Traveling with Companions: The Social Customer Journey

The importance of distal influences

Some research suggests that under some circumstances, distal influences can be more powerful than proximal ones. For example, Duhan et al. (1997) found that consumers with high subjective knowledge, or those who encounter instrumental cues, tend to seek recommendations from more distal social sources. Adding a further nuance, Kim, Zhang, and Li (2008) found that preference for socially proximal sources was moderated by temporal distance, while Zhao and Xie (2011) showed that others’ recommendations are most persuasive when their social distance aligns with the temporal distance of the decision itself. More generally, powerful normative influences often reflect the preferences of diffuse, unknown others.

Changes in perceived social distance

Changes in the actual or perceived nature of one or several of the social distance dimensions can move the social other from the more distal toward the more proximal end of the continuum, and vice versa. Relatedly, the perceived distance from social others is malleable. For example, reviewers may be perceived as similar to the consumer (Naylor, Lamberton, and West 2012), which would narrow the perceived social distance between the consumer and a typically distal social other, potentially increasing their influence (Gershoff, Mukherjee, and Mukhopadhyay 2003). Similarly, influence as a function of changes in perceived social distance is an idea that must increasingly be applied to nonhuman social companions, such as Amazon’s Alexa and Apple’s Siri (Novak and Hoffman 2019). As technology evolves, these entities will increasingly take on the person-roles of information gatekeepers, experts, and possibly even decision makers (a type of outsourced journey, per Lee et al. 2018). As they become more familiar and proximal—in our homes, in our cars, and on our persons—these AI agents may be seen as more proximal social others by some consumers.

Differing roles of proximal versus distal social others

Finally, we suggest that the precise roles of social others may change along with differences in perceived social distance. A friend may provide valuable information about a product, but the friend’s social closeness to the consumer may exert influence in other ways, such as by serving as a basis for social comparison or because the consumer desires to maintain a good relationship with that friend. Reviewers are more distal in social connection, and their impact is likely to be more informational. Yet as some or all of the social distance factors begin moving toward the more proximal or the more distal end of the continuum, corresponding changes in the respective roles may also change.

Proximal social others as motivational drivers

Consumers are motivated to affiliate with others and may do so by matching the consumption behavior of others (e.g., Tanner et al. 2008). For example, what or how much one chooses to eat is often motivated by wanting to associate with social others more than one’s own physiological need (McFerran et al. 2010). That desire to affiliate depends on social distance; for example, consumers form stronger connections with brands that are associated with in-groups as opposed to out-groups (Escalas and Bettman 2003). Yet consumers also make purchases as a way to differentiate from others (Chan, Berger, and Van Boven 2012), particularly in product categories that convey identity (Berger and Heath 2007). Consumers are less likely to choose products that are associated with out-groups. For example, White and Dahl (2006) showed that men had more negative evaluations of, and were less inclined to choose, a product associated with a female reference group. In short, the desire to affiliate or dissociate may be as dominant a motivation as addressing the underlying functional need (e.g., food, clothing, shelter), highlighting the importance of social motivation in understanding customer behavior.

Consumers are also motivated to engage in identity signaling, and they do so through the consumption of brands, products, and experiences that contain cultural meanings or associations with social status, traits, and aspirations (Kirmani and Ferraro 2017; Oyserman 2009). For example, choice of feature-rich products can signal wealth, technological skills, and openness to new experiences (Thompson and Norton 2011). Even if consumers are motivated to pursue a purchase for functional or other benefits, they may leverage the consumption opportunity for identity signaling. For example, consumers who are motivated to consume in an environmentally responsible manner may also use that consumption to signal how virtuous they are (Griskevicius, Tyber, and Van den Bergh 2010). The desire to signal status transcends socioeconomic boundaries. Ordabayeva and Chandon (2011) found that social competition can lead to increased conspicuous consumption among consumers of low socioeconomic status. While most signaling via consumption is likely done to influence proximal others, we expect that such signaling may be especially effective among distal others, as proximal others will tend to have more existing knowledge of the consumer.

