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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.

Scholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.

To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.

The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.

Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.

The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.

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.

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.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

Despite similarities, research across family business and business advising forwards contradictory conclusions when considering family business advising. The authors seek to integrate these literature and in doing so uncover both the hurdles facing family business advisors attempting to adapt tools developed in corporate advising to the family business context as well as the potential for greater integration of these streams in ways that contribute to both family business and advising research and practice.

Primary data were collected both in the form of a survey questionnaire and website marketing content. In the survey, 47 family business advisors evaluated the distinctiveness of their family business clients across structural, cognitive and relational social capital dimensions. Motivated by unexpected findings, a content analysis of advisor websites uncovered specific marketing themes that illustrate the divides between family business advising and scholarship.

Family business advisors reliably acknowledge structural and cognitive social capital as preeminently characterizing the distinctiveness of their family business clients. Expanding on this, the authors’ findings suggest that the urgency signaled in advisor marketing via their websites may inspire tactics misaligned with the long-term time horizon typically characterizing family businesses strategy.

The few family business advising studies that exist predominantly consider post-hoc evaluation of advising by family business clients. The primary data the authors collect are unique in the literature in that the data detail how family business advisors perceive and engage with potential clients.

This study aims to propose a longitudinal analysis of motivations for Integrated Reporting (IR) adoption, internal changes, the benefits of IR implementation and compliance challenges.

The authors analyse a longitudinal case study of an Italian-listed company (Eni) participating in the IR-Pilot Programme (PP) and covering 10 years of IR adoption. The analysis was based on a mixed-method approach that included semi-structured interviews, content analysis of annual reports and triangulation with other data sources. Results are discussed regarding institutional theory, legitimacy theory and diffusion of innovation theory.

The study suggests that motivations for adopting IR change over time and participation in the IR-PP helps Eni acquire a comprehensive and substantial integrated view of value creation over time, makes integrated culture a key factor for strategic business sustainability and confirms the readiness of early adopters to comply with the non-financial Directive (NFD).

This study, among the few longitudinal case studies, provides organisations, regulators and academics with insights into the motivations driving the successful adoption and implementation of IR and the NFD. The results may help companies consider one of the tools currently deemed to bring sustainability into action and participation in pilot groups.

This study aims to review the current status of quality management (QM) research in Arab countries between 2001 and 2020.

The study adopted the content analysis methodology, searching through various databases and search engines for relevant publications using several keywords. The selected publications were classified according to several criteria and the obtained results were presented in the form of frequencies and percentages.

Most research publications regarding QM were journal articles. The number of publications has steadily increased between 2001 and 2020. Moreover, QM research largely uses the quantitative research design. Questionnaire surveys are widely used as a data collection method; basic statistical analysis techniques are commonly employed to analyze the data. There is a tendency toward empirical research versus conceptual research. A few journal articles were published in reputed peer-reviewed international journals with low citation. Overall, Arab scholars research on QM and related topics over the past two decades is not significant for the field considering the number of published papers, citations and the papers published in reputed peer-reviewed international journals.

This study has several limitations. First, it does not cover non-English information sources due to the overall lack of Arabic publication databases. Second, it uses a limited number of criteria to classify the selected publications. Third, it adopts the content analysis methodology to classify the selected publications. This method has several limitations, which may negatively affect the results. Nevertheless, the study offers several implications for research scholars, educators and practitioners.

This is the first study to attempt a comprehensive overview of the state of research on QM in Arab countries between 2001 and 2020 using the content analysis methodology.

The purpose of this study is to conduct a thematic content analysis of facial emotion recognition (FER) research within the context of the hospitality and tourism industry. Through this analysis, the study aims to identify key themes, trends and implications of the utilization of FER technology in enhancing customer emotions and experiences within hospitality and tourism settings.

This is qualitative research that utilizes thematic content analysis. The research data were obtained from the Scopus database. A total of 45 articles (titles, abstracts and keywords) were coded into MAXQDA and VOSWiever programs for data analyses and mapping.

Based on the analyses, the predominant term used in titles was emotion, indicating its centrality in the research domain. Moreover, the most prevalent concepts in this field were emotion and experience. Notably, facial emotion recognition emerged as the most frequently utilized term within this context. Within the hospitality and tourism industry, FER was primarily employed within the travel sub-branch. Finally, the research culminated in the visualization of the theoretical framework and conceptual background, offering a comprehensive overview of the field.

There is a growing demand for using FER technology specifically within the hospitality and tourism industry context; therefore, growing scientific research has been conducted on this topic recently. By conducting a thematic content analysis, this study uncovered novel insights into the utilization of this technology to enhance customer emotions and experiences, thereby contributing to a deeper understanding of its potential implications and applications within the hospitality and tourism industry.

This paper aims to illustrate, by means of a content analysis of 278 weekly School Meeting minutes, the ways in which student voice is actualized in one democratic free school in Germany.

This paper uses a qualitative content analysis methodology of 278 weekly School Meetings minutes.

This paper uses Fielding’s (2012) patterns of partnership typology to illustrate what counts as student voice and participation in a democratic free school.

Limitations included being reliant on translations of German texts, some missing minutes from the entire set, the lack of a single author for the minutes (and thus degree of detail differs) and the fact that the School Meeting minutes make reference to other meetings for various sub-committees for which no minutes exist, and thus, findings on the degree of student voice may be limited. And because this is a study of one school, generalizability may be difficult. Future research into these sub-committee meetings would prove helpful as well as content analyses of other democratic free schools’ meeting minutes.

This study can help people more deeply understand what goes on in democratic free schools and what student voice and participation can mean within this context.