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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.

The purpose of this technical paper is to explore the application of analytics and Six Sigma in the manufacturing processes for iron foundries. This study aims to establish a causal relationship between chemical composition and the quality of the iron casting to achieve the global benchmark quality level.

The case study-based exploratory research design is used in this study. The problem discovery is done through the literature survey and Delphi method-based expert opinions. The prediction model is built and deployed in 11 cases to validate the research hypothesis. The analytics helps in achieving the statistically significant business goals. The design includes Six Sigma DMAIC (Define – Measure – Analyze – Improve and Control) approach, benchmarking, historical data analysis, literature survey and experiments for the data collection. The data analysis is done through stratification and process capability analysis. The logistic regression-based analytics helps in prediction model building and simulations.

The application of prediction model helped in quick root cause analysis and reduction of rejection by over 99 per cent saving over INR6.6m per year. This has also enhanced the reliability of the production line and supply chain with on-time delivery of 99.78 per cent, which earlier was 80 per cent. The analytics with Six Sigma DMAIC approach can quickly and easily be applied in manufacturing domain as well.

The limitation of the present analytics model is that it provides the point estimates. The model can further be enhanced incorporating range estimates through Monte Carlo simulation.

The increasing use of prediction model in the near future is likely to enhance predictability and efficiencies of the various manufacturing process with sensors and Internet of Things.

The researchers have used design of experiments, artificial neural network and the technical simulations to optimise either chemical composition or mould properties or melt shop parameters. However, this work is based on comprehensive historical data-based analytics. It considers multiple human and temporal factors, sand and mould properties and melt shop parameters along with their relative weight, which is unique. The prediction model is useful to the practitioners for parameter simulation and quality enhancements. The researchers can use similar analytics models with structured Six Sigma DMAIC approach in other manufacturing processes for the simulation and optimisations.

The purpose of this study is to analyze how strategic planning is used as critical success factors (CSF’s) in not-for-profit (NFP) organizations. This was because many nonprofits had to innovate their operations owing to the global fiscal crises, the continuing international economic instability, natural disasters or the increasing man-made worldwide terrorism. Additionally, the objective is to identify what successful nonprofit organizations actually do to remain effective at the national association level of analysis.

A constructivist research design ideology is applied (in contrast to the customary positivist philosophy to collect quantitative). The literature is critically reviewed to identify NFP CSF’s and terms such as capacity building. NFP institutions are theoretically sampled using US-based retrospective data to identify practitioner CSF activities. Applying a constructivist research design ideology, the theoretical CSF’s from the literature review are compared to practitioner activities. Representatives of NFP organizations are invited to participate in a strategic planning exercise to identify the most important CSF’s from the literature and practice that would be needed in the future.

Seven of the nine United Nations NFP capacity building CSF’s are similar to NFP nine practitioner best practices. In comparison to the general literature, NFP practitioners applied leadership, strategic planning, innovation, documented procedures/training, human/technology resource management, financial management, accountability practices, ethical standards with professional communications policies, collaborative fundraising and marketing initiatives along with performance success evaluations.

The sample was drawn theoretically from 44 nonprofit state-centered institutions in the USA. Although statistically the results pertain strictly to US-based nonprofits, the principles should generalize to other countries as revealed by the similarity with United Nations innovation and strategic planning recommendations.

The authors applied a strategic planning exercise with the 44 participants at their recommendations to prioritize the CSF’s. The result was an innovative SWOT-TOWS diagram that summarized how the nine CSF’s were prioritized and grouped into the three categories of market performance, ethical responsibility and human resources.

The purpose of this paper is to develop and test a series of conceptual models that investigates the impact of management accounting (MA) (systems and information) on firms’ structural capital and business performance. It also replicates previous research in this area which focused on the interplay between the three primary elements of intellectual capital (IC) (i.e. human capital, structural capital and relational capital) and business performance.

A survey instrument was used to collect the data required to conduct the study. All respondents who participated occupied the role of chief financial officer or equivalent and were employed by firms competing within the indigenous Irish information and communications technology sector. Consistent with prior quantitative IC-based research, a form of structural equation modelling called partial least squares was used to test the data collected.

The findings reject the suggestion that MA is most appropriately situated as an element of firms’ structural capital. The findings support a plausible and statistically significant relationship between advanced MA systems and business performance. The findings also generally support previous research on the relationship between the three elements of IC and business performance.

