People in context—The social perspective

People in context—The social perspective

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Suzanne Higgs*,†, Helen Ruddock*, Nicolas Darcel† *School of Psychology, University of Birmingham, Birmingham, United Kingdom, † AgroParisTech, Paris, France

2.1

Introduction

Consumer choice and product evaluation are affected by the social context in which those decisions are made. For example, social context has a profound influence on food choices and amounts eaten (Herman, Roth, & Polivy, 2003; Rozin, 1996). People make different choices when they eat in company compared with when they eat alone (Higgs & Thomas, 2016). In addition, the same food may be evaluated very differently if it is consumed with others, compared with when it is consumed on a solo dining occasion: a shared consumption experience is rated as more pleasant than a non-shared experience (Boothby, Clark, & Bargh, 2014). Here, we define social context as the people who may be present when a decision is made, as well as knowledge of the behaviors and evaluations of others. There is evidence that mere knowledge of the food selections made by others in a similar context affects consumer behavior, even if those other people are not present at the time of choosing (e.g., Robinson, Benwell, & Higgs, 2013). In addition, many choices are made jointly by romantic partners or family members, or influenced indirectly by knowledge about the preferences of close others (Cavanaugh, 2016). Hence, the social context of a consumer includes the people who may be present when choices are made, but also our understanding of the choices and preferences of socially connected others, social norms, and aspects of the situation that infer such norms. Despite the wealth of evidence that has accumulated over many years on social influences on consumer behaviors, social context remains a neglected factor in product development and evaluation, particularly in relation to the development of new food products (K€ oster & Mojet, 2018; Meiselman, 2013). The aim of this chapter is to provide a brief overview of the methods that have been used to study consumer behavior in social contexts, and the ways in which social context influences consumer behavior. We will also discuss how social context might be better incorporated into consumer research. The emphasis will be on research that has examined social context and food-related decision making, but where appropriate, reference will be made to other consumer experiences.

Context. https://doi.org/10.1016/B978-0-12-814495-4.00002-7 Copyright © 2019 Elsevier Inc. All rights reserved.

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2.2

Context

Studying the social context of consumer behavior

Research examining how social context affects consumer behavior has been conducted in laboratory settings, as well as home and field settings, and has used both quantitative and qualitative methods. The literature examining social influences on eating therefore provides a good example of how data collected using a range of techniques has converged to provide strong support for the validity of the findings. Much of the research into social context and eating has examined the extent to which food intake varies in accordance with the number, or type, of people present at a meal. To do so, diary studies (e.g., de Castro & Brewer, 1992) and observational techniques (e.g., Klesges, Bartsch, Norwood, Kautzrnan, & Haugrud, 1984) have been used to identify associations between eating behavior and social context in usual eating situations. Subsequent experimental, laboratory-based studies have examined the causal relationship between social context and eating behavior by systematically manipulating the number/type of people present at that eating occasion (e.g., Clendenen, Herman, & Polivy, 1994). Other lab-based studies have focused on how people adapt their eating behavior in response to the behavior of others (e.g., Goldman, Herman, & Polivy, 1991; Prinsen, de Ridder, & de Vet, 2013). Observational investigations of dyadic eating interactions assess the degree to which eating partners match their intake. However, such investigations are theoretically problematic, as modeling of intake between non-randomly assigned dyads may occur because of pre-existing similarities between partners. It is also important to note that data from this type of study should be analyzed using multilevel modeling, due to the non-independence of the data (e.g., Salvy, Vartanian, Coelho, Jarrin, & Pliner, 2008). An alternative approach is to fix the intake of one eating partner by employing a confederate (usually another researcher) who pretends to be a participant. In these studies, the confederate always eats a set amount of food, so that the extent to which the real participant matches this amount can be assessed. A variant on this design is to use a ‘remote confederate,’ in which the participant is exposed to fictional accounts of the amount of food consumed by previous study participants, rather than a person playing the role of a participant (Feeney, Polivy, Pliner, & Sullivan, 2011; Pliner & Mann, 2004; Roth, Herman, Polivy, & Pliner, 2001). Such remote confederate designs allow researchers to examine the extent to which matching of intake occurs in the absence of other social influences (e.g., attempts to bond with the confederate). Studies using the “live” and “remote” confederate design have generally yielded similar results (Feeney et al., 2011). It is important to bear in mind that participants’ behavior may be affected by their knowledge of the fact that they are taking part in an experiment. It is possible that some participants eat differently in company, not because there is a real influence of the dining partner on consumption, but because they think that is what is expected of them in the experiment. To minimize the influence of such demand characteristics, experimenters should provide a convincing cover story to reduce awareness of the aims of the study, and to distract the participants from the experimenter’s interest

