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Scenario Technique to Elicit Research and Training Needs in Digital Government Employing Disruptive Technologies

Published: 18 June 2019 Publication History

Abstract

Technologies such as artificial intelligence, machine learning, Internet of things, chatbots and other disruptive technologies may bring innovations to the public sector. However, the way in which such disruptive technologies could be deployed in various areas of digital government needs systematic investigation to understand emerging research and training needs. Future scenarios can be used as a method to elicit potential future evolutions of new technologies. In this paper, we suggest a future scenarios technique to identify research and training needs along the introduction of new disruptive technologies in the public sector. The paper describes the methodology of this scenario approach and an exemplification of identifying research and training needs relating to the implementation of Internet of things in public service provisioning, based on the application of the scenario approach. The methodology foresees expert engagement in the interactive workshops aimed at identification and prioritisation of the needs through a moderated discussion of the pre-constructed future scenarios. The methodology proved to be a useful tool for the identification of the research and training needs based on the expert input and produced useful and useable results during its application.

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Cited By

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  • (2022)A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with DisabilitiesSensors10.3390/s2214514222:14(5142)Online publication date: 8-Jul-2022
  • (2022)Government 3.0: Scenarios and Roadmap of ResearchScientific Foundations of Digital Governance and Transformation10.1007/978-3-030-92945-9_13(335-360)Online publication date: 2-Mar-2022
  • (2020)Gamification in Public Service Provisioning: Investigation of Research NeedsProceedings of the 21st Annual International Conference on Digital Government Research10.1145/3396956.3398256(294-300)Online publication date: 15-Jun-2020
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cover image ACM Other conferences
dg.o '19: Proceedings of the 20th Annual International Conference on Digital Government Research
June 2019
533 pages
ISBN:9781450372046
DOI:10.1145/3325112
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 18 June 2019

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  1. E-government
  2. Government 3.0
  3. future scenarios

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dg.o 2019

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Cited By

View all
  • (2022)A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with DisabilitiesSensors10.3390/s2214514222:14(5142)Online publication date: 8-Jul-2022
  • (2022)Government 3.0: Scenarios and Roadmap of ResearchScientific Foundations of Digital Governance and Transformation10.1007/978-3-030-92945-9_13(335-360)Online publication date: 2-Mar-2022
  • (2020)Gamification in Public Service Provisioning: Investigation of Research NeedsProceedings of the 21st Annual International Conference on Digital Government Research10.1145/3396956.3398256(294-300)Online publication date: 15-Jun-2020
  • (2020)Blockchain in Digital Government: Research Needs IdentificationInformation Systems10.1007/978-3-030-63396-7_13(188-204)Online publication date: 21-Nov-2020
  • (2019)Using Disruptive Technologies in Government: Identification of Research and Training NeedsElectronic Government10.1007/978-3-030-27325-5_21(276-287)Online publication date: 2-Sep-2019

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