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Globalization and the COVID (post) pandemic continue to pose significant challenges to managing employee performance across geographic locations because there is a need to simultaneously implement procedures that are standardized and yet applicable to different contexts. This study aims to describe five universal principles in performance management that can be adapted to specific contexts to address the performance management standardization vs adaptation dilemma.

Critical literature review of evidence-based recommendations for practice.

This study describe five universal principles in performance management, how they can be adapted to specific contexts around the world and actions that organizations can take to implement them: (1) cultural congruence, (2) strategic congruence, (3) performance evaluation thoroughness, (4) inclusiveness and (5) effective feedback.

This study provide valuable and actionable knowledge for organizations facing performance management challenges around the world.

With the rapid economic development, China has been increasingly paying special attention to its sports and health industries. In October 2014, the State Council raised the goal to upscale the sports industry to the net worth of 5 trillion yuan by 2025. To gain further insights into the current development of tennis in China, especially the development of Women’s Tennis Association (WTA) tennis competitions, an interview with the director of Wuhan Opening Fabrice Chouquet and Mr Guoqing Yi of Wuahan Sport Investment Inc. was conducted. The paper aims to discuss these issues.

To gain further insights into the current development of tennis in China, especially the development of WTA tennis competitions, an interview with the director of Wuhan Opening Fabrice Chouquet and Mr Guoqing Yi of Wuahan Sport Investment Inc. was conducted.

The interview comprised a series of topics including motivators of tennis industries in China, the strategic position of China for WTA development, WTA choice of hosting city, effects of WTA competition events toward the hosting city, and the operation of WTA tennis competitions. Integrating the aforementioned topics with Wuhan Tennis Open, the interviewees also provided suggestions toward the future development of tennis industry in China.

This study has several limitations that are of concern to researchers in the future. First, the interviewees of this research came from Wuhan Tennis Open. Though Mr Fabrice used to be the managing director of WTA in Asia, he only knows about women tennis in China, and his viewpoints have their own limitation. Therefore, the interviewee in the future has to be the person who could hold the opinion on tennis development in China at the macro scale. Second, the questions in the interview have a wide scale, but lack depth and penetration. For instance, the interview only relates to the motive of tennis development but does not go deeply into the relation between the above two aspects. Moreover, it mentions the problems related to the tennis development. It only concentrates on the tennis tournaments but does not discuss the tennis culture and the industry. These questions need to be considered when the authors research on the development of tennis in China in the future.

Through the analysis on WTA and tennis in China, the authors can understand the development of sports in China and the Chinese sports industry in recent years.

This paper aims to identify key sectors in carbon footprint responsibility, an introduced concept depicting CO₂ responsibilities allocated through the supply chain containing sectoral activities and interactions. In detail, various key sectors could be identified according to comparative advantages in trade, sectoral linkage and sectoral synergy within the supply chain.

A semi-closed input–output model is used to make the household income–expenditure relationship endogenous through the supply chain where sectoral CO₂ emissions are calculated, and the production-based responsibility (PR) principle is evaluated. Thus, according to “carbon footprint responsibility”, modified hypothetical extraction method is applied to decompose sectoral CO₂ in terms of comparative advantages in trade, sectoral linkage and synergy. Finally, key sectors are identified via sectoral shares and associated decompositions in carbon footprint responsibility.

Compared to 2005, in 2012, the PR principle failed to track sectoral CO₂ flow, and embodied CO₂ in import and interprovincial export increased, with manufacturing contributing the most; manufacturing should take more carbon responsibilities in the internal linkage, and tertiary sectors in the net forward and backward linkage, with sectors enjoying low carbonization in the mixed linkage; inward net CO₂ flows of manufacturing and service sectors were more complicated than their outward ones in terms of involved sectors and economic drivers; and residential effects on CO₂ emissions of traditional sectors increased, urban effects remained larger than rural ones and manufacturing and tertiary sectors received the largest residential effects.

The value of this paper is as follows: the household income–expenditure relationship got endogenous in intermediate supply and demand, corresponding to the rapid urbanization in megacities; key sectors were observed to change flexibly according to real sectoral activities and interaction; and the evaluation of the PR principle was completed ahead of using a certain CO₂ accounting principle at the city level.

