Applying Noise-Based Reverse Correlation to Relate Consumer Perception to Product Complex Form Features

Jose Antonio Diego-Mas, Jorge Alcaide-Marzal, Rocio Poveda-Bautista and Ning (Chris) Chen

Complexity, 2022, vol. 2022, 1-10

Abstract: Consumer behavior knowledge is essential to designing successful products. However, measuring subjective perceptions affecting this behavior is a complex issue that depends on many factors. Identifying visual cues elicited by the product’s appearance is key in many cases. Marketing research on this topic has produced different approaches to the question. This paper proposes the use of Noise-Based Reverse Correlation techniques in the identification of product form features carrying a particular semantic message. This technique has been successfully utilized in social sciences to obtain prototypical images of faces representing social stereotypes from different judgements. In this work, an exploratory study on subcompact cars is performed by applying Noise-Based Reverse Correlation to identify relevant form features conveying a sports car image. The results provide meaningful information about the car attributes involved in communicating this idea, thus validating the use of the technique in this particular case. More research is needed to generalize and adapt Noise-Based Reverse Correlation procedures to different product scenarios and semantic concepts.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2022/4641932.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2022/4641932.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:4641932

DOI: 10.1155/2022/4641932

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().