Techno-Cultural Factors Affecting Policy Decision-Making: A Social Network Analysis of South Korea’s Local Spatial Planning Policy

Abstract

Increasing interest in various local construction forms necessitate examining its link to human life. Construction culture should be adapted and applied to the contemporary context to create a harmonious coexistence with diverse local cultures and to strengthen regional sustainability, avoiding the rigid, one-dimensional local construction development. Thus, this study aims to analyze the factors of influence needed for policy decision-making at the local spatial planning stage, with regional technologies and cultural contents from a convergent perspective taken into consideration. This study derived tangible and intangible policy decision-making factors during the spatial planning stage using text mining analysis. Additionally, social network analysis was also used to seek multi-angle correlations among factors. Through big data analytics, 16 key decision-making contents in the spatial planning stage were derived, with ‘regional development, urban policy’ as most influential. Such a result indicates the need for regional and urban policy engagement with strategic development from a holistic perspective—in view of socio-cultural relations and forms of change—and local perceptions of spatial value and significance affecting decision-making in the local spatial planning stage (LSPS). Understanding the decision-making process in the spatial planning stage requires a holistic approach with both visible technological factors (structure, form, and construction method) and invisible cultural factors (ways of life projected during space formation, zeitgeist, religion, learning, and art) included.

A Cognitive Systems Engineering Approach Using Unsupervised Fuzzy C-Means Technique, Exploratory Factor Analysis and Network Analysis—A Preliminary Statistical Investigation of the Bean Counter Profiling Scale Robustness

“…To en-399 sure the correctness of our network analysis, the network needs to be a sufficiently accu-400 rate representation of the underlying data in order to guarantee the scientific accuracy 401 [61]. Since the aim of this study is to bring as much as possible statistical evidence for the six-factorial structure of the Bean Counter Profiling Scale, we have employed network 403 analysis for adding empirical rigor [62][63][64][65] besides results obtained with factorial and 404 Additional fit indices yielded an RMSEA of 0.052 and a TLI of 0.816 for the entire 68-item BCPS, indicating an acceptable general model fit.…”