Pleasant Ambient Scents: A Meta-Analysis of Customer Responses and Situational Contingencies

“Ambient scent” is defined as a scent present as a part of the retail or service environment, and its effect is measured by comparing customers’ responses in an unscented condition with those in a scented condition (Doucé and Janssens 2013). The literature discusses three functions of ambient scent—attracting attention, priming affect, and facilitating information retrieval—which trace back to the unique biological features of olfaction. One such feature is that basic olfactory processing, such as odor detection, occurs in more primitive areas of the brain, requiring little to no cognitive effort (Herz and Engen 1996). Thus, ambient scent may attract attention because of an unprompted processing of olfactory information. Another feature is the privileged neural link that the olfactory nerve shares with the neural area for emotional memory (Herz 2010). Thus, a scent may trigger the remembrance of positive emotions and memories and so prime affect (Herz and Engen 1996). It may also serve as an especially good cue for retrieving information stored under its presence (Herz and Engen 1996).

Studies on pleasant ambient scents test their effects in experiments on numerous customer responses, such as mood activation, mood valence, and mood control; product evaluations, environmental quality, and shopping satisfaction; recall and time elusiveness; purchase intentions and intention to recommend; and expenditures and lingering. Their organization follows Morrin (2010) and Nibbe and Orth (2017). Table 1 provides the operational definitions used to integrate the constructs of the individual studies.

As a starting point for our framework (Figure 1), we propose that the presence (vs. absence) of a pleasant ambient scent positively influences customer responses. RLB found scent-facilitated mood valence, satisfaction, and behavioral intentions, and physiological evidence suggests that scents attract attention, prime affect, and facilitate information retrieval (Herz 2010; Herz and Engen 1996). However, scholars also caution about scent effects on customer responses, because the underlying processes are poorly understood and the evidence to date offers inconsistent results, partially depending on the type of response (Nibbe and Orth 2017). We thus also examine the relative effect sizes of affective (mood) and cognitive (evaluations and memory) responses. For mood, research describes the results as mixed and not always clear; the results for evaluations are comparatively more compelling and robust (Bone and Ellen 1999; Morrin 2010; Nibbe and Orth 2017). The results also seem to favor memory effects, though they are less frequently studied (Nibbe and Orth 2017). Overall, the evidence appears stronger for cognitive than for affective responses. Thus,

We next discuss how situational contingencies influence the effect sizes of customer responses (Figure 1). Situational variation may explain why the influence of ambient scent varies across studies, providing insight into scent selection and usage in different settings and the expected change in outcomes. That insight allows reflection on industry reports, such as by Nike, claiming a scent-elicited 80% increase in purchase intent (Scent Australia 2019).

Scent characteristics describe morphological aspects of a pleasant ambient scent and include quality (in-kind description), congruency (fit with the environment and its products and services), and structure (single vs. multiple aroma dimensions). Quality characterizes the perception of the scent in kind and so differentiates it to others (Dravnieks 1982). While people distinguish well among many scents they have previously smelled, they have difficulty providing a verbal or semantic label for them and thus often experience a feeling of recognizing a scent, without being able to identify it (tip-of-the-nose effect; Morrin 2010). Because scents act as a memory cue even without identification (Herz and Engen 1996), the specific pleasant ambient scent used is often based on other scent characteristics and perceptual properties.

One such characteristic is congruency. Our conceptualization of it follows suggestions that people respond not necessarily to discrete elements but rather to their total configuration (Bitner 1992). We therefore combine prior fit considerations that use the environment and particular offerings as reference points (Bosmans 2006; Herrmann et al. 2013). Congruency may facilitate effect sizes, as people are positively predisposed to it (Herz 2010) and incongruent ambient scents interfere with information processing (Mitchell, Kahn, and Knasko 1995). In terms of structure, whether pleasant ambient scents contain a single aroma dimension (unidimensional) or multiple aroma dimensions (multidimensional) represents a version of Herrmann et al.’s (2013) simple versus complex differentiation to allow for greater cross-study generalizability. Herrmann et al. (2013) find that the ambient scent orange-basil with green tea (multidimensional) is less effective than orange (unidimensional) because it is more challenging to process. Thus,