People in context—The social perspective

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in social eating behavior. Participants may be either provided with a non-food related cover story for the experiment (e.g., Kaisari & Higgs, 2015), or told that they are participating in a taste-test study, in which the interest is in their evaluations of the food, rather than amounts consumed (Goldman et al., 1991). The extent to which participants are aware of the experiment aims can be assessed during the post-experiment debrief, by asking participants to write down their thoughts on the aims of the study. Laboratory assessment of social influences on eating has yielded similar findings to those obtained within field settings, including work in lunchroom settings, canteens, restaurants, and supermarkets (e.g., Mollen, Rimal, Ruiter, & Kok, 2013; Gierl & Huettl, 2010; Salmon et al., 2015; Thomas et al., 2017). Evidence for the importance of the social context of eating is also derived from large-scale surveys of self-reported food intake (e.g., Pedersen, Grønhøj, & Thøgersen, 2015; Pelletier, Graham, & Laska, 2014), and social network analysis (e.g., Haye, Robins, Mohr, & Wilson, 2013; Pachucki, Jacques, & Christakis, 2011). In the latter technique, participants are asked to nominate others with whom they are socially connected, and associations between these networks and dietary patterns are analyzed. Data from these studies suggest that people’s eating choices are influenced by the eating choices of those with whom they are socially connected. This pattern of results reflects findings obtained from qualitative research based on interviews exploring people’s eating experiences (Kristensen, Holm, Raben, & Astrup, 2002) and meal time interactions (Laurier & Wiggins, 2011).

2.3

How does social context influence consumer behavior?

The literature documenting the effects of social context on eating behaviors has been divided into three broad areas: (1) social facilitation, (2) modeling, and (3) impression management. These phenomena have been well described for eating behaviors, but similar effects are observed for social contextual effects on other consumer behaviors, including shopping and responses to artwork, ads, and TV programs (e.g., Geller, Russ, & Altomari, 1986; Sommer, Wynes, & Brinkley, 1992).

2.3.1 Social facilitation Social facilitation is the term used to describe the finding that the mere presence of other people enhances the predominant behavioral responses in that situation. Social facilitation of eating was first described in detail by John de Castro, who conducted a series of diary studies in which participants were asked to record what and how much they ate over 7 days, alongside information about where and with whom they ate. Data from these studies revealed that people ate much more food when they ate in company than when they ate alone (de Castro & Brewer, 1992; de Castro & de Castro, 1989). These findings were observed for meals consumed during weekends and weekdays, thus ruling out the possibility that the social facilitation of eating reflects an artifact

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Context

that arises because people eat more, and are more likely to eat with others, during weekends (de Castro, 1991). Social facilitation has been observed consistently across different meal types, including breakfast, snacks, meals eaten at home, and meals eaten without alcohol (de Castro, 1991). Further analyses by de Castro also suggested that the amounts eaten increases as the number of diners increases, such that groups of twelve consumed, on average, 60% more than did groups of two. Indeed, de Castro concluded that social facilitation was the single most powerful influence on eating, and that “the number of people eating with the subject …is the best predictor of how much food an individual will consume” (Redd & de Castro, 1992). The conclusions based on these diary studies have been corroborated by results obtained from studies examining social facilitation within laboratory and field settings. For example, Berry, Beatty, and Klesges (1985) found that participants ate much more ice cream in 3- or 4-person groups than when alone. Similarly, Klesges et al. (1984) found that people dining out in a restaurant ate more in groups than when eating alone. The weight of evidence from numerous studies employing different methodological approaches supports the suggestion that social facilitation of eating is a real phenomenon (see Herman, 2015 for a review). There are some factors that moderate the extent to which social facilitation of eating is observed. Social facilitation of eating is more likely to occur when friends eat together than when strangers dine in a group (de Castro, 1994). In fact, when eating with strangers, people may eat less than they would if they were eating alone, possibly because they feel self-conscious about their choices (e.g., Hetherington, Anderson, Norton, & Newson, 2006; Peneau et al., 2009). In this situation, impression management concerns may override any effect of social facilitation: intake may be supressed to avoid appearing “greedy.” Similarly, people with obesity have been observed to eat less in a group than when dining alone, and it has been proposed that this is due to concerns about the stigma associated with appearing to eat excessively (Krantz, 1979). Recently, it has also been observed that people with higher BMI were more likely to consume high-energy snacks when alone, and were more likely to consume low-energy snacks in the presence of others eating (Sch€uz, Revell, Hills, Sch€uz, & Ferguson, 2017). The gender composition of a group can also moderate social facilitation effects. Specifically, Brindal, Wilson, Mohr, and Wittert (2015) reported that males eating in mixed-sex larger groups ate more than those eating in mixed- or same-sex pairs (reflective of social facilitation). Conversely, females eating in mixed-sex larger groups did not eat more than those eating in pairs, and ate significantly less than those eating in same-sex larger groups. These findings have been attributed to concerns about the image portrayed to others, such that when in mixed-sexed groups, women may eat less in order to convey a feminine impression (Brindal et al., 2015; Pliner & Chaiken, 1990). Whether people eat more in very large groups (e.g., in a crowd) has not been thoroughly investigated. The results of a recent series of studies suggest that eating in a crowded environment is associated with increased intake (Hock & Bagchi, 2017), although other work suggests that eating in a very large group of more than 50 people does not facilitate intake (Hirsch & Kramer, 1993). Further work is required to assess

