Abstract

This work provides a systematic literature review of blockchain-based applications across multiple domains. The aim is to investigate the current state of blockchain technology and its applications and to highlight how specific characteristics of this disruptive technology can revolutionise "business-as-usual" practices. To this end, the theoretical underpinnings of numerous research papers published in high ranked scientific journals during the last decade, along with several reports from grey literature as a means of streamlining our assessment and capturing the continuously expanding blockchain domain, are included in this review. Based on a structured, systematic review and thematic content analysis of the discovered literature, we present a comprehensive classification of blockchain-enabled applications across diverse sectors such as supply chain, business, healthcare, IoT, privacy, and data management, and we establish key themes, trends and emerging areas for research. We also point to the shortcomings identified in the relevant literature, particularly limitations the blockchain technology presents and how these limitations spawn across different sectors and industries. Building on these findings, we identify various research gaps and future exploratory directions that are anticipated to be of significant value both for academics and practitioners.

Keywords

BlockchainComputer scienceSystematic reviewCurrent (fluid)Data scienceComputer securityPolitical scienceMEDLINEEngineering

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Publication Info

Year
2018
Type
article
Volume
36
Pages
55-81
Citations
2215
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

2215
OpenAlex
78
Influential
1815
CrossRef

Cite This

Fran Casino, Thomas K. Dasaklis, Constantinos Patsakis (2018). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics , 36 , 55-81. https://doi.org/10.1016/j.tele.2018.11.006

Identifiers

DOI
10.1016/j.tele.2018.11.006

Data Quality

Data completeness: 81%