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The study aims to provide concrete service remediation and enhancement for LLM developers such as getting user forgiveness and breaking through perceived bottlenecks. It also aims to improve the efficiency of app users' usage decisions.

This paper takes the user reviews of the app stores in 21 countries and 10 languages as the research data, extracts the potential factors by LDA model, exploratively takes the misalignment between user ratings and textual emotions as user forgiveness and perceived bottleneck and uses the Word2vec-SVM model to analyze the sentiment. Finally, attributions are made based on empathy.

The results show that AI-based LLMs are more likely to cause bias in user ratings and textual content than regular APPs. Functional and economic remedies are effective in awakening empathy and forgiveness, while empathic remedies are effective in reducing perceived bottlenecks. Interestingly, empathetic users are “pickier”. Further social network analysis reveals that problem solving timeliness, software flexibility, model updating and special data (voice and image) analysis capabilities are beneficial in breaking perceived bottlenecks. Besides, heterogeneity analysis show that eastern users are more sensitive to the price factor and are more likely to generate forgiveness through economic remedy, and there is a dual interaction between basic attributes and extra boosts in the East and West.

The “gap” between negative (positive) user reviews and ratings, that is consumer forgiveness and perceived bottlenecks, is identified in unstructured text; the study finds that empathy helps to awaken user forgiveness and understanding, while it is limited to bottleneck breakthroughs; the dataset includes a wide range of countries and regions, findings are tested in a cross-language and cross-cultural perspective, which makes the study more robust, and the heterogeneity of users' cultural backgrounds is also analyzed.

The purpose of this study is to investigate how expressing gratitude and forgiveness on social media platforms relates to the overall sentiment of users, aiming to understand the impact of these expressions on social media interactions and individual well-being.

The hypothesis posits that users who frequently express gratitude or forgiveness will exhibit more positive sentiment in all posts during the observed period, compared to those who express these emotions less often. To test the hypothesis, sentiment analysis and statistical inference will be used. Additionally, topic modelling algorithms will be used to identify and assess the correlation between expressing gratitude and forgiveness and various topics.

This research paper explores the relationship between expressing gratitude and forgiveness in X (formerly known as Twitter) posts and the overall sentiment of user posts. The findings suggest correlations between expressing these emotions and the overall tone of social media content. The findings of this study can inform future research on how expressing gratitude and forgiveness can affect online sentiment and communication.

The authors have demonstrated that social media users who frequently express gratitude or forgiveness over an extended period of time exhibit a more positive sentiment compared to those who express these emotions less. Additionally, the authors observed that BERTopic modelling analysis performs better than latent dirichlet allocation and Top2Vec modelling analyses when analysing short messages from social media. This research, through the application of innovative techniques and the confirmation of previous theoretical findings, paves the way for further studies in the fields of positive psychology and machine learning.

This study aims to examine the theoretical links among three important variables by empirically testing the cases of two international brands.

By using a comparative case study design, this study conducts a content analysis of a total of 490 Facebook comments regarding online confrontational crises: Dolce & Gabbana’s photo fiasco and Laneige’s discriminative sales incident.

The findings suggest that when evaluating whether or not a company has shouldered responsibility in online confrontational crises, social media users tend to be more influenced by how timely, active and consistent the organization’s reaction is than by the organization’s mere use of concession crisis communication strategies (CCSs). The individual-level perception (perceived degree of organizational crisis responsibility-taking) is a stronger predictor of social media users’ reaction than organization strategies. The earlier that social media user has a perceived improvement in the organization, the more effective is the organization’s strategy to minimize the effects of social media as crisis mobilizer.

This study confirms theories formulated in a Western context with actual cases from Eastern cultures. Theoretically, this study sheds light on the importance of the individual-level perception for effective use of organization strategy in crisis. This study also suggests the relative significance of positive forms of crisis response, concessions CCSs and their relationship with the perceived degree of crisis responsibility-taking.

The purpose of this study is to explore consumer reviews with robotics based on the five dimensions for evaluating user experiences (i.e. embodiment, emotion, human-oriented perception, feeling of security and co-experience), as derived from research in human-robot interactions (HRI).

The study first reviews the five dimensions for evaluating user experiences in HRI and then analyzes user experiences with robotics at four hotels (i.e. Yotel New York, Aloft Cupertino, Henn-na Hotel Japan and Marriott Residence Inn LAX) based on reviews on TripAdvisor, Agoda, Yelp and Booking.com.

The findings highlight the influence of robotic embodiment and human-oriented perceptions on consumer experiences. The findings also suggest that users and robots can co-create novel experiences, with some guests even proactively seeking new opportunities to interact and communicate with robots to develop a certain level of “relationship” with them.

An understanding of user experiences from HRIs can inform future hospitality and tourism research and management.

