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Conceptual Model of Big Data Technologies Adoption in Smart Cities of the European Union

In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020

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  • Pivar, Jasmina

Abstract

Big data technologies enable cities to develop towards a smart city. However, the adoption of big data technologies is challenging, which is why it is essential to identify factors that influence the adoption of big data technologies in cities. The main goal of the paper is to propose a conceptual model of big data technologies adoption in smart cities of the European Union. In order to derive the conceptual model following is done: i) overview of the previous Technology-Organisation-Environment framework - based research on the adoption of selected information and communications technologies crucial for the development of smart cities, and ii) selection of factors based on the critical examination of the previous research. Selected factors, Absorptive Capacity, Technology Readiness, Compatibility, City Managements Support, the Existence of Smart City Strategy and Stakeholders Support, were incorporated into the conceptual model of big data technologies adoption in smart cities of the European Union.

Suggested Citation

  • Pivar, Jasmina, 2020. "Conceptual Model of Big Data Technologies Adoption in Smart Cities of the European Union," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2020), Virtual Conference, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, pages 572-585, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    • Handle: RePEc:zbw:entr20:224723
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    References listed on IDEAS

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    1. G. M.P. Swann, 2009. "The Economics of Innovation," Books, Edward Elgar Publishing, number 13211.
    2. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    3. Kevin Zhu & Kenneth L. Kraemer & Sean Xu, 2006. "The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business," Management Science, INFORMS, vol. 52(10), pages 1557-1576, October.
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    Cited by:

    1. Ana Cecilia Quiroga Gutierrez & Daniel J. Lindegger & Ala Taji Heravi & Thomas Stojanov & Martin Sykora & Suzanne Elayan & Stephen J. Mooney & John A. Naslund & Marta Fadda & Oliver Gruebner, 2023. "Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action," IJERPH, MDPI, vol. 20(2), pages 1-15, January.

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    More about this item

    Keywords

    smart city; big data technologies; adoption; TOE framework;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General

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