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Open Access
Article
Publication date: 10 May 2022

Simone Fanelli, Lorenzo Pratici, Fiorella Pia Salvatore, Chiara Carolina Donelli and Antonello Zangrandi

This study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

Abstract

Purpose

This study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

Design/methodology/approach

A systematic literature review was carried out. The research uses two analyses: descriptive analysis, describing the evolution of citations; keywords; and the ten most influential papers, and bibliometric analysis, for content evaluation, for which a cluster analysis was performed.

Findings

A total of 48 articles were selected for bibliographic coupling out of an initial sample of more than 5,000 papers. Of the 48 articles, 29 are linked on the basis of their bibliography. Clustering the 29 articles on the basis of actual content, four research areas emerged: quality of care, quality of service, crisis management and data management.

Originality/value

Health-care organizations believe strongly that big data can become the most effective tool for correctly influencing the decision-making processes. Thus, more and more organizations continue to invest in big data analytics, and the literature on this topic has expanded rapidly. This study seeks to provide a comprehensive picture of the different streams of literature existing, together with gaps in research and future perspectives. The literature is mature enough for an analysis to be made and provide managers with useful insights on opportunities, criticisms and perspectives on the use of big data for health-care organizations. However, to date, there is no comprehensive literature review on the big data analysis in health care. Furthermore, as big data is a “sexy catchphrase,” more clarity on its usage may be needed. It represents an important tool to be investigated and its great potential is often yet to be discovered. This study thus sheds light on emerging issues and suggests further research that may be needed.

Details

Management Research Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-8269

Keywords

Book part
Publication date: 3 November 2014

Robin James Smith

This chapter critically discusses implications of working with ‘big data’ from the perspective of qualitative research and methodology. A critique is developed of the…

Abstract

Purpose

This chapter critically discusses implications of working with ‘big data’ from the perspective of qualitative research and methodology. A critique is developed of the analytic troubles that come with integrating qualitative methodologies with ‘big dataanalyses and, moreover, the ways in which qualitative traditions themselves offer a challenge, as well as contributions, to computational social science.

Design/methodology/approach

The chapter draws on Interactionist understandings of social organisation as an ongoing production, tied to and accomplished in the actual practices of actual people. This is a matter of analytic priority but also points to a distinctiveness of sociological work which may be undermined in moving from the study of such actualities, suggesting an alternative coming crisis of empirical sociology.

Findings

A cautionary tale is offered regarding the contribution and character of sociological analysis within the ‘digital turn’. It is suggested that ‘big dataanalyses of traces abstracted from actual people and their practices not only miss and distort the relation of social practice to social product but, consequentially, can take on an ideological character.

Originality/value

The chapter offers an original contribution to current discussions and debates surrounding ‘big data’ by developing enduring critiques of sociological methodology and analysis. It concludes by pointing to contributions and interventions that such an empirical programme of qualitative research might make in the context of the ‘digital turn’ and is of value to those working at the interface of traditional and digital(ised) inquiries and methods.

Details

Big Data? Qualitative Approaches to Digital Research
Type: Book
ISBN: 978-1-78441-050-6

Keywords

Article
Publication date: 25 August 2020

Xinyi Zhang, Yanni Yu and Ning Zhang

This study aims to provide a literature review and bibliometric analysis of sustainable supply chain management using big data. We reviewed the literature on sustainable…

1133

Abstract

Purpose

This study aims to provide a literature review and bibliometric analysis of sustainable supply chain management using big data. We reviewed the literature on sustainable supply chain management under big data from 2012 to 2019 and extracted 777 articles.

Design/methodology/approach

We conducted quantitative analysis and data network visualization of the chosen literature, including authors, journals, countries, research institutions and citations.

Findings

We discovered that the development of this interdisciplinary field has gained increasing popularity among researchers around the world, such as China and the US publishing the most articles and Western states having more cooperation, which indicates this research topic is growing in significance globally.

Originality/value

Scientific and technological revolutions such as big data have been incorporated in various industries. Modern supply chain management has also been combined with the advances in data science to achieve sustainability goals. No studies have reviewed the sustainable supply chain management based on big data. This study fills this gap.

Details

Journal of Enterprise Information Management, vol. 34 no. 1
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 4 February 2021

Karen Mcbride and Christina Philippou

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data

Abstract

Purpose

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same.

Design/methodology/approach

The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis.

Findings

Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes.

Research limitations/implications

The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research.

Practical implications

This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes.

Social implications

The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics.

Originality/value

While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.