This paper aims to investigate the effect of frictional heat on the wear of high-speed rotary lip seals in engines.

In this research paper, the authors focus on the high-speed rotating lip seal of aircraft engines. Using the hybrid lubrication theory, a thermal-fluid-solid coupled numerical simulation model is established to investigate the influence of parameters such as contact pressure distribution, temperature rise and leakage rate on the sealing performance under different operating conditions. By incorporating the Rhee wear theory and combining simulation results with experimental data, a method for predicting the wear of the rotating seal lip profile is proposed. Experimental validation is conducted using a high-speed rotating test rig.

The results indicate that as the speed increases, the rise in frictional heat leads to a decrease in the sealing performance of the lip seal contact region. The experimental results show a similar trend to the numerical simulation results, and considering the effect of frictional heat, the predicted wear of the lip seal profile aligns more closely with the actual wear curve. This highlights the importance of considering the influence of frictional heat in the analysis of rotating seal mechanisms.

This study provides a reference for the prediction of wear profiles of engine high-speed rotary lip seals.

This paper aims to study the impact of transient velocity changes on sealing performance during reciprocating sealing processes.

Establish a model of transient mixed lubrication, solve the transient Reynolds equation, consider the effect of temperature rise at the seal interfaces, and determine the behavior of the seal interfaces, such as film thickness and fluid pressure. Evaluation with friction and leakage rate, calculate the variation of sealing performance with reciprocating velocity under different working conditions, and verify it through bench experiments.

Within a reciprocating stroke, the frictional force decreases with increasing velocity, and the frictional force of the outstroke is greater than that of the instroke; at the time of the stroke transition, the fluid pressure is smallest and the rough peak contact pressure is greatest. At present, the dynamic pressure effect of fluids is the largest, and the friction force also increases, which increases the risk of material wear and failure. Friction and leakage increase with increasing pressure and root mean square roughness. As temperature increases, friction increases and leakage decreases. In studying the performance variations of seal components through a reciprocating sealing experiment, it was found that the friction force decreases with increasing velocity, which is consistent with the calculated results and more similar to the calculated results considering the temperature rise.

This study provides a reference for the study of transient sealing performance.

Resilience while learning is the capacity to initiate, persist and direct effort toward learning when experiencing unpleasant affective states. The underlying mechanisms of resilience are emotional buffering and self-regulation when experiencing unpleasant affective states. The authors identified four factors that support resilience while learning: positive emotional engagement, creative problem-solving, learning identity and social support. The authors developed and tested scales and found evidence to support the four-factor model of resilience. The authors offer a person-centered approach to resilience in learning by conducting a latent profile analysis that tested the likelihood of resilience based on profiles of differences in scores on these factors under two affective conditions: (unpleasant) learning during frustration versus (pleasant) learning during progress. A quarter of individuals activated the four resilience factors in pleasant and unpleasant affective states, while 75% of participants saw decrements in these factors when faced with frustration. The results support a four-factor, person-centered approach to resilience while learning.

The authors develop and test a four-factor model of resilience and test the model in a group of 330 management undergraduate and graduate students. Each participant identified two learning episodes in their responses, one while frustrated and one while making progress, and ranked the level of intensity on the four resilience factors. Analysis on an additional 88 subjects provided additional support for the validation and reliability of scales.

Results revealed 2 latent profiles groups, with 25% of the sample associated with resilience (low difference on resilience factors between the two learning episodes) and 75% who remain susceptible to unpleasant emotions (high difference between the two learning episodes).

The study supports a person-centered approach to resilience while learning (in contrast to a variable centered approach).

The study provides a means to classify individuals using a person-centered, rather than a variable-centered approach. An understanding of how individuals buffer and self-regulate while experiencing unpleasant affect while learning can help educators, consultants and managers develop better interventions for learning.

This study addresses the growing concern over student success associated with increased dropout rates among undergraduate business students, and the failure of many management developments and executive training efforts. This study suggests that looking at specific variables may not provide insight into the complex relationship between learning outcomes and factors that support resilience in learning.

