2020, Vol.27, No.4

Informatization Policy 2023 KCI Impact Factor : 4.08

pISSN : 1598-3498
https://journal.kci.go.kr/nia

It has been two decades since Green Information System attracted scholars in information systems research. The surge of sustainability issues over the world naturally made Information Systems scholars to turn their attention to understanding how the use of Information Systems is making impact to our society and environments. This paper reviews studies on Green Information Systems(Green ISs) to evaluate efforts made in the last decade. Based on a systematic approach, 64 articles published in peer-reviewed international journals in Information Systems and Business & Management disciplines are analyzed to identify research gaps and propose future research agenda in Green ISs that include the application of psychological theory in the design of Green ISs, energy efficient IT/IS to respond to accelerated virtualization, and contribution of Green ISs to biodiversity.

This study proposes a way to timely forecast open government data (OGD) demand(i.e., OGD requests, search queries, etc.) by using keyword network analysis. According to the analysis results, most of the OGD belonging to the high-demand topics are provided by the domestic OGD portal(data.go.kr), while the OGD related to users’ actual needs predicted through topic association analysis are rarely provided. This is because, when providing(or selecting) OGD, relevance to OGD topics takes precedence over relevance to users’ OGD requests. The proposed keyword network analysis framework is expected to contribute to the establishment of OGD policies for public institutions in the future as it can quickly and easily forecast users’ demand based on actual OGD requests.

The rapid development of artificial intelligence technologies such as machine learning and deep learning is expanding its impact in the public administrative and public policy sphere. This paper is an exploratory study on policy decision-making in the age of artificial intelligence to design automated configuration and operation through data analysis and algorithm development. The theoretical framework was composed of the types of policy problems according to the degree of problem structuring, and the success and failure cases were classified and analyzed to derive implications. In other words, when the problem structuring is more difficult than others, the greater the possibility of failure or side effects of decision-making using artificial intelligence. Also, concerns about the neutrality of the algorithm were presented. As a policy suggestion, a subcommittee was proposed in which experts in technical and social aspects play a professional role in establishing the AI promotion system in Korea. Although the subcommittee works independently, it suggests that it is necessary to establish governance in which the results of activities can be synthesized and integrated.