Search results

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

Rhetorical figures are frequently used in English-Chinese advertisement translation, but their impact on consumers is an under-studied issue. This research aims to explore Chinese consumers’ perception of two categories of rhetorical figures in bilingual advertising, namely schemes and tropes, via an eye-tracking experiment.

It takes rhyme and antithesis as representative examples of schemes and takes metaphor as an example of tropes. Eighty-six native Chinese speakers with satisfactory English proficiency participated in the experiment. Thirty English slogans without rhetorical figures were selected for the experiment. Two parallel Chinese versions were prepared, one with rhetorical figures and the other without. Participants were randomly assigned to read the English version and one Chinese version.

Data analyses show that the translations with rhetorical figures generally involve higher fixation duration or count, smaller saccade amplitude and higher subjective ratings. In particular, the impact of metaphor is more prominent than that of rhymes and antitheses. Still, English texts receive higher ratings than Chinese translations except for cases where metaphors are used in translations.

The results suggest that incorporating rhetorical figures particularly tropes into Chinese translations is effective in increasing Chinese consumers’ attention and processing depth and to present English advertisements together with Chinese translations can be helpful for affecting their attitude.

It applies the eye-tracking approach to explore Chinese consumers’ perception of rhetorical figures. It provides empirical evidence for the effectiveness of tropes.

This paper proposes a new intra-layer partition adaptive slicing algorithm for FDM 3D printing, aiming to further improve forming efficiency based on the adaptive slicing algorithm while preserving the surface finish quality of the formed model.

This method initially applies a large layer thickness for primary slicing, then refines layer thickness in layer height ranges with significant cross-sectional contour changes. Refined layers are partitioned: the internal region uses the large layer thickness for efficiency, while the external region uses a smaller layer thickness for surface quality. A thickness ratio and transition zone between regions prevent overlaps and gaps in printing paths.

The experimental results show that, compared to traditional adaptive slicing algorithms, the intra-layer partition adaptive slicing algorithm can effectively improve forming efficiency for most models while ensuring the model’s surface finish, with minimal impact on the bonding strength of the model.

The intra-layer partition adaptive slicing algorithm is a novel algorithm improved upon the traditional adaptive slicing algorithm, enabling models to achieve higher printing efficiency while maintaining the surface finish provided by the conventional adaptive slicing algorithm. This algorithm is of significant importance to vendors and individual users who provide printing services for large-sized fused deposition modeling models, as it can greatly enhance their production efficiency.

In social media context, service failures become public domain, making them visible to vast audiences of customers who are virtually present. Thus, this study aims to discuss the roles of service failure type and management response on observers’ perceived helpfulness.

The authors conducted econometric analyses on a dataset incorporating 107,984 reviews and 34,641 management responses to negative reviews.

The results reveal that, for process failures, specifying a form of action (initiatives for solving the problem) is associated with more perceived helpfulness of reviews than accounts (explanation) or acknowledgments (recognition and acceptance), while responding to an outcome failure by providing an account is associated with increased perceived helpfulness of reviews.

For process failures, managers should make every effort to convince observers through specific actions that similar failures are less likely to occur in the future. For outcome failures, managers should strive to provide clear and fast explanations of the failure causes to restore observers’ evaluation of the firm’s capabilities.

The authors’ work extends sparse insights on observers and sheds new light on the effect of service failure type and response strategy on observers’ actual behaviors. The interplay between service failure type and response strategy provides guidance on how to use management responses to influence potential customers.

Since the COVID-19 epidemic, the number of restaurant service quality studies has continued to increase. However, until now, there has not been an overall perspective or accurate instructions for research on restaurant service quality and experiential value enhancement. This study conducts multiple comparison studies to discover differences between consumer-perceived service quality and satisfaction perspectives on hotel fine dining and chain restaurants.

This study integrates a hotel’s fine dining and chain restaurant to obtain 636 participants (e.g. Study 1 has 318 hotel fine dining customers; Study 2 has 318 chain restaurant customers), mainly expanding the SERVQUAL model and stimulus–organism–response (S–O–R) theory.

The results of Study 1 show that value co-creation has a mediating effect on the relationship between service quality and satisfaction. In addition, customer experiences have a significant moderating effect on customer satisfaction. The outcomes of Study 2 showed that experiential value has a significant mediating effect on the relationship between service quality and satisfaction. In addition, customer relationship quality is a critical criterion in regulating the process of experience value delivery.

