Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-31084
Publication type: | Conference paper |
Type of review: | Peer review (publication) |
Title: | An intelligent platform-based tool for the development of digital transformation strategies |
Authors: | Gatziu Grivas, Stella Hanne, Thomas Imhof, Denis Bugmann, Diego Schmitter, Paul |
et. al: | No |
DOI: | 10.1016/j.procs.2024.05.114 10.21256/zhaw-31084 |
Published in: | Procedia Computer Science |
Volume(Issue): | 237 |
Page(s): | 344 |
Pages to: | 353 |
Conference details: | International Conference on Industry Science and Computer Sciences Innovation (iSCSi), Lisbon, Portugal, 4-6 October 2023 |
Issue Date: | 2024 |
Publisher / Ed. Institution: | Elsevier |
ISSN: | 1877-0509 |
Language: | English |
Subjects: | Digital transformation; Digital strategy development; Digital maturity assessment; Online assessment; Consulting |
Subject (DDC): | 004: Computer science 658: General Management |
Abstract: | Digital transformation strategies are of elementary importance for organizations competing in the digital age. Challenges such as faster changing customer needs, new value creation structures in digital eco-systems, or the use of collective intelligence to innovate business models require leveraging digital technologies. To achieve this and remain competitive, appropriate digital transformation strategies need to be in place. Yet, studies show that organizations struggle with strategy formulation and implementation. Based on workshops with practitioners the authors obtained concrete needs, pains, and gains as requirements for the development of an own, new intelligent and platform-based assessment tool. The proposed tool collects, calculates, and visualizes in a self-service manner, relevant company data to support decision-makers and organizations in digital transformation strategy development and implementation. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/31084 |
Fulltext version: | Published version |
License (according to publishing contract): | CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International |
Departement: | Life Sciences and Facility Management |
Organisational Unit: | Institute of Facility Management (IFM) |
Published as part of the ZHAW project: | DC4HC – Digital Competence for Healthcare |
Appears in collections: | Publikationen Life Sciences und Facility Management |
Files in This Item:
File | Description | Size | Format | |
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2024_GatziuGrivas-etal_Development-of-digital-transformation-strategies_iSCSi.pdf | 1.08 MB | Adobe PDF | ![]() View/Open |
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Gatziu Grivas, S., Hanne, T., Imhof, D., Bugmann, D., & Schmitter, P. (2024). An intelligent platform-based tool for the development of digital transformation strategies [Conference paper]. Procedia Computer Science, 237, 344–353. https://doi.org/10.1016/j.procs.2024.05.114
Gatziu Grivas, S. et al. (2024) ‘An intelligent platform-based tool for the development of digital transformation strategies’, in Procedia Computer Science. Elsevier, pp. 344–353. Available at: https://doi.org/10.1016/j.procs.2024.05.114.
S. Gatziu Grivas, T. Hanne, D. Imhof, D. Bugmann, and P. Schmitter, “An intelligent platform-based tool for the development of digital transformation strategies,” in Procedia Computer Science, 2024, vol. 237, pp. 344–353. doi: 10.1016/j.procs.2024.05.114.
GATZIU GRIVAS, Stella, Thomas HANNE, Denis IMHOF, Diego BUGMANN und Paul SCHMITTER, 2024. An intelligent platform-based tool for the development of digital transformation strategies. In: Procedia Computer Science. Conference paper. Elsevier. 2024. S. 344–353
Gatziu Grivas, Stella, Thomas Hanne, Denis Imhof, Diego Bugmann, and Paul Schmitter. 2024. “An Intelligent Platform-Based Tool for the Development of Digital Transformation Strategies.” Conference paper. In Procedia Computer Science, 237:344–53. Elsevier. https://doi.org/10.1016/j.procs.2024.05.114.
Gatziu Grivas, Stella, et al. “An Intelligent Platform-Based Tool for the Development of Digital Transformation Strategies.” Procedia Computer Science, vol. 237, Elsevier, 2024, pp. 344–53, https://doi.org/10.1016/j.procs.2024.05.114.
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