Search results

The performance of corporations in sustainable development is not only a concern of investors, but has also captured ever-increasing attention from consumers. However, the evidence on how these good practices would ultimately benefit brands economically remains insufficient. This study tests the causal effect between corporate Environmental, Social, and Governance (ESG) performance, media coverage, and brand value to reveal the underlying mechanisms of how consumers would react to high ESG performance.

This study uses panel data regression analysis with a sample of Chinese A-share non-financial listed companies from 2010 to 2021. ESG performance, brand value, and media coverage are assessed with Huazheng ESG Rating, the rankings from the China’s 500 Most Valuable Brands' list published by the World Brand Lab, and media index compiled by the Chinese Research Data Services Platform (CNRDS) respectively.

This research confirmed that ESG performance positively impacted brand value in terms of profitability, and that media coverage played a role as a megaphone in this relationship. Large-scale corporates, compared to small ones, benefited more from good ESG ratings due to increased media coverage.

The findings provide evidence of the megaphone effect of media coverage on the relationship between firms’ ESG engagements and brand value in the product market, which has extended the knowledge of media’s monitoring role in the financial market. And this megaphone effect is strengthened by firm size in which larger firms have spotlight effect in draw public attention due to higher expectations in terms of social responsibility.

The purpose of this paper is to achieve the multi-objective optimization design of novel tubular switched reluctance motor (TSRM).

First, the structure and initial dimensions of TSRM are obtained based on design criteria and requirements. Second, the sensitivity analysis rules, process and results of TSRM are performed. Third, three optimization objectives are determined by the average electromagnetic force, smoothing coefficient and copper loss ratio. The analytic hierarchy process-entropy method-a technique for order preference by similarity to an ideal solution-grey relation analysis comprehensive evaluation algorithm is used to optimize TSRM. Finally, a prototype is manufactured, a hardware platform is built and static and dynamic experimental validations are carried out.

The sensitivity analysis reveals that parameters significantly impact the performance of TSRM. The results of multi-objective optimization show that the average electromagnetic force and smoothing coefficient after optimization are better than before, and the copper loss ratio reduces slightly. The experimental and simulated results of TSRM are consistent, which verifies the accuracy of TSRM.

In this paper, only three optimization objectives are selected in the multi-objective optimization process. To improve the performance of TSRM, the heating characteristics, such as iron loss, can be considered as the optimization objective for a more comprehensive analysis of TSRM performance.

A novel motor structure is designed, combining the advantages of the TSRM and the linear motor. The established sensitivity analysis rules are scientific and suitable for the effects of various parameters on motor performance. The proposed multi-objective optimization algorithm is a comprehensive evaluation algorithm. It considers subjective weight and objective weight and fully uses the original data and the relational degree between the optimization objectives.

The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images.

First, the baseline network based on the U-net is trained under a large-scale dataset of remote sensing imagery. Then, the cross-modal training data are used to fine-tune the first two convolutional layers of the pre-trained network to achieve the adaptation to the local features of the cross-modal data. For the cross-modal data of different band, an autoencoder is designed to achieve data conversion and local feature extraction.

The experimental results show the effectiveness and practicability of the proposed method. Compared with the ordinary method, the proposed method gets much better metrics.

The originality is the transfer learning strategy that fine-tunes the low-level layers for the cross-modal data application. The proposed method can achieve satisfied road segmentation with a small amount of cross-modal training data, so that is has a good application value. Still, for the similar application of cross-modal data, the idea provided by this paper is helpful.

Scenarios are cognitive aids for thinking about the future in a sustained and disciplined manner. Because scenarios must be facilitated, scenarios must be considered in the context of their practice. In the strategic management literature, there has been a considerable conversation on the practical difference between “hot” and “cold” cognition. Thinking in this conventional literature demonstrates how the facilitators of scenario planning workshops establish and channel the productive cognition of their clients away from hot cognition and toward cold cognition. But how? As a thought experiment, we examine whether the sociological concept of “emotional labor” helps explain the cognition management of clients by facilitators during scenario planning. We end by considering how a deeper practical understanding of emotional labor might help facilitators identify mechanisms and adapt their tools to better manage the cognitive-affective dimensions of scenario planning in practice.

Drawing on power approach-inhibition theory, this study develops a conditional indirect effect model to explore how team vertical leader position and expert power indirectly impact members’ shared leadership through vertical leader’s empowering behaviors.

Multi-source data was collected using a field survey research design. The final sample includes 944 employees in 164 teams from 14 companies in China.

This study found that the interaction of team vertical leader position power and expert power was positively related to their empowering behaviors, which in turn were positively associated with shared leadership. Moreover, our post hoc-analysis revealed the moderating effect of team power distance orientation on the relationship between vertical leader empowering behaviors and shared leadership.

This study sheds light on shared leadership literature by examining vertical leader position and expert power as antecedents. We also offer new directions for exploring how power functions by discussing leadership through the lens of power approach-inhibition theory.

This study seeks to investigate strategies for building personal relationships with an organization's members, and to examine the outcomes of personal relationships in an advocacy organization.

The case study includes interviews with 39 staff people at national, state, and affiliate levels of the organization; 58 members; and five former members, for a total of 102 participants. Document analysis and participant observation were supplemental methods.

The following strategies for cultivating personal relationships were identified: direct engagement, task sharing, constitutive rhetoric, peer linking, hat‐in‐your‐hand, investment in local relationships, and targeting of aware affiliates for diversity efforts. To contribute to the discussion about the value of personal relationships in organizations, the study also investigated the outcomes of personal relationships. The outcomes found in the study include affective commitment, political leverage, social capital, member recruitment, and member retention.

