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This study aims to assess the level of development of the digital economy by constructing a comprehensive measurement system. It explores regional differences within China’s digital economy, highlighting the varying degrees of digital infrastructure, industrialization, governance and innovation capabilities across provinces.

A multidimensional analytical framework including digital infrastructure, industrialization, digitization, governance and innovation was developed. Entropy methods were used to calculate the weights of each dimension. The coupled coordination degree model and the Tobit model with random effects panel are applied to analyze the current situation, discrepancies and influencing factors.

This study reveals significant regional differences in the development of China’s digital economy, characterized by a pattern of “strong in the east, weak in the west; high in the south, low in the north.” This geographical imbalance exacerbates the “polarization effect” and the “siphon effect,” where resources and growth tend to concentrate in already developed areas, further intensifying regional inequalities. The development of the digital economy is driven by principles of innovation, coordination and sharing, which facilitate the creation and dissemination of new technologies and collaboration across different sectors. However, this progress is also constrained by considerations of environmental sustainability (green) and economic openness.

This paper contributes to the body of knowledge by providing a novel multidimensional measurement system for the level of digital economy development. The unique application of the coupled coordination degree model and Tobit model to analyze regional differences and influencing factors provides insights into the dynamics of China’s digital economy.

The paper proposes a research method to verify the perception bias of consumers on the freshness preservation effects of vacuum packaging (VP) and modified atmosphere packaging (MAP) chilled pork packages, the influence of “sensory experience” on correcting consumers' perception bias of packaging performance and willingness-to-pay (WTP) enhancement channels.

Using data from 458 and 188 participants who completed the contingent valuation method (CVM) and auction experiment, respectively, the study aimed to uncover consumers' packing quality perception bias and WTP, and investigated the societal factors that contribute to variations in WTP.

The CVM experiment revealed that although consumers' high perception bias rate toward MAP to maintain freshness, as compared to lab test results, came along with low WTP premium to cost rate with sensory experience in the auction experiment, the proportion of consumers with quality perception bias decreased from 49.85% to 34.46%, while the WTP premium to cost rate for MAP increased largely by 36.7%. Perceptive embedding has a positive effect on chilled pork packaging WTP, while normative embedding decreases WTP.

The findings emphasize the need of public policies to promote positive consumption attitudes, while whittling the negative consumption norms, to increase the WTP for packaged child pork and promote the chilled pork market formation.

Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Antagonistic relationship among the participants of construction projects has significantly improved, and further improving cooperation quality are committed. In this context, expanding new ways to improve cooperation quality has become a new topic in cooperation research. This study is dedicated to exploring the mechanism of cross-organizational private relationships on cooperative behaviors, which is rarely addressed in current research on construction projects, and provides reference for the rational use of cross-organizational private relationships.

Based on analysis of studies related to relational governance theory, institutional theory and project complexity, this study constructs the theoretical model. This study uses survey data from 395 construction professionals in China to test the theoretical model by using structural equation modeling (SEM) and explains the direct and indirect mechanism of cross-organizational private relationships on cooperation behavior.

(1) Cross-organizational private relationships have direct and indirect facilitating effect on cooperation behavior. (2) Relational norms as mediating variables contribute to the expansion of the positive effects of cross-organizational private relationships on cooperation behavior. (3) Institutional environment and project complexity have the moderating effect between cross-organizational private relationships and cooperation behavior.

This research investigates the impact mechanisms and boundary conditions of cross-organizational private relationships at the micro level on the cooperative behaviors in construction projects and conducts empirical research. It is a topic that has not been adequately researched in the field of project management. The research results expand the scope of research on relational governance and deepen the research on the antecedents of relational norms. It provides the base for the proposed contingency theory of relational governance.

This study examines the phenomenon of “knowledge influencers,” individuals who convey expert information to non-expert audiences and attract users to subscribe to their self-created knowledge products. It seeks to address two research questions: (1) What are the antecedents that promote perceived attractiveness of knowledge influencers? and (2) How does perceived attractiveness of knowledge influencers affect users’ willingness to subscribe to knowledge products?

Guided by self-branding theory, which suggests that individuals strategically shape user perceptions and interactions to create an appealing image, the study employed a sequential mixed-methods approach. Qualitative interviews were conducted with knowledge influencers and their subscribers, followed by a quantitative survey of users with knowledge subscription experience to validate the findings.

Results suggested that knowledge influencers could enhance their attractiveness to users by promoting perceived professionalism, perceived familiarity, and perceived connectedness. Perceived attractiveness of knowledge influencers could directly affect users’ willingness to subscribe or indirectly through the role of users’ attachment to knowledge influencers.

By understanding the factors driving users’ subscription intentions, platform operators and influencers can refine their strategies to enhance user attachment and optimize monetization opportunities through personalized interactions and tailored content offerings.

