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For over a decade now, various stakeholders in accounting education have called for the integration of technology competencies in the accounting curriculum (Association to Advance Collegiate Schools of Business (AACSB), 2013, 2018; Accounting Education Change Commission (AECC), 1990; American Institute of Certified Public Accountant (AICPA), 1996; Behn et al., 2012; Lawson et al., 2014; PricewaterhouseCoopers (PWC), 2013). In addition to stakeholder expectations, the inclusion of data analytics as a key area in both the business and accounting accreditation standards of the AACSB signals the urgent need for accounting programs to incorporate data analytics into their accounting curricula. This paper examines the extent of the integration of data analytics in the curricula of accounting programs with separate accounting AACSB accreditation. The paper also identifies possible barriers to integrating data analytics into the accounting curriculum. The results of this study indicate that of the 177 AACSB-accredited accounting programs, 79 (44.6%) offer data analytics courses at either the undergraduate or graduate level or as a special track. The results also indicate that 41 (23.16%) offer data analytics courses in their undergraduate curriculum, 61 (35.88%) at the graduate level, and 12 (6.80%) offer specialized tracks for accounting data analytics. Taken together, the findings indicate an encouraging trend, albeit slow, toward the integration of data analytics into the accounting curriculum.

This chapter examines the application of learning analytics techniques within higher education – learning analytics – and its application in supporting “student success.” Learning analytics focuses on the practice of using data about students to inform interventions aimed at improving outcomes (e.g., retention, graduation, and learning outcomes), and it is a rapidly growing area of educational practice within higher education institutions (HEIs). This growth is spurring a number of commercial developments, with many companies offering “analytics solutions” to universities across the world. We review the origins of learning analytics and identify drives for its growth. We then discuss some possible implications for this growth, which focus on the ethics of data collection, use and sharing.

The purpose of this study is to identify the specific skills and abilities within the broad category of data analytics that current business professionals believe are most important for accounting graduates. Data analytics knowledge is clearly important, but this category is broad. Therefore, this study identifies the specific skills and abilities that are most important for accounting graduates so that faculty can create classroom materials most beneficial for the future accounting graduates. In 2013, the Association to Advance Collegiate Schools of Business developed new standards for accounting programs, including standard A7, related to information technology and analytics. The intent of the standard clearly focuses on increasing the level of technology and analytics studied within the accounting curriculum. However, the specific details and methods for achieving the intent of A7 remain an open question. This chapter uses prior research focused on business analytics education to identify potential analytic skills, tools, techniques, and management issues of concern within the accounting profession. A survey of 342 accounting professionals identifies suggested areas of analytic competencies for accounting graduates. Specifically, the authors find preferences for skills related to data interpretation and communication over any individual technical skills or statistical knowledge. These skills suggest a role for accountants as intermediaries who may need to translate analytic activities into business language. Post hoc, the authors examine the survey results for differences based on respondent characteristics. Interestingly, female respondents report lower beliefs about the importance of analytic skills. The authors also find some differences when examining different demographics within the respondents.

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.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

The awareness of probability was observed in ancient cultures through the discovery of primitive dice games made with animal bones. The history of analytics in the workplace, as it is currently known (defined as predictive analytics), probably started in ancient Roman times, when the concept of insurance was first created. While the previous example showed that analytics for business had been around for some time, it is only relatively recently that there is an increased emphasis on the use of analytics in the modern firm. Credit card firms and retail catalog companies relied on analytics to drive their business models, for most of the latter half of the twentieth century. The use of advanced analytics for business also grew around the Millennium since the widespread use of data warehousing and relational databases on client servers. Moreover, Machine Learning and Artificial Intelligence Techniques, which have been around for many decades, have had very few breakthrough successful applications up until recently when cloud computing and being able to take advantage of the infrastructure of companies, such as Amazon and Google, with their Cloud Services enabled these algorithms to be used to their full extent in firms. This powerful infrastructure availability coupled with BIG DATA is creating breakthrough applications across many business models on a consistent basis. This chapter explores the use of advanced analytics across different business functional areas. It also introduces some breakthrough models, which include Netflix, Pandora, eHarmony, Zillow, and Amazon, and explores how these are not only changing the lives of consumers but also changing the nature of the workplace and creating new issues for firms such as data protection and liabilities for the actions of automated algorithms.

In this paper, we explore the effects of emerging digital technologies on professionalization within organizations. Specifically, we examine how the emergence of data analytics as a new cross-functional profession rooted in new digital technologies is challenging human resources (HR) as an established organizational profession. Our qualitative study reveals how rhetorical work and material work have established a symbiosis between data science and HR. Rather than leading to de-professionalization, new technologies are enabling HR practices to be augmented and new actors to be integrated into the professionalization project, thereby elevating the status of HR. These findings contribute to the literature on the role of technology in institutional theory and its influences on the professionalization.

Purpose: This study aims to review the existing literature on human resource management (HRM) as a major theme and sub-theme of human resource (HR) analytics. This chapter has the objective of analysing the trends in HR analytics.

Design/Methodology/Approach: It covered the publications between 2010 and 2021. There was a total of 500 articles sourced through ProQuest. The systematic literature review is applied as a research methodology. The metadata analysis was carried out to understand the trends, challenges, best practices, and scope of HR analytics. The authors have taken the help of keywords, journals, authors, domains, and topics for sourcing, screening, shortlisting, and finalising the article for review purposes.

Findings: It was found that research published in the early period concentrated on HRM’s theoretical and conceptual frameworks. In the middle phase, HR analytics gained momentum while the recent publications have reiterated on adopting various tools and technologies for optimum utilisation of resources and sustainable organisational development.

Practical Implications: This research will add to the literature in the HR analytics domain. It also provides a deeper insight to the researcher to explore and analyse more trends in the area that needs further exploration.

Originality: This study is relevant and comprehensive within the context of digitalisation, people analytics and competition management. It provides valuable insights to analyse the present scenario of the volatility, uncertainty, complexity, and ambiguity environment exploring new horizons for organisational efficiency and human capital.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.