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Meta-model method has been widely used in structural reliability optimization design. The main limitation of this method is that it is difficult to quantify the error caused by the meta-model approximation, which leads to the inaccuracy of the optimization results of the reliability evaluation. Taking the local high efficiency of the proxy model, this paper aims to propose a local effective constrained response surface method (LEC-RSM) based on a meta-model.

The operating mechanisms of LEC-RSM is to calculate the index of the local relative importance based on numerical theory and capture the most effective area in the entire design space, as well as selecting important analysis domains for sample changes. To improve the efficiency of the algorithm, the constrained efficient set algorithm (ESA) is introduced, in which the sample point validity is identified based on the reliability information obtained in the previous cycle and then the boundary sampling points that violate the constraint conditions are ignored or eliminated.

The computational power of the proposed method is demonstrated by solving two mathematical problems and the actual engineering optimization problem of a car collision. LEC-RSM makes it easier to achieve the optimal performance, less feature evaluation and fewer algorithm iterations.

This paper proposes a new RSM technology based on proxy model to complete the reliability design. The originality of this paper is to increase the sampling points by identifying the local importance of the analysis domain and introduce the constrained ESA to improve the efficiency of the algorithm.

Because of the high computational efficiency, response surface method (RSM) has been widely used in structural reliability analysis. However, for a highly nonlinear limit state function (LSF), the approximate accuracy of the failure probability mainly depends on the design point, and the result is that the response surface function composed of initial experimental points rarely fits the LSF exactly. The inaccurate design points usually cause some errors in the traditional RSM. The purpose of this paper is to present a hybrid method combining adaptive moving experimental points strategy and RSM, describing a new response surface using downhill simplex algorithm (DSA-RSM).

In DSA-RSM, the operation mechanism principle of the basic DSA, in which local descending vectors are automatically generated, was studied. Then, the search strategy of the basic DSA was changed and the RSM approximate model was reconstructed by combining the direct search advantage of DSA with the reliability mechanism of response surface analysis.

The computational power of the proposed method is demonstrated by solving four structural reliability problems, including the actual engineering problem of a car collision. Compared to specific structural reliability analysis methods, the approach of modified DSA interpolation response surface for structural reliability has a good convergent capability and computational accuracy.

This paper proposes a new RSM technology based on proxy model to complete the reliability analysis. The originality of this paper is to present an improved RSM that adjusts the position of the experimental points judiciously by using the DSA principle to make the fitted response surface closer to the actual limit state surface.

National identity has a profound impact on building a modern state, maintaining social stability and promoting economic development. Based on three waves of data collected from the World Values Survey (WVS) in Hong Kong between 2005 and 2018, this study aims to examine the changes in the national identity awareness of Hong Kongese over time.

The data used in this paper originate from the WVS. The WVS is a cross-country time-series survey that has been carried out in seven waves in 85 countries around the world, since 1981. There are three waves of data involving Hong Kong, which were obtained from the surveys in 2005, 2014 and 2018.

This study examined the changes in the national identity awareness of Hong Kongese over time and found that this has shown both continuity and rupture. Extreme groups lacking national identity have emerged and become more common over the decades and the elites’ national identity is much stronger than that of the lower and middle classes. It also shows that political trust, social capital, subjective well-being and possession of authoritarian personality have strong explanatory power for the changes in Hong Kongese national identity over time, but their explanatory strength varies across eras.

Based on three waves of surveys conducted by the WVS in Hong Kong in 2005, 2014 and 2018, respectively, this paper charts these changes over time and explores the differences in how they are influenced by political trust, social capital, subjective well-being and authoritarian personality.

The purpose is to provide decision support for tourists recommending scenic spots and corresponding suggestions for the management of scenic spots.

Based on the Baidu index data generated, this paper analyzes the temporal and spatial characteristics of network attention of 5A scenic spots in Sichuan Province. The online comment data are used to build the assessment model of scenic spots based on network attention, and the comment information of tourists is mined and analyzed through statistical analysis. At the same time, the key attributes of scenic spots from the perspective of network attention are evaluated and analyzed by using the probabilistic linguistic term set. Finally, this paper further constructs a recommendation model based on the key attribute set of scenic spots.

This paper uses different types of tourism network information, integrates multi-types of data and methods, fully excavates the value information of tourism network information, constructs the research framework of “scenic spot assessment + scenic spot recommendation” from the perspective of network attention, analyzes the network attention characteristics of scenic spots, evaluates the performance of scenic spots, and implements scenic spot recommendation.

This paper integrates multi-source data and multidisciplinary theoretical methods to form a scenic spot research framework of “assessment + recommendation” from the perspective of network attention.

Considering the network externalities of online selling, this paper builds three different online direct selling models: manufacturer direct selling (MN model), network platform direct selling (NN model) and retailer direct selling (RN model). The optimal advertising and pricing decision and corporate profits under each selling model are investigated.

