Rural communities across Europe face accelerating retail desertification as traditional shops close under demographic contraction, rising labor costs, and insufficient consumer density. In Sweden's Småland region, entrepreneurs have responded with two contrasting unmanned retail models: fully automated stores gated by BankID, Sweden's national digital identification system, and honesty-based farm shops and kiosks where customers enter freely and pay on trust. This exploratory study employs observational netnography to map and classify 70 unmanned and self-service retail operations across Småland's three counties (Jönköping, Kronoberg, and Kalmar), drawing on naturally occurring digital-trace data from Google Maps. An inductively developed four-tier digitalization framework reveals a pattern the study terms digital polarization: rather than progressing along a linear adoption curve, rural retail appears to bifurcate into two contrasting logics, one oriented toward technological control and the other toward social trust, with a thin and apparently fragile hybrid middle ground. Fully automated stores (n = 28) offer continuous access but tend to exclude international tourists, elderly residents, and foreign workers through digital authentication barriers. Trust-based shops (n = 24) achieve broad accessibility and the highest consumer ratings in the dataset yet appear difficult to scale beyond high-social-capital settings. The 2023 bankruptcy of the region's most capital-intensive operator signals that high-capital-expenditure automation may be economically fragile in low-density markets. The most persistent innovations observed are also the least digital, a pattern that qualifies the assumption that digital transformation improves commercial sustainability in peripheral regions. The resulting framework and testable propositions offer an exploratory foundation for confirmatory research.
Sustainable technology infrastructures are becoming increasingly important for organizations operating in environments characterized by volatility, uncertainty, and systemic disruption. Among emerging digital infrastructures, AI-enabled information systems integrating satellite data, geospatial intelligence, and machine learning provide new capabilities for supporting strategic decision-making under uncertainty. However, empirical understanding of how these externally distributed intelligence systems contribute to organizational resilience remains limited.Drawing on dynamic capabilities theory and contingency theory, this study examines how AI-enabled external intelligence enhances corporate crisis resilience through Geo-Temporal Strategic Agility, conceptualized as a higher-order capability enabling firms to interpret and act upon spatial-temporal information in real time. The study further investigates whether industry disruption sensitivity conditions these relationships.Using survey data collected from 237 senior managers across disruption-sensitive industries, the study employs structural equation modelling to test a moderated mediation framework. The findings indicate that AI-enabled external intelligence positively influences corporate crisis resilience both directly and indirectly through Geo-Temporal Strategic Agility. In addition, the results show that these relationships are significantly stronger in industries characterized by high disruption sensitivity.This study contributes to the literature by reconceptualizing organizational sensing as an externally augmented, AI-enabled decision-support capability rather than a purely internal process. It further introduces Geo-Temporal Strategic Agility as a novel mechanism linking external intelligence to adaptive strategic action and resilience outcomes. Practically, the findings provide guidance for managers and policymakers seeking to leverage AI-enabled information systems as sustainable technology infrastructures for enhancing organizational resilience in complex and disruption-prone environments.
Purpose Amidst pressure on emerging economies to reconcile economic development with environmental goals, understanding the drivers of sustainable entrepreneurial intention (SEI) is crucial. This study examines the determinants of SEI among university students, extending the Theory of Planned Behavior (TPB) by incorporating sustainability-oriented entrepreneurial cognition (SOEC) and entrepreneurial skills (ES), and exploring the mediating role of TPB components. Methodology A cross-sectional survey gathered 173 responses from business students in a Latin American emerging economy. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to test explanatory direct and mediating effects among attitudes toward sustainable entrepreneurship (ATS), subjective norms (SN), perceived behavioral control (PBC), ES, SOEC, and SEI. Results Findings show that ATS and PBC strongly predict SEI, whereas SN and ES have no significant direct effects. Crucially, SOEC exerts significant indirect effects on SEI primarily through ATS and PBC. The indirect path via SN is non-significant. These results suggest that SOEC strengthens SEI by acting as a foundational catalyst for pro-sustainability attitudes and perceived control. Practical and theoretical implications Theoretically, this study clarifies SOEC's role not as a direct predictor that increases explanatory power, but as a distal antecedent that structures the TPB’s cognitive architecture. Furthermore, the insignificance of SN reflects the emerging economy context, where traditional corporate careers are socially favored over perceived high-risk sustainable ventures, making profound personal conviction (ATS) and self-efficacy (PBC) essential. Practically, entrepreneurship education should prioritize cultivating SOEC, attitudes, and perceived control alongside concrete skills to foster sustainability-driven mindsets.
