Search Results (702)

Background: This paper presents the development of digital tools created within the BIORADAR European project to improve user access to Life Cycle Assessment (LCA) results from the project’s use cases and to enable users to upload, benchmark and analyze their own data. The work addresses common challenges in circularity and environmental impact assessment, particularly data availability and expert-assisted self-assessment for users such as small- and medium-sized enterprises. Methods: The LCA data for the project use cases is calculated using the Environmental Footprint methodology. Benchmarking compares bio-approach use cases with traditional approaches across three key sectors selected by the BIORADAR project: fertilizer, textile and packaging. These sectors are recognized by the European Commission as three of the most important sectors in terms of environmental impact. Case impact factor data are normalized using a reference statistic, and a weighting is assigned to each key performance indicator to calculate the global score. Individual impact factor values can also be used for benchmarking. Synthetic data are generated through an advanced statistical decomposition algorithm. Advanced data analytics are provided with clustering and a decision tree algorithm using supervised machine learning. Results: Two examples of decision-oriented case studies are used to illustrate how the platform can support the interpretation and use of already computed LCA results in realistic settings. The web-based expert-assisted self-assessment tool, developed in JavaScript, allows users to input their data, benchmark them against project results and perform multidimensional data analysis. The resulting digital tools provide access to LCA data for each use case, generate realistic synthetic datasets preserving key statistical properties, support benchmarking of both project and user-uploaded cases, and perform data analytics, which complement the benchmarking module with a structural and exploratory interpretation of the data. Conclusions: Overall, the tools integrate use case benchmarking, data processing, advanced analytics and user interfaces to facilitate environmental self-assessment and comparison within the BIORADAR framework. Full article

The maritime and logistics sector is undergoing digital transformation, positioning digital logistics platforms (DLPs) as important tools for improving operational coordination, information visibility, and organizational performance (OP). However, prior studies have mainly examined platform adoption, digital capabilities, or macro-level performance outcomes, while paying insufficient attention to the micro-level cognitive and experiential mechanisms through which DLP innovation diffusion is translated into OP, particularly in the Chinese maritime logistics context. Grounded in an integrated framework combining the Stimulus–Organism–Response (SOR) paradigm, Diffusion of Innovations Theory (IDT), and the Extended Technology Acceptance Model (ETAM), this study investigates how DLP innovation diffusion affects OP through perceived usefulness (PU), perceived ease of use (PEOU), and flow experience (FE). Using survey data from 400 professionals in Chinese maritime and logistics enterprises and second-order structural equation modeling (SEM), the results show that DLPs’ innovation diffusion significantly enhances PU, PEOU, and FE. PU has the strongest standardized effect among the paths from DLPs’ innovation diffusion to the mediators (β = 0.779), whereas FE has the strongest direct effect on OP (β = 0.279) and the largest mediating effect. These findings clarify the cognitive–experiential pathway linking DLPs’ innovation diffusion to OP and inform DLPs’ implementation in maritime logistics. Full article

Digital platforms are increasingly presented as instruments for sustainable tourism governance, yet destinations often remain data-rich and governance-poor: digital traces are dispersed across actors, indicators are weakly standardised and communities frequently lack meaningful access to the information that shapes destination decisions. This article addresses this problem through the conceptual design and preliminary formative evaluation of ORVE (Optimisation of Resources and Valorisation of Experiences), a destination-level platform designed to connect tourists and residents, companies and institutions and Destination Management Organisations (DMOs) through a circular data ecosystem, understood as feedback loops across stakeholder levels. Methodologically, the study adopts Design Science Research (DSR). It operationalises problem identification, definition of solution objectives, artefact design and development, preliminary demonstration and formative evaluation, while recognising that full-scale causal evaluation remains a future research stage. The empirical component draws on a real-world pre-test with 12 tourism companies mediated by Biosphere Portugal, two Biosphere-administered pilot-company surveys involving 58 and 52 companies and scenario-based testing by 14 student groups involving more than 60 final-year students from Tourism and Tourism and Hospitality Management programmes. These sources are interpreted as exploratory and formative evidence rather than as a representative adoption study or a causal impact evaluation. The results suggest perceived usefulness for structuring sustainability information, supporting indicator monitoring and informing decision making, while also revealing operational constraints related to usability, data-entry flexibility, privacy communication, validation mechanisms, data availability in micro and small enterprises and the need for close onboarding support. The article contributes a refined platform architecture, a governance requirements matrix, design principles, an operationalisation roadmap and an evaluation protocol for sustainable tourism platform governance. Full article

