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The purpose of this study is to examine the effect of stakeholder orientation in mediating the impact of female directors on environmental innovation. The authors argue that female directors are eco-friendly and more concerned with multi-stakeholder interests and demands. Thus, they promote environmental innovation by including more stakeholder-oriented values and beliefs in firm decision-making.

As the dependent variable in this study is a nonnegative count variable, the authors use a count data model based on Poisson regression. A sample of Chinese listed firms between 2010 and 2020 is used to test the hypotheses.

The results of this study show that female directors can enhance environmental innovation. Further, stakeholder orientation represents an intermediate channel that accounts for the effects of female directors on environmental innovation. This suggests that having women on a board can lead to better stakeholder management, which, in turn, positively affects environmental innovation. The authors also reveal that female directors contribute more to stakeholder orientation with the presence of female chairpersons.

A significant limitation in the literature is that little attention has been paid to the mechanisms linking female directors to firm outcomes. In the context of environmental innovation, while previous studies have investigated the influence of female directors on environmental innovation, the underlying channels of that influence remain largely unexplored. Therefore, the findings of this study advance the understanding of the effects of female directors on environmental innovation by revealing an important underlying channel – stakeholder orientation.

Malicious attacks extremely traumatize knowledge networks due to increasing interdependence among knowledge elements. Therefore, exposing the damage of malicious attacks to knowledge networks has important theoretical and practical significance. Despite the insights being offered by the growing research stream, few studies discuss the diverse responses of knowledge networks’ robustness to different target-attacks, and the authors lack sufficient knowledge of which forms of malicious attacks constitute greater disaster when knowledge networks evolve to different stages. Given the irreversible consequences of malicious attacks on knowledge networks, this paper aims to examine the impacts of different malicious attacks on the robustness of knowledge networks.

On the basic of dividing malicious attacks into six forms, the authors incorporate two important aspects of robustness of knowledge networks – structure and function – in a research framework, and use maximal connected sub-graphs and network efficiency, respectively, to measure structural and functional robustness. Furthermore, the authors conceptualize knowledge as a multi-dimensional structure to reflect the heterogeneous nature of knowledge elements, and design the fundamental rules of simulation. NetLogo is used to simulate the features of knowledge networks and their changes of robustness as they face different malicious attacks.

First, knowledge networks gradually form more associative integrated structures with evolutionary progress. Second, various properties of knowledge elements play diverse roles in mitigating damage from malicious attacks. Recalculated-degree-based attacks cause greater damage than degree-based attacks, and structure of knowledge networks has higher resilience against ability than function. Third, structural robustness is mainly affected by the potential combinatorial value of high-degree knowledge elements, and the combinatorial potential of high-out-degree knowledge elements. Forth, the number of high in-degree knowledge elements with heterogeneous contents, and the inverted U-sharp effect contributed by high out-degree knowledge elements are the main influencers of functional robustness.

The authors use the frontier method to expose the detriments of malicious attacks both to structural and functional robustness in each evolutionary stage, and the authors reveal the relationship and effects of knowledge-based connections and knowledge combinatorial opportunities that contribute to maintaining them. Furthermore, the authors identify latent critical factors that may improve the structural and functional robustness of knowledge networks.

First, from the dynamic evolutionary perspective, the authors systematically examine structural and functional robustness to reveal the roles of the properties of knowledge element, and knowledge associations to maintain the robustness of knowledge networks. Second, the authors compare the damage of six forms of malicious attacks to identify the reasons for increased robustness vulnerability. Third, the authors construct the stock, power, expertise knowledge structure to overcome the difficulty of knowledge conceptualization. The results respond to multiple calls from different studies and extend the literature in multiple research domains.

With the growing global emphasis on environmental, social and governance (ESG) criteria, it is crucial to investigate the factors that influence individuals' intentions to invest in ESG and to understand the underlying mechanisms at play. This study constructs a theoretical model, grounded in the Fogg behavioral model (FBM), and explores the mediating role of ESG investment attitudes in shaping individuals' ESG investment behaviors.

A survey was conducted among ESG investors and potential ESG investors in China, resulting in 613 valid responses regarding ESG investment. The partial least squares structural equation modeling (PLS-SEM) approach was utilized to evaluate the proposed model and test the hypotheses.

