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Artificial intelligence (AI) agents have been increasingly applied in the tourism and hospitality industry. However, AI service failure is inevitable. Thus, AI service recovery merits empirical investigation. This study aims to explore how AI empathic accuracy affects customers’ satisfaction in the context of AI service recovery.

A moderated mediation model was presented to describe the effect of empathic accuracy on customer satisfaction via four scenario-based experiments.

The results reveal the positive impact of AI empathic accuracy on customer satisfaction and the mediating effects of perceived agency and perceived experience. Moreover, anthropomorphism moderates the empathic accuracy effect.

This paper expanded AI service studies by exploring the significance of empathic accuracy in customer recovery satisfaction. The results provide a novel theoretical viewpoint on retaining customers following AI service failure.

人工智能（AI）设备已越来越多地应用于旅游业和酒店业。然而, AI服务失败是不可避免的。因此, AI服务补救值得进一步实证研究。本研究探讨了AI共情准确性如何影响顾客对AI服务补救的满意度。

通过四个基于场景的实验, 提出了一个有调节的中介模型来描述共情准确性对顾客满意度的影响。

结果揭示了AI共情准确性对顾客满意度有积极影响, 感知能动性和感知感受性具有中介效应。此外, 拟人化调节了共情准确性的效应。

本文通过探讨共情准确性在顾客服务补救满意度中的作用, 拓展了AI服务研究。研究结果为AI服务失败后如何留住顾客提供了新的理论视角。

Las agentes de inteligencia artificial (IA) se aplican cada vez más en el sector del turismo y la hostelería. Sin embargo, los fallos de los servicios de IA son inevitables. Por lo tanto, la recuperación de servicios de IA merece una investigación empírica. Esta investigación explora cómo la precisión empática de la IA afecta a la satisfacción de los clientes con la recuperación del servicio de IA.

Se presentó un modelo de mediación moderado para describir el efecto de la precisión empática en la satisfacción del cliente mediante cuatro experimentos basados en escenarios.

Los resultados revelan el impacto positivo de la precisión empática de la IA en la satisfacción del cliente y los efectos mediadores de la agencia percibida y la experiencia percibida. Además, el antropomorfismo modera el efecto de la precisión empática.

Este artículo amplía los estudios sobre los servicios de IA al investigar el papel de la precisión empática en la satisfacción del cliente. Los resultados aportan un punto de vista teórico novedoso sobre la retención de clientes tras el fallo de un servicio de IA.

Prosocial lending in online crowdfunding has flourished in recent years, and it has become a new way to fundraise for philanthropy. However, there is almost a 70% user attrition rate in crowdfunding. The purpose of this study is to understand what the lender’s lending experience and social connection influence lender retention of online prosocial lending from a self-determination perspective.

Drawing on self-determination theory (SDT), this research utilizes a quantifiable method for factors of the lender's lending experience and social connection. Additionally, the research constructs economic models to explore the impacts of these factors acting as the necessary conditions for basic psychological needs on lender retention, using a large-scale sample of over 380,000 lenders from Kiva.

The results indicate that, from the lender's lending experience aspect, the loan narratives with more profit language in the last lending and the failure of past participation are negatively related to lender retention. Regarding the lender's social connection aspect, their friends or small lending teams are positively related to lender retention, while whether they are invited and lending team size show negative influence. Furthermore, results indicate the moderating effects of the disclosure of lending motivation.

This research explores the mechanism of lender retention of online prosocial lending, providing a self-determination perspective about how previous experience influences long-term lending behavior. The study offers significant implications for the literature on online philanthropy, SDT and user retention of online platforms. At the same time, the study provides an understanding of the effects of different aspects of SDT.

Smart cities show a “booming” trend both in the academia and the industry in recent years. Scholars across the world have been investigating how new technologies are applied to develop new services to the inhabitants and cities all over the world also address the “smart cities” challenges by promoting policymaking and governance. This paper aims to conduct in-depth research on smart cities by combining the study of governance policy study and information technology study.

