Search Results (979)

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

Collaborative business ecosystems (CBEs) face persistent disruptions, including pandemics, geopolitical instability, climate shocks, cyber threats, resource scarcity, and sustainability transition pressures. Building on prior CBE resilience–antifragility research and a mathematical framework that introduced plasticity as a viable below-baseline response trajectory, this integrative review aims to develop a sustainability-oriented framework explaining how CBEs can align response modes, strategies, capabilities, governance mechanisms, and enabling infrastructures under persistent disruption. The review synthesizes the 2019–2026 literature on sustainable business model innovation (SBMI), circular and regenerative perspectives, digital capability infrastructures, and ecosystem governance. Drawing on 99 sources, it proposes a six-layer Sustainable Resilience–Antifragility Framework for CBEs (SRA-CBE Framework), linking disruption sources, ecosystem vulnerabilities, viable response modes, strategy and capability portfolios, governance mechanisms, and sustainability-oriented outcomes. The synthesis shows that sustainable CBEs require aligned strategy bundles, adaptive and sustainability-oriented capabilities, governance arrangements that prevent collaboration and digitalization from becoming fragility sources, and enablers such as SBMI, circularity, scenario simulation, and governed digital infrastructures. The paper contributes by sharpening the link between disruption response and sustainability-oriented ecosystem design, repositioning viable response modes as design positions, and outlining managerial and research implications for sustainable collaborative ecosystems. Full article

Although the green belt around Greater Lomé (GBGL) is a vital ecological buffer, it is currently facing significant degradation. This decline appears to be associated with a combination of various socioeconomic uses by the local community and formal operations of established businesses. Grounded in the cultural materialism framework, this study aims to contribute to a better understanding of the dynamics of the socioeconomic uses of the green belt around Greater Lomé in a context of degradation and investigates the dynamics of these socioeconomic uses and their environmental impacts through a multidisciplinary methodology. This approach combines anthropological analysis based on field observation, 53 semi-structured interviews and 5 focus groups, a quantitative questionnaire survey (n = 384) and an analysis of land use and land cover (LULC) dynamics derived from Landsat imagery (2003–2023). The results reveal six main types of socioeconomic uses of the GBGL (notably land transactions, agriculture, breeding and grazing, exploitation of wood energy, timber and utility wood, sand mining, and waste disposal), which lead to complex social dynamics ranging from conflicts to alliances among stakeholders. The LULC dynamics analysis indicates a staggering 468.26% expansion in built-up areas over the last 20 years, at the expense of swamp vegetation/gallery forest (−76.79%), tree-and-shrub savanna (−53.47%) and plantations (−49.43). This study provides a scientific basis supporting the urgent necessity to establish the GBGL as a legally protected entity and argues in favour of an inclusive management model that is designed to reconcile the socioeconomic survival needs of local populations with sustainable preservation of essential ecosystem services. Full article

In biodiversity-rich borderlands, some humanitarian settlements are rapidly expanding. This creates a profound conflict: refugees need a place to live, and ecosystems need protection. However, how settlement growth spatially affects the ecology surrounding protected areas remains understudied. This study takes as an example the city of Teknaf in Bangladesh, one of the world’s largest refugee gathering areas, to explore how settlement expansion changes the ecological structure and function of protected area boundaries, with a focus on two questions: Are there critical spatial thresholds? What is the role of climate feedback mechanisms? We build an analysis framework that integrates several types of data: multitemporal remote sensing images, land-use changes, ecological indicators (NDVI, LST, HQ), landscape pattern indices, gradient analysis, and 2036 simulations based on the business-as-usual scenario. Through this framework, we identify the ecological threshold at the junction of settlements and forests within the Teknaf Wildlife Sanctuary. The expansion of settlements has turned the landscape, which was originally dominated by vegetation, into fragmented hard patches. At the same time, the habitat is severely degraded, and heat stress intensifies. Notably, a critical transition zone emerges at approximately 300–500 m from the protected area boundary, where landscape fragmentation intensifies, habitat quality declines, and heat stress reaches its peak, highlighting a spatial hotspot of ecological vulnerability. If there are no intervention measures, future scenario simulations show that the continued expansion of settlements will only isolate protected areas and accelerate ecological degradation. On the basis of gradient analysis for spatial diagnosis, we propose a zoning management framework and regeneration landscape strategy with the direct goal of coordinating ecological protection and humanitarian needs in crisis-prone border areas. Full article

