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This study investigates how working-age Asian immigrants' educational attainment and professional abilities when arriving in the United States have evolved over the past 4 decades and draws inferences on the impact of the US employment based visa policies.

Using data from the 1980, 1990 and 2000 census and American Community Survey for 2001 to 2019, the study adopts multivariate regression and regression discontinuity design to investigate the trends in educational and occupation selection among Asian immigrants and the association with policy changes in the H1B visa program.

The findings suggest that new Asian immigrants were more positively selected for education than non-Asian immigrants and US natives and this pattern of positive selection increased over time. Newly arrived South Asian and East Asian immigrants had the highest share of highly educated professionals than Southeast Asians and US-born persons. I infer that the enactment and changes in the H1-B program might have contributed to the changing patterns of the educational and occupational selection among East and South Asian Immigrants. The results also shed light on how Asian immigrants' skill selection might be related to the size of Asian diasporas in the US and sending countries' income, inequality and education level.

The story of changing the skill profile (educational and occupational profile) of newly arrived Asian immigrants during 1980–2019 can provide valuable policy implications. US immigration policies are routinely criticized for being inefficient and outdated. The economic prosperity of Asian countries over time also provides an excellent opportunity to test the theories pertaining to how sending countries' income, inequality and education level of the population are associated with Asian migrants' education and occupation when arriving in the US. This study can provide insightful perspectives for policymakers and business decision-makers to adapt to the changing demographics of Asian migrant workers. The most recent reports on Asian immigrants in the US highlighted the aggregated trends of migration flow and education. Still, none have provided a longitudinal and nuanced review of Asian immigrants' educational and occupational selection into the US.

Corporations, as key contributors of greenhouse gas emissions, have been increasingly scrutinized by governments and stakeholders. Corporations have been asked to disclose their carbon-related information. This study investigates public corporate carbon disclosure, an imperative communication channel between firms.

This study uses generalized estimation equation models with a longitudinal panel data of 311 listed firms in the China A-share stock index from 2010 to 2020. This study collected firm-level data from the Carbon Disclosure Project survey, the China Stock Market and Accounting Research, and the National Economic Research Institute of China. Stata was used as the primary statistic software in empirical analyses.

This study finds that compared to state-owned enterprises (SOEs), private firms are more willing to disclose carbon information under legitimate environmental pressure, and firms in highly distorted factor-markets are reluctant to disclose carbon information. This study finds that factor-distortion markets further moderate ownership and lead private firms in highly distorted factor-markets to behave like SOEs by significantly reducing their carbon disclosures.

This study intends to contribute to the corporate carbon disclosure literature by adding important institutional determinants to the conversation in the context of China.

Team leaders play a vital role in achieving superior team performance. However, their role in implementing the organizational customer orientation strategy is not well understood. Drawing on social exchange theory, this study investigates how team leader customer orientation affects team customer orientation climate and team performance (i.e. customer satisfaction) as well as the moderating role of transformational leadership in such effect.

This study builds on survey data collected from matched team leaders, employees and customers nested in 81 service teams and employs hierarchical multiple regression analysis to test the hypotheses.

The findings indicate that team leader customer orientation increases team customer orientation climate, which leads to a higher level of customer satisfaction. Leaders' transformational leadership moderates the link between a leader customer orientation and team customer orientation climate in an unexpected way. When a team leader is transformational, the team customer orientation climate is enhanced, regardless of the level of team leader customer orientation. When a team leader's transformational leadership is low, the higher leader customer orientation is and the higher team customer orientation climate is.

This study contributes to the customer orientation, transformational leadership and service literature by unraveling team leaders' roles in boosting team customer orientation climate and team effectiveness.

This study aims to explore how online reviews and users’ social network centrality interact to influence idea popularity in open innovation communities (OICs).

This study used Python to obtain data from the LEGO Innovation Community. In total, 285,849 reviews across 4,475 user designs between March 2019 and March 2021 were extracted to test this study’s hypotheses.

The ordinary least square regression analysis results show that review volume, review valence, review variance and review length all positively influence idea popularity. In addition, users’ in-degree centrality positively interacts with review valence, review variance and review length to influence idea popularity, while their out-degree centrality negatively interacts with such effects.

Drawing on the interactive marketing perspective, this study employs a large sample from the LEGO community and examines user design and idea popularity from a community member’s point of view. Moreover, this study is the first to confirm the role of online reviews and user network centrality in influencing idea popularity in OICs from a social network perspective. Furthermore, by integrating social network analysis and persuasion theories, this study confirms the interaction effects of review characteristics and users’ social network centrality on idea popularity.