The resilience of Pharmaceutical Cold Chains (PCCs) is vital for maintaining the quality and efficacy of temperature-sensitive medications and vaccines. Considering the field of sustainable technology entrepreneurship, the strategic implementation of AI-based innovations within the logistics sector of the pharmaceutical industry is a significant economic choice with clear consequences for organizational sustainability. This study proposes a structured decision-making framework to evaluate and prioritize Artificial Intelligence (AI)-driven technologies through a comprehensive, multi-dimensional approach that enhances PCC resilience across technical, business, environmental, and social dimensions. The framework integrates advanced fuzzy Multi-Criteria Decision-Making (MCDM) methods to systematically assess technologies under uncertainty. This research develops a novel framework for decision-making that makes two main contributions. First, from a methodological perspective, it proposes a p,q-Quasirung Orthopair Fuzzy (p,q-QOF) extension to the existing Ranking Alternatives with Weights of Criteria (RAWEC) method that incorporates a p,q-QOF Delphi-Lawshe’s Content Validity Ratio (CVR) to address high-order expert uncertainty and hesitancy. Secondly, from a substantive perspective, it presents a strategic approach to the prioritization of AI-driven technological solutions that identify predictive analytics and ML models as the most impactful solutions for enhancing the resilience of PCCs. This research thus closes the gap between fuzzy modeling theory and resource allocation for high-stakes medical logistics applications. It reveals that while technical robustness is the primary concern for technical systems, sustainability in the multi-dimensional approach to business agility and human capital development is a critical factor for resilience. The findings offer actionable economic guidance for pharmaceutical organizations navigating technology investment decisions under regulatory and operational uncertainty.
Remote agroforestry systems face persistent logistical constraints arising from dispersed production, weak infrastructure, and intermittent connectivity. These constraints often limit their capacity to generate stable economic value, participate in regional markets, and contribute to sustainable rural economic development. This paper reframes logistics in such settings as a foundational socio-technical and entrepreneurial capability that enables the emergence of viable agroforestry enterprises and sustainable bioeconomic activity rather than merely a downstream operational function. Integrating insights from sustainable entrepreneurship, rural economics, circular economy thinking, socio-technical transitions, and logistics systems design, the study develops the Two-Phase Agroforestry Logistics Architecture (TPALA)—a conceptual framework explaining how logistics capability can emerge, stabilise, and evolve under infrastructural scarcity. Using abductive reasoning and contextual illustration, TPALA identifies a sequenced pathway in which analogue stabilisation—predictable mobility, decentralised aggregation, custodian-led governance, and low-tech verification—establish the behavioural and informational reliability required for digital augmentation. The second phase introduces geo-tagged verification, offline-capable reporting, layered assurance mechanisms, and predictive analytics, enhancing traceability, coordination, and economic participation while preserving community-embedded routines. By linking logistics capability to opportunity formation and enterprise development, the framework specifies how logistics functions as entrepreneurial infrastructure in distributed agroforestry systems. The paper contributes to sustainable technology and entrepreneurship research by theorising logistics capability formation as a socio-technical mechanism enabling enterprise emergence under infrastructural constraint, with implications for sequenced investment, governance alignment, and the development of digital absorptive capacity in remote agroforestry systems.
Access to global markets remains a major challenge for social entrepreneurs, as international expansion requires capabilities and networks that often exceed local conditions. Accordingly, the recognition of strong internationalization intentions constitutes a relevant step in understanding how such expansion may be enabled. This study identifies the factors influencing the intention of social entrepreneurship initiatives to engage in internationalization processes. A structural equation model using Partial Least Squares (PLS-SEM) was developed, drawing on Ajzen’s Theory of Planned Behavior and Teece and Pisano’s Dynamic Capabilities Theory. Data were collected through a questionnaire administered to a representative sample of 375 social entrepreneurship in Ecuador, with a 95% confidence level and a 5% margin of error. The results support the validity of the measurement and structural models, indicating that partners’ social commitment, subjective norms, collaborative networks, and market diversification activities positively affect internationalization intentions, explaining 42% of the variance (R² = 0.42). The proposed model provides quantitative evidence to inform strategies aimed at strengthening the internationalization of social entrepreneurship.
