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By exploring the impact of digital knowledge resources (DKR) on the carbon emission intensity of the pig industry (PCEI), this study aims to reveal the role of DKR in reducing PCEI.

Based on provincial panel data in China from 2011 to 2021, this study uses the entropy and Intergovernmental Panel on Climate Change coefficient methods to calculate the evaluation index system of DKR and PCEI, respectively. Empirical analysis using a panel fixed-effects model examines the influence of DKR on PCEI and its underlying mechanisms.

DKR can significantly reduce PCEI. This conclusion still holds even after undergoing endogeneity treatment and a series of robustness tests. Mechanism test results indicate that DKR can operate indirectly through the mediation mechanism of rural human capital (RHC) and pig breeding technology innovation (PTI), while environmental regulation intensity (ERI) plays a positive moderating role in the relationship between DKR and PCEI. The magnitude of the impact of DKR on PCEI depends on ERI. Further studies found that the impact of DKR on PCEI has obvious heterogeneity characteristics, and the promotion effect is more obvious in regions with good integration degrees and high development potential.

This paper divides DKR into three dimensions: digital technology knowledge (DTK), digital management knowledge (DMK) and digital application knowledge (DAK), providing a new framework for research and enriching the understanding of the relationship between DKR and PCEI. Furthermore, the research results reveal the application potential of DKR in the pig industry, particularly in terms of resource allocation efficiency. This is of great significance for promoting low-carbon development in the pig industry and provides insights for the low-carbon transformation of other industries. In addition, the study emphasizes the moderating effect of ERI on the mechanism of carbon reduction in the pig industry through DKR. This offers a new perspective for understanding the relationship between knowledge management and environmental governance, providing a reference basis for policy formulation in related fields.

This paper further enriches the role of DKR in the livestock industry. Integrating DKR with traditional industries promotes knowledge innovation, information distribution and utilization and scientific decision-making. This has significant value in promoting the development and application of carbon reduction technologies, enhancing industrial competitive advantages, and other aspects.

This paper aims to improve the refractive index sensor performance for analytes with large refractive index by adopting the technology of microstructured fiber (MF) and surface plasmon resonance (SPR).

The structure adopts an MF with a hexagonal lattice cladding structure composed of all-circular air holes, and three defect regions are introduced. The liquid analyte that needs to be tested is filled in the defect area. The surface plasmon polarition mode is generated and coupled with the core mode, thus forming a refractive index sensing channel. When the resonance conditions are satisfied, the resonance wavelength will be changed with the refractive index of the liquid analyte. All parameters that may affect the performance of the sensor are numerical simulated, and the structure is optimized through a large number of calculations.

The results demonstrate that the maximum dynamic sensitivity (S_R) can reach to 24,260 nm/RIU, and the average sensitivity (S_R-AV) can reach to 18,046 nm/RIU when the refractive index range is from 1.42 to 1.47. Besides, the sensitivity linearity (R²) is approximately 0.965, and its resolution is 4.1 × 10^–6 RIU. The comparison with some literature results shown that the proposed sensor has certain advantages over the sensors reported in these literatures.

This work proposed an SPR-based refractive index sensor with a simple MF structure. It has a certain reference significance for the design and optimization of SPR-based MF sensors. Moreover, owing to its simple structure, high refractive index sensitivity and linear sensing performance, this sensor will play an important role in the detection of high refractive index liquid analytes.

As more hotels adopt artificial intelligence (AI), it becomes inevitable for employees to rely on abilities enhanced by the use of AI to complete tasks. However, our understanding of how employees adapt to this shift in work design remains limited. Therefore, the purpose of this study is to explore hotel employees’ approach and avoidance behavioral reactions to dependence on AI.

A three-wave field study was conducted, collecting data from 303 hotel employees and analyzed using Mplus 8.3.

Dependence on AI can be construed as a positive stimulus, augmenting employees’ harmonious work passion and subsequently promoting approach job crafting. The promotion focus of employees positively moderates this process. On the other hand, dependence on AI also can be perceived as a negative stimulus, heightening employees’ feelings of AI threat and, consequently, fostering avoidance job crafting. In this case, the prevention focus of employees positively moderates the process.