Access to operational information in industrial plants forces operators to interrupt their tasks, walk to the human–machine interface (HMI) terminals, and navigate heterogeneous platforms—namely programmable logic controllers (PLC), supervisory control and data acquisition (SCADA) systems, manufacturing execution systems (MES), and enterprise resource planning (ERP) systems—consuming 15–30 min per query. Previous work integrated local large language models (LLMs) into a five-layer cognitive architecture deployed in a precast concrete plant, reducing that time to 14–23 s through voice-based conversational queries; however, model inference accounted for 55.3% of total latency and the system remained reactive. This work incorporates the event-driven paradigm as a non-intrusive augmentation layer that keeps the operational context permanently updated, continuously monitoring the process and refreshing knowledge only when significant changes occur. The architecture is fully local, cloud-independent, graphics processing unit (GPU)-free, and containerized via Docker Compose. Experimental results demonstrate a 26–31% reduction in response times (means of 9.84 s, 11.23 s, and 16.47 s for simple, moderate, and complex queries), an 8.4 °C reduction in peak hardware temperature (from 79.6 °C to 71.2 °C), a 41.6% decrease in thermal variability, and an expansion of the safety margin before central processing unit (CPU) throttling from 5.4 °C to 13.8 °C. The system achieved 100% success rate and availability over 30 min of autonomous operation, validated in a real industrial environment. 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

Documentary videos on green energy consumption are widely distributed via platforms such as YouTube, yet the verbal framing strategies embedded in their subtitle transcripts remain systematically understudied. This study applies the Analysis of Topic Model Networks (ATMN)—an unsupervised machine learning approach combining LDA topic modeling, semantic network analysis, and hierarchical clustering—to subtitle transcripts extracted from 60 YouTube green energy consumption documentaries. Three distinct framing communities are identified: (1) the Technological Supply Frame, which foregrounds zero-carbon resources, renewable generation, smart grid systems, and AI-enabled energy management as the technical foundation of decarbonization; (2) the Socioeconomic Transition Frame, the most thematically expansive, which positions the energy transition simultaneously as an economic opportunity, a behavioral imperative, and a systemic industrial transformation spanning green investment, end-use substitution, industrial decarbonization, and green mobility; and (3) the Ecological Governance Frame, which integrates ecological co-benefits with international climate commitments to construct the transition as a globally mandated planetary responsibility. Together, these frames reveal a richer and more multi-dimensional verbal framing landscape than previously documented in the green energy communication literature, extending beyond techno-optimism or environmentalism to encompass financial, governance, and behavioral dimensions within a single integrated corpus. The identified framing strategies offer actionable guidance for policymakers, energy enterprises, and media producers seeking to accelerate green energy consumption transition through targeted, evidence-based video communication. Full article

As an emerging application, AI shopping has received increasing attention from both enterprises and users. Based on the push–pull–mooring (PPM) model, this research examined user switching intention from traditional online shopping to AI shopping. We conducted an online survey to collect 422 valid responses and adopted a mixed method of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The results show that choice overload and perceived inefficiency lead to online shopping fatigue, while perceived convenience, perceived anthropomorphism, and perceived coolness affect AI shopping attractiveness. Online shopping fatigue, AI shopping attractiveness, and inertia determine user switching intention. These results provide a comprehensive understanding of the mechanism underlying user switching from traditional online shopping to emerging AI shopping. They also imply that e-commerce platforms need to mitigate online shopping fatigue and increase AI shopping attractiveness in order to expand their user base and maintain a competitive advantage. Full article

Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857. 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

In the volatility, uncertainty, complexity, and ambiguity (VUCA) era, platform enterprises face the critical challenge of balancing flexibility and controllability to sustain competitive advantage. Existing studies have examined these two dimensions separately but offer limited insight into their synergistic mechanisms within complex adaptive ecosystems. Drawing on the structural, cognitive, and relational dimensions of social capital theory, with a particular emphasis on institutional-level collaborative social capital among platform enterprises and their supply-chain partners, this study employs dynamic fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify the configurational pathways through which Chinese platform enterprises achieve high sustainable development performance. Using panel data from Chinese A-share listed platform enterprises (2014–2024), we identify three equifinal configurational pathways sufficient for high sustainable development performance: (1) a cognitive-relational alliance-driven path, (2) a structural-relational hybrid synergy path, (3) a structural-cognitive flexible innovation path. Innovation value consensus emerges as a core condition across all pathways. These configurations are sufficient for platforms to exhibit flexible market responsiveness and strategic controllability under varying environmental conditions. The study advances social capital theory by demonstrating the multidimensional, synergistic, and context-sensitive nature of flexibility-controllability coupling in platform governance. It also provides managers with actionable insights for tailoring social capital investments to specific regional and strategic contexts. Full article

