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The outbreak and continuation of COVID-19 have spawned the transformation of traditional teaching models to a certain extent. The Chinese Ministry of Education’s guidance on “keep learning and teaching during class suspension” has made OTC and learning (OTC) become routinized, and the public’s emotional attitudes toward OTC have also evolved over time. The purpose of this study is to segment the emotional text data and introduce it into the topic model to reveal the evolution process and stage characteristics of public emotional polarity and public opinion of OTC topics during public health emergencies in the context of social media participation. The research has important guiding significance for the development of OTC and can influence and improve the efficiency and effect of OTC to a certain extent. The analysis of online public opinion can provide suggestions for the government and media to guide the trend of public opinion and optimize the OTC model.

This paper takes the topic of “OTC” on Zhihu during the COVID-19 epidemic as an example, combined with the characteristics of public opinion changes, chooses Boson emotional dictionary and time series analysis method to build an OTC network public opinion theme evolution analysis framework that integrates emotional analysis and topic mining. Finally, an empirical analysis of the dynamic evolution of the communication network for each stage of the life cycle of a specific topic is realized.

This paper draws the following conclusions: (1) Through the emotional value table and the change trend chart of the number of comments, the analysis found that the number of positive comments is greater than the number of negative comments, which can be inferred that the public gradually accepts “OTC” and presents a positive emotional state. (2) By observing the changing trend of the average daily emotional value of the public, it is found that the overall emotional value shows a stable development trend after a large fluctuation. From the actual emotional value and the fitted emotional value curve, it can be seen that the overall curve fit is good, so ARIMA (12, 1, 6) can accurately predict the dynamic trend of the daily average emotional value in this paper. Therefore, based on the above-mentioned public opinion, emotional analysis research, relevant countermeasures and suggestions are put forward, which is conducive to guiding the development direction of public opinion in a positive way.

Taking the topic of “OTC” in Zhihu as an example, this paper combines Boson emotional dictionary and time series to conduct a series of research analyses. Boson emotional dictionary can analyze the public’s emotional tendency, and time series can well analyze the intrinsic structure and complex features of the data to predict the future values. The combination of the two research methods allows for an adequate and unique study of public emotional polarization and the evolution of public opinion.

Nowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.

The article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.

It was found that the sequence has typical non-linear chaotic characteristics; its correlation dimension indicates that it contains obvious fractal characteristics; the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.

Based on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

Microblogging is an important channel used to disseminate online public opinion during an emergency. Analyzing the features and evolution mechanism of online public opinion during an emergency plays a significant role in crisis management.

This paper uses the event of Hurricane Irma and combines it with the life cycle of online public opinion evolution to understand the effect of different types of emotional (joy, anger, sadness, fear, disgust) microblogs (tweets) on information dissemination. The research was performed in the context of Hurricane Irma by using tweets associated with that event.

This paper demonstrates that negative emotional information has a greater communication effect, and further, the target audience that receives more exposure to negative emotional microblogs has a stronger tendency to retweet. Meanwhile, emotions expressed in tweets and the life cycle of public opinion evolution exert interactive effects on the retweeting behavior of the target audience.

For future research, a professional dictionary and the context should be taken into consideration to make the modeling in the text more normative and analyzable.

This paper aims to reveal how the emotions of a tweet affect its virality in terms of diffusion volume in the context of an emergency event.

The conclusion made in this paper can shed light on the real-time regulation and public opinion transmission, as well as for efficient intelligence service and emergency management.

In this study, Hurricane Irma is taken as an example to explore the factors influencing the information dissemination during emergencies on the social media environment. The relationship between the sentiment of a tweet and the life cycle of public opinion and its effect on tweet volume were investigated.

Hospitality workers are emotional labor workers because they must display appropriate emotions to their customers to provide outstanding service. Emotional intelligence (EI) helps employees regulate their emotions and display appropriate emotions, and hence should help hospitality workers provide outstanding service. However, the strength of the relationship between EI and hospitality workers’ job performance substantially varied across studies. Hence, the purpose of the present study is to clarify the mixed findings and to examine if EI can improve hospitality workers’ job performance.

A meta-analysis was performed to investigate the relationship between EI and hospitality workers’ job performance as well as the moderators which condition this relationship.

The present meta-analysis indicated that EI is positively related to hospitality workers’ job performance (ρ̅̂ = 0.54); the relationship between EI and hospitality workers’ job performance is stronger when the percentage of married subjects is low and in feminine cultures; and this relationship does not differ between male-dominated and female-dominated studies, across educational levels, between collectivistic and individualistic cultures, between low and high power distance cultures and between low and high uncertainty avoidance cultures.

This study uncovers theoretically important moderators that contribute to cross-cultural research, work–family literature and gender-related literature in hospitality research.

The present study builds a theoretical foundation and performs a meta-analysis to elucidate the relationship between EI and hospitality workers’ job performance and to identify the moderators which condition this relationship.

