Modeling of open government data for public sector organizations using the potential theories and determinants-A systematic review

• Main Authors: Khurshid, Muhammad Mahboob, Zakaria, Nor Hidayati, Rashid, Ammar, Ahmad, Mohammad Nazir, Arfeen, Muhammad Irfanullah, Shehzad, Hafiz Muhammad Faisal
• Format: Article
• Language: English
• Published: MDPI AG 2020
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/90262/1/NorHidayatiZakaria2020_ModelingofOpenGovernmentDataforPublicSector.pdf; http://eprints.utm.my/id/eprint/90262/; http://dx.doi.org/10.3390/INFORMATICS7030024
• Institution: Universiti Teknologi Malaysia

Open government data (OGD) has huge potential to increase transparency, accountability, and participation while improving effciency in operations, data-driven and evidence-based policymaking, and trust in government institutions. Despite its potential benefits, OGD has not been widely and successfully adopted in public sector organizations, particularly in developing countries. Therefore, the purpose of this study is to explore the theories/frameworks and potential determinants that influence the OGD adoption in public sector organizations. To ascertain the various determinants of OGD adoption in public sector organizations, this study involved a systematic review of already established theories and determinants addressed in the public sector open data domain. The review revealed that the TOE (technology, organization, environment) framework was dominantly employed over theories in the earlier studies to understand organizational adoption to OGD followed by institutional theory. The results, concerning potential determinants, revealed that some of the most frequently addressed determinants are an organization's digitization/digitalization capacity, compliance pressure, financial resources, legislation, policy, regulations, organizational culture, political leadership commitment, top-management support, and data quality. The findings will enrich researchers to empirically investigate the exposed determinants and improve the understanding of decision-makers to leverage OGD adoption by taking relevant measures.