The results reveal that future orientation, ESG investment bias and perceived ESG investment performance are significant determinants of ESG investment intentions, with attitude playing a partially mediating role. Furthermore, government support moderates the relationship between perceived ESG investment performance and investment intention.

This study expands the application of the FBM to the context of ESG investment and introduces a novel conceptual framework for understanding ESG investment behavior. The findings provide valuable insights for enterprises and institutions involved in ESG investment, aiding them in identifying and targeting potential investors more effectively. Additionally, the study offers a foundation for policymakers to devise strategies that promote sustainable development.

The purpose of this paper is to explore how small and medium-sized enterprises (SMEs) in China leverage their strengths to engage stakeholders in knowledge co-creation processes and get mutual benefit via knowledge-based view (KBV).

Based on KBV, the authors conduct a multiple-case study of five SMEs in China to embrace the knowledge co-creation practice using semi-structured interview, organizational documents and onsite observation.

This study highlights how SMEs leverage their strengths to engage stakeholder to co-create knowledge and practice for the better capturing and utilization of external and internal knowledge. The authors identify three processes of knowledge co-creation for SMEs based on knowledge sharing, knowledge integration and knowledge application in the B2B context. This study finds that SMEs engage their stakeholders in knowledge sharing by building and maintaining trust. The knowledge integration process was driven by the owner’s openness. Mutual learning facilitates the knowledge application process of SMEs.

This study relies on a limited number of case studies and considers only firms’ perspective to analyze the SMEs co-create knowledge with their stakeholders. Further studies could examine the challenge of knowledge co-creation in multiple stakeholders’ relationships in B2B contexts, i.e. in relation to product and service innovation with complexity and uncertainly.

Managers need to make choices when designing knowledge co-creation process in collaborative product development activities. The use of online and offline approaches can help balance requirements in terms of joint problem-solving across firms, the efficiency of knowledge co-creation and effective of knowledge leakage.

The conceptualization of knowledge co-creation as knowledge sharing and knowledge integration and knowledge application extends existing perspective on knowledge co-creation as either a transfer of knowledge or as revealing the novel situation of pertinent knowledge with entirely assimilate it. The findings point to the complexity of knowledge co-creation as a process influenced by stakeholder engagement, perspectives on knowledge, trust of multiple stakeholders, openness of firm boundaries and mutual learning of SMEs with their stakeholders.

The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using wavelet analysis and gray-level co-occurrence matrix (GLCM), and the samples are evaluated using SVM classifiers. The study aims to solve the problem of inappropriate parameters and large required samples in objective seam pucker evaluation.

Initially, seam pucker image was captured, and Edge detection and Hough transform were utilized to normalize the seam position and orientation. After cropping the image, the intensity was adjusted to the same identical level through histogram specification. Then, the standard deviations of the horizontal image and diagonal image, reconstructed using wavelet decomposition and reconstruction, were calculated based on parameter optimization. Meanwhile, GLCM was extracted from the restructured horizontal detail image, then the contrast and correlation of GLCM were calculated. Finally, these four features were imported to SVM classifiers based on genetic algorithm for evaluation.

The four extracted features reflected linear relationships among five grades. The experimental results showed that the classification accuracy was 96 percent, which catches up to the performance of human vision, and resolves ambiguity and subjective of the manual evaluation.

There are large required samples in current research. This paper provides a novel method using finite samples, and the parameters of the methods were discussed for parameter optimization. The evaluation results can provide references for analyzing the reason of wrinkles during garment manufacturing.

The purpose of this paper is to study how to improve the performance of RFID robot system by anti-collision algorithms. For radio frequency identification (RFID) robots operating in mobile scenes, effective anti-collision algorithm not only reduces missed reading but also enhances the speed of RFID robots movement.

An effective anti-collision algorithm is proposed to accelerate tag identification in RFID robots systems in this paper. The tag collisions in the current time slot are detected by a new method, and then further resolve each small tag collision to improve system throughput, rather than the total tags number estimation. After the reader detected the collision, three different collision resolution methods were described and studied, and the situation of missing tag caused by reader moving is also discussed.

