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Introduction: All countries are interested in attracting foreign direct investment (FDI) as it provides for productivity gains and modernisation for attaining sustainable development goals. Multinational corporations (MNCs) collect a vast volume of structured and unstructured big data when seeking international expansion by the FDI route in the insurance sector, but concluding these data may not be practically feasible. So nowadays, for finalising their FDI ventures, MNCs depend on machine-based algorithms for quick analysis of big data sets.

Purpose: This chapter explores how emerging big data analytics and predictive modelling fields can scale and speed up FDI decisions in the insurance sector.

Methodology: The author used a descriptive study based on secondary data from sources like World Bank, The Organisation for Economic Co-operation and Development (OECD), World Trade Organisation (WTO), and International Finance Corporation (IFC) data repositories to identify variables such as risks, costs, trade agreements, regulatory policies, and gross domestic product (GDP) that affect FDI movements. This chapter highlights the process flow that can be beneficial to convert big data sets using statistical tools and computer software such as Statistical Analytics Software (SAS), IBM SPSS Statistics.

Findings: The application of artificial intelligence-based statistical tools on FDI variables can help derive time-series graphs and forecast revenues. The authors found that foreign investors can narrow their prospect search for industry or product to manageable from varied investment opportunities in host countries. Advancements in big data analysis offer cost-effective methods to improve decision-making and resource management for enterprises.

Social media platforms, along with networked devices and applications, enable their user base to produce, access and circulate large volumes of data. On the one hand, this development contains an empowering potential for users, who can make otherwise obscured aspects of social life visible, and coordinate social action in accordance. Yet the preceding activities in turn render these users visible to governments as well as the multinational companies that operate these services. Between these two visions lie more nuanced accounts of individuals coordinating via social data for reactionary purposes, as well as policing and intelligence agencies struggling with the affordances of big data.

This chapter considers how individual users as well as police agencies respectively actualise the supposedly revolutionary and repressive potentials associated with big data. It briefly considers the broader social context in which ‘big data’ is situated, which includes the hardware, software, individuals and cultural values that render big data meaningful and useful. Then, in contrast to polarising visions of the social impact of big data, it considers two sets of practices that speak to a more ambivalent potentiality. First, recent examples suggest a kind of crowd-sourced vigilantism, where individuals rely on ubiquitous data and devices in order to reproduce law and order politics. Second, police agencies in various branches of European governments report a sense of obligation to turn to social data as a source of intelligence and evidence, yet attempts to do so are complicated by both practical and procedural challenges. A combination of case studies and in-depth interviews offers a grounded understanding of big data in practice, in contrast to commonly held visions of these technologies.

First, big data is only ever meaningful in use. While they may be contained in databases in remote locations, big data do not exist in a social vacuum. Their impact cannot be fully understood in the context of newly assembled configurations or ‘game-changing’ discourses. Instead, they are only knowable in the context of existing practices. These practices can initially be the sole remit of public discourse shaped by journalists, tech-evangelists and even academics. Yet embodied individual and institutional practices also emerge, and this may contradict or at least complicate discursive assertions. Secondly, the range of devices and practices that make up big data are engaged in a bilateral relation with these practices. They may be a platform to further reproduce relations of information exchange and power relations. Yet they may also reconfigure these relations.

This research is limited to a sample of respondents based in the European Union, and based at a particular stage of big data and social media monitoring uptake. Subsequent research should look at how this uptake is occurring elsewhere, along with the medium to long-term implications of big data monitoring. Finally, subsequent research should consider how citizens and other social actors are coping with these emerging practices.

This chapter considers practices associated with big data monitoring and draws from cross-national empirical data. It stands in contrast to overly optimistic as well as well as totalising accounts of the social costs and consequences of big data. For these reasons, this chapter will be of value to scholars in internet studies, as well as privacy advocates and policymakers who are responsive to big data developments.

