Big data analytics as a tool for fighting pandemics: a systematic review of literature

The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance.

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A Systematic Review of Big Data Analytics Using Model Driven Engineering

Proceedings of the 2017 International Conference on Cloud and Big Data Computing - ICCBDC 2017 ◽

10.1145/3141128.3141138 ◽

2017 ◽

Cited By ~ 1

Author(s):

Muhammad Nouman Zafar ◽

Farooque Azam ◽

Saad Rehman ◽

Muhammad Waseem Anwar

Keyword(s):

Systematic Review ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Model Driven Engineering ◽

Model Driven

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Comprehensive Contemplation of Probabilistic Aspects in Intelligent Analytics

International Journal of Service Science Management Engineering and Technology ◽

10.4018/ijssmet.2020010108 ◽

2020 ◽

Vol 11 (1) ◽

pp. 116-141 ◽

Cited By ~ 2

Author(s):

Neeti Sangwan ◽

Vishal Bhatnagar

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Big Data Analysis ◽

Future Research ◽

Learning Approaches ◽

Text Analytics ◽

Review Of Literature ◽

Classification Framework

In Big Data analysis, the application of machine learning has proven to be a revolutionary. The systematic review of literature shows that research has been carried out on the domain of big data analytics particularly text analytics with the inclusion of machine learning approaches. This extensive survey deals with the data at hand that provides different ways and issues while combining the machine learning approaches with the text. During the course of the survey, various publications in the field of synchronous application of machine learning in text analytics were searched and studied. Classification framework is proposed as the contribution of machine learning in text analytics. A classification framework represented the various application areas to motivate researchers for future research on the application of two emerging technologies.

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Big Data Analytics for Customer Relationship Management: A Systematic Review and Research Agenda

Communications in Computer and Information Science - Advances in Computing and Data Sciences ◽

10.1007/978-981-15-6634-9_39 ◽

2020 ◽

pp. 430-438

Author(s):

Sarika Sharma

Keyword(s):

Systematic Review ◽

Big Data ◽

Customer Relationship Management ◽

Research Agenda ◽

Data Analytics ◽

Big Data Analytics ◽

Relationship Management ◽

Customer Relationship

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Big data analytics in E-commerce: a systematic review and agenda for future research

Electronic Markets ◽

10.1007/s12525-016-0219-0 ◽

2016 ◽

Vol 26 (2) ◽

pp. 173-194 ◽

Cited By ~ 162

Author(s):

Shahriar Akter ◽

Samuel Fosso Wamba

Keyword(s):

Systematic Review ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Future Research

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The emerging big data analytics and IoT in supply chain management: a systematic review

Supply Chain Management An International Journal ◽

10.1108/scm-03-2018-0149 ◽

2018 ◽

Vol 25 (2) ◽

pp. 141-156 ◽

Cited By ~ 13

Author(s):

Arun Aryal ◽

Ying Liao ◽

Prasnna Nattuthurai ◽

Bo Li

Keyword(s):

Systematic Review ◽

Big Data ◽

Supply Chain ◽

Data Analytics ◽

Big Data Analytics ◽

Research Approach ◽

Network Systems ◽

Content Type ◽

And Performance ◽

Over Time

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.

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