A Study and Analysis of the Open Government Data Ecosystem Models

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
The conversion of published data by government to a value and social effect is not done automatically and it requires a number of actors with different functions That by activating and connecting them with each other, we can use data. The sum of these actors, stakeholders and their functions is called the Open Government Data Ecosystem. This study aims to understand the ecosystem of open government data and analysis of selected open government ecosystem models is formed. Present study is an exploratory study that seeks to analysis of international models of open government data ecosystem, Identify and monitor the elements and components of the open government data ecosystem. open government data ecosystem models were redefined and its contents were extracted by content analysis method and then compared and evaluated. Units in this study, open government data ecosystem models and sampling is targeted sampling. In the first stage, 36 models were identified by searching and reviewing the models of open government data ecosystems. Finally, by studying these models and identifying the similarities and aggregating of some models, based on targeted sampling, the researchers considered 12 models of open government data ecosystem and by examining them, they extracted the data and information from these models. The reason for choosing these 12 models is their reputation, influence, comprehensiveness and authority. By examining existing models, it has become clear that there is still no comprehensive model of an open government data ecosystem that could be the basis for developing an open government data policy.  Summarizing or generalizing, paying attention to a part of the ecosystem, using a cyclic or value chain approach, instead of ecosystems approach, and some of the models are an experience or specific area of the characteristics of ecosystem models.
Language:
Persian
Published:
Journal of Information Processing and Management, Volume:34 Issue: 2, 2019
Pages:
609 to 636
magiran.com/p1950461  
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