Search Results (404)

Coal-fired power plants are increasingly required to operate flexibly in power systems with high shares of variable renewable energy. Molten-salt thermal energy storage (MSTES) offers a promising retrofit option because it can be coupled with the steam-water cycle, boiler-side heat sources, flue gas, or electric heaters to decouple heat generation from electricity output. This review summarizes recent progress in MSTES-assisted flexible operation of coal-fired power plants from the perspectives of integration configurations, operating strategies, performance evaluation, and engineering challenges. The main charging routes include electric heating, main-steam extraction, reheat-steam extraction, flue-gas heat recovery, and hybrid heat sources, while the main discharging routes include feedwater heating, condensate heating, and steam generation. Representative studies show that MSTES can reduce minimum net load, enhance power-boosting capability, improve ramping performance, and support frequency regulation; however, reported efficiencies and economic indicators depend strongly on system boundaries and market assumptions. Dynamic modeling and coordinated control are essential for practical implementation, particularly for mode switching, heater-group reserve allocation, pump-flow coordination, and safety-limit management. Major challenges remain in salt freezing, corrosion, thermal decomposition, heat-exchanger inertia, field validation, and revenue uncertainty. Future research should focus on modular design, control-oriented models, digital-twin-assisted validation, standardized performance reporting, and multi-service economic optimization. Full article

Context: The increasing adoption of Internet of Things (IoT) technologies has transformed digital marketing into a software-intensive, data-driven ecosystem requiring continuous optimization under uncertainty. Existing decision-support approaches primarily optimize engagement or cost independently and rarely integrate behavioral dynamics, sustainability constraints, and managerial preferences within a unified information systems framework. Objectives: This study develops and evaluates a fuzzy multi-objective optimization framework that supports intelligent software-based marketing decision making by simultaneously maximizing customer engagement, minimizing digital resource consumption, and reducing behavioral saturation in IoT-enabled environments. Methods: A multi-objective mathematical model was developed in which customer responsiveness is represented through probabilistic engagement parameters, while fuzzy membership functions and a Max–Min satisfaction criterion represent imprecise managerial aspiration levels across the conflicting objectives. The small-scale experiment was solved exactly in GAMS to obtain reference Pareto-optimal solutions, whereas the large-scale experiment was conducted as a simulation study using NSGA-II and MOPSO to evaluate scalability and algorithmic performance. Both experimental settings relied exclusively on synthetically generated datasets; no real-world enterprise, customer-level, or campaign-level marketing data were used. Performance was assessed through Pareto-front analysis, key performance indicators, sensitivity analysis, and scenario-based managerial evaluation. Results: The proposed framework successfully generated high-quality Pareto-optimal solutions across multiple optimization objectives. NSGA-II consistently achieved superior customer engagement, personalization efficiency, and behavioral balance, whereas MOPSO demonstrated faster execution and lower sustainability costs. Sensitivity analysis confirmed the robustness of the framework under varying behavioral parameters, while scenario analysis showed that different optimization strategies can be selected according to organizational priorities. The principal limitation is that the framework has been evaluated only in controlled synthetic environments, which limits direct empirical generalization to operational enterprise marketing settings. Future research should validate the framework using longitudinal enterprise marketing data, real-time IoT interaction streams, and field-based deployment studies. Conclusions: The proposed framework contributes to information systems research by integrating fuzzy decision support, multi-objective optimization, and behavioral modeling into a scalable software architecture for IoT-enabled marketing. The approach enables adaptive, explainable, and sustainable decision making, providing organizations with a practical decision-support system capable of balancing customer experience, operational efficiency, and digital sustainability in intelligent marketing ecosystems. Full article