This paper empirically mapped the trends of smart city development, outstanding scholars and hot topics about smart cities by analyzing important references using CiteSpace. The authors visualized references and topics to analyze smart city research, based on empirical data from Web of Science. Furthermore, two most important research branches – topics from smart city governance research and those from information systems (IS) research were studied, respectively.

First, the authors mapped the development of research and divided the development into three different stages. Second, the authors explored important, influential and instructive publications and publications’ attributes including authors, institutions, journals and topics. Third, the authors found there are different characteristics between the IS group and the governance group in publication situations, influential institutions, journals and authors, although the research points of the two branches are overlapping and fragmented. Finally, the authors proposed important topics, which include “internet of things (IoT)”, “big data”, “smart city systems” and “smart city management” and the authors predicted that “IoT” and “smart city challenge” would be future trends in recent years.

This study is an innovative research of its category because it visualized the development of smart city research, analyzed both governance and technology branches of smart city research synthetically using CiteSpace and forecasted future trends of smart city research by topics analysis and visualization of evolution.

This paper aims to study the effects of MnO₂ on the ZnO–Bi₂O₃-based varistor prepared via flash sintering (FS)

MnO₂-doped ZnO–Bi₂O₃-based varistors were successfully prepared by the FS with a step-wise increase of the .current in 60 s at the furnace temperature <750°C under the direct current electric field of 300 V cm⁻¹. The FS process, microstructure and the electrical performance of ZnO–Bi₂O₃-based varistors were systematically investigated.

The doping of MnO₂ significantly decreased the onset temperature of FS and improved the electrical performance of FS ZnO varistor ceramic. The sample with 0.5 mol% MnO₂ doping shows the highest improvement, with the nonlinear coefficient of 18, the leakage current of 16.82 µA, the threshold voltage of 459 V/mm and the dielectric constant of 1,221 at 1 kHz.

FS is a wonderful technology to enhance ZnO varistors for its low energy consumption, and a short sintering time can reduce grain growth and inhabit Bi₂O₃ volatilize, yet few research studies work on that. In this research, the authors analyzed the FS process and improved the electrical characteristics through MnO₂ doping.

This paper aims to investigate the interaction effect of job demands (e.g. display rules; DRs) and personal resources (e.g. emotional intelligence; EI) on work engagement and whether this interaction effect varies between managers and non-managers.

This study has been conducted on 572 hotel employees (153 managers and 388 non-managers) in China, and multiple linear regression analyses have been used to analyze the data.

The results indicate that the direct impact of DRs on work engagement was significantly positive for both hotel managers and non-managers; DRs boosted the positive impact of EI on work engagement, but only among managers; and EI boosted the positive impact of DRs on work engagement, but only among managers with high EI.

Previous studies have primarily focused on the antecedents and outcomes of work engagement, but little is about the direct impact of job demands and the interaction effect of job demands and personal resources on work engagement. This is the first study investigating DRs and its interaction effect with EI on work engagement among hotel managers and non-managers, respectively.

This study aimed to bibliometrically and visually analyze and review hospitality and tourism marketing studies published from 2000–2020.

A total of 3,942 articles collected from the databases of Social Science Citation Index (SSCI) and Science Citation Index Expanded (SCI-E) in the Web of Science (WoS) along with their references were used for analyses. The bibliometric software HistCiteTM and literature measurement visualization tools, VOSviewer and CiteSpace, were employed to analyze the selected articles.

The results of the study demonstrated top influential scholars and institutions, intellectual structure and emerging trends of the study topics, and future research opportunities in the field of hospitality and tourism marketing.

First, academic influence of a scholar was evaluated by citations of his/her publications, which did not take the order of authorship into consideration. Second, this study was restricted to the English language journals. Third, other types of published documents related to the studied field such as review papers were not considered by this research.

In comparison to traditional qualitative analysis such as content analysis, bibliometric analysis is a more objective approach to vividly demonstrate trends and performance of a research field, offers unique insights for its advancement with wider inclusiveness of a larger amount of data.

To examine how shareholding affects optimal profits, R&D innovation, NEV market scale and social welfare in two supply chain models with partial and cross ownership patterns.

