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The development of urban underground complexes (UUCs) has great positive significance for improving urban safety. Therefore, it is necessary to identify the key factors of the people's behavior of evacuation route selection (BERS) for fire emergencies and UUCs’ development. This study aims to find out the factors affecting people's BERS in the evacuation process of UUCs.

This study aims to find out the factors affecting people's BERS in the evacuation process of UUCs. To achieve this goal, the authors conducted a field experiment in F City. Furthermore, the people's BERS are obtained by using a structural equation model and compared with the field test results.

The authors found that the key factors for people's BERS are lighting conditions, route distance, flow direction guidance and indication. The results of this study contribute to the safety field by providing key factors for fire emergencies. It can also be used to improve fire safety management, evacuation strategies and assist in the development of intelligent evacuation systems.

The results of this study contribute to the safety field by providing key factors for fire emergencies. It can also be used to improve fire safety management, evacuation strategies and assist in the development of intelligent evacuation systems.

This study aims to examine the effects of multidimensional factors of platform features, group effects and emotional attitudes on social media users’ privacy disclosure intention.

This study collected the data from 426 respondents through an online questionnaire survey and conducted two approaches of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) for theoretical hypothesis testing and configuration analysis of the data.

The results show that social media platform features (rewards of information disclosure, personalized service quality and data transparency), group effects (group similarity, group information interaction and network externality), individual emotional attitudes (trust and privacy concern) and control variable (gender) have a significant impact on privacy disclosure intention, as well as trust and privacy concern play mediating roles. Additionally, the fsQCA method reveals five causal configurations that explain high privacy disclosure intentions. Furthermore, the study reveals that male users pay more attention to platform features, while female users are more inclined to group effects.

This study attempts to construct a comprehensive model to examine the factors that affect users' intention to disclose their privacy on social media platforms. Drawing on the cognition-affect-conation model and multidimensional development theory, the model integrates multidimensional factors of platform features, group effects, trust and privacy concern to complement existing theoretical frameworks and privacy disclosure literature. By understanding the complex dynamics behind privacy disclosure, this study helps platform providers and policymakers develop effective strategies to ensure the vitality and momentum of the social media ecosystem.

This empirical paper is an endeavor to explore the relationship between social media advertising (SMA) and customers' purchase intention (CPI) in three South Asian countries. SMA was further divided into three relevant dimensions: perceived relevance (PR), perceived informativeness (PI) and perceived credibility (PC). Furthermore, the authors incorporated a single mediator: customers' brand consciousness (CBC) to test the mediating effects on the direct relationships.

The authors purposively selected 1937 fashion-conscious individuals based on a cross-sectional survey design. The authors applied SPSS 25 for explanatory statistics and structural equation modeling (SEM) (through AMOS 25) for testing the hypothesized relationships.

Based on the responses and the application of statistical measures, the authors revealed that all of the three dimensions of SMA have significant positive relationships with CPI. CBC is also significantly and positively related to CPI. Regarding the mediating effects, CBC was identified to have full mediation effects on the relationships between PR and CPI and PI and CPI. On the contrary, the same variable was found to have partial mediation on the relationship between PC and CPI.

South Asia is a growing business hub and the largest consumer market in terms of population. This empirical study was undertaken to reveal the role of SMA on CPI in the three highly populated South Asian countries, which is rare in academia. The outcomes of this empirical study are expected to be useful for further research attempts regarding SMA and consumer behavior. Businesses and policymakers are also expected to benefit from formulating SMA-related strategies to retain present buyers as well as attract the prospective ones.

Drawing on anthropomorphism and the stereotype content model, this study aims to explore the impacts of robot anthropomorphism on hotel guests’ word of mouth and continuous usage intention through perceived warmth and competence. This study also investigates the moderating effects of guests’ expertise.

A total of 524 valid data was collected with Chinese hotel guest through an online survey. Data were analyzed by using structural equation modeling.

The results indicate that robot anthropomorphism positively affects perceived warmth and competence. Perceived warmth positively affects guests’ word of mouth and continuous usage intention, while perceived competence only positively influences guests’ continuous usage intention. The findings further show that guest expertise weakens the positive relationship between robot anthropomorphism and perceived warmth and competence.

