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Sustainable, technological, and innovative challenges post Covid-19 in health, economy, and education sectors

Author

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  • Molleví Bortoló, Gemma
  • Álvarez Valdés, Jesús
  • Nicolas-Sans, Ruben

Abstract

The impact of the Covid-19 pandemic has caused an evolution in the business use of Big Data, Artificial Intelligence and New Technologies in general. The general objective of the article is to assess how this process developed during the pandemic in the use and standardization of Big Data, digitalization, the use of data in the private sector and in the public administration and to assess whether it has been used to modernize and digitalize the post-pandemic society. The specific objectives of the article are: 1) the impact of new technologies on society during confinement; 2) to understand the use of Big Data for the creation of new products and businesses and 3) to assess which businesses and companies and from which economic sectors have emerged, which have been transformed and which have disappeared.

Suggested Citation

  • Molleví Bortoló, Gemma & Álvarez Valdés, Jesús & Nicolas-Sans, Ruben, 2023. "Sustainable, technological, and innovative challenges post Covid-19 in health, economy, and education sectors," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
  • Handle: RePEc:eee:tefoso:v:190:y:2023:i:c:s0040162523001099
    DOI: 10.1016/j.techfore.2023.122424
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    References listed on IDEAS

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    1. Papadopoulos, Thanos & Baltas, Konstantinos N. & Balta, Maria Elisavet, 2020. "The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice," International Journal of Information Management, Elsevier, vol. 55(C).
    2. Amankwah-Amoah, Joseph & Khan, Zaheer & Wood, Geoffrey & Knight, Gary, 2021. "COVID-19 and digitalization: The great acceleration," Journal of Business Research, Elsevier, vol. 136(C), pages 602-611.
    3. Fernández-Rovira, Cristina & Álvarez Valdés, Jesús & Molleví, Gemma & Nicolas-Sans, Ruben, 2021. "The digital transformation of business. Towards the datafication of the relationship with customers," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
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    Cited by:

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    2. Su, Miao & Li, Jiankun & Kim, Woohyoung, 2025. "Port ship congestion and Port-oriented cities air pollution: the role of machine learning models in transportation environmental governance," Transport Policy, Elsevier, vol. 171(C), pages 896-915.
    3. Lasarov, Wassili & Hoffmann, Stefan, 2026. "Invasive rapid innovation: An introduction and exploration of their acceptance," Technology in Society, Elsevier, vol. 84(C).

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