No rose without a thorn: Hedonic testing of live rose plants

Grygorczyk, Alexandra; Jenkins, Amy

doi:10.1111/joss.12526

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Studies have used CA for various research purposes such as to summarizes differences among consumers for their likes and dislikes of products, group specific products or attributes for various product categories, and for product optimization in new product development (Zielinski et al, 2014). For example, CA was used to segment consumers for liking similarities of large potato varieties (Sharma et al, 2020), mate tea varieties for sensory properties (Godoy et al, 2020), and non-food products (Grygorczyk et al, 2019) and plant proteins snack development (Saint-Eve et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Unreliability of clustering results in sensory studies and a strategy to address the issue

Kumar,

Chambers

2024

Front. Food. Sci. Technol.

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Researchers commonly use hierarchical clustering (HC) or k-means (KM) for grouping products, attributes, or consumers. However, the results produced by these approaches can differ widely depending on the specific methods used or the initial “seed” aka “starting cluster centroid” chosen in clustering. Although recommendations for various clustering techniques have been made, the realities are that objects in groups can, and do, change their clusters. That can impact interpretation of the data. Researchers usually does not run the clustering algorithms multiple times to determine stability, nor do they often run multiple methods of clustering although that has been recommended previously. This study applied hierarchical agglomerative clustering (HAC), KM and fuzzy clustering (FC) to a large descriptive sensory data set and compared attribute clusters from the methods, including multiple iterations of same methods. Sensory attributes (objects) shuffled among clusters in varying ways, which could provide different interpretations of the data. That frequency was captured in the KM output and used to form the “best possible” clusters via manual clustering (MC). The HAC and FC results were studied and compared with KM results. Attribute correlation coefficients also were compared with clustering information. Using results from one clustering approach may not be reliable, and results should be confirmed using other clustering approaches. A strategy that combines multiple clustering approaches, including a MC process is suggested to determine consistent clusters in sensory data sets.

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Section: Introductionmentioning

confidence: 99%

Unreliability of clustering results in sensory studies and a strategy to address the issue

Kumar,

Chambers

2024

Front. Food. Sci. Technol.

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“…To date, most studies concerning flower preference have been focused on customer experience and have mainly been used for marketing purposes (Boumaza et al, 2010; Grygorczyk et al, 2019; Santagostini et al, 2014; Wang et al, 2017; Yue & Behe, 2010). This is not surprising given the fact that global floral sales reach more than €15 billion per year (Mamias, 2018).…”

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confidence: 99%

Does flower preference differ across cultures? A study of Czech and Kenyan populations.

Hůla,

Šámalová

2024

Psychology of Aesthetics, Creativity, and the Arts

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Despite the ubiquity of floral themes and motifs in art and culture, human esthetic responses to flowers remain a largely unexplored area of research. Our previous studies revealed that Czechs, irrespective of gender, age, education, or level of expertise, display very similar esthetic responses to flowers of diverse shapes and colors, that is, they like and dislike the same flowers. We wanted to explore further whether these findings are culturally or ecologically dependent. This study compares the flower preferences of the inhabitants of two culturally, geographically, and ecologically highly distinct countries-Czechia (n = 54 and n = 48) and Kenya (n = 54). We asked each subject to rank photographs of 40 species of Czech flowers from the most liked one to the least liked one. We then computed the mean ranking of each flower species in the Kenyan and Czech samples and compared them. We found a very strong positive correlation (r = .79 and r = .77) between the samples, suggesting that Czechs and Kenyans prefer the same flowers. This concordance was unexpected, especially given the small sample sizes and the large differences between them. Moreover, the Czech flower species used as stimuli were probably familiar to Czechs but unknown to Kenyans. Thus, our results suggest that cultural and individual factors might play only a minor role in flower preference, whereas more general inborn preferences may be more important.

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