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Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): a co-word analysis

Author

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  • Bei-Ni Yan

    (Anhui University
    Fu Jen Catholic University)

  • Tian-Shyug Lee

    (Fu Jen Catholic University)

  • Tsung-Pei Lee

    (Fu Jen Catholic University)

Abstract

The study utilized co-word analysis to explore papers in the field of Internet of Things to examine the scientific development in the area. The research data were retrieved from the WOS database from the period between 2000 and 2014, which consists of 758 papers. By using co-word analysis, this study found 7 clusters that represent the intellectual structure of IoT, including ‘IoT and Security’, ‘Middleware’, ‘RFID’, ‘Internet’, ‘Cloud computing’, ‘Wireless sensor networks’ and ‘6LoWPAN’. To understand these intellectual structures, this study used a co-occurrence matrix based on Pearson’s correlation coefficient to create a clustering of the words using the hierarchical clustering technique. To visualize these intellectual structures, this study carried out a multidimensional scaling analysis, to which a PROXCAL algorithm was applied.

Suggested Citation

  • Bei-Ni Yan & Tian-Shyug Lee & Tsung-Pei Lee, 2015. "Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): a co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1285-1300, November.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:2:d:10.1007_s11192-015-1740-1
    DOI: 10.1007/s11192-015-1740-1
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    Cited by:

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    2. Delgosha, Mohammad Soltani & Hajiheydari, Nastaran & Talafidaryani, Mojtaba, 2022. "Discovering IoT implications in business and management: A computational thematic analysis," Technovation, Elsevier, vol. 118(C).
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    8. Arif Mehmood & Gyu Sang Choi & Otto F. Feigenblatt & Han Woo Park, 2016. "Proving ground for social network analysis in the emerging research area “Internet of Things” (IoT)," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 185-201, October.
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    12. Qikai Cheng & Jiamin Wang & Wei Lu & Yong Huang & Yi Bu, 2020. "Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1923-1943, September.
    13. Manuel Castriotta & Michela Loi & Elona Marku & Luca Naitana, 2019. "What’s in a name? Exploring the conceptual structure of emerging organizations," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 407-437, February.
    14. Qing Yang & Yanxia Zhu & Xingxing Liu & Lingmei Fu & Qianqian Guo, 2019. "Bayesian-Based NIMBY Crisis Transformation Path Discovery for Municipal Solid Waste Incineration in China," Sustainability, MDPI, vol. 11(8), pages 1-21, April.
    15. Lu, Yang & Papagiannidis, Savvas & Alamanos, Eleftherios, 2018. "Internet of Things: A systematic review of the business literature from the user and organisational perspectives," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 285-297.
    16. Aliakbar Pourhatami & Mohammad Kaviyani-Charati & Bahareh Kargar & Hamed Baziyad & Maryam Kargar & Carlos Olmeda-Gómez, 2021. "Mapping the intellectual structure of the coronavirus field (2000–2020): a co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6625-6657, August.
    17. Hao Wang & Sanhong Deng & Xinning Su, 2016. "A study on construction and analysis of discipline knowledge structure of Chinese LIS based on CSSCI," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1725-1759, December.
    18. Sung Kim & Derek Hansen & Richard Helps, 2018. "Computing research in the academy: insights from theses and dissertations," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 135-158, January.
    19. Xiuwen Chen & Jianping Li & Xiaolei Sun & Dengsheng Wu, 2019. "Early identification of intellectual structure based on co-word analysis from research grants," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 349-369, October.
    20. Xuefeng Wang & Shuo Zhang & Yuqin liu, 2022. "ITGInsight–discovering and visualizing research fronts in the scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6509-6531, November.
    21. Zhu, Lin & Cunningham, Scott W., 2022. "Unveiling the knowledge structure of technological forecasting and social change (1969–2020) through an NMF-based hierarchical topic model," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    22. Takano, Yasutomo & Kajikawa, Yuya, 2019. "Extracting commercialization opportunities of the Internet of Things: Measuring text similarity between papers and patents," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 45-68.

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