Details

Accounting Research Journal, vol. 35 no. 1
Type: Research Article
ISSN: 1030-9616

Keywords

Article
Publication date: 27 March 2018

Chen Chi Chang

The purpose of this paper is to explore the data connection, spatial distribution characteristics and trends in genealogical information. First, it implements a…

Abstract

Purpose

The purpose of this paper is to explore the data connection, spatial distribution characteristics and trends in genealogical information. First, it implements a spatial-temporal visualization of the Hakka genealogical information system that makes these individual family pedigree charts appear as one seamless genealogy to family and researchers seeking connections and family history all over the world. Second, this study applies migration analysis by applying big data technologies to Hakka genealogies to investigate the migration patterns of the Hakka ethnic group in Taiwan between 1954 and 2014. This innovative library service enhances the Hakka genealogical migration analysis using big data.

Design/methodology/approach

The platform is designed for the exchange of genealogical data to be used in big data analysis. This study integrates big data and geographic information systems (GIS) to map the population distribution themes. The general procedure included collecting genealogical big data, geographic encoding, gathering the map information, GIS layer integration and migration map production.

Findings

The analytical results demonstrate that big data technology is highly appropriate for family migration history analysis, given the increasing volume, velocity and variety of genealogical data. The spatial-temporal visualization of the genealogical research platform can follow family history and migration paths, and dynamically generate roadmaps to simplify the cartographic steps.

Practical implications

Technology that combines big data and GIS is suitable for performing migration analysis based on genealogy. A web-based application for spatial-temporal genealogical information also demonstrates the contribution of innovative library services.

Social implications

Big data play a dominant role in library services, and in turn, provide an active library service. These findings indicate that big data technology can provide a suitable tool for improving library services.

Originality/value

Online genealogy and family trees are linked with large-volume, growing data sets that are complex and have multiple, autonomous sources. The migration analysis using big data has the potential to help genealogy researchers to construct minority ethnic history.

Article
Publication date: 28 December 2018

Arun Aryal, Ying Liao, Prasnna Nattuthurai and Bo Li

The purpose of this study is to provide insights into the way in which understanding and implementation of disruptive technology, specifically big data analytics and the…

5445

Abstract

Purpose

The purpose of this study is to provide insights into the way in which understanding and implementation of disruptive technology, specifically big data analytics and the Internet of Things (IoT), have changed over time. The study also examines the ways in which research in supply chain and related fields differ when responding to and managing disruptive change.

Design/methodology/approach

This study follows a four-step systematic review process, consisting of literature collection, descriptive analysis, category selection and material evaluation. For the last stage of evaluating relevant issues and trends in the literature, the latent semantic analysis method was adopted using Leximancer, which allows more rapid, reliable and consistent content analysis.

Findings

The empirical analysis identified key research trends in big data analytics and IoT divided over two time-periods, in which research demonstrated steady growth by 2015 and the rapid growth was shown afterwards. The key finding of this review is that the main interest in recent big data is toward overlapping customer service, support and supply chain network, systems and performance. Major research themes in IoT moved from general supply chain and business information management to more specific context including supply chain design, model and performance.

Originality/value

In addition to providing more awareness of this research approach, the authors seek to identify important trends in disruptive technologies research over time.

Details

Supply Chain Management: An International Journal, vol. 25 no. 2
Type: Research Article
ISSN: 1359-8546

Keywords

Article
Publication date: 19 October 2015

Wasim Ahmad Bhat and S.M.K. Quadri

The purpose of this paper is to explore the challenges posed by Big Data to current trends in computation, networking and storage technology at various stages of Big Data

3130

Abstract

Purpose

The purpose of this paper is to explore the challenges posed by Big Data to current trends in computation, networking and storage technology at various stages of Big Data analysis. The work aims to bridge the gap between theory and practice, and highlight the areas of potential research.

Design/methodology/approach

The study employs a systematic and critical review of the relevant literature to explore the challenges posed by Big Data to hardware technology, and assess the worthiness of hardware technology at various stages of Big Data analysis. Online computer-databases were searched to identify the literature relevant to: Big Data requirements and challenges; and evolution and current trends of hardware technology.

Findings

The findings reveal that even though current hardware technology has not evolved with the motivation to support Big Data analysis, it significantly supports Big Data analysis at all stages. However, they also point toward some important shortcomings and challenges of current technology trends. These include: lack of intelligent Big Data sources; need for scalable real-time analysis capability; lack of support (in networks) for latency-bound applications; need for necessary augmentation (in network support) for peer-to-peer networks; and rethinking on cost-effective high-performance storage subsystem.

Research limitations/implications

The study suggests that a lot of research is yet to be done in hardware technology, if full potential of Big Data is to be unlocked.

