Technology adoption in socially sustainable supply chain management: Towards an integrated conceptual framework

This study aims to systematically review existing literature on digital technology adoption for socially sustainable supply chain management (SSSCM) and propose a theoretical framework that outlines the central concepts. A content analysis-based systematic literature review approach was adopted to analyze 49 articles published from 2017 to 2024. The findings of this study identify critical antecedents, barriers, practices, enablers, and outcomes of digital technology adoption for SSSCM. The proposed conceptual model based on technology–organization–environment (TOE) framework and diffusion of innovation (DOI) theory captures these relationships among the identified factors and provides insights into how they can support the development of a socially sustainable supply chain. Furthermore, this study explores the potential positive and negative effects of technology adoption for SSSCM. It highlights the opportunities and challenges that arise from using digital technology in SSSCM, such as the emergence of Industry 4.0 and the need to ensure the ethical use of technology. This study is the first comprehensive review of the role of digital technology in SSSCM. The suggested framework offers guidance for upcoming research in this field, outlining the key areas that require further investigation

Tank container operators’ profit maximization through dynamic operations planning integrated with the quotation-booking process under multiple uncertainties

Tank Container Operators (TCOs) are striving to maximize profit through the integration of their global Tank Container (TC) operations with the job quotation-booking process. However, TCOs face a set of unique challenges not faced by general shipping container operators, including the process uncertainties arising from TC cleaning and the use of Freight Forwarders (FFs). In this paper, a simulation-based two-stage optimization model is developed to address these challenges. The first stage focuses on tactical decisions of setting inventory levels and control policy for empty container repositioning. The second stage integrates the dynamic job acceptance/rejection decisions in the quotation-booking processes with container operations decisions in the planning and execution processes, such as job fulfilment, container leasing terms, choice of FFs considering cost and reliability, and empty tank container repositioning. The solution procedure is based on the simulation model combined with heuristic algorithms including an adjusted Genetic Algorithm, mathematical programming, and heuristic rules. Numerical examples based on a real case study are provided to illustrate the effectiveness of the model.Tank Container Operators (TCOs) are striving to maximize profit through the integration of their global Tank Container (TC) operations with the job quotation-booking process. However, TCOs face a set of unique challenges not faced by general shipping container operators, including the process uncertainties arising from TC cleaning and the use of Freight Forwarders (FFs). In this paper, a simulation-based two-stage optimization model is developed to address these challenges. The first stage focuses on tactical decisions of setting inventory levels and control policy for empty container repositioning. The second stage integrates the dynamic job acceptance/rejection decisions in the quotation-booking processes with container operations decisions in the planning and execution processes, such as job fulfillment, container leasing terms, choice of FFs considering cost and reliability, and empty tank container repositioning. The solution procedure is based on the simulation model combined with heuristic algorithms including an adjusted Genetic Algorithm, mathematical programming, and heuristic rules. Numerical examples based on a real case study are provided to illustrate the effectiveness of the model

Enhancing firms’ innovation persistence in the circular economy through government-supported green supply chain demonstrations: cost leadership or differentiation?

Government support is pivotal in guiding firms towards adopting green supply chain (GSC) practices aligned with the circular economy. Our study addressed this critical issue through a quasi-natural experiment of GSC demonstration in China. We conducted a difference-in-differences estimation to assess the variation in the persistent innovation capabilities between the treatment and control groups. The results indicate that the GSC demonstration, as an external policy change, stimulated the development of firms’ persistent innovation capabilities. These capabilities have positive effects on both innovation input and output dimensions. Additionally, we explored the interaction between firms’ competitive strategies and government support. Findings indicate that differentiation strategies have a stronger positive impact on innovation persistence, while cost leadership strategies weaken this link. These results emphasize the government's critical role in fostering GSC adoption, offering implications for effective government-business collaboration towards a circular economy and sustainable planning across social, environmental, and technological innovation factors

Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network

Credit risk prediction for small and medium enterprises (SMEs) has long posed a complex research challenge. Traditional approaches have primarily focused on enterprise-specific variables, but these models often prove inadequate when applied to SMEs with incomplete data. In this innovative study, we push the theoretical boundaries by leveraging data from adjacent enterprises to address the issue of data deficiency. Our strategy involves constructing an intricate network that interconnects enterprises based on shared managerial teams and business interactions. Within this network, we propose a novel relational graph attention network (RGAT) algorithm capable of capturing the inherent complexity in its topological information. By doing so, our model enhances financial service providers' ability to predict credit risk even in the face of incomplete data from target SMEs. Empirical experiments conducted using China's SMEs highlight the predictive proficiency and potential economic benefits of our proposed model. Our approach offers a comprehensive and nuanced perspective on credit risk while demonstrating the advantages of incorporating network-wide data in credit risk prediction

Leveraging social media in new product development: organisational learning processes, mechanisms and evidence from China

Purpose The main purpose of this paper is to investigate how social media can provide important platforms to facilitate organisational learning and innovation in new product development (NPD) process. Design/methodology/approach Using a multiple case-study approach, this study assesses qualitative data collected via 56 interviews from 13 world-leading Chinese companies in the high-technology industry. Findings The study identified three distinct types of organisational learning mechanisms for firms to extract potential innovation inherent in social media. It further determined various organisational enablers that facilitate the connections between these mechanisms and NPD performance. Research limitations/implications This research contributes to the emerging literature on digital product development and organisational learning. The cases were conducted in the Chinese context, hence, the results may not be fully generalisable to other organisations, industries and countries without appropriate re-contextualisation. Practical implications The empirical evidence showcases the various mechanisms adopted by managers in different NPD phases. It identifies several technological and organisational adaptations that managers can apply to smartly scale their social presence and facilitate NPD. Originality/value Despite the exponential growth of social media use in identifying and interacting with external stakeholders, managerial practice and academic research have paid little attention to how social media can be leveraged for NPD. The value of this research comes from applying a qualitative method to gain in-depth insights into the mechanisms for leveraging social media to facilitate innovation in NPD

Leveraging Social Media in New Product Development: Organisational Learning Processes, Mechanisms and Evidence from China

Purpose – The main purpose of this paper is to investigate how social media can provide important platforms to facilitate organisational learning and innovation in new product development (NPD) process.Design/methodology/approach – Using a multiple case-study approach, this study assesses qualitative data collected via 56 interviews from 13 world-leading Chinese companies in the high-technology industry.Findings – The study identified three distinct types of organisational learning mechanisms for firms to extract potential innovation inherent in social media. It further determined various organisational enablers that facilitate the connections between these mechanisms and NPD performance.Research limitations/implications – This research contributes to the emerging literature on digital product development and organisational learning. The cases were conducted in the Chinese context, hence, the results may not be fully generalisable to other organisations, industries and countries without appropriate re-contextualisation.Practical implications – The empirical evidence showcases the various mechanisms adopted by managers in different NPD phases. It identifies several technological and organisational adaptations that managers can apply to smartly scale their social presence and facilitate NPD.Originality/value – Despite the exponential growth of social media use in identifying and interacting with external stakeholders, managerial practice and academic research have paid little attention to how social media can be leveraged for NPD. The value of this research comes from applying a qualitative method to gain in-depth insights into the mechanisms for leveraging social media to facilitate innovation in NPD

Bi-objective optimization of last-train timetabling with multimodal coordination in urban transportation

When urban rail transit (URT) does not provide 24-hour services, passengers who travel at late night may not be able to reach their destinations with only URT trains. As a result, passengers have to find alternative transport means, or combine URT trains with other transport services to fulfill their journeys. This paper investigates the integrated optimization of last train timetabling and bridging service design with consideration of passenger path choices. Two bridging services are considered: taxis and buses. Based on pre-constructed path sets, a bi-objective mixed-integer nonlinear programming (MINLP) model is developed, aiming at minimizing total passenger travel time and total passenger travel cost. To reduce the model scale and improve solution efficiency, three path dominance principles are proposed to remove redundant passenger paths without loss of optimality. An adaptive iterative algorithm is designed to obtain the Pareto frontier curve. The proposed model and solution methods are demonstrated on the Chengdu URT network. Results indicate that passenger travel costs and travel times can be significantly reduced by the integrated optimization. It also provides passengers with a safer night travel environment due to the reduction in passenger travel times in taxis.ISSN:0968-090