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This research explored the intersection of cognitive processes, emotions and their impacts on digital game-based vocabulary learning (DGVL) among university students. Recognizing the scant research in this area, especially with integrating innovative technologies, this study aims to understand the influence of these elements using advanced monitoring tools.

This inquiry was carried out as an observational study involving 44 university students segmented into three English language proficiency levels: high, intermediate and low based on their English course scores. The methodological tools included a portable eye tracker to observe visual behaviors and deep learning technology to identify and analyze the participants’ emotional responses and engagement with the DGVL during the learning process.

The results showed that distinct fixation sequences and variations in visual attention during DGVL were correlated with different levels of competency, suggesting a direct correlation between visual engagement and language competence. In addition, emotional transitions, predominantly from engagement (“flow”) to challenge (“frustration”), were common among participants, reflecting the emotional dynamics of learning. Furthermore, all participants consistently focused on the English vocabulary definitions, indicative of their targeted approach to understanding and test preparation. These findings highlighted the intricate dynamics between emotions and cognitive processes in learning environments.

Contribution of this study shows the interplay of cognitive engagement and emotional experiences in the context of DGVL. It underscored the complex nature of these factors and their collective influence on learners’ visual and emotional engagement, offering valuable implications for educational strategies and technological applications in language learning.

This article introduces the Responsible AI for Service Excellence (RAISE) framework. RAISE is a strategic framework for responsibly integrating AI into service industries. It emphasizes collaborative AI design and deployment that aligns with the evolving global standards and societal well-being while promoting business success and sustainable development.

This multidisciplinary conceptual article draws upon the United Nations' Sustainable Development Goals (SDGs) and AI ethics guidelines to lay out three principles for practicing RAISE: (1) Embrace AI to serve the greater good, (2) Design and deploy responsible AI and (3) Practice transformative collaboration with different service organizations to implement responsible AI.

By acknowledging the potential risks and challenges associated with AI usage, this article provides practical recommendations for service entities (i.e. service organizations, policymakers, AI developers, customers and researchers) to strengthen their commitment to responsible and sustainable service practices.

This is the first service research article to discuss and provide specific practices for leveraging responsible AI for service excellence.

Recent archiving and curatorial practices took advantage of the advancement in digital technologies, creating immersive and interactive experiences to emphasize the plurality of memory materials, encourage personalized sense-making and extract, manage and share the ever-growing surrounding knowledge. Audiovisual (AV) content, with its growing importance and popularity, is less explored on that end than texts and images. This paper examines the trend of datafication in AV archives and answers the critical question, “What to extract from AV materials and why?”.

This study roots in a comprehensive state-of-the-art review of digital methods and curatorial practices in AV archives. The thinking model for mapping AV archive data to purposes is based on pre-existing models for understanding multimedia content and metadata standards.

The thinking model connects AV content descriptors (data perspective) and purposes (curatorial perspective) and provides a theoretical map of how information extracted from AV archives should be fused and embedded for memory institutions. The model is constructed by looking into the three broad dimensions of audiovisual content – archival, affective and aesthetic, social and historical.

This paper contributes uniquely to the intersection of computational archives, audiovisual content and public sense-making experiences. It provides updates and insights to work towards datafied AV archives and cope with the increasing needs in the sense-making end using AV archives.

This study aims to focus on tourists with physical disabilities and examine their perceptions of service robots in hotels and their influence on the overall experience, behavioral intentions and hotel attachment.

A survey was conducted via the Prolific platform to collect data from 409 respondents. The data was analyzed using partial least squares–based SEM.

The salient PLS-SEM findings show that perceived safety and functional benefits significantly affect a hotel's overall experience and intention to revisit. Perceived privacy did not relate to the hotel's overall experience and intention to revisit. Moreover, the hotel's overall experience significantly affects the intention to revisit, further influencing hotel attachment.

This research only examines the perceptions of tourists with physical disabilities of hotel service robots. Findings are based on a survey, and thus, the results cannot be generalized.

Current research indicates the impact of the intention to revisit and hotel attachment. Moreover, this study investigates the mediating role of overall hotel experience in the association between perceived privacy, perceived safety and functional benefits regarding revisit intention.

本研究关注身体残障游客, 并检查他们对酒店服务机器人的感知, 以及对整体体验、行为意图和酒店情感联系的影响。

通过Prolific平台进行调查, 收集了409名受访者的数据。数据使用PLS-SEM进行分析。

显著的PLS-SEM结果表明, 感知安全性和功能性收益显著影响酒店的整体体验和再次入住意愿。感知隐私与酒店的整体体验和再次入住意愿无关。此外, 酒店的整体体验显著影响再次入住意愿, 进而影响酒店情感联系。

本研究仅考察了身体残障游客对酒店服务机器人的感知。研究结果基于调查, 因此无法推广到整体。

当前研究表明再次入住意愿和酒店情感联系的影响。此外, 本研究探讨了整体酒店体验在感知隐私、感知安全和功能性收益与再次入住意愿之间的关联中的中介作用。

This study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews.

This paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment.

The research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions.

This research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.