Scent perceptual properties refer to the hedonic perception of a scent, which is an affective evaluation, centering on whether someone likes it or not (Herz 2010). Liking a scent is due to acquired emotional associations learned over time and carries hedonic meaning (Herz 2010). Thus, in contrast to scent characteristics, which are analytical and unrelated to acquired associations, the perceptual properties reflect feelings and meanings that are linked to and result from the sensation of an ambient scent. Because scent liking is learned, it is also more subjective and emphasized as culture bound (Morrin 2010). The cultural context is relevant because most studies were conducted in North America and Europe. Researchers use pleasantness, activation, familiarity, and intensity, which are not necessarily independent and refer to scent liking in terms of meaning and physiological perception (Nibbe and Orth 2017).

We propose that pleasantness, familiarity, and activation are positively related to effect sizes. In theory, a scent must be perceived as pleasant (i.e., enjoyable) to positively prime affect, because scent-based associations and hedonic perceptions are contingent on each other (Herz and Engen 1996). To stimulate customers through emotionally potent memories, a scent also needs to be familiar (Herz and Engen 1996). Familiarity is not about identifying a scent by name but rather being acquainted with its aroma. If a scent is unfamiliar, no meaningful associations that can be remembered and elicit affect could have been formed (Herz and Engen 1996). Familiarity also leads to liking (Rindfleisch and Inman 1998), which may transfer to the environment and its elements. Finally, certain ambient scents can induce greater activation (i.e., their perception as stimulating; Mattila and Wirtz 2001) than others do. Activating customers may amplify the positive in-store experience (Spangenberg, Crowley, and Henderson 1996) and result in responses that are more positive. Thus,

Across studies, scent intensity is typically assessed on a perceptual level. To examine its effects, we therefore use perceived concentration, defined as the proportion of respondents who detected (i.e., realized the presence of) the scent in the environment (Morrin and Ratneshwar 2003). Perceived concentration reflects perceptual detection on a group level, which is a required condition for scents to have any effect; there are no subliminal effects of odors (Herz 2010). Higher intensities also generally extend the time until the sense of smell is blunted by too much exposure (olfactory adaptation; Herz 2010). In addition, research suggests an inverted U-shaped relationship: with increasing intensity, a pleasant scent is evaluated as more positive up to a point where it overpowers and becomes aversive (Nibbe and Orth 2017). Because the value range of perceived concentration is limited to 100%, when every respondent detects the presence of an ambient scent, it may not tap into the area where a scent overpowers. Thus,

The nonlinear relationship is part of an interplay between pleasantness and intensity. We apply this logic to the interactions of pleasantness with congruency, dimensionality, familiarity, and activation. This interplay for scents in general (not only pleasant ones) is described as follows: while intensity makes pleasant scents more positive (up to a point), it decreases the hedonic value for scents that may only be acceptable, such as a weak fishy smell (Herz 2010). Thus, the inverted U-shaped curve occurs because pleasantness shifted the turning point, allowing people to experience scents at higher intensities (Spangenberg, Crowley, and Henderson 1996). Similarly, pleasantness may buffer against (1) less congruent scents by increasing tolerance for a perceptual misfit, (2) multidimensional scents by easing their processing, (3) less familiar scents by evoking (less but) additional positive emotional associations, and (4) less activating scents by making them more attractive. Thus, we propose that pleasantness buffers against levels in scent characteristics and perceptual properties that are associated with smaller effect sizes. Thus,

Given the wide applicability in retail stores, entertainment venues, and medical facilities, we are next interested in the following factors proving (un)favorable ambient scent conditions: service versus nonservice exchange, multi- versus single-store environment, and presence versus absence of (in)congruent music (Mattila and Wirtz 2001; Morrin and Chebat 2005; RLB).

Service exchanges refer to activities performed on or for the customer (e.g., by a spa). Compared with nonservice exchanges, service exchanges may favor pleasant ambient scents for two reasons. First, service exchanges have a high degree of intangibility (Bitner 1990). If there are no tangible features, environmental cues may gain importance as decision criteria. Such a shift is reflected in the stronger reliance on word of mouth when consumers cannot try an offering (You, Vadakkepatt, and Joshi 2015). Second, service exchanges include a high degree of person-to-person interactions, where scent is shown to facilitate peer perceptions and helping behaviors (Baron 1997; Bitner 1990).