This chapter uses multiple research methods, including quantitative science mapping analysis (SciMat) and a qualitative literature review, to provide insight into the academic debate unfolding at the intersection of big data and business processes. SciMat analysis based on keyword co-occurrence enabled identifying 12 of the most productive research themes, as reflected in a poll of 301 articles about big data and business processes. The three most important themes are: firm performance, Industry 4.0, and innovation. The traditional literature review on firm performance indicated that big data analytics (BDA) positively influence business process performance and have a beneficial impact on a firm’s performance, that is, the role of big data is viewed as critical in the context of Industry 4.0 because it enhances productivity and improves business processes. The benefits of BDA can be achieved only if the organizational obstacles related to planning, workforce attitude, and alignment with strategy are overcome. Moreover, big data is perceived as a significant source of innovation in an organization and can be conceptualized with the use of a resource-based view (RBV) of the firm. BDA positively influence business processes, which is strengthened by adequate implementation and openness to innovation.

Introduction: Big data is that disruptive force that affects businesses, industries, and the economy. In 2021, insurance analytics will include more than simply analysing statistics. According to current trends, new insurance big data analytics (BDA) methods will enable firms to do more with their data. The insurance business has traditionally been conservative, but adopting new technology is no longer only a current trend; it must be competitive. Big data technologies aid in processing a huge amount of data, improve workflow efficiency, and lower operating costs.

Purpose: Some of the most recent developments in big data for insurance and how insurers may use the information to stay ahead of their competitors are discussed in this chapter. This chapter’s prime purpose is to analyse how artificial intelligence (AI), blockchain, and mobile technology change the outlook and working of the insurance sector.

Methodology: To achieve our research purpose, we analyse case studies and literature that emphasise how BDA revolutionises the insurance market. For this purpose, various articles and studies on BDA in the insurance market will be selected and studied.

Findings: From the analysis, we find that the use of big data in the insurance business is growing. The development of BDA has proven to be a game-changing technology in insurance, with a slew of benefits. The insurance sector is now grappling with the risks and opportunities that modern technology presents. Big data offers opportunities that every company must avail of. We can safely argue that big data has transformed the insurance sector for the better. The BDA’s consequences have enabled insurers to target clients more accurately. This chapter highlights that new tools and technologies of big data in the insurance market are increasing. AI is emerging as a powerful technology that can alter the entire insurance value stream. The transmission of any type of digital proof for underwriting, including the use of digital health data, might be a blockchain use case (electronic health record (EHR)). As digital forensics becomes easier to include in underwriting, it must expect price and product design changes in the future. In the future, the internet of things (IoT) and AI will combine to automate insurance processes, causing our sector to transform dramatically. We highlight that these technologies transformed insurance practices and revolutionalised the insurance market.

The objective of the research is to explore the importance of online reputation management through some core concepts: technologies and entrepreneurship. Specifically, the research will explore how in a tourism ecosystem context, it is strategically relevant through the use of Big Data Analytics (BDA) to manage and improve online reputation management. An emphasis will also be placed on the concept of entrepreneurship and dynamic capabilities. Finally, the research also explores empowerment issue to shed some considerations on the development of tourists' online reviews.

There has been a financial revolution lead by technology firms over the past decade. Many large established technology giants, from Google, Apple to Amazon in the US are entering the financial service industry. Smaller start-ups, in particular robotic advisors, a.k.a. Robo-Advisors, have been taking market shares from traditional asset management firms. In China, firms like Tencent and Alibaba have created a whole new field of online finance. At the center of our study is a critical examination of the key components of the financial innovation over the past 10 years. Mobile banking was the beginning, followed by trading, investment, and insurance business. We study innovation through several cases. Due to the size and number o firms in Financial technology (FinTech) space, the US and China are the focus of the chapter. Artificial intelligence (AI) and machine learning are included for discussion in this chapter. We emphasize a market approach in our study, albeit, incorporating the historical and cultural perspectives in our analysis. Our goal is to develop a thorough understanding of the art and science of financial innovation, from both bottom-up market indicators and a top-down holistic view. We want to demonstrate that the technological changes are just the beginning of a new world of financial services. Unprecedented changes are still yet to come and it is crucially important to be prepared and even embrace the changes. A special discussion was devoted to the phenomenon of FinTech boom in Asia. Lastly, many new technologies are being developed to combat fraudulent activities in the FinTech space.

