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This study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research.

This study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022.

Findings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites.

The applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes.

The paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set.

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.

Despite a significant focus on customer evaluation and sentiment analysis, limited attention has been paid to discrete emotional perspective in terms of the emotionality used in text. This paper aims to extend the general-sentiment dictionary in Chinese to a restaurant-domain-specific dictionary, visualize spatiotemporal sentiment trends, identify the main discrete emotions that affect customers’ ratings in a restaurant setting and identify constituents of influential emotions.

A total of 683,610 online restaurant reviews downloaded from Dianping.com were analyzed by a sentiment dictionary optimized by the authors; the main emotions (joy, love, trust, anger, sadness and surprise) that affect online ratings were explored by using multiple linear regression methods. After tracking these sentiment review texts, Latent Dirichlet Allocation (LDA) and LDA models with term frequency-inverse document frequency as weights were used to find the factors that constitute influential emotions.

The results show that it is viable to optimize or expand sentiment dictionary by word similarity. The findings highlight that love and anger have the highest effect on online ratings. The main factors that constitute consumers’ anger (local characteristics, incorrect food portions and unobtrusive location) and love (comfortable dining atmosphere, obvious local characteristics and complete supporting services) are identified. Different from previous studies, negativity bias is not observed, which poses a question of whether it has to do with Chinese culture.

These findings can help managers monitor the true quality of restaurant service in an area on time. Based on the results, restaurant operators can better decide which aspects they should pay more attention to; platforms can operate better and can have more manageable webpage settings; and consumers can easily capture the quality of restaurants to make better purchase decisions.

This study builds upon the existing general sentiment dictionary in Chinese and, to the best of the authors’ knowledge, is the first to provide a restaurant-domain-specific sentiment dictionary and use it for analysis. It also reveals the constituents of two prominent emotions (love and anger) in the case of restaurant reviews.

Drawing upon theories of modern discrimination, the present study focuses on cyber incivility and interpersonal incivility as mechanisms through which race leads to perceived discrimination. Participants included 408 full-time working adults who responded to an online survey. The results indicate that Non-White employees experience subtle forms of discrimination through the use of e-mail, which accentuate the need for organizations to eradicate workplace mistreatment so that their employees can avoid the adverse outcomes associated with experiencing cyber incivility. The purpose of this paper is to extend the understanding of selective incivility and concludes with directions for future research and practical implications.

Participants included 408 full-time working adults who responded to the survey online.

The results indicate that race was indirectly associated with discrimination through cyber incivility. The results indicate that Non-White employees experience subtle forms of discrimination through the use of technology and cyber space which accentuate the need for organizations to eradicate workplace incivility so that their employees can evade the adverse outcomes associated with experiencing incivility at work.

This study extends the understanding of selective incivility and concludes with directions for future research and practical implications.

This paper is the first to explore the relationship between race, cyber incivility and discrimination.

This study explored graduate students' academic success by testing a hypothesized model based on the self-determination theory (SDT), which posits that motivation, time management and career aspiration predicts perceived success.

A quantitative methodology was employed to garner data from a population of 324 graduate students, and then analyzed using structural equation modeling in R.

Intrinsic motivation was the strongest motivation type that predicted graduate students' perceived success. Time management was another important predictor of perceived success, while career aspiration did not impact students' perception of success. Doctoral students showed significantly higher relatedness when compared to master degree students. In addition, male students showed significantly higher career aspirations than females, while female students showed significantly higher time management than their male counterparts. The results of this study support the SDT as a framework to understand graduate students' academic success.

Implementing the research findings may increase graduate students' academic success. This study suggests direct ways of increasing graduate students' achievement through intrinsic motivation, time management and autonomy, as well as reducing amotivation (lack of motivation) to indirectly enhance academic success.

This study aims to reveal the influencing factors of user churn behavior and explore how these factors influence user churn behavior of rural public digital cultural services (RPDCS), and then, to provide the avoidance strategies for user churn behavior of RPDCS.

Combined with the stimulus–organism–response theory and cognitive load theory, this study constructed a mixed model of user churn behavior. Data collected through online and offline questionnaire survey were tested using the partial least squares structural equation modeling approach, and finally, the authors proposed a user churn behavior model of RPDCS.

The results indicate that the environmental stimulus factors of RPDCS affected user churn behavior via user organism factors. This study suggests that administrators should pay more attention to the information demand of users and strengthen the effective supply of RPDCS. Meanwhile, it is necessary to improve the information literacy of rural users to restrain the user churn behavior and improve the effectiveness of RPDCS.

The research findings on the influencing factors of user churn behavior shed light on the user churn behavior in public digital cultural services, add new knowledge to the construction of the public cultural services system and provide empirical evidence for how to improve the utilization and effectiveness of RPDCS.

