Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/13483
Title: Predictive models of emotion from product design features
Authors: Seva, Rosemary R.
Keywords: DRNTU::Engineering::Industrial engineering::Human factors engineering
Issue Date: 2008
Source: Seva, R. R. (2008). Predictive models of emotion from product design features. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: The use of affect as a means of conceptualizing and evaluating designs required the development of a measurement system that is appropriate for the context. Emotions that users experience when they inspect and evaluate products are called pre-purchase affect and consist of a unique set of emotions. Pre-purchase affect is important as this is the stage when consumers are contemplating on buying a product and therefore designs must be able to elicit intense emotions that would prompt them to buy. A Pre-purchase Emotion Set (PES) was developed in this study that enumerates the emotions that consumers typically experience while shopping. The set was obtained from a field study that considered several products. The set included eighteen emotions that were predominantly positive compared to other emotion sets found in literature. Earlier models of emotions were regarded to be inappropriate for subjective measurement of emotion in pre-purchase product evaluation because the components were broad that includes even the post-purchase context. This makes them insensitive and ineffective in measuring pre-purchase affect (PPA). Further analysis of the PES using multidimensional scaling and factor analysis revealed a four-dimensional solution. The dimensions were labeled: amazement, positive enthusiasm, optimism, and satisfaction.
URI: http://hdl.handle.net/10356/13483
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
Appears in Collections:MAE Theses

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