Although much has been written about the potential role for MA in the IC area, little empirical evidence has yet emerged. This exploratory research begins to address this deficiency by developing and testing a series of MA-related constructs within the IC research domain.

The purpose of this paper is to identify the main variables that affect the establishment of a good faculty-student pedagogical relationship in representative samples from a main university in Ecuador.

In the first phase of this exploratory mixed methods study, focus groups were conducted to identify the variables of interest, and in a second phase, these variables were rated in relevance by applying the “pile-sort” method.

Results showed that for students, the variable that most affects the establishment of a good relationship with their faculty is the “faculty’s knowledge,” while the variable that showed the least effect is the “number of students in the classroom.” On the other hand, faculty members responded that the variables that most affect the establishment of a good pedagogical relationship are “empathy with students,” “vocation” and “faculty’s knowledge,” while they considered that the least relevant variables were “context” variables such as “the number of students in the classroom” and “the physical conditions of the classroom.”

These results provide relevant insights into the importance that students place on the theoretical resources that faculty members show as a foundation for establishing positive relationships. In the same way, the relevance that faculty members place on the elements “empathy,” “vocation” and “knowledge” as key variables needed to establish positive interactions.

Previous research had underlined the importance that positive faculty-students relationships have on achieving learning goals. However, the variables that would affect the establishment of these relationships were not clearly recognized.

This paper aims to contribute to the literature on human resources (HR) digitalization, specifically on HR analytics, disentangling the concept of analytics applied to HR and explaining the factors that hinder companies from moving to analytics. Therefore, the central research questions addressed in this study are: what does HR analytics encompass? What impedes the adoption of analytics in HR within organizations?

The authors performed a comprehensive literature review on analytics as applied in HR. The authors relied on two of the major multidisciplinary publication databases (i.e. Scopus and WoS). A total of 64 manuscripts from 2010 to 2019 were content analyzed.

The results reveal that there is an ongoing confusion on HR analytics conceptualization. Yet, it seems that there is an emerging consensus on what HR analytics encompasses. The authors have identified 14 different barriers for HR analytics adoption grouped into four categories, namely, data and models, software and technology, people and management. Grounding on them the authors propose a set of 14 key factors to help to successfully adopt HR Analytics in companies.

This paper brings clarity over the conceptualization of HR analytics by offering a comprehensive definition. Additionally, it facilitates business and HR leaders in making informed decisions on adopting and implementing HR analytics. Moreover, it assists HR researchers in positioning their paper more explicitly in current debates and encouraging them to develop some future avenues of research departing from some questions posed.

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.

Prior research (Harrast, Olsen, & Sun, 2023) analyzes the eight emerging topics to be included in future CPA exams and discusses their importance to career success and appropriate teaching locus in light of survey evidence. They find that the general topic of data analytics is the most important of the eight emerging topics. To further understand the topics most important to career success, this study analyzes subtopics underlying the eight emerging topics. The results show that advanced Excel analysis tools, data visualization, and data extraction, transformation, and loading (ETL) are the most important data analytics subskills for career success according to professionals and that these topics should be both introduced and emphasized in the accounting curriculum. The results provide useful information to educators to prioritize general emerging topics and specific subtopics in the accounting curriculum by taking into account the most pressing needs of the profession.

The purpose of this paper is to provide an overview of predictive analytics, summarize how it is impacting knowledge creation in marketing, and suggest future developments in marketing and predictive analytics for both organizations and researchers.

Survival in a knowledge‐based economy is derived from the ability to convert information to knowledge. To do so, researchers and managers increasingly are relying on the field of predictive analytics. Data mining identifies and confirms relationships between explanatory and criterion variables. Predictive analytics uses confirmed relationships between variables to predict future outcomes. The predictions are most often values suggesting the likelihood a particular behavior or event will take place in the future.

Data mining and predictive analytics are increasingly popular because of the substantial contributions they can make in converting information to knowledge. Marketing is among the most frequent applications of the techniques, and whether you think about product development, advertising, distribution and retailing, or marketing research and business intelligence, data mining and predictive analytics increasingly are being applied.

In the future, we can expect predictive analytics to increasingly be applied to databases in all fields and revolutionize the ability to identify, understand and predict future developments, data analysts will increasingly rely on mixed‐data models that examine both structured (numbers)and unstructured (text and images) data, statistical tools will be more powerful and easier to use, future applications will be global and real time, demand for data analysts will increase as will the need for students to learn data analysis methods, and scholarly researchers will need to improve their quantitative skills so the large amounts of information available can be used to create knowledge instead of information overload.