Search Conferences

Type in any word, words or author name. This searchs through the abstract title, keywords and abstract text and authors. You may search all conferences or just select one conference.

 All Conferences
 EMAC 2019 Annual Conference
 EMAC 2020 Annual Conference
 EMAC 2020 Regional Conference
 EMAC 2021 Annual Conference
 EMAC 2021 Regional Conference
 EMAC 2022 Annual
 EMAC 2022 Regional Conference
 EMAC 2023 Annual
 EMAC 2023 Regional Conference

EMAC 2023 Annual

Attribute non-attendance in choice experiment-based latent-class models: The role of self-reported information and visual attributes

Published: May 24, 2023


Nelyda Campos-Requena, Universidad del Desarrollo; Jun Yao, Macquarie University; Harmen Oppewal, Monash University


When making their buying decisions, consumers often only attend to of subset of all product attributes. This is known as attribute non-attendance. Whereas attribute non-attendance can be expected to influence segmentation analyses based on attribute weights as estimated in choice experiments, the marketing literature has not much considered this potential impact. This study aims to assess how outcomes of a latent class-based segmentation analysis would differ when attribute non-attendance is accounted for. In particular, we incorporate for self-reported attribute non-attendance and the presentation format of an attribute in the model estimations. The results of a choice experiment involving the yoghurt category show that accounting for attribute non-attendance improves the model estimation and uncovers additional segments. The results also reveal how the influence of visual attribute representations differs between classes and consequently would affect the segmentation results.