Predictions of consumer acceptance are often based on hedonic scores, but these are determined not only by the consumer level of product liking, but also by consumer scale usage, which in turn is affected by thinking style and experimental contexts. To improve the validity and reliability of consumer acceptance measurement, a new indirect scaling method, the ‘Degree of Satisfaction-Difference (DOSD)’, was developed using a reminder design and signal detection theory (SDT). In DOSD, a product-specified ‘cognitive warm-up’ was used to evoke the consumer personal context and the internal evaluative criteria prior to product evaluation. In DOSD, each test product was presented together with a fixed-reference (identified as such) and consumers were asked to evaluate their satisfaction with the reference first with a sureness rating, and then to evaluate the test product for both absolute satisfaction and comparative satisfaction to the reference. The reliability of DOSD was tested against traditional hedonic scaling using an independent samples design of two consumer groups with equivalent cognitive reflection test profiles, each including High Reflection Thinkers (HRTs) and Low Reflection Thinkers (LRTs) in equal proportion. Each group tested two sets of skin lotions differing in product range, either using DOSD or hedonic scaling. When examining the affective discriminations of the two common products in terms of d′ values between product sets, the LRT subjects generated inconsistent responses with hedonic scaling, but reproducible responses with DOSD. The HRT subjects performed consistently using both scaling methods. These results validate DOSD's superior reliability in affective tests and demonstrate its potential as an alternative consumer acceptance measurement to hedonic scaling.
Bibliographical noteFunding Information:
This research was supported by Unilever R&D and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2015R1A1A1A05001170). We gratefully acknowledge the support of Unilever R&D. We thank the following collaborators at Unilever R&D Trumbull for their input and preparing the samples: Kevin Blot, Jeremy Shen and Brian Dobkowski.
© 2017 Elsevier Ltd
- Acceptance test
- Affective product discrimination
- Indirect scaling
- Range effects
- Reference framing