This study examines how reintegration policies shape sustainable return migrant entrepreneurship through financial inclusion and post-migration economic resilience. Drawing on Institutional Theory and the New Economics of Labor Migration (NELM), the study conceptualizes reintegration as a mechanism through which institutional support is translated into long-term economic outcomes. A quantitative survey was conducted among Indonesian return migrants who previously worked in Hong Kong. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to assess both direct and indirect relationships among the constructs. The results indicate that reintegration policy quality does not have a significant direct effect on productive entrepreneurship. Instead, its influence operates indirectly through financial inclusion and post-migration economic resilience. Financial inclusion significantly enhances economic resilience, which in turn strongly predicts entrepreneurial activity. The findings confirm an indirect-only mediation mechanism, suggesting that institutional interventions affect entrepreneurial outcomes through sequential economic processes. This study contributes to the literature by integrating Institutional Theory and NELM into a unified framework explaining post-migration economic transformation. It also offers policy implications by highlighting the importance of strengthening financial inclusion and economic resilience to support sustainable entrepreneurship among return migrants.
The integration of Artificial Intelligence (AI) in the personal care and cosmetics sector has significantly transformed consumer experiences, offering AI-powered beauty recommendations, augmented reality (AR) try-on tools, and personalized skincare solutions. However, despite these advancements, consumer trust remains a critical barrier to adoption due to concerns over algorithmic fairness, inclusivity, data privacy, and transparency. This study investigates consumer perceptions of AI-driven beauty solutions within the UK and Ireland, exploring the factors influencing trust, adoption, and ethical considerations in AI-powered cosmetics retail. Employing a quantitative research approach, this study utilizes an online survey distributed to a diverse consumer base in the UK and Ireland, with data collected between 2020 and 2021, capturing insights from AI adopters and skeptics. The data were analyzed using descriptive statistics, regression analysis, and factor analysis to determine key drivers of consumer trust in AI-powered beauty solutions. The study is theoretically underpinned by the Technology Acceptance Model (TAM), Trust and E-Trust Theories, and Critical Realism, providing a socio-technical lens to examine how AI influences consumer decision-making, confidence, and engagement. Findings reveal that while AI has the potential to enhance shopping experiences, consumer skepticism remains high due to biases in AI recommendations (85.96%), lack of algorithmic transparency (62%), and concerns over data privacy (79%). The research also identifies the gap between AI innovation and consumer literacy, highlighting the need for explainable AI (XAI) models and fairness-aware algorithms. This study contributes to the growing discourse on responsible AI adoption by emphasizing the need for inclusive AI models, transparent decision-making frameworks, and stronger data privacy measures. The findings provide actionable recommendations for AI-driven beauty brands, including the implementation of bias audits, privacy-preserving AI frameworks, and consumer education initiatives to enhance trust and adoption. By integrating fairness-aware AI governance, the personal care and cosmetics sector can foster ethical AI deployment that aligns with consumer expectations and industry best practices.
