Consumer segmentation based on health-related motive orientations and fruit and vegetable consumption

Abstract

Purpose More tailored interventions and campaigns are needed to increase fruit and vegetable consumption to recommended levels. The purpose of this paper is to explore which consumer groups exist based on both their fruit and vegetable consumption level and their health-related motive orientations (HRMO), and to compare the revealed consumer clusters regarding their fruit and vegetable product attribute importance. Design/methodology/approach In the Netherlands an online panel survey was carried out resulting in 1,296 respondents. The clusters based on HRMO and fruit and vegetable intake are profiled with respect to demographics and product attribute importance. Findings Cluster analysis revealed six homogeneous consumer clusters with different HRMO and fruit and vegetable consumption levels. In addition, these clusters show a different socio-demographic profile and differ in their importance ratings of fruit and vegetable product attributes. Practical implications The results show that health is a multidimensional construct suggesting that there is a need for addressing health in interventions and campaigns in a more tailored approach. Originality/value This study shows that the combination of both usage- and psychographic segmentation variables provide valuable and interesting information that give insights in addressing different target groups. Moreover, this study elaborates on previous research by showing that health is a multidimensional construct and that Dutch consumers differ in their HRMO.

Exploring health-related motive orientations among Dutch seniors

“…In accordance with the studies by Geeroms et al (2008b) and Raaijmakers et al (2018). this study showed that health is a construct comprising multiple psychosocial dimensions that are weighted differently between consumers.…”

“…The results revealed a six-factor solution in line with a previous Dutch study (Raaijmakers et al, 2018). Within the current study, nine items were deleted, because their factor loadings were too low (o0.55) (Osborn et al, 2008) and five items were deleted because they loaded on a different dimension than expected based on Raaijmakers et al (2018). The remaining factors included the individualistic oriented motives "achievement", "active", "well-being", and "appearance", and the altruistic oriented motives "caring" and "social" (see Table I).…”

“…An exploratory and confirmatory factor analysis (EFA and CFA) with Oblimin rotation were conducted to confirm the six and eight factors reported by Geeroms et al (2008a, b). The results revealed a six-factor solution in line with a previous Dutch study (Raaijmakers et al, 2018). Within the current study, nine items were deleted, because their factor loadings were too low (o0.55) (Osborn et al, 2008) and five items were deleted because they loaded on a different dimension than expected based on Raaijmakers et al (2018).…”

“…this study showed that health is a construct comprising multiple psychosocial dimensions that are weighted differently between consumers. In line with Raaijmakers et al (2018), also performed in a Dutch population, our study divided health in six orientations: four individual-focussed health orientations, i.e., "active", "well-being", "achievement" and "appearance"; and two altruistic-focussed health orientations, i.e., "caring" and "social". As compared to the studies by Geeroms et al (2008a, b), performed in a population including both young and older adults from Belgium, there are some differences with regard to the revealed factor structure of the six distinct health orientations constructs.…”

Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide

“…Demographic variables can act as a further sanity check: if seatbelt use rises with age, that variable should also follow a similar distribution in the health lifestyle clusters (i.e., more older people would be expected in more health-oriented clusters). As another example, interventions campaigns focusing on alcohol abuse in the US might use smoking as an outcome measure correlated with heavy drinking [22,23].…”