A novel two-step rating-based ‘double-faced applicability’ test. Part 2: Introducing a novel measure of affect magnitude (d′A) for profiling consumers’ product usage experience based on Signal Detection Theory

In Ah Kim, Andrew Hopkinson, Danielle van Hout, Hye Seong Lee

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

To measure consumers’ product usage experience throughout the various product usage stages, a novel two-step rating-based ‘double-faced applicability’ test has recently been proposed by Kim et al. (2017). In this method, a ‘two-step’ rating (forced-choice Yes/No questions followed by 3-point sureness ratings) and ‘double-faced’ descriptors (a pair of semantic-differential descriptors) are used for each attribute to improve the product discriminability by reducing consumers’ response bias and variations. In this paper, we introduce a novel measure that can be computed from the data from the ‘double-faced applicability’ test to provide a new way to generate affective product usage experience profiles. The novel measure was a nonparametric estimate of affect magnitude, named as d-prime affect magnitude (d′A), computed by considering the response ratio of positivity to negativity as the ratio of signal to noise in the context of Signal Detection Theory (SDT). The advantage of using this new measure d′A was that it meaningfully reflected the consumers’ affective product usage experience for each product independently (and how this affect valence changed through a usage process), yet it can still be used to compare between products. The practical application of using d′A was demonstrated in comparison to the more conventional SDT measure d′.

Original languageEnglish
Pages (from-to)141-149
Number of pages9
JournalFood Quality and Preference
Volume59
DOIs
StatePublished - 1 Jul 2017

Bibliographical note

Funding 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). The authors also thank Emma Elliott at Unilever R&D Port Sunlight for preparing the samples, Hyun-Kyung Shin, Yu-Na Jeong, Ji-Young Yoon, Hye-Jong Yoo, Bi-A Kang and So-Yub Lee at Ewha Womans University for their assistance in conducting experiments, and Timo Giesbrecht at Unilever for reviewing and improving the manuscript.

Publisher Copyright:
© 2017 Elsevier Ltd

Keywords

  • Affect measurement
  • Consumer test
  • Product discrimination
  • Product testing
  • Scale estimation
  • Usage experience

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