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This paper aims to understand the current development situation of scientific data management policy in China, analyze the content structure of the policy and provide a theoretical basis for the improvement and optimization of the policy system.

China's scientific data management policies were obtained through various channels such as searching government websites and policy and legal database, and 209 policies were finally identified as the sample for analysis after being screened and integrated. A three-dimensional framework was constructed based on the perspective of policy tools, combining stakeholder and lifecycle theories. And the content of policy texts was coded and quantitatively analyzed according to this framework.

China's scientific data management policies can be divided into four stages according to the time sequence: infancy, preliminary exploration, comprehensive promotion and key implementation. The policies use a combination of three types of policy tools: supply-side, environmental-side and demand-side, involving multiple stakeholders and covering all stages of the lifecycle. But policy tools and their application to stakeholders and lifecycle stages are imbalanced. The development of future scientific data management policy should strengthen the balance of policy tools, promote the participation of multiple subjects and focus on the supervision of the whole lifecycle.

This paper constructs a three-dimensional analytical framework and uses content analysis to quantitatively analyze scientific data management policy texts, extending the research perspective and research content in the field of scientific data management. The study identifies policy focuses and proposes several strategies that will help optimize the scientific data management policy.

Although the prior literature has identified the relevance of dealer participation for multinational enterprises (MNEs), it is unclear whether such participation could also be an important means for local dealers to learn from MNEs. By adopting local firms’ viewpoint, our study draws on organizational learning theory to examine how local dealers benefit from their participation with foreign suppliers in Africa.

The empirical setting is a combinative dataset of secondary data and primary survey of 164 small- and medium-sized local dealers with nine subsidiaries of a Chinese motorcycle company in six countries of Sub-Saharan Africa.

This research shows that dealer participation is positively associated with dealer performance, and this positive effect is stronger when local dealers operate in regions with low government corruption and high government support. However, the positive relationship is weaker when local dealers use the local tongue extensively but becomes stronger when their foreign suppliers have a high dealer coverage.

By taking a local-participant perspective, our study extends the participation literature to show how firms from a resource-constrained region may benefit from their proactive participation with foreign counterparts. Additionally, we identify the boundary conditions of institutional factors and strategic choices of local dealers and foreign suppliers, providing a nuanced understanding of firm behaviors in complex and uncertain markets.

The current chapter proposes a theoretical framework to assess the sustainability of economic growth in the Gulf Cooperation Council (GCC) States. The authors integrate insights from endogenous growth models and consider the unique socioeconomic characteristics of the GCC region to provide a comprehensive and tailored approach to understanding the determinants of economic growth and formulating effective policy measures to foster sustainable development and growth. This chapter highlights the environmental challenges faced by GCC; based on this, the authors suggested indicators to construct a theoretical framework (Economic Growth, Climatic Indicators, Energy Indicators, Social Indicators, and Economic Resources Indicators). The authors propose that policymakers and researchers in GCC States should take these factors into account when devising policies or conducting research aimed at fostering sustainable economic growth. Overall, this chapter presents significant insights for policymakers, researchers, and stakeholders involved in promoting the sustainable economic advancement of the GCC States.

This study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.

Based on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.

The findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.

The study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.

Interorganizational power dependence has become an increasingly important factor for small and medium-sized enterprises (SMEs) to improve product innovation. This paper examines the role of power dependence in SMEs' product innovation trade-offs between exploration and exploitation. The article further studies the mediating effect of supply chain adaptability and the moderating effect of knowledge acquisition on the relationship between power dependence and product innovation.

The study proposes a model to verify the impact of power dependence on SMEs' product innovation trade-offs based on social network theory. Two conceptually independent constructs, “availability of alternatives (ALTRN)” and “restraint in the use of power (RSPTW),” are used to evaluate the power dependence. The model also analyzed how these effects are mediated by supply chain adaptability and moderated by knowledge acquisition. The authors test these relationships using data collected from 224 SMEs in China.

The empirical analysis shows that ALTRN has a more substantial effect on exploration for product innovation, while RSTPW has a more significant impact on exploitation for product innovation. Moreover, empirical data indicate a partial mediating effect by supply chain adaptability between power dependence and product innovation of SMEs. The results also show that knowledge acquisition positively moderates the relationship between ALTRN/RSTPW, supply chain adaptability and product innovation.

Overall, the findings of the study advance the understanding of the roles of power dependence in product innovation for SMEs. In addition, the research also uncovers the impact mechanisms of existing theoretical frameworks and extends the boundaries of the theory.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Engaging in webcare, i.e. responding to online reviews, can positively affect consumer attitudes, intentions and behavior. Research is often scarce or inconsistent regarding the effects of specific webcare strategies on business performance. Therefore, this study tests whether and how several webcare strategies affect hotel bookings.

