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The purpose of this study is to investigate the key predictors and outcomes of task–technology fit (TTF) of facial recognition payment systems with the moderating role of cultural differences in the restaurant industry.

The survey responses were collected from 336 South Korean and 336 US restaurant customers.

The results revealed that function significantly affected TTF in both groups. Unique to the Korean sample, emotion was found to be a significant determinant of TTF, whereas convenience and social influence were key predictors of TTF only for the US sample. TTF had significant and positive effects on the three dimensions of behavioral intentions in both groups. The result of multi-group analysis showed that cultural differences moderated the effect of convenience on TTF and the effect of emotion on TTF.

The authors provided recommendations for restaurant operators and technology companies seeking to improve customer TTF and acceptance of facial recognition payment systems for the first time.

本研究旨在调查面部识别支付系统任务技术匹配（TTF）的关键前置因素和影响, 以文化差异为调节变量, 研究其在餐饮行业的应用。

我们收集了来自336名韩国和336名美国餐厅顾客的调查回答。

结果显示, 在两组中, 功能显著影响TTF。对于韩国样本来说, 情感被发现是TTF的重要决定因素, 而对于美国样本来说, 方便性和社会影响是TTF的关键预测因素。在两组中, TTF对行为意向的三个维度均产生了显著且积极的影响。多组分析结果显示, 文化差异在方便性对TTF的影响以及情感对TTF的影响中起到了调节作用。

我们首次为寻求改善顾客TTF和接受面部识别支付系统的餐厅经营者和技术公司提供了建议。

Fact-checking is a process of seeking and displaying facts to confirm or counter uncertain information, which reduces the spread of fake news. However, little is known about how to promote fact-checking posts to online users on social media. Through uncertainty reduction theory and message framing, this first study examines the effect of fact-checking posts on social media with an avatar on online users' trust, attitudes, and behavioral intentions. The authors further investigate the congruency effects between promotional message framing (gain/loss/neutral) and facial expressions of the avatar (happy/angry/neutral) on online users' trust, attitudes, and behavioral intentions in the second study.

The authors conducted two studies and statistically analyzed 120 samples (study 1) and 519 samples (study 2) from Facebook users.

Results showed that including the neutral facial expression avatar in fact-checking posts leads to online users' greater trust and more positive attitudes. Furthermore, the congruency effects between loss message framing and the angry facial expression of the avatar can effectively promote online users' trust and attitudes as well as stronger intentions to follow and share.

This study offers theoretical implications for fact-checking studies, and practical implications for online fact-checkers to apply these findings to design effective fact-checking posts and spread the veracity of information on social media.

Based on the cognition–affect–conation pattern, this study explores the factors that affect the intention to use facial recognition services (FRS). The study adopts the driving factor perspective to examine how network externalities influence FRS use intention through the mediating role of satisfaction and the barrier factor perspective to analyze how perceived privacy risk affects FRS use intention through the mediating role of privacy cynicism.

The data collected from 478 Chinese FRS users are analyzed via partial least squares-based structural equation modeling (PLS-SEM).

The study produces the following results. (1) FRS use intention is motivated directly by the positive affective factor of satisfaction and the negative affective factor of privacy cynicism. (2) Satisfaction is affected by cognitive factors related to network externalities. Perceived complementarity and perceived compatibility, two indirect network externalities, positively affect satisfaction, whereas perceived critical mass, a direct network externality, does not significantly affect satisfaction. In addition, perceived privacy risk generates privacy cynicism. (3) Resistance to change positively moderates the relationship between privacy cynicism and intention to use FRS.

This study extends knowledge on people's use of FRS by exploring affect- and cognitive-based factors and finding that the affect-based factors (satisfaction and privacy cynicism) play fully mediating roles in the relationship between the cognitive-based factors and use intention. This study also expands the cognitive boundaries of FRS use by exploring the functional condition between affect-based factors and use intention, that is, the moderating role of resistance to use.

Beneficiary photos on charity appeals are believed to engender prosocial behavior. This study explores photo framing of healthy and unhealthy beneficiary photos in the context of photo-rich online medical crowdfunding. Based on framing theory, emphasis framing effect (i.e. unhealthy photos only vs both healthy and unhealthy photos) and equivalency framing effect (i.e. healthy photos prior to unhealthy photos vs unhealthy photos prior to healthy photos) are identified.

