[Purpose/Significance] To explore the essential connotation and boundary of data goodness and to explore the driving factors of its realization, to help China’s digital ecology realize the normalization of data goodness development. [Method/Process] This paper explored the intrinsic elements and scope of data goodness through theoretical tracing and defined its essential concept, then analyzed the driving factors of data subjects’ adoption of data goodness using the ISM model, and finally analyzed the dilemma and future of data goodness. [Result/Conclusion] The paper found that the motivations that drive data subjects to adopt data for good can be divided into core, indirect and surface motivations, and choosing the right driver is the key to achieving data for good. In the future, if we want to popularise data for good at a societal level, we need to start from a number of dimensions such as regulations, laws and market environment.
Chu Jiewang
,
Li Jiaxuan
. How Data for Good is Possible in the Digital Ecology: Origins, Elements, Drivers and Dilemmas[J]. Library and Information Service, 2023
, 67(10)
: 3
-14
.
DOI: 10.13266/j.issn.0252-3116.2023.10.001
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