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The aim of this study is first, to investigate the relative effect of user-generated content (UGC) and firm-generated content (FGC) on online brand advocacy, and second, to examine the mediation effect of customer engagement and the moderation effect of brand familiarity in the relationship between UGC and FGC and online brand advocacy. The differential impact of UGC and FGC on consumer behavior has yet to receive sufficient academic attention among hospitality scholars.

Based on social learning theory, cognitive consistency theory and schema theory, this study established an integrated research framework to explain the relationship between the constructs of the study. This study adopts a scenario-based experimental design in two separate studies within contexts to examine the proposed hypotheses.

The results revealed that UGC is a stronger predictor of online brand advocacy than FGC. A mediation analysis supported that the effect of digital content marketing types on online brand advocacy occurs because of customer engagement. Further, when the brand was familiar, participants showed a higher level of online brand advocacy than when they were exposed to FGC (vs. unfamiliar brand), whereas the effect of familiar and unfamiliar brands on online brand advocacy remains slightly close to each other when the participants were exposed to UGC. Brand familiarity positively enhanced participants’ engagement when they were exposed to UGC. Further, customer engagement is only a significant mediator when the brand is unfamiliar.

This paper presents significant managerial implications for hospitality companies about how they can effectively enhance brand advocacy in the online medium.

This research provides a novel contribution by examining the differential impact of UGC and FGC on online brand advocacy as well as uncovering the underlying mechanism of how and under what conditions user- and firm-generated content promotes online brand advocacy in the hospitality context.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

This study aims to empirically test how sponsored video customization (i.e. the degree to which a sponsored video is customized for a sponsoring brand) affects video shares differently depending on influencer characteristics (i.e. mega influencer and expert influencer) and brand characteristics (i.e. brand establishment and product involvement).

This study uses a unique real-world data set that combines coded variables (e.g. customization) and objective video performance (e.g. sharing) of 365 sponsored videos to test the hypotheses. A negative binomial model is used to analyze the data set.

This study finds that the effect of video customization on video shares varies across contexts. Video customization positively affects shares if they are made for well-established brands and high-involvement products but negatively influences shares if they are produced by mega and expert influencers.

This study extends the influencer marketing literature by focusing on a new media modality – sponsored video. Drawing on the multiple inference model and the persuasion knowledge theory, this study teases out different conditions under which video customization is more or less likely to foster audience engagement, which both influencers and brands care about. The chosen research setting may limit the generalizability of the findings of this study.

The findings suggest that mega and expert influencers need to consider if their endorsement would backfire on a highly customized video. Brands that aim to engage customers with highly-customized videos should gauge their decision by taking into consideration their years of establishment and product involvement. For video-sharing platforms, especially those that are planning to expand their businesses to include “matching-making services” for brands and influencers, the findings provide theory-based guidance on optimizing such matches.

This paper fulfills an urgent research need to study how brands and influencers should produce sponsored videos to achieve optimal outcomes.

This study aims to provide a holistic review of social media influencers (SMIs) research based on a unique approach of bibliometric analysis and content analysis between 2011 and 2020. The review examines the main influential aspects, themes and research streams to identify research directions for the future.

The sample selection and data collection were done from the Scopus database. The sample dataset was refined based on the inclusion and exclusion criteria to determine the final dataset of 183 articles. The dataset was exported in the BibTeX format and then imported into the BiblioShiny app for bibliometric analysis. The content analysis was done following the theory-context-methodology framework.

