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The ongoing COVID-19 pandemic highlights the importance of destination marketing and media profiling to re-engage international tourists. However, potential crisis-induced nation brand (NB) deficits must be addressed to re-ignite tourism demand. The study examines the possible intervening effect of the contemporary NB in the international destination marketing and media-travel motives nexus.

A deductive quantitative study was undertaken with an online Amazon Mechanical Turk sample of n = 454 respondents. Hypotheses were tested using PROCESS Macro, Model 4.

The results show that the NB [people and negative events] had a practically significant partial mediating effect in the destination marketing – nature-cultural oriented travel motivation nexus.

New insights are provided via a practical model which facilitates the measurement of potential nuances in the influence of destination marketing and media profiling on leisure tourists' travel motives amid crises. The intervening effect implies that a better understanding of the NB as an indirect antecedent to travel motivation may result in more effective crisis communications and tourism recovery-oriented marketing.

The study is amongst the first to extend marketing and behavioural theory to explore the interplay between the marketing and media profile, a nation's brand and tourists' travel behaviour amid a crisis. The study addresses a discernible dearth of knowledge related to the influence of the NB on tourist behaviour from an emerging market perspective.

As higher education moves to formats that are not face-to-face classes in the wake of a global pandemic, educators need research-based guidelines to inform instructional planning and implementation. This study aims to provide recommendations for teaching with social media, as a complement and enhancement to traditional online teaching approaches.

The study draws on reviews of the research literature and the authors’ own experiences in studying and integrating social media into remote teaching and learning in university settings.

Learning environments that blend asynchronous online elements, where students can go at their own pace, on their own time, have some choice over their learning and are regularly and meaningfully engaging with other students, their teacher and the subject matter are most successful for student learning. Social media, with its affordances for personal profiling, relationship-building, content creation and socializing, when thoughtfully integrated into an online education plan, can help students and teachers stay connected while apart, enhance students’ engagement and make remote learning seem less remote.

The paper includes instructional guidelines for instructors and instructional designers in various post-secondary settings who seek to integrate social media as part of their strategy for remote higher education.

This study fulfills an identified need for pragmatic approaches to online higher education using social media.

Partisan news media, which often publish extremely biased, one-sided or even false news, are gaining popularity world-wide and represent a major societal issue. Due to a growing number of such media, a need for automatic detection approaches is of high demand. Automatic detection relies on various indicators (e.g. content characteristics) to identify new partisan media candidates and to predict their level of partisanship. The aim of the research is to investigate to a deeper extent whether it would be appropriate to rely on the hyperlinks as possible indicators for better automatic partisan news media detection.

The authors utilized hyperlink network analysis to study the hyperlinks of partisan and mainstream media. The dataset involved the hyperlinks of 18 mainstream media and 15 partisan media in Slovakia and Czech Republic. More than 171 million domain pairs of inbound and outbound hyperlinks of selected online news media were collected with Ahrefs tool, analyzed and visualized with Gephi software. Additionally, 300 articles covering COVID-19 from both types of media were selected for content analysis of hyperlinks to verify the reliability of quantitative analysis and to provide more detailed analysis.

The authors conclude that hyperlinks are reliable indicators of media affinity and linking patterns could contribute to partisan news detection. The authors found out that especially the incoming links with dofollow attribute to news websites are reliable indicators for assessing the type of media, as partisan media rarely receive links with dofollow attribute from mainstream media. The outgoing links are not such reliable indicators as both mainstream and partisan media link to mainstream sources similarly.

In contrast to the extensive amount of research aiming at fake news detection within a piece of text or multimedia content (e.g. news articles, social media posts), the authors shift to characterization of the whole news media. In addition, the authors did a geographical shift from more researched US-based media to so far under-researched European context, particularly Central Europe. The results and conclusions can serve as a guide how to derive new features for an automatic detection of possibly partisan news media by means of artificial intelligence (AI).

The peer review history for this article is available at the following link: https://publons.com/publon/10.1108/OIR-10-2020-0441.

It is generally recognised that companies spend approximately 50% of their marketing budget on promotional activities. Advertising belongs to the most visible areas of a company’s activity. Therefore, it should not be surprising that the average recipient associates marketing with advertising, competitions and leaflets about new promotions delivered to houses or offices. Advertising, especially Internet advertising, is one of the most effective forms of marketing and one of the fastest developing areas of business. New channels of communication are emerging all the time – the Internet, digital television, mobile telephony; accompanied by new forms, such as the so-called ambient media. Advertising benefits from the achievements of many fields of science, that is, psychology, sociology, statistics, medicine and economics. At the same time, it combines science and the arts – it requires both knowledge and intuition. Contemporary advertising has different forms and areas of activity; yet it is always closely linked with the operations of a company – it is a form of marketing communication.

