This study explores how the digital business ecosystem enhances purchasing and supply management performance in the hospitality industry, focusing on the mediating role of service innovation capabilities and the moderating roles of collaborative network capabilities and knowledge sharing. Data were gathered through surveys from 401 managers of four- and five-star hotels in Türkiye, including purchasing managers and supply management professionals, and analyzed using structural equation modeling and moderated mediation analysis based on Hayes’ PROCESS macro. Digital business ecosystems positively influence purchasing and supply management performance, both directly and indirectly, via their service integration capability. Collaborative network capability and knowledge sharing serve as moderators in the research model. The findings confirm that integrating digital infrastructure, innovation capabilities, and collaborative networks improves purchasing efficiency, supplier collaboration, and overall supply management effectiveness in service-intensive environments.

The question of cybersecurity has turned into a necessity of credible digital finance, defining the expansion capacity of fintech sectors to increase access to secure and safe financial services. The phenomenon of fintech inclusion is becoming subject to the impact of institutional and corporate protection factors, which minimize perceived risk, and safeguard users, as well as confidence in digital financial channels. This paper is based on institutional and risk governance views and explores the influence of cybersecurity practices on fintech inclusion and suggests digital policies as a mediating factor that converts cybersecurity preparedness to inclusive fintech performance. Although increased attention is paid to cybersecurity and the modernization of regulations, there is still a lack of empirical data on the impact of cybersecurity practices on the inclusion of fintech based on policy-driven processes in a unified analytical framework. The 300 respondents who worked in the positions of information security managers, compliance and risk officers, fintech product managers, IT governance specialists, and senior executives were sampled to gather the data across the fintech-enabled organizations. The proposed hypothesized relationships were tested using Structural Equation Modeling based on the PLS-SEM. These results suggest that cybersecurity practices affect considerably digital policies and fintech inclusion, whereas digital policies affect considerably fintech inclusion. The mediation analysis ensures that the effect of cybersecurity practices on fintech inclusion is partly transmitted through the digital policies, which indicate that a better result is observed when backed by the clear governance structure and policy frameworks. The findings have practical implications to managers and policymakers who aim to improve the inclusion of fintech using secure and policy-driven digital finance environments.

The digital transformation has been a main engine of economic growth by transforming the organizational structures, operations and strategic decisions with the help of sophisticated digital technologies. The mediating role of Big Data Analytics in this transformation is that it helps organizations to transform digital initiatives into real economic performance. It will utilize the Technology Acceptance Model and recent literature on the concept of digital transformation and the capabilities of data to identify the effects of digital transformation on economic growth in the mediator Big Data Analytics. The research paper is based on three aspects of digital transformation: preparations of digital infrastructure, process integration based on technology and data-driven decisions support. A sample of 300 respondents was taken, who are representatives of digitally intensive organizations, including senior executives, technology managers, and analytics specialists. Structural Equation Modeling on the Partial Least Squares algorithm was used to test the research framework. The results prove that digital transformation has a positive impact on economic growth and improves the capabilities of Big Data Analytics to a large extent. Big Data Analytics, in its turn, can help improve economic outcomes due to its contribution to enhancing efficiency, the ability to innovate, and responsiveness. The mediation analysis supports the claims and confirms that Big Data Analytics is a major transmission channel upon which the digital transformation initiatives create sustainable economic value. The research has its implications on organizational leaders, technology strategy, and policymakers. Digital transformation can only be maximized by strategic investment in digital infrastructure, development of analytics capability and data governance. To create the digital policies that can support both the data-driven innovation and inclusive economic growth, policymakers can rely on these findings.

