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The purpose of this paper is to reveal some basic characteristics in social and economic process, and lay the analytic foundation for advance‐retreat course (ARC).

The paper presents the analytic model of stochastic ARC (SARC), which is based on the partial distribution and partial process (belonging to the probability theory and stochastic process), and describes some important characteristics of social and economic process in a quantitative method.

The successful socio‐economic process, including many biological process, are usually divided into three basic stages: the weak growth, the quick development and the swift decline. In general, rapid growth brings with it the weak persistence, and slow growth brings with it the strong persistence. The socio‐economic fluctuations are mainly caused by the excessive environmental pressures. The duration of the socio‐economic growth is inverse with the environmental pressure.

All the basic variables and parameters in an ARC model should be no less than zero.

Based on US GDP (chained) price index data (1940‐2005), American economic process in recent 70 years is analyzed, and the analysis indicates, American economic motivity is clearly insufficient after 2008, and the present economic recovery will be very arduous and prolonged.

The environmental pressures will become the main problem for future global socio‐economic development.

SARC model in this paper presents a special way to analyze the social development and economic growth, and is helpful to related academic research and socio‐economic decision making.

This paper aims to unveil the importance of knowledge management on a firm’s strategic emergency response during the great negative shock from global public health threats. Through analyzing how representative firms in China’s new economy industries dealt with the COVID-19 pandemic before, during and after the crisis, the significant problems confronted by these firms are pointed out, and the important role knowledge management capabilities played in solving these problems is proven.

The open data of listed companies regarding the important role knowledge management played in firms’ strategic emergency response during the COVID-19 pandemic are qualitatively analyzed. Based on theoretical sampling, this paper selects representative samples of enterprises and analyzes the positive response measures they took after being hit by this public health event to gain qualitative insight into the importance of knowledge management capabilities in strategic emergency response.

Three aspects of the important role of knowledge management capabilities in a firm’s strategic emergency response during the COVID-19 pandemic are introduced: before the crisis, firms should strengthen the acquisition, sharing and integration of knowledge so that they can intensify their monitoring for uncertain risks; during the crisis, firms should boost the transmission, transformation and diffusion of knowledge to improve emergency cooperation; and after the crisis, companies should reinforce knowledge evaluation, creation and application to enhance “immunity” in similar emergencies.

This paper has important implications for bolstering strategic emergency management practice and knowledge management capability among firms. Future research must focus on the following two aspects for further investigation: the dynamic relationship between firm knowledge management capability and strategic emergency response ability; and the collaboration system between firm knowledge management and strategic emergency response behaviors.

This paper discusses the important role knowledge management capabilities play in firms’ strategic emergency responses based on insights gained from the significant changes that the COVID-19 pandemic caused to representative Chinese new economy firms. By analyzing the three stages of before, during and after the emergency, this paper proposes the exact efforts that new economy companies should make in improving knowledge management capability.

The purpose of this paper is to investigate the role of narcissistic supply for the association between CEO narcissism and corporate risk taking.

The authors investigate a sample of 281 non-financial S&P 1500 firms and a corresponding 457 CEOs in the 10-yr period 2006–2015.

The association between CEO narcissism and corporate risk taking depends on the admiration, attention, and affirmation of own superiority (“narcissistic supply”) that the CEO receives given her/his current position. Thus, a narcissistic CEO with an insufficient narcissistic supply (small firm/small compensation) will crave for more and take more risks (“rock the boat”) while a narcissistic CEO with a sufficient narcissistic supply (large firm/large compensation) will protect the status quo and be reluctant to take new risks. Specifically, the authors find that a change from a slightly narcissistic CEO to a strongly narcissistic CEO, for positions entailing limited (abundant) narcissistic supply, is associated with an increase (a decrease) in corporate risk of 6%–8% (11%–27%).

Previous research indicates a positive association between CEO narcissism and corporate risk taking in specific domains such as M&A and R&D activities. This paper provides a novel contribution to the existing literature by identifying and assessing the important role of narcissistic supply for the association between CEO narcissism and corporate risk taking in general.

The purpose of this paper is to present a symmetrical method to extract smooth signal from linear mixtures in the frequency domain; with experimentations, the method is thereafter evaluated.

The Second‐order Frequency Identification (SOFI) algorithm has been presented to retrieve baseband signals which have inactive bands and different bandwidths in the frequency domain. However, the SOFI method will bring about accumulative error, therefore an improved method is proposed in this paper by using symmetric extraction mode. In contrast to the SOFI algorithm, the ISOFI method can extract simultaneously the signal with the highest degree of smoothness and the signal with the lowest degree of smoothness. This means that the signals are not extracted one by one; instead, they are extracted in parallel. Experimental results in both noise‐free and noisy scenarios verified that the proposed method has a significant improvement compared with the SOFI algorithm.

An improved SOFI (ISOFI) method is proposed to reduce the accumulated error encountered in the SOFI algorithm. In the proposed method, the symmetric mode is utilized to extract in parallel the signals with different smooth degrees. Experimental results demonstrated that the ISOFI has a higher accuracy and lower accumulated error compared to the original algorithm.

The paper demonstrates use of the symmetric extraction mode to overcome the disadvantage of accumulated errors existing in the SOFI algorithm.

The purpose of this paper is to draw attention to an indigenous community that lives in the periphery of Taiwan. The Dao on Orchid Island have had to face serious abuse of their human rights in terms of ecological exploitation and environmental injustice. The article highlights the empowerment of the indigenous group through collective learning. In this process the importance of being represented at both local and international levels is emphasized. Despite the democratization and implemented mechanism to protect indigenous peoples' rights and dignity, the issue of self‐determination and dependency on financial compensation still remains unsolved.