Sustainability represents a pressing challenge for the textile and apparel (T&A) industry, driving a fast-growing but highly fragmented research stream. To tackle this issue, the study proposes a novel methodology, the LACE (Literature Analysis for thematic Classification and Evidence via topic Modelling) protocol, a data-driven approach that synthesizes extensive academic literature. Based on 881 peer-reviewed publications (2014–2024), the protocol combines systematic literature review principles with text mining techniques and topic modelling algorithms. The analysis identifies eight thematic areas shaping the research landscape: supply chain, circular economy, business models, environmental impact, social responsibility, sustainable practices, Industry 4.0, and consumer behaviour. Topic modelling reveals four cross-cutting patterns connecting these domains: performance-oriented optimization, governance-led stabilization, innovation-oriented transformation, and human-centered reconfiguration. The synthesis draws attention to the need for more integrative research approaches that examine how optimization, governance, innovation, and socially embedded practices interact across organizational and value chain contexts. Further research is needed to understand how digital and design-based technologies shape sustainability governance, how sustainability-oriented innovations are stabilized and scaled within institutional settings, and how social relations and consumption practices influence the adoption and persistence of innovative sustainability solutions and business models. Theoretically, the study advances a transparent and replicable approach for large-scale literature reviews, providing a structured synthesis of how sustainability is conceptualized in the T&A industry literature. Practically, the findings offer a lens for firms, innovators, and policymakers to assess current sustainability strategies and to identify where greater integration of governance arrangements, technological solutions, and business model innovation may be required to support more scalable and coherent sustainability initiatives across global value chains.
Recycling is a fundamental component of the circular economy. It is implemented in urban waste management services through citizen co-production, infrastructure provision, and communication promotion. This study examines the psychological and situational determinants of recycling among Generation Z within an expanded Theory of Planned Behaviour (TPB) framework, translating the findings into pragmatic, technology-based, and entrepreneurship-oriented proposals. Using a mixed-methods design, we first tested a simultaneous equation model with data from 826 young Spaniards in Barcelona and evaluated the moderating role of the Big Five personality traits and intuitive-rational processing style. Subsequently, we conducted 36 in-depth interviews to explore in greater depth the mechanisms, barriers, and frictions in the service system. The results show that moral norms are the strongest predictor of intention, and that intention is the main driver of behaviour, while situational factors weaken it. Moderating variables reveal heterogeneous behaviour patterns, with each segment systematically modulating the causal pathways within the TPB model. Focusing on the application of circular economy programmes, the study identifies the most appropriate messages to promote greater engagement in recycling among different segments of young citizens.
The integration of sustainable technologies and artificial intelligence (AI) into organisational processes is reshaping the strategic foundations of cooperatives. However, empirical evidence on how AI-enabled marketing capabilities can boost organisational performance in the social economy remains fragmented. This study explores the role of sustainable marketing strategies and the perceived benefits of AI in shaping cooperatives’ organisational capabilities and financial performance. An empirical exploratory study was conducted using partial least squares structural equation modelling (PLS-SEM) with bootstrapping in SmartPLS 4. Primary data were collected through a survey of Spanish cooperatives. The analysis provides exploratory findings on how marketing-driven capabilities are structured in cooperatives. These findings reveal the absence of direct performance effects and highlight the contextual role of perceived AI benefits. Marketing strategies exert a significant effect on organisational capabilities. However, the perceived benefits of AI do not significantly alter the relationship between market-oriented learning and performance. The low significance of the negative direct effect of perceived AI benefits on performance suggests ambivalent perceptions of the strategic risk of AI use in cooperatives. Despite its exploratory design, this study can help cooperatives by revealing appropriate strategies to improve or maintain performance.
Digitalization is an essential element for the development strategy of Small and Medium-sized Enterprises (SMEs) and a key factor for their Operational Performance. This study develops an extended TOE (Technology-Organization-Environment) model, incorporating the Individual and Economic factors (TOE-IE), aiming to identify and quantify the causal drivers of technology application in the firm and its subsequent impact. The research was designed as a quantitative and confirmatory study, and the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique was applied to analyze data from a sample of 270 SMEs in the Extremadura region (Spain) in 2024. The results reveal that Digitalization serves as a full mediator and a robust predictor of Operational Performance, with the Individual Factor (leadership, training, and digital talent) being the most robust driver of adoption. Critically, the Economic and Technological Factors were found to be non-significant, suggesting that public support policies have managed to mitigate the financial barrier, leaving the leader's will and staff capabilities as the main bottleneck.It is concluded that the essential strategic resource for digital transformation is human and attitudinal capital, not financial capital. This study is highly relevant for consultants, SME managers, and public policymakers, as it justifies the reorientation of investments from capital subsidies towards leadership development and digital upskilling programs. This study contributes to the field of sustainable technology by demonstrating that, within the framework of current economic policies, the primary driver of digital transformation in SMEs is not financial capital but the strategic leadership of the manager.
