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Detection of paradigm shifts and emerging fields using scientific network: A case study of Information Technology for Engineering

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  • Prabhakaran, Thara
  • Lathabai, Hiran H.
  • Changat, Manoj

Abstract

Detection of emerging fields in any industry is of great importance to the industrialists, engineers and policy makers of business as well as state administration. Exact awareness of the paradigm which governs current research activities and chances of likely paradigm shifts which could redefine the research approaches, is very crucial for the actors of scientific community and policy makers. Excellent technologies in IT, even accelerated the scientific and applied ontological pursuit in both academia as well as industry. In this work, network approach is advocated for the identification of innovations, new paradigms and emerging fields in the IT industry in the research area ‘engineering’. The network is a scientific network of research publications which reflects the volume and flow of scientific activities. Centrality analysis, path analysis, cluster analysis, etc. are used to identify the key papers of paradigm shifts, emerging fields, relatively important clusters and works respectively. A new metric, flow vergence index is devised for cluster analysis. The paradigm shift identified from this network is RFID technology, related with the supply chain management. With proper economic and policy supports, there are some good reasons to look forward for more wonders from the industry.

Suggested Citation

  • Prabhakaran, Thara & Lathabai, Hiran H. & Changat, Manoj, 2015. "Detection of paradigm shifts and emerging fields using scientific network: A case study of Information Technology for Engineering," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 124-145.
    • Handle: RePEc:eee:tefoso:v:91:y:2015:i:c:p:124-145
    DOI: 10.1016/j.techfore.2014.02.003
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    Cited by:

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    2. Susan George & Hiran H. Lathabai & Thara Prabhakaran & Manoj Changat, 2020. "A framework towards bias-free contextual productivity assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 127-157, January.
    3. Xian Li & Ronald Rousseau & Liming Liang & Fangjie Xi & Yushuang Lü & Yifan Yuan & Xiaojun Hu, 2022. "Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2105-2122, April.
    4. Hiran H. Lathabai & Susan George & Thara Prabhakaran & Manoj Changat, 2018. "An integrated approach to path analysis for weighted citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1871-1904, December.
    5. de Paulo, Alex Fabianne & Nunes, Breno & Porto, Geciane, 2020. "Emerging green technologies for vehicle propulsion systems," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    6. Wei, Xiaoyong & Lo, Chris.K.Y. & Jung, Sojin & Choi, Tsan-Ming, 2021. "From co-consumption to co-production: A systematic review and research synthesis of collaborative consumption practices," Journal of Business Research, Elsevier, vol. 129(C), pages 282-294.
    7. Xianzhong Cao & Bo Chen & Yi Guo & Zhenzhen Yi, 2023. "The Impact of Intra-City and Inter-City Innovation Networks on City Economic Growth: A Case Study of the Yangtze River Delta in China," Land, MDPI, vol. 12(7), pages 1-17, July.
    8. Lathabai, Hiran H. & Prabhakaran, Thara & Changat, Manoj, 2015. "Centrality and Flow Vergence gradient based Path analysis of scientific literature: A case study of Biotechnology for Engineering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 157-168.
    9. 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.
    10. Ba, Zhichao & Ma, Yaxue & Cai, Jinyao & Li, Gang, 2023. "A citation-based research framework for exploring policy diffusion: Evidence from China's new energy policies," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    11. Thara Prabhakaran & Hiran H. Lathabai & Susan George & Manoj Changat, 2018. "Towards prediction of paradigm shifts from scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1611-1644, December.
    12. Huenteler, Joern & Schmidt, Tobias S. & Ossenbrink, Jan & Hoffmann, Volker H., 2016. "Technology life-cycles in the energy sector — Technological characteristics and the role of deployment for innovation," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 102-121.
    13. Sohrabi, Babak & Khalilijafarabad, Ahmad, 2018. "Systematic method for finding emergence research areas as data quality," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 280-287.
    14. Hiran H. Lathabai & Thara Prabhakaran & Manoj Changat, 2017. "Contextual productivity assessment of authors and journals: a network scientometric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 711-737, February.
    15. Hwang, Seonho & Shin, Juneseuk, 2019. "Extending technological trajectories to latest technological changes by overcoming time lags," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 142-153.

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