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In China, rural-to-urban migrant workers who are from the same place of origin tend to concentrate in the same workplace. If the concentration is sufficiently dense, it means that these migrant workers build up a social network which could be defined as native place enclave (NPE). In this paper, the authors discussed whether there are behavioral differences between enclave workers and non-enclave workers when they have conflicts with their employers.

The authors put two questions to empirical tests. First, do enclave workers experience less sense of deprivation than non-enclave workers? Second, compared to non-enclave workers, are enclave workers more willing to participate in collective action against their employers? Using data from a survey of migrant workers in Pearl River Delta and Yangzi River Delta in 2010, the authors made a comparison between enclave workers and non-enclave workers with respect to sense of deprivation and willingness-to-participate by using a propensity score matching method.

The authors found that the relationship between NPE and sense of deprivation was negative, so was the relationship between NPE and willingness-to-participate. Meanwhile, the two relationships were stronger than what had been found after the propensity score matching method was used.

The results implied that employers can reduce labor conflicts by using NPE to mitigate migrant workers’ sense of deprivation and by lowering the risk of their collective actions. In this way, NPE may contribute to the upkeep of workplace order and even social order.

There have been hot debates on how NPE would affect migrant workers’ collective action. Resource mobilization theory pointed out that NPE was positively related to workers’ collective action while production politics theory held an opposite view. Our findings provided empirical evidences for the debates.

This study serves two purposes: (1) to evaluate the effects of organizational ambidexterity by examining how the balanced and the combined sales–service configurations of chatbots differ in their abilities to enhance customer experience and patronage and (2) to apply information boundary theory to assess the contingent role that chatbot sales–service ambidexterity can play in adapting to customers' personalization–privacy paradox.

An online survey of artificial intelligence chatbots users was conducted, and a mixed-methods research design involving response surface analysis and polynomial regression was adopted to address the research aim.

The results of polynomial regressions on survey data from 507 online customers indicated that as the benefits of personalization decreased and the risk to privacy increased, the inherently negative (positive) effects of imbalanced (combined) chatbots' sales–service ambidexterity had an increasing (decreasing) influence on customer experience. Furthermore, customer experience fully mediated the association of chatbots' sales–service ambidexterity with customer patronage.

First, this study enriches the literature on frontline ambidexterity and extends it to the setting of human–machine interaction. Second, the study contributes to the literature on the personalization–privacy paradox by demonstrating the importance of frontline ambidexterity for adapting to customer concerns. Third, the study examines the conduit between artificial intelligence (AI) chatbots' ambidexterity and sales performance, thereby helping to reconcile the previously inconsistent evidence regarding this relationship.

This paper aims to study the sampling methods (or design of experiments) which have a large influence on the performance of the surrogate model. To improve the adaptability of modelling, a new sequential sampling method termed as sequential Chebyshev sampling method (SCSM) is proposed in this study.

The high-order polynomials are used to construct the global surrogated model, which retains the advantages of the traditional low-order polynomial models while overcoming their disadvantage in accuracy. First, the zeros of Chebyshev polynomials with the highest allowable order will be used as sampling candidates to improve the stability and accuracy of the high-order polynomial model. In the second step, some initial sampling points will be selected from the candidates by using a coordinate alternation algorithm, which keeps the initial sampling set uniformly distributed. Third, a fast sequential sampling scheme based on the space-filling principle is developed to collect more samples from the candidates, and the order of polynomial model is also updated in this procedure. The final surrogate model will be determined as the polynomial that has the largest adjusted R-square after the sequential sampling is terminated.

The SCSM has better performance in efficiency, accuracy and stability compared with several popular sequential sampling methods, e.g. LOLA-Voronoi algorithm and global Monte Carlo method from the SED toolbox, and the Halton sequence.

The SCSM has good performance in building the high-order surrogate model, including the high stability and accuracy, which may save a large amount of cost in solving complicated engineering design or optimisation problems.

The purpose of this paper is to investigate the impact of buyer-seller interpersonal interactions on the purchase intention of buyers, incorporating swift guanxi as a mediator.

Based on survey data obtained from 336 Taobao Live users, PLS techniques were used to test hypotheses.

Swift guanxi exists in buyer-seller interactions and matters, as it drives buyers' purchase intention in live stream shopping. Perceived expertise, perceived similarity and perceived likeability are found to be the three essential interpersonal interaction factors promoting the formation of swift guanxi. Perceived familiarity is also found to be significant but to a lesser extent. In addition, all these interpersonal interaction factors are found to significantly affect purchase intention through the mediation of swift guanxi.