There is growing interest in understanding resilience factors from a person-centered perspective using analytical methods such as latent profile analysis. This is the first study to look at how individuals can be grouped into similar profiles based on four resilience factors.

Classifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which performs very low efficiency and high cost. Therefore, how to improve the classification accuracy of textile fabric defects by using current artificial intelligence and to better meet the needs in the textile industry, the purpose of this article is to develop a method to improve the accuracy of textile fabric defects classification.

To improve the accuracy of textile fabric defects classification, an ensemble learning-based convolutional neural network (CNN) method in terms of textile fabric defects classification (short for ECTFDC) on an enhanced TILDA database is used. ECTFDC first adopts ensemble learning-based model to classify five types of fabric defects from TILDA. Subsequently, ECTFDC extracts features of fabric defects via an ensemble multiple convolutional neural network model and obtains parameters by using transfer learning method.

The authors applied ECTFDC on an enhanced TILDA database to improve the robustness and generalization ability of the proposed networks. Experimental results show that ECTFDC outperforms the other networks, the precision and recall rates are 97.8%, 97.68%, respectively.

The ensemble convolutional neural network textile fabric defect classification method in this paper can quickly and effectively classify textile fabric defect categories; it can reduce the production cost of textiles and it can alleviate the visual fatigue of inspectors working for a long time.

This study aims to pose one major research question, i.e. why and how to use knowledge management methods in order to enhance knowledge creation in academia – at universities and research institutes?

The paper defines KM in academia as any systematic activity related to support and enhancement of the creation of scientific knowledge and achievement of research goals, including both social process and relevant computer technology tools. Two surveys and case studies were carried out to achieve the research purpose at Japan Advanced Institute of Science and Technology (JAIST). The first survey focused on knowledge management in academia and investigated the current KM situations, special and diverse requirements from researchers. The second survey concentrated on supporting the creative processes of academic research and investigated which aspects of knowledge creation processes should be supported in particular. Based on survey findings, the practical solutions are further presented aimed to improve the creative environment for scientific knowledge creation.

The findings from the first survey showed that the KM obstacles reflected on various aspects: technological support, the people involved in creation activities, laboratory cultural, and so on. The seven most critical questions and three most important questions were evaluated by responders with respect to academic knowledge creation process in the second survey.

The study advances the belief that knowledge management (KM) is applicable not only in industrial and market organizations, but also in academia.

With respect to the survey results, it is suggested that a creative environment in academia should be enhanced from both “soft” and “hard” aspects under the guidelines of a systems thinking framework for KM in scientific labs. From the soft side, by using personalization strategies, a knowledge‐sharing culture has to be built in labs to facilitate scientific communication, debate and team work. From the hard side, by using technology strategies, a practical example is presented in JAIST concerning the implementation of the hard aspect of creative environment. It is hoped that the research can launch further debate and prompt practical steps to help research institutes or universities improve their management and increase the research efficiency.

An essential point is that the study is based on the feedback from knowledge creators in a typical knowledge creation organization, which makes the analyses and conclusions more comprehensive and persuasive from both the theoretical and practical points of view. The research not only explores some hidden or tacit problems existing in academic knowledge management and scientific knowledge creation, but also proposes solutions for scientific knowledge creation that were found to be valuable by university management.

The purpose of this paper is to analyze the effect of environmental policy China’s national program to address climate change on carbon emission efficiency.

Based on the directional distance function, the provincial total factor carbon emission efficiency was measured. Then, the authors analyzed the effect of environmental policy on carbon emission efficiency based on a difference in difference model.

Carbon emission efficiency has been significantly improved since the environmental policy China’s national program to address climate change was put forwarded, but the positive impact in different periods and regions is different. In addition, the environmental policy improves the carbon emission efficiency through the reduction of energy intensity and adjustment of the industrial structure.

This is the first time to use difference in difference model to use a difference in difference model to quantitatively assess the influence of environmental policy China’s national program to address climate change on carbon emission efficiency.

Online portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.

We propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.

Empirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.

The proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.