People in context—The social perspective

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the influence of eating in a crowd on intake, and to determine the limits of social facilitation in terms of group size. Several explanations have been forwarded to explain social facilitation of eating (Herman, 2015). One theory is that social meals last longer than do solo meals, due to social interaction, thus extending the opportunity for eating (de Castro, 1990). However, exactly why lingering over a meal causes people to eat more is unclear. It has been suggested that social interaction may distract people from monitoring how much they are eating, or their awareness of internal cues that might inhibit eating (e.g., fullness). Additionally, seeing others eating may automatically trigger eating based on the conditioning of appetite to the social context (Sch€uz, Bower, & Ferguson, 2015). In this manner, social facilitation of eating may become habitual, or part of the ritual of commensal meals. Another possibility is that meals eaten alone are smaller than social meals because eating alone is not as enjoyable as eating with company. However, there is only indirect evidence in support of this assumption. de Castro (1990) found that people were generally happier when eating with others than when eating alone, but his analysis found that mood and the number of people present contributed independently to variance in intake. In a more recent study, Boothby et al. (2014) reported that participants’ ‘liking’ evaluations of a good-tasting chocolate were higher when in the presence of a co-eater. However, the researchers did not assess participants’ food intake. Overall, there is some evidence that social meals may be larger because eating with others is more enjoyable, and the presence of others may disrupt usual processes associated with satiety. However, there has yet to be a systematic investigation of the effects of social context on these aspects of appetite while controlling for the amount consumed. Importantly, explanations for social facilitation of eating fail to address the fact that, in order to eat more during a social meal, more food must be available. It is possible that both social and lone eaters serve the same amount of food, but that lone eaters do not finish all of their portion. This seems unlikely because recent evidence suggests that people tend to serve themselves the amount of food they believe will make them feel comfortably full, and then eat all of that portion (Brunstrom & Shakeshaft, 2009). In other words, decisions about portion size are made before eating in the pre-meal planning stage (e.g., Fay et al., 2011). Therefore, one explanation of social facilitation of eating is that people plan to provide more food when they know they will be eating socially (Herman, 2015). For example, people might cook or order larger portion sizes (or a greater number of dishes), per person, for meals with others versus meals eaten alone. In support of this idea, Cavazza, Graziani, and Guidetti (2011) reported that the number of dishes ordered per person, within a restaurant setting, increased as a direct function of group size. Although we know that social factors influence people’s food intake, very little is known about the relationship between social eating and obesity. There is evidence that obesity spreads via social networks (Christakis & Fowler, 2007), and one plausible underlying mechanism is that eating in social groups promotes food intake. However, as most direct evidence for social facilitation of eating comes from laboratory studies, in which intake is measured at one eating occasion, it remains unclear whether social