This study advances robot service literature by integrating the stereotype content model with robot anthropomorphism in exploring hotel guests’ perceptions on service robots. This study also discovers the vital boundary role of guest characteristics in human–robot interaction. This study provides valuable information for hoteliers to design and adopt better robots for optimal guest experiences.

基于拟人化和刻板印象内容模型, 本研究探讨了机器人拟人化通过感知温暖和感知能力对酒店客人的口碑和持续使用意向的影响。此外, 本研究也探讨了客人专业知识的调节作用。

通过在线调查问卷, 我们共收集了来自中国酒店客人的524份有效数据, 并采用结构方程模型对这些数据进行了分析。

研究结果表明, 机器人拟人化积极影响感知温暖和感知能力。感知温暖会对客人的口碑和持续使用意向产生积极影响, 而感知能力只对客人的持续使用意向产生积极影响。研究结果进一步表明, 客人的专业知识会削弱机器人拟人化与感知温暖和能力之间的积极关系。

为了探索酒店客人对服务机器人的感知（状况/情况）, 本研究将机器人拟人化因素整合到刻板印象内容模型中, 其结果推动了机器人服务研究的发展。本研究还发现了客人特征在人机互动中的重要边界作用。本研究为酒店经营者设计和采用更好的机器人以获得最佳的客人体验提供了有价值的参考。

Se recopiló datos válidos de un total de 524 clientes de hoteles chinos mediante una encuesta en línea. Los datos fueron analizados mediante un modelo de ecuaciones estructurales.

Basándose en el antropomorfismo y en el modelo de contenido estereotipado, este estudio explora el impacto del antropomorfismo de los robots en el boca a boca de los clientes del hotel y en la intención de uso continuado a través de la calidez y la competencia percibidas. El estudio investiga también los efectos moderadores de la experiencia de los huéspedes.

Los resultados indican que el antropomorfismo del robot afecta positivamente a la calidez y a la competencia percibidas. La calidez percibida influye positivamente en el boca a boca y en la intención de uso continuado de los huéspedes, mientras que la competencia percibida sólo influye positivamente en la intención de uso continuado de los huéspedes. Los resultados muestran, además, que la experiencia del cliente debilita la relación positiva entre el antropomorfismo del robot y la percepción de calidez y de competencia.

Este estudio supone un avance en la literatura sobre robots de servicio al integrar el modelo de contenido estereotipado con el antropomorfismo de los robots, en la exploración de las percepciones de los huéspedes de hoteles sobre los robots de servicio. Este estudio también muestra el papel vital que desempeñan las características de los huéspedes en la interacción humano-robot. El estudio proporciona información valiosa para que los hoteleros diseñen y adopten mejores robots para ofrecer experiencias óptimas a los huéspedes.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

Artificial intelligence (AI) used in service has sparked fundamental changes in how enterprises engage their customers. AI specifically designed for customer service is denoted as service-oriented AI. Through the lens of social information processing theory and the pleasure-arousal-dominance (PAD) emotional state model, this study aims to examine the underlying mechanisms for service-oriented AI anthropomorphism to influence customers' propensity for value co-creation.

Our data were collected from hotel customers who had experienced interactions with service-oriented AI. Through purposive sampling, 350 survey responses were collected. We analyzed the survey data using covariance-based structural equation modeling (CB-SEM).

This study has two key findings. Firstly, customers' propensity for value co-creation is favorably facilitated by the anthropomorphism of service-oriented AI. Secondly, the anthropomorphism of service-oriented AI can strengthen customers' perceptions of cuteness and service capacity, elicit a sense of novelty, and enhance customers' propensity to collaborate with service-oriented AI to create value. These findings address the research gaps by focusing on customer engagement through service-oriented AI and provide a theoretical basis for subsequent practical endeavors in the field.

Integrating the PAD emotional state model with the social information processing theory, this study explores the effects of service-oriented AI's anthropomorphism on customers' propensity for value co-creation.

This study aims to investigate the intervening role of lean management (LM) in the direct relationships between green market orientation, green value-based innovation, green reputation and enterprise social performance.

Data for the study is carefully garnered from 217 managers in Ghanaian small- and medium-sized enterprises. The methodological technique used to validate all hypothesized relationships is partial least squares structural equation modelling.