Effective collaboration between artificial intelligence and human capital has become an important organizational foundation for improving corporate carbon emissions. Drawing on socio-technical systems theory, we examine the effect of human–AI collaboration on carbon emissions performance and its moderating effects using Chinese A-share listed firms from 2011 to 2023. The results show that human–AI collaboration is positively associated with corporate carbon emissions performance, suggesting that the combination of artificial intelligence capabilities and human managerial judgment is related to improved emissions efficiency. Executives’ green awareness positively moderates the relationship, whereas digital transformation weakens this relationship. In addition, the positive effect is more pronounced among non-state-owned firms, high-tech firms, and firms operating in less competitive markets. From a socio-technical coordination perspective, our study extends research on the environmental performance implications of human–AI collaboration, advances strategy research on resource orchestration in carbon-constrained markets, and provides theoretical foundations and practical guidance for firms navigating the dual digital and green transformation. Full article

This study examines the extent to which a medium-sized Portuguese metalworking company, engaged in the production of aluminium goods for residential exterior applications, has embedded the three foundational pillars of Industry 5.0 - organisational resilience, environmental sustainability, and a people-centred strategy - together with digital maturity as a critical enabling dimension into its operational and strategic practices, with the aim of producing an empirically grounded, multi-vector maturity profile capable of informing strategic decision-making aligned with the European Union’s vision for responsible industrial transformation. A quantitative cross-sectional survey was conducted using a structured questionnaire administered face-to-face to 20 managers and second-line supervisors spanning all six organisational departments, combining a bespoke 48-item Likert-scale instrument (1 = Strongly Disagree; 5 = Strongly Agree) addressing resilience, sustainability, and people-centred dimensions with the European Commission’s validated Open DMAT instrument for digital maturity. Descriptive statistical analysis was performed using Minitab Statistics and MS Excel. The company achieved an overall digital maturity score of 43% (Open DMAT). Resilience scores clustered around the scale-point 3.0 (“Partially Agree”) (median = 3.0 across items), with interdepartmental coordination as the most critical deficit (mean = 2.444). Sustainability exhibited a differentiated profile: use of recyclable materials scored highest (mean = 4.150), while sustainability as a market development driver remained undervalued (mean = 2.563). The people-centred vector attained the strongest overall performance, led by worker participation culture (mean = 4.105), though investment in ergonomic workstations requires reinforcement (mean = 3.056). This study provides an empirically grounded, simultaneous assessment of the three Industry 5.0 pillars, together with digital maturity as an enabling dimension, in a single Portuguese metalworking SME, combining a bespoke organisational survey with the EU-endorsed Open DMAT instrument, and fills a gap in the empirical literature on export-oriented manufacturers navigating the sustainability and digitalisation transition mandated by European industrial policy. The single-organisation design limits external generalisability; future research should extend the analysis to cross-sector comparative studies and longitudinal designs. The maturity profiles produced here enable practitioners to prioritise investment in data governance (Open DMAT score: 25%), interdepartmental integration, AI-enabled quality inspection, and ergonomic workstation infrastructure as the highest-leverage enablers of Industry 5.0 adoption. Full article

Digital humans are emerging as a form of mediated communication, appearing as virtual influencers, virtual agents, avatars, VTubers, AI companions, and other humanlike digital entities across social media, entertainment, commerce, and online communities. Research on how users perceive and respond to these entities remains fragmented across disciplines. This scoping review synthesizes evidence on users’ perceptions of and responses to digital humans. Following established scoping review guidance and PRISMA-ScR reporting principles, we searched Scopus, Web of Science, and EBSCO for English-language, peer-reviewed studies published between 2016 and 2025. Fifty-one studies met the inclusion criteria and were synthesized descriptively and thematically. The synthesis identified three recurring dimensions of user perception (i.e., perceived agency, perceived embodiment, and perceived social role) and three broad response domains: psychological, relational, and behavioral. These findings suggest that users’ responses to digital humans are shaped by more than human-likeness alone, with capability, embodied presence, and social role also emerging as important interpretive dimensions. The evidence base was concentrated in marketing and commerce, with limited attention to broader media contexts, long-term interaction, cultural diversity, and negative or ambivalent responses. Overall, this review provides an evidence-organizing framework for understanding how users interpret, relate to, and act toward digital humans. Full article