Swift guanxi has been less explored in live stream shopping. This study takes the lead in empirically examining the mediating role of swift guanxi in the relationship between interpersonal interaction factors and purchase intention and offers a description of key buyer-seller interpersonal interaction factors (perceived expertise, perceived similarity and perceived likeability), thereby helping to extend the swift guanxi literature in social commerce.

This paper aims to present a robust method for the determination of α- and ß-2, 7, 11-cembratriene-4, 6-diols (α, ß-CBT-diol) in tobacco samples which was developed and validated by using the self-made α, ß-CBT-diol with higher purity as the standard.

After the ultrasonic extraction and clean-up procedures, samples were analyzed by gas chromatography/mass spectrometry in selected ion monitoring mode and full scan mode at the same time. A 1-heptadecanol was used as an internal standard. The important parameters, such as extraction conditions and derivation conditions, were optimized.

Under the optimal conditions, good results in terms of linearity (R² > 0.999) and recoveries (93.2-107 per cent) were achieved. The limits of detection were 0.120 and 0.180 μg/ml for α- and ß-CBT-diol, respectively. α, ß-CBT-diol level of analyzed tobacco was found in the range of 34.2-1.26 × 103 μg/g with relative standard deviations below 6 per cent.

Such a strategy opens a new door towards the development of a simple, robust and sensitive method for the determination of α, ß-CBT-diol in real samples.

The purpose of this article is to conduct a main path analysis of 627 articles on the theme of Pythagorean fuzzy sets (PFSs) in the Web of Science (WoS) from 2013 to 2020, to provide a conclusive and comprehensive analysis for researchers in this field, and to provide a study on preliminary understanding of PFSs.

The research topic of Pythagorean fuzzy fields, through keyword extraction and describing the changes in characteristic themes over the past eight years, are firstly examined. Main path analysis, including local and global main paths and key route paths, is then used to reveal the most influential relationships between papers and to explore the trajectory and structure of knowledge transmission.

The application of Pythagorean fuzzy theory to the field of decision-making has been popular, and combinations of the traditional Pythagorean fuzzy decision-making method with other fuzzy sets have attracted widespread attention in recent years. In addition, over the past eight years, research interest has shifted to different types of PFSs, such as interval-valued PFSs.

This paper implicates to investigate the growth in certain trends in the literature and to explore the main paths of knowledge dissemination in the domain of PFSs in recent years.

This paper aims to identify the topics in which researchers are currently interested, to help scholars to keep abreast of the latest research on PFSs.

Since the early 2000s, China has been actively promoting school collaboration to narrow down educational gaps between schools to achieve inclusive and quality education for all. Among the reforms, building education groups has become one of the most widely adopted approaches for school improvement. This chapter aims at visualizing a hybrid model of school collaboration formulated by both hierarchist and egalitarian approaches in the Chengdu City of China. It starts with policy review on the construction of education groups for education improvement in China to interpret how social cohesion and regulation are constructed at policy level to promote school collaboration. Through data collected from education groups in District A of Chengdu, it then provides an analysis of the practice of improving education quality through promoting education groups among public schools in this province. The study reveals top-down policy initiatives, bottom-up school autonomy, and a shared responsibility for constructing quality education for all are key factors which enabled education groups to contribute to school improvement. The research also reveals how a lack of policy coordination and limited shared value and trust within education groups have become barriers to this reform. It concludes by discussing possible solutions for further promoting a sustainable school collaboration based on experiences of some ongoing practices at school level.

Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.

The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.

As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.

The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

In many cases, the external pipelines of aero-engine are subjected to random excitation. The purpose of this paper is to reduce the vibration response of the pipeline system effectively by adjusting the hoop layout.

In this paper, a spatial pipeline supported by multi-hoops is taken as the object, the methods of solution of the vibration response of the pipeline system by using pseudo excitation and hoop layouts optimization with amplitude reduction of vibration response as the goal are presented. First, the finite element model of the spatial pipeline system is presented. Then, an optimization model spatial pipeline is established. Finally, a case study is carried out to prove the rationality of the random vibration response analysis of the pipeline system. Furthermore, the proposed optimization model and genetic algorithm are applied to optimize the hoop layout.

The results show that the maximum response variance after optimization is reduced by 32.8%, which proves the rationality of the developed hoop layout optimization method.

The pseudo excitation method is used to solve the vibration response of aero-engine pipeline system, and the optimization of the hoop layout for aero-engine spatial pipelines under random excitation to reduce random vibration response is studied systematically.