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Compared to traditional (domestic) e-commerce consumers, cross-border electronic commerce (CBEC) consumers may face greater information asymmetry in the CBEC purchase process. Given this background, however, the literature has paid limited attention to the informational antecedents that influence consumers' perceptions of transaction costs and their CBEC purchase intentions. To fill this gap, this study integrates the elaboration likelihood model (ELM) and transaction cost theory (TCT) to develop a model for exploring how product (website informativeness, product diagnosticity and website interactivity as the central route) and external (country brand, website policy and vendor reputation as the peripheral route) informational antecedents affect consumers’ evaluations of transaction costs in terms of uncertainty and asset specificity and their CBEC purchase intentions.

This study employs a survey approach to validate the model with 766 Generation Z CBEC consumers based on judgment sampling. The partial least squares (PLS) technique is adopted for data analysis.

The results show that all the proposed central and peripheral informational antecedents reduce consumers’ perceptions of uncertainty and asset specificity, which in turn negatively influences their CBEC purchase intentions.

Through this investigation, this study increases our understanding of how product and external informational antecedents affect consumers’ evaluations of transaction costs, which subsequently determine their CBEC purchase decisions. This study offers theoretical contributions to existing CBEC research and has practical implications for CBEC organizations and managers.

Benefiting from the development and innovation of artificial intelligence and affective computing technology, social chatbots that integrate cognitive analysis and affective social services have flooded into the consumer market. For cognition and emotion-oriented tasks, social chatbots do not always receive positive consumer responses. In addition, consumers have a contradictory attitude toward the anthropomorphism of chatbots. Therefore, from the perspective of mind perception and the two dimensions of social judgment, this research explores the mechanism of consumer responses to anthropomorphic interaction styles when social chatbots complete different service tasks.

This paper utilizes three behavior experimental designs and survey methods to collect data and the ANOVA, t-test and bootstrap analysis methods to verify the assumed hypotheses.

The results indicate that when the service task type of a social chatbot is cognition-oriented, compared to a warm anthropomorphic interaction style, a competent anthropomorphic interaction style can improve consumer responses more effectively. During this process, agent-mind perception plays a mediating role. When the service task type of a social chatbot is emotion-oriented, compared with a competent anthropomorphic conversation style, a warm anthropomorphic conversation style can improve consumer responses. Experience-mind perception mediates this influencing relationship.

The research results theoretically enrich the relevant research on the anthropomorphism of social chatbots and expand the application of the theory of mind perception in the fields of artificial intelligence and interactive marketing. Our findings provide theoretical guidance for the anthropomorphic development and design of social chatbots and the practical management of service task scenarios.

This study investigated consumer perceptions of AI chatbots focusing on sentiment analysis across Twitter (X) and Reddit during ChatGPT3 through ChatGPT4 launches. It identifies user sentiments: positive, negative, or neutral and explores their impact on chatbot deployment in interactive marketing. The goal was to understand consumer engagement dynamics and provide insights for enhancing marketing strategies and consumer interactions with chatbots.

Using sentiment analysis, this research examined the nature and scope of discussions surrounding AI chatbots. This methodological approach allowed for a nuanced understanding of the predominant sentiments: positive, negative, or neutral, expressed by users providing insights into consumer engagement and interaction patterns.

The findings reveal a diverse range of consumer sentiments toward ChatGPT 3, reflecting varying degrees of acceptance and skepticism. These varied sentiments are crucial for organizations in shaping their interactive marketing strategies, particularly in how they deploy chatbots for consumer engagement and brand interaction.

Trust and positive sentiment toward ChatGPT, particularly on platforms like Twitter and Reddit, suggest it is becoming part of everyday life. However, concerns about its impact on human jobs and the lack of emotional intelligence persist. Users still weigh the benefits and drawbacks of ChatGPT, with negative and neutral sentiments reflecting these worries. For interactive marketers, this presents an opportunity to differentiate through human interaction in customer-facing roles. Addressing the risks and ethical concerns of AI, marketers can better engage consumers and refine strategies for future chatbot deployments, ensuring AI enhances rather than detracts from the customer experience.

This paper makes a unique contribution to the existing literature by demonstrating how consumer perceptions, rather than mere acceptance, directly influence the strategic use of AI chatbots in interactive marketing. By focusing on perceptions, this study offers deeper insights from social media sentiment analysis that can refine marketing campaigns and enhance consumer engagement with emerging technologies in the digital landscape.

Generative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users’ guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI’s cognitive and emotional conversational skills affect users’ guidance through different types of user engagement, and how these effects are moderated by users’ relationship norm orientation.

Based on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.

The results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users’ guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.

First, drawing on social exchange theory, this study empirically examined the drivers of users’ guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance. The findings will deepen our understanding of users’ guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.

Polycentricity has become a crucial spatial development strategy. This paper aims to clarify the impact and mechanism of polycentric spatial structure on the urban–rural income gap, provide better countermeasures to narrow it and make development suggestions for cities of different scales.