This study’s results highlight that users should actively interact and share with reviewers their professional product design knowledge and/or the journey of their design to improve the volume of reviews on their user designs. Moreover, users could also draw more attention from other users by actively responding to heterogeneous reviews. In addition, users should be cautious with the number of people they follow and ensure that they improve their in-degree rather than out-degree centrality in their social networks.

This study integrates social network analysis and persuasion theories to explore the effects of online reviews and users’ centrality on idea popularity in OICs, a vital research issue that has been overlooked.

The purpose of this paper is to design a solar sail heliocentric transfer orbit which can meet the requirements of control system and capture orbit, and to provide the change of angles for attitude control system.

Aiming at the problem of solar sail heliocentric transfer orbits design, this paper addresses the derivation of analytical optimal control law. The control laws can realize the combination of the control of each orbit element, but they can only give local optimal solution to meet the practical needs of mission. In order to solve this problem and meet the capture orbit and the attitude control system requirements, the modified genetic algorithm based on the analytical control law is introduced.

The algorithm addressed by this paper includes results closer to the global optimization, and also can meet the engineering constraints.

The analytical optimal control law can be applied to the future onboard sail control systems. The blending optimal algorithm is demonstrated to be suitable as a method of preliminary design for solar sail deep space exploration mission.

A blending optimal algorithm combining the analytical control law and genetic algorithm is proposed; the algorithm can search for global optimization based on the local optimal results of analytical control law.

While the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail expectations and may even pose negative impacts on user experience. The purpose of the study is to empirically explore the negative user experience with chatbots and understand how users respond to service failure caused by chatbots.

This study adopts a qualitative research method and conducts thematic analysis of 23 interview transcripts.

It identifies common areas where chatbots fail user expectations and cause service failure. These include their inability to comprehend and provide information, over-enquiry of personal or sensitive information, fake humanity, poor integration with human agents, and their inability to solve complicated user queries. Negative emotions such as anger, frustration, betrayal and passive defeat were experienced by participants when they interacted with chatbots. We also reveal four coping strategies users employ following a chatbots-induced failure: expressive support seeking, active coping, acceptance and withdrawal.

Our study extends our current understanding of human-chatbot interactions and provides significant managerial implications. It highlights the importance for organizations to re-consider the role of their chatbots in user interactions and balance the use of human and chatbots in the service context, particularly in customer service interactions that involve resolving complex issues or handling non-routinized tasks.

This paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.

An intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.

The fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.

In most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

Notwithstanding its significance as a form of strategic human capital investment, employee training has not yielded consistent conclusions among scholars regarding its impact on organizational performance. Some studies deem it effective, while others regard it as ineffective. We contend that distinct types of training impact various facets of firm performance.

In this study, we categorize employee training as either exploitative or explorative. Specifically, we examine their impact on two aspects of organizational performance: short-term performance and long-term competence, using a quasi-experimental setting and a difference-in-differences (DID) method.

We find that exploitative training is more effective in improving firms’ short-term performance (e.g. firms’ sales revenue), while explorative training is more effective in enhancing firms’ long-term competence (e.g. firms’ innovation output).

The findings of this study illuminate concrete benefits of training for practitioners, suggesting that firms can strategically select employee training category to maximize returns on their investment in strategic human capital based on their strategic orientations.

This paper proposes an improved fatigue life analysis method for optimal design of electric multiple units (EMU) gear, which aims at defects of traditional Miner fatigue cumulative damage theory.

A fatigue life analysis method by modifying S–N curve and considering material difference is presented, which improves the fatigue life of EMU gear based on shape modification optimization. A corrected method for stress amplitude, average stress and S–N curve is proposed, which considers low stress cycle, material difference and other factors. The fatigue life prediction of EMU gear is carried out by corrected S–N curve and transient dynamic analysis. Moreover, the gear modification technology combined with intelligent optimization method is adopted to investigate the approach of fatigue life analysis and improvement.

The results show that it is more corresponded to engineering practice by using the improved fatigue life analysis method than the traditional method. The function of stress and modification amount established by response surface method meets the requirement of precision. The fatigue life of EMU gear based on the intelligent algorithm for seeking the optimal modification amount is significantly improved compared with that before the modification.

The traditional fatigue life analysis method does not consider the influence of working condition and material. The life prediction results by using the method proposed in this paper are more accurate and ensure the safety of the people in the EMU. At the same time, the combination of intelligent algorithm and gear modification can improve the fatigue life of gear on the basis of accurate prediction, which is of great significance to the portability of EMU maintenance.