Distal social others as motivational drivers

Distal social others may also have a significant impact on consumer decision making, especially on the altruistic or prosocial motivations of consumers (Chaney, Sanchez, and Maimon 2019; Oyserman 2009). For example, consumers may be motivated to consume (or not consume) for purposes of the greater good, and their decisions may include various forms of environmentally responsible consumption (Haws, Winterich, and Naylor 2014), as illustrated by recent interest in discontinuing the usage of single-use plastics (Madrigal 2018). In such cases, motivational influences can come from abstractly defined groups of social others and yet have a strong impact on one’s own motivation for both adoption and disadoption of certain products or practices. Businesses are not immune from this kind of distal social pressure. Firms’ decisions to source sustainably, to support (or not support) LGBTQ communities, or to force environmental, safety, or ethical standards on their suppliers are all examples of distal social influence on managers’ motivations.

An intriguing example of how distal social groups can influence motivation relates to the role of political orientation in consumer decision making (Crockett and Wallendorf 2004; Jost 2017). Ordabayeva and Fernandes (2018) showed that political ideology affects how consumers differentiate from others, such that conservatives are more likely to choose products signaling hierarchical status whereas liberals are more likely to choose products signaling uniqueness. Mohan et al. (2018) found that higher CEO-to-worker pay ratios were associated with lower consumer purchase intentions, but only among Democrats and independents. Kim, Park, and Dubois (2018) found that political ideology affects the relative motivation of maintaining status versus advancing status in the preference for luxury goods. In particular, political conservatives are more motivated by status maintenance in their consumption of luxury goods than are political liberals.

Social media platforms have increased the general revelation of one’s political beliefs as well as the intermixing of consumer decision making and politics. Bringing political views and related social causes to the forefront and aligning such views with influencers or brands may change the perceived social distance of the traveling companion vis-à-vis the alignment with one’s own political viewpoint. Interesting questions arise as to how consumers navigate conflicts between, for example, existing brand preferences and alignment with their social networks’ political ideologies.

Motivation in joint journeys

When the DMU has more than one member, there are unique opportunities for understanding the social nature of the customer journey because joint decision making introduces the need to negotiate different, often conflicting, motivations. Each member of the DMU must be on board with the motivation to engage in a specific journey, which means that individual members may need to persuade one another. Often this occurs in family settings in which DMUs share a collective identity and goals (Thomas, Epp, and Price 2020). This situation introduces roles for pro-relationship behaviors (e.g., Dzhogleva and Lamberton 2014), such as empathy (Burns and Granbois 1977), as well as pitfalls involving power (Corfman and Lehmann 1987), which may even influence future decision journeys in unrelated domains. In a B2B context, decision making often formalizes and codifies the split motivations of a DMU. When a major purchase is under consideration, the motivations of employees representing purchasing, engineering, legal, and operations may be at odds (e.g., minimizing cost vs. maximizing reliability). A particularly interesting question relates to how conflicting motivations among individual members of a joint journey DMU are reconciled, and what marketers can do to facilitate and influence that reconciliation.

Emergent research areas

Viewing the motivation phase of the social customer journey with an emphasis on both proximal and distal others can lead to exciting research avenues with clear implications for researchers and practitioners. At the more distal level, cultural values affect the form that needs take (e.g., functional vs. symbolic) as well as the means consumers use to fulfill those needs (e.g., luxury vs. nonluxury car). Social media brings to light new and conflicting views about what is desirable, raising interesting research questions. When are desires more influenced by distal groups rather than by proximal influences? How does the consumer reconcile situations in which those closest to the consumer provide motivational inputs that are in opposition to larger but more distal societal norms? Some motivational conflicts may lead to journey abandonment instead of continuation, whereas others may involve a choice between two different journey paths. As Louro, Pieters, and Zeelenberg (2007) found, perceived progress toward one goal can motivate people to pursue alternative goals if the focal goal is close. The factors determining perceived progress are contextually dependent, making this a fruitful area for future research. The converse direction of influence is also worth considering. The motivation of the decision maker may exert its own influence on the social other. People infer the motivation of others from observable cues (Cheng, Mukhopadhyay, and Williams 2020), and an actor who signals a high level of motivation may well motivate observers to set off on their own journeys.

In addition to the influence of social others on a consumer’s motivations, these traveling companions can also influence when and how motivation leads to action. It is possible, even likely, that traveling companions can spur decision makers to the next stage of the journey. If so, which companions are most likely to move a consumer from the motivation stage to the information search stage and beyond? Motivations stemming from proximal social others might lead to quicker movement than motivations arising from distal others; understanding when this is and is not the case is important. The precise genesis of one’s socially inspired motivations is likely to influence how one goes about continuing a customer journey.

