Search results

Purpose: This chapter identifies performance factors that have the strongest impact on companies’ sustainable outcomes and compares the obtained results across different sectors.

Methodology: About 3,384 observations were gathered from 2015 to 2022 from companies in communication services, energy, financials, real estate, and utilities sectors that comprise the ‘STOXX Global ESG Leaders Select 50’ index. The multiple regression model is constructed with companies’ ESG scores as dependent variables and independent variables representing operational, financial, and market performance.

Findings: Companies that tend to have higher operational and financial performance in the financial sector are more likely to have higher ESG performance. The financial performance results of companies showed the strongest statistically significant relationship with environmental and the weakest with governance scores.

Implications: Results benefit private and institutional investors aiming to create more sustainable portfolios. The obtained results indicate that these investors should focus on companies operating in the financial and energy sectors with higher performance results. Better ROE, ROA, and Tobin’s Q may have a negative impact on sustainable outcomes for companies operating in the real estate and utility sectors.

Limitations: Firstly, not all ESG index providers disclose information about their index constituents. Secondly, within the chosen ‘STOXX Global ESG Leaders Select 50’ index, not all constituents had complete ESG data available on the Bloomberg platform. When selecting the analysis period, it was observed that the accessible ESG data on Bloomberg covers a relatively short time span, only from 2015 onwards.

Future research: A larger number of companies by choosing a more comprehensive available ESG index.

The 6D pose estimation is a crucial branch of robot vision. However, the authors find that due to the failure to make full use of the complementarity of the appearance and geometry information of the object, the failure to deeply explore the contributions of the features from different regions to the pose estimation, and the failure to take advantage of the invariance of the geometric structure of keypoints, the performances of the most existing methods are not satisfactory. This paper aims to design a high-precision 6D pose estimation method based on above insights.

First, a multi-scale cross-attention-based feature fusion module (MCFF) is designed to aggregate the appearance and geometry information by exploring the correlations between appearance features and geometry features in the various regions. Second, the authors build a multi-query regional-attention-based feature differentiation module (MRFD) to learn the contribution of each region to each keypoint. Finally, a geometric enhancement mechanism (GEM) is designed to use structure information to predict keypoints and optimize both pose and keypoints in the inference phase.

Experiments on several benchmarks and real robot show that the proposed method performs better than existing methods. Ablation studies illustrate the effectiveness of each module of the authors’ method.

A high-precision 6D pose estimation method is proposed by studying the relationship between the appearance and geometry from different object parts and the geometric invariance of the keypoints, which is of great significance for various robot applications.

This paper aims to investigate the suppression of end-point vibrations in industrial robot systems that exhibit joint flexibility and are subject to external disturbances.

The real-time position tracking error is effectively decomposed by using feedforward control based on a dynamic model. Various proportional-derivative controllers and adapted versions are used to compute real-time compensation torque for different position tracking errors. This approach aims to simultaneously achieve rapid response and stability in the control system, resulting in reduced end vibration in the industrial robot.

Experiments were conducted in torque compensation on a 6R industrial robot platform. Compared to the dynamic model calculate torque feedforward compensation method, the maximum reduction of the root mean square of the position error of each joint reached 77% and the minimum reduction was 36.2%. This enhancement improves the trajectory tracking accuracy and effectively suppresses the end-effector vibration.

An improved torque feedforward compensation method is proposed and verified. According to the experimental results, the method can effectively suppress vibration and further improve the trajectory tracking accuracy.

The purpose of this study is to value the thermal and hydraulic transport augmentation of turbulent fluid flow within the round-pipe axis fixed by a twisted-staggered concave/convex dimples tape.

This study meets the report’s novel design by axis-inserting a twisted plastic tape with staggered concave/convex dimples of varying diameters (4 and 6 mm) and depths (1, 1.4 and 1.8 mm). Introducing a realizable model integrated with an improved wall function and SIMPLE solving procedure evaluates the thermo-hydraulic transport as Reynolds number is feasible as 5,000, 10,000, 15,000 and 20,000. In addition, using the findings from the present experimental work validates the numerical methodology.

This paper reveals that the staggered concave/convex dimples on the axis-fixed plastic tape can significantly improve thermo-hydraulic transport within this outer-heated tube. Furthermore, the processed dimples can cause flow disturbance, which increases turbulent kinetic energy and accelerates fluid mixing around a twisted plastic tape, resulting in enhanced thermal convection. The six kinds of twisted tapes (C1−C6) result in the thermo-hydraulic performance index (η) of 1.18–1.32 at Re = 5000. Among all the cases, the dimples using 4 mm combined with 6 mm diameter and 1.4 mm height (C4) earn the highest, around 1.40 at Re = 5,000.

The conditions of constant hydraulic-thermal characteristics of working fluid (air), steady Newtonian fluid considered, and the ignored radiative heat transfer and gravity are the research limitations of the numerical simulation.

The given results can benefit from a round tube design of a thermal apparatus axis fixed by a twisted-staggered concave/convex dimples tape to augment the thermo-hydraulic transport.