The proposed algorithm achieves theoretical system throughput of about 0.48, 0.50 and 0.61 and simulates to show that the proposed algorithm performance is significantly improved compared with the existing ALOHA-based algorithm.

The proposed RFID anti-collision algorithm is beneficial to improve the moving speed and identification reliability of the RFID robots in complex environments.

Based on the theory of performance feedback, this study aims to explore the theoretical relationship between performance shortfalls and the financialization of non-financial enterprises. It further analyzes the moderating effect of economic policy uncertainty (EPU) and organizational redundant resources.

Multiple regression analysis is used on 16,555 initial samples of 2,658 Chinese A-share issuing enterprises from 2007 to 2019 to empirically test the relationship between performance shortfalls and the financialization of non-financial enterprises, and an instrumental variables-generalized moments estimation model is also used to verify the robustness of the results.

The results reveal that the greater the performance gap below the aspiration level, the higher the degree of enterprise financialization. Moreover, EPU strengthens the relationship between performance shortfalls and financialization, whereas organizational redundant resources weaken the relationship between performance shortfalls and financialization.

Decision-makers should determine the aspirated performance level of enterprises to make investment decisions that are most conducive to the long-term development of enterprises. Each enterprise should establish scientific management evaluation and supervision systems to avoid financial investment behaviors that place too much emphasis on short-term performance.

This study finds that financialization is one of the reactions when performance of enterprises is lower than the aspiration level, thus expanding the functional dimensions of performance feedback and supplementing the research on the influencing factors of enterprise financialization. The results also reveal information about situational factors, helping identify the boundary conditions through which performance below aspirations affects enterprise financialization.

This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.

The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.

The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.

The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.

The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

Bibliotherapy, a therapeutic approach that uses books and reading materials to promote psychological well-being and personal growth, has become more prevalent in recent years. This scientometric study aims to provide a comprehensive view of the bibliotherapy research landscape by highlighting its evolution, trends, and noteworthy contributions using Biblioshiny and VOSviewer.

The academic literature on bibliotherapy is evaluated in-depth in this study utilizing scientometric techniques, including citation and co-citation analysis. A thorough search of the Scopus database revealed 1,703 papers between 1942 and 2023 that dealt with bibliotherapy. For data analysis, the renowned applications Biblioshiny and VOSViewer are employed.

The study reveals that the output of publications has fluctuated, reflecting scholarly interest in this discipline. The distribution of research across various countries, organizations and academic subjects is investigated further to highlight the diverse and global extent of bibliotherapy research. By analyzing co-citation networks and locating pertinent publications and authors, this scientometric method analyzes the intellectual structure of bibliotherapy research.

Bibliometric analysis enriches the theoretical understanding of bibliotherapy by unveiling the networks, influential works and existing gaps in the literature, thus guiding a more informed and collaborative approach to future research and practice in the domain.

Employing bibliometric analysis in bibliotherapy can refine practices and training programs, ensuring they are evidence-based and practical, enhancing the quality of therapeutic services provided to individuals.

It is a valuable resource for academics, practitioners and policymakers interested in the field since it offers a thorough and current assessment of the bibliotherapy research landscape. The findings of this study have the potential to steer future research, guide the development of bibliotherapeutic interventions supported by evidence and enhance the use of bibliotherapy as a therapeutic modality.

3D garment design technology is developing rapidly thereby creating a need for different approaches to developing the patterns. The purpose of this paper is to evaluate the 3D dynamic ease distribution for a 3D garment design.

Standard garments were created from Size 2 to Size 14 for ten human subjects. Landmarks location on both human body and the standard garment under dynamic postures are recorded, and he fit and comfort evaluation of the standard garment were collected from the ten human subjects. Finally, these data were used to evaluate the 3D dynamic ease distribution for a 3D garment design.

3D dynamic ease evaluation is challenging and the findings showed that the upper-arm design is a core element of the whole 3D garment design. The upper arm is not only a connecting part for both front and back pieces of the garment, but is also the main active part of the body, so it is the essential element to affect the comfort and fit of the garment under dynamic postures.

This research provides a novel 3D ease evaluation by analyzing the landmarks location of both human body and standard garment, and fit and comfort evaluation of the standard garment, which are all carried under dynamic postures.