This study diagnoses the systemic sustainability challenges in Iran's national automotive supply chain using Soft Systems Methodology to analyze the interrelated economic, environmental, social, technological, and managerial dimensions. The goal is to develop a conceptual model that reflects these complexities, validate it with real-world case data, and propose practical and desirable changes to improve sustainable supply chain management practices. The research addresses a critical gap in sustainability strategies for emerging markets with structurally constrained and politically sensitive industrial ecosystems. The study adopts an integrated approach combining systemic methodology and thematic analysis, utilizing semi-structured interviews with senior automotive industry experts and a targeted literature review. Rich pictures, conceptual modeling, and iterative validation ensured alignment between systemic challenges and practical realities, with data coded to identify barriers and enablers across the supply chain. Comparing conceptual and real-world models revealed key areas for intervention, including blockchain-enabled traceability, water recycling, supplier diversification, and digital integration to enhance sustainability. Findings highlight issues such as reliance on foreign suppliers, currency volatility, limited domestic manufacturing capacity, inefficient logistics, weak governance, and slow adoption of green technologies. Environmental concerns include water scarcity, inadequate vehicle scrappage systems, and high emissions from diesel transport. The study provides actionable recommendations for policymakers and industry managers, such as diversifying suppliers, implementing digital platforms, and introducing transparent governance. These measures can improve efficiency, reduce environmental impact, and increase resilience against political and economic disruptions. The research also offers future research needed to test the proposed interventions and explore policy frameworks for large-scale adoption.

This study develops a generalized finite-horizon inventory model that integrates complex demand patterns, deterioration, seasonal effects, and stochastic variability within a Monte Carlo–based simulation framework in MATLAB. The model incorporates fixed, holding, procurement, deterioration, transportation, backordering, and lost sales costs, enabling comprehensive cost evaluation. Simulation results show that deterioration and seasonality significantly affect optimal replenishment cycles and order quantities, and that the model yields robust cost estimates under uncertainty. Sensitivity analysis further indicates that the base demand rate has the strongest effect on cost outcomes, followed by procurement and backorder costs, whereas seasonality and holding cost parameters exhibit negligible influence.

The study explores the impact of digitalization and supply chain integration on the financial performance of the Tunisian agro-industrial sector, considering the growing importance of digital technologies in modern business operations. The objective is to examine the relationship between these two factors and financial performance in this sector. Data were collected through a questionnaire and analyzed using SPSS26 software, employing principal component analysis and linear regression. The results show that digitalization and supply chain integration significantly influence the financial performance of agro-industrial organizations in Tunisia. This study makes a theoretical contribution by shedding light on how digital advancements and supply chain strategies affect financial outcomes in this context. In conclusion, the findings highlight the significant effects of digitalization and supply chain integration on the financial performance of Tunisian agro-industrial companies.

Green supply chain management (GSCM) has emerged as a crucial strategy for organizations to address environmental sustainability concerns. This study aims to examine the knowledge domain of GSCM practices by conducting a bibliometric analysis of Scopus data from 2010 to 2023. Using the VOS viewer software, the researchers examined the publication trends, influential authors, institutions, journals, and keywords in the GSCM research landscape. The methodology involves systematically searching and retrieving relevant publications from the Scopus database, then data cleaning and preprocessing. The resulting dataset comprises 530 articles, which are then analyzed using the VOS viewer to create a network visualization map. Various bibliometric indicators, such as co-authorship, co-citation, and keyword co-occurrence, are used to uncover patterns and relationships among the articles. The bibliometric analysis revealed a growing interest in GSCM, with a significant increase in publications over the 14 years. The study identified the most influential authors, institutions, and journals in the field, highlighting the prominent contributors to GSCM research. Also, looking at how often keywords were used together revealed the main theme groups in the GSCM field, such as sustainable supply chain management, sustainable operations, sustainable governance, and sustainable organizational performance. The findings of this study provide valuable insights into the intellectual structure and evolution of GSCM research. Visualizing the knowledge domain using the VOS viewer facilitates a comprehensive understanding of the interconnections between various aspects of GSCM, such as the adoption of green practices, environmental performance, and supply chain sustainability. These insights can inform researchers, practitioners, and policymakers in developing strategic initiatives and future research directions to advance the field of GSCM.

