In the post-pandemic era, the uncertain global market and rising social-environmental issues drive organizations to adapt their supply chain strategies to more dynamic, flexible models, leveraging advanced technologies like AI, big data analytics, and decision support systems. This review paper aims to examine the current research on AI-integrated technologies in sustainable supply chain management (SSCM) to inform future research directions. We adopted bibliometric and text analysis, targeting 170 articles published between 2004 and 2023 from the Scopus database following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. We confirm that AI-integrated technologies have demonstrated the capability to enable SSCM across various sectors. We generated ten future research topics using the Latent Dirichlet Allocation (LDA) method and proposed 20 propositions. The results show that AI-integrated technologies in supply chain processes primarily address sustainability, focusing on environmental and economic issues. However, there is still a technological gap in tackling social issues like working conditions and fair dealing. Thus, we proposed a dynamic framework of AI in SSCM to help researchers and practitioners synthesize AI-integrated technologies in SSCM and optimize their supply chain models in future directions.

This study aims to promote the protection and inheritance of cultural heritage in private gardens in the Jiangnan area of China. By establishing a precise visual labeling system and accelerating the construction of a database for private garden features, we deepen the understanding of garden design philosophy. To this end, we propose an improved Jiangnan private garden recognition model based on You Only Look Once (YOLO) v8. This model is particularly suitable for processing garden environments with characteristics such as single or complex structures, rich depth of field, and cluttered targets, effectively enhancing the accuracy and efficiency of object recognition. This design integrates the Diverse Branch Block (DBB), Bidirectional Feature Pyramid Network (BiFPN), and Dynamic Head modules (DyHead) to optimize model accuracy, feature fusion, and object detection representational capability, respectively. The enhancements elevated the model's accuracy by 8.7%, achieving a mean average precision (mAP@0.5) value of 57.1%. A specialized dataset, comprising 4890 images and encapsulating various angles and lighting conditions of Jiangnan private gardens, was constructed to realize this. Following manual annotation and the application of diverse data augmentation strategies, the dataset bolsters the generalization and robustness of the model. Experimental outcomes reveal that, compared to its predecessor, the improved model has witnessed increments of 15.16%, 3.25%, and 11.88% in precision, mAP0.5, and mAP0.5:0.95 metrics, respectively, demonstrating exemplary performance in the accuracy and real-time recognition of garden target elements. This research not only furnishes robust technical support for the digitization and intelligent research of Jiangnan private gardens but also provides a potent methodological reference for object detection and classification research in analogous domains.

The rapid evolution of artificial intelligence (AI) has significantly transformed industries, positioning the fashion sector as a critical area of study due to its mass production and pressing sustainability challenges. As the world’s largest apparel producer, China faces unique hurdles in terms of integrating AI technologies, highlighting the intersection of technological innovation and sustainability within this industry. In this context, this study aims to provide the initial exploratory correlations between AI adoption and open innovation from apparel manufacturing micro-, small-, and medium-size enterprises (MSMEs) managers’ perspectives, identifying knowledge absorptive capacity (KACAP)’s significant impacts through an integrated and extended TAM-TOE model. We conducted PLS-SEM to empirically validate the antecedents of AI adoption and its consequential effects on KACAP and open innovation by collecting information from 269 of the apparel manufacturing MSMEs’ top managers. The results show that the TAM-TOE structural model explains 60.7% of the variance in AI adoption, 47.4% in KACAP, and 55.4% in open innovation, which suggests that the model has good explanatory capacity, and that all these Q² values indicate a sizeable predictive accuracy threshold. Drawing on the proposed model, the study has identified technological (e.g., perceived usefulness) and environmental factors (e.g., competitive pressure, market uncertainty, and government support and policy) that significantly impact AI adoption, while organizational factors (e.g., organizational readiness) directly impact KACAP, and environmental factors (e.g., competitive pressure, supplier involvement, and market uncertainty) directly impact open innovation. Subsequently, the AI construct is having a significant influence on MSMEs’ open innovation through KACAP. This fills existing theoretical gaps by linking AI technology to organizational innovation processes and demonstrates the mediating influence of KACAP. Also, the proposed model provides a foundation for future research by exploring the intersection of AI and innovation in similar industries.

