The concept of “GovTech” has emerged as a business-oriented model and practice for enabling the public sector to take advantage of digital solutions as service towards the citizen, while the private for-profit sector is responsible for innovation, development, and profitable maintenance of the GovTech services, hence making the whole area of solutions seemingly desirable to invest in. However, the current literature on stakeholder views of the GovTech market remains rather generic, less connected to concrete examples of GovTech solutions as these are perceived within a given and delimited GovTech domain. The objective of this paper is to apply exploratory quantitative data analysis for phenomena detection and evaluation. We explore to what extent the constructs of the area actually are disparate and ill-suited to use for quantitative GovTech research, and find five factors showing a degree of mistrust between the public and private sector and prescribe further research into developing constructs that can cut across the research area, enabling for a build-up of a stronger theoretical base for tech business in the public-private markets.
CITATION STYLE
Svahn, M., Larsson, A., Macedo, E., & Bandeira, J. (2023). Construct Hunting in GovTech Research: An Exploratory Data Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14130 LNCS, pp. 3–17). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-41138-0_1
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