Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature. Issue 7 (8th August 2016)

• Record Type: Journal Article
• Authors: Mishra, Deepa; Gunasekaran, Angappa; Childe, Stephen J.; Papadopoulos, Thanos; Dubey, Rameshwar; Wamba, Samuel
• Editors: Angappa Gunasekaran, Dr Nachiappan Subramanian and Dr Manoj Kumar Tiwari, Dr
• Is Part Of: Industrial management & data systems. Volume 116:Issue 7(2016)
• Page Start: 1331
• Page End: 1355
• Publication Date: 2016-08-08
• Subjects: Vision -- Internet of Things -- Bibliometrics -- Applications -- Network analytics; Industrial management -- Periodicals; Electronic data processing -- Periodicals; Business -- Periodicals; Industrial management -- Great Britain -- Periodicals; 658.05
• DOI: 10.1108/IMDS-11-2015-0478
• Languages: English
• ISSNs: 0263-5577

Abstract

Abstract : Purpose: – The emergent field of Internet of Things (IoT) has been evolving rapidly with a geometric growth in the number of academic publications in this field. The purpose of this paper is to review the literature of IoT in past 16 years using rigorous bibliometric and network analysis tools, offering at the same time future directions for the IoT research community and implications for managers and decision makers. Design/methodology/approach: – The authors adopted the techniques of bibliometric and network analysis. The paper reviewed the articles published on IoT from 2000 to 2015. Findings: – This study identifies top contributing authors; key research topics related to the field; the most influential works based on citations and PageRank; and established and emerging research clusters. Scholars are encouraged to further explore this topic. Research limitations/implications: – This study focusses only on vision and applications of IoT. Scholars may explore various other aspects of this area of research. Originality/value: – To the best of authors' knowledge, this is the first study to review the literature on IoT by using bibliometric and network analysis techniques. The study is unique as it spans a long time period of 16 years (2000-2015). The study proposes a five-cluster classification of research themes that may inform current and future research in IoT.