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This paper aims to explore blockchain integration strategies within a three-level livestock meat supply chain in which consumers have a preference for quality trust in livestock meat products. The paper investigates three questions: First, how does consumers’ preference for quality trust affect blockchain integration and transaction decisions among supply chain participants? Second, under what circumstances will retailers choose to participate in the blockchain? Finally, how can other factors such as blockchain costs and supplier–retailer partnership value affect integration decisions?

This paper formulates a supply chain network equilibrium model and employs the logarithmic-quadratic proximal prediction-correction method to obtain equilibrium decisions. Extensive numerical studies are conducted using a pork supply chain network to analyze the implications of blockchain integration for different supply chain participants.

The results reveal several key insights: First, suppliers’ increased blockchain integration, driven by higher quality trust preference, can negatively affect their profits, particularly, with excessive trust preferences and high blockchain costs. Second, an increase in consumers’ preference for quality trust expands the range of unit operating costs for retailers engaging in blockchain. Finally, the supplier–retailer partnership drives retailer blockchain participation, facilitating enhanced information sharing to benefit the entire supply chain.

This study provides original insights into blockchain integration strategies in an agricultural supply chain through the application of the supply chain network equilibrium model. The investigation of several key factors on equilibrium decisions provides important managerial implications for different supply chain participants to address consumers’ preference for quality trust and enhance overall supply chain performance.

As a potentially disruptive technology, blockchain technology ensures that all the data cannot be merely tampered with once they are recorded on-chain. However, the fake source information may be input into the blockchain, which is mistaken for truthful data and results in a trust divide between the on-chain and the actual world. One missing perspective from previous studies is information manipulation at the source still exists under the blockchain mode. The authors’ goal was to analyze how blockchain technology affects the information deception of the agricultural product supply chain (APSC) under this premise. Also, the authors further analyzed some factors that influence the effectiveness of blockchain technology.

The authors build an APSC game model consisting of a farmer and an agricultural product broker, which employs the principal–agent game model to explore the conditions for achieving the mutual trust equilibrium between the two parts. Then, through numerical simulation, the authors further analyze how the quality of on-chain information and the numbers of on-chain firms affect blockchain’s effect on deception in APSC and examine the circumstances in which blockchain technology is more suitable.

The authors demonstrate that only by meeting the threshold of high-quality on-chain information and having a sufficient number of on-chain firms, can the blockchain-based supply chain initiate a better information ecosystem, which helps eradicate deception in the APSC.

This paper provides valuable insights for participants in supply chains as well as is probably generalizable to other industrial products that require similar services in the early stage of blockchain.

In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

This study explores the role of the Internet of Things (IoT) as an enabler for Lean Construction principles and tools in construction projects.

In response to the scarcity of studies about IoT functionalities in construction, a two-round systematic literature review (SLR) was undertaken. The first round aimed to identify IoT functionalities in construction, encompassing an analysis of 288 studies. The second round aimed to analyze their interaction with Lean Construction principles, drawing insights from 43 studies.

The outcome is a comprehensive Lean Construction-IoT matrix featuring 54 interactions. The highest levels of interaction were found in the Lean Construction principle “flow” and the functionality of “data transfer and real-time information sharing”.

The study focuses on the role of IoT as an enabler for Lean Construction. Future work can cover the role of Lean as an enabler for advanced technology implementation in construction.

The Lean Construction-IoT matrix serves as a resource for researchers, practitioners, and decision-makers seeking to enhance Lean Construction by leveraging IoT technology. It also provides various examples of how advanced technology can support waste elimination and value generation in construction projects.

This study adopts the perspective of dynamic capabilities to investigate influencing factors and proposes improvement strategies of supply chain resilience of prefabricated construction.

The structural equation model (SEM) is used to identify and verify the relationship between factors influencing supply chain resilience of prefabricated construction from the perspective of dynamic capabilities. The system dynamics (SD) model is constructed to dynamically simulate the specific effects of different influencing factors.

Results indicate that: (1) An evaluation index system for supply chain resilience of prefabricated construction containing five first-level indicators and 36 second-level indicators is constructed; (2) Ability to anticipate, ability to respond, ability to adapt, ability to recover and ability to learn are positively correlated with the supply chain resilience of prefabricated construction and (3) ANT3 (information system), RES1 (quick response), ADA3 (buffer stock) and LEA4 (trust) are the most leading factors influencing supply chain resilience of prefabricated construction over time.

This study fulfills the need for an in-depth exploration of the various influencing factors on supply chain resilience of prefabricated construction from the perspective of dynamic capabilities. Furthermore, this study provides improvement strategies to enhance supply chain resilience of prefabricated construction in China.

Culture is considered a critical aspect of social media usage. The purpose of this paper is to explore how cultures and languages influence multilingual users' cross-cultural information sharing patterns.

This study used a crowdsourcing survey with Amazon Mechanical Turk to collect qualitative and quantitative data from 355 multilingual users who utilize two or more languages daily. A mixed-method approach combined statistical, and cluster analysis with thematic analysis was employed to analyze information sharing patterns among multilingual users in the Chinese cultural context.

It was found that most multilingual users surveyed preferred to share in their first and second language mainly because that is what others around them speak or use. Multilingual users have more diverse sharing characteristics and are more actively engaged in social media. The results also provide insights into what incentives make multilingual users engage in social media to share information related to Chinese culture with the MOA model. Finally, the ten motivation factors include learning, entertainment, empathy, personal gain, social engagement, altruism, self-expression, information, trust and sharing culture. One opportunity factor is identified, which is convenience. Three ability factors are recognized consist of self-efficacy, habit and personality.

