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Published in Walter R. Paczkowski, Deep Data Analytics for New Product Development, 2020
I covered the early stage product design in this chapter. Conjoint analysis is useful at this stage because it helps to identify the product attributes that are important to the customers who will buy and use the product. The drawback to conjoint is that it is materialistically oriented – only attributes and their levels count for defining a product. The emotions a customer may attach to some features are ignored. The Kansei approach to product design tries to rectify this.
Assessing performance of mined business process variants
Published in Enterprise Information Systems, 2021
Lucas Van Den Ingh, Rik Eshuis, Sarah Gelper
To determine what values a well performing P2P process variant should have on the dimensions of the devil’s quadrangle, a conjoint analysis was executed. This is an analysis that provides insight in the preference a respondent has for a certain attribute (dimension), as well as the preferred level of that attribute, according to Hair et al..(Hair et al. 2014). There are three types of conjoint analysis: choice-based conjoint, traditional conjoint and adaptive choice. Choice-based conjoint can handle a maximum of six attributes and has the advantage over other conjoint techniques that it creates a realistic choice task (thanks to a ‘no choice’ option) and can measure the interaction effect between attributes (Hair et al. 2014), and is therefore the conjoint type that was chosen. If respondents answer more than 30 choice tasks, the quality of the answers decreases (Hair et al. 2014).
The effect of gender on willingness to pay for mass customised running shoes
Published in Journal of Global Fashion Marketing, 2021
Hassan Daronkola Kalantari, Lester W Johnson, Chamila R. Perera
The data were analysed using conjoint analysis (Green & Rao, 1971; Rao, 2014). Conjoint analysis is a well-known method which allows inclusion and combinations of a number of attributes to describe a hypothetical situation in which participants evaluate the situation rather than evaluating each attribute individually. This method makes preference statements more realistic. Conjoint analysis first was proposed by Green and Rao (1971). There is growing interest in the use of conjoint analysis as a tool for estimating customer preferences within the economic evaluation of different products and services. It has been suggested that where cost is included as one of the attributes within the exercise, conjoint analysis can be used to estimate willingness to pay (Kulshreshtha et al., 2018). There are many studies that have used conjoint analysis to estimate the willingness to pay (e.g. Tokimatsu et al., 2016).