Based on data from Chinese A-share listed companies from 2018 to 2024, this study examines the impact of artificial intelligence (AI) adoption on corporate data assetization and underlying mechanisms from a systems perspective. The findings reveal that AI adoption is significantly and positively associated with corporate data assetization, robust to firm fixed effects, propensity score matching, instrumental variable estimation, and COVID-19 exclusion. Mediation analysis indicates that digital human capital is a mediating channel, while the market-based allocation of data factors exhibits a directionally positive but statistically weak moderating effect. Heterogeneity analysis further reveals that the AI–data assetization effect is largely universal across ownership, technology intensity, competition, and region, with the sole statistically significant boundary being a stronger effect in non-manufacturing than manufacturing. This study reveals how technological application, organizational capabilities, and the institutional environment form an interconnected system that jointly shapes data assetization. Fuzzy-set qualitative comparative analysis (fsQCA) reveals that the conjunction of high AI adoption, strong digital human capital, and advanced data marketization is sufficient for high data assetization, corroborating the configurational systems perspective. It provides micro-level evidence on how AI empowers corporate value creation in the digital economy. Full article

Power mini-grids are being transformed from locally automated electrical systems into cyber-physical ecosystems in which heterogeneous distributed energy resources, storage, converters, flexible demand and uncertain external conditions must be coordinated. Existing surveys commonly treat microgrid control, artificial intelligence (AI), multi-agent systems, communications, digital twins and cybersecurity as separate research streams. In this survey, the cognitive power mini-grid is introduced as a unifying paradigm in which physical and social contexts are perceived by distributed agents, task-relevant semantic information is exchanged, auditable coordination readiness is checked and proposed actions are subject to independent safety checks. A corpus of 247 scholarly publications, standards and technical sources is synthesized across microgrid engineering, distributed AI, semantic communication, language models, federated learning, neuro-symbolic AI, digital twins, causal reasoning, runtime assurance, cybersecurity and community energy markets. A six-layer architecture is proposed in which fast deterministic control is separated from semantic coordination, distributed learning, agentic deliberation and assurance. Representative studies are compared by problem, method, control horizon, information assumptions, validation environment, hardware-in-the-loop (HIL) status, reported outcome, limitations and architectural relevance. Applications are organized into balancing, resilience, demand-response, maintenance and inter-mini-grid markets. Cross-layer challenges are identified in stability under asynchronous interaction, semantic interoperability, hallucination, common-knowledge failure, edge resources, privacy and cyber-physical security constraints. A research agenda is developed around proof-carrying actions, causal digital twins, edge small language models, federated multimodal foundation models, neuromorphic semantic control and human-agent governance. Cognition is therefore positioned as a safety-gated coordination capability above verified physical control loops rather than as a replacement for established control. Full article

Accurate cryptocurrency price forecasting represents an important yet challenging problem in financial time-series analysis due to the highly volatile, nonlinear, and noise-sensitive nature of digital asset markets. Although transformer-based architectures have recently demonstrated strong capabilities in temporal sequence modeling, their behavior under high-frequency cryptocurrency dynamics and the role of explainability-guided feature refinement remain insufficiently explored. To address this gap, this study presents a comprehensive transformer-based forecasting framework for hourly cryptocurrency price prediction and investigates the impact of explainability-guided feature optimization on forecasting performance, robustness, and interpretability. Five transformer architectures—Vanilla Transformer, Informer, Autoformer, Reformer, and Temporal Fusion Transformer (TFT)—are systematically evaluated across five major cryptocurrency assets: Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Dogecoin (DOGE), and Ripple (XRP). The experimental framework employs Open, High, Low, Close, and Volume (OHLCV) data together with a broad set of engineered technical indicators and evaluates model performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). To improve interpretability and reduce feature redundancy, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are integrated directly into the forecasting pipeline. Based on the resulting explanations, asset-specific feature subsets are constructed, and all models are subsequently retrained using the refined feature representations. The results show that explainability-guided feature refinement provides compact, model-aware, and interpretable feature subsets with competitive forecasting performance; however, its effect on prediction accuracy is dependent on the cryptocurrency asset, transformer architecture, retained feature subset size, and market conditions. Additional robustness, sensitivity, alternative feature-selection, and statistical significance analyses indicate that the SHAP–LIME Top-15 subset should be interpreted as a conservative dimensionality-reduction strategy rather than a universally optimal feature-selection rule. The findings further reveal that transformer architectures incorporating sparse attention, decomposition mechanisms, or gating structures generally provide stronger performance than the Vanilla Transformer under highly volatile hourly market conditions. Overall, the proposed framework demonstrates that combining transformer-based forecasting with explainability-guided feature refinement can support interpretable and parsimonious high-frequency financial time-series modeling, while highlighting the importance of evaluating robustness, feature-selection sensitivity, and statistical variability alongside average forecasting errors. Full article