Multistore environments, such as a mall or a bookstore with a café, contain multiple single stores. A pleasant ambient scent may render an environment distinct and so attract attention by setting off the particular object from its environment (e.g., a single store from other stores). Krishna, Lwin, and Morrin (2010) argue that the capability to enhance an object’s contextual distinctiveness lies not in the uniqueness of the scent itself but in the number of objects with which it is associated. Thus, by being associated with multiple stores (e.g., a mall) compared with a single store (e.g., a florist shop), an ambient scent’s capability to render the environment distinct is stretched over many objects, making it less effective.

Finally, we consider whether music is playing in the environment and, if so, whether it matches the pleasant ambient scent. Such a cross-modal congruency goes back to findings showing that customers respond more positively when the music tempo matches the ambient scent in terms of being activating (fast) or relaxing (slow), compared with a mismatch (Mattila and Wirtz 2001). In addition, Spangenberg, Grohmann, and Sprott (2005) found similar results for the cross-modal congruency of music theme (Christmas) with scent quality (pine). The presence of incongruent music likely interferes with ambient scent effects because customers respond holistically to an environment (Mattila and Wirtz 2001). For congruent music, no prediction can be made. Its presence may amplify, attenuate, or fail to change the effect of ambient scent (Stein et al. 2009). We explore this constellation without a hypothesis. Thus,

In their experiments, researchers obtain control over extraneous factors through statistical control (controlling vs. not controlling for extraneous influences) and the design of the research setting (fictitious vs. actual) and the stimuli (imagined vs. experienced offering). To assess a statistical method of isolating factors besides the ambient scent manipulation, we distinguish whether the absence–presence comparison of ambient scent controls for effects from other variables or not (based on raw means). These are controlled for when the effect size is extracted from a multivariate model or the reported univariate statistic itself is adjusted for covariates. Raw means may lead to larger effect sizes, as they capture other variables with which they are collinear. However, controlling for other effects may also lead to larger effect sizes, as variance from extraneous factors is partialled out (Carlson and Wu 2012). We follow the latter argument because it reflects scholars’ attempts to achieve precision in results.

The design of research settings can be differentiated as based on fictitious (artificial laboratory) or actual (field or in-store) environments. Fictitious environments may allow researchers to better isolate extraneous factors (Shadish, Cook, and Campbell 2002) and so may lead to larger effect sizes than actual environments do. The design of the experimental stimuli may provide control over response errors. Photographic or video materials require respondents to imagine (parts of) the product or service offering. In such cases, respondents must make projections about reality, which translate into overstated reactions (Wilson and Gilbert 2003). In contrast, simulated store environments or field settings allow respondents to actually sense and experience the offering. Thus,

Age and gender represent the final set of variables. First, physiological evidence shows women outperforming men in odor response (detection, discrimination, identification, and memory) and being emotionally more sensitive and responsive to scent than men are (Doty and Cameron 2009; Herz and Engen 1996). In the atmospherics domain, Lehrner et al. (2000) find support for this and show that women exhibited a more positive mood from ambient scent than men. Other tests could not detect gender differences in responses to ambient scent (e.g., Baron 1997; Krishna, Lwin, and Morrin 2010), which may be due to lower power than in an aggregated analysis. Second, Chebat, Morrin, and Chebat (2009) observed scent-facilitated expenditures for younger (<35 years) but not for older (≥35 years) respondents. Differences in individuals’ susceptibility to ambient scent trace back to olfactory capabilities that change during a life span. Loss of olfactory function in old age is well established (Correia et al. 2016). Research indicates that olfactory performance peaks between 20 and 40 years of age and notably declines afterward, with a rapid decrease after age 70 (Correia et al. 2016). Thus,

Table 3 shows the results from the meta-analytic effect size integration. The presence (vs. absence) of ambient scent produced significant (p < .01) and positive effects on customer responses, except for mood control and expenditures. Of the significant effects, the r_as ranged from .078 to .177 and the fail-safe N-values from 49 to 3,018. The results largely supported H_1a. In regard to H_1b, the r_as show that ambient scent caused larger effects on evaluation and memory responses (r_a = .131) than on mood responses (r_a = .094). Contrasting both response types through a dummy variable yielded a significant result (p = .023), in support of H_1b. Furthermore, the presence (vs. absence) of ambient scent increased the level of the customer responses by 3.2% to 15.4%, with the standardized values tending to be somewhat lower than the raw mean values. Table 4 presents the meta-analytic correlation matrix, providing a first indication that the presence (vs. absence) of ambient scent exhibits downstream links to expenditures.