Modelling constitutes a disciplined transformation process through which heterogeneous, unstructured evidence is translated into structured representations that support reasoning and decision-making. The integration of generative artificial intelligence into such processes introduces new possibilities for automation, yet risks undermining methodological rigour, traceability, and human accountability. This paper proposes a methodology-grounded multi-agent architecture for constructing structured business ecosystem maps from unstructured document collections. The architecture decomposes the modelling lifecycle into specialised agent functions covering boundary specification, source discovery, document analysis, semantic extraction, and controlled model editing, addressing four of the five methodology stages while leaving automated completeness verification outside the current scope. A central orchestrator coordinates agents while enforcing ontological constraints derived from a formal modelling methodology. All proposed modifications are staged for human review before execution, and each map element maintains explicit provenance links to source material. To evaluate the reliability and correctness of generative modelling pipelines, a hybrid evaluation framework integrates operational metrics, semantic assessment using an LLM-based judge, and human agreement validation. Empirical evaluation across 34 generative models and 4382 experimental runs characterises capabilities across modelling tasks. In a controlled single-document extraction task, text-based extraction achieves a mean semantic match score of 0.947, whereas interaction extraction scores 0.431 and visual diagram interpretation scores 0.470, identifying relational reasoning and multimodal interpretation as principal bottlenecks. Model performance varies across agent roles, with task-aligned model selection associated with larger performance changes than hyperparameter tuning; the architecture’s causal contribution is not isolated, and comparison against monolithic or ablated baselines remains future work. Full article

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

The promotion of rapidly scaling technology startups has become a major policy priority. Sustainable startups are increasingly viewed as potential contributors to resilient and environmentally responsible economies, as they may combine economic growth with environmental and social objectives. Based on entrepreneurial ecosystem theory, the resource-based view, and Schumpeterian creative destruction, this study identifies innovation ecosystem conditions associated with sustainable startup growth. Turnover growth is used as a proxy for the economic pillar of the Triple Bottom Line framework and as a measure of startup scaling capacity. K-means clustering is applied to identify distinct growth profiles. To analyse relationships between startup growth and innovation ecosystem variables, the study employs a multi-method semiparametric framework. The results show multifaceted associations between ecosystem factors and startup growth. Market access and human capital are positively associated with global business models and innovation, while sectoral relatedness and knowledge spillovers may show negative associations, potentially through stronger competition and higher talent acquisition costs. Venture capital is positively associated with startup growth, whereas public R&D investment and direct government funding show no consistent positive relationship. The study is limited by using financial growth as a proxy for economic sustainability and by focusing on four European innovation ecosystems. Full article

While digital communication is critical for business growth, there is a notable lack of research concerning the specific digital and corporate strategies of bio-health start-ups in regional ecosystems like Andalusia. This article addresses this gap by analysing the corporate and digital strategies of the leading bio-health start-ups at the Andalusian Health Technology Park. The research focuses on innovation in the health sector and builds on the broader discourse surrounding science communication as applied to Andalusian companies. Health innovation companies are implementing their digital corporate strategies to raise their profile and reach their target audience. For Andalusian bio-health start-ups, the main focus is on their websites; this is why they are analysed here from different perspectives, with the aim of evaluating the information they share and its effectiveness. To this end, a mixed approach combining quantitative and qualitative content analysis is proposed, and data analysis tools are applied to web traffic and performance factors, as well as to the analysis of corporate culture and brand identity. The results indicate that these companies are consistent with digital communication strategies typical of B2B models, that is, emerging and highly specialised companies. In the corporate sphere, there is generally a strong focus on positioning within a framework that fosters organisational culture, employee recognition and the key elements of effective brand architecture. Full article

