secondary raw materials. The model enables real-time data sharing and supports sustainability-oriented matching mechanisms. Overall, the study suggests that digital platforms like DREAM can play a key role in strengthening industrial symbiosis practices and supporting SMEs in their transition toward circular production systems. Full article

Decentralized finance (DeFi) has been studied mainly as a financial and technological system, while the role of digital entrepreneurial capability in shaping sustainable user traction remains underexplored. This study repositions DeFi as a digitally mediated entrepreneurial ecosystem and examines whether retention-oriented user behavior is associated with three capability dimensions—entrepreneurial visibility, network embeddedness, and organic acquisition efficiency—together with ecosystem-finance conditions such as total value locked and decentralized-exchange activity. Using an exploratory, correlational design with monthly aggregated data from five incumbent DeFi platforms during the post-FTX recovery period (October 2022–September 2023), the analysis combines canonical correlation analysis, partial least squares regression, and ridge regression. Results indicate a significant multivariate association between ecosystem-finance conditions and the entrepreneurial-capability block, and show that returning-visitor behavior is more coherently linked to the predictor set than broad visitor inflow. Entrepreneurial Visibility Capital and Network Embeddedness emerge as the most stable positive correlates of user retention, while Organic Acquisition Efficiency shows a directionally mixed pattern. Because the sample is small, the findings are interpreted as preliminary evidence rather than confirmatory claims. Overall, the study offers an integrative framework that connects DeFi, digital entrepreneurship, and sustainability-oriented business-model research, and identifies the joint configuration of digital capability and financial conditions as a promising direction for future, larger-scale investigation. Full article

Offshore oil and gas exploitation is one of the riskiest businesses to invest in and is dominated by various uncertainties: high deepwater pressure, low temperatures, remote operation, long-distance tiebacks and transportation, as well as environmental factors such as wind, waves and ocean currents. Serving as a profitability threshold, the minimum economic field size is defined as the economic recoverable reserve level that an oilfield must exceed to achieve economic returns. This paper develops an approach for determining the minimum economic field size of offshore oil and gas reservoirs. It categorizes the capital expenditure into four major components: drilling and completion costs, platform costs, pipeline costs, and subsea production system costs. The regression models of drilling costs and subsea production costs are developed respectively, with water depth and recoverable reserves as key influencing factors. The pipeline costs are estimated using the unit pipeline cost per mile and pipeline length. A profit model for the offshore field is established under the constraints of the contract, which allocates the oilfield’s production profits between the contractor and the government according to the contractual fiscal terms. Finally, taking the Lucius oilfield in the Gulf of Mexico as a case study, the paper simulates its investment, operating costs, and oilfield revenues. The minimum economic field size is calculated, accompanied by the derivation of the sensitivity boundaries for the primary parameters. Full article