Drawing on the parameter estimates of Model 3 (Table 5), we calculated the percentage change of customer expenditures through the r_as (standardized approach) that the model predicts for different levels of the significant situational contingencies. Theme 3 in the Web Appendix provides the statistical details for this analysis, and Table 5 shows the results in the last column of Model 3. On average, when all situational contingencies are at their mean, the presence (vs. absence) of ambient scent produced a 3% increase in expenditures. Combining more favorable conditions, the presence (vs. absence) of a congruent, unidimensional, and familiar ambient scent resulted in a 10% increase. If a service exchange without incongruent music and a female-dominant sample were also predicted, the increase was 23%. If the respective less favorable conditions were combined, the presence of ambient scent caused a reduction of 17%, compared with its absence. Set in relation to the distribution of the raw mean values, the estimated range of +23% to −17% falls within the average deviation from the mean (±1 SD), rendering the estimates adequate to gauge the percentage change. We also obtained similar results for lingering and for an average across all customer responses, summarized in Theme 7 in the Web Appendix.

Both increases and decreases in the levels of the responses span up to 60%; success stories from the industry claim even larger positive changes (Scent Australia 2019). We estimated that the presence (vs. absence) of ambient scent yields a 3% increase in expenditures for an average setting and a 23% increase for a most favorable condition, with similar results for lingering and an average across the response variables (see Theme 7 in the Web Appendix). Thus, industry reports such as increased breakfast sales (Novotel) and a 16% rise in store traffic (Dunkin Donuts) lie within our predictions, whereas claims of an 80% higher purchase intent (Nike) do not (Scent Australia 2019). Moreover, the success stories inevitably portray the positive side. While a less favorable condition may often lead to diminished effects, our predicted 17% decrease for a least favorable condition and the negative percentage changes in the data also show the possibility for negative effects.

The predictions need to be set in context. Although they combine a large volume of data, they represent forecasts, which are not free from error. They inform about the potential of ambient scent and its situational sensitivity according to currently available evidence. Larger numbers are typically more attractive. However, situational considerations (such as wide reach or great leverage on profit) may make even small changes desirable. Overall, ambient scent is able to facilitate expenditures, either by doing so directly or by moving consumers down the purchase funnel, thus supporting the business press belief “smell sells” (Bone and Ellen 1999).

Ambient scent positively affected most responses. Nevertheless, recommendations are not straightforward, because scent-facilitated responses may not always be desirable or occur for alternative mechanisms. Undesirable responses may include scent-enhanced recall and activation intensifying negative experiences (e.g., complaint handling, waiting) and scent-facilitated lingering worsening performance indicators (e.g., the number of processed consumers). Responses occurring for alternative mechanisms may include that ambient scent reduces cognitive efforts (Morrin, Chebat, and Chebat 2010), which can be responsible for better evaluations and the perception that less time has passed than is actually true.

To leverage ambient scent effects, marketing executives should consider situational aspects, guided by two questions. First, which pleasant scent should be selected? Using lavender in a French florist shop (Jacob, Stefan, and Guéguen 2014) may illustrate a best-practice example. Lavender is a fit to the store, is familiar to most French people (who were interviewed), and contains a single aroma. Other cases may be less obvious, especially regarding familiarity, which is not about identifying a scent but rather being acquainted with its aroma and the memories and emotions attached to it. Popular scents may be known but are not necessarily familiar. Thus, firms may need to test the responses from their target group.