The several findings of this study include (1) Co-word analysis of most used keywords; (2) Longitudinal thematic evolution; (3) The focus of the research papers as per the theory-context-methodology review protocol are persuasion knowledge model, fashion and beauty industries, Instagram and content analysis, respectively; and (4) The network analysis of the research studies is known as the co-citation analysis and depicts the intellectual structure in the domain. This analysis resulted in four clusters of the research streams from the literature and two emergent themes (Chen et al., 2010)

In general, the previous reviews in the area are either domain, method or theory-based. Thus, this study aims to complement and extend the existing literature by presenting the overall picture of the SMI research with the help of a unique combined approach and further highlighting the trends and future research directions based on the findings of this study.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

Today social media capabilities have enabled businesses and enterprises to more collaboration, engagement and co-creation with their customers. So the current paper expands on this notion. The aim of this study is a bibliometric analysis to examine the trends of publications in the field of co-creation based on social media.

To data collection of quantitative analysis, Scopus database was selected and the collected data were analyzed using Bibliometrix-package. The Web of Science also was selected to retrieve highly cited and hot papers for qualitative part of analysis besides top 10 Scopus highest citation per year documents on June 6, 2020.

The results indicate insights into research trends pertaining to social media-based co-creation, as follows: starting jump to the publications occurred in this researches from the year 2008 and the growth trend is progressing in recent years; the stressful points are “co-design,” “co-creation” and “value co-creation” and concepts such as “open innovation,” “co-innovation” and “co-new product design” are new topics that guide future direction; the USA and UK are leaders in number of multiple and single publications; the most active and top journals that are better suited to achieving a high citation rate per year for a related paper were introduced. In addition, the top documents and highly cited papers were qualitatively analyzed on the basis of times cited per year.

The current study is not free of limitations. The database was limited to only Scopus. So the patterns and trends generated in the study may not be generalized to all social media-based co-creation research. Of course, the authors did not intend to ignore other contributions. It is mainly because of the number of documents retrieved from Scopus database and the coverage, Scopus was selected. Moreover, other types of research techniques such as correspondence analysis can be incorporated to generate additional meaningful insight.

In this time of social media and user-generated content portals, co-creation through social media has become quite popular. So the main innovation of this study is providing a visual presentation of the trends and patterns in the evaluation of social media-based co-creation from the first document about the research area published till 2020. The results of this paper can shed light on the factors that strengthen the contribution of studies in a research area. Generally, the bibliometric items the authors analyzed essentially show the entire field picture and guide researchers toward understanding future trends to produce impactful studies.

Cybersecurity competitions can effectively develop skills, but engaging a wide learner spectrum is challenging. This study aims to investigate the perceptions of cybersecurity competitions among Reddit users. These users constitute a substantial demographic of young individuals, often participating in communities oriented towards college students or cybersecurity enthusiasts. The authors specifically focus on novice learners who showed an interest in cybersecurity but have not participated in competitions. By understanding their views and concerns, the authors aim to devise strategies to encourage their continuous involvement in cybersecurity learning. The Reddit platform provides unique access to this significant demographic, contributing to enhancing and diversifying the cybersecurity workforce.

The authors propose to mine Reddit posts for information about learners’ attitudes, interests and experiences with cybersecurity competitions. To mine Reddit posts, the authors developed a text mining approach that integrates computational text mining and qualitative content analysis techniques, and the authors discussed the advantages of the integrated approach.

The authors' text mining approach was successful in extracting the major themes from the collected posts. The authors found that motivated learners would want to form a strategic way to facilitate their learning. In addition, hope and fear collide, which exposes the learners’ interests and challenges.

The authors discussed the findings to provide education and training experts with a thorough understanding of novice learners, allowing them to engage them in the cybersecurity industry.

This study aims to explore how social media affects decision-making among tourists and whether there is a potential effect of age, which is studied through generations. For this purpose, baby boomers, Gen X, Gen Y and Gen Z tourists are studied and real-time implications are offered.

The study adopts a descriptive and exploratory design where the conceptual model of social media-based decision-making is developed through a review of the literature. Quantitative analysis is conducted on primary data from 600 Indian tourists. This is done using a self-administered questionnaire adopted from Gulati (2022) after checking its validity and reliability. The statistical analysis for hypothesis testing is done using PLS-SEM path modelling on pooled data. To study the categorical moderating effect of generations, partial least squares multigroup analysis (PLS-MGA) is performed as a paired comparison on every successive generation.