This study examines how micro and small enterprises (MSMEs) enact value propositions (VPs) to sustain competitiveness and economic resilience under crisis conditions. While prior research has emphasized VP design and communication, limited work has explored their enactment as strategic and economic practices within fragile and resource-constrained environments. Using a mixed-method design, the study combines Participatory Action Research (PAR) with nine Lebanese MSMEs and a survey of 106 enterprises analyzed through Principal Component Analysis (PCA). The findings show that MSMEs enact value propositions as adaptive, relational, and practice-based mechanisms embedded in daily entrepreneurial decision-making, relying on customer trust, improvisation, and resource bricolage rather than standardized tools. These enactment practices support economic continuity, value co-creation, and survival in the absence of stable institutional and policy frameworks. The PCA yields a three-component model comprising Integrated Value Proposition Practice, Customer-Driven Value Foundations, and Performance Accountability Gaps. While MSMEs demonstrate strong adaptive and customer-centered value practices, systematic economic evaluation and accountability mechanisms remain underdeveloped, constraining long-term sustainability and scalability.By reframing value propositions as enactment-based mechanisms of sustainable entrepreneurship, this study contributes to entrepreneurship and sustainability research and offers policy-relevant insights for supporting MSMEs in fragile and crisis-affected economies.
Industry 4.0 technologies, such as artificial intelligence, machine learning, and the Industrial Internet of Things (IIoT), are reshaping industries and hold considerable promise for advancing productivity and sustainable economic development. However, their adoption is fraught with risks, particularly for small and medium-sized enterprises (SMEs) that play a pivotal role in driving entrepreneurship, innovation, and the circular economy. Based on survey data collected between May and July 2020 from 307 professionals in mining, automotive manufacturing, and construction industries, this study examines critical risks across three stages of technology adoption: identification and selection; pilot testing, and full-scale implementation. Using the fuzzy analytical hierarchy process, the analysis reveals that technical risks dominate across stages and industries, while key social and environmental risks for achieving sustainability goals remain consistently deprioritized. However, risk profiles diverge by firm size and industry: SMEs are disproportionately affected by organizational and financial risks, whereas larger firms demonstrate consistent approaches through established risk management and economic policy frameworks. These findings demonstrate the necessity of a staged, context-sensitive, and sustainability-oriented adoption strategy. This study contributes to understanding how SMEs can balance economic efficiency and sustainable technology adoption in the transition toward Industry 4.0 by offering industry-specific propositions and managerial implications.
This study examines how ineffective sustainability communication contributes to perceived greenwashing and its cascading effects on consumer behavior, with critical implications for sustainable technology entrepreneurs and circular economy business models. By integrating Elaboration Likelihood Model (ELM), Signaling Theory, and Trust-Commitment Theory, we develop a comprehensive framework explaining how communication deficiencies trigger dual pathways of consumer response, addressing a critical gap in sustainable entrepreneurship literature by linking communication quality to economic outcomes in green technology adoption. We employed a three-stage approach combining PRISMA methodology, Structural Topic Modeling (STM), and bibliometric analysis to systematically analyze 111 articles (2010-2024) from the Scopus database. STM analysis revealed three dominant thematic clusters: greenwashing practices and perception (41 %), consumer behavior and intentions (33 %), and trust erosion and skepticism (26 %). Findings demonstrate that inadequate sustainability communication creates substantial market barriers for green technology entrepreneurs, with trust erosion translating directly into reduced purchase intentions in circular economy sectors. Our Integrated Theoretical Model establishes dual impact pathways (psychological and behavioral) through which inadequate sustainability information triggers consumer responses, mediated by emotional, cognitive, attributional, and evaluative mechanisms. For sustainable technology entrepreneurs, findings highlight substantial economic risks of misleading environmental claims. From a policy perspective, results suggest environmental communication regulations may protect legitimate sustainable ventures from market confusion. The framework reveals economic mechanisms through which communication deficiencies translate into market failures in sustainable innovation ecosystems, positioning communication integrity as an essential economic strategy for circular economy viability and green market development.