This study contributes to hospitality and tourism management by highlighting current practices with robotics to suggest areas of improvements for enhancing future consumer experiences.

Consumer experiences will change rapidly as hospitality and tourism management deploys robotics in the future.

This is one of the early studies in the field to explore consumer experiences with robotics based on the five dimensions for evaluating user experiences from research in HRI. In doing so, this study provides a number of theoretical and managerial implications relevant for hospitality and tourism research and practice.

An empirical study investigated the antecedents to perceived usefulness (PU) and its consequences in the context of smoking cessation online health communities (OHCs).

To validate a research model for perceived informational support, perceived emotional support and perceived esteem support, the authors conducted a partial-least-squares analysis of empirical data from an online survey (N = 173) of users of two smoking cessation OHCs. The proposed model articulates these as antecedents to PU from a social support perspective, and knowledge sharing and continuance intention are expressed as consequences of PU.

The empirical study identified that the PU of smoking cessation OHCs is influenced by perceived emotional support and perceived esteem support, and perceived informational support indirectly affects PU via these factors. In turn, PU exerts a positive influence on both knowledge sharing and continuance intention. Also, knowledge sharing positively affects continuance intention.

The study contributes to scholarship on users' postadoption behavior in the context of smoking cessation OHCs by disentangling the antecedents to PU from a social support perspective and pinpointing some important consequences of PU. The research also has practical implications for managing smoking cessation OHCs.

The purpose of this paper is to explore the Values in Action (VIA) classification of human strengths and virtues by using unsupervised machine learning techniques, specifically topic modeling algorithms, on a sample of X (formerly known as Twitter) posts. This study aims to investigate if and to what extent the structure of posts with the highest positive sentiment, as determined by topic modeling algorithms, aligns with the structure of the VIA classification.

This study uses a sample of X posts as the data set for the analysis. Unsupervised machine learning techniques, specifically topic modeling algorithms, are used to extract and categorize topics from X posts. The sentiment analysis algorithm is used to identify posts with the most positive sentiment. The structure and representation of these positive sentiment posts are then compared with the structure of the VIA classification.

The results of this study reveal a correlation between the structure of posts with the highest positive sentiment, as determined by topic modeling algorithms, and the structure of the VIA classification. This indicates that the topic structures derived from the X posts exhibit similarities to the categorization of character strengths proposed by the VIA classification. The findings of this study provide empirical validation for the VIA classification framework when applied to social media data.

This paper contributes to the literature by using unsupervised machine learning techniques to validate the VIA classification on social media data. The use of these innovative methods adds a novel dimension to the research on character strengths and virtues.

This research investigates the role of customer forgiveness as the result of online service recovery transparency in predicting customer engagement. It also examines the moderating roles of timeliness and personalization in this proposed model.

An online survey study using retrospective experience sampling and a scenario-based experimental study were conducted to test the proposed hypotheses.

Customer forgiveness positively influences customer engagement and plays a mediating role in the relationship between service recovery transparency and customer engagement. Additionally, timeliness and personalization moderate the positive influence of service recovery transparency on customer forgiveness. The positive influence of service recovery transparency on customer forgiveness is more apparent when levels of timeliness and personalization decrease.

To retain focal customers' engagement after a service failure, firms must obtain their forgiveness. One of the firm's online complaint handling strategies to increase the forgiveness level of focal customers is to provide a high level of service recovery transparency (i.e. responding to their complaints in a public channel), especially when the firm is unable to respond to online complaints quickly or provide highly personalized responses.

This research provides new insights into the underlying mechanism of customer engagement by applying the concept of customer forgiveness. It also contributes to the social influence theory by applying the essence of the theory to explain how other customers' virtual presence during the online complaint handling influences the forgiveness of focal customers in order to gain their engagement. Additionally, it provides insight into the conditions under which the role of service recovery transparency can be very effective in dealing with online complaints.

In the workplace, forgiveness can increase positive interactions between the victim and offender in the aftermath of a conflict. As an important intrapersonal factor in an organization, a victim’s power motives may shape one’s forgiveness. However, previous research shows inconsistent results because it only considers explicit power motives while ignoring the possible contingent role of implicit power motives in influencing forgiveness. This paper aims to consider both implicit and explicit power motives and aims to examine their joint effect on interpersonal forgiveness in the workplace conflict.

Polynomial regressions with response surface analysis were conducted with 415 Amazon Mechanical Turk users. Implicit power motives were measured by using a modified picture story exercise technique, whereas explicit power motives were measured via self-response scales.