Hotels’ fine dining should pay attention to the item risk in the value co-creation factor, while chain restaurants should enhance the item service excellence in the experiential value factor to satisfy the changing customer requirements.

This study provides several alternative models to verify the robustness of the empirical results.

1. This research has brought clarity to the diverse mediation-moderation models that compare of hotel fine dining and chain restaurant consumer perceived service quality and satisfaction predictions.

2. These models delve into how different service quality requirements after the epidemic that affect customer satisfaction, as perceived by customers consumed in hotel fine dining and chain restaurant.

3. Value cocreation and experiential value emerge as pivotal factors, they act as mediators between service quality and satisfaction.

4. They demonstrate a moderation effect of customer experiences between value cocreation and satisfaction, as well as customer relationship quality between experiential value and satisfaction.

This research has brought clarity to the diverse mediation-moderation models that compare of hotel fine dining and chain restaurant consumer perceived service quality and satisfaction predictions.

These models delve into how different service quality requirements after the epidemic that affect customer satisfaction, as perceived by customers consumed in hotel fine dining and chain restaurant.

Value cocreation and experiential value emerge as pivotal factors, they act as mediators between service quality and satisfaction.

They demonstrate a moderation effect of customer experiences between value cocreation and satisfaction, as well as customer relationship quality between experiential value and satisfaction.

The purpose of this study is to examine the applicability of the technology acceptance model (TAM) in libraries considering the advanced technologies and users’ behaviour.

The research uses a critical reflective approach to review and synthesize a body of recent academic literature on the use of TAM in libraries. The review included assessing TAM’s historical evolution, its limitations and how it could be improved.

The findings indicated that, although TAM can be viewed as an appropriate theoretical model to explain the users’ intention towards technology acceptance, it is limited in explaining both the users’ attitude towards advanced technology and their behaviour in advanced library settings.

To enhance the practicality of TAM in libraries, several recommendations for strategic advancements have been proposed such as contextualizing TAM to libraries, exploring AI-driven adoption, integrating library-specific constructs, understanding cultural differences and using holistic research approaches.

The findings contribute to a deeper understanding of technology adoption in libraries and to the future possibilities of TAM.

The study aims to investigate the implementation of green supply chain management (GSCM) in the footwear industry. It focuses on identifying key factors (dimensions and corresponding sub-dimensions) that promote GSCM and examines the importance and relevance of these factors in improving sustainability and competitive advantage within the footwear manufacturing supply chain.

A literature review and expert interviews were conducted to identify the factors for GSCM in the footwear industry. The DEMATEL-based ANP (DANP) method was used to analyze the importance and interrelationships of the identified dimensions and sub-dimensions. The data were collected through questionnaires and analyzed across different levels of the supply chain to compare the significant factors affecting GSCM implementation.

The study reveals that green purchasing is the core dimension, playing a critical role in the footwear industry. Organizational and environmental management emerges as the most influential causal factors, strongly correlated with other factors. However, as to sub-dimensions, different level of supply chain members should focus on different sub-dimensions.

This research provides valuable insights into the key dimensions and sub-dimensions and their causal relationships in the footwear industry. It offers practical suggestions for manufacturers to prioritize organizational and environmental management as a strategic approach to maximize the benefits of GSCM.

In response to the complex external structure of high-precision aviation plugs, which makes it difficult to search outside the hole and adjust inside the hole during automated assembly. This paper aims to propose an assembly framework that combines multi-agent search and variable parameter compliant control to solve this problem.

First, a multi-agent search strategy (MAS) based on Gaussian Mixture Model and Deep Q-Network was proposed to optimize displacement direction and actions, thereby improving search speed and success rate. Then, a variable parameter admittance control method (RL-VPA) based on dual delay depth deterministic policy gradient (TD3) was proposed, which dynamically optimized the internal parameters of the admittance controller and adopted state space discretization to improve convergence speed and assembly efficiency.