Green finance has evolved into a crucial institutional catalyst that shapes sustainable technology deployment, entrepreneurial activity, and economy-wide transitions toward the Sustainable Development Goals (SDGs). Situated at the intersection of sustainability, economics, and entrepreneurship, green finance influences how capital is mobilized, how institutions govern innovation, and how entrepreneurial ecosystems translate financial flows into long-term socio-economic and environmental value. This study synthesizes the global body of green finance–SDG research published between 2010 and 2025 through an integrated review combining quantitative science–mapping with theory–driven qualitative analysis. Drawing on institutional economics, innovation systems, and sustainability transition perspectives, the analysis reveals that, while green finance has accelerated investments in renewable energy and climate-oriented technologies, persistent gaps remain in institutional coordination, economic policy coherence, entrepreneurial inclusivity, technology diffusion across regions, and the integration of circular economy principles. Building on these insights, a conceptual framework was developed that positions green finance as an institutional enabler linking financial governance, sustainable technology innovation, entrepreneurial ecosystems, and SDG outcomes, offering actionable implications for policymakers and practitioners seeking inclusive, innovation-led, and resilient sustainable development pathways.
AI-driven dynamic pricing has evolved from an optimisation technique into a core infrastructure of the digital economy, such as the European Union Artificial Intelligence Act and global guidelines for trustworthy AI move towards implementation, questions of fairness, transparency, and consumer trust in algorithmic pricing have become urgent for firms, regulators, and entrepreneurial ventures alike. Start-ups and scale-ups are often at the frontier of deploying these systems as sustainable technology for resource-efficient demand management and revenue resilience, yet their dependence on legitimacy makes them particularly exposed to ethical, reputational, and market-acceptance failures. Despite the rapid growth of research on AI ethics, the specific intersection between algorithmic design, normative imperatives, and consumer-centric outcomes remains conceptually fragmented and insufficiently mapped.This study provides a bibliometric mapping of the emerging ethical agenda in AI-driven dynamic pricing at the interface of entrepreneurship, economics, and sustainable technology. A dataset of 38 peer-reviewed articles (2019–2025) was retrieved from Scopus using a targeted search combining dynamic pricing, Artificial Intelligence, and ethics/transparency/consumer trust. Using VOSviewer and Biblioshiny, the analysis integrates performance indicators, keyword co-occurrence, co-citation structure, and thematic evolution. The findings reveal a clear post-2022 shift from optimisation-centric work towards a more integrated discourse in which fairness, transparency, and trust become structurally central. Two dominant clusters emerge, pricing mechanisms with distributive implications and AI-enabled methodologies, while recent literature increasingly links technical design to consumer protection and economic governance, echoing policy developments associated with the European Union Artificial Intelligence Act and debates on circular economy-compatible market practices.
Purpose This study investigates the influence of digital literacy, entrepreneurial networking, knowledge sharing, and innovation on entrepreneurial success within emerging Middle Eastern economies. It further examines the moderating role of cultural support, assessing how societal norms and institutional expectations shape digital entrepreneurship, innovative behaviour, and the transition toward sustainable, knowledge-based economic systems. Design/methodology/approach A quantitative research design was applied using structured survey data from 305 entrepreneurs across diverse sectors in the Middle East. Validated measurement scales were employed to ensure reliability and construct validity. Structural equation modeling (SEM) was used to test the hypothesized relationships and evaluate the moderating effect of cultural support on the links between digital literacy, networking, knowledge sharing, innovation, and entrepreneurial success. Findings The results show that digital literacy and entrepreneurial networking significantly enhance knowledge sharing and innovation, which in turn improve entrepreneurial success. Cultural support, however, negatively moderates several relationships, indicating that traditional norms and institutional rigidities may constrain digitally driven entrepreneurial activity. Practical implications The findings highlight the need for policies that strengthen digital literacy, promote open innovation, and support sustainable technology adoption through inclusive and culturally adaptive entrepreneurial ecosystems. Originality/value The study contributes to entrepreneurship and digital transformation scholarship by applying Social Capital Theory to explain how digital competencies and networking interact with cultural dynamics, offering new evidence on sustainable entrepreneurship in rapidly modernizing Middle Eastern economies.
