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To delve into the integration of global logistics and supply chain networks amidst the digital transformation era. This study aims to investigate the potential role of China’s Belt and Road Initiative (BRI) in facilitating the integration of global flows encompassing both tangible goods and intangibles. Additionally, the study seeks to incorporate third-party logistics activities into a comprehensive global logistics and supply chain integration framework.

Prior research is synthesised into a global logistics and supply chain integration framework. A case study was undertaken on Yuan Tong (YTO) express group to investigate the framework, employing qualitative data analysis techniques. The study specifically examined the context of the BRI to enhance comprehension of its impact on global supply chains. Information was collected in particular to two types of supply chain flows, the physical flow of goods, and intangible information and cash flows.

The proposed framework aligns well with the case study, leading to the identification of global logistics and supply chain integration enablers. The results demonstrate a range of ways BRI promotes global logistics and supply chain integration.

The case study, with multiple examples, focuses on how third-party logistics firms can embrace global logistics and supply chain integration in line with BRI. The case study approach limits generalisation, further applications in different contexts are required to validate the findings.

The framework holds promise for aiding practitioners and researchers in gaining deeper insights into the role of the BRI in global logistics and supply chain integration within the digital era. The identified enablers underscore the importance of emphasising key factors necessary for success in navigating digital transformation within global supply chains.

The purpose of the paper is to present an empirical study on the logistics innovation capability and its impacts on the supply chain risk in the Australian courier firms. Based on the resource-based review, logistics innovation capability provides valuable insight into mitigating supply chain risks in the Industry 4.0 era.

The research model focuses on the relationships between logistics innovation capability and supply chain risk. Partial least squares approach for structural equation modelling is used to validate the research model by empirically analysing survey data.

The empirical result shows negative relationships between logistics innovation capability and supply chain risks. These relationships may imply that firms can mitigate the negative impacts of supply chain risks by developing logistics innovation capabilities. The findings demonstrate the applicability of logistics innovation capability for mitigating supply chain risks in the Australian courier firms.

There are very few empirical studies on the mitigating supply chain risk through logistics innovation capability. The empirical results provide an insight into innovation management and risk management in logistics and supply chain. This insight offers practical guidance for developing and deploying logistics innovation capability to support and enable supply chain risk management strategies in the Industry 4.0 era.

Due to increasing supply chain complexity, the supply chain uncertainty has become an imperative issue, which hinders the development of modern logistics and supply chain management. The paper attempts to conceptualize reverse logistics uncertainty from supply chain uncertainty literature and present the types of reverse logistics uncertainty in a triadic model.

The concept of reverse logistics uncertainty is developed based on a triadic model of logistics uncertainty and supply chain uncertainty literature. A desk research is conducted to develop a taxonomy of reverse logistics uncertainty. To better depict the reverse logistics uncertainty, we use case studies to discuss the types of reverse logistics uncertainty in the triadic model.

The study reveals four types of supply chain uncertainties in the reverse logistics. We call them reverse logistics uncertainty. Type-A and Type-B uncertainty are new types of supply chain uncertainty in the reverse logistics.

The types of reverse logistics uncertainty have not been empirically validated in industries. Especially, the two new types including Type-A and Type-B reverse uncertainty need further exploration.

Although reverse logistics has been discussed in the past decades, very few studies have been conducted on the supply chain uncertainty in returns management arena. The paper offers valuable insights to better understand the supply chain uncertainty in the reverse logistics. This also provides suggestions for both managers and researchers to reflect on the reverse logistics uncertainty management and business sustainability.

Under the constraints of given passenger service level and coupling travel demand with train departure time, this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.

The authors optimize the train operational plan in a special network layout, i.e. an urban rail corridor with dead-end terminal yard, by decomposing it into two sub-problems: train timetable optimization and rolling stock circulation optimization. As for train timetable optimization, the authors propose a schedule-based passenger flow assignment method, construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm. For the optimization of rolling stock circulation, the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.

The case study shows that the train operational plan developed by the study's approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.

The example verifies the efficiency of the model and algorithm.

This paper aims to outline a map of all the research that exists on mobile technology and tourism archived in the two main databases worldwide (Web of Science and Scopus). Accordingly, with the identification of all the scientific articles that deal with both mobile technology and tourism, the authors seek to ascertain the evolution of mobile technology in the tourism sector through the years, countries, universities and authors and determine the various collaborations brought about between authors, universities, institutions and/or companies in various research projects. Finally, it also allows the authors to distinguish the main topics under study within the scope of ‘mobile tourism’.

A mixed methodology has been carried out. The search focused on the principal databases of bibliographic references and citations of periodical publications, such as articles from scientific journals, books and other types of printed material. Once the results were obtained in the respective databases, it was necessary to be able to work with them. In this respect, the authors had to extract the relevant data and dump it in a bibliographic reference manager, for which they chose Mendeley. After this, the tabulation of data was performed in Excel and tables and graphs were created from all the data collected.

The main results obtained and analyzed are the number of articles per year, countries and universities. In the same way, it is interesting to highlight the number of countries and universities that participate in each article under study. On the other hand, an analysis has been carried out regarding the number of articles per author, as well as the topics dealt with in the different articles.

This analysis reveals the role that has been played by mobile phones in tourism since the first scientific article was recorded in 2002. In this regard, in recent years there has been a significant increase in the number of articles, finally resulting in moderate figures in relation to countries (40) and universities (233) that have formed part of the subject matter under study. In contrast to other areas of research in tourism, the relevance of this subject is therefore evident, as is the need for greater background knowledge to establish research models adapted to the new reality of tourism in a world of ever-increasing mobility.

This paper introduces the second part of a AAAJ special issue on accounting, accountability and management during the COVID-19 emergency. The authors analyse the themes that emerge from the second part of the special issue, which allows us to identify the diverse accounting and accountability practices across different geographical and organisational contexts. The authors also provide an overall picture of the contributions of the special issue, with insights into avenues of future research.

Building on the first part of the AAAJ special issue, the paper draws together and identifies additional emerging themes related to research into the COVID-19 pandemic and how it impacts accounting, accountability and management practices. The authors reflect on the contributions of the special issue to the interdisciplinary accounting research project.

The authors identify two macro-themes and outline their contributions to the accounting literature. The first deals with the changes and dangers of accounting and accountability practices during the pandemic. The second considers accountability practices in a broader sense, including reporting, disclosure and rhetorical practices in the management of COVID-19.

The paper shows the pervasive role of accounting and accountability in the unprecedented and indiscriminate health crisis of COVID-19. It highlights the important role of special issues in producing timely research that responds to unfolding events.

This paper contributes to current debates on the roles of accounting and accountability during COVID-19 by drawing together the themes of the special issue and identifying future interdisciplinary accounting research on the pandemic's aftermath.

This study aims to test the compensatory consumption theory with the explicit hypothesis that China's new-rich tend to waste relatively more food.

In this study, the authors use Heckman two-step probit model to empirically investigate the new-rich consumption behavior related to food waste.

The results show that new-rich is associated with restaurant leftovers and less likely to take them home, which supports the compensatory consumption hypothesis.

Understanding the empirical evidence supporting compensatory consumption theory may improve forecasts, which feed into early warning systems for food insecurity. And it also avoids unreasonable food policies.

This research is a first attempt to place food waste in a compensatory-consumption perspective, which sheds light on a new theory for explaining increasing food waste in developing countries.

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.

The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.

This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.

Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.