Integrating affective features with engineering features to seek the optimal product varieties with respect to the niche segments

- Smart cameras are characterized by affective features and engineering features.
- User attitude toward affective features forms a basis of market partitioning.
- Classification tree is constructed to build a Kansei information system.
- Correspondence analysis is applied to elicit user perceptions of engineering features.
- VIKOR ranking is used to derive the optimal portfolios w.r.t. the niche segments.

In recent years, the popularity of smart phones substantially leads to poor sales of the low-end digital cameras. One of the most astounding industry news is Kodak's bankruptcy in 2011 although Kodak was a pioneer in the field of digital still cameras. In reality, not only functional capability but also affective design can influence user purchase intentions on consumer electronics. In this paper, both affective features (AFs), and engineering features (EFs) are considered to achieve successful product planning. In particular, two critical issues are addressed: (1) market partitioning and (2) product differentiation. Initially, Kansei engineering is employed to capture user attitude toward AFs. Then, a classification tree is constructed to carry out effective market partitioning. Secondly, correspondence analysis is applied to capture user perceptions of EFs for identifying the core features that best characterize distinct market segments. Finally, VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) ranking is conducted to prioritize various product portfolios to accomplish product differentiation. In summary, the presented framework can help industrial practitioners transform diverse customer requirements into attractive alternatives while keep controllable manufacturing costs.

As shown the following Figure, a Kansei information system is built to develop and assess product varieties of smart cameras. In particular, both affective features (i.e. zoom lens, screen type, and flash light) and engineering features are considered. In this paper, a novel framework is presented to complete two critical tasks: market partitioning (using Kansei engineering and classification tree) and product differentiation (using correspondence analysis and VIKOR ranking).201