Although many of the cultivation strategies and outcomes are likely to apply to various contexts, some of them may be specific to the context of an advocacy organization that has a grass‐roots culture and layers of leadership, such as local, state, and national offices.

Organizations can read the study to identify potential strategies they can use to cultivate strong personal relationships with their stakeholders.

The study produces new cultivation strategies and outcomes for personal relationships and engages in a critical discussion of the existing literature.

The primary aim is to identify and examine the critical success factors (CSFs) in FinTech that influence social responsibility (SR). This study aims to differentiate between internal factors (e.g. risk management, innovation, regulatory compliance, technology integration) and external factors (e.g. market conditions and regulatory changes) and offer insights into effective FinTech practices that enhance SR performance.

A comprehensive literature review using the Web of Science database analyzed 143 relevant articles. This review categorizes CSFs and assesses how FinTech practices impact SR.

The findings indicate that strategic FinTech practices, such as innovation in financial services, effective data management and stakeholder collaboration, are crucial for enhancing operational efficiency, fostering sustainability and improving responsiveness to social and environmental needs. This study also highlights current trends in FinTech and SR, emphasizing the integration of FinTech into broader SR strategies.

This study provides valuable insights for practitioners on how to leverage FinTech to enhance SR, including strategies for integrating innovative technologies and improving stakeholder engagement.

By advancing the understanding of how FinTech can contribute to SR, this research highlights the potential for FinTech to drive positive social and environmental outcomes, fostering greater corporate responsibility and sustainability.

This research provides a novel bibliometric analysis of FinTech’s role in advancing SR, offering a comprehensive overview of the CSFs and emerging trends in this field.

The set-union knapsack problem is one of the most significant generalizations of the Non-deterministic Polynomial (NP)-hard 0-1 knapsack problem in combinatorial optimization, which has rich application scenarios. Although some researchers performed effective algorithms on normal-sized instances, the authors found these methods deteriorated rapidly as the scale became larger. Therefore, the authors design an efficient yet effective algorithm to solve this large-scale optimization problem, making it applicable to real-world cases under the era of big data.

The authors develop three targeted strategies and adjust them into the adaptive tabu search framework. Specifically, the dynamic item scoring tries to select proper items into the knapsack dynamically to enhance the intensification, while the age-guided perturbation places more emphasis on the diversification of the algorithm. The lightweight neighborhood updating simplifies the neighborhood operators to reduce the algorithm complexity distinctly as well as maintains potential solutions. The authors conduct comparative experiments against currently best solvers to show the performance of the proposed algorithm.

Statistical experiments show that the proposed algorithm can find 18 out of 24 better solutions than other algorithms. For the remaining six instances on which the competitor also achieves the same solutions, ours performs more stably due to its narrow gap between best and mean value. Besides, the convergence time is also verified efficiency against other algorithms.

The authors present the first implementation of heuristic algorithm for solving large-scale set-union knapsack problem and achieve the best results. Also, the authors provide the benchmarks on the website for the first time.

For the dramatically developed high voltage direct current (HVDC) power transmission, HVDC cables play a vital role in the power transmission across seas and connections with renewable power sources. However, the condition monitoring of HVDC cables is still a challenging research topic. This paper aims to understand the influence of external factors, namely, current, cavity location and material properties, on partial discharge (PD) characteristics in HVDC cable in a numerical way referring to the refined Niemeyer’s model.

The influences of the three external factors are studied by a proposed numerical model for DC PDs based on the modification of a conventional PD model for AC voltage via a finite element analysis method.

The external factors can influence the discharge magnitude and discharge repetition rate via affecting the electrical conductivity of the material: DC PD is more frequent and with higher discharge magnitude when the cavity is closer to the conductor or the current through the conductor is higher. Both DC PD repetition rate and average discharge magnitude in long-term aged material are lower than virgin material. The effect of discharge on insulation degradation becomes decreasingly significant.

The current work is based on the numerical modelling of DC PDs. Further experimental validations and comparisons are essential for improving the model.

The studies of the influence factors for PDs under HVDC voltage can benefit the research and practical power transmission on DC PDs, contributing the design and test of DC PDs in HVDC cables, exploring the understandings of the DC PDs’ mechanism.

This paper, to the best of author’s knowledge, first studies the influence factors on DC PDs based on the numerical modelling work.

Increased interest in team dynamics has resulted in new methods for measuring teamwork over time. The primary purpose of this chapter is to provide a survey of recent developments in teamwork/collaboration measurement in an educational context. Key topics include conceptual frameworks, large-scale assessments, and innovative measurement techniques.

A range of methods for collecting and analyzing teamwork data are discussed, and five frameworks for measuring collaborative problem solving (CPS) over time are compared. Frameworks from Programme for International Student Assessment (PISA), Assessment and Teaching of 21st Century Skills (ATC21S) project, Educational Testing Service (ETS), ACT, and von Davier and Halpin (2013) are discussed. Results of assessments developed from these frameworks are also considered.

New techniques for measuring team dynamics over time have great potential to improve education and work outcomes. Preliminary results of the assessments developed from these frameworks show that important advances in teamwork measurement have been enabled by innovative task designs, data-mining techniques, and novel applications of stochastic models.

This novel overview and comparison of interdisciplinary approaches will help to indicate where progress has been made and what challenges are ahead.