Artificial intelligence (AI) is changing how firms search for, interpret, and act on externally generated knowledge in digital innovation ecosystems. Existing research motivates closer examination of how resource-constrained SMEs organize the internal workflows through which AI-supported customer, market, competitor, and platform information becomes usable for innovation. This study examines how cross-border e-commerce SMEs organize AI-supported workflows and team roles to handle external knowledge for emerging inbound open innovation. The empirical design is an exploratory, theory-elaborating qualitative multiple-case study of two contrasting SMEs. The analysis draws on ten face-to-face semi-structured interviews across strategic, managerial, technical, and frontline roles, totaling 183 min 32 s (approximately 184 recorded minutes). An abductive thematic case-analysis approach combines data-near coding, within-case analysis, cross-case synthesis, and iteration with relevant theory. The analysis suggests an analytically ordered framework rather than a verified longitudinal sequence. Case A exhibits experiment-led diffusion, whereas Case B exhibits technical-partner-led workflow design. Across the cases, participants described modular human–AI–human sequences, broader task integration, changing feedback arrangements, and greater emphasis on human review. These reported arrangements are associated with three emerging capability-related practice dimensions: external knowledge sensing, knowledge recombination, and agile implementation. Disconfirming accounts show that the patterns depend on task–AI fit and that overreliance can weaken independent market judgment. The study contributes to research on AI-enabled inbound open innovation by showing that access to AI and platform data is insufficient: external inputs become actionable when SMEs reorganize workflows and roles around prompting, interpretation, recombination, and human accountability. It also elaborates how human–AI collaboration operates as an internal microfoundation of customer-centric, platform-mediated openness. For managers, the findings suggest moving from isolated tool use toward capability-oriented workflow design while retaining human review. The conclusions offer contextualized theoretical insight rather than statistical generalization. Full article

Qatar offers small and medium-sized enterprises (SMEs) an unusually uniform environment for digitalization, with advanced infrastructure, generous public programs, and strong market pressure, yet digital adoption varies widely between firms. This study asks whether that environment still explains the variation. Using the technology-organization-environment (TOE) framework, we surveyed owners and managers of service-sector SMEs through 165 invitations in April 2026 and analyzed 158 screened responses with partial least squares structural equation modeling (PLS-SEM). The model tested the effects of technological readiness, organizational readiness, environmental support, competitive pressure, and customer expectations on digital adoption, each measured on five-point scales. Organizational readiness was the strongest predictor ($\beta$ = 0.671), technological readiness had a smaller significant effect ($\beta$ = 0.297), and the model explained 74.5% of the variance. No environmental construct was significant, although four of the five environmental conditions were rated near the top of the scale. We interpret this as environmental saturation, a reading the diagnostics favor over a measurement ceiling: conditions shared by nearly all firms no longer distinguish adopters, leaving a divide in internal capability. Policymakers should fund diagnosed capability gaps, training, and implementation advice rather than further general provision, and managers should prioritize leadership commitment and staff skills. Full article

