Adel Aazami

Digital technologies are widely expected to reshape supply chains, yet the conditions under which they deliver operational and sustainability-related value in practice remain insufficiently understood. This study examines how digital technologies influence supply chain operations and performance, where performance is operationalized through demand forecasting accuracy, cost, lead time, visibility, and inter-organizational collaboration, and asks how these technologies can support more resilient, resource-efficient, and sustainable supply chain operations. The study adopts an exploratory qualitative design based on semi-structured interviews with eight supply chain professionals at manager level or above, drawn from the aluminum, elevator, railway, food, and consumer goods industries in Austria, Slovakia, and the Czech Republic, and conducted between March and June 2025. A structured literature review complements the interview evidence. Within this exploratory sample, Artificial Intelligence and Data Analytics were the most widely adopted technologies (five of eight participants each), followed by the Internet of Things (four of eight) and Automation (five of eight), while no participant reported active Blockchain deployment. Reported benefits concentrated on forecasting accuracy, operational efficiency, visibility, and collaboration, whereas high implementation costs, legacy system integration, skill shortages, and regulatory uncertainty formed the principal barriers. The central finding is a conditional relationship between adoption and competitiveness: internal operational gains did not automatically translate into competitive advantage among the professionals interviewed, but appeared to require strategic alignment, cross-functional integration, and performance measurement. Because digitally enabled forecasting, inventory positioning, and resource optimization also reduce waste and improve resource utilization, the findings link digital supply chain transformation to sustainable development objectives. This exploratory study of eight participants therefore provides practitioner-level propositions and a research agenda for digital and sustainable supply chain transformation rather than statistically generalizable findings.

This study examines how heritage-based small and medium enterprises (SMEs) cope with economic shocks and institutional constraints in a semi-urban context. The study focuses on identifying context-specific barriers that shape SME growth and sustainability in heritage-based, semi-urban settings. Using a mixed-methods design, survey data from 200 SMEs were analyzed with PLS-SEM, and 20 semi-structured interviews were examined through thematic analysis, collected in Kashan, Iran (March–May 2025). We find that inflation and limited access to finance are the primary barriers to firm growth, followed by regulatory delays and administrative complexity. Qualitative findings reveal five recurring adaptive routines, short-cycle cash management, cooperative input purchasing, product simplification/micro-pivoting, reliance on local networks, and minimalist digitalization, that operate in a discernible temporal sequence to sustain firm continuity. By integrating resource-based and institutional perspectives, the paper advances meso-level theorizing on SME resilience and proposes a set of low-cost, actionable policy measures (e.g., streamlined e-licensing, targeted mobile microfinance, and buyer–supplier matchmaking) for local authorities and development practitioners.

Electromobility provides an effective solution for developing countries to reduce dependence on fossil fuels, enhance energy security, and increase environmental sustainability. The current study evaluates the feasibility of implementing electric vehicles (EVs) powered by renewable energy in developing countries. Based on qualitative methods, including expert interviews, it discusses existing transportation systems, the benefits of EVs, and significant constraints such as poor infrastructure, high initial investment, and ineffective policy structures. Evidence further suggests that EV adoption is likely to bring considerable benefits, particularly in cities with high population densities, adequate infrastructure, and supportive regulations that facilitate rapid adoption. Countries like India and Kenya have reduced their fuel import bills and created new jobs. At the same time, cities such as Bogota and Nairobi have seen improved air quality through the adoption of electric public transit. However, the transition requires investments in charging infrastructures and improvements in power grids. Central to this is government backing, whether through subsidy or partnership. Programs like India’s Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) initiative and China’s subsidy program are prime examples of such support. The study draws on expert interviews to provide context-specific insights that are often absent in global EV discussions, while acknowledging the limitations of a small, regionally concentrated sample. These qualitative findings complement international data and offer grounded implications for electromobility planning in developing contexts. It concludes that while challenges remain, tailored interventions and multi-party public–private partnerships can make the economic and environmental promise of electromobility in emerging markets a reality.

