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Τίτλος: Digital Transformation Strategy in Post-COVID Era: Innovation Performance Determinants and Digital Capabilities in Driving Schools
Συγγραφείς: Nousopoulou, Evangelia
Kamariotou, Maria
Kitsios, Fotis
Τύπος: Article
Θέματα: FRASCATI::Engineering and technology
FRASCATI::Social sciences
Λέξεις-Κλειδιά: digital transformation
innovation performance
digital capabilities
strategy
digitalization
Ημερομηνία Έκδοσης: 4-Ιου-2022
Πηγή: Information
Τόμος: 13
Τεύχος: 7
Πρώτη Σελίδα: 323
Επιτομή: Businesses affected by the pandemic have realized the importance of incorporating digital transformation into their operations. However, as a result of the market lockdown, they realized that they needed to digitalize their firms immediately and make greater attempts to enhance their economic situation by integrating a greater number of technological components. While there have been numerous studies conducted on the adoption of digital transformation in small–medium enterprises, there has been no research carried out on the implementation of digital transformation in the specific industry of driving schools. This paper investigates the significance of digital transformation, as well as the potential for its application in this industry’s business setting and the ways in which it can be utilized to improve innovation capabilities and performance. The data for this study came from 300 driving instructors in Greece and Cyprus. Multivariate regression analysis was used to analyze the data. The outcomes suggest that driving schools have a generally positive reaction to and acknowledgement of the increasing speed of digital transformation. The results also give driving school owners useful information that helps them show how important digital transformation is to their businesses. Using the findings of this study, driving schools will be able to improve their operational capabilities and accelerate their development in the post-COVID era.
URI: https://doi.org/10.3390/info13070323
https://ruomo.lib.uom.gr/handle/7000/1730
ISSN: 2078-2489
Αλλοι Προσδιοριστές: 10.3390/info13070323
Εμφανίζεται στις Συλλογές: Τμήμα Εφαρμοσμένης Πληροφορικής

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