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Context

eating leads to cumulative increases in energy intake, and ultimately weight gain. There is evidence to suggest that the effect of merely providing large portion sizes to individuals, which increases food intake, is not compensated for by eating less at subsequent meals (Rolls, Roe, & Meengs, 2007). Hence, it is possible that socially-induced increases in food intake might not be offset by subsequent reductions in intake. In support of this suggestion, Hirsch and Kramer (1993) found that total daily caloric intake of soldiers increased as a function of the number of meals eaten socially. The phenomenon of social facilitation has been observed for other consumer behaviors, including alcohol and coffee drinking (Geller et al., 1986; Sommer & Sommer, 1989) and shopping (Sommer et al., 1992). People have also been shown to attend more to visual marketing stimuli when they are viewed in the presence of others, versus when they are viewed alone (Pozharliev, Verbeke, Van Strien, & Bagozzi, 2015). Taken together, these data suggest that social facilitation of consumer behaviors is a robust phenomenon, and that lone consumption experiences are not the same as consumption experiences shared with others.

2.3.2 Modeling Modeling refers to the tendency to adapt one’s behavior to conform to that of other people, or to what is thought to be ‘normal’ in that environment. In the case of eating behavior, this means that decisions about how much, and what, to eat are influenced by people’s perceptions of the choices of others. The eating behavior of others provides a norm of appropriate intake in that context (Vartanian, Sokol, Herman, & Polivy, 2013). Modeling occurs when the appropriate behavior is set by another present person (i.e. another diner), but appropriate behavior may also be communicated by environmental cues (e.g., portion sizes), or by the transmission of information about how other people behave. People may also model culturally agreed upon norms, such as cuisine rules. Indeed, research suggests that people tend to eat more when in the presence of someone who is eating a large amount, and less when with someone eating a small amount, compared with when they are eating alone. This is true for both adults and children (e.g., Bevelander, Ansch€ utz, & Engels, 2012; Robinson et al., 2013; Salvy et al., 2008). There is also evidence from lab-based studies that people model the food choices of others (Prinsen et al., 2013; Robinson & Higgs, 2013). Being accompanied in a cafeteria by others who choose dessert has been reported to increase the likelihood of choosing a dessert (Guarino, Fridrich, & Sitton, 1995), and providing information about the most popular dish choices in a restaurant considerably increases demand for those dishes (Cai, Chen, & Fang, 2009). Two systematic reviews of a large collection of literature have provided evidence that modeling of eating behavior is an extremely robust phenomenon that occurs regardless of current hunger state, dieting status, current health goals, age, or familiarity with the model (Cruwys, Bevelander, & Hermans, 2015; Vartanian, Spanos, Herman, & Polivy, 2015). A few factors have been shown to moderate the extent to which modeling is observed. In particular, people are more likely to model the behavior of others who

People in context—The social perspective

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are perceived as similar in some way, and are less likely to model the behavior of a social “out-group” (Cruwys et al., 2012; Stok, Ridder, Vet, & Wit, 2014). McFerren and colleagues reported that non-overweight participants were less influenced by the choices of an overweight confederate at a buffet than a lean model (McFerran, Dahl, Fitzsimons, & Morales, 2010). Similarly, lean (but not underweight) participants in another study did not model an obviously underweight person, possibly because they did not identify with the model (Hermans, Larsen, Herman, & Engels, 2008). These findings suggest that people are more likely to model others of a similar body size. In addition, the tendency to eat more when served a large portion size (the portion size effect) is reduced when participants are told that the size of the portion served is based on the behavior of a less relevant social group (Versluis & Papies, 2016). On the other hand, eating norms may exert a stronger influence on behavior if there is a strong motivation to associate oneself with the referent group. Indeed, Guendelman and colleagues reported that a desire to prove they belong in America motivated U.S. immigrants to consume more typically American food (e.g., fast food) (Guendelman, Cheryan, & Monin, 2011). Similar motivations underlie, at least in part, the persuasiveness of celebrity endorsements of products (Hoffman & Tan, 2015). There is some evidence that the tendency to model is reduced if we are already quite certain about how to behave in a particular context (Leone, Pliner, & Herman, 2007). For example, modeling is attenuated for meals such as breakfast and lunch, for which people may have clear expectations about how much one should eat (Hermans, Herman, Larsen, & Engels, 2010). On the other hand, people are more likely to model others’ choices under conditions in which they are uncertain about their own preferences (Huh, Vosgerau, & Morewedge, 2014). Interestingly, the choices that participants make in a group setting in a restaurant appear to be dynamically influenced by competing influences. People may be less likely to conform to the norm when the unanimity of others’ choices is low, and more likely to conform if a dominant preference of the group emerges (Quester & Steyer, 2009). However, reactance may be observed when preference is very strong around a given option leading to divergence from the norm (Quester & Steyer, 2009). One reason why people model the eating behavior of others is because doing so enhances feelings of social connectedness. Humans are social creatures with a strong desire to be liked (Baumeister & Leary, 1995), and this goal of affiliation may be achieved through modeling. When we imitate the behavior of another person, this has the effect of increasing the sense of rapport we have with that person (Chartrand & Bargh, 1999). The idea that people model eating behavior to affiliate is supported by findings that modeling is reduced in circumstances in which participants feel socially accepted (Hermans, Engels, Larsen, & Herman, 2009; Robinson, Tobias, Shaw, Freeman, & Higgs, 2011), and is enhanced for individuals who are low in self-esteem (Robinson et al., 2011). Interestingly, there is evidence that the feelings of rapport and liking generated by mimicking others’ behavior can have spill-over effect on consumer behaviors more generally. Being mimicked can enhance both product preferences and memory for the consumption experience (Kulesza et al., 2017; Ramanathan & McGill, 2007; Tanner, Ferraro, Chartrand, Bettman, & Van Baaren, 2008). Furthermore, expressing

