Three methods for identifying novel affordances. Issue 3 (28th July 2015)

• Record Type: Journal Article
• Authors: Brown, David C.; Maier, Jonathan R.A.; Shu, L.H.; Srivastava, J.; Chou, A.; Lai, S.
• Is Part Of: AI EDAM. Volume 29:Issue 3(2015)
• Page Start: 267
• Page End: 279
• Publication Date: 2015-07-28
• Subjects: Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060415000219
• Languages: English
• ISSNs: 0890-0604

Abstract

<abstract abstract-type="normal"> <title>Abstract</title> <p>We describe three approaches to identify novel product affordances: affordance of absence; insights from lead users, specifically do-it-yourselfers (DIYers); and natural-language searches. While these approaches were separately pursued, we show their connection to each other in this paper. We begin by describing the affordance of absence, inspired by insights on affordances arising from a lack of resources. For example, in the absence of specialized tools, more general tools are used to accomplish similar tasks. Such absence clarifies how other tools could be modified to add relevant features and identifies critical features of the absent tool. In addition, the temporary removal of physical features and objects enables user interaction in ways that may not emerge in their presence. Affordance of absence has the potential to more fully specify affordances for a given object and to help overcome functional fixedness. For the second approach, we describe insights from DIYers obtained from the "IKEA hackers" online community. We consider DIYers lead users for seeking out and exploiting product affordances, often transforming product functions dramatically. We also discuss their projects through the lens of affordance of absence. For the third approach, we outline our natural-language approach to affordance extraction, beginning with consumer product reviews provided for Canadian Tire, a major Canadian retailer. We describe efforts toward automatically identifying less common affordances, and the use of cue phrases to highlight insightful DIY transformations from the IKEA hackers community. Finally, we comment on the potential value of this work for product design in general.</p> </abstract>