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Campaigns to promote the COVID-19 vaccination messages to vaccine-hesitant consumers in the late stages of the pandemic are often met with resistance. This study aims to explore a way to leverage positive emotions induced from entertainment media consumption to promote vaccination messages to this audience group.

An online experiment was conducted with vaccine-hesitant consumers (N = 409). Participants viewed personally relevant entertainment music videos or mundane videos and vaccinated messages embedded in user-generated comments.

Data revealed that feelings of inspiration and nostalgia induced from entertainment media consumption increased vaccination intentions via increased risk perceptions and reduced anti-vaccination attitudes.

Social marketers should consider leveraging the combined effect of entertainment media-induced positive emotions and user-generated comments to motivate behavioral change among vaccine-hesitant individuals in the late stages of the COVID-19 pandemic.

The present study adds to social marketing literature by showing mechanisms that positive emotions induced from entertainment social media consumption might lead to health behavioral change.

This study aims to use big data analysis and sheds light on key hotel features that play a role in the revisit intention of customers. In addition, this study endeavors to highlight hotel features for different customer segments.

This study uses a machine learning method and analyzes around 100,000 reviews of customers of 100 selected hotels around the world where they had indicated on Trip Advisor their intention to return to a particular hotel. The important features of the hotels are then extracted in terms of the 7Ps of the marketing mix. This study has then segmented customers intending to revisit hotels, based on the similarities in their reviews.

In total, 71 important hotel features are extracted using text analysis of comments. The most important features are the room, staff, food and accessibility. Also, customers are segmented into 15 groups, and key hotel features important for each segment are highlighted.

In this research, the number of repetitions of words was used to identify key hotel features, whereas sentence-based analysis or group analysis of adjacent words can be used.

This study highlights key hotel features that are crucial for customers’ revisit intention and identifies related market segments that can support managers in better designing their strategies and allocating their resources.

By using text mining analysis, this study identifies and classifies important hotel features that are crucial for the revisit intention of customers based on the 7Ps. Methodologically, the authors suggest a comprehensive method to describe the revisit intention of hotel customers based on customer reviews.

This study aims to describe the development of hospitality research in terms of research methods and data sources used in the 2010s.

Content analyses of the research methods and data sources used in original hospitality research published in the 2010s in the Cornell Hospitality Quarterly (CQ), International Journal of Hospitality Management (IJHM), International Journal of Contemporary Hospitality Management (IJCHM), Journal of Hospitality and Tourism Research (JHTR) and International Hospitality Review (IHR) were conducted. It describes whether the time span, functional areas and geographic regions of data sources were related to the research methods and data sources.

Results from 2,759 original hospitality empirical articles showed that marketing research used various research methods and data sources. Most finance articles used archival data, while most human resources articles used survey designs with organizational data. In addition, only a small amount of research used data from Oceania, Africa and Latin America.

This study sheds some light on the development of hospitality research in terms of research method and data source usage. However, it only focused on five English-based journals from 2010–2019. Therefore, future studies may seek to understand the impact of the COVID-19 pandemic on research methods and data source usage in hospitality research.

This is the first study to examine five hospitality journals' research methods and data sources used in the last decade. It sheds light on the development of hospitality research in the previous decade and identifies new hospitality research avenues.

This study aims to investigate the relative impact of firm-created (FC) and user-generated (UG) social media marketing communication on fashionwear customers' purchase intention (CPI) in Sri Lanka. The primary objective is to identify the influence of social media marketing on the purchasing intention (PI) of customers in the fashionwear industry in Sri Lanka.

A standardized online survey was conducted, generating 312 datasets for analysis.

The empirical findings reveal that both firm-produced and UG social media fashionwear marketing communication has a significant influence on CPI. However, firm-produced social media fashionwear brands demonstrate a higher impact on CPI.

This study highlights the importance of social media marketing communication in shaping customers’ PI in the fashionwear industry in Sri Lanka. Both FC and UG content on social media platforms play a crucial role in influencing customers' intention to purchase fashionwear products. However, firm-produced social media fashionwear brands exert a stronger impact on CPI. These findings emphasize the need for marketers to incorporate effective social media strategies, including both FC and UG content, to enhance customer engagement and drive purchase decisions in the fashion-wear industry.

This study aimed to empirically examine the effectiveness of disclosing user comment history without disclosing personal identity as a nudge policy to refrain users from posting malicious content online.