First, congruence in power motives was associated with higher empathy and forgiveness than incongruence. In addition, high-implicit/high explicit power motives led to higher level of empathy and forgiveness than low-implicit/low-explicit power motives. Furthermore, directional power motive incongruence had an additive effect on forgiveness, such that discrepantly low-implicit/high-explicit power motives were more detrimental to empathy and forgiveness than discrepantly high-implicit/low-explicit power motives. Finally, empathy underlies the combined effect of implicit and explicit power motives on forgiveness.

The findings suggest that in a workplace conflict, managers should consider an employee’s explicit and implicit motives. To build the harmony group work climate, managers can improve intrapersonal implicit–explicit power motive congruence by providing developmental support and training focusing on self-enhancement and self-affirmation with the low-explicit-powerful employees. Further, to promote forgiveness in a conflict, organizations can use empathy-based exercise and provide team building activities to increase employees’ empathy and perspective-taking toward others.

By integrating implicit–explicit framework, this paper conciliates previous studies investigating the relationship between power and forgiveness by proposing that the two types of power motives, implicit and explicit power motives, jointly influences a victim’s forgiving tendency. This study serves as a meaningful touchstone for future research to consider both implicit and explicit power motives into the organizational conflict framework.

Artificial intelligence-based chatbots are frequently used to handle customer complaints in the hospitality and tourism sectors; however, little is known about their recovery strategies. Further, the widespread usage of chatbots is anticipated to affect customers' favorable responses. Therefore, this study aims to examine how chatbots’ symbolic recovery influences customer forgiveness through customer empathy and explore the moderating effect of time pressure on it. Moreover, it investigates the effect of customer forgiveness on customer reconciliation and customer continuous trust.

Structural equation modeling was used to analyze data collected from 994 customers who have experienced chatbot recovery in tourism and hospitality during the past four months.

The results show that chatbots’ symbolic recovery stimulates customer forgiveness, which subsequently positively affects customer reconciliation and customer continuous trust. Moreover, customer empathy partially mediates the effect of chatbots’ symbolic recovery on customer forgiveness, and time pressure plays a moderating role in the relationship between chatbots’ symbolic recovery and customer forgiveness.

The results offer highly persuasive insights that may be used to promote chatbots’ symbolic recovery in tourism organizations. The effectiveness of chatbots’ symbolic recovery in achieving customer forgiveness will motivate tourism organizations to use chatbots efficiently in service recovery.

This study extends the theoretical scope of chatbot research by investigating the symbolic recovery capabilities of chatbots. Moreover, it expands the application of SOR theory in the context of chatbot service recovery and reveals the underlying mechanism behind the impact of chatbots’ symbolic recovery on customer forgiveness, thus building and testing an integrative model of chatbot service recovery.

系统评估阻碍因素作为技术接受模型（TAM）的先驱方面存在一定的空白。本研究调查了三个阻碍因素, 即不适感、不安全感和风险。此外, 本研究提出了调节变量 - 个人能力（PC）, 并测试其对感知有用性（PU）、感知易用性（PEU）和行为意图（BI）之间关系的影响。

使用量化数据分析验证了通过Smart PLS4使用的调整模型。对327名有效受访者的数据进行了分析。

不适感是影响PU和PEU的显著先驱因素。不安全感和风险分别是PEU和PU的抑制因素。本研究在稀缺文献中贡献了个人能力的调节效应, 积极调节PU和BI之间影响的研究。

本研究通过纳入阻碍因素并探索个人能力在AR眼镜方面的调节作用, 为TAM提供了一种新的拓展。此外, 该研究还使创新公司能够通过用户的反馈来增强其产品和服务的设计。

This study aims to examine how brand transgression (BT) affects brand relationship quality (BRQ). Brand forgiveness (BF) and brand evangelism (BE) are tested as mediators between BT and BRQ. This study advances knowledge in consumer behavior by showing how consumers offer to deal with their relationships with brands through BE and BF. This provides relevant information to managers to seek strategies to obtain forgiveness from consumers in case the unfortunate happens. Not only must they seek to obtain forgiveness but also seek ways to ensure BE of their brands. These act as buffers for the brands in case a transgression happens.

Using a survey approach, this study tests whether forgiveness mediated the relationship between BT and BRQ. Also, whether BE mediated the relationship between BT and BRQ was examined. The test was conducted using PROCESS bootstrapping method (Model 4 of Hayes [2018]).

Consistent with the predictions, this study finds that, following a BT, consumers engage in BE and BF to maintain the relationship they have already established with their brands.

This study extends the existing literature by showing that after a BT, response from consumers may take different forms. The consumer’s response or the relationship with the brand may be affected by whether the consumer forgives the brand or evangelizes about the brand. Based on the cognitive dissonance theory, the results of this study imply that, forgiveness and BE act as important mechanisms in understanding consumer-brand relationships after brands act unacceptably. Further, this study contributes to the social media brand management literature by investigating a real-world BT case of social media.