Practical implications

The study suggests that practitioners need to meticulously choose the hardware infrastructure for Big Data considering the limitations of technology.

Originality/value

This research arms industry, enterprises and organizations with the concise and comprehensive technical-knowledge about the capability of current hardware technology trends in solving Big Data problems. It also highlights the areas of potential research and immediate attention which researchers can exploit to explore new ideas and existing practices.

Details

Industrial Management & Data Systems, vol. 115 no. 9
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 9 February 2022

Sena Başak, İzzet Kılınç and Aslıhan Ünal

The purpose of this paper is to examine the contribution of big data in the transforming process of an IT firm to a learning organization.

Abstract

Purpose

The purpose of this paper is to examine the contribution of big data in the transforming process of an IT firm to a learning organization.

Design/methodology/approach

The authors adopted a qualitative research approach to define and interpret the ideas and experiences of the IT firms’ employees and to present them to the readers directly. For this purpose, they followed a single-case study design. They researched on a small and medium enterprise operating in the IT sector in Düzce province, Turkey. This paper used a semi-structured interview and document analysis as data collecting methods. In all, eight interviews were conducted with employees. Brochures and website of the organization were used as data sources for the document analysis.

Findings

As a result of in-depth interviews and document analysis, the authors formed five main themes that describe perception of big data and learning organization concepts, methods and practices adopted in transforming process, usage areas of big data in organization and how the sample organization uses big data as a learning organization. The findings of this paper show that the sample organization is a learning IT firm that has used big data in transforming to learning organization and in maintaining the learning culture.

Research limitations/implications

The findings contribute to literature as it is one of the first studies that examine the influence of big data on the transformation process of an IT firm to a learning organization. The findings reveal that IT firms benefit from the solutions of big data while learning. However, as the design of the research is single-case study, the findings may be specific to the sample organization. Future studies are required that examine the subject in different samples and by different research designs.

Originality/value

In literature, research on how IT firms’ managers and employees use big data in organizational learning process is limited. The authors expect that this paper will shed light on future research that examines the effect of big data on the learning process of the organization.

Details

VINE Journal of Information and Knowledge Management Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2059-5891

Keywords

Book part
Publication date: 19 September 2019

Emre Soyer, Koen Pauwels and Steven H. Seggie

While Big Data offer marketing managers information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn’t necessarily improve their…

Abstract

While Big Data offer marketing managers information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn’t necessarily improve their decision-making. Managers would still be vulnerable to confirmation bias, control illusions, communication problems, and confidence issues (the 4Cs). The authors argue that traditional remedies for such biases don’t go far enough and propose a lean start-up approach to data-based learning in marketing management. Specifically, they focus on the marketing analytics component of Big Data and how adaptations of the lean start-up methodology can be used in some combination with such analytics to help marketing managers improve their decision-making and innovation process. Beyond the often discussed technical obstacles and operational costs associated with handling Big Data, this chapter contributes by analyzing the various learning and decision-making problems that can emerge once the 4Vs of Big Data have materialized.

Details

Marketing in a Digital World
Type: Book
ISBN: 978-1-78756-339-1

Keywords

Book part
Publication date: 10 February 2020

Burcu İşgüden Kılıç

Professionals who carry out the forensic accounting profession must have an extensive knowledge of accounting, as well as an effective knowledge of law, auditing, internal…

Abstract

Professionals who carry out the forensic accounting profession must have an extensive knowledge of accounting, as well as an effective knowledge of law, auditing, internal audit, business management, psychology, crime science, and, in particular, computer technologies. In today’s digital business environment, it has become difficult to identify fraudulent transactions with traditional methods. Developments in information (data) and information technology have helped increase anti-fraud control programs and fraud research opportunities. In particular, fraudulent financial reporting disrupts the reliability, accuracy, and efficiency of financial markets in terms of existence and continuity. The forensic accounting profession has been able to improve the effectiveness of inspections by using big data techniques, data analytics, and algorithms (Rezaee, Lo, Ha, & Suen, 2016; Seda & Kramer, 2014; Singleton & Singleton, 2010).

The aim of the author, in this chapter, is to evaluate the contribution of using big data techniques in forensic accounting applications and the skills that will be provided to students while integrating these techniques in forensic accounting trainings. For this purpose, studies on forensic accounting education and their applications were reviewed. In addition, opinions were evaluated by considering the relevant literature about the importance of big data, benefits of big data, use of big data techniques, and interest shown of them.

Details

Contemporary Issues in Audit Management and Forensic Accounting
Type: Book
ISBN: 978-1-83867-636-0

Keywords

1 – 10 of over 83000