This study provides theoretical foundations and decision-making references for management practice. Managers should implement measures to guide employees in developing a proper understanding of AI and provide them with emotional support and institutional safeguards.

This study unveils the consequences of dependence on AI for employees, offering new perspectives for AI research in the hotel industry. By differentiating job crafting, this study theorizes and tests a dual-path model of how dependence on AI may influence hotel employees’ approach and avoidance job crafting, thereby enriching the AI–job crafting literature.

This study aims to explore the relationship between artificial intelligence (AI) awareness and physical and psychological withdrawal behaviors of enterprises employees through the mediating roles of job security and emotional exhaustion as well as the moderating role of emotional intelligence.

Data were collected through a self-administered questionnaire from six fields with the highest level of AI application with a sample of 1,129 Vietnamese enterprises employees. Data were analyzed using SmartPLS, a bootstrapping technique was used to analyze the data. The mediating effect of job security and emotional exhaustion and the moderating effect of emotional intelligence were performed.

The research showed that the proposed moderated mediation model was accepted because the relationships between the constructs were statistically significant. The results of the data analysis supported a positive relationship between AI awareness and physical and psychological withdrawal behaviors, as well as a mediating effect of job security and emotional exhaustion. The findings also confirmed that there is a moderating effect of emotional intelligence between AI awareness and job security, emotional exhaustion, physical and psychological withdrawal behaviors.

Sample data was only collected at a few Vietnamese enterprises in six fields with the highest level of AI application which are e-commerce, transportation and logistics, education, real estate, finance and agriculture, which may be limiting generalizability of research results.

This study offers several practical and useful management implications, such as anticipating negative attitudes, feelings and behaviors of employees to prepare a response plan; conducting interviews, investigate employees’ AI awareness and do their best to minimize its negative effects on employees’ psychological states and behaviors; and paying attention to recruiting and selecting employees with good emotional intelligence.

This study contributes to the growing literature on AI by elucidating the mediating roles of job insecurity and emotional exhaustion in the relationship between AI awareness and physical and psychological withdrawal behavior. This study also makes a significant step forward in examining the moderating mechanisms of emotional intelligence in attenuating the effects of AI awareness on job insecurity, emotional exhaustion, physical and psychological withdrawal behavior.

The effectiveness of the Just-in-Time (JIT) production system in non-Japanese contexts is a topic of diverse findings. This study conducts a meta-analysis of empirical research on JIT and its relationship with performance, focusing on studies published since 1995. Additionally, it examines the moderating influence of National Culture (NC) values on JIT outcomes.

A total of 59 empirical studies with 211 effects and 17,008 observations from 18 countries are meta-analyzed. A meta-regression using hierarchical linear modeling (HLM) is performed to explore how four dimensions of National Culture (NC) moderate the impact. (viz. institutional collectivism, uncertainty avoidance future orientation, and power distance,) based on the Global Leadership and Organizational Behavior Effectiveness (GLOBE) culture model.

The meta-analysis results show that improved production efficiency, product quality and reduced wastes achieved through JIT deployments translate into the overall performance of organizations. The meta-regression results shed light on how local cultures influence the effectiveness of JIT across different countries.

The findings of meta-analysis have implications for multinational manufacturers in realizing efficacy of JIT. The research adds to the international operations management literature by examining how NC values influence strategies and decisions in operations management.

Unlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.

By administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.

The optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.

While our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.

The established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.

Given the popularity of online health communities (OHCs) and medical question-and-answer (Q&A) services, it is increasingly important to understand what constitutes useful answers and user-adopted standards in healthcare domain. However, few studies provide insights into how health information characteristics, provider characteristics and recipient characteristics jointly influence user information adoption decisions. To fill this research gap, this study examines the combined effects of physicians' certainty tone as information characteristics, seniority as provider characteristics and disease severity as recipient characteristics on patients' health information adoption.

