Form-Driven Value Evaluation of Eco-Products

As perceived by consumers, the value of an eco-product can be enhanced by its product form in addition to physical product attributes. This paper develops a neural network (NN) and multiattribute decision making (MADM) approach for determining the design combination of product form elements that match a given eco-product value (EPV) and product image. A morphological analysis is used to extract form elements from these sample office chairs. The experimental study identifies 7 office chair product form elements and 27 representative office chairs as experimental samples for developing NN models. A best-performing NN model is chosen to examine the complex relationship between 7 product form elements and 6 product images as well as 15 EPV attributes which are identified and categorized into aesthetic, functional, and environmental dimensions. With the NN model, an office chair design database is built consisting of 960 different combinations of product form elements, together with their associated EPV and product image values. The application of the database and the MADM method provides product designers with the best combination of product form elements for illustrating the aesthetic, functional, and environmental-friendly attributes as well as particular design concept represented by product image word pairs to an office chair design. The approach developed has general application in evaluating the value of eco-products with a diversity of design elements.