Purpose: The main purpose of this study is to provide a decision-making basis for the development of a sustainability strategy by importance-performance analysis (IPA) methods. However, traditional and modified IPA methods do not accurately reveal the impact of the various dimensions of sustainable development (SD) on cultural landscape (CL). Based on the structural equation model (SEM), four main dimensions of SD were identified from the available data. This paper conducted an optimized IPA by building three SEMs based on importance, performance and the importance of four dimensions.Design/methodology/approach: This paper proposes the use of exploratory factor analysis (EFA) and SEM to develop the CL characteristic index evaluation model to determine the load of each indicator of importance and performance, and the SD-based measurement model to further determine the weight of the sustainability of each indicator in importance and performance. Afterwards, proposed sustainable strategies to the corresponding index were obtained.Findings: This study offers useful insights into the actual operation and theoretical aspects of the IPA method.Practical implications: This study offers useful insights into the actual operation and theoretical aspects of the IPA method. This can solve the incongruous and uncertainty problem in the sustainable planning strategy, and the case study proves the feasibility of the IPA method combined with the influence of the structural equation.Social implications: The research is conducive to acceptance by local people with regard to their preferences in the perspective of sustainable development. It provides a basis for the future use of the IPA method in a comprehensive way based on an administrative angle.Originality/value: The case study proves the feasibility of the IPA method combined with the influence of the structural equation. This provides a basis for the future use of the IPA method in a comprehensive way based on an administrative angle.

The rapid advancement of artificial intelligence (AI) in the traditional-apparel-manufacturing sector is accelerating innovation and transformation, as cutting-edge AI applications have been increasingly integrated into the industry in recent years. While China has made outstanding achievements in applying AI in the apparel-manufacturing sector, the adoption of AI by traditional apparel manufacturers has progressed slowly. This study aims to develop a sustainable triple-layer framework of an AI-enabled innovation ecosystem from grounded required AI capabilities and barriers to AI adoption, thereby generating the conceptual propositions for micro, small, and medium-sized Chinese apparel manufacturing. Through semi-structured interviews conducted with 20 organizations, this study qualitatively analyzes interviews with representatives from enterprises, universities, and apparel associations to determine the required AI capabilities and barriers to adopting AI. It proposes 13 propositions within a theoretical framework that addresses barriers and aligns multi-actor collaborations, ultimately forming a sustainable AI-enabled Triple-Layer Innovation Ecosystem Framework. This novel framework reflects the dynamic interplay between external knowledge absorption capacity and a firm’s internal innovation capacity, providing a theoretical foundation for understanding and advancing AI-driven innovation in the apparel-manufacturing sector.

This study demonstrates a new intangible cultural heritage (ICH) design course and its effectiveness of interdisciplinary teams with digital technology beyond conventional single-disciplinary teams. A design guideline for ICH was built on the basis of Bronfenbrenner’s Ecological Systems Theory (EST). Then, an evaluation standard involving five criteria (culture fit, creativity, aesthetics, experientiality, diversity) was established. Thirty students participated in the course and were divided into four teams of two types as follows: two interdisciplinary teams consisting of students from different backgrounds and two single-disciplinary teams consisting of students from the visual communication department. Experts assessed the design works and design process of the four teams according to the evaluation criteria as follows: (1) there are differences in the application of the ICH design guideline between the interdisciplinary approach with digital technology and the conventional teaching method, and (2) the ICH design course positively affects the five criteria. The interdisciplinary approach with digital technology in ICH design is more likely to stimulate students’ creativity. These findings emphasize the importance of new ICH design courses and provide insights for future design educators.