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This chapter more clearly identifies the distinction between Electronic Health Record (EHR) and Electronic Medical Record (EMR), and states their value in obtaining individual-level data. Synthetic medical records may be used as a surrogate for EHR data in order to ensure digital data privacy is maintained during the development of the LHS. Synthea is an open-source simulation tool available through GitHub.¹ Extensive descriptive analysis of synthesized data is provided as a foundation for the analysis in Chapter 7.

The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.

This chapter outlines how the comprehensive North American and European datasets were collected and explains the ensuing data cleaning process outlining three alternative methods applied to deal with missing values, the complete case, the multiple imputation (MI), and the K-nearest neighbor (KNN) methods. The complete case method is the conventional approach adopted in many mainstream management studies. We further discuss the implied assumption underlying use of this technique, which is rarely assessed, or tested in practice and explain the alternative imputation approaches in detail. Use of North American data is the norm but we also collected a European dataset, which is rarely done to enable subsequent comparative analysis between these geographical regions. We introduce the structure of firms organized within different industry classification schemes for use in the ensuing comparative analyses and provide base information on missing values in the original and cleaned datasets. The calculated performance indicators derived from the sampled data are defined and presented. We show how the three alternative approaches considered to deal with missing values have significantly different effects on the calculated performance measures in terms of extreme estimate ranges and mean performance values.

It is difficult to argue against the fact that research has focussed on artificial intelligence (AI) and robotisation over the past few decades. Additionally, during the past several years, it has taken off and is now extensively used in numerous businesses across various industries. Most of the time, AI has been associated with some industrial sector process automation. Still, recently, the authors have noticed more positive technology uses, especially in the financial services industry. Due to several factors, the financial sector needs to adopt AI and recognise its potential. The industry has historically been concerned about unpredictability, legislation, stronger cybersecurity, technological limitations and disruption of established lucrative operations.

Never before has there been more discussion about AI due to the advantages it provides to businesses that are providing financial services. That may explain why this change is referred to as the fourth industrial revolution. Both positively and negatively, it is quite disruptive. The effectiveness, accuracy and cost-effectiveness of solutions greatly increase. However, immense power also entails great responsibility.

Precautions and security are more crucial than ever for businesses since the financial sector is changing significantly and quickly. The various benefits and drawbacks of this technology are yet unknown to humans. Although AI was first shown to us in the 1950s, it has recently gained new prominence as processing power, and the available quantity of data has increased dramatically.

Purpose: The banking sector took the initiative to improve it by releasing a new blockchain application. This innovative approach connects customers from various geographic locations and also gives them a sense of banks’ global presence. Competition is one of the most important market factors because consumer tastes, interests and demands constantly change, making it difficult to meet these problems.

Methodology: Blockchain develops a Blue Ocean Approach in this competitive climate by enticing numerous market segments and giving the financial industry a fresh perspective that benefits the potential consumer. This chapter illustrates how the Blue Ocean Approach can be unlocked by a disruptive technology called blockchain, which generates value innovation and renders the competition obsolete.

Findings: This paradigm shifts the emphasis away from the present competition and generates value and demand for the product. The researcher advises that the Blue Ocean Strategy in retail banking, which uses blockchain technology, works very well since it eliminates cut-throat competition and favours costs, operations, and meeting financial targets on time.

Practical Implications: The study focuses on the bank’s real-world application of the Blue Ocean Strategy and the discovery of sustainable marketing strategies that will aid in their pursuit of innovation. It also highlights the elements introduced in the banking industry to support innovation and the development of long-lasting markets.

The purpose of this chapter is to develop academic answers to the key rural areas and smart villages and digital agriculture. This chapter analyses the National level initiatives of Government of India Mission to convert rural areas into smart cities. The Union Ministry of urban development collaborates with State Government and nominate a particular city or cities in their state. Financial incentives or benefits will be provided to enhance the quality of the city. Coimbatore being a cosmopolitan city it is also a combination of rural villages and urban township. The main objective of this chapter is to identify and explore the initiatives of SMART CITIES MISSION a joint venture activity initiated by Government of India and State Government of Tamil Nadu. The results clearly indicate how digital technologies play a pivotal role to enhance the quality of eco-friendly initiatives and to improve the smart villages and agriculture. The key recommendations are the lessons learnt from other smart cities initiatives in other states and how Coimbatore can be an example and adopt key takeaways from other states and cities around the world.