Drawing on dual-process theory and information adoption model, an extended information adoption model is established in this study to examine the effect of attitude certainty on patients' health information adoption, and the moderating effects of online seniority and offline seniority, as well as patient motivation level—disease severity. Utilizing logit regression models, the authors empirically tested the hypotheses based on 4,224 Q&A records from a popular Chinese OHC.

The results show that (1) attitude certainty has a significant positive impact on patients' health information adoption, (2) the relationship between attitude certainty and information adoption is negatively moderated by physicians' online seniority, but is positively moderated by offline seniority; (3) there is a negative three-way interaction effect of attitude certainty, online seniority and disease severity on patients' health information adoption.

This study extends the information adoption model to examine the two-way interaction between argument quality and source reliability, as well as the three-way interaction with user motivation level, especially for health information adoption in the healthcare field. These findings also provide direct practical applications for knowledge contributors and OHCs.

Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the moderating influence of extrinsic informal institutions (foreign culture) and intrinsic formal institutions (property rights).

This study constructs a comprehensive database comprising 9,759 firm-year observations in China by using a sample of Chinese A-share listed firms from 2016 to 2020. Subsequently, the hypotheses are examined and confirmed, with the validity of the results being upheld even after conducting endogenous and robustness tests.

The findings of this study offer robust and consistent evidence supporting the notion that Confucianism positively affects technology for social good through both incentive effect and normative effect. Moreover, this positive influence is particularly prominent in organizations with limited exposure to foreign culture and in nonstate-owned enterprises.

The findings contribute to the literature by fostering a deep understanding of technology for social good and Confucianism research, and further provide a nuanced picture of the role of foreign culture and property rights in the process of technology for social good in China.

How to successfully drive open innovation (OI) has become an important issue. However, the existing literature on the determinants of OI focuses on organizational and situational factors, while the “human side” of it remains poorly understood. To address such problem, this paper examines the impact of two core qualities of CEOs – CEO power and social capital – on three representative OI modes from a micro-level perspective.

This paper uses the data of 4,213 firm-year observations from Chinese A-share listed companies. A panel logit model is used to test the hypotheses, and the author also uses clustering robust standard errors to ensure the robustness of the model.

A powerful CEO can drive the firm's adoption of technology, organization and market-oriented OI, and different types of social capital have a differential impact on such relationship. Specifically, the CEO's political social capital has a negative moderating effect, while his/her stronger business social capital can enhance the positive relationship between CEO power and various types of OI activities, as well as mitigate the negative effect of political social capital.

This paper echoes the call for more attention to be paid to the microfoundations of OI and provides theoretical implications for research on the convergence of OI and strategic leadership.

Integrating transaction costs economics and task-technology fit theory, this study distinguishes two categories of social media–enabled interactions, namely task-related interactions and tie-related interactions, and explores the match between these two and firms' use of contracts in achieving safeguarding and coordinating purposes in interfirm governance.

Two studies were conducted to test the hypotheses. In Study 1, this study collaborated with a professional market research firm and collected responses from Chinese manufacturing firms in a survey. In Study 2, this study designed a scenario-based experiment and collected 239 participants from the Credamo platform.

This study categorized social media–enabled interactions into task-related interactions and tie-related interactions and conducted two studies to reveal that the safeguarding purpose of contract specificity is amplified by tie-related interactions, whereas the coordinating purpose of contract specificity is strengthened by task-related interactions.

This study assumes that firms permit and encourage the use of social media. However, some firms might prohibit the use of social media due to risk issues, or their partners may be prohibited from using social media.

Given that social media–enabled interactions have joint effects with contracts in achieving safeguarding and coordinating purposes, a firm's employees should match their goals with an appropriate type of social media–enabled interactions.

This study enriches the interfirm governance literature by uncovering the roles of these two types of interactions in matching contract specificity to achieve safeguarding and coordinating purposes, which provides actionable insights for managers in governing interfirm relationships.