Combining the characteristics of online direct selling, this paper formulates direct selling models that are dominated by different companies as Stackelberg game models. Numerical analyses are carried out, along with the comparison of the equilibrium solutions for each model.

The authors' research shows that increasing network externalities is conducive to the development of enterprises. The network platform's profit is the lowest in the RN model and the highest in the NN one. The comparison of manufacturers' profits between the MN model and the NN model primarily depends on consumers' sensitivities for sales price and advertising promotion level. The manufacturer does not benefit from the RN model due to the lowest efficiency.

Coupled with three different online direct selling models and detailed analyses of the optimal solutions, this study has enriched the theoretical foundation of online direct selling. Moreover, this study extends the research of network externalities to the field of e-commerce, revealing the network externalities' influence on the decision-making of the e-supply chain.

The current study aims to examine the antecedents and outcomes of emotional labor strategies (i.e. surface acting and deep acting) among service employees in China. The study proposed employees’ perceived closeness with customers and customers’ socioeconomic status will predict deep acting and surface acting, respectively. It further examined the mediating role of emotional labor between perceived customer attributes and employee well-being (i.e. burnout and job satisfaction).

One hundred and one employees at a jewelry store in China completed a survey regarding their perceptions of customers, use of emotional labor and well-being (e.g. job satisfaction and burnout). Correlational and regression analyses were conducted to examine the predictors and outcomes of different emotional labor strategies.

Perceived closeness with the customer group predicted employees’ use of deep acting, whereas perceived customer socioeconomic status did not predict the use of surface acting. Deep acting was negatively related with burnout, whereas surface acting did not predict burnout. Deep acting mediated the relationship between perceived closeness with customers and burnout.

To maintain employee well-being, organizations can promote a service climate to enhance employees’ perceived relationship with customers.

The study specifies the interpersonal context in which employees use different emotional labor strategies; the perceived closeness with customers predicts less burnout via the use of more deep acting. This study also supplements the existing research on emotional labor based on a Chinese sample; deep acting predicts employee well-being.

The elevator installation in old communities (EIOC) can effectively improve the public infrastructure of urban communities. However, differences in the decision-making behaviours of stakeholders lead to frequent conflicts, thereby hindering the implementation of EIOC. The purpose of this study is to explore the decision-making behavior of core stakeholders which are the government, community owners and elevator enterprises at different stages in the EIOC using the evolutionary game method.

A tripartite evolutionary game model involving the government, community owners and elevator enterprises was developed, and their evolutionary stabilisation strategies were explored in different stages. The dynamic change of the stakeholders' decision-making behaviours at different stages of the project and the influencing mechanism of the key factors on the decision-making behaviours of the three stakeholders were analysed through numerical simulation.

The results of this study showed that: Divergent interests led the government, community owners and elevator enterprises to adopt distinct decision-making behaviours at different stages, resulting in diverse attitudes and actions among stakeholders. A dynamic reward and penalty mechanism effectively motivated community owners and elevator enterprises to engage actively, fostering broad participation. However, the high regulatory cost diminished the government's regulatory effectiveness. This imbalance between penalties and incentives posed a challenge, impacting the overall effectiveness and efficiency of implementing the EIOC.

Existing research lacks exploration of the decision-making behaviours of stakeholders in community public infrastructure. This study developed a dynamic tripartite evolutionary game model in the EIOC from the gaming perspective. The results of this study provide a reference for dealing with the stakeholders' interests in the community public infrastructure and contribute to the theoretical basis for establishing an effective supervision mechanism.

This article documents accounts of the tactics parents and siblings of autistic persons in Hong Kong deploy to manage social encounters. This article aims to consider the impact of such tactics and their enmeshment with factors that combine to limit satisfactory outcomes and outlines a project intent upon contriving dialogue between persons.

This qualitative project elicited stories and accounts (or narratives) produced by persons involved in encounters involving autistic persons. This project also intervened in these encounters.

Persons involved in social encounters are de-fused, in the sense of being disconnected. The emotions persons experience through these encounters (e.g. and especially anxiety) remain hidden. Dialogue has the capacity to re-fuse, not only reconnecting but also rejecting unsatisfactory arrangements.

The dialogue produced in the article may extend beyond the specific circumstances and persons considered, potentially reducing the barriers and distances between autistic persons and others.

In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.

The study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.

In the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.

Previous research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

This article reviews and discusses issues in the translation of international brand names to Chinese, and provides a framework for international brand managers who want to expand into China. Linguistic differences between Chinese and English are wide and deep, making translation of brand names difficult. Cultural context, pronunciation, written vs. oral language, and the meaning of characters are just a few examples of such difficulties. We discuss four global product‐naming strategic alternatives available to country/brand managers, along with their usage. The four approaches include (1) dual extension, (2) brand meaning extension, (3) brand feeling extension, and (4) dual adaptation. We also provide examples of brands utilizing the different approaches.