A scenario-based experiment with 135 participants was used to empirically test the proposed research hypotheses. The subjects were randomly assigned to three treatment groups, with 45 subjects in each group. ANOVA, linear regression, and multiple mediation analysis were used to analysis data.

The results reveal that disclosing both healthy and unhealthy photos can elicit stronger sympathy and perceived need than merely disclosing unhealthy photos. Moreover, the order of unhealthy photos prior to healthy photos leads to a higher level of sympathy than the order of healthy photos prior to unhealthy photos. Furthermore, sympathy and perceived need are positively related to donation intention.

First, this study extends the photo-related research limited to certain characteristics of a single photo to the sequence effect of multiple photos. Second, this study contributes to framing theory by introducing photo framing, particularly the equivalence and emphasis framing effect of beneficiary photos. Finally, this study reveals the emotional and cognitive routes through which beneficiary photos stimulate prosocial behavior. It also offers practical guidance in the aspects of the framing effect of beneficiary photos for crowdfunding management.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

Grounded in emotional dissonance and social presence theories, this study examines whether the characteristics of employee–customer interaction (frequency, routineness and duration) and emotional intelligence (EI) have an impact on emotional labor (surface acting (SA), deep acting and naturally felt emotions (NFE)) and whether the type of interaction (face to face, voice to voice and online) moderates this relationship.

A survey method was employed to collect data from employees working in hotels, customer care and e-booking services (n = 604). The model was tested using structural equation modeling (SEM).

The study showed that EI was positively linked to deep acting and NFE but negatively associated with SA. Frequency of interaction had a negative relationship with deep acting and NFE but a positive association with SA. Duration of interaction (DOI) had a positive relationship with deep acting and NFE but a negative association with NFE. Routineness of interaction had a negative relationship with deep acting and NFE but surprisingly had a negative relationship with SA. Online interaction moderated the relationship between EI and deep acting.

This pioneering study examines the relationship between EI and characteristics of employee–customer interaction with emotional labor in the Indian hospitality context. While the association between EI and emotional labor has been studied, this study is unique in substantiating the moderating effects of interaction type and is among the first to do so empirically.

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.

The deployment of artificial intelligence (AI) technologies in travel and tourism has received much attention in the wake of the pandemic. While societal adoption of AI has accelerated, it also raises some trust challenges. Literature on trust in AI is scant, especially regarding the vulnerabilities faced by different stakeholders to inform policy and practice. This work proposes a framework to understand the use of AI technologies from the perspectives of institutional and the self to understand the formation of trust in the mandated use of AI-based technologies in travelers.

An empirical investigation using partial least squares-structural equation modeling was employed on responses from 209 users. This paper considered factors related to the self (perceptions of self-threat, privacy empowerment, trust propensity) and institution (regulatory protection, corporate privacy responsibility) to understand the formation of trust in AI use for travelers.

Results showed that self-threat, trust propensity and regulatory protection influence trust in users on AI use. Privacy empowerment and corporate responsibility do not.

Insights from the past studies on AI in travel and tourism are limited. This study advances current literature on affordance and reactance theories to provide a better understanding of what makes travelers trust the mandated use of AI technologies. This work also demonstrates the paradoxical effects of self and institution on technologies and their relationship to trust. For practice, this study offers insights for enhancing adoption via developing trust.

This study aims to investigate how despotic leadership affects employee well-being through bullying behavior. The study further investigates emotional intelligence as a conditional variable on the association between bullying behavior and employee well-being.

The data from 257 nurses and their immediate supervisors (dyads) were collected on convenience basis using a cross-sectional design. Further, structural equation modeling was used to analyze the data.

The study noted that despotic leadership negatively affects employee well-being. Specifically, despotic leaders were noted to trigger employees’ bullying behavior that ultimately diminish their well-being. The study noted emotional intelligence as a conditional variable such that individuals with high emotional intelligence are more likely to buffer the negative association between bullying behavior and employee well-being.

This study highlights the importance of employee well-being and suggests the management focus on their leadership style. Further, the study suggests to Human Resource practitioners the importance of personality traits (emotional intelligence) at the time of recruitment, as it serves as a coping strategy to diminish employee well-being.

Drawing upon the conservation of resources, this study shed light on the mediating role of bullying behavior between negative leadership (despotic) and well-being. In addition, emotional intelligence has not been examined as a conditional variable between bullying behavior and employee well-being.