Second, which contexts are beneficial? Service exchanges favor ambient scent effects, making the 1 Hotels group a positive example (Minskey, Fahey, and Fabrigas 2018). Scent also appears to be beneficial when female consumers represent the main group in the facility or in a spatially separated area (e.g., women’s sections in retailing and spa facilities). In either context, the scent needs to be perceived, which is different from consumers often being unaware that scents affect them (Herz 2010). If ambient music is present, it should match the ambient scent (via both stimuli’s arousing properties). We also saw the possibility of negative effects. Even a pleasant ambient scent may create negative sentiments, such as when it leads to overstimulation (Chebat and Morrin 2005), is so strong that it turns aversive (Herz 2010), or distorts the perception of the environment. Because also other as-yet-undiscovered individual differences may account for ambient scent effects, it is again advisable to use field tests.

A special strategy of firms is to use bespoke fragrances, such as Hyatt Place’s unique “Seamless” scent (Minskey, Fahey, and Fabrigas 2018). Because people can distinguish between many thousands of scents they have previously smelled, signature scents appear suited to build an olfactory identity. At the same time, positive associations have yet to be formed. Thus, there is little margin for failure, a challenge for services (Gelbrich and Roschk 2011), and they may elicit effect only after repeated exposure. Furthermore, because they are often compounds, unidimensionality appears more difficult to achieve, which pleasantness can partially compensate for.

Meta-analyses have strengths but also inherent limitations. First, they are constrained by the available primary data. Thus, our framework should be considered a summary of the most commonly studied variables. Data constraints are also reflected in the focus of this meta-analysis on pleasant scents and its geographic focus on North America and Europe. The scent perceptual properties are learned and carry culture-bound meaning. In consequence, generalizations may be more difficult, which probably accounts for the absence of main effects for scent pleasantness and activation, and our results may not extend to other cultures. Furthermore, the results on multistore versus single-store distinction and mean respondent age were not predictive. The store distinction may be too coarse to reflect the idea that ambient scent creates distinctiveness as a function of the number of associated objects. The absence of significance for age is likely because olfactory deficits become salient after 70 years old (Correia et al. 2016), an age group that is only barely covered by the average sample (mean age 32.6 years). The second limitation is that situational contingencies accounted for 12.6% of the within-sample variance in effect sizes, indicating that variability could not be explained fully and heterogeneity remained. To explain variability, the model assumed linearity. However, the situational contingencies may exhibit nonlinear effects, leading to positive but not negative effect sizes or vice versa. They may also exert response-specific effects that our analysis could not reveal. Third, though we attempted to address publication bias, studies that failed to establish ambient scent effects may not have been available, positively biasing our summarized results.

Table 7 offers a compilation of six areas for future research and various questions on meeting market challenges. While firms attempt to perfect their in-store experience with pleasant ambient scents, unpleasant smells may cause detrimental effects, likely to a larger extent than pleasant scents yield positive effects (Herz 2010). Given the lack of research and the various conceivable sources for malodors (e.g., kitchens, building sites), an exploration and understanding of unpleasant ambient scents is encouraged (area 1). To stand out in the crowd, marketers also use ambient scents to create an olfactory identity in the form of signature scents. While such trends illustrate the markets’ interest in ambient scents, we have little understanding of why we observed our reported effects. Thus, insights into process explanations (area 2) and relations with other stimuli (area 3) are needed to fully leverage ambient scent effects and expand its fields of application (areas 4–6).

Hyatt Place, for example, is currently active in 23 countries. However, cross-cultural knowledge on ambient scent is scarce (area 4). Because scent preferences are culture bound, should Hyatt Place adapt its “Seamless” scent to local preferences, or can it use its scent to target a multinational audience? Like using music to cue healthier choices (Dong, Huang, and Labroo 2019), ambient scent may also be viewed as a nudge for social betterment (area 5). For instance, by making time pass faster, it may promote health-related behaviors, such as spending more time in physical activities. For prudent use, it is also necessary to understand when this leads to undesired consequences. Finally, are ambient scents doomed to be relevant only in the physical world (area 6)? People are capable of imagining a scent, so scents provide a sensory appeal that can be reexperienced in a digital environment (Bleier, Harmeling, and Palmatier 2019; Krishna, Morrin, and Sayin 2014). Scholars also see potential solutions to problems in artificial intelligence through the way olfactory information is processed (Cepelewicz 2018). Because ambient scent facilitates social interactions (Baron 1997), might it also support human–computer interactions, such as with robots?