Staggered concave/convex dimples on the surface of a twisted tape allow for impinging and swirling flow along the tape. These processed dimples can induce flow disturbance, which increases the turbulent kinetic energy and facilitates fluid mixing in a twisted tape. Furthermore, the hybrid-diameter dimples have enough flow channels for fluid separation-reattachment, and the thermo-hydraulic performance index has improved. This paper then presents a helpful passive approach for cooling a thermal device.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

This paper proposes a framework for director evaluation in the context of Chinese state-owned enterprises (SOEs), taking into account the influences of traditional and modern Chinese ideologies.

Following the Delphi method, a series of semi-structured interviews were conducted with Chinese SOE directors.

The framework used has been validated by examining seven dimensions of virtue and four dimensions of competence functions in Chinese SOEs. Effective and representative characteristics of each dimension are identified through interviews.

First, through this research, indicators of virtue have been materialized and those of competence have been specified in a broader range. Second, this research provides advice for training of candidate directors whose experience were in private firms before they step in as SOE directors.

This study aims to examine the effect of motivating language on employee performance and assesses the mediating roles of organisational citizenship behaviour and employee engagement between motivating language and employee performance in the Indian health-care sector, which is a highly demanding work environment, wherein employee burnout is high.

The study was in the context of COVID-19 pandemic set for health-care workers in India. To collect data and test the proposed research model, 328 questionnaires were respondent by multi-level health-care professionals from private and government hospitals in North India.

The findings suggest that leader’s motivating language is crucial for health-care leaders, inducing employee’s performance in context to patientcare, safety and satisfaction. Underpinning theory of leader member exchange substantiates that the role of leader is pivotal in daily interaction with the stakeholders. Self-determination theory of motivation is determined by psychological needs satisfaction inducing employee engagement and organisational citizenship behaviour, amplified through the leader’s motivating language, resulting into improved patientcare and patient safety. The findings state that leader’s motivating language impacts the high culture context like health-care professionals, as observed in the Indian health-care sector during COVID-19. The findings are indicative of developing non-cognitive personality traits for managerial skills.

The study substantiates the pivotal role of the leader’s communication with stakeholders such as patients/attendants and health-care staff. The findings, which are an indicator of patientcare, as an outcome of patient compliance, will be indicative of developing the non-cognitive skills in the personality traits of managerial skills, inducing patients’ trust in their health-care providers, using motivating language. Therefore, the health-care professionals must be trained in the application of motivating language with stakeholders, namely, patients/attendants and staff.

The findings state that leader’s motivating language impacts on employees of high culture context like health-care professionals, as observed in the Indian health-care sector during the recent global medical emergency of COVID-19, whereas the earlier studies posited leader’s motivating language to be effective on employees with low-cultural context. The role of leader is pivotal in daily interaction with the stakeholders, namely, patients/attendants and health-care staff.

This study aims to assess the mediating and driving roles of knowledge cooperation in the effectiveness of G60 Sci-tech Innovation Corridor (G60 STIC) for regional collaborative innovation within the knowledge economy context. Furthermore, it focuses on whether knowledge cooperation is more effective than resource cooperation in terms of spatial spillover and its mediating effects on collaborative innovation.

This study employs multiple statistical and econometric approaches, including social cooperation network, Super-DEA, spatial difference-in-difference model (SDID) and mediating effect model, to measure the effectiveness of knowledge cooperation and resource cooperation paths within the framework of the G60 STIC on regional collaborative innovation in the Yangtze River Delta region (YRD) from 2002 to 2022.

First, the knowledge cooperation networks validate the strengthening of collaborative innovation is primarily centred on provincial cities and leading manufacturing locales, with smaller cities radiating outwards from these centres. The knowledge cooperation network was generally stronger than the resource cooperation network. Second, the G60 STIC significantly enhances collaborative innovation efficiency by intensifying knowledge, resource and interactive cooperation networks. Third, within the context of the knowledge economy, knowledge cooperation presents a stronger spillover and mediating effect in stimulating collaborative innovation than resource cooperation.

This study clarifies the existence of a knowledge cooperation network and its mediating role in stimulating the effectiveness of strategic, innovative platforms on collaborative innovation. This further verifies the stronger role of the knowledge cooperation than the resource cooperation, which serves as a vital element in promoting strategic innovative platforms to optimise collaborative innovation.

In the quest to build a sense of human contact, e-retailers are increasingly depending on the scalability of chatbots to promote assistive dialogue during online shopping. Not much is known about the experiential value of customer interaction. This research proposes and evaluates a conceptual model for understanding the value perceptions emanating from the experiences of fashion shoppers utilising e-retail chatbots.

Data were collected using an online survey administered to 460 online panellists. Structural equation modelling was used to test the proposed research model.

Continued chatbot use intentions (CUIs) are influenced positively by perceived hedonic and utilitarian experiential value. Perceived social experiential value had a negative effect on shoppers’ continued intention to use the chatbot. Both perceived chatbot anthropomorphism and perceived chatbot intelligence positively and significantly affect shoppers’ experiential value while perceived chatbot risk yields a significantly negative effect.

By using conversational artificial intelligence chatbots, engagement at e-retail stores can be driven based on the user data and made more interactive.

The study introduces an e-retail chatbot model which asserts the power of selected chatbot attributes as catalysts of shoppers’ experiential value. Cumulatively, the model is a first-step approach providing a novel and balanced (both positive attributes and negative risks) view of chatbot continued use intentions.