We apply machine learning classifiers to secondary data (webcare messages) to classify webcare variables to be included in a regression analysis looking at the effect of these strategies on hotel bookings while controlling for possible confounds such as seasonality and hotel-specific effects.

The strategies that have a positive effect on bookings are directing reviewers to a private channel, being defensive, offering compensation and having managers sign the response. Webcare strategies to be avoided are apologies, merely asking for more information, inviting customers for another visit and adding informal non-verbal cues. Strategies that do not appear to affect future bookings are expressing gratitude, personalizing and having staff members (rather than managers) sign webcare.

These findings help managers optimize their webcare strategy for better business results and develop automated webcare.

We look into several commonly used and studied webcare strategies that affect actual business outcomes, being that most previous research studies are experimental or look into a very limited set of strategies.

Joint ventures (JVs) serve as a viable tool in mitigating some of the challenges posed to the effective delivery of construction projects. However, JVs are highly susceptible to failure in most developing countries. Therefore, this study seeks to unravel the critical factors influencing the failure of JVs in the South African construction industry.

A quantitative approach was adopted for the study using a well-structured questionnaire as the instrument for data collection. Respondents for the study were built environment professionals in Gauteng province in South Africa. Data elicited from respondents were analyzed using a four-pronged process which included descriptive statistics, one sample t-test, exploratory factor analysis and confirmatory factor analysis.

Resulting from the analysis conducted, four critical components emerged as the major factors influencing the failure of JVs in the South African construction industry, which are inefficient financial framework, divergent organizational culture, poor project governance and inadequacies from project stakeholders.

The outcome of this study presents a roadmap for stakeholders in the construction industry with the requisite knowledge of the critical factors leading to the failure of JVs, consequently providing a clear path for the successful delivery of JV mandates.

Evidence from literature suggests that several studies have been conducted on the various aspects of JVs in the South African construction industry; however, none has focused on the leading factors attributed to the failure of JVs. Also, the findings of this study cultivate a good theoretical platform for future studies on JVs.

Job stressors have a considerable influence on workplace safety behaviors. However, the findings from previous studies regarding the effect of different types of job stressors have been contradictory. This is attributable to, among other factors, the effectiveness of job stressors varying with occupations and contexts. This study examines the effects of challenge and hindrance stressors on construction workers' informal safety communication at different levels of coworker relationships.

A three-dimensional framework of informal safety communication is adopted, including self-needed, citizenship and participatory safety communication. Stepwise regression analysis is then performed using questionnaire survey data collected from 293 construction workers in the Chinese construction industry.

The results demonstrate that both challenge and hindrance stressors are negatively associated with self-needed and citizenship safety communication, whereas their relationships with participatory safety communication are not significant. Meanwhile, the mitigation effects of the coworker relationship (represented by trustworthiness and accessibility) on the above negative impacts vary with the communication forms. Higher trustworthiness and accessibility enable workers faced with challenge stressors to actively manage these challenges and engage in self-needed safety communication. Similarly, trustworthiness promotes workers' involvement in self-needed and citizenship safety communication in the face of hindrance stressors, but accessibility is only effective in facilitating self-needed safety communication.

By introducing the job demands-resources theory and distinguishing informal safety communication into three categories, this study explains the negative effects of challenge and hindrance job stressors in complex and variable construction contexts and provides additional clues to the current inconsistent findings regarding this framework. The diverse roles of challenge and hindrance job stressors also present strong evidence for the need to differentiate between the types of informal safe communication.

This research explores the impact of classical aesthetics (e.g. order and symmetry) and expressive aesthetics (e.g. creativity and distinctiveness) on consumer green consumption.

This research conducted three studies. Study 1 explored the main effect of appearance aesthetics (appearance: plain vs classical vs expressive) on green products purchase intention through a one-factor between-subjects design. Study 2 verified the mediating role of perceived naturalness through two types of appearance aesthetics (appearance: classical vs expressive) between-subjects design. Study 3 verified the moderating role of product identity-symbolic attributes through a 2 (product identity-symbolic attributes: non-identity-symbolic vs identity-symbolic attributes) × 2 (appearance: classical aesthetics vs expressive aesthetics) between-subjects design.

Consumers will be more likely to purchase a green product that has classical aesthetics appearance (vs expressive aesthetics). Perceived naturalness mediates the effect of aesthetic appearance on consumer green consumption. Product identity symbol attributes moderate this effect. Specifically, for non-identity-symbolic green products, classical aesthetics can effectively enhance consumer purchase intention. For identity-symbolic green products, expressive aesthetics can effectively enhance consumer purchase intention.

Existing research suggests that aesthetic appearance can increase consumers’ evaluation of electronic products, beauty products and food, but the difference between aesthetics has not yet been explored. This research compares two aesthetics, contributing to the literature on aesthetic appearance in green products and offering valuable insights for managers’ green products marketing.