With the increasing complexity of supply chain management, supply chain concentration (SCC) has become a prominent research topic in academia and practice. To clarify the developmental context and research trends within this field, this study utilizes the Web of Science core collection as the data source, selecting 362 English-language publications from 1975 to 2025. CiteSpace 6.2 was employed to conduct a visual bibliometric analysis, systematically examining the social structure, conceptual structure, and intellectual structure of SCC research through co-authorship, co-word, and co-citation analyses. The results indicate rapid growth in SCC research since 2020, with China and the United States being the major contributing countries, and collaborations exhibiting regional characteristics. High-frequency keywords prominently include "customer concentration," "supplier concentration," and "performance," with research themes progressively extending toward frontier topics such as "digital transformation," "green innovation," and "corporate social responsibility." Co-citation analysis identified representative works by authors such as Panos Patatoukas, Dan Dhaliwal, and Murillo Campello, highlighting a shift in research focus from traditional performance perspectives to governance mechanisms and sustainable strategies within a digital context. This study summarizes core literature clusters, evolutionary paths of clusters, and significant citation bursts, revealing interdisciplinary integration and paradigm shifts in SCC research. The paper provides a systematic review of future directions in SCC studies.

In the face of digitization in manufacturing industries, the judicious evaluation and selection of cutting-edge CNC machines play a pivotal role in achieving production-grade precision, accuracy and manufacturing agility. The evaluation of 3-axes CNC machines incorporates most sought-after subjective and objective criteria having significant relative weights and green impacts. This research paper presents a novel heterogeneous expert based decision making (HGEDM) framework incorporating a diversified combination of experts having distinct impact factors. The experts’ impact factors so calculated impart significant contributions in computing weighted aggregated performance ratings of the alternatives. To establish the effectiveness of the suggested approach, three practical selection problems are illustrated. The calculated findings are validated with few well-established approaches demonstrating the realistic nature of the suggested methodology. To assess the stability and robustness of the proposed approach, a sensitivity analysis is performed. Besides, Spearman’s rank correlation measure demonstrates that the ranks obtained using the proposed approach are highly close to those derived from several existing methods. Furthermore, both Pearson correlation coefficient and Sample correlation coefficient measures show a strong association between the proposed approach and existing ones. Therefore, the proposed HGEDM approach is considered to be a consistent and effective tool for supporting optimal selection.

Auto accessories such as car covers provide an added extra in automotive styling both in the look and construction. Any fault in these components will reduce customer satisfaction and result in higher warranty expenses among manufacturers. Automotive sector as per IATF 16949 requirements requires a very effective and strong control of its processes to reduce the defects and enhance productivity. Thus, improved methods for defect identification and higher levels of quality assurance during production are critical issues of current concern. This research focuses on the use of Artificial intelligence (AI) in the automotive industry with an emphasis of using computer vision for superior improvement of quality KPIs. The purpose is to provide an efficient system and organizational approach to the further optimization of the end-of-line inspection of covers for vehicles, and to improve the efficiency of the identification of defects under IATF 16949 regulations. This study is unique in adopting a case based on smart splicing technology implemented in the cutting area of the automobile manufacturing lines. This paper simultaneously applies AI and IoT in order to understand its degree of influence in the definitive performance KPIs. Insignificance may be identified through the application of linear regression used to analyze the correlation between the applied technology and subsequent performance gains. Experimental outcome shows a significant decline on the number of defects that are identified at the last inspection process as well as an improvement on the rate of production. AI particularly contributed to enhancement of inspection processes thereby minimizing non-value adding activities and hence improving overall quality of the products. The current study also encourages manufacturers to adopt intelligent technologies since the AI technologies implemented within the IATF 16949 standards can boost the automotive production quality and decrease the costs and customer dissatisfaction. The automotive industry has changed today due to the implementation of IoT and AI in manufacturing, as this work has shown, with an exciting horizon of the constant automation process and increasing quality indications to deliver on the promise of the redefined definition of success in this industry.