Nature of social information

Not only has technology dramatically changed consumers’ ability to search for information by enabling access to practically infinite sources of information, it has also changed the nature of the information on offer. Consumers may still access traditional sources of independent information (e.g., Consumer Reports), but they also may access personalized recommendations and evaluative information presented by countless distal consumers through product reviews. Recent research reflects the importance of product reviews within the customer journey. Key findings suggest that online reviews do not necessarily converge with independent expert reviews, such as those provided by Consumer Reports, and that consumers heavily weight average reviewer ratings without appropriately accounting for the sample size (De Langhe, Fernbach, and Lichtenstein 2015). Consequently, the inferences that consumers draw as they encounter and process this information directly affect the relative weight given to it in the evaluation stage. Consumers also draw inferences even in the absence of explicitly offered information, such as when social others display their preferences on platforms such as Pinterest. Even silence, particularly from a proximal social other, may be construed as assent or dissent with the decision maker’s own opinion, despite the social other not having intended to play an active role in the decision-making process (Weaver and Hamby 2019).

Sources of social information

The idea that the silence of one’s traveling companions can influence a customer’s journey highlights the profound shifts in information search brought about by technology. Technology has helped blur the barriers between unknown sources, representing weak ties, and known sources, representing strong ties. This shift has led to the rise of interest groups focused on specific topics, to which people with similar tastes turn to find information, but it has also led to the rise of echo chambers, where people gain comfort from others holding similar opinions (Shore, Baek, and Dellarocas 2018) and where negative opinions lead to more negative opinions (Hewett et al. 2016). However, technology has also resulted in information searches that are broader and more open-ended, with consumers crowdsourcing inputs from social networks and thereby incorporating social others who previously would not have had any input on that journey. Finally, the opinion leaders and market mavens of times past are increasingly being replaced by social media influencers who cultivate fame by providing product information through blogs and other online platforms (Hughes, Swaminathan, and Brooks 2019).

Information search in joint journeys

As Sheth (1974) and Burns and Granbois (1977) demonstrate, DMU members vary in expertise. They have a priori access to different information, and such differences will naturally manifest in the information search stage. Joint information search raises the possibility of an expanded knowledge base or one that is acquired more efficiently. However, it also raises the possibility that decision-relevant information will not be equally well understood by all members, creating asymmetries. Furthermore, joint decision making may change the nature of the information acquired. Members of the DMU may seek different types of information regarding choice options when they anticipate having to defend their evaluations or persuade partners than when they are gathering information for an independent decision. They may also change their reliance on various traveling companions to increase persuasiveness within the DMU; for example, they may have personally been persuaded by a social media influencer, whereas they believe that a decision partner may be more influenced by a perceived expert, and thus they may adjust their own search accordingly. Similarly, information gathering in a B2B decision setting may be motivated by the need to defend one’s preferred option against the anticipated arguments of others in the DMU with a different set of preferences.

Emergent research areas

Although a large literature has addressed information search, we suggest that the focus on social influence opens opportunities to expand that research further. One drawback of easy access to information from social others is a sense of information overload, and with more information comes more variation in the nature and valence of the information. How do consumers sift and integrate this information, and what role does social distance play in determining the type of information that is most impactful? It is likely that consumers will turn to different sources of information for various inputs, such that certain social others become the go-to source for certain product categories or types of information. We further expect that the ease of access to multiple influencers, ironically, may serve as an impediment to information gathering, as the proliferation of experts makes information search seem never truly complete. Just as “Sale!” signs become less effective when too many items in a category have them (Anderson and Simester 2001), the simplifying heuristic of seeking out an expert’s opinion can lose its simplifying power when dozens of experts’ opinions must be weighed against each other.

Because technology has reduced search costs, it is likely to generate feelings of an incomplete information search. Thus, understanding how and when the information obtained from various proximal and distal social others propels a consumer on to the evaluation stage deserves further attention. Hildebrand and Schlager (2019) find that exposure to information on Facebook leads to more conventional feature choices, as looking at Facebook makes social others more salient, thereby increasing fear of negative evaluation from those others. Given that consumers acquire product information via Facebook, this may affect how they evaluate the possible options. Do social information sources further blur the line between information search and evaluation as others tend to simultaneously provide information and recommendations? Is the form of social influence different for specific social sources (e.g., informational from proximal others and normative from distal others), and how do consumers organize and categorize this information?