In interactive digital commerce environments, negative electronic word-of-mouth (NeWOM)—particularly negative online reviews—profoundly shapes consumer perceptions and brand relationships. Yet, the underlying mechanisms through which NeWOM influences consumer conformity behavior remain underexplored from a qualitative, process-oriented perspective. This study adopts a grounded theory approach to analyze 1405 authentic negative smartphone reviews from a leading Chinese e-commerce platform. Through systematic open, axial, and selective coding, we develop a processual model that reveals how NeWOM triggers two interconnected yet distinct psychological mechanisms: the formation of generalized negative brand schema, driven by service/product failures and the internalization of psychological expectations, driven by unmet brand expectations and poor service attitudes. These mechanisms jointly shape consumer conformity behavior—the tendency to align one’s judgments and actions with perceived peer consensus reflected in negative reviews. Importantly, enterprises’ responsive improvements based on negative feedback operate as a feedback loop that can sustain or restore consumer–brand congruence. By reconceptualizing NeWOM as a dynamic, dialogic trigger within interactive marketing systems, this study extends electronic commerce theory and provides context-sensitive insight into how consumer conformity emerges and evolves in digital marketplaces. Full article

Autonomous mobile robots (AMRs) are increasingly used in warehouse intralogistics to improve material flow, flexibility, productivity, and operational continuity. However, selecting an appropriate AMR is no longer limited to mechanical performance or acquisition cost, since modern warehouse robots operate as networked cyber-physical systems that must interact with enterprise software, fleet management platforms, communication infrastructures, and cybersecurity mechanisms. This study proposes an integrated Pythagorean fuzzy multi-criteria decision-making (MCDM) framework for evaluating AMR alternatives in warehouse operations by jointly considering economic, technical, physical, software-related, integration-oriented, and security-related criteria. Expert judgments obtained from eight specialists, including four academics and four private-sector professionals, were modeled using Pythagorean fuzzy numbers to capture uncertainty and hesitation in linguistic assessments. The Pythagorean Fuzzy Indifference Threshold-Based Attribute Ratio Analysis (PF-ITARA) method was employed to determine criterion weights based on threshold-sensitive discrimination among alternatives, while Pythagorean Fuzzy VIšekriterijumsko KOmpromisno Rangiranje (PF-VIKOR) was used to rank four AMR alternatives according to a compromise solution logic. The results show that investment cost, maneuverability, total cost of ownership, integration and validation requirements, and ease of programming and commissioning are the most influential criteria. Cybersecurity-related criteria, particularly data confidentiality, system integrity, monitoring and incident response readiness, authentication control, and role-based access control, also received notable importance levels. Among the evaluated alternatives, MiR250 achieved the best overall performance and emerged as the most suitable compromise solution, followed by OMRON LD-250, HIKROBOT Forklift AGV, and KUKA KMP 600-S diffDrive. The proposed framework provides a transparent and practically applicable decision-support tool for AMR procurement by integrating operational performance, digital interoperability, and cybersecurity readiness into a unified evaluation structure. 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

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

Employees’ voice is an important source of organizational learning and adaptive change. As algorithmic management is increasingly applied across organizational management processes, an urgent practical question arises: Does it affect employees’ participation in organizational improvement through voice? To address this challenge, drawing on signaling theory, this study examines the differential effects of distinct dimensions of algorithmic management on voice, while also considering work locus of control as a key moderating variable. We collected one-to-one matched data from 351 employees and their supervisors in a large Chinese platform-based enterprise. We tested the hypothesized theoretical model using structural equation modeling and bootstrapping procedures. The results show that algorithmic feedback enhances employees’ felt responsibility for constructive change, which in turn promotes employees’ voice. In contrast, algorithmic directing, algorithmic scheduling, and algorithmic monitoring undermine employees’ felt responsibility for constructive change and thereby inhibit voice. In addition, work locus of control moderates these relationships: employees with an external work locus of control strengthen the negative effects of algorithmic directing, algorithmic scheduling, and algorithmic monitoring, whereas employees with an internal work locus of control strengthen the positive effect of algorithmic feedback. These findings deepen our understanding of how different dimensions of algorithmic management shape voice and offer practical insights for fostering voice in contexts characterized by algorithmic management. Full article