Why does the same host economy see foreign investors co-build vocational training institutions in some industries but not others, even when FDI volumes are comparable? We argue that part of the answer lies in the governance mode of the global value chain (GVC) an investor is embedded in, alongside the liability-of-foreignness logic that dominates the co-creation literature. Relational and captive governance (high transaction complexity, low codifiability of required capabilities, and supplier competence that cannot be bought off the shelf) create a mutual dependence that can make joint institutional investment rational for both firms and the host state, whereas modular and market governance do not. Morocco’s aerospace and automotive value chains sit at the relational/captive end of this spectrum; its textile and agro-processing value chains sit closer to modular/market governance. We examine the argument in two stages. First, using a national-level 2SLS/DOLS/FMOLS estimation on 44 annual observations (1977–2020), we find FDI inflows positively associated with secondary-school enrollment nationally (β = 7.18 USD billions, p < 0.001 under 2SLS, corroborated by DOLS but not by FMOLS), though the supporting evidence is not uniform across estimators and diagnostic tests, and a national aggregate cannot, by itself, explain sector-by-sector variation. Second, we contrast the aerospace/automotive and textile/agro-processing value chains directly: the relational/captive chains show co-designed curricula, co-funded institutes, and co-governed placement systems, while the modular/market chains show comparable FDI intensity but no comparable institutional response. This sectoral contrast, documented in greater depth for aerospace and automotive than for the comparison sectors, is consistent with GVC governance mode, rather than FDI volume or liability of foreignness alone, playing a role in whether institutional co-creation occurs, though the evidence here is suggestive rather than conclusive. We report the national-level estimation transparently, including a set of diagnostic limitations (cointegration-rank and integration-order ambiguity across Johansen, Gregory–Hansen, and ARDL bounds tests; an instrument-validity caveat that persists even after removing individual instruments; a digital-infrastructure composite missing its fixed-broadband component; and an estimator-sensitive FDI coefficient that DOLS corroborates but FMOLS does not), which qualify the macro evidence and should be read alongside, rather than in place of, the sectoral comparison. Full article

Building Information Modelling (BIM) has become a key driver of digital transformation in the construction sector, increasing the demand for professionals capable of managing BIM-enabled projects across their entire life cycle. However, despite the growing importance of the BIM Manager (BIMM), there is still no widely accepted competency framework applicable across different regulatory and market contexts. This study aims to identify the core competencies required of BIM Managers operating in countries with different digital construction policies and to evaluate the extent to which the Polish labour market and educational system align with international expectations. A qualitative comparative approach was adopted, combining a narrative literature review, an analysis of 48 job advertisements from 6 countries representing different BIM policy and implementation contexts, with 8 advertisements analysed per country, and an exploratory qualitative comparative study of selected Polish academic and professional educational programs. To complement the qualitative approach, an auxiliary text frequency analysis was conducted by extracting the most frequently occurring post-related vocabulary from the analysed job advertisements. The extracted terms were subsequently grouped into broader competency domains to support the interpretation of employer expectations. The results demonstrate that employer expectations are remarkably consistent and extend beyond technical BIM expertise to include leadership, communication, information management, and strategic decision-making competencies. The auxiliary text frequency analysis confirmed the consistency of the qualitative findings by demonstrating that the most frequently occurring competency-related terms could be meaningfully interpreted within four complementary categories: Technical BIM Competencies, Managerial & Operational Competencies, Information & Data Management Competencies, and Behavioral (Soft) Competencies. In addition, prospective Emerging Digital Competencies are discussed as a future-oriented conceptual extension reflecting the ongoing digital transformation of BIM practice. Although Poland represents an emerging BIM market without mandatory BIM implementation in public procurement, its labour market and educational offers largely reflect international competency expectations. The identified competency domains provide a foundation for developing a standardized BIM Manager profile and support future efforts toward harmonizing higher education, professional certification, and industry practice in digitally transforming construction markets. Full article

