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Current e-learning platforms are based on relational database management systems (RDBMS) and are well suited for handling structured data. However, it is expected from e-learning solutions to efficiently handle unstructured data as well. The purpose of this paper is to show an alternative to current solutions for unstructured data management.

Current repository-based solution for file management was compared to MongoDB architecture according to their functionalities and characteristics. This included several categories: data integrity, hardware acquisition, processing files, availability, handling concurrent users, partition tolerance, disaster recovery, backup policies and scalability.

This paper shows that it is possible to improve e-learning platform capabilities by implementing a hybrid database architecture that incorporates RDBMS for handling structured data and MongoDB database system for handling unstructured data.

The study shows an acceptable adoption of MongoDB inside a service-oriented architecture (SOA) for enhancing e-learning solutions.

This research enables an efficient file handling not only for e-learning systems, but also for any system where file handling is needed.

It is expected that future single/joint e-learning initiatives will need to manage huge amount of files and they will require effective file handling solution. The new architecture solution for file handling is offered in this paper: it is different from current solutions because it is less expensive, more efficient, more flexible and requires less administrative and development effort for building and maintaining.

The analysis of unstructured information, particularly in the form of text, has long been a technique in the armory of social scientists, who have to deal with conversational records, historical documents, unstructured interviews, and the like. Unsurprisingly, a considerable amount of methodological literature has developed on the subject. The methods of “qualitative data analysis” have now spread to areas of information analysis as diverse as market research and legal evidence analysis. Related computer techniques, from database management systems and word‐processors to specialized qualitative data analysis software, have been pressed into use. This article discusses the information processing methodology and theory assumed by computer‐based qualitative data analysis software; and, in particular, describes and analyzes the methodology of the NUDIST system developed by the authors.

Contact centers (CCs) are one of the main touch points of customers in an organization. They form one of the inputs to customer relationship management (CRM) to enable an organization to efficiently resolve customer queries. CCs have an important impact on customer satisfaction and are a strategic asset for CRM systems. The purpose of this paper is to review the current literature on CCs and identify their shortcomings to be addressed in the current digital age.

The current literature on CCs can be classified into the analytical and the managerial aspects of CCs. In the former, data mining, text mining, and voice recognition techniques are discussed, and in the latter, staff training, CC performance, and outsourced CCs are discussed.

With the growth of information and communication technologies, the information that CCs must handle both in terms of type and volume, has changed. To deal with such changes, CCs need to evolve in terms of their operation and public relations. The authors present a state-of-the-art review of the challenges in identifying the gaps in order to have the next generation of CCs. Lack of an interactive CC and lack of data integrity for CCs are highlighted as important issues that need to be dealt with properly by CCs.

As far as the authors know, this is the first paper that reviews CCs’ literature by providing the comprehensive survey, critical evaluation, and future research.

Environmental, social and governance (ESG) factors have become increasingly important in investment decisions, leading to a surge in ESG investing and the rise of sustainable investment assets. Nevertheless, challenges in ESG disclosure, such as quantifying unstructured data, lack of guidelines and comparability, rampantly exist. ESG rating agencies play a crucial role in assessing corporate ESG performance, but concerns over their credibility and reliability persist. To address these issues, researchers are increasingly utilizing machine learning (ML) tools to enhance ESG reporting and evaluation. By leveraging ML, accounting practitioners and researchers gain deeper insights into the relationship between ESG practices and financial performance, offering a more data-driven understanding of ESG impacts on business communities.

The authors review the current research on ESG disclosure and ESG performance disagreement, followed by the review of current ESG research with ML tools in three areas: connecting ML with ESG disclosures, integrating ML with ESG rating disagreement and employing ML with ESG in other settings. By comparing different research's ML applications in ESG research, the authors conclude the positive and negative sides of those research studies.

The practice of ESG reporting and assurance is on the rise, but still in its technical infancy. ML methods offer advantages over traditional approaches in accounting, efficiently handling large, unstructured data and capturing complex patterns, contributing to their superiority. ML methods excel in prediction accuracy, making them ideal for tasks like fraud detection and financial forecasting. Their adaptability and feature interaction capabilities make them well-suited for addressing diverse and evolving accounting problems, surpassing traditional methods in accuracy and insight.

The authors broadly review the accounting research with the ML method in ESG-related issues. By emphasizing the advantages of ML compared to traditional methods, the authors offer suggestions for future research in ML applications in ESG-related fields.

We use design thinking in the context of accounting pedagogy to exploit recent advances in cybernetics in the form of generative artificial intelligence technology. Relying on the intuition that supplementing or augmenting human argumentation (natural intelligence or NI) with parallel AI output can produce better student written assignments, we posit the “augmentation premise,” that is, ((NI + AI) > AI > NI). To test the augmentation premise, we compare student written submissions in an Accounting Information Systems (AIS) course with and without the benefit of parallel generative AI output. We then evaluate how the generative AI output enhances student-crafted revisions to their initial submissions. Using a summative quality improvement index (QII) consisting of quantitative and qualitative assessments, we present preliminary evidence supporting the augmentation premise. The augmentation premise likely extends to other accounting subdisciplines and merits generalization for enriching accounting pedagogy.

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

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’.

This paper aims to critically examine the accounting and information systems literature to understand the changes that are occurring in the management accounting profession. The changes the authors are interested in are linked to technology-driven innovations in managerial decision-making and in organizational structures. In addition, the paper highlights research gaps and opportunities for future research.

The authors adopted a grounded theory literature review method (Wolfswinkel et al., 2013) to achieve the study’s aims.

The authors identified four research themes that describe the changes in the management accounting profession due to technology-driven innovations: structured vs unstructured data, human vs algorithm-driven decision-making, delineated vs blurred functional boundaries and hierarchical vs platform-based organizations. The authors also identified tensions mentioned in the literature for each research theme.

Previous studies display a rather narrow focus on the role of digital technologies in accounting work and new competences that management accountants require in the digital era. By contrast, the authors focus on the broader technology-driven shifts in organizational processes and structures, which vastly change how accounting information is collected, processed and analyzed internally to support managerial decision-making. Hence, the paper focuses on how management accountants can adapt and evolve as their organizations transition toward a digital environment.

The data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.

This paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.

The accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.

The proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.

Nowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.

This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics.

The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day.

Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions.

This is a historical perspective from the early days of analytics to present day use of analytics.