Sustainable supply chain management has been an important research issue for the last two decades due to climate change. From a global perspective, the United Nations have introduced sustainable development goals, which point towards sustainability. Manufacturing supply chains are among those that produce harmful effluents into the environment in addition to social issues that impact societies and economies where they operate. New developments in information and communication technologies, especially big data analytics (BDA), can help create new insights that can detect parts and members of a supply chain whose activities are unsustainable and take corrective action. While many studies have addressed sustainable supply chain management (SSCM), studies on the effect of BDA on SSCM in the context of manufacturing supply chains are limited. This conceptual paper applies Toulmin’s argumentation model to review relevant literature and draw conclusions. The study identifies the elements of big data analytics as data processing, analytics, reporting, integration, security and economic. The aspects of sustainable SCM are transparency, sustainability culture, corporate goals and risk management. It is established that BDA enhances SSCM of manufacturing supply chains. Cyberattacks and information technology skills gap are some of the challenges impeding BDA implementation. The paper makes a conceptual and methodological contribution to supply chain management literature by linking big data analytics and SSCM in manufacturing supply chains by using the rarely used Toulmin’s argumentation model in management studies.

Sustainability awareness across various economic sectors requires firms to use logistics outsourcing to reduce logistics-related emissions with compliant logistics service providers (LSPs). LSPs apply advanced information technologies to help achieve high efficiency, effectiveness, and sustainability goals. While logistics outsourcing has received considerable attention from researchers, limited research has identified the elements of logistics outsourcing and established research trends regarding technology and sustainability aspects of logistics outsourcing. This study aims to establish trends in technology and sustainability in logistics outsourcing and identify the important elements of logistics outsourcing. A bibliometric analysis technique using the Biblioshiny package in R. A SCOPUS search resulted in 2019 documents. Research on technology and sustainability in logistics outsourcing is growing, especially in developed countries, with little or no research from developing countries. Sustainable supply chains and third-party logistics themes dominated the past research. Current research is on reverse logistics, circular economy, and green logistics; the latter is likely to dominate the future, focusing on risk management, pollution control, and innovation through advanced technologies such as artificial intelligence, machine learning, and big data. Essential elements of logistics outsourcing are identified as maintaining a good relationship based on trust and cooperation, LSP multi-selection criteria that include sustainability and technology capabilities, proper contract management, and an appropriate in-house versus outsourcing balance for competitiveness. LSP managers are advised to develop sustainability and technology capabilities, including reverse logistics. The paper contributes to logistics management theory by identifying the elements of logistics outsourcing and presenting a bibliometric result to guide future research on sustainability and technology capabilities in logistics outsourcing.

Transportation systems globally face challenges related to congestion, decreased quality of life, limited accessibility, increased harmful emissions and costs, growing use of private cars and in some cases lack of intra and intermodal integration. Smart Mobility is believed to be a solution to some of these challenges by providing comprehensive and intelligent mobility services, decreasing transportation costs, promoting safety, and combating pollution and traffic congestion. Despite this potential, there is still uncertainty surrounding what smart mobility is and whether it is moving toward improving the quality of life and making cities more sustainable. To address this gap, this paper conducts a bibliometric review of 3223 Web of Science Core Collection-indexed documents to provide a comprehensive understanding of smart mobility research. The findings reveal a lack of multi-disciplinary approaches in previous studies with a strong emphasis on technological aspects and limited social or economic considerations in current research. The review identifies four distinct periods of smart mobility research, with recent interest sparked by advancements in big data, deep learning, artificial intelligence, and real-time technologies in transport systems. However, there is a dearth of research on smart mobility in developing countries, where urban populations are rapidly increasing. Thus, the review proposes a research agenda to address the current gaps in knowledge. Furthermore, the review provides an updated and integrated definition of smart mobility as the use of advanced technologies, such as the Internet of Things (IoT), big data analytics, and artificial intelligence, to improve transportation efficiency, mobility for all, and sustainability while safeguarding the quality of life. The primary challenge for smart mobility is the co-evolution with existing transport systems, making further research on integration with these systems and real-time technologies essential for advancing smart mobility research. The paper’s main contribution is an integrated conceptualisation of smart mobility research and novel research topics that build on this unified base.

Population growth in urban cities has created transport problems such as traffic congestion, pollution, accidents, and infrastructure failures. These problems often lead to disruption of mobility, especially in emerging economies. The goal of any city is to have a resilient transport system that is free from frequent and severe disruptions. Resilience can be incorporated into smart mobility systems to create a robust, integrated and intelligent transport system. Therefore, this study sought to determine resilience factors required for a resilient smart mobility system in emerging economies. The study used interpretive structural modelling (ISM) to establish the causal interrelationships among the factors drawing from data from experts in smart cities, city transport and academia. It was found that the most critical factor to drive resilience is foresight – the ability to anticipate disruptions. Three other resilience factor categories identified were dynamism, certainty and optimisation, which all point to the importance of building dynamic capabilities. The key implication for practice highlights the role that human and artificial intelligence can play in anticipating natural, social and infrastructure disruptions. ISM offered a mechanism to determine the critical causal resilience factors to be prioritised amongst the competing demands on the limited resources of emerging economies. Apart from foresight, which was found to be a driving factor, the other resilience factors are equally strong linkage factors (influence each other), thus further revealing the complexities that decision-makers in urban cities of emerging economies constantly face. A research agenda for resilience in smart mobility in emerging economies was developed.

Academic plagiarism is increasingly becoming a challenge to academic integrity worldwide, owing to the ease of access to free information online. The aim of this paper was twofold; first, to ascertain the perceptions of transport and logistics management university students regarding academic plagiarism, and second, to determine the predictors of university students’ plagiarism practices. A self-designed structured questionnaire was developed to collect information from the students of their understanding of plagiarism (UP), the plagiarism practices (PP), the understanding of the university plagiarism policy (UPP), the understanding of the departmental plagiarism policy (DPP), the awareness of the university and departmental training workshops (TOP), and the adequacy of the university and departmental training workshops (AOT). Independent t-tests were computed for the differences in plagiarism, based on home language and gender. Also, a one-way ANOVA was computed to test if the year of study, the degree enrolled for, and race, had an impact on plagiarism practices. Lastly, a regression model was computed to determine the impact of the plagiarism predictors on the plagiarism practices. The results of this study revealed high-levels of the understanding of plagiarism, and an awareness of the university and departmental plagiarism policies. However, an analysis of the plagiarism practices revealed moderate levels of plagiarism, indicating a likelihood of intentional plagiarism among students. Two significant predictors of plagiarism practices among university students were identified as; the understanding of plagiarism and the understanding of the university-wide plagiarism policy. University instructors and education managers are informed through the findings of this study that clear plagiarism policies are important in reducing academic dishonesty among students. It is important to continuously train students on what plagiarism entails and how to avoid academic dishonesty.

The Journal of Transport and Supply Chain Management (JTSCM) seeks to disseminate research results and to support high level learning, teaching and research in the areas of traffic and transportation (operations, policy, planning), customer service, warehousing, inventory management, procurement, packaging, materials handling, reverse logistics, demand forecasting, distribution communications and information technology.