As companies are forced to conceive innovative products to stay competitive, designers face the challenge of developing products more suited to users' needs and perceptions in order to be accepted, thus reducing project risk failure. Evaluating users' acceptability has become an important research problem. Current approaches leave the acceptance evaluation question to be answered in the last stages of product development process (NPD), when an almost finished prototype is available and when there is no time left for important modifications. Acceptability evaluation methods suitable for use from the early stages of the NPD process are thus needed. This paper proposes a method for acceptability evaluation and analysis that can be used in the early stages of the development cycle. It is based on the evaluation of the solution concept by the users. The relationships among the factors (or criteria) are made explicit, thus helping designers to identify the key factors for acceptance. As the users' tests and the maturity of the concept prototype are limited in this stage, the proposed method exploits the inference properties of Bayesian networks making it possible to make useful estimations and allowing the exploration of actions that could improve the product acceptability level. Two case studies are presented in order to illustrate the method, the first related to a technological product design for a home-health care service provider and the second to a work-related musculoskeletal disorder prevention software design. Relevance to industry: The article describes an acceptability assessment and an analysis approach to be used by industrial engineers, designers and ergonomists in the early phases of design projects. The method can help the design team to identify the levers (key factors) for enhancing product acceptance and to identify different actions (e.g. product modification, deployment strategy, and training).
Arbelaez Garces, G., Rakotondranaivo, A., & Bonjour, E. (2016). An acceptability estimation and analysis methodology based on Bayesian networks. International Journal of Industrial Ergonomics, 53, 245–256. https://doi.org/10.1016/j.ergon.2016.02.005