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The purpose of this paper is to present knowledge acquired through examining three cruise lines’ social media strategies over a three-year period, analyzing the network structures involved and demonstrating the value of the STAR (storytelling, triggers, amusement and reaction) model for enhancing social media activity.

This study gathered data from three cruise lines’ official websites and Facebook and Twitter accounts, examining variables such as internet presence, engagement and fans/followers. Furthermore, the work also addresses several issues that researchers encounter when using the STAR model.

Digital activity was found to vary significantly between the three cruise lines’ websites and Facebook and Twitter accounts, with the companies adopting different approaches and obtaining different results. Each company tended to have its own base of fans and followers, who shared a common language, reflected in the hashtags they used. The results show that cruise lines wishing to develop a content-oriented strategy that maximizes engagement in social media should share rich multimedia content that supports storytelling values and can be used on multiple platforms.

This work can be of interest to practitioners aiming to use a comparison and assessment tool for their digital activity. It could also assist future researchers focusing on cruise line activity, as few researchers have analyzed the online content strategies of cruise lines, particularly on Facebook and Twitter.

The digital relationship between brand and users, and brand and endorsers has been analyzed from different angles. The purpose of this paper is to investigate how these three elements co-create online the brand personality of the firm, through user-generated content.

This study gathered data from the hotels’ websites, Facebook, Twitter and TripAdvisor accounts, examining the content posted by the hotel, by tourists and by the celebrity endorsing some of the hotels. To pursue the aims, the brand personality dimensions communicated online were assessed through content analysis for the global presence and for each social network by user typology to establish the alignment of brand personality traits communicated.

Digital communication was found to vary significantly between the hotels and tourists in different social networks. The amount of content created by tourists is significantly higher than the ones produced by the hotel. The sincerity dimension of brand personality was confirmed in both communications. However, tourists’ brand image impacts brand personality differently than the hotels themselves. Furthermore, an analysis of the influence of customers on social networks indicates that celebrity personality traits seem to impact on the image of a hotel brand.

This research can be used to help brand managers to understand better the digital co-branding with clients and celebrity, as well as to identify gaps in their brand personality strategy. It could also assist future researchers focusing on digital celebrity endorsement since few researchers have analyzed digital communication in different social networks.

O relacionamento das marcas com os consumidores e das marcas com os endorsers tem sido analisado por diversos prismas. O objectivo deste trabalho é analisar como estes três elementos se relacionam e co-criam a personalidade de marca da empresa, através dos conteúdos digitais criados por estes.

Neste trabalho foram recolhidos os conteúdos e comentários com origem na empresa, nos clientes e nos endorsers existentes nas páginas web oficiais dos hotéis, bem como no Facebook, Twitter e TripAdvisor. Através da análise de conteúdo foram determinadas as dimensões da personalidade de marca existentes em cada uma das redes e para cada tipo de utilizador, com vista a determinar a consistência e o alinhamento da comunicação de marca existente.

Os resultados desta investigação apontam para a existência de diferenças significativas entre a comunicação com origem nos hotéis e a originada pelos turistas, nas redes sociais: os turistas criam mais conteúdos que os hotéis; e as dimensões da personalidade de marca comunicadas não são coincidentes, embora a dimensão sinceridade tenha sido encontrada nos conteúdos de ambos. As evidências sugerem que os traços de personalidade do endorser tendem a influenciar a personalidade da marca.

Este trabalho ajuda os gestores a se consciencializarem da importância da co-criação da imagem de marca que ocorre no domínio digital, bem como permite que identifiquem as lacunas existentes na comunicação da personalidade de marca das suas empresas. Este trabalho pode ser útil também para os investigadores que queiram analisar o papel das celebridades na comunicação digital.

The hospitality industry values segmentation and loyalty programs (LPs), but there is limited research on new methods for segmenting loyalty program members, so managers often rely on conventional techniques. This study aims to use big data-driven segmentation methods to cluster customers and provide a new solution for customer segmentation in hotel LPs.

Using the k-means algorithm, this study examined 498,655 profiles of guests enrolled in a multinational hotel chain’s loyalty program. The objective was to cluster guests according to their consumption behavior and monetary value and compare data-driven segments based on brand preferences, demographic data and monetary value with loyalty program tiers.

This study shows that current tier-based LPs lack features to improve customer segmentation, and some high-tier members generate less revenue than low-tier members. Therefore, more attention should be given to truly valuable customers.

Hotels can segment LP members to develop targeted campaigns and uncover new insights. This will help to transform LPs to make them more valuable and profitable and use differentiated rewards and strategies.

As not all guests or hotel brands benefit equally from LPs, additional segmentation is required to suit varying guest behaviors. Hotel managers can use data mining techniques to develop more efficient and valuable LPs with personalized strategies and rewards.

The rise of social media has led to the emergence of influencers and influencer marketing (IM) domains, which have become important areas of academic inquiry. However, despite its prominence as an area for study, several significant challenges must be addressed. One significant challenge involves identifying, assessing and recommending social media influencers (SMIs). This study proposes a semantic network model capable of measuring an influencer's performance on specific topics or subjects to address this issue. This study can assist managers in identifying suitable SMIs based on their estimated reach.

Data from popular YouTube influencers and publicly available performance measures (views and likes) are extracted. Second, the titles of the past videos made by the influencer are used to develop a semantic network connecting all the videos to other videos based on similarity measures. Third, the nearest neighbor approach extracts the neighbors of the target title video. Finally, based on the set of neighbors, a range prediction is made for the views and likes of the target video with the influencer.