There has been a financial revolution lead by technology firms over the past decade. Many large established technology giants, from Google, Apple to Amazon in the US are entering the financial service industry. Smaller start-ups, in particular, robotic advisors, a.k.a. robo-advisors have been taking market shares from traditional asset management firms. In China, firms like Tencent and Alibaba have created a whole new field of online finance. The center of our study is a critical examination of the essential components of the financial innovation over the past 10 years. Mobile banking was the beginning, followed by trading, investment, and insurance business. Artificial intelligence and machine learning are included for discussion in this chapter. Our goal is to develop a thorough understanding of the art and science of financial innovation, from both bottom-up market indicators and a top-down holistic view. Then, we apply to the situation in Russia. We want to demonstrate that the technological changes are likely to have a significant impact on Russia’s sustainable finance and banking development.

The focus of this chapter is the intersection of social media, publication, data sharing, and research ethics. By now there is an extensive literature on the use of social media in research. There is also excellent work on challenges of postpublication sharing of social media, primarily focused on legal restrictions, technical infrastructure, and documentation. This chapter attempts to build upon and extend this work by using cases to deepen the analysis of ethical issues arising from publishing and sharing social media data. Publishing will refer to the presentation of data extracts, aggregations, or summaries, while sharing refers to the practice of making the underlying data available postpublication for others to use. It will look at the ethical questions that arise both for researchers (or others) sharing data, and those who are using data that has been made available by others, emphasizing the inherently relational nature of data sharing. The ethical challenges researchers face when considering sharing user-generated content collected from social media platforms are the focus of the cases. The chapter begins by summarizing the general principles of research ethics, then identifies the specific ethical challenges from sharing social media data and positions these challenges in the context of these general principles. These challenges are then analyzed in more detail with cases from research projects that drew upon several different genres of social media. The chapter concludes with some recommendations for practical guidance and considers the future of ethical practice in sharing social media data.

Digital technologies have fundamentally changed organizations, industries, and even the society. Although institutional theory provides rich array of perspectives to both the content and dynamics of such changes, research at the intersection of institutional scholarship and digitalization has remained scarce. In this essay, I draw on the institutional logics perspective to elaborate digitalization as involving a new set of interconnected managerial beliefs and norms, organizational practices, and diverse material and social structures that together complement and challenge the established logics in organizations and institutional fields. I draw attention to two central organizing principles in the logic of digitalization: the pursuit of digital omniscience – the efforts to represent and conceive the world through digital data – and digital omnipotence – the efforts to bring activities inside and outside organizations under the control of information systems. I conclude the essay by elaborating how the institutional logics perspective can help understand organization-level efforts to leverage digitalization by incumbent corporations and new digital-native companies.

This chapter delves into the impact of digital initiatives on firms and sheds light on how they can be explained through market reactions and the resource/capabilities mechanism. By providing a novel conceptual framework that reflects the potential impact of digital initiatives on the sensing, seizing and transforming capabilities of dynamic capabilities, this chapter reveals the tremendous potential of digital initiatives to help firms become more adaptive to their environment and create sustainable competitive advantages that elicit positive market responses. This conceptual framework represents an original contribution to the literature. It enhances the understanding of the resource-based view and efficient market hypothesis, providing a fresh perspective on the influence of digital initiatives on firm performance and the dynamic capabilities mechanism that has hitherto been overlooked. As a result, this chapter enables researchers to develop testable hypotheses that examine the causal relationships between digital initiatives, dynamic capabilities and market performance using robust quantitative research methods. Furthermore, this chapter offers valuable insights for managers seeking to develop a more focused approach to digital transformation and enhance their competitive advantage. By exploring the impact of digital initiatives on sensing, seizing and transforming capabilities, managers can gain a deeper understanding of how they can leverage digital initiatives to improve their organisational performance and respond more effectively to the demands of an ever-changing landscape.