Affective Human Factors Design with Ambient Intelligence for Product Ecosystems

Affective Human Factors Design with Ambient Intelligence for Product Ecosystems

Roger J. Jiao (Georgia Institute of Technology, USA) and Qianli Xu (Nanyang Technological University, Singapore)
DOI: 10.4018/978-1-60566-260-2.ch010
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The fulfillment of affective customers needs may award the producer extra premium in gaining a competitive edge. This entails a number of technical challenges to be addressed, such as, the elicitation, evaluation, and fulfillment of affective needs, as well as the evaluation of capability of producers to launch the planned products. To tackle these issues, this research proposes an affective human factor design framework to facilitate decision-making in designing product ecosystems. In particular, ambient intelligence techniques are applied to elicit affective customer needs. An analytical model is proposed to support affective design analysis. Utility measure and conjoint analysis are employed to quantify users’ affective satisfaction, while the producers’ capability to fulfill the respective customer needs is evaluated using a capacity index. Association rule mining techniques are applied to model the mapping of affective needs to design elements. Configuration design of product ecosystems is optimized with a heuristic genetic algorithm. A case study of designing the living room ecosystem is reported with dual considerations of customers’ satisfaction and producer’s capacities. It is demonstrated that the affective human factors design framework can effectively manage the elicitation, analysis, and fulfillment of affective customer needs.
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From a business perspective, product development aims at maximizing of the overlap of the producers’ capabilities with the window of customers’ needs in the marketplace. This can be achieved either through expanding producers’ capabilities by developing the company’s portfolio, including products, services, equipments, and skills that market demands, or through channelling customers to the total capacity of the company so that customers are better served. The former strategy is largely the research focus of product planning and platform-based product development, where strategic development of product and process platforms gives the producer an advantage of improved resource utilization (Meyer, 1997; Sanderson, 1991). The latter strategy advocates directing market needs to the capacity of a producer, where a clear understanding of customer needs and subsequent fulfilment of the customer needs with the appropriate design elements suggest themselves to be critical issues.

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