The purpose of this paper is to systematically review the literature on innovation and the food supply chain to synthesise and explore their interactions, determining what it is known and what gaps there are in the knowledge regarding these subjects.

A systematic review of technological innovation and the food supply chain was conducted based on the Methodi Ordinatio protocol. This method seeks to select and rank papers according to their scientific relevance.

This study has highlighted the importance of research focused on specific matters, such as food packaging, integration, Big Data and bio-economy. Considering the stages of innovation, the portfolio has focused mainly on innovations’ generation. As for adoption, the multiple obstacles responsible for the few successful innovations were highlighted. Adopting these innovations has become complex, with a high level of failure and several critical points, ranging from the level of research to acceptance and purchase, with consumer indifference and even negative positions towards innovation.

This paper contributes to the debate about innovations in the food supply chain, providing a research agenda.

The purpose of this paper is to assess the reliability and validity of the Spanish version of the Davidson trauma scale (DTS-S) and to determine the prevalence and correlates of post-traumatic stress disorder (PTSD) symptoms in a non-clinical random sample of prison inmates.

Probabilistic samples of 1,179 inmates from 26 penal institutions in Puerto Rico were selected using a multistage sampling design. Population estimates and correlations were obtained for PTSD, generalized anxiety and depression. The reliability, factor structure, and convergent validity of the DTS-S were assessed. Cross-validation was employed to confirm the results of the factor analyses.

Using the cut-offs adopted by the scale's author, 136 (13.4 percent) of the inmates are likely to have current PTSD and 117 (11.6 percent) reach the cut-off for sub-threshold PTSD. Confirmatory factor analysis generated two factors explaining 53 percent of the variance. High reliabilities were obtained for the total scale (α=0.95) and for the frequency and severity scales (α=0.90 and 0.91). Significantly higher DTS-S scores were found for females (t=2.26, p<0.025), for inmates diagnosed with depression or anxiety (t=2.02, p<0.05), and those reporting suicide attempts (t=4.47, p<0.0001).

Findings support that the DTS-S is a reliable and valid measure to assess PTSD symptoms in Latino inmate populations and to identify individuals at risk for the disorder that require confirmatory diagnosis and clinical interventions.

This paper aims to examine the impact of CEO (chief executive officer) compensation and CEO attributes on the level of tax aggressiveness of French companies.

The sample comprises 180 firm-year observations of 40 companies listed on the CAC 40 during the period ranging from 2008 to 2018. For the purpose of overcoming the problems of heteroscedasticity and autocorrelation, the authors apply the generalized least square panel regression.

This study’s results corroborate the importance of CEO compensation and CEO attributes as determinants of tax aggressiveness. In addition, the authors come up with the fact that CEO compensation has a negative effect on tax aggressiveness, and that older CEOs and CEOs with accounting expertise are negatively linked with tax aggressiveness. The authors also find out that there is a positive relationship between the CEO tenure and tax aggressiveness. Moreover, the authors report that foreign CEOs are more likely to engage in tax aggressiveness practices than local CEOs.

The unavailability of all annual reports and the use of only one proxy to measure tax aggressiveness present limitations. This study shows significant implications for shareholders, regulators and researchers. As a matter of fact, shareholders will observe the effect of appointing a foreign CEO on the tax aggressiveness level. This study may also provide regulators with new ideas regarding the role of the CEO and its impact on aggressive decision-making. And it brings forth new insight for researchers through adding a foreign CEO as a new determinant of tax aggressiveness.

According to the authors’ knowledge, this study is the first to provide empirical evidence regarding the effect of both CEO compensation and CEO attributes on tax aggressiveness. It also looks into the impact of a foreign CEO on tax aggressiveness.

This study aims to explore the relationship between elderly users' trust in public digital cultural services (PDCS) and their intention to use PDCS, and reveal the factors affecting their intentions from the perspective of trust to make recommendations that will increase their intention to use PDCS.

Combined with the trust building model and social exchange theory, this study constructed a conceptual model of elderly users' intention to use PDCS. Data collected from Chinese elderly users who have reached the age of 60 through questionnaire surveys were tested using the structural equation model with partial least squares. Finally, the authors proposed a model of elderly users' intention to use PDCS.

This study finds that elderly users' trust positively affects their intention to use PDCS from two aspects: service features and user features of PDCS. Concerning the service features, system quality directly affects elderly users' trust in PDCS most significantly, followed by information quality and service reputation. Concerning the user features, perceived value has a higher impact on elderly users' trust than that of service features, and information literacy and information quality directly affect perceived value.

This study adds new knowledge to the users' behavior of PDCS and enriches the prior description of PDCS. The recommendations made in this study provide a series of strategies for practitioners and researchers to improve the elderly users' intention to use PDCS and bridge the silver digital divide, which offers new ideas for improving the efficiency of PDCS.