Data are collected and elaborated primarily using recognized research methods that are common in social anthropology (Bernard). Field research was conducted in 2007, 2008, and 2011 in Taiwan. A variety of interviews and formal and informal conversations formed the basis of data collection. According to qualitative content analysis, concepts and procedures developed by Gläser and Laudel, Bernard, as well as Schmidt were applied.

The outcomes of the research highlight the need to investigate the dynamic between modern technologies and the fight of vulnerable minorities in multicultural and democratic countries. Collective learning, solidarity, and presence at national and international levels are essential to improve empowerment to gain justice and respect of human rights.

The indigenous Dao were excluded from decision making processes regarding the government's appropriation of land on Orchid Island to be used to store nuclear waste. The Dao gained power due to educational technologies within a local and global network. Although Taiwan promotes its cultural diversity nowadays, social and environmental justice is still a goal to reach.

The paper provides valuable insights of an indigenous community in Taiwan and their struggles against environmental injustice and exploitation. Due to the fieldwork, a unique and authentic picture of the indigenous peoples' situation is offered.

The purpose of this paper is to explore the influence mechanism of job satisfaction and positive affect on knowledge sharing among project members in Chinese construction industry, and test the moderating role of organizational commitment between them in order to find a new approach from the perspective of psychology to improve the knowledge sharing performance within project management organizations in China constantly.

An empirical study was used based on confirmatory factor analysis and hierarchical regression analysis with a sample of 540 project members from 80 project management organizations in China.

Research results showed that job satisfaction and positive affect of project members both have a significant positive impact on knowledge sharing; organizational commitment could moderate the influence of job satisfaction and positive affect on knowledge sharing among project members partially within the Chinese context.

A questionnaire study from China only represents the relationship and regular pattern within a shorter time interval in the Chinese context. It is necessary to continue to implement a longitudinal study in a relatively long period in future research.

Knowledge sharing among project members can be enhanced through improving job satisfaction and positive affect, and strengthening project members’ organizational commitment can amplify the influence effect of job satisfaction and positive affect on knowledge sharing.

This paper clarifies the direct influence mechanism of project members’ job satisfaction and positive affect on explicit knowledge sharing (EKS) and tacit knowledge sharing (TKS), and further tests the partial moderating effect of organizational commitment on the influence relationship of job satisfaction and positive affect on EKS and TKS.

The purpose of this paper is to gain an in-depth understanding into the research progress, hot spots and future trends in smart gripping technology in the field of apparel smart manufacturing.

This work scrutinised the current research status of the five automatic grasping methods for garment fabrics including the pneumatic suction grasping, the electrostatic grasping, the intrusive grasping and the dexterous grasping. Specifically, the principles, characteristics, main devices and the impact on garment production were discussed.

In particular, soft finger of the dexterous grasping method has good flexibility and adaptability in the process of fabric grasping, which provides a new solution for garment production automation. Up to now, the reviewed method in general exhibit good grasping speed, high grasping stability and flat grasping process. However, in the face of complex fabric materials which are thin and flexible and do not return their original shapes when deformed in practical applications, the gripper for automatic fabric grasping need new technological breakthroughs in the positioning accuracy, grab efficiency and flexible grasping.

The outcomes offered an overview of the research status and future trends of the automatic grasping methods for garment fabrics in the field of apparel intelligent manufacturing. It could not only provide scholars with convenience in identifying research hot spots and building potential cooperation in the follow-up research but also assist beginners in searching core scholars and literature of great significance.

This article examines how personality types of tutors affect group interaction at a distance learning institution in Hong Kong. All participants of this study are Chinese, and they were given two personality assessment tools to complete: (1) the Myers‐Briggs Type Indicator which was developed in the West in the early 1940s; and (2) the Chinese Personality Assessment Inventory which was developed in Hong Kong in the early 1990s. The CPAI consists of personality constructs of specific interest to people of Chinese culture but that were not covered in most of the translated personality inventories. The purpose of using two personality tests is to explore whether a culturally specific instrument will be a more comprehensive measure that can reveal particular distinctive factors in the relevant culture.

For comfort evaluation of underwear pressure, this paper proposes an improved GA algorithm to optimize the weight and threshold of BP neural network, namely PSO-GA-BP neural network prediction model.

The objective parameters of underwear, body shape data, skin deformation and other data are selected for simulation experiments to predict the objective pressure and subjective evaluation in dynamic and static state. Compared with the prediction results of BP neural network prediction model, GA-BP neural network prediction model and PSO-BP neural network prediction model, the performance of each prediction model is verified.

The results show that the BP neural network model optimized by PSO-GA algorithm can accelerate the convergence speed of the neural network and improve the prediction accuracy of underwear pressure.

PSO-GA-BP model provides data support for underwear design, production and processing and has guiding significance for consumers to choose underwear.

The purpose of this paper is to improve the prediction accuracy of the body shape prediction model and provide some reference value for the design of underwear.

The body size data of 250 male youths is measured to analyze the body shape of the lower body. And there is a total of 56 measurement items, which are clustered by GA-BP-K-means, K-means, optimal segmentation method for ordered samples, wavelet coefficient analysis, regression analysis and Naive Bayes Algorithm. Finally, a test male sample of an unknown body shape was clustered to verify the superiority of the GA-BP-K-means.

This paper presented the key factors for body shape clustering, and experimental results have shown that the GA-BP neural network model is higher in speed and precision than other algorithm prediction models.

It was clarified which is the key to body shape clustering. At the same time, the GA-BP-K-means algorithm can promote the popularization and application of the prediction model in body shape clustering.