Product Semantics: The Emotional Design Language
07 Jan 2021-Vol. 221, pp 607-618
TL;DR: In this article, the authors explored the co-existence of emotions and cognition in humans to build and propose a framework that helps correlate "likeability" and "sellability" of a product by introduction of a relatively new term (USF).
Abstract: This study explores the co-existent nature of emotions and cognition in humans to build and propose a framework that helps correlate ‘likeability’ and ‘sellability’ of a product by introduction of a relatively new term—unique selling factor (USF). The framework runs on context-based logical correlations among its constituents. The aim of this framework is to qualitatively express the emotional characteristics of a product. As emotions work alongside with cognition, the design attributes of the product under the lenses are first analysed as per the three levels of our brain’s processing. Each design feature corresponds to the processing level based on the consumers’ probable preferences to choose that feature in the first place. After this cognitive breakdown, we further diverge the semantic analysis at emotional levels. Each design feature when stated with the consumers’ probable preference and the cognition level involved can now help develop context of the scenario. This context that triangulates the connect between the design feature, consumers’ preference and processing level plays significant role as the backbone of emotionality in the analysis overall. To apply the understanding built, a logical study is done considering a black V-neck T-shirt as the product under the lenses. For this product, we define the likeability, sellability and the unique selling factor. For analysis, we create a feature analysis table that subdivides the product features first, into its design characteristics. Second, against these characteristics are explored the probable reasons the consumer might have had for opting for those characteristics. Third, each reason for the preference for its respective design characteristic is assigned to its corresponding levels of brain processing. Fourth, for the context developed so far, we can assign emotions involved. The co-existence of emotion and cognition paves way for this product-semantic design language. Thus, the framework proposed works evidently on emotion and cognition and helps provide a novel perspective—that of the most significant stakeholder of all—the customer and the people we design for. The framework follows an ecosystemic approach that provides it with appropriate literature and a holistic approach.
TL;DR: In this article, a comparison of predictions from alternative surveys, one of subjective purchase probabilities and the other of buying intentions, indicates that purchase probabilities explain about twice as much of the cross-section variance in automobile purchase rates as buying intentions.
Abstract: Surveys of consumer intentions to buy are inefficient predictors of purchase rates because they do not provide accurate estimates of mean purchase probability. This is a consequence of the fact that intentions surveys cannot detect movements in mean probability among nonintenders, who account for the bulk of actual purchases and for most of the time-series variance in purchase rates. Comparison of predictions from alternative surveys, one of subjective purchase probabilities and the other of buying intentions, indicates that purchase probabilities explain about twice as much of the cross-section variance in automobile purchase rates as buying intentions. Similar but not quite so conclusive differences are obtained from analysis of selected household durables. The probability variable predicts more accurately than the intentions variable largely because it divides nonintenders, and those who report that they “don't know” about their buying intentions, into subgroups with systematically different p...