After testing every successive younger generation with an older generation through PLS-MGA, none of the pairs found any significant differences in path coefficients, as the values obtained were 0.05 < p < 0.95 for all five paths (SM → NR, SM → IS, SM → E, SM → P, SM → PPB). This indicates all the generations behave in a similar manner irrespective of them being older or younger, and age does not moderate social media’s impact on decision-making among Indian tourists.

The study establishes India as a unique geographical market and suggests tourism marketers to treat all generations at par, irrespective of age, as they behave and interact with social media in a similar manner. But, because this study is restricted to a single geographical location, i.e. India, further regions can be explored for global generalisation. Future research can also explore other demographics for combined, moderated analysis. Findings from the study suggest that marketers should ensure that equal attention is given to all generations as they engage with social media in a similar manner. Targeted marketing using artificial intelligence can help in ensuring custom ads. Personalisation according to generations can also facilitate greater purchases.

The study fills a major population and knowledge gap by exploring a topic that has been highly under-researched. Also, the study adopts an inclusive approach by analysing all the generations, both younger and older, to understand the potential effect of age on moderating the impact that social media has on tourist decision-making. Further, real-time suggestions and implications are offered to tourism marketers with special reference to the Indian tourism industry.

Online food delivery applications (OFDAs) provide an expedient platform, and consumers’ access to food has been drastically altered, especially during and after the COVID-19 pandemic. This study aimed to completely explore the attributes that influence consumers' purchase intention and how an app's aesthetics can evoke feelings that predict continuous usage intentions for OFDAs. The food industry, especially restaurants, heavily relies on mobile technology to facilitate critical online food delivery during the pandemic crisis.

The data for this study are gathered from 477 food consumers located in the federal capital territory (FCT) of Islamabad, Pakistan, through convenient sampling by developing a self-administrated online survey. SmartPLS is used for structural equation modeling to test the proposed research model and perform bootstrapping and algorithmic analysis.

Our findings revealed that perceived value positively predicted consumers’ purchase intentions. Moreover, perceived value mediates the association of information quality, familiarity, time-saving, usability and reputation with purchase intentions and fear of COVID-19 moderates the relationship between perceived value and purchase intention.

This research work has significant implications for researchers, web developers, app designers, delivery services, restaurants and other enterprises as it demonstrates the importance of aesthetically pleasing OFDAs in eliciting positive emotions and bolstering consumers’ intentions to continue using the app for efficient food delivery services.

This study expanded the application of the technology acceptance model (TAM) and attention, interest, desire and action (AIDA) by examining consumers’ purchase intentions in the context of OFDAs. Further, the successful utilization of TAM enhanced the understanding of consumer perceptions and behavioral intentions about the usage of OFDAs.

This study of job advertisements for internal communication practitioners aims to investigate the signals that organisations are sending the profession about what is required of these roles. The concept of corporate voice – the “voice” of the organisation – is problematised to explore tensions in vocality. The aim is to support communication practitioners to navigate multi-vocality in the evolving professional context of digital communication technologies and changes in the workplace.

This qualitative study considers the role of voice in corporate communication practices and offers insights into “digital disruption” and the discursive pressure of employers' priorities on the profession and its practices. Job advertisements for internal communication practitioners were examined during 6-month periods in 2018, 2020 and 2022, which was a significant time of change for the profession with the global COVID-19 pandemic.

Qualitative content analysis of 514 internal communication job advertisements identifies that control and consistency are valorised, and continue to dominate descriptions of internal communication skills and responsibilities. The digital affordances that communication practitioners rely on has not changed significantly and a preference for “broadcasting” is evident.

This study provides insights into how Australian organisations shape and sustain univocal corporate communication practices, and the incompatibility of narrow configurations of voice with emerging organisational challenges such as social connectedness.