Compared to spiral search and single-agent search, the average search success rate has improved by approximately 10% and 6.6%. Compared to fixed admittance control and other RL-based methods, the average assembly success rate has increased by approximately 38.6%, 22% and 8.6%. Compared with the training results of the model without state discretization, it was found that state discretization helps the model converge quickly. To verify the generalization ability of the assembly framework, experiments were conducted on three different pin counts of aviation plugs, the assembly success rate reached 86.7%, all of which showed good assembly results. Finally, combining state space discretization to reduce the impact of environmental noise, improve training effectiveness and convergence speed.

MAS has been proposed to optimize displacement direction and action, improving search speed and success rate. RL-VPA is designed to dynamically optimize the internal parameters of the admittance controller, enhancing the robustness and generalization ability of the model. Additionally, state space discretization is combined to improve training effectiveness and convergence speed.

To support digital humanities research more effectively and efficiently, this study develops a novel Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) for the Digital Humanities Research Platform for Biographies of Chinese Malaysian Personalities (DHRP-BCMP) based on artificial intelligence (AI) technology that would not only allow humanities scholars to look at the relationships between people but also has the potential for aiding digital humanities research by identifying latent relationships between people via relationships between people and organizations.

To verify the effectiveness of KGAT-PO, a counterbalanced design was applied to compare research participants in two groups using DHRP-BCMP with and without KGAT-PO, respectively, to perform people relationship inquiry and to see if there were significant differences in the effectiveness and efficiency of exploring relationships between people, and the use of technology acceptance between the two groups. Interviews and Lag Sequential Analysis were also used to observe research participants’ perceptions and behaviors.

The results show that the DHRP-BCMP with KGAT-PO could help research participants improve the effectiveness of exploring relationships between people, and the research participants showed high technology acceptance towards using DHRP-BCMP with KGAT-PO. Moreover, the research participants who used DHRP-BCMP with KGAT-PO could identify helpful textual patterns to explore people’s relationships more quickly than DHRP-BCMP without KGAT-PO. The interviews revealed that most research participants agreed that the KGAT-PO is a good starting point for exploring relationships between people and improves the effectiveness and efficiency of exploring people’s relationship networks.

The research’s limitations encompass challenges related to data quality, complex people relationships, and privacy and ethics concerns. Currently, the KGAT-PO is limited to recognizing eight types of person-to-person relationships, including couple, sibling, parent-child, friend, teacher-student, relative, work, and others. These factors should be carefully considered to ensure the tool’s accuracy, usability, and ethical application in enhancing digital humanities research.

The study’s practical implications encompass enhanced research efficiency, aiding humanities scholars in uncovering latent interpersonal relationships within historical texts with high technology acceptance. Additionally, the tool’s applications can extend to social sciences, business and marketing, educational settings, and innovative research directions, ultimately contributing to data-driven insights in the field of digital humanities.

The research’s originality lies in creating a Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) using AI, bridging the gap between digital humanities research and AI technology. Its value is evident in its potential to efficiently uncover hidden people relationships, aiding digital humanities scholars in gaining new insights and perspectives, ultimately enhancing the depth and effectiveness of their research.

The deviant behavior (DB) of construction workers has always been a troubling event in project management. Although scholars continue to search for the main causes of this behavior to curb it at the source, the authors know less about the role and contribution of the team. This study aims to uncover the mechanisms and conditions under which collective moral judgment focus on self (CMJS) effectively enhances DB.

Adopting Chinese construction enterprises as samples, a hierarchical linear model (HLM) is used to test the results of the hypothesis. Moderated mediating effects are used to analyze the potential mechanisms and boundary conditions of DB.

The results of the HLM analysis show that CMJS could directly and significantly induce DB, and moral disengagement (MD) plays a mediator role in this association. In addition, the positive relationship between MD and DB is stronger when performance-avoidance goal orientation (PaGO) or overqualification (Overq) is higher.

The conditions and mechanisms that influence DB are not unique. Future study could examine the explanatory and weighting mechanisms of DB from other perspectives or to construct a framework and summarize the factors that may influence DB.

This study provides a rich theoretical basis for the prevention and correction of construction workers' DB in Chinese construction firms from the perspective of CMJS. In addition, objective moral judgments contribute to guiding employees' moral cognitive processes and positive work.

This study extends existing research on DB and advances the practical outcomes of construction project governance. It not only illustrates that CMJS has a direct impact on DB but also clarifies the mechanisms and conditions that predispose to the generation of DB, filling the research gap on construction workers' DB from cross-level mechanisms and also enriching the theoretical system for preventing this behavior.