IDENTIFYING INTERIOR SPATIAL DIMENSIONS ACCORDING TO USER PREFERENCE: AN ASSOCIATIVE CONCEPT NETWORK ANALYSIS
Keywords:interior spatial dimensions, user preference, associative concept, network analysis
This study proposed a fundamental technique for evaluating the preferences of interior space users by capturing their verbally expressed preferences and then determining word associations. To accomplish this, the Pajek visualization software for large network analysis was employed in conjunction with the USF Word Association dictionary to visualize the structures and network depths of the derived associative meanings. The generated associative words were then qualitatively categorized into taxonomic word groups to reveal 13 dimensions of perceived interior-environmental quality, as follows: House-related, Territorial, Impression, Activity, Active Element of Nature, Nature, Building Materials, Companion, Household Basics, Color, Location, Composition, and Time Period. A factor analysis was then conducted to sort the generated associative words according to Out- Degree Centrality/ODC score. These were validated into five factors that appeared to influence the comfort levels of interior space users. These five factors and 13 dimensions are useful as objective bases for determining the composition of adjectival pairs through the Semantic Differential (SD) method, which helps designers and architects evaluate interior space preferences.
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), pp. B-141.
De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek (Vol. 27). Cambridge University Press.
Georgiev, G.V, & Nagai, Y. (2011). A conceptual network analysis of user impressions and meanings of product materials in design. Materials and Design, 32(8-9), pp. 4230-4242.
Georgiev, G.V. et al. (2012). Analysis of user feelings during interface operation: implications for creative design, DS 73-2 Proceedings of the 2nd International conference on Design Creativity, Vol. 2.
Goldschmidt, G. (1990). Linkography: assessing design productivity. Cyberbetics and System '90, pp. 291-298.
Goldschmidt, G. (2014). Linkography: unfolding the design process. MIT Press.
Junaidy, D.W., & Nagai, Y. (2013) The in-depth cognitive levels of imagination of artisans and designers. Journal of Design Research, 11(4), pp. 317-335.
Janssens, J., & K1/4ller, R. (1986). Utilizing an environmental simulation laboratory in Sweden. Foundations for visual project analysis, pp. 265-275.
K1/4ller, R. (1972), A semantic model for describing perceived environment. Stockholm. National Swedish Institute for Building Research, D:12.
K1/4ller, R. (1975). Semantisk miljbeskrivning (SMB). Stockholm, Psykologifrlaget.
K1/4ller, R. (1979). A semantic test for use in cross-cultural studies. Man-environment Systems, 9(4-5), pp. 253-256.
K1/4ller, R. (1980). Architecture and emotions In B. Mikellides (Ed), Architecture for People (pp. 87-100). London: Studio Vista.
K1/4ller, R. (1991). Environmental assessments from a neuropsychological perspective. In T. Garling and G. W. Evans (Eds.), Environment, cognition and action: An integrated approach (pp. 111-147). New York: Oxford University Press.
Leskovec, J. (2008). Dynamics of large networks (Doctoral dissertation, Carnegie Mellon University, School of Computer Science, Machine Learning Department).
Maki, W. S, & Buchanan, E. (2008). Latent structure in measures of associative, semantic and thematic knowledge. Psychon Bull Rev, 15(3), pp. 598-603.
Mwihaki, A. (2004). Meaning as use: A functional view of semantics and pragmatics. Swahili Forum, 11, 127-139.
Nagai, Y., & Georgiev, G. V. (2011a). The role of impressions on users' tactile interaction with product materials: An analysis of associative concept networks. Materials & Design, 32(1), pp. 291-302.
Nagai, Y., Georgiev, G. V., & Zhou, F. (2011b). A methodology to analyse in-depth impressions of design on the basis of concept networks. Journal of Design Research, 9(1), pp. 44-64.
Nelson, D. L, McEvoy, C.L., & Schreiber, T.A. (2004). The University of South Florida free association, rhyme, and word fragment norms. Behavior Research Methods, Instruments and Computers, 36(3), pp. 402-407.
Osgood C., Suci, G., & Tannenbaum, P. (1957). The measurement of meaning. Urbana: University of Illinois Press.
Pawlak, Z. (1991). Imprecise categories, approximations and rough sets. In Rough sets (pp. 9-32). Dordrecht: Springer.
Taura, T. E., Yamamoto, M. Y., Fasiha, N., & Nagai, Y. (2010). Virtual impression networks for capturing deep impressions. In J.S. Gero (Ed.) Design Computing and Cognition DCC'10, (pp. 559-578). Springer.
Wikstrm, L. (2002). Produktens budskap: Metoder fr vardering av produkters semantiska funktioner ur ett anvandarperspektiv. Chalmers University of Technology.
Yamamoto, E., Mukai, F., Yusof, N. F. M., Taura, T., & Nagai, Y. (2009). A method to generate and evaluate a creative design idea by focusing on associative process. In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 1003-1011). American Society of Mechanical Engineers.