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Context

views that are in agreement with a person sharing that experience enhances enjoyment of that experience, and triggers a more holistic (less analytic) processing, which affects how the experience is evaluated retrospectively (Raghunathan & Corfman, 2006). Furthermore, Bhargave and Montgomery (2013) found that people were more likely to be influenced by first impressions of an experience when remembering a shared, compared with an unshared, experience. Modeling also occurs because the information provided by others offers information about what is the most appropriate or “correct” choice in that context (Deutsch & Gerard, 1955). This has been demonstrated in studies that have used a remote confederate, in which the norm set by the confederate does not serve to promote affiliation (i.e. because there is no other person present). Instead, the intake of the fictitious participants indicates the “right” way to behave in terms of how much to eat or what foods to choose, and so that norm is adopted (e.g., Roth et al., 2001). Similarly, social norms for other behaviors, including recycling behavior, avoidance of littering, and energysaving behaviors, operate when others are not physically present (e.g., Cialdini, Reno, & Kallgren, 1990; Kallgren, Reno, & Cialdini, 2000; Larimer, Turner, Mallett, & Geisner, 2004). These data are consistent with the suggestion that people look to others to provide information about the most appropriate way to act. The mechanisms underlying modeling are unclear, but some evidence suggests that modeling of consumer behaviors is underpinned by a fundamental propensity to imitate the behavior of others (Chartrand & Bargh, 1999). In line with this idea, people mimic the consumption behaviors of others directly by taking a sip or reaching for food immediately after an observed person performs the same behavior (Hermans et al., 2012; Koordeman, Kuntsche, Anschutz, van Baaren, & Engels, 2011; Larsen, Engels, Granic, & Overbeek, 2009; Sharps et al., 2015). This behavior may be underpinned by basic neural processes that link perception with action, known as the “mirror neuron system” (Rizzolatti & Craighero, 2004). Indeed, it has been reported recently that modeling of eating is associated with activity in the mirror neuron system (Mcgeown & Davis, 2018). Modeling is also likely underpinned by changes in preferences for modeled items. If we learn that peers have a preference for a product, then we expect to like it too, and will place a higher value on the item (Nook & Zaki, 2015; Robinson & Higgs, 2012). Such expectancies can also produce placebo effects. Participants who consumed water falsely-labeled as containing caffeine experienced more alertness and demonstrated stronger product endorsement when a confederate reported a similar response, relative to when the confederate reported no response (Crum, Phillips, Goyer, Akinola, & Higgins, 2016). Another study found that labelling a product with information that more and more people are reducing their salt intake, increased product choice (Zandstra, Carvalho, & Van Herpen, 2017). Interestingly, products that connect with a person’s social identity are also evaluated more positively. Hackel and colleagues found that participants whose Canadian social identity was made salient demonstrated greater preference for maple syrup (versus honey), compared with those whose Canadian identity was not made salient (Hackel, Coppin, Wohl, & Van Bavel, 2018). These data suggest that modeling affects food choice and intake by altering the sensory/ hedonic evaluation of foods.

