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In the almost twenty years since Vasarhelyi and Halper (1991) reported on their pioneering implementation of what has come to be known as Continuous Auditing (CA), the concept has increasingly moved from theory into practice. A 2006 survey by PricewaterhouseCoopers shows that half of all responding firms use some sort of CA techniques, and the majority of the rest plan to do so in the near future. CA not only has an increasing impact on auditing practice, but is also one of the rare instances in which such a significant change was led by the researchers. In this paper we survey the state of CA after two decades of research into continuous auditing theory and practice, and draw out the lessons learned by us in recent pilot CA projects at two major firms, to examine where this unique partnership between academics and auditors will take CA in the future.

Industries’ projects are associated with large amounts of data throughout the project lifecycle. As the size of the projects grows, so does the data associate with the projects. To generate useful information from Big Data, appropriate predictive analysis and data visualisation tools are required. Therefore, the use of visualisation techniques will target this expected evolution of big data. Thus, this study aims to give an overview of the migrations of an existing computer-based application, used to analyse multidata sources of enterprises, to a web-based application. Essential development aspects were concerned with the process of application development, platform development, software utilisation, and efficient display interface according to the enterprise’s requirement. Two solutions such as Microsoft Power Business Intelligence (BI) and the Dynamic Web System have been presented that proven to be widespread because these solutions have met the enterprises’ requirements in terms of expenses, policy, security, maintenance, and interactive interface. Also, an assessment of these solutions and challenges to adopting has been presented. Eventually, the system developed on the web overcomes computer-based systems in solving their problems such as deploying and maintaining applications and providing efficient and richer user interfaces for users. The web-based visualisation application has wide beneficial features; it provides high-performance real-time data analysis, data multimodal visualisation, and friendly to use. Web-based applications will increase confidence in decision-making based on data visualisation, which will positively reflect on comprehensive data analysis processes for project success.

Technology for training military teams has evolved through a convergence of advances in simulation technology for individual and collective training, methods for analyzing teamwork and designing training solutions, and intelligent tutoring technologies that adapt training to the student, to accelerate learning. A number of factors have slowed this evolution toward intelligent team tutoring systems (ITTS), including the challenges of processing communications data, which are the currency of teamwork, and the paucity of automated and generalizable measures of team work. Several systems fulfill a subset of the features required of an ITTS, namely the use of team training objectives, teamwork models, measures of teamwork, diagnostic capability, instructional strategies, and adaptation of training to team needs. We describe these systems: the Advanced Embedded Training System (AETS), Synthetic Cognition for Operational Team Training (SCOTT), the AWO Trainer, the Benchmarked Experiential System for Training (BEST), and the Cross-Platform Mission Visualization Tool. We close this chapter with recommendations for future research.

This research aims to explore the potential of big data technology for sustainable management and investigate its impact on tourism. Its goal is to obtain meaningful results related to sustainable tourism to understand better how big data technology plays a role in decision-making by looking at it through the lens of various studies.

A systematic review, which is a qualitative method, was used in this study. The analysis was conducted using secondary data from the Web of Science Core Collections databases.

Big data technology has many economic benefits for businesses, but it also has managerial benefits such as forecasting, decision-making and tracking human and machine behaviour. Furthermore, big data technology offers sustainability benefits such as resource efficiency, preventive quality systems, carbon reduction and environmentally friendly production.

Big data's capabilities enable businesses to make more informed business decisions, improve overall business performance and contribute to achieving various SDGs. Big data, which aids in developing smart and sustainable tourism in the tourism sector, assists tourism managers in making economically, socially and environmentally sound decisions.

Central theme: The present chapter discusses the integration of data science methods in devising economic policies in different countries with special reference to India.

Purpose: It explains how the policy-making process in countries can be transformed from estimate-based policies to evidence-based policies with the help of techniques such as artificial intelligence (AI), big data, and data analytics. It answers the research question of whether the data science techniques can make the economic policy process efficient or not in developing countries like India.

Research methodology: Data are collected from secondary sources such as government websites, journals, corporate reports, and research databases to conduct this descriptive analysis. Research papers from Scopus/Web of Science (WoS) database are extracted, and exclusion/inclusion criteria are applied for extracting papers relevant to this research.

Findings: The chapter found out various opportunities which India can tap by gaining new insights on critical macroeconomic issues such as unemployment, labour markets, and water crises and would be able to resolve the problems with the help of predictive modelling. The findings exhibit the possibility of building models that could explain how to integrate data science techniques into the policy-making process. It also highlights the challenges that Indian economy is facing in incorporating these techniques in its policy-making process. It states the need to design different evaluation schemes based on information and communication technology (ICT) and data science for different policies, since one methodology does not suit all.

The main goal of this research is to describe the talent management process via social networking sites (SNS) in emerging economies of India and Mexico.

The chapter uses a conceptual approach to detail the subject matter and adopts two theoretical frameworks to enhance understanding of the topic.

India and Mexico have welcomed various forms of SNS as strategic organizational tools for talent management. In India, Orkut is adopted to attract technical employees, Facebook is used to source overseas applicants, and Linkedin is sourced for management professionals. In Mexico, Facebook, Twitter, and YouTube predominantly provide applicants for young talent, while LinkedIn provides applicants for upper-level talent. This research also identifies the role of national cultures and data privacy laws in both these countries.

This research is a conceptual paper and any future research will benefit from having either empirical or qualitative research to substantiate the results.

Practitioners might also benefit from this chapter as it distinguishes how different cultures use SNS for recruitment practices. Some cultures prefer to use SNS predominantly for its social aspect while others favor its practicality value. Global managers may be interested to identify where social networks are easily accepted for work-related practices.

There seems to be a paucity of research on SNS in emerging economies. This chapter provides pioneer work in two emerging economies by identifying relevant statistics, developing a model, and listing current SNS. Thus this research helps fill the gap in the extant cross-cultural literature on SNS.