Government data openness and knowledge management: configurational patterns for national competitiveness

Autor: Juyeon Ham, Yunmo Koo, Jae Nam Lee
Rok vydání: 2022
Předmět:
Zdroj: Industrial Management & Data Systems. 122:2710-2736
ISSN: 0263-5577
DOI: 10.1108/imds-03-2022-0188
Popis: PurposeIn the data economy era, despite the tremendous effort of governments to actively provide and use open data, its effect on national performance such as competitiveness differs widely from country to country. A sufficient knowledge base and its appropriate management are important to effectively derive the potential value from open data. A country can implement multiple and equally viable means to effectively align open data with knowledge management, which lead to high national performance. However, previous studies lack consideration of the possibility of these various configurations. To fill the research gap, this study aims to investigate the configurational patterns constituted by government data openness and knowledge management for national competitiveness.Design/methodology/approachFrom the open innovation perspective, this study collected data from the global reports of 76 countries and examined them through fuzzy-set qualitative comparative analysis (fsQCA).FindingsFour configurational patterns are identified, namely, coupled (outbound-focused)-, coupled (inbound-focused)-, inbound-focused-, and outbound-focused national competitiveness.Originality/valueThis study provides a foundation that enables researchers to build a holistic and balanced perspective that can manage open government data and develop knowledge management capability.
Databáze: OpenAIRE