The authors collected the number of comments and posters from the leading portal website in South Korea, Naver.com. To causally investigate the impacts of the new nudge policy on the number of comments and posters, the authors used the regression discontinuity design (RDD) approach.

The authors found that the new policy reduced all types of comments, including the number of malicious comments, self-deleted comments and current comments. This resulted in an overall decrease in the total number of posted comments, which is considered a side effect. In addition, the authors found that the effect of the nudge policy, which disclosed user comment history, has a stronger effect on older female users than their counterparts.

The study findings extend the current knowledge on a nudge policy being implemented by a website as a means to reduce malicious online content and how it impacts user content posting behaviors.

Several high street retail banks are extending their brands into digital banking through fully digital, app-only neobanks, which have been described as traditionally-driven neobanks (TDNBs). These TDNBs are considered a form of brand extension, representing the increased complexity of branding banks and financial institutions. This study explicitly addresses the branding strategies employed by TDNBs.

This study has adopted a case study research design, using a multi-stage data collection strategy. Initially, interviews were conducted with bank managers, followed by interviews with customers. Later, user-generated content was extracted through verified reviews from the app store. Subsequently, these three strands of data were thematically analysed and triangulated, in order to gain a holistic understanding of the branding strategies used by TDNBs.

Three key themes emerged regarding the branding strategies of the TDNBs: aligning with the parent brand, reinforcing the digital experience, and enhancing the brand image.

This study contributed to the growing body of research on marketing, branding, and digital transformation of bank services. As more traditional banks are exploring opportunities to pivot and explore other fintech options, this study offers significant insights that will help in managing brand experience and promotion across customer journeys in the banking sector.

This study contributes to the growing body of research on marketing, branding, and digital transformation of bank services. Even as more traditional banks explore opportunities to pivot as well as other fintech options, this study offers significant insights to help manage brand experience and promotion across customer journeys in the banking sector.

While previous studies on banking and financial services have concentrated on traditional retail and high street banks, there is a need for a greater understanding of the brand positioning of digital banks, especially those created by traditional banks.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

This study aims to investigate replies to the top 10 comments under Always “Like a Girl” YouTube femvertising video to gauge consumers’ responses regarding femvertising as well as relationships among commenters.

This study adopted a mixed research methods design. A user analysis and a qualitative content analysis were conducted to examine the replies of the top 10 comments with the most replies to reveal not only the topics but also relationships and patterns among those comments and commenters.

The user analysis found that across all the 10 comment-and-reply units, in 8 units, the user of the original primary comment, the conversation starter, was also the user who was targeted most often. The qualitative content analysis revealed four themes from the 10 comment-and-reply units: multilayered emotional responses, a gendered society, complex coexisting relationships and a melting pot.

The findings of this research offer significant extensions to the understanding of public sphere theory within the contemporary digital media landscape. By analyzing the nature of replies to digital advertisements, the study illuminates how various types of user engagement–whether it be inquiry, laudation, debate, or flame–play a critical role in shaping the digital public sphere.

The study underscores the importance for marketers to scrutinize both comments and replies to effectively utilize femvertising on social media, particularly YouTube. By understanding the emotional dynamics of user interactions, marketers can craft strategies that evoke positive responses and mitigate negative ones. Engaging with users who are open to changing their views or mediating discussions can also be beneficial, as can the use of AI tools to maintain focus on the content rather than on individual commenters. Such approaches can enhance the perception of femvertising campaigns and foster a more constructive dialogue within the social media space.

This study contributes to the literature by investigating the replies of comments, interactions, relationships and patterns among YouTube commenters that may generate valuable insights for advertisers and marketers to be aware of the possible issues and monitor the sentiment of commentaries, thus, developing effective strategies to better connect with consumers. This extends the understanding of public sphere theory in the contemporary digital media landscape.

With the growing popularity of digital engagement, this study explores the interrelationships among digital engagement, interactivity and engagement strategies from the perspective of practitioners.

Individual in-depth interviews were conducted with 27 practitioners who have been involved in marketing communication activities in Hong Kong.

It was found that practitioners interpreted digital engagement mainly from the cognitive and behavioral dimensions and organizations engaged with their target audiences with either transactional or transitional communications. Functional interactivity and medium interactivity were perceived as the basis of digital engagement.

This qualitative analysis enriches the extant literature in marketing and public relations by delineating the relationships between interactivity and the use of different levels of digital engagement strategies, as well as guiding practitioners in setting effective digital engagement strategies.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.