People in context—The social perspective

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Strong modeling effects have also been observed for alcohol consumption (Caudill & Kong, 2001; Larsen et al., 2009; Robinson et al., 2016), and perceptions of normative behavior have been linked to various health-related behaviors, including stair climbing, drunk driving, smoking, and unsafe sex (Burger & Shelton, 2011; Chernoff & Davison, 2005; DeJong, Larimer, Wood, & Hartman, 2009; Linkenbach & Perkins, 2003; Perkins, Linkenbach, Lewis, & Neighbors, 2010). There is also evidence that people follow norms provided by others for a wide range of consumer behaviors. For example, social norm messages have been reported to increase towel reuse behaviors (Goldstein, Cialdini, & Griskevicius, 2008), energy conservation behaviors (Schultz, Nolan, Cialdini, Goldstein, & Griskevicius, 2007) littering (Cialdini et al., 1990), and recycling (Schultz, 1999). In summary, there is strong evidence that behavior of consumers is influenced by the preferences of others. This is because other people provide information about “good” choices, and aligning our choices with valued others enhances our personal relationships and sense of self (Wood & Hayes, 2012).

2.3.3 Impression management Adapting one’s behavior to create a particular impression of oneself to others is known as impression management (Leary, 1995). We are usually motivated to present ourselves in a positive light to others, especially to people who do not know us well (Baumeister & Leary, 1995). We are also aware that our behavior reflects the kind of person we are, and so we may make specific consumption choices to express something about ourselves to others (Vartanian, Herman, & Polivy, 2007). For example, when having lunch with colleagues for the first time, we may choose to eat a salad rather than a burger, because this may fit with the image of ourselves we might want to convey to colleagues at that moment. Impression management is based on shared assumptions about the personal characteristics of people who engage in particular behaviors (also known as stereotypes). In the case of eating behavior, people readily make judgements about others based on what and how much they eat, and there is some evidence that people may use these stereotypes to manage how they are perceived, especially in the context of unfamiliar others (Vartanian et al., 2015). Research on consumption stereotypes has identified that certain foods and food choices are associated with impressions of masculinity or femininity. Meat eating is associated with masculinity (Rothgerber, 2013; Rozin, Hormes, Faith, & Wansink, 2012), whereas meat avoidance and consumption of vegetables, salad, fish, and sweet foods is associated with femininity (Cavazza, Guidetti, & Butera, 2015; Jensen & Holm, 1999; Rothgerber, 2013; Ruby & Heine, 2011). In a study of Japanese students, Kimura and colleagues found that sweet foods and salad were more likely to be associated with feminine names, whereas meat dishes were associated with masculine names (Kimura et al., 2009). These findings suggest that people implicitly categorize foods as either feminine or masculine. Furthermore, there is evidence that eating “good” foods is seen as feminine, and eating “bad” foods is seen as masculine. Stein and Nemeroff (1995) reported that men who

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Context

ate “bad” foods (i.e. high-calorie foods thought to be bad for health) were rated as more masculine (and less feminine) than were men who ate “good” foods (i.e. low-calorie foods thought to be good for health). Both women and men were judged as being more feminine and less masculine when they were depicted eating low-fat foods, compared with when they were depicted eating high-fat foods (Barker, Tandy, & Stookey, 1999). Other evidence suggests that food choices reflect the character of the consumer more generally. Overall, people who eat “good” foods are perceived as being “better” people than are those who eat “bad” foods. For example, people who eat “good” foods were judged to be more attractive, healthier, more moral, and more intelligent than were consumers of “bad” foods (Stein & Nemeroff, 1995). However, they were also judged as more serious and less likable (Barker et al., 1999). Conversely, consumers of high-fat diets were perceived to be unattractive, unintelligent, and working class, but also fun-loving, happy, and sociable (Barker et al., 1999). Consumption stereotypes also extend to judgments about amounts eaten. Bock and Kanarek (1995) provided participants with descriptions of a target person eating a small, moderate, or large amount. As meal size increased, participants rated both female and male targets as more masculine and less feminine. Eating smaller meals has also been associated with perceptions of neatness and attractiveness, especially for women (Bock & Kanarek, 1995; Chaiken & Pliner, 1987), and small portions of foods are rated as more feminine than are larger portions of the same food (Cavazza et al., 2015). There is indirect evidence that people use consumption stereotypes to express something about themselves within social interactions. This evidence is derived from studies examining eating behavior in situations where people may be particularly motivated to manage impressions. For example, women tend to eat lightly in the company of men, and this has been suggested to be because eating a small amount is an affirmation of gender identity (Pliner & Chaiken, 1990). White and Dahl (2006) conducted a study in which participants were asked to imagine that they had been sent on a training course by their employer. They read a scenario that described that they had been in workshops all day, and were planning on ordering something from the room service menu for dinner. To encourage the choice of steak, and in particular, a small steak, participants were told: “You aren’t feeling too hungry because you had a late lunch; however, you are tempted to select steak for dinner.” They were then asked to select from a hypothetical menu, and to evaluate each menu option. Men were less likely to choose a small steak (versus a large steak) when it was described as a ladies’ cut than when it was described as a chef’s cut. However, this was only observed when men thought they would be consuming the steak in public, and not when they thought they would be consuming the steak in private. The men also evaluated the ladies’ cut steak less favorably than the chef’s cut steak. The authors suggested that the menu choices were motivated by impression management concerns, such that the men avoided choosing a food associated with femininity to maintain their masculine identity. A similar conclusion was drawn by Gal and Wilkie (2010), who found that men were less likely to choose stereotypically feminine foods, after their masculine identity was challenged. However, this effect was observed only when they were not under time pressure, and thus had the cognitive recourses to regulate their choice to maintain their gender identity. More recently, Cavazza and colleagues (Cavazza et al., 2015)

