Abstract/Summary

Since the Victorian era, orchids have been in continuous demand within the horticultural trade. As a result, the entire family of over 29,000 species are listed in the Convention on International Trade in Endangered Species (CITES), making up over 73% of the species listed on CITES. With the continued threat from (often illegal) over-exploitation of orchids and discovery of new species of potential horticultural value, this raises the question as to whether we can develop statistical models to identify species likely to be threatened through over-exploitation by identifying those plant attributes that attract the highest price premium. Using species of Southeast Asian slipper orchids (Paphiopedilum) as a model system, we analyse market level data from a systematic survey of online trade using hedonic regression analysis to assess the relationship between price and plant attributes. Our analysis reveals that a range of plant attributes (taxonomic sections, plant size, plant colour and rarity on the market) influence price and that these vary by species. Importantly, our study reveals that coding observable attributes such as colour (primary and other) for use in statistical analysis is complicated. Future research on how colour and other attributes affect choice would benefit from employing more advanced consumer research techniques such as eye-tracking that can help inform what is specifically driving consumer choice.