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The Nationwide Suspicious Activity Reporting Initiative (NSI) is the focal point of the Information Sharing Environment (ISE), a radical reformulation of policies governing government intelligence activities within US borders. In the wake of the September 11th attacks, long-standing informational norms for the production, use, and circulation of domestic intelligence records containing personal information are being replaced with far less restrictive norms, altering a status quo that had been in effect since mid-1970s. Although the NSI represents an unprecedented expansion of human resources dedicated to the collection and production of domestic intelligence, it is not well known in privacy advocacy community. This chapter considers these and other terms in the context of relevant US law and policy, including the Privacy Act of 1974, the E-Government Act of 2002, Executive Order 12333, and 28 CFR Part 23. In addition to describing the federal (ISE-SAR) standard, the chapter examines the critical role of guidance in the logic of suspicious activity report (SAR) production, and the problematic role finished ISE-SARs seem to play in the matrix of federal and state-level watch lists. The program, if not properly regulated, could pose a considerable threat to personal privacy and the life chances and self-determination of all US persons. The chapter considers this threat in terms of Nissenbaum's (2010) “contextual integrity,” a theory of context-relative informational norms.

The consequences of a media-shaped perceptual destination attribute during the pandemic on a commitment to embrace and thread trip intent are explored in this research. The influence of four elements related to the choice of destination, involving reliability, strategic planning, health service, and friendly relationship on tourism performance expectancy are chosen depending on visitors' previous experience of a certain place, using the notion of psychological tourist satisfaction and an interactive personality questionnaire. Industry 4.0 paradigms are either viewed with skepticism or eagerness by organizations. Organizations that develop different innovative strategies to leverage the digital world doing and offering greater opportunity will certainly drive the evolution in tourism. Any revolutionary technology has the potential to benefit or harm enterprises. An Industry “4.0” data type depicts the technological transformation occurring inside a given business, from Industrial Revolution 4.0 to dealing with customers 4.0. However, one instance is Tourism 4.0. Social networking is not unique. Hospitality organizations, especially, must be acutely conscious of the customer's power as a result of social networking sites. The increase in security concerns by preventing the rate of cybercrime and by enhancing the secure money transactions in online reputation management consolidates the socioeconomic relationship. The comparative study has been propagated in relevance to the pre- and post-pandemic effects on the tourism industry.

Introduction: In recent years, fresh big data ideas and concepts have emerged to address the massive increase in data volumes in several commercial areas. Meanwhile, the phenomenal development of internet use and social media has not only added to the enormous volumes of data available but has also posed new hurdles to traditional data processing methods. For example, the insurance industry is known for being data-driven, as it generates massive volumes of accumulated material, both structured and unstructured, that typical data processing techniques can’t handle.

Purpose: In this study, the authors compare the benefits of big data technologies to the needs for insurance data processing and decision-making. There is also a case study evaluation concentrating on the primary use cases of big data in the insurance business.

Methodology: This chapter examines the essential big data technologies and tools from the insurance industry’s perspective. The study also included an analytical analysis that supported several gains made by insurance companies, such as more efficient processing of large, heterogeneous data sets or better decision-making support. In addition, the study examines in depth the top seven use cases of big data in insurance and justifying their use and adding value. Finally, it also reviewed contemporary big data technologies and tools, concentrating on their key concepts and recommended applications in the insurance business through examples.

Findings: The study has demonstrated the value of implementing big data technologies and tools, which enable the development of powerful new business models, allowing insurance to advance from ‘understand and protect’ to ‘predict and prevent’.