Exploring Digital Competitiveness through Bayesian Belief Networks
This study assesses national digital competitiveness by analyzing interdependencies among key factors influencing overall performance. Unlike conventional ranking models that assume equal weighting of pillars, this study uses Bayesian belief network (BBN) models to capture complex, non-linear relati...
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| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | article |
| منشور في: |
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/26362 |
| الوسوم: |
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| _version_ | 1864513441630257152 |
|---|---|
| author | Qazi, Abroon |
| author_facet | Qazi, Abroon |
| author_role | author |
| dc.creator.none.fl_str_mv | Qazi, Abroon |
| dc.date.none.fl_str_mv | 2025-09-23T09:23:44Z 2025-09-23T09:23:44Z 2025 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Qazi, A. (2025). Exploring digital competitiveness through Bayesian belief networks. Journal of Competitiveness, 17(2). https://hdl.handle.net/11073/26362 10.7441/joc.2025.02.03 1804-1728 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Tomas Bata University in Zlín |
| dc.relation.none.fl_str_mv | https://doi.org/10.7441/joc.2025.02.03 |
| dc.rights.none.fl_str_mv | Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ |
| dc.subject.none.fl_str_mv | Digital competitiveness Bayesian belief network Future readiness Knowledge Policymaking |
| dc.title.none.fl_str_mv | Exploring Digital Competitiveness through Bayesian Belief Networks |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This study assesses national digital competitiveness by analyzing interdependencies among key factors influencing overall performance. Unlike conventional ranking models that assume equal weighting of pillars, this study uses Bayesian belief network (BBN) models to capture complex, non-linear relationships, offering a more precise identification of critical determinants. The methodology involves constructing BBN models using data from the IMD Digital Competitiveness Ranking 2023 for 64 countries. Three states were assigned to variables—low, medium, and high performance—and the tree augmented naive Bayes (TAN) algorithm was applied to model interdependencies. Thefindings highlight future readiness and knowledge as the most influential pillars, with high-performing countries demonstrating strengths in these areas. Additionally, critical sub-pillars such as adaptive attitudes and regulatory frameworks play pivotal roles. Unlike traditional approaches, this study identifies ripple effects within sub-pillars, demonstrating how targeted improvements in key areas can amplify digital transformation. The results emphasize the importance of a holistic strategy that considers these interconnections rather than isolated improvements. By providing a data-driven prioritization of key factors, this study offers policymakers a novel framework for resource allocation and strategic interventions. It contributes to the literature by challenging traditional schemes, advocating for a more comprehensive understanding of digital competitiveness, and offering guidance for targeted interventions tailored to each country's unique context. |
| format | article |
| id | aus_0cf31a5f8372b41a7677a7336f9bfcdc |
| identifier_str_mv | Qazi, A. (2025). Exploring digital competitiveness through Bayesian belief networks. Journal of Competitiveness, 17(2). 10.7441/joc.2025.02.03 1804-1728 |
| language_invalid_str_mv | en |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/26362 |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Tomas Bata University in Zlín |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ |
| spelling | Exploring Digital Competitiveness through Bayesian Belief NetworksQazi, AbroonDigital competitivenessBayesian belief networkFuture readinessKnowledgePolicymakingThis study assesses national digital competitiveness by analyzing interdependencies among key factors influencing overall performance. Unlike conventional ranking models that assume equal weighting of pillars, this study uses Bayesian belief network (BBN) models to capture complex, non-linear relationships, offering a more precise identification of critical determinants. The methodology involves constructing BBN models using data from the IMD Digital Competitiveness Ranking 2023 for 64 countries. Three states were assigned to variables—low, medium, and high performance—and the tree augmented naive Bayes (TAN) algorithm was applied to model interdependencies. Thefindings highlight future readiness and knowledge as the most influential pillars, with high-performing countries demonstrating strengths in these areas. Additionally, critical sub-pillars such as adaptive attitudes and regulatory frameworks play pivotal roles. Unlike traditional approaches, this study identifies ripple effects within sub-pillars, demonstrating how targeted improvements in key areas can amplify digital transformation. The results emphasize the importance of a holistic strategy that considers these interconnections rather than isolated improvements. By providing a data-driven prioritization of key factors, this study offers policymakers a novel framework for resource allocation and strategic interventions. It contributes to the literature by challenging traditional schemes, advocating for a more comprehensive understanding of digital competitiveness, and offering guidance for targeted interventions tailored to each country's unique context.Tomas Bata University in Zlín2025-09-23T09:23:44Z2025-09-23T09:23:44Z2025Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfQazi, A. (2025). Exploring digital competitiveness through Bayesian belief networks. Journal of Competitiveness, 17(2).https://hdl.handle.net/11073/2636210.7441/joc.2025.02.031804-1728enhttps://doi.org/10.7441/joc.2025.02.03Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/oai:repository.aus.edu:11073/263622025-09-23T11:45:15Z |
| spellingShingle | Exploring Digital Competitiveness through Bayesian Belief Networks Qazi, Abroon Digital competitiveness Bayesian belief network Future readiness Knowledge Policymaking |
| status_str | publishedVersion |
| title | Exploring Digital Competitiveness through Bayesian Belief Networks |
| title_full | Exploring Digital Competitiveness through Bayesian Belief Networks |
| title_fullStr | Exploring Digital Competitiveness through Bayesian Belief Networks |
| title_full_unstemmed | Exploring Digital Competitiveness through Bayesian Belief Networks |
| title_short | Exploring Digital Competitiveness through Bayesian Belief Networks |
| title_sort | Exploring Digital Competitiveness through Bayesian Belief Networks |
| topic | Digital competitiveness Bayesian belief network Future readiness Knowledge Policymaking |
| url | https://hdl.handle.net/11073/26362 |