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This chapter reviews the growth of data analytics in sport by taking a social media perspective. There has been an emphasis on the use of real-time statistics by sport organizations in order to increase their performance in the marketplace. This has been made possible by increased emphasis on technological innovations that enable data analytics to be conducted. The current developments and areas of application to the sport industry are stated and prospects for future advancement noted. This includes a focus on emerging trends in artificial intelligence and social media marketing. A concluding section focusing on the implications of current research and practice stresses the need for more novel approaches to be used.

This chapter examines the ways social media, analytics, and disruptive technologies are combined and leveraged by political campaigns to increase the probability of victory through micro-targeting, voter engagement, and public relations. More specifically, the importance of community detection, social influence, natural language processing and text analytics, machine learning, and predictive analytics is assessed and reviewed in relation to political campaigns. In this context, data processing is examined through the lens of the General Data Protection Regulation (GDPR) effective as of May 25, 2018. It is concluded that while data processing during political campaigns does not violate the GDPR, electoral campaigns engage in surveillance, thereby violating Articles 12 and 19, in respect to private life, and freedom of expression accordingly, as stated in the 1948 Universal Declaration of Human Rights.

International students make valuable educational and economic contributions in higher education institutions (HEIs) all across the world. They contribute to the diversity and add different perspective in classrooms. Attracting international students is one of the objectives of HEIs. International students being present all over the world access information about the university online. Hence, all the relevant information must be available on the HEI website. Institutions are also present on social media platforms for interaction purposes. The owned media, including website, social media, discussion forums, etc. of institutions must be regularly monitored and analysed. Equally important is to analyse what students are discussing online about institutions, that is, what are their sentiments. This chapter is an attempt to develop a guide for HEI to draw the interest of international students for an institution through web analytics and social listening. Web analytics will help in understanding the behaviour of the visitor to the HEI website. Key performance indicators that will help in achieve the objective of attracting international students will be identified. On the other hand, analysing social listening on different platforms will help in understanding international students’ perspectives towards a specific institution.

To outline the current trajectories in digital social research and to highlight the roles of qualitative research in those trajectories.

A secondary analysis of the primary literature.

Qualitative research has shifted over time in relation to rapidly changing digital phenomena, but arguably finds itself in ‘crisis’ when faced with algorithms and ubiquitous digital data. However, there are many highly significant qualitative approaches that are being pursued and have the potential to contextualize, situate and critique narratives and practices of data.

To situate current debates around methods within longer trajectories of digital social research, recognizing their conceptual, disciplinary and empirical commitments.

Introduction: The world is passing through a technology explosion phase where one technology is being replaced by another very quickly. Emerging technologies play more important roles in the insurance sector directly or indirectly. These technologies have a high potential to change the insurance paradigm.

Purpose: In this chapter, we discuss emerging technologies such as artificial intelligence (AI), big data, blockchain, the internet of things (IoT), mobile technology, predictive analytics, social media, telematics, chatbots, low codes, and drones in the context of the insurance industry.

Methodology: To carry out our analysis, we searched for data using the keywords for each technology from the Web of Science (WoS) coral database. Certain inclusion and exclusion criteria were followed to select the articles for further analysis. R-studio was used for the data analysis and visualisation.

Findings: It was found that the highest number of research articles published are related to big data, followed by AI and social media. The first article on AI in insurance appeared in 1975. Social media is the highest cited new technology, whereas the low codes are the undiscovered paradigm for the insurance sector with no published research. Research on the impact of chatbots, drones, and mobile technology in the insurance industry is still at a nascent stage. We also noticed that the United States is leading the research on emerging technologies in the insurance sector.

Implications: This chapter audits the emerging technologies in the insurance sector and identifies technological areas with the highest, least, or no research, dominant journals, authors, and countries. This holistic overview empowers managers and academicians to decide the future course of action.