The empirical results of the study suggest that although green market orientation has a positive impact on green value-based innovation, the effect is not significant. However, the results confirm that green market orientation has a significant positive impact on green reputation and enterprise social performance. The results further suggest that LM has a significant positive impact on green value-based innovation, green reputation and enterprise social performance. The mediation analysis provides empirical evidence to suggest that LM fully mediates the relationship between green market orientation and green value-based innovation. Lastly, the results of the mediation analysis suggest that LM plays a complementary partial mediation role between green market orientation, green reputation and enterprise social performance.

Despite the flourishing research on green market orientation in marketing management and environmental literature, no study has been carried out to explore the intervening role of LM in the relationships between green market orientation, green value-based innovation, green reputation and enterprise social performance. Thus, considering LM as a missing link between green market orientation, green value-based innovation, green reputation and enterprise social performance is a noteworthy research gap which this study fills.

Drawing on person–environment fit theory, this study aims to investigate how the relationships between service task types (i.e. utilitarian and hedonic service tasks) and perceived authenticity (i.e. service and brand authenticity) differ under different conditions of service providers (human employee vs service robot). This study further examines whether customers’ stereotypes toward service robots (competence vs warmth) moderate the relationship between service types and perceived authenticity.

Using a 2 × 2 between-subjects experimental design, Study 1 examines a casual restaurant, whereas Study 2 assesses a theme park restaurant. Analysis of covariance and PROCESS are used to analyze the data.

Both studies reveal that human service providers in hedonic services positively affect service and brand authenticity more than robotic employees. Additionally, the robot competence stereotype moderates the relationship between hedonic services, service and brand authenticity, whereas the robot warmth stereotype moderates the relationship between hedonic services and brand authenticity in Study 2.

Restaurant managers need to understand which functions and types of service outlets are best suited for service robots in different service contexts. Robot–environment fit should be considered when developers design and managers select robots for their restaurants.

This study blazes a new theoretical trail of service robot research to systematically propose customer experiences with different service types by drawing upon person–environment fit theory and examining the moderating role of customers’ stereotypes toward service robots.

The service industry is facing the huge impact of digital transformation, in which artificial intelligence (AI) plays one of the most important roles. This study aims to expand the understanding of the AI acceptance framework and confirm whether consumers’ digital skills have a moderating effect on the research model.

Hypotheses were tested using a data set of 1,641 individuals. Partial least squares structural equation modeling and multi-group analysis were used to estimate the model.

The results indicate that antecedent factors influence consumers’ willingness to use AI devices in services. The two groups of different digitally savvy respondents differ because the influence of anthropomorphism, social influence and hedonic motivation on respondents’ perceived efforts to use AI devices in service delivery depends on respondents’ digital skills.

The novel contribution of this study is reflected in a comprehensive model that explains the moderating effect of individual digital skills on willingness to use AI devices. The attitudes of experienced and digitally skilled consumers are valuable and highlight some important theoretical, practical implications and future lines of research.

This study aims to uncover marketing strategies to restore a positive image in times of pandemic crisis to bring tourists back to a popularly affected tourism city in China, Wuhan in Hubei Province. The paper argues that the process of restoring city image requires understanding the perceptions of Generation Z, a segment of the population who have a high propensity to travel after COVID-19.

This study employed a constructivist grounded theory approach to explore the perspectives of China’s Generation Z consumers of Wuhan’s future image to restart tourism. The data is collected through semi-structured interview of 29 respondents and analyzed with the initial, focused and theoretical coding process.

Start by narrowing the gap between projected and perceived image, the findings suggest that Generation Z could be pulled to visit Wuhan city through four core products such as dark tourism activities, special-interest leisure, heroism and storytelling.

The destination image restoration framework after health-related crisis is creatively proposed. It combines the analysis of crisis and urban characteristics from the perspective of the target audience (Generation Z) and provides specific strategies to restore the tourism city image from cognitive, affective and conative dimensions. Significantly, two novel characteristics of Generation Z (i.e. lighthearted and patriotic) emerge in addition to the typical features of this generational cohort. This study also found a high preference for a technologically oriented type of attraction that reverses the morbid COVID-19 memories into an entertainment tool.