This paper examines the state of the academic literature on the development of after-sales and maintenance services for electric vehicles (EVs), highlighting their critical yet underexplored role in the transition to electrified mobility. Against the backdrop of rising EV sales, this study investigates how service ecosystems influence long-term adoption. A systematic review was conducted to identify recurring themes, barriers, and proposed solutions related to EV maintenance and after-sales systems. The findings indicate that, despite lower mechanical complexity compared to internal combustion vehicles, EVs generate new service demands due to their reliance on electronics, software, and high-voltage systems. Key barriers to EV adoption include high purchase costs, limited charging infrastructure, and shortages of skilled technicians, which collectively affect consumer confidence beyond the point of acquisition. The analysis shows that after-sales services constitute both a technical and economic bottleneck in large-scale EV diffusion. The existing literature predominantly emphasizes theoretical solutions, such as digitalized maintenance and data-driven business models, with limited focus on practical implementation strategies. This paper concludes that sustainable EV adoption depends not only on technological and infrastructural progress but also on workforce adaptation, proposing a transitional management framework to support independent workshops in shifting toward fully electric service operations. Full article

This article examines how smart cities can act as catalysts for enterprise development by integrating technological, infrastructural, governance and human capital dimensions into a coherent urban innovation ecosystem. Drawing on an extensive literature review, the study first conceptualizes smart cities as adaptive systems that combine physical infrastructure, digital data layers, and institutional frameworks, creating conditions for knowledge spillovers, entrepreneurial opportunities, and business model innovation. Empirically, the research is based on an expert survey conducted among 54 specialists from academia, business, and public administration, who assessed the importance of technological, infrastructural, governance, innovation ecosystem, and human capital factors for enterprise development in the context of smart cities. The results suggest that advanced digital technologies, smart infrastructure, open data, R&D support, startup programs and talent development are perceived by experts as key, mutually complementary drivers of firms’ innovation, efficiency, sustainable growth, and competitiveness, with notable differences between expert groups. On this basis, the study proposes a synthetic model of relationships and impact pathways linking smart city components with enterprise outcomes. The paper concludes with a discussion of the study’s limitations, related to the expert-based, country-specific, and perceptional character of the data, and outlines directions for further quantitative and qualitative research on the firm-level effects of smart city development. Full article

Vehicle-to-grid (V2G) can provide flexibility and storage for low-carbon power systems while supporting sustainable mobility, yet real-world deployment remains largely confined to pilots despite substantial technical progress. This article presents a PRISMA-guided systematic review of 974 V2G/V2X studies published between 2009 and 2025 to explain why implementation lags and how it can be accelerated. Within this corpus, a total of 162 implementation-critical articles are identified and, within these, 95 studies that primarily address non-technical dimensions such as policy, markets, user behavior, and ecosystem coordination. Drawing on full-text coding, a four-domain socio-technical framework is developed that clusters recurring non-technical barriers and enablers into business–economic, governance–policy, social, and infrastructure and ecosystem domains. The analysis reveals (i) a temporal shift from technical dominance to multidisciplinary acceleration after 2021; (ii) distinct regional priorities in which Europe emphasizes regulation and business models, Asia focuses on infrastructure scaling, and the Americas on frequency services and resilience; and (iii) persistent revenue uncertainty, regulatory gaps, user resistance, and grid unreadiness as cross-cutting obstacles. For each domain, concrete transition levers and indicative deployment key performance indicators (KPIs) are derived, such as multi-actor revenue-sharing mechanisms, aggregator recognition in market rules, privacy-by-design user participation models, and targeted bidirectional charging deployment in constrained grids. Synthesizing these insights, three archetypal V2G transition pathways are proposed—regulation-led, infrastructure-first, and service-driven—that reflect regional conditions and offer alternative routes to large-scale adoption. The framework and roadmap provide researchers, policymakers, system operators, and mobility providers with an integrated basis for designing, monitoring, and evaluating V2G policies, business models, and pilots in line with energy system decarbonization goals. Full article