Generative artificial intelligence (GenAI) is reshaping digital marketing by expanding opportunities for personalization, content generation, automation, and consumer interaction, while simultaneously raising concerns about intrusion, manipulation, authenticity, autonomy, and trust. This conceptual study examines how GenAI-enabled marketing can create value without undermining the relational conditions required for sustainable consumer–brand relationships. Drawing on the Daedalus–Icarus myth as a theoretical lens for technological ambivalence, the study conceptually integrates research on GenAI marketing, responsible AI, responsible communication, and consumer trust to develop the Daedalus–Icarus Framework for Sustainable AI-Driven Marketing. The resulting framework explains how the same technological capabilities may follow a Daedalian trajectory of responsible technological empowerment or an Icarian trajectory toward perceived technological overreach. It distinguishes the organization-defined Responsible AI Boundary from the consumer-perceived Icarus Threshold and identifies Boundary–Threshold Alignment as the central mechanism connecting responsible GenAI use with consumer acceptability. Responsible communication is positioned as supporting this alignment, while consumer trust functions as a relational sustainability mechanism. The study concludes that sustainable AI-driven marketing depends not on maximizing technological capability, but on continuously calibrating GenAI use across technological possibilities, organizational responsibility, and consumer-perceived acceptability. Full article

Generative artificial intelligence (GenAI) is reshaping higher education and early-career work, raising questions about how universities can support pedagogically sustainable career readiness. This study examines undergraduate students’ perceptions of AI, automation, entry-level employment, and perceived preparedness for an AI-augmented labor market. Survey data were collected from 153 undergraduate students. The questionnaire examined awareness of AI and automation, perceived risks to traditional entry-level work, anxiety and perceived preparedness regarding post-graduation employment, skill priorities, and desired institutional support. The findings indicate substantial awareness of AI-related change, with many students expecting routine junior tasks such as data entry, basic research, report generation, customer support, and simple coding-related work to be affected. However, confidence in academic preparation was weaker and more uncertain. Students emphasized human-centered capabilities, including critical thinking, creativity, communication, and problem solving, alongside AI literacy and practical exposure to digital tools. The study identifies an awareness-preparedness gap and argues that higher education institutions should strengthen GenAI-era curriculum design, AI-authentic assessment, experiential learning, career guidance, and ethical AI literacy to support perceived preparedness and sustainable career readiness. Full article

Emerging-market exporters encounter global sustainability governance not as a treaty or a target but as a product rule: an energy performance threshold, a restricted-substance limit, and a recyclability declaration. Those rules are published, and publication is not the same as access. A firm with a regulatory affairs department can establish which version of a measure is in force, which products it covers and what evidence the destination market will accept; the small- and medium-sized enterprises that dominate emerging-market export bases usually cannot, so rules written to raise environmental standards can exclude the firms least equipped to read them. This paper asks whether emerging digital technologies can convert the transparency infrastructure of the trading system into sustainable business capability. We develop GRACE, a WTO-informed decision-support system that resolves regulatory versions and timelines before interpretation begins, ranks evidence by the authority of its source, declines to state any obligation that no official passage supports, and escalates to human experts when the record is incomplete. Evaluation covers 214 held-out notification families under family-level splits and five seeds, against direct prompting, generic retrieval-augmented generation, a domain-adapted single agent with the same retriever and supervision, an always-on version of the same agent set, a hierarchical-audit baseline and a frozen frontier model, with 72 cases scored blind by trade-law assessors. Against the domain-adapted single agent, it raises citation support from 87.4% to 92.6%, a 5.2-point gain (95% bootstrap CI for the difference [3.6, 6.9]), lifts requirement-action coverage from 82.9% to 88.4%, reduces unsupported claims by 45.3%, and matches the always-on pipeline at 36.7% fewer tokens; blind expert scores are 4.16 of 5 against 3.60. Within the evaluated stack, interpretive capacity, not information supply, is what converts environmental regulation into sustainable business practice. Full article