A qualitative framework for data collection and analysis in participation processes

被引:0
作者
Marzouki, Amal [1 ]
Mellouli, Sehl [1 ]
Daniel, Sylvie [2 ]
机构
[1] Laval Univ, Dept Informat Syst, Quebec City, PQ, Canada
[2] Laval Univ, Dept Geomat, Quebec City, PQ, Canada
来源
PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH (DGO2019): GOVERNANCE IN THE AGE OF ARTIFICIAL INTELLIGENCE | 2019年
关键词
Participation processes; Data collection; Data analysis; Grounded Theory Method; Natural Language Processing; PUBLIC INVOLVEMENT PROCESSES; GROUNDED THEORY; CITIZEN PARTICIPATION; PERCEPTIONS;
D O I
10.1145/3325112.3325227
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Participation processes (PPs) are more and more requested in different areas given their capability to promote constructive exchange and innovative ideas and to bring a great value to decision-making processes. PPs aim to generate new data that decision-makers can consider and transform into relevant knowledge to support their decision-making processes. In some cases, collecting data could be problematic since stakeholders might lack willingness, capacity and/or suitable means to participate. In other cases, PPs might generate a large amount of data to be analyzed and scrutinized. At the same time, PPs are subject to time-effectiveness pressures to provide timely reports. Based on a case study and using the grounded theory method, we propose in this paper a qualitative framework to guide data collection and analysis in PPs in order to better inform decision-makers.
引用
收藏
页码:398 / 408
页数:11
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