Studentpreneurship has gained prominence in South Africa as universities are increasingly expected to foster innovation, job creation, and youth participation in the economy. However, despite the establishment of incubators, entrepreneurship centres, mentorship programmes, and EDHE-aligned initiatives, support for studentpreneurs remains unevenly implemented, poorly integrated, and inconsistently accessible, particularly within a historically disadvantaged university. This study examines how university support mechanisms shape the experiences, challenges, and business development trajectories of studentpreneurs in a South African university. Guided by Entrepreneurial Ecosystem Theory, the study adopts a qualitative research design involving in-depth interviews with 15 studentpreneurs. Thematic analysis reveals significant gaps in awareness, accessibility, and continuity of institutional support. While students valued motivational workshops, pitching opportunities, and limited mentorship, these interventions lacked sustained follow-up, sector-specific guidance, and financial or infrastructural resources necessary for business growth. The study contributes to South African entrepreneurship scholarship by highlighting the lived realities of studentpreneurs at a historically disadvantaged university and by proposing institutional reforms to build more coherent, equitable, and sustainable studentpreneurship ecosystems. 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

This research mapped mentoring typologies implemented by business incubators in Spain and examined the role of these typologies in fostering sustainable entrepreneurship. Using a quantitative multivariate approach, this study identified and classified mentoring models on the basis of 28 variables related to the mentoring process. The analysis drew on data from the Funcas 2025 survey of Spanish business incubators, which provided detailed information on mentoring practices across the participating incubators (initial responses: n = 100; final analytical sample after listwise deletion of missing values: n = 93). Principal component analysis was applied to extract the main latent dimensions underlying mentoring activities, and cluster analysis was subsequently used to group incubators into homogeneous mentoring typologies. The analysis identified three distinct mentoring profiles: (i) advanced mentoring, characterized by formalized programs with systematic evaluation, rigorous mentor selection, and continuous training; (ii) moderate mentoring, defined by partial integration into incubation services and the use of basic monitoring and evaluation mechanisms; and (iii) incipient mentoring, grounded ad hoc interactions, low formalization, and the absence of structured evaluation systems. Incubators with structured, continuous, and expert-driven mentoring systems were associated with higher entrepreneurial survival rates and stronger contributions to sustainable business development. From a public policy perspective, the findings highlighted mentoring as a strategic policy instrument for advancing Sustainable Development Goals related to Decent Work and Economic Growth (SDG 8), Industry, Innovation and Infrastructure (SDG 9), and Sustainable Cities and Communities (SDG 11). The proposed mentoring typology provided an evidence-based framework to support differentiated incubation policies, improve the targeting of public resources, and design stage-specific mentoring interventions. By moving beyond uniform policy approaches, public authorities can more effectively strengthen entrepreneurial ecosystems and promote resilient, innovative, and sustainable territorial development. Full article

This paper addresses the challenge of providing operational access to current metadata in complex, ever-changing relational data warehouses. Traditional catalogs struggle to keep up with changes in schemas, code, and processes. The paper presents a methodological approach based on a dual-loop architecture with ReAct agents and retrieval-augmented generation. The first loop, managed by an Ingestion Agent, continuously updates the semantic layer by automatically analyzing changes. The second loop uses an Assistant Agent to give analysts, developers, and support engineers an intelligent interface. This interface combines semantic search over a vector database with direct execution of diagnostic queries through an extensible set of tools. The main goal is to create a self-updating metadata ecosystem that provides operational access to contextual information for different user groups. The approach’s practical effectiveness is demonstrated through end-to-end scenarios, such as creating complex queries based on business terms or diagnosing extract-transform-load processes. Full article