This study presents an integrated assessment of technological development in the aerial mobility innovation ecosystem, combining network analysis, patent clustering, and a multicriteria strategic evaluation model. The empirical corpus centers on unmanned aerial vehicle (UAV) logistics, unmanned aircraft system (UAS) applications, urban air mobility (UAM) platforms, and the avionics, sensing, communication, and materials technologies that enable supply‐chain‐oriented aerial operations, rather than commercial passenger air transport. Patent data sourced from Lens.org were analyzed using Gephi, revealing a highly modular applicant collaboration network shaped by five dominant communities centered around Huawei, Apple, Qualcomm, NVIDIA, and UATC. These applicant communities form structurally cohesive ecosystems that anchor major technological directions within the field. To uncover the thematic organization of innovation, text clustering of patent abstracts identified five coherent technological clusters: data and sensing layer, platform design and manufacturing, network infrastructure and communication, agrotech applications, and materials and component innovation. Together, these clusters represent the core thematic architecture of aerial mobility, capturing the interplay between perception technologies, engineering design, communication infrastructures, domain‐specific applications, and advancements in materials and components. A subsequent TOPSIS‐based strategic evaluation reveals that the data and sensing layer, materials and component innovation, and network infrastructure and communication clusters possess the highest strategic potential based on their citation influence, cross‐domain relevance, and integration across applicant communities. Overall, the findings show that aerial mobility innovation emerges through the interaction of five structurally dominant applicant collaboration communities and five thematically distinct technological clusters, offering a comprehensive understanding of how technological trajectories evolve. The proposed integrated framework offers a replicable patent‐based prioritization signal that can inform preliminary research scanning, monitoring, and agenda‐setting in this rapidly progressing technological domain, without substituting for market, regulatory, or operational assessment.

This study introduces the Multi-Objective Sustainable Vehicle Routing Problem (MOSVRP) with time windows, designed for congested urban networks. The model simultaneously addresses economic, environmental, and social sustainability by minimizing costs and emissions while maximizing customer satisfaction through enhanced service at pickup nodes. To manage the complexity of large-scale urban routing, we propose a novel Voronoi diagram-based network shrinking procedure that significantly reduces computational effort. More specifically, the model incorporates time-dependent traffic patterns to capture realistic urban conditions. For the solution, we propose a tailored metaheuristic, the enhanced Multi-Objective Volleyball Premier League (MOVPL) algorithm, which incorporates reference point guidance, disruption operators, and adaptive weight adjustment. This hybrid approach effectively balances conflicting objectives and improves solution diversity. Applied to Tehran’s urban freight network, the proposed method demonstrates superior performance across all metrics compared to benchmark algorithms and exact methods. Results show notable reductions in fuel consumption and travel distance, alongside improved service equity. Furthermore, the framework offers a scalable and transferable solution for sustainable logistics in other urban contexts.

This study maps how Natural Language Processing (NLP) contributes to sustainability. Using a PRISMA‐guided review of 131 English‐language articles from Web of Science (2018–2025), we combine bibliometric co‐word mapping with BERTopic to derive complementary structural and semantic views. Four themes emerge: Topic 0—Climate Change Discourse and Energy Innovation (SDG 13, SDG 7); Topic 1—Sustainability Policy and Public Engagement (SDG 11, SDG 16); Topic 2—Tech‐Driven Environmental Solutions (SDG 6, SDG 9, SDG 15); and Topic 3—Corporate Sustainability and Sustainable Infrastructure (SDG 8, SDG 9, SDG 12). Social equity remains comparatively under‐covered, with limited attention to SDG 5 and SDG 10. Theoretically, we position NLP as a bridge that supports sensemaking, coordination, and accountability across the environmental, social, and economic pillars. Practically, our synthesis highlights deployable uses of NLP for real‐time narrative monitoring, environmental, social, governance (ESG) claim assessment, and horizon scanning for emerging risks and innovation niches. For policy, coupling narrative analytics with policy‐text tracking can create feedback loops that enhance communication, legitimacy, and policy‐mix coherence for SDG implementation. By jointly leveraging network‐based and embedding‐based evidence, the review reveals thematic frontiers, makes SDG linkages explicit, and offers a topic‐based agenda with testable propositions.