Premised on the conservation of resources (COR) theory, our study attempts to investigate if emotional exhaustion (EE) is a mediator in linking customer incivility (CI) and employee job performance (JP). Additionally, it examines the moderating role of PsyCap in customer incivility and job performance relationships. Furthermore, we explore whether PsyCap moderates the mediating effect of emotional exhaustion in customer incivility and job performance relationships.

Using a multi-phase survey, data was gathered from 469 frontline employees working in diverse service sectors in India. All analyses were done with SPSS 25 and AMOS 23 to determine the study's findings.

The study substantiates a partial mediation effect, revealing that customer incivility intensifies emotional exhaustion among FLEs, resulting in decreased job performance. In addition, the interaction of PsyCap seemed to weaken the relationship between CI and employee JP. However, PsyCap does not mitigate the indirect association of CI and employee JP via emotional exhaustion. To our surprise, we observed a slight amplification in the mediating impact of emotional EE at elevated levels of PsyCap.

The study findings suggest that training employees and cultivating their PsyCap could be a strategic solution for effectively managing and mitigating the adverse effects of customer incivility in the workplace.

Our study marks the first empirical exploration of PsyCap's moderating role between customer incivility and both job performance and emotional exhaustion linkage.

This paper investigates the factors affecting individual dispositions toward intentions. Specifically, this study aims to investigate the effects of emotional intelligence, entrepreneurial motivation and creativity on the entrepreneurial intentions of university students in Jordan.

The data were gathered through self-administered questionnaires from a sample of 745 university students in Jordan. The hypotheses were tested using partial least squares (PLS) analysis.

The results indicate a robust relationship between entrepreneurial motivation and creativity and entrepreneurial intention. However, an insignificant relationship was found between emotional intelligence and entrepreneurial intention. The findings of this study reveal that both creativity and emotional intelligence exert a significant and positive indirect effect on entrepreneurial intentions through the mediation of entrepreneurial motivation.

This work suggests that university administrators should consider the significant roles that emotional intelligence, entrepreneurial motivation and creativity play in shaping the entrepreneurial intentions of university students.

While there is limited existing research on this matter within the Middle Eastern context, especially in Jordan, this study aims to address this gap in the Jordanian entrepreneurial literature. As the first of its kind conducted within Jordanian universities, this study contributes significantly to the understanding of entrepreneurial dynamics in the region. Using the Wong and Law Emotional Intelligence Scale, this study evaluates emotional intelligence in Jordan. The results contribute to social psychology literature and suggest actionable government measures, integrated into education systems, to encourage entrepreneurial paths for students and alleviate graduate unemployment.

In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.

The study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.

In the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.

Previous research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

This paper aims to focus on how salespeople use emotions to build connections and facilitate value proposition co-creation (VPCC) in B2B complex services sales.

This paper uses video recordings of authentic B2B sales meetings in a two-part qualitative study.

This paper proposes a set of salesperson emotional behaviors that influence the co-creation of value propositions with customers. This paper uncovers five salesperson emotional behavior archetypes influencing VPCC.

This study advances the value proposition literature by linking salespeople’s emotional behaviors with micro-level activities in the collaborative crafting of value propositions. The unique methodology may encourage researchers to apply video recordings in future studies.

The study provides managerial guidelines for improved selling competence and sales team organization.

This study’s findings represent a new insight into the actual manifestations of salesperson emotional behaviors that are commonly discussed but rarely observed directly.

Given the socialisation of men and women to their gender roles and expression of emotion, this study aims to investigate whether there are gender differences in the use of emotive language in electronic word-of-mouth (eWOM), specifically in online reviews. The authors propose that female reviewers will use strong emotive terms, such as love, more frequently in online reviews than do male reviewers. The authors further propose that the gender of the reviewer influences audience responses to the reviewer’s use of emotive terms in online reviews.

The authors conducted secondary data analysis of restaurant reviews (Study 1) to provide evidence on whether the gender of the reviewer affects the frequency of use of emotive terms in an online review. In addition, three separate experiments (Studies 2–4) were conducted to test the theoretical arguments.

The results of the secondary data analysis indicated that female online reviewers used the term “love” much more frequently in their reviews than male reviewers, whereas there was no usage difference for the term “like”. The experimental studies further showed that an emotive review by a male reviewer containing the word “love” resulted in a higher evaluation of the restaurant being reviewed than a non-emotive review containing the word “like”. This difference was stronger when the overall rating was less salient and for consumers who believe (vs do not believe) that men and women use emotional language differently.

First, the paper extends our understanding of gender differences in emotional expression within the domain of eWOM and online reviews as well as our understanding of consumer responses to these gender differences. Second, the authors identify a boundary condition for these gender effects, namely, the overall rating score. Third, the authors find that consumer beliefs regarding gender stereotypes in emotional expression provide an explanation for these effects.

The results of the research indicate that the electronic algorithms operating on review sites might be modified in terms of their criteria for selecting the reviews to display to consumers, as consumer decision-makers may find greater utility in reviews written by male reviewers that contain strongly positive emotive terms.

The research extends the knowledge on gender differences in emotional expression in online reviews by demonstrating the actual usage patterns and differing responses to the emotional expressions of each gender.