The findings are conducive to promoting the active participation of multilingual users in online communities, increasing global resource sharing and information flow and promoting the consumption of digital cultural content.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

Within the labor-intensive construction industry characterized by distinctly structural shortages in the labor force worldwide, efficient and effective migration of construction workers across regions is critical for the smooth operation of construction activities. This study aims to investigate how the interregional migration patterns of construction workers are impacted by the disparities in both employment opportunities and environment amenities between the origin and destination provinces.

Drawing on the push and pull theory and the archival data on 13,728 migrant construction workers in China, descriptive analyses are first performed to characterize the interregional migration patterns of the investigated construction workers. Combining regional data in the National Bureau of Statistics of China, this study uses hierarchical regression modeling techniques to empirically test the relative importance of the employment-related and environment-related factors in driving the interregional migration of construction workers after controlling for the effects of related economic and geographic factors.

The results provide evidence that the interregional migration of construction workers is principally driven by the disparities in employment opportunities while disparities in environment amenities (including climate comfort disparity, medical service disparity and educational service disparity) generally play much fewer substantive roles. With regard to the impacts of employment opportunities, the results provide evidence that compared with the disparity in job market size, the disparities in job income and industry development level are more significantly relevant factors, which positively pull and adversely push the interregional migration flows, respectively.

This study contributes to a deepened understanding of how workers specifically balance their employment and amenity needs to make temporary migration decisions in the “laggard” labor-intensive construction industry. This study also adds to the literature on population migration by characterizing the specific characteristics of construction workers and the temporary nature of the workers' migration activities. The findings hold important practical implications for construction organizations and policymakers for effectively managing the mobility of migrant construction workers.

The extant literature on migrant construction workers has primarily focused on the consequences of international migration and the generalization of empirical findings on population migration mechanisms in other domains to the construction industry is substantially limited by the specific characteristics of construction workers and the temporary nature of their migration activities. In addressing this gap, this study represents an exploratory effort to quantitatively characterize the interregional migration patterns of construction workers in the labor-intensive construction industry and examines the roles of employment opportunity and environmental amenity in driving interregional migration.

This study aims to explore the potential role of supply chain digital transformation on collaborative knowledge creation, supply chain innovation, and value co-creation in new norms. It also examines the impact of collaborative knowledge creation and supply chain innovation on value co-creation. Furthermore, the study examines the impact of collaborative knowledge creation on supply chain innovation. Finally, it investigates the possible mediating role of knowledge absorptive capacity and relationship quality in shaping these interactions.

To establish the empirical part of this study, the collection of data involved distributing a questionnaire to 247 managers working in manufacturing companies. The measurement model assessment and hypothesis testing were performed employing the PLS-SEM approach.

The findings indicate that supply chain digital transformation significantly impacts collaborative knowledge creation, supply chain innovation, and value co-creation. This study also confirms the significant impact of collaborative knowledge creation on supply chain innovation and value co-creation. Furthermore, it reveals that knowledge absorptive capacity mediates the impact of supply chain digital transformation on collaborative knowledge creation. It also shows that the impact of collaborative knowledge creation on supply chain innovation and value co-creation is mediated by relationship quality among participants.

The findings of this study make significant contributions to academic theory, existing literature, and the scholarly community within the realms of supply chain management, innovation, knowledge management, and value co-creation. It also offers practical implications for managers to strategically navigate the evolving norms of supply chain management. Companies can use these insights to improve their innovation processes and knowledge management, while policymakers can consider the study's findings when developing supportive frameworks for the manufacturing sector.

It has been ten years since the policy was implemented, but the effect of the policy needs to be tested empirically. This paper aims to explore the mechanism of policy influence on regional innovation ability by measuring the effectiveness of policy by innovation ability indicators. Further, it reflects the problems in the process of the transformation and development of resource-based cities in recent years and points out the direction for the development of the cities in the future. In addition, this paper discusses the differences between regions and cities in China and seeks the path to narrow the gap.

This paper mainly uses the difference-in-difference method for the research. This study divided China’s resource-based cities and non-resource-based cities into experimental groups and control groups, and explored the effect of the transformation and development of resource-based cities and the changes of their innovation ability under the influence of the National Sustainable Development Plan for Resource-based Cities (NSDPRC). More carefully, this paper uses the fixed effects regression model, propensity score matching method, bootstrap method and other methods to improve the empirical results.

This paper finds that NSDPRC significantly improves the innovation ability of resource-based cities, although there is some lag in this effect. Research on the influence mechanism of policies shows that NSDPRC improves the marketization degree of resource-based cities and reduces the proportion of the secondary industry in such cities. Finally, the results of the heterogeneity analysis confirm that policies are more popular in western China and that resource-based cities in growth, maturity and decline are more vulnerable to policy influence. The development of policy effectiveness also requires the size of a city, and maintaining a healthy and reasonable scale is necessary for urban development.

First, the existing research on the development of resource-based cities is mainly from the perspective of economy and environment, but rarely from the perspective of innovation ability, and the index to measure urban development is relatively single. This paper will compensate for this deficiency. Second, different from the European and American countries that have basically completed the industrial transformation, the research on Chinese cities will provide a reference for the transformation of developing countries. Finally, from the perspective of resource endowment theory and innovation theory, this paper discusses the influence of SDPNRBC mechanism on the innovation ability improvement of resource-based cities, and further improves and enriches the theory.