Based on the panel data of Chinese cities from 2010 to 2019, this paper empirically examines the specific impact of polycentric spatial structure on the urban–rural income gap by utilizing a two-way fixed effects model.

The results show that the polycentric spatial structure is favorable for reducing the urban–rural income gap, and upgrading the industrial structure and improving transportation infrastructure are important mechanisms. Moreover, the role of polycentric spatial structure is more significant in large cities. However, in small and medium-sized cities, the role of polycentric spatial structure is not significant.

The limitations of this study include two main aspects. One is that this paper only analyzes the mediating effect of industrial structure and transport infrastructure, and we do not examine other mechanisms. The second is that due to data availability, this paper uses data from 2010 to 2019, and the research period can be further extended in future studies.

This study has policy implications for building a polycentric city network and also provides insights into the planning of cities of different scales.

The polycentric spatial structure is important for narrowing the urban–rural income gap, especially in large cities. Improvement of transportation infrastructure and upgrading of industrial structure are important mechanisms. However, the polycentric spatial structure does not play a significant role in small and medium-sized cities. Thus, the polycentric spatial structure is not suitable for every kind of city. Relevant departments should carry out urban planning according to local conditions.

As a critical concern of urban planning, polycentricity significantly affects the urban–rural income gap. Relevant studies have analyzed the impact of polycentric spatial structure on regional income disparity, and few studies have focused on the relationship between polycentric spatial structure and the urban–rural income gap. Using city panel data in China, this paper explores the impact of polycentric spatial structure on the urban-rural income gap. Meanwhile, we test the mediating role of transportation infrastructure improvement and industrial structure upgrading. This study broadens the analysis of the urban–rural income gap’s influencing factors and provides a path reference for narrowing the urban–rural income gap.

Grassland degradation is a global ecological issue that inevitably leads to low livestock production efficiency (LPE). Adoption of appropriate technology is an effective way to improve productivity. However, the rate of technology adoption among herders in less developed pastoral areas is low. Therefore, it is critical to improve the level of technology adoption in order to increase LPE.

Based on remote sensing data and survey datasets of herder households in China’s Qinghai–Xizang Plateau, this paper innovatively constructs a stochastic production frontier model incorporating grassland productivity (i.e. grassland total net primary productivity) to accurately evaluate LPE and uses fractional regression models to determine the impact of technology adoption on LPE.

The results show that grassland productivity is essential to estimating LPE, and failing to account for it will result in overestimation. Technology adopters have a technical advantage with respect to average LPE (0.596) when compared with non-adopters (0.540), and technology adoption positively contributes to LPE. Furthermore, compared with profit-seeking technology, pro-environmental technology contributes more to improving LPE, and the combined adoption of both technologies leads to a markedly greater enhancement in LPE.

Few studies have empirically analyzed the economic benefits of technologies that most smallholders can afford, and few measure LPE considering grassland productivity. This study fills these gaps, and the findings are highly relevant for policies aimed at encouraging technology adoption and facilitating more efficient livestock production.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

During metro construction using the shield method, the construction process's complexity, the construction environment's variability, and other factors can easily lead to tunnel construction accidents. This paper aims to explore the interconnections between risk factors and related accident types, as well as the risk chain formed between risk factors, and to analyze the key risk factors and vulnerabilities in shield tunnel construction through empirical data.

Based on the social network analysis theory, the connection of various risk factors in subway shield tunnel construction is explored, and the mechanism of multiple risk factors is studied. Through literature analysis, articles on safety risk factors in metro shield tunnel construction are organized and studied, and the identified safety risk factors can comprehensively reflect the significant risks that need to be concerned in metro shield tunnel construction.

The results show that a small world characterizes the SNA network of safety risk factors for metro shield tunnel construction: The frequency of association between the five risk factors “unsafe behavior,” “site management,” “safety supervision and inspection,” “safety education system” and “safety protection” is higher than that of other factors. Only a few risks, such as “site management,” “safety supervision and inspection,” and “rapid response capability,” directly lead to accidents. In addition, risk factors such as the “safety education system” and “safety protection” will indirectly cause unsafe behaviors of construction personnel.

During construction, the probability of occurrence of risk factors may vary with the construction phase and area and is not considered in this paper. In addition, although this paper identifies, determines and analyzes the risk factors affecting the safety of metro shield tunnel construction, including the importance of each risk factor and the connection between them, more detailed information before and after the accident could not be obtained based on the accident investigation report alone. Therefore, future research can collect the same accident case from more sources to obtain more information.

The theory of accident causation has been improved at the theoretical level. The identified safety risk factors can comprehensively reflect the significant risks that need to be paid attention to in metro shield tunnel construction. From a practical point of view, the results of the study provide a basis for the rational control of the risk factors in the construction of subway shield tunnels, which can help guide practitioners to do a good job of risk prevention before the construction of metro shield tunnels and reduce the probability of related accidents.

This study expands the application of social network analysis in the field of subway tunnel construction risk, quantitatively analyzes the key risk factors and vulnerabilities in shield method tunnel construction and proposes policy recommendations for future metro tunnel construction safety management.