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Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.

This paper analyzes the advance selling and pricing strategies of fresh products supply chain where the e-retailer provides wholesale contract or agency contract to the fresh products supplier.

This paper constructed a two-period sequential-move game of fresh products supply chain members.

This analysis showed that the supply chain members had different preferences for contracts under different market conditions. The advance selling of fresh products was not a decision of the seller, but also required the support of other supply chain members. And the advance selling strategy was not always beneficial to all supply chain parties. Under the two contracts, there were market conditions in which the profits of supply chain members were Pareto-improved through the implementation of advance selling.

The model presented in this study focuses solely on the context of monopoly, overlooking the competition from alternative suppliers or retailers. Consequently, exploring the competitive landscape within the fresh products supply chain, particularly in relation to pre-sale pricing, emerges as a crucial avenue for further investigation. By employing empirical research methods, valuable insights are gleaned, thereby significantly augmenting the existing body of relevant theories.

The decision to pre-sell fresh products should be based on market conditions. Supply chain members can control production costs and fresh products circulation losses to maximize profits.

From the perspective of game theory, this study analyzed the optimal advance selling and pricing strategies of fresh products supply chain members under two kinds of contracts. These results can provide practical implications for fresh products suppliers and e-retailers.

The aim of this study is to analyze the influence mechanism of real estate enterprises' status on debt default risk and explore the heterogeneity effect of the characteristics of enterprises.

Against the background of the “three red lines” regulation of the financing of real estate enterprises and the COVID-19 pandemic, the authors select 123 real estate enterprises listed on China's Shanghai and Shenzhen A-shares markets from the first quarter of 2021 to the second quarter of 2022 as a research sample. The social network analysis method and Z-score financial risk early warning model are used to measure real estate enterprises' status and debt default risk. The authors construct a panel regression model to analyze how the status of real estate enterprises influences their debt default risk.

The results show that the status of real estate enterprises negatively and significantly affects their debt default risk. Economic policy uncertainty and financing constraints play negative moderating and mediating roles, respectively. Further research has found that the effect of real estate enterprises' status on debt default risk is characterized by heterogeneity in equity characteristics, i.e. it is significant in the sample of nonstate-owned enterprises but not in the sample of state-owned enterprises.

It is helpful for real estate enterprises to attach importance to the value of social networks, and the authors provide policy suggestions for real estate enterprises to constantly improve their risk management systems.

Using economic policy uncertainty as the moderating variable and financing constraints as the mediating variable, the authors analyze how the status of real estate enterprises influences debt default risk, which contributes to a better understanding of the formation of the debt default risk of real estate enterprises.

Recently, autonomous vehicles (AV) acceptance has been studied intensively. This paper aims to map and analyze the bibliometric characteristics of AV acceptance literature. Furthermore, this research aims to identify research gaps and propose future research opportunities.

The bibliometric analysis was performed. Scopus database was used as the source of the literature. This study selected and analyzed 297 AV acceptance papers. The performance and science mapping analysis were performed.

The developed countries tended to dominate the topic. The publication outlet tended to be in transportation or technology journals. There were four research themes in existing literature. Technology acceptance model (TAM) and UTAUT2 tended to be used for explaining AV acceptance. AV acceptance studies tended to use two types of psychological concepts for understanding AV acceptance, namely risk related concepts and functional utilitarian benefit related concepts. In the context of research design, quantitative approach tended to be used. Self-driving feature was the most exploited feature of AV in the existing literature. Three research gaps were mapped and future research opportunities were proposed.

This paper provided a comprehensive information that allowed scientists to develop future research on AV acceptance.

There is lack of paper that discussed the bibliometric characteristics of AV acceptance literature. This paper fulfilled the gap.

This study aims to examine the impact of autonomous vehicles adoption motivations (i.e. technological, ecological and intrinsic motivation) on tourists’ pro-environmental behavior and verify the mediating role of tourists’ green self-image between the relationship of eco-friendly attitudes and autonomous vehicles adoption motivations.

The data from 586 national and international tourists were analyzed using the partial least squares method.