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found that certain aspects of a dish (e.g., food type, portion size and presentation) affected the perception of the dish as feminine or masculine. These perceptions influenced women’s intentions to consume the food such that women were more likely to say they intended to consume a small portion of salad when it was elegantly presented, because they perceived it to be more feminine. Drawing upon the preceding information, there is substantial evidence to suggest that foods and dishes can be perceived as “good” or “bad” and “feminine” or “masculine,” and that people are judged on their food choices on the basis of these stereotypes. There is also some evidence that people adjust their eating behavior in light of these stereotypes to create and/or maintain a particular self-image. There are clear implications of these findings for the marketing of foods, and for the design of healthy eating campaigns.

2.4

Competing social influences

We have reviewed the ways in which social context influences consumer choices, using eating behaviors as an example. In doing so, we have highlighted (1) that the mere presence of others facilitates eating behaviors, (2) that people tend to follow social norms when making decisions about what and how much to eat, and (3) that people also adapt these choices based on impression management concerns. What has been less well studied is what happens in real-world consumption contexts when there are multiple social influences at play. In real-world contexts, there will be potentially conflicting social motivations that influence behavior. For example, in the case of food choices, a woman might be motivated to model the hearty appetite of a male dining companion to ingratiate herself, but this might conflict with attempts to convey a feminine gender identity. Interestingly, when faced with such competing motives in an experimental study, women used portion size to signal their gender identity (choosing a smaller portion of a food), but modeled the food choices of their partner by choosing a similar dish (Cavazza, Guidetti, & Butera, 2017). Further research is required to examine how competing motives play out in different scenarios, particularly in the case of joint decision making in couples and families (Wood & Hayes, 2012). For example, how would someone reconcile a desire to align their choices with a romantic partner with potentially conflicting motives regarding their desire to create a good impression on their wider social group? Understanding of consumer behavior will be advanced by a clearer account of the salient motives that govern choice in specific social contexts.

2.5

Implications for consumer research and product development

It is clear from the evidence reviewed thus far that the utilization and evaluation of a product varies according to the social context. Some choices will be specific to certain contexts, and that context will influence the evaluation of the consumption experience via processes such as social facilitation and modeling. Moreover, the same choices

