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Compared to traditional (domestic) e-commerce consumers, cross-border electronic commerce (CBEC) consumers may face greater information asymmetry in the CBEC purchase process. Given this background, however, the literature has paid limited attention to the informational antecedents that influence consumers' perceptions of transaction costs and their CBEC purchase intentions. To fill this gap, this study integrates the elaboration likelihood model (ELM) and transaction cost theory (TCT) to develop a model for exploring how product (website informativeness, product diagnosticity and website interactivity as the central route) and external (country brand, website policy and vendor reputation as the peripheral route) informational antecedents affect consumers’ evaluations of transaction costs in terms of uncertainty and asset specificity and their CBEC purchase intentions.

This study employs a survey approach to validate the model with 766 Generation Z CBEC consumers based on judgment sampling. The partial least squares (PLS) technique is adopted for data analysis.

The results show that all the proposed central and peripheral informational antecedents reduce consumers’ perceptions of uncertainty and asset specificity, which in turn negatively influences their CBEC purchase intentions.

Through this investigation, this study increases our understanding of how product and external informational antecedents affect consumers’ evaluations of transaction costs, which subsequently determine their CBEC purchase decisions. This study offers theoretical contributions to existing CBEC research and has practical implications for CBEC organizations and managers.

Cybersecurity has received growing attention from academic researchers and industry practitioners as a strategy to accelerate performance gains and social sustainability. Meanwhile, firms are usually prone to cyber-risks that emanate from their supply chain partners especially third-party logistics providers (3PLs). Thus, it is crucial to implement cyber-risks management in 3PLs to achieve social sustainability in supply chains. However, these 3PLs are faced with critical difficulties which tend to hamper the consistent growth of cybersecurity. This paper aims to analyze these critical difficulties.

Data were sourced from 40 managers in Nigerian 3PLs with the aid of questionnaires. A novel quantitative methodology based on the synergetic combination of interval-valued neutrosophic analytic hierarchy process (IVN-AHP) and multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA) is applied. Sensitivity analysis and comparative analysis with other decision models were conducted.

Barriers were identified from published literature, finalized using experts’ inputs and classified under organizational, institutional and human (cultural values) dimensions. The results highlight the most critical dimension as human followed by organizational and institutional. Also, the results pinpointed indigenous beliefs (e.g. cyber-crime spiritualism), poor humane orientation, unavailable specific tools for managing cyber-risks and skilled workforce shortage as the most critical barriers that show the highest potential to elicit other barriers.

By illustrating the most significant barriers, this study will assist policy makers and industry practitioners in developing strategies in a coordinated and sequential manner to overcome these barriers and thus, achieve socially sustainable supply chains.

This research pioneers the use of IVN-AHP-MULTIMOORA to analyze cyber-risks management barriers in 3PLs for supply chain social sustainability in a developing nation.

With the change in climate and increased pollution, there has been a need to reduce environmental carbon emissions. This research aims to develop a framework for reducing environmental carbon footprints to improve business performance.

This study uses Scientific Procedures and Rationales for the Systematic Literature Reviews (SPAR-4-SLR) approach. Articles are searched in the Scopus database using various keywords and their combinations. It resulted in 651 articles initially. After applying different screening criteria, 61 articles were considered for the final study.

This study provided four themes and sub-themes within each category. This research also used theories, methodologies and context (TMC) framework to provide future research questions. This study used the antecedents, decisions and outcomes (ADO) framework for synthesising the findings. The ADO framework will help to achieve carbon neutrality and improve firms' supply chain (SC) performance.

This study provides theoretical implications by highlighting the various theories that can be used in future research. This study also states the practical implications for the achievement of carbon neutrality by the firms.

This study contributes to the literature linking carbon neutrality with business performance.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

Metro stations have become a crucial aspect of urban rail transportation, integrating facilities, equipment and pedestrians. Impractical physical layout designs and pedestrian psychology impact the effectiveness of an evacuation during a metro fire. Prior research on emergency evacuation has overlooked the complexity of metro stations and failed to adequately consider the physical heterogeneity of stations and pedestrian psychology. Therefore, this study aims to develop a comprehensive evacuation optimization strategy for metro stations by applying the concept of design for safety (DFS) to an emergency evacuation. This approach offers novel insights into the management of complex systems in metro stations during emergencies.

Physical and social factors affecting evacuations are identified. Moreover, the social force model (SFM) is modified by combining the fire dynamics model (FDM) and considering pedestrians' impatience and panic psychology. Based on the Nanjing South Metro Station, a multiagent-based simulation (MABS) model is developed. Finally, based on DFS, optimization strategies for metro stations are suggested.

The most effective evacuation occurs when the width of the stairs is 3 meters and the transfer corridor is 14 meters. Additionally, a luggage disposal area should be set up. The exit strategy of the fewest evacuees is better than the nearest-exit strategy, and the staff in the metro station should guide pedestrians correctly.

Previous studies rarely consider metro stations as sociotechnical systems or apply DFS to proactively reduce evacuation risks. This study provides a new perspective on the evacuation framework of metro stations, which can guide the designers and managers of metro stations.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.

This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.

The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.

This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

Urban villages are prevalent informal settlements within Chinese cities, arising from urban expansion. These areas frequently face systematic demolition during urban renewal due to their disorderly layout and outdated appearance. Urban village renovation (UVR) entails balancing diverse interests and navigating complex conflicts, particularly within China’s dual property rights system encompassing urban and rural land. The purpose of this study is to avoid the fierce interest conflict of UVR.

This study utilized the theoretical framework of value co-destruction. Initially, text mining and literature analysis were employed to identify concept nodes and interaction relationships. Subsequently, the structural equation model (SEM) was used to verify the causal model. Finally, the fuzzy cognitive map (FCM) was developed to dynamically simulate value co-destruction scenarios within UVR across various hypothetical situations.

The concept nodes influencing value co-destruction in UVR form a complex system with multiple levels. This includes three cause nodes and one result node. Among these, actor-to-actor emerges as a primary and underlying cause influencing value co-destruction in these projects. Furthermore, strategies for UVR should prioritize integrated interventions that enhance actor-to-actor relationships.

This study introduced a novel mixed methodology aimed at systematically simulating the dynamic process of value co-destruction during UVR. It also provided a fresh perspective on reverse assessment to mitigate the prevalent interest conflicts in UVR, thereby contributing to theoretical advancements and practical strategies for UVR.

For the purpose of saving nickel, this study aims to develop new duplex stainless steel cored wires suitable for wire arc additive manufacturing (WAAM) with the addition of nitrogen.

The effect of nitrogen content on the microstructure and mechanical properties of the thin-walled deposits is investigated in detail.

The microstructure of thin-walled deposits mainly consists of austenite, ferrite and secondary austenite. With increasing nitrogen content, the austenite in the deposited metals increases. The austenite proportion in the bottom region is more than that in the top region of the deposited metals. The χ phase is randomly distributed at the grain boundaries and within ferrite. The σ phase is mainly precipitated at ferrite and austenite grain boundaries. With increasing nitrogen content, the tensile strength of the deposited metals increases, but the impact toughness of the deposited metals deteriorates.

This study proposes new duplex stainless steel cored wires for WAAM, which realizes the objective of saving nickel.