The findings revealed that eco-friendly attitude is a significant predictor of tourists’ green self-image; tourists’ green self-image is a significant predictor of autonomous vehicles adoption motivations; and autonomous vehicles adoption motivations are significant predictors of tourists’ pro-environmental behavior. In addition, tourists’ green self-image mediated the relationship between eco-friendly attitudes and autonomous vehicles adoption motivations.

These outcomes provide valuable guidance for the future development of green destination tourism and allow interesting implications for the tourism industry and autonomous vehicles adoption.

本研究探讨自动驾驶汽车采纳动机（即技术、生态和内在动机）对游客环保行为的影响, 并验证游客绿色自我形象在环保态度和自动驾驶汽车采纳动机之间的中介作用。

收集586份来自中国国内外游客的数据, 采用偏最小二乘法进行分析。

研究结果表明, 环保态度显著影响游客绿色自我形象, 进而影响自动驾驶汽车采纳动机, 带来游客的环保行为。此外, 游客的绿色自我形象在环保态度与自动驾驶汽车采纳动机之间起到中介作用。

本研究提出了游客绿色自我形象的概念, 将游客与环保人士的日常行为进行区分。研究结果为绿色目的地旅游业的未来发展提供了方向, 对旅游业和自动驾驶汽车的采纳产生影响。

Este estudio tiene como objetivo examinar el impacto de las motivaciones para la adopción de vehículos autónomos (es decir, motivaciones tecnológicas, ecológicas e intrínsecas) en el comportamiento proambiental de los turistas y verificar el papel mediador de la autoimagen ecológica de los turistas en la relación entre las actitudes ecológicas. y las motivaciones para la adopción de vehículos autónomos.

Se analizaron los datos de 586 turistas nacionales e internacionales mediante el método de mínimos cuadrados parciales.

Los hallazgos revelaron que la actitud ecológica es un predictor importante de la autoimagen ecológica de los turistas; la autoimagen ecológica de los turistas es un predictor importante de las motivaciones para la adopción de vehículos autónomos; y las motivaciones para la adopción de vehículos autónomos son predictores importantes del comportamiento proambiental de los turistas. Además, la autoimagen ecológica de los turistas medió la relación entre las actitudes ecológicas y las motivaciones para la adopción de vehículos autónomos.

Estos resultados proporcionan una orientación valiosa para el desarrollo futuro del turismo de destino ecológico y permiten implicaciones interesantes para la industria turística y la adopción de vehículos autónomos.

1) The OE-MLPG penalty meshfree method is developed to solve cracked structure.(2) Smartening the numerical meshfree method by combining the particle swarm optimization (PSO) optimization algorithms and Voronoi computational geometric algorithm. (3). Selection of base functions, finding optimal penalty factor and distribution of appropriate nodal points to the accuracy of calculation in the meshless local Petrov–Galekrin (MLPG) meshless method.

Using appropriate shape functions and distribution of nodal points in local domains and sub-domains and choosing an approximation or interpolation method has an effective role in the application of meshless methods for the analysis of computational fracture mechanics problems, especially problems with geometric discontinuity and cracks. In this research, computational geometry technique, based on the Voronoi diagram (VD) and Delaunay triangulation and PSO algorithm, are used to distribute nodal points in the sub-domain of analysis (crack line and around it on the crack plane).

By doing this process, the problems caused by too closeness of nodal points in computationally sensitive areas that exist in general methods of nodal point distribution are also solved. Comparing the effect of the number of sentences of basic functions and their order in the definition of shape functions, performing the mono-objective PSO algorithm to find the penalty factor, the coefficient, convergence, arrangement of nodal points during the three stages of VD implementation and the accuracy of the answers found indicates, the efficiency of V-E-MLPG method with Ns = 7 and ß = 0.0037–0.0075 to estimation of 3D-stress intensity factors (3D-SIFs) in computational fracture mechanics.

The present manuscript is a continuation of the studies (Ref. [33]) carried out by the authors, about; feasibility assessment, improvement and solution of challenges, introduction of more capacities and capabilities of the numerical MLPG method have been used. In order to validate the modeling and accuracy of calculations, the results have been compared with the findings of reference article [34] and [35].