This study contributes to the literature by elucidating the relationship between perceived attractiveness and users’ subscription intentions, offering new insights into the dynamics of online knowledge consumption.

To systematically characterize and objectively evaluate basic railway safety management capability, creating a closed-loop management approach which allows continuous improvement and optimization.

A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards, railway safety rules and regulations and existing safety data from railway transport enterprises is presented. The system comprises a guideline layer including safety committee formation, work safety responsibility, safety management organization and safety rules and regulations as its components, along with an index layer consisting of 12 quantifiable indexes. Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.

The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.

Construction of an evaluation index system that is quantifiable, generalizable and accessible, accurately reflects the main aspects of railway transportation enterprises’ basic safety management capability and provides interoperability across various railway transportation enterprises. The application of the game theoretic combination weighting method to derive composite weights which combine experts’ subjective evaluations with the objectivity of data.

This paper aims to develop a novel grey Bernoulli model with memory characteristics, which is designed to dynamically choose the optimal memory kernel function and the length of memory dependence period, ultimately enhancing the model's predictive accuracy.

This paper enhances the traditional grey Bernoulli model by introducing memory-dependent derivatives, resulting in a novel memory-dependent derivative grey model. Additionally, fractional-order accumulation is employed for preprocessing the original data. The length of the memory dependence period for memory-dependent derivatives is determined through grey correlation analysis. Furthermore, the whale optimization algorithm is utilized to optimize the cumulative order, power index and memory kernel function index of the model, enabling adaptability to diverse scenarios.

The selection of appropriate memory kernel functions and memory dependency lengths will improve model prediction performance. The model can adaptively select the memory kernel function and memory dependence length, and the performance of the model is better than other comparison models.

The model presented in this article has some limitations. The grey model is itself suitable for small sample data, and memory-dependent derivatives mainly consider the memory effect on a fixed length. Therefore, this model is mainly applicable to data prediction with short-term memory effect and has certain limitations on time series of long-term memory.

In practical systems, memory effects typically exhibit a decaying pattern, which is effectively characterized by the memory kernel function. The model in this study skillfully determines the appropriate kernel functions and memory dependency lengths to capture these memory effects, enhancing its alignment with real-world scenarios.

Based on the memory-dependent derivative method, a memory-dependent derivative grey Bernoulli model that more accurately reflects the actual memory effect is constructed and applied to power generation forecasting in China, South Korea and India.

Explore how the degree of humanization affects user misconduct, and provide effective misconduct prevention measures for the wide application of artificial intelligence in the future.

Based on the “Uncanny Valley theory”, three experiments were conducted to explore the relationship between the degree of humanization of service machines and user misbehavior, and to analyze the mediating role of cognitive resistance and the moderating role of social class.

There is a U-shaped relationship between the degree of humanization of service machines and user misbehavior; Social class not only regulates the main effect of anthropomorphism on misbehavior, but also regulates the intermediary effect of anthropomorphism on cognitive resistance, thus affecting misbehavior.

The design of the service robot can be from the user’s point of view, combined with the user’s social class, match different user types, and provide the same preferences as the user’s humanoid service robot.

This study is an important reference value for enterprises and governments to provide intelligent services in public places. It can prevent the robot from being vandalized and also provide users with a comfortable human-computer interaction experience, expanding the positive effects of providing smart services by government and enterprises.

This study avoids and reduces users' misbehavior towards intelligent service robots, improves users' satisfaction in using service robots, and avoids service robots being damaged, resulting in waste of government, enterprise and social resources.

From the perspective of product factors to identify the inducing factors of improper behavior, from the perspective of social class of users to analyze the moderating effect of humanization degree and user improper behavior.

This study aims to investigate account managers’ dual embeddedness (customer and internal embeddedness) in solution co-creation. The authors examine the mediating role of two-way matching between suppliers and customers and the moderating role of customer requirement complexity.

The authors use a questionnaire to collect data from 566 account managers of supplier companies in China and conduct hypothesis testing through multiple linear regression analysis and bootstrapping.

The findings demonstrate that customer and internal embeddedness are distinct with different dimensions and are positively related to solution co-creation performance. Customer and internal embeddedness affect solution co-creation performance through two-way matching in the customer requirement definition and solution integration phases, respectively. The interaction term of customer and internal embeddedness indirectly affect solution co-creation performance through two-way matching, and customer requirement complexity strengthens this main effect.

To the best of the authors’ knowledge, this study is the first to examine dual embeddedness at the individual level and distinguish between the customer and internal embeddedness of account managers by different dimensional classifications. The authors clarify the difference and relationship between customer and internal embeddedness in solution co-creation and investigate the mediating and moderating roles of two-way matching and customer requirement complexity, respectively. This study expands the theoretical research on social embeddedness theory and business-to-business solutions and provides useful insights into the solution co-creation practice for account managers and suppliers.