Cue use is the cognitive process of gathering information from the external environment and using it to make a decision (Olson, 1978). There are many cues available in the shopping environment (e.g., labels/signs and the merchandise itself), which the consumer could use to make product assessments or a purchase decision. Olson (1972) categorized cues as either intrinsic (e.g., product ingredients or the product itself) or extrinsic (e.g., price, brand, package, etc.). He also posited a two-step cue assessment theory in which consumers first identify important cues before using them in judgments. Decades of consumer research has documented the persistent impact that price has on product perceptions (Dodds et al., 1991; Gabor and Granger, 1961; Janakiraman et al., 2006; Rao, 2005; Vanhuele et al., 2006). Other extrinsic cues such as brand (Allison and Uhl, 1964; Richardson et al., 1994) and packaging (Koutsimanis et al., 2012; McDaniel and Baker, 1977) are often also assessed when consumers make product choices.
With respect to plant purchasing, plant quality is nearly always identified as an important purchase factor to consumers (Behe and Barton, 2000; Hudson et al., 1997; Klingeman et al., 2004). Because plants are sold with very little packaging and often do not have brand names, consumers mainly use intrinsic cues (the plant themselves) and extrinsic cues (tags or signs when available) in the purchase decision. Understanding the consumer segment that uses each type of cue can help retailers improve the shopping process, which may lead to greater customer satisfaction and improved sales.
To better understand the role of intrinsic and extrinsic cues, researchers have typically relied on various techniques such as focus groups and experiments. As technology has evolved, researchers have explored the use of new techniques to examine drivers of purchase. Recently developed technologies such as eye tracking technology (ETT) allow researchers to see exactly what the consumer sees, thereby allowing for a better understanding of the consumer mind set. Given its recent adoption, the literature using ETT on retail plant displays is sparse. To fill this void and examine the potential impact on the green industry, we used ETT to investigate what captures attention in horticultural retail displays to better understand cue use. Our objective was to investigate the use of intrinsic cues (plants) and extrinsic cues (signs) in retail plant displays with ETT. Using visual behavior data within conjoint analysis framework, we gain a better understanding of how consumers view cues during the purchase decision.
An important contribution of this study to both methodological and applied research is the unique combination of visual behavior and stated preference data within the choice analysis framework. The first component—conjoint analysis—has routinely been used to understand the effects of product attributes and demographic characteristics on choice decisions. Conjoint studies have been used as a means to elicit consumer preferences for a wide range of ornamental products such as Christmas trees (Behe et al., 2005b), landscapes (Behe et al., 2005a; Zagaden et al., 2008), plant containers (Hall et al., 2010), and mixed flowering annual containers (Mason et al., 2008). Hall et al. (2010) found that 13% of plant preference survey participants valued an extrinsic cue, carbon footprint label, more than other product cues such as price, plant container type, and waste composition in the container. Building on that study, Behe et al. (2013b) used a conjoint design to identify nine consumer segments, focusing on their gardening purchases, and documented differences in consumer preferences for plant provenance and environmental attributes of transplants.
Although techniques such as conjoint analysis are invaluable to understanding the consumer mind set, most cases use stated preference data, which has been subject to criticism for systematic biases (e.g., hypothetical commitment bias) (Carson and Louviere, 2011). Hence, the second component of our framework—ETT—can be applied in conjunction with commonly used experimental techniques (e.g., conjoint analysis) to mitigate these biases and gain an in-depth understanding of behavioral mechanisms underlying consumers’ choice decisions. Wedel and Pieters (2008) reported that, “The areas in the visual brain are highly specialized to process information collected during eye fixations and continuously interact with areas that direct eye movement to salient and/or informative locations in visual scenes and stimuli, which enables purposeful and goal-directed eye movement” (p. 13–14). In other words, people do not look randomly and the subconscious movement of the eye is guided by the type of information sought and its value to the task at hand. The bulk of the peer-reviewed studies using ETT investigated the process of reading by following eye movements (see Rayner, 1998, for a 20-year review of this subject).
Visual behavior data were previously used to investigate whether branded products receive more visual attention compared with unbranded products regardless of product size (Teixeira et al., 2010). Meissner and Decker (2010) demonstrated that consumers spent more time (fixations) viewing product attributes that were more important to them. Kuisma et al. (2010) found that animation in online advertisements drew the viewer’s attention more for vertical advertisements compared with horizontal advertisements. Patalano et al. (2009) documented that consumer indecisiveness was positively related to time spent viewing information about the purchase as well as time spent looking away from information directly related to that choice task. ETT methods have also been used to examine the effects of package label design on individual choice decision-making behavior. For example, Bix et al. (2009) investigated the prominence of package warnings on over-the-counter medicines and found that they were not readily viewed by consumers. Sorensen et al. (2012) showed that a product name on a label attracted six times more attention than any organic production claim, whereas illustrations captured more attention than health claims, even if the illustration was irrelevant. Behe et al. (2013a) recently demonstrated the potential use of ETT for analyzing green industry consumer behavior.
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