The housing/real estate sector is one of the most important sectors in any country. However, existing marketing literature on the home buying behavior and the decision-making process is still in the early stage of development. The purpose of this study is to examine the home buying behavior from the consumers’ perspective in a high-context culture, namely, Kyrgyzstan and its managerial and/or public policy implications to other countries which are at a similar level of socio-economic development as Kyrgyzstan.

Using a questionnaire, data for the study (n = 300) is collected from households in Bishkek, Kyrgyzstan. Personal interviews were used to collect data from the four administrative regions of Bishkek.

Results of this study show that the physical, environmental and financial dimensions of the homes influenced consumers’ home buying intentions. A few statistically significant differences in terms of preferences for the proximity of the property to schools and shopping districts, having public sewer and water connections, and safety characteristics of the neighborhood were found between the first-time homebuyers and the repeat homebuyers.

The most important limitation of the study is the use of convenience sampling. Although the sample size is reasonably large, the selection of the responses was done based on using convenience and connections. Representativeness of the results may be limited.

Along with the physical, environmental and financial dimensions of the homes, home buying is a high-involvement decision; it is not as much of an emotional purchase but rather a main residence and a good long-term value for Kyrgyz households. Both marketing and social stimuli did not have any statistically significant effect on purchase intentions. Therefore, housing and real estate developers should focus on understanding how their offering meets individual customers’ tangible and intangible expectations and assist them in their highly involved decision-making process.

To the best of the authors’ knowledge, this study is the first to conduct an empirical study to analyze the home buying decisions of Kyrgyz households. This study contributes to marketing literature by filling the existing gaps in understanding various facets of the high-context consumers’ home buying decision-making.

Artificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP.

Drawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas.

AIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions.

New conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda.

This paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives.

This is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.

Cyberloafing is an organization-directed counterproductive work behavior (CWB). One stream of literature deems cyberloafing to be bad for organizations and their employees, while another suggests cyberloafing is a coping response to stressful work events. Our work contributes to the latter stream of literature. The key objective of our study is to examine whether cyberloafing could be a means to cope with a stressful work event-abusive supervision, and if yes, what mediating and boundary conditions are involved. For this investigation, the authors leveraged the Stressor-Emotion-CWB theory which posits that individuals engage in CWB to cope with the negative affect generated by the stressors and that this relationship is moderated at the first stage by personality traits.

Using a multi-wave survey design, the authors collected data from 357 employees working in an Indian IT firm. Results revealed support for three out of the four hypotheses.

Based on the Stressor-Emotion-CWB theory, the authors found that work-related negative affect fully mediated the positive relationship between abusive supervision and cyberloafing, and work locus of control (WLOC) moderated the positive relationship between abusive supervision and work-related negative affect. The authors did not find any evidence of a direct relationship between abusive supervision and cyberloafing. Also, the positive indirect relationship between abusive supervision and cyberloafing through work-related negative affect was moderated at the first stage by the WLOC such that the indirect effect was stronger (weaker) at high (low) levels of WLOC.

This work demonstrates that cyberloafing could be a way for employees to cope with their abusive supervisors.

This paper seeks to investigate the differences in the teachers’ professional development (TPD) by mentorship in workplace. The authors examined the role of mentorship in the PD of teachers and conducted a meta-analysis of pertinent empirical data.

Using data from over 2,900 individuals, 66 experiments and 12 countries, the authors presented a meta-analysis of the association between workplace mentorship and TPD.

The authors concluded that mentoring activities could boost the TPD to some extent. It contributes positively to the discipline of science and language, kindergarten, individual mentoring and curriculum research. In addition, the periodicity should not exceed 1 year.

The results of the meta-analysis are restricted to short-term mentorship activities, and the sample size is modest. Building upon the findings from the literature review and meta-analysis, the authors delineated a research agenda for prospective investigations. This includes an imperative for further exploration into the nexus between mentoring and the PD of educators.

Based on the available literature and meta-analysis findings, the authors developed a framework for the “Experts in the classroom” TPD pattern.

This is the first meta-analysis evaluating the association between mentorship and TPD.