The gradual retreat of government subsidies has directly weakened the financial support available to the stakeholders of new energy vehicles (NEVs). In this context, upstream and downstream enterprises of NEV are constantly seeking new business models of cooperation to achieve possible win-wins. NEV supply chain shareholding is an emerging new practice for such explorations. However, its performance in the NEV supply chain is seldom investigated. In this paper, we employ a Stackelberg game model to investigate how partial and cross-ownership affect the optimal decisions in a NEV supply chain.

Results showed that: (1) Compared with the unilateral shareholding model, the battery supplier will benefit from cross-ownership in the supply chain, while the NEV manufacturer will not necessarily benefit from it. At the same time, cross-ownership will bring the greatest incentive for battery R&D (2) Supply chain downstream competition will not necessarily lead to the improvement of the total consumption of NEVs or the level of battery design. Pareto improvement can be brought only when one of the manufacturers holds less than a certain equity threshold. In addition, downstream competition will also not necessarily bring more benefits to the battery supplier.

At present, NEV supply chain management has attracted widespread attention from scholars from all walks of life. Previous studies have been carried out that covers topics such as pricing strategies and optimal profits and the role of NEV in the sustainable development of the automotive industry supply chain, or disparate impacts of government subsidies and carbon emission regulation on supply chain members. However, as far as the authors know, compared with the new emerging NEV corporate practice, the shareholding phenomenon between upstream and downstream in the supply chain of NEV has not been studied in the existing studies.

This study aims to examine the long memory as well as the effect of structural breaks in the US and the Chinese stock markets. More importantly, it further explores possible causes of the differences in long memory between these two stock markets.

The authors employ various methods to estimate the memory parameters, including the modified R/S, averaged periodogram, Lagrange multiplier, local Whittle and exact local Whittle estimations.

China's two stock markets exhibit long memory, whereas the two US markets do not. Furthermore, long memory is robust in Chinese markets even when we test break-adjusted data. The Chinese stock market does not meet the efficient market hypothesis (EMHs), including the efficiency of information disclosure, regulations and supervision, investors' behavior, and trading mechanisms. Therefore, its stock prices' sluggish response to information leads to momentum effects and long memory.

The authors elaborately illustrate how long memory develops by analyzing not only stock market indices but also typical individual stocks in both the emerging China and the developed US, which diversifies the EMH with wider international stylized facts and findings when compared with previous literature. A couple of tests conducted to analyze structural break effects and spurious long memory demonstrate the reliability of the results. The authors’ findings have significant implications for investors and policymakers worldwide.

This paper aims to analyze and model consumer behavior on hotel online search interest in the USA.

Discrete Fourier transform was used to analyze the periodicity of hotel search behavior in the USA by using Google Trends data. Based on the obtained frequency components, a model structure was proposed to describe the search interest. A separable nonlinear least squares algorithm was developed to fit the data.

It was found that the major dynamics of the search interest was composed of nine frequency components. The developed separable nonlinear least squares algorithm significantly reduced the number of model parameters that needed to be estimated. The fitting results indicated that the model structure could fit the data well (average error 0.575 per cent).

Knowledge of consumer behavior on online search is critical to marketing decision because search engine has become an important tool for customers to find hotels. This work is thus very useful to marketing strategy.

This research is the first work on analyzing and modeling consumer behavior on hotel online search interest.

The purpose of the study was to examine the moderating effect of self-efficacy and gender on work engagement.

The survey study was conducted on 149 restaurant employees, and multiple regression analyses were used to analyze the data.

Self-efficacy significantly moderated the impact of perceived organizational support on work engagement, but the moderating effect of self-efficacy was only significant for women with low self-efficacy. Self-efficacy did not moderate the impact of work engagement on intent to leave.

The results of the study provide suggestions for managing men and women with different levels of self-efficacy in the hospitality workplace.

The focus of previous studies on work engagement has primarily been on its antecedents and outcomes, but little is known about individual differences in the relationship between work engagement and its antecedents/outcomes. This is the first study investigating self-efficacy and gender as moderators of work engagement in the hospitality industry.