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Context

may be made in different social contexts, but the underlying motives may differ. For example, I may choose French cheese to signal national identity in one context, and because my dining partners are choosing it in another context. These motives may also change the nature of the consumption experience. Therefore, if the social context of consumption is not taken into account when developing a product, important information will be missed. It has been argued that new product failures can often be attributed to not studying consumers in the contexts in which they actually interact with the products (K€ oster & Mojet, 2018), and this includes the social context of consumption. Other people are often part of the context in which consumption occurs, but social factors shape the meaning of consumption, interactions with products, and product decision making. The social context of eating and the meaning attached to such contexts will also vary depending on the prevailing cultural context, which highlights that social effects on consumption will also vary cross-culturally (Danesi, 2017). In the case of food products, it may be particularly important to examine the interaction between consumer and social context, because many consumption experiences are social events. Commensality is often seen as an inherent core of meals (Fischler, 2011), and food and eating play integral parts in our social lives, as they reinforce social connections and help us communicate and express ourselves (M€akel€a, 2009; Murcott, 1983). Many people make time to eat together, especially at the evening meal. A 2014 YouGov survey found that 77% of UK adults eat as a household at least once a week, and 48% eat together daily. A survey from the USA (NPD Group, 2014) found that 68% of those surveyed reported eating an evening meal socially. According to figures from the Credoc Research Institute, 80% of meals are taken with other people in France. It is also important to acknowledge that choices today are often made in the context of social media and signifiers of social popularity (e.g., “likes”) that are also likely to have a large influence on preferences. There have been extensive discussions around how best to incorporate contextual influences in consumer-based research generally (e.g., see Meiselman, 1992). Various methods/approaches have been proposed, including item-by-use methods, home use testing, experimental restaurants, and evoked contexts (for reviews see Jaeger & Porcherot, 2017). However, the social context of eating has perhaps been a neglected factor in some of these methods, possibly because appropriate social eating environments are seen as difficult to recreate in controlled settings (Delarue & Boutrolle, 2010). There is scope for future research to provide a more detailed assessment of the specific social contexts in which consumption occurs, and how this relates to product evaluation and decision making. Insights can be gained from the exploration of social contextual effects on consumer behavior using ethnographic and qualitative methods, as well as context evocation using written scenarios. Developments in the use of virtual reality techniques also offer opportunities to create immersive environments that combine contextual influences with product interaction (Bangcuyo et al., 2015). Finally, there is the potential to harness recent advances in artificial intelligence research to analyze information from large sets of observations of eating behaviors, and uncover the complex relationships between social context and eaters’ decisions (Akkoyunlu, Manfredotti, Cornuejols, Darcel, & Delaere, 2017). Non-supervised algorithms such as Bayesian network approaches (Getoor & Taskar, 2007;

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31

Heckerman, 2013) or machine learning techniques to measure causality links (Scholkopf & Janzing, 2018) can be developed and employed on very large numbers of behavioral observations to disentangle relationships between social contexts and food decisions or food preferences. Although very promising, this emerging field of research still requires major scientific and methodological improvements to fully benefit consumer and sensory sciences. Methodological research in this domain will be needed to facilitate the acquisition and the thorough pre-processing of a sufficient number of observations. Also, development of algorithms will be needed to adapt to the specificity of food decisions because they are mostly expressed as positive choices (foods that are chosen), and information on negative decisions (foods that are not chosen) are therefore often scarce or missing.

2.6

Conclusions

The social context of consumption has powerful and pervasive effects on consumer choices and experiences. Social influences on consumption are many and varied, and considerable work has been done recently, particularly in the area of social influences on eating behaviors, which has expanded the evidence base and our appreciation of the factors that moderate social influences on consumption. Many unanswered questions remain, but one area ripe for further work is how best to incorporate social context into consumer research. Greater understanding of people and products in their social context is necessary to provide a more complete understanding of consumer behaviors.

Acknowledgments Funding: This work and the work cited herein were supported by a grant from the Economic and Social Research Council (ESRC), grant number: ES/P01027X/1. The funder had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report, and in the decision to submit the article for publication.

Conflict of Interest Suzanne Higgs and Helen Ruddock and Nicolas Darcel declare they have no conflict of interest.

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Further reading Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591–621. Feunekes, G. I., de Graaf, C., Meyboom, S., & van Staveren, W. A. (1998). Food choice and fat intake of adolescents and adults: Associations of intakes within social networks. Preventive Medicine, 27(5), 645–656. Higgs, S. (2015). Social norms and their influence on eating behaviours. Appetite, 86, 38–44. Lakin, J. L., & Chartrand, T. L. (2003). Using nonconscious behavioral mimicry to create affiliation and rapport. Psychological Science, 14(4), 334–339. Robinson, E., Fleming, A., & Higgs, S. (2014). Prompting healthier eating: Comparing the use of health and social norm based messages. Health Psychology, 33, 1057–1064. Tavoularis, G., & Mathe, T. (2010). Le mode`le alimentaire franc¸ais contribue a` limiter le risque d’obesite. Consommation et modes de vie, 232.