The results show that the model can predict an accurate range of views and likes based on the suggested video titles and the content creator, with 69–78% accuracy across different influencers on YouTube.

The current study introduces a novel and innovative approach that exploits the textual association between a SMI's previous content to forecast the outcome of their future content. Although the findings are encouraging, this research recognizes various constraints that upcoming researchers may tackle. Forecasting views of posts concerning novel subjects and precisely adjusting video view counts based on their age constitute two primary limitations of this study.

Managers interested in hiring influencers can employ the suggested approach to evaluate an influencer's potential performance on a specific topic. This research aids managers in making informed decisions regarding influencer selection, utilizing data-based metrics that are simple to comprehend and explain.

The study contributes to outreach evaluation and better estimating the impact of SMIs using a novel semantic network approach.

This paper aims to determine whether the implementation of knowledge management (KM) is linked to e‐business performance and to identify the nature of the relationship existing in the different components of knowledge‐sharing and application and internet‐based KM.

This paper establishes a new model of the practices and results of the KM which has been tested in European companies. For that purpose, a structural equation modelling analysis was used.

The results show that product innovation and external employees’ access to databases have a strong positive effect on the maximization of internet‐based KM and that internet‐based KM has also a positive impact on e‐business performance.

Limitations of this study include the need for more research into the KM cycle. This paper contributes to the research on this topic with new evidence in a broad sample.

These results point to KM's usefulness in improving every day e‐business processes. Therefore managers should be aware of these benefits.

The present study advances knowledge on the nature of the relative importance of different components of internet‐based KM as drivers of e‐business performance.

The main goal of this study is to employ unsupervised (lexicon-based) learning approaches to identify readers' emotional dimensions and thumbs-up empathy reactions to reviews of online travel agency apps based on appraisal and stimulus–organism–response (SOR) theories.

Using the Google Play Scraper, we gathered a total of 402,431 reviews from the Google Play Store for two travel agency apps, Tripadvisor and Booking.com. Following the filtering and cleaning of user reviews, we used lexicon-based unsupervised machine learning algorithms to investigate the associations between various emotional dimensions of reviews and review readers' thumbs-up reactions.

The study's findings reveal that the sentiment of different sorts of reviews has a substantial influence on review readers' emotional experiences, causing them to give the app a thumbs up review. Furthermore, readers' thumbs-up responses to the text reviews differed depending on the eight emotional aspects of the reviews.

The results of this research can be applied in the development of online travel agency apps. The findings suggest that app developers can enhance users' emotional experiences by considering the sentiment and emotional aspects of reviews in their design and implementation. Additionally, the results can be used by travel agencies to improve their online reputation and attract more customers by providing a positive user experience.

The findings of this research have the potential to have a significant impact on society by providing insights into the emotional experiences of users when they engage with online travel agency apps. The study highlights the importance of considering the emotional aspect of user reviews, which can help app developers to create more user-friendly and empathetic products.

The current study is the first to evaluate the impact of users' thumbs-up empathetic reactions on user evaluations of online travel agency applications using unsupervised (lexicon-based) learning methodologies.

The aim of this research is to explore content, traffic and visibility on four social platforms to boost social visibility.

The study explores content, traffic and visibility in the context of Spanish beer brands. A sample of 3,332 beer brands' social media (SM) sites, specifically the four most commonly used platforms amongst Spaniards, was analysed. An inductive content analysis by a panel of experts identified the main contents. A cluster analysis then divided the significantly different beer brand SM sites, and a Kruskal–Wallis test confirmed the significant differences by content and traffic. To determine and predict SM visibility, a binary logistic regression was conducted.

The findings reveal that traffic is not significantly correlated with social visibility. Moreover, the SM sites with the highest traffic show significant leisure content. Twitter is significantly different network in traffic and content, whilst YouTube is the best for boosting social visibility.

The study's findings constitute valuable information in understanding how content, traffic and visibility are correlated and help in managing brands' public presence and exposure on SM.

This study contributes to the existing literature by exploring four SM platforms (Twitter, Instagram, YouTube and Facebook), two dimensions of SM interactions (traffic and social visibility) and three main focal points of contents (leisure, product and promotion). This research bridges the gap amongst content, traffic and social visibility and ascertains how to gain traffic and boost social visibility.

This study unravels the intellectual structure of virtual reality (VR) in digital marketing (DM) research, identifies core research gaps and presents future research avenues. The study also conducts a performance analysis of publications in the field and identifies the most important stakeholders of the stated topic.

The Web of Science database was used to retrieve the publications that were relevant to the topic between 2012 and 2022. Biblioshiny, a shiny app for the Bibliometrix R package, is used to conduct a bibliometric analysis by decoding the results into several visual representations.

This report includes the most prolific contributors, keyword analysis results, productive nations, authors and connections, as well as the most often cited publications on VR in DM. In DM research, numerous perspectives on VR were looked at, explored and revealed.

The findings provide a new perspective and understanding of the issue for researchers in order to improve their research insights in the field. This study will also benefit marketing practitioners in ensuring the sustainability and innovativeness of technology used to run their DM campaigns.

This research provides the first bibliometric analysis of the citation works and productivity in the field of VR in DM using Biblioshiny, identifies core research gaps and provides future research agenda which can be useful to advance the research understanding in this context.