Consumer-assisted selection (CAS) is a strategy to understand the desirable aspects of innovation and change in a plant product through communication and input from the consumer. This type of selection is designed to increase purchasing interest and use of a product or service (<http://www.plantinnovation.org>). A primary example of CAS is the cultivation, breeding, and consumption of a perishable food crop like strawberry (Fragaria ×ananassa Duch.). In the past, industry efforts have mainly focused on supply chain traits like berry yield, shipping quality, and size (Qin et al., 2008). Meanwhile traits like berry flavor, aroma, and texture have been widely underused (Duffy, 2007; Ulrich, 2010). From the industry perspective this concept is rational; e.g., a strawberry producer should be concerned with overall structural integrity and the volume to market numbers or the bottom line so to speak. Because strawberry traits selected for by industry breeders are known and can act as dependent variables, the consumer can then be asked which feature of strawberry interests them or would promote higher frequencies of purchase, i.e., CAS. These newly discovered independent variables may be berry color, flavor, aroma, texture, storage, etc. Therefore, allowing a greater number of variables affecting market value and/or sales of a perishable strawberry product through a system like CAS may result in an increased market share for industry members and an increased satisfaction of consumers at large (Colquhoun et al., 2012).
Consumer perception and/or preference for an object, experience, or concept is extremely difficult to assay without introducing cognitive bias (Redelmeier and Dickinson, 2011). In general, bias may lead to inaccurate conclusions about the consumer perception of a product. When an individual understands a question or situation regarding an object, experience, or concept, cognition and rationalization are conserved mental functions that order or structure an idea and response. Thus, cognition can lead to cognitive bias whether it is founded in heuristics, social influences, and/or individual motivational factors (Gilovich et al., 2002).
Most interactive issues can be addressed by using a modified conjoint analysis approach with a relatively large population of human subjects (Green and Krieger, 1991). This method is a rapid and relatively inexpensive way to assay human perception and/or preference for individual elements that together describe a product. In the modified conjoint analysis performed here, product elements are presented in combinations of terms to determine consumer response to a mixture of ideas. Each element is considered an independent variable. For this reason, the individual elements that motivate the consumer reaction in a positive or negative manner can be determined. This method is called rule developing experimentation (RDE) and uses a software suite (IdeaMap®) as a human interface and data collection system (Moskowitz and Gofman, 2007). The impact of each product element on potential purchasing behavior as well as the affective state of human subjects can be objectively assessed.
Here RDE is applied for the first time to flowers, a terminal plant product with extremely high economic, psychological, and social value. The U.S. Department of Agriculture calculated that the expanded wholesale value of domestic floriculture crops from 15 program states has averaged close to four billion U.S. dollars (USD) per year during the last decade (US-NASS 2011). Additionally, the United States imports a substantial percentage of floriculture crops like cut flowers every year that are not accounted for in the latter statistic (≈500 million USD). The United States is not alone with an interest in flowers as an end product. In 1993 Sir John (Jack) Rankine Goody, famous for his work related to comparative anthropology in literacy, published a book entitled The Culture of Flowers (Goody, 1993). In his book, Dr. Goody illustrated that almost every human culture has written about flowers, used flowers in social rituals, or incorporated flower imagery in native art, except a number of African cultures. Interestingly, many of these African cultures refer to flowers by what they produce (e.g., fruit, seeds, dyes, medicine, etc.). Michael Pollan constructed a convincing argument for the innate attraction humans may have toward flowers, much like that of a bee (Anthophila), in his 2001 book The Botany of Desire (Pollan, 2001). More recently, flowers have been proposed to function through a positive emotional niche for humans, i.e., flowers influence socioemotional behavior. In theory, flowers effect a positive affective state that in turn increases overall human health and wellness (Haviland-Jones et al., 2005).
Despite the clear fascination humans have with flowers, very little empirical research has been conducted to investigate the biological, psychological, and/or neurological basis for this attraction. Here we use psychophysics to explore what aspects of flowers interest various demographic groups and segments of human subjects. Interpretation of these results can provide a framework for defining “the iconic flower,” but goes further to illustrate how the definition is really of iconic flower(s) with variation between definable groups of people.
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Comparison of the fielding house (FH) and plants, gardens, and you (PG&Y) subjects by top and bottom elements of each category.z
Age group comparison of the fielding house (FH) and plants, gardens, and you (PG&Y) subjects.z
Ethnicity comparison of the fielding house (FH) and plants, gardens, and you (PG&Y) subjects.z
Location of residence comparison of the fielding house (FH) and plants, gardens, and you (PG&Y) subjects.z
Total sample clustered into two segments for the fielding house (FH) and plants, gardens, and you (PG&Y) subjects.z