The indices of marketing communication presented in this chapter are generally known and used not only by advertising agencies but also by the marketing departments of many organisations. Brand awareness, advertising scope and frequency, the penetration index or the response rate belong to the most widely used indices; others, like the conversion rate or the affinity index, will get increasingly more significant along with the process of professionalisation of the environment of marketing specialists in Poland and with increased pressure on measuring marketing activities. Marketing indices are used for not only planning activities, but also their evaluation; some of them, such as telemarketing, mailing and coupons, provide an extensive array of possibilities of performance evaluation.

Now more than ever, businesses need to understand what the media is saying about them. The authors describe three types of media analysis: salience and sentiment analysis; theme and contradiction analysis; and problem and solution analysis, the first two of which are routinely commissioned by many companies. Using four case studies the authors describe how problem and solution analysis can be used to save costs and increase revenues.

Four case studies are used to illustrate the financial value that problem and solution media analysis can play in understanding and solving a range of business problems.

The authors show how the analysis of media commentary helped a public company to identify its most influential investment commentators; helped an appliance manufacturer to change its sales force compensation scheme; helped a financial services company to position its IPO; and helped an internet‐based share trading company to understand some conflicting research results. The financial value of these outcomes often far exceeded the price paid.

The authors compare and contrast three styles of media analytics. The review suggests that the problem and solution analysis technique is novel and financially valuable in situations where media coverage creates problems.

This article provides insight into researchers' use of academic web profiles and an understanding of how the influencing factors highlighted in the literature interact with each other, affecting researchers' motivation to use web profiles.

Based on motivation theory and literature related to the use of online profile platforms and academic web profiles, the authors present a conceptual framework for motivation factors influencing researchers' use of academic web profiles. The authors use qualitative interviews with researchers to explore and enrich the conceptual framework.

The conceptual framework of researchers' motivation space shows the relationships and influences between internal and external motivation in connection to three main categories (Identity and professional goals, Organisation and guidelines, Platforms and technology) and 12 more specific aspects of motivation that all play a role in choices regarding academic online profiles and platforms. Personality also plays an important role in itself – and not always in support of professional goals or workplace guidelines.

The study shows that a holistic perspective is necessary to understand the high degree of complexity in terms of researchers' motivation to use academic online profiles, and the presented conceptual framework can be used to understand and activate motivation factors.

The study aimed to examine how different communities concerned with dementia engage and interact on Twitter.

A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.

Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.

The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.

This study focuses on managers' perceptions of employees' communicative role in social media, and explores the changes in the contractual nature of employment relations in mediatized workplaces in which the boundaries of professional and private life are becoming more fluid.

A qualitative approach was employed to explore this relatively new phenomenon. The data, comprising 24 interviews with managers responsible for corporate communication and human resources in knowledge-intensive organizations, was thematically analysed.

The analysis shows that employees' work-related social media use creates new types of exchanges and dependencies between an organization and individual employees, which relate to employees' representation, knowledge and networks.

The study is among the first to examine the exchanges and dependencies in an employment relationship that emerge from increased use of social media for professional purposes.

This paper aims to detect opinion leaders, who they play a vital role as influencers of their community, which will help companies to improve their image in social media. This idea came with the fast development of social media, where individuals are increasingly sharing their personal experiences, opinions and critiques about products through these platforms. Thus, the new customers can rely on these spontaneous recommendations to proceed with the purchase without risk of disappointment. Therefore, the mismanagement of the e-reputation can cause huge losses for companies.

In this study, a product reputation framework based on the prediction of opinion leaders is presented. To do so, opinion mining has been used to determine the product reputation in social media. In addition to posts processing, the profile information has also exploited to predict opinion leaders. To achieve the authors’ goal, spammers and duplicated profiles have been detected to improve the product reputation results.

The effectiveness of this approach has been tested using a social media simulation. The obtained results show that this approach is efficient and more accurate compared to the classical solutions.

The key novelty is the gathering of spammer detection criteria with different weights and the profiles matching by providing the suitable matching methods that take into account the profile’s attributes types. Consequently, a different similarity measure was assigned for each of the considered four attributes types. These two steps can ensure that the results obtained from social media are actually supported by opinions extracted directly from the real physical consumers.

Advances in artificial intelligence have ushered in new opportunities for marketers in the domain of content generation. We discuss approaches that have emerged to generate text and image content. Drawing on the customer equity framework, we then discuss the potential applications of automated content generation for customer acquisition, relationship development, and customer retention. We conclude by discussing important considerations that businesses must make prior to adopting automated content generation.