Big Data Analytics (BDA) usage in the industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision-making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in the organizational domain. Big Data has been defined in various ways and, the past literature about the classification of BDA and its capabilities is explored in this research. We conducted a literature review using PRISMA methodology and integrated a thematic analysis using NVIVO12. By adopting five steps of the PRISMA framework—70 sample articles, we generate five themes, which are informed through organization development theory, and develop a novel empirical research model, which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.

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Big data analytics utilizes different analytics techniques to transform large volume and diversified big dataset. The analytics uses various computational methods such as different Machine Learning (ML) in convert raw data to valuable insights. The ML assist individuals to perform work activities quicker and better, and empower decision-makers in system use. Since academics and industry practitioners have growing interests on ML, how different applications of ML in specific problem domains have been explored, but not in a holistic manner from the past literature. This paper aims to promote the utilization of intelligent literature review for researchers by introducing a step-by-step framework on a case providing the code template. We offer an intelligent literature review to obtain in-depth analytical insight of ML applications in the clinical domain to: a) develop the intelligent literature framework using traditional literature and Latent Dirichlet Allocation (LDA) topic modeling, b) analyze research documents using traditional systematic literature review revealing ML applications, and c) identify topics from documents using LDA topic modeling. We used a PRISMA framework for the traditional literature review, reviewed four databases (e.g. IEEE, PubMed, Scopus, and Google Scholar), which are published between 2016 and 2021 (September). The framework comprises two stages – Traditional systematic literature review and LDA topic modeling. The intelligent literature review framework reviewed 305 research documents in a transparent, reliable, and faster way.

Big data analytics utilizes different techniques to transform large volumes of big datasets. The analytics techniques utilize various computational methods such as Machine Learning (ML) for converting raw data into valuable insights. The ML assists individuals in performing work activities intelligently, which empowers decision-makers. Since academics and industry practitioners have growing interests in ML, various existing review studies have explored different applications of ML for enhancing knowledge about specific problem domains. However, in most of the cases existing studies suffer from the limitations of employing a holistic, automated approach. While several researchers developed various techniques to automate the systematic literature review process, they also seemed to lack transparency and guidance for future researchers. This research aims to promote the utilization of intelligent literature reviews for researchers by introducing a step-by-step automated framework. We offer an intelligent literature review to obtain in-depth analytical insight of ML applications in the clinical domain to (a) develop the intelligent literature framework using traditional literature and Latent Dirichlet Allocation (LDA) topic modeling, (b) analyze research documents using traditional systematic literature review revealing ML applications, and (c) identify topics from documents using LDA topic modeling. We used a PRISMA framework for the review to harness samples sourced from four major databases (e.g., IEEE, PubMed, Scopus, and Google Scholar) published between 2016 and 2021 (September). The framework comprises two stages—(a) traditional systematic literature review consisting of three stages (planning, conducting, and reporting) and (b) LDA topic modeling that consists of three steps (pre-processing, topic modeling, and post-processing). The intelligent literature review framework transparently and reliably reviewed 305 sample documents.

The study focuses on usability provisions of learning management systems (LMS) in a corrective service organization. Under the influence of goal-directed interaction design principles in design science, the study outlines a new innovative artifact as a conceptual LMS solution that addresses the employee-centric learning model. The collective artifact represents relevant design knowledge assistive to meeting stakeholders’ contextual learning needs, for instance, demonstrating the potential significance of the developed mobile-based approach to support correctional officers in improving their learning on safety professional development. The paper concludes with a practical model that could be applied to assist correctional officers in their in-house training purposes.

Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

Big Data Analytics (BDA) have been proliferated to academic researchers and industry practitioners over the past few years. As a prominent data-driven decision application, the BDA capabilities in organisation have been recognised, but limited studies have successfully attempted to communicate an authentic understanding on BDA capabilities that may enhance the current theoretical knowledge. While big data have been defined in various ways with its characteristics of shared definitions, it is important to explore the classification of BDA and its capabilities considering its advantageous opportunities. This study conducts a review study adopting the well-known PRISMA methodology, integrating a thematic analysis approach using NVIVO12. The study analyses 70 elected sample articles for generating new insights of BDA, informing through organisation development theory and leading to this an empirical research model is outlined for further validity assessment. It is anticipated that the findings would be contributing to address dynamic clarity and relevance of adopting BDA application.

Big Data Analytics (BDA) usage in industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in organizational domain. Big Data has been defined in various ways and , the past literature about classification of BDA and its capabilities is explored in this research . We conduct a literature review using PRISMA methodology, and integrate a thematic analysis using NVIVO12. By adopting five steps of PRISMA framework - , 70 sample articles we generate five themes, which informed through organization development theory, and develop a novel empirical research model which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.