本研究通过分析酒店评论文本和图像之间情感特征的不一致性来检测消费者的讽刺。

本文提出了一种基于多模态深度学习的讽刺检测模型, 使用从两个旅行平台收集的三个酒店品牌的评论, 该模型能够识别模态内部和模态之间的情感不一致性。利用图神经网络（GNN）探索文本-图像交互信息, 以检测讽刺情感中的关键线索。

研究结果显示, 多模态深度学习模型优于其他基线模型, 这有助于理解酒店服务评估, 并为酒店经理提供决策建议。

该研究可以在两方面帮助酒店业者：检测服务质量和制定策略。通过选择参考酒店品牌, 酒店业者可以更好地评估其服务质量水平（随之而来的是最佳资源分配）, 因此, 讽刺检测研究不仅有助于寻求提高服务质量的酒店经理。本研究介绍的多模态深度学习方法可以在其他行业复制, 帮助旅行平台优化其产品和服务。

Subjective well-being has attracted attention in relation to its impact on job satisfaction and performance. As a result, companies would do well to encourage positive emotions and minimize negative ones in the workplace. This study aims to examine the factors that contribute to emotional contagion in the workplace in Japan.

The study builds upon previous research on emotional contagion by considering both personal and professional events using vignette questions and multiple regression analysis. A large group of permanent Japanese employees is included in the study.

Emotional contagion in the Japanese workplace was found to be primarily influenced by the level of familiarity between sender and receiver, and to a lesser extent by the receiver’s emotional susceptibility, group climate and the hierarchical difference between sender and receiver. Contrary to past research in other countries, several hypothesized predictors had little or no effect on emotional contagion among Japanese employees, such as communication frequency or empathy.

Given the persistent labor shortage and aging population, it is crucial for Japanese companies to understand how positive and negative emotions are transmitted in the workplace. This knowledge can assist them in establishing a work environment that can optimize employee well-being, performance and retention.

While the antecedents of emotional contagion have been extensively studied, their effects within Japanese corporations remain underexplored. Thus, this research investigates the factors that can foster the contagion of positive emotions while mitigating the transmission of negative emotions within the Japanese workplace, thereby addressing the prevailing challenges faced by Japanese companies.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

The study aims to closely look at the phenomenon of transformational leadership and the psychological capital of followers by using affective process theory (APT). It has empirically tested the mediation of the perceived emotional labor (EL) of a leader and susceptible emotional contagion (EC) of followers when studying the effect of transformational leadership on the psychological capital (PsyCap) of followers.

The method adopted was mixed methodology. The data were collected from the 120 respondents and their perception regarding the construct as identified by previous literature was captured through a structured questionnaire. The relationships and hypotheses were tested by the structural equation modeling (SEM) model using SMART PLS. Further 20 semi-structured interviews were conducted using a qualitative approach.

The current research has empirically shown how specific aspects of transformational leadership, i.e. individual consideration perceived by followers also show high use of perceived deep acting strategy. Deep acting EL strategy is impacting positive EC and positive EC is leading to higher PsyCap of followers generating more work efficacy, hope, optimism and resilience. Mediation of positive EC between Deep acting EL and PsyCap was also observed. In qualitative studies done with the participants, major themes that emerged were transformational leadership, EL strategies, EC and PsyCap.

In times of uncertainty and stress after the post-COVID scenario, employees are facing emotional burnout due to increased work pressure and workload. Transformational leadership has become very critical to manage the PsyCap of followers by using correct EL strategies. Leaders can focus on the optimism and resilience aspect of PsyCap.

The current research has taken affective process theory (APT) as a foundation to understand the connection between transformational leadership and the PsyCap of followers. The study has specifically picked up the fourth mechanism of affective linkage as suggested by Elfenbein (2014) called emotional recognition and seen how emotions are transferred from source (leaders) to recipient (followers). The research has contributed by empirically testing the mediation of the perceived EL of leaders and the susceptible EC of followers and how they affect the PsyCap of followers.

There is need for definitional clarity when using psychological concepts in tourism research. The same term used in ‘folk psychology’, or behavioural or cognitive psychology can have quite different definitions and associated paradigmatic assumptions. This chapter argues that an understanding of mainstream cognitive psychology is needed when using concepts that are within its explanatory scope. Emotion is one such concept that has been extensively studied in cognitive psychology. The chapter discusses emotion and related terms such as feelings, novelty, unexpectedness and surprise to provide conceptual clarity. This will benefit researchers studying psychology of tourism emotions, tourism behaviour and decision-making.

Previous research suggests that emotional intelligence (EI) may benefit managers when resolving conflicts. However, past studies relied on self-reports of EI and conflict management styles, and a theoretical model explaining the mechanisms of the link between EI and conflict management outcomes for managers is still missing. This study aims to test a theoretical model proposing that during conflicts, managers with higher performance-based ability EI are perceived as warmer and more competent, which in turn contributes to higher conflict management effectiveness.

A total of 108 Executive MBA students with managerial experience completed a performance-based EI test designed for the workplace and engaged in a conflict management exercise during which they were videotaped. In the exercise, managers spontaneously responded to video-based vignettes in which “employees” addressed them regarding a work-related conflict (e.g. a disagreement regarding tasks and working hours). Independent observers (n = 262) rated the managers’ videotaped responses on items tapping warmth, competence and conflict management effectiveness.

Managers with higher performance-based EI (in particular, emotion regulation in oneself and emotion management in others) received higher observer ratings on warmth, competence and conflict management effectiveness. Warmth and competence fully mediated the link between EI and effectiveness.

These results demonstrate that managers’ performance-based EI translates into actual work-related behaviors and outcomes. Implications for training EI and effective conflict management are discussed.