Evaluation of the source

Given the emergence of new social sources of information, including social media influencers, it is important to reconsider classic notions of what makes a source persuasive. Credibility, trustworthiness, likability, and attractiveness have been examined in many consumer contexts for their role in shaping evaluations of a source. Extensive research on the customers' perceptions of salespeople has yielded relevant insights. For example, attractive or likable salespeople are more persuasive because they are seen as less likely to have an ulterior motive, compared with their unattractive or disliked counterparts (Reinhard, Messner, and Sporer 2006). Furthermore, an attractive celebrity endorser positively affects brand attitudes under low involvement (Petty, Cacioppo, and Schumann 1983), or under high involvement when attractiveness is relevant to the product category (Kahle and Homer 1985).

In the online environment, mechanisms for establishing credibility for distal influencers include inputs such as the number of likes, followers, or reviews. As consumers regularly interact with bloggers and social media influencers, those relationships become more familiar and thereby more proximal, potentially enhancing credibility. However, this credibility can also be altered when firms become involved, for example, through sponsored posts. Recent findings suggest that greater blogger expertise has stronger effects for raising awareness than for spurring product trial, and expertise has differential effects based on the specific online platform (e.g., Facebook vs. a blog; Hughes, Swaminathan, and Brooks 2019).

Evaluation of the information

Although they are closely intertwined, the evaluation of the source of social information and of the information itself can be different, such that the nature of the social information customers receive can influence the evaluation of that specific product information. Learning about an attribute from multiple sources might increase the perceived importance of that attribute; for example, hearing from several friends that a movie has surprising twists might increase the importance of plot complexity in evaluating information about the movies currently showing. Structured customer evaluations (e.g., Audible.com encourages customer ratings and feedback along several specific dimensions) are likely to increase the importance of comparable attributes relative to anecdotal word-of-mouth, which might privilege noncomparable attribute information. For informationally complex products, customers can simplify the choice by evaluating the overall customer rating to the exclusion of nearly all other information. Increasingly, information in the form of aggregate customer reviews serves as a substitute for specific attribute information. Interestingly, moderately positive reviews have been found to be more persuasive than extremely positive ones because they are perceived to be more thoughtful, thereby enhancing credibility (Kupor and Tormala 2018). Social influence operating through social norms can also affect how attribute information is weighted. Attributes that might have received little attention in the past, including whether something has a small carbon footprint, is cruelty-free, or is made from recycled materials, can drive choice when social norms change what is important to customers. Firms have proven to be especially sensitive to changes in these norms, both downstream, as a way of enticing customers, and upstream, as a criterion for selecting among vendors.

We expect social distance to further moderate the way social influence affects the evaluation of information. While social norms are a distal social influence, previous research suggests that when these norms are filtered through socially proximal exemplars, the effects on how information is evaluated can be even more influential. For example, in the context of green consumption, Allcott and Rogers (2014) found that energy usage was affected by information about the energy consumption of one’s neighbors, and Goldstein, Cialdini, and Griskevicius (2008) showed that hotel guests’ reuse of towels was affected by the towel usage behavior of other hotel guests. These findings suggest that combinations of distal social influences (e.g., norms) and proximal influences (e.g., neighbors, previous occupants) might be especially powerful in influencing information evaluation.

Evaluation in joint journeys

Multiple members of a DMU exerting influence on the outcome opens the door to new factors influencing the evaluation process, including the perceived fairness of the decision-making process and the weighting of outcomes of past journeys. Members of a DMU may track who “won” in past decision journeys and use this history to “equalize gains” this time around (Corfman and Lehmann 1987). As Greenhalgh and Chapman (1995) put it, the relationship between the parties is not a “constraint on utility maximization,” which causes a “deviation from the base state of independent or autonomous decision making” (p. 170). Rather, joint utility maximization is the “central explanatory concept” for understanding the decision-making process.

These interesting dynamics have led to research on how evaluation is affected by the relative standing of individuals within the DMU. When members within a DMU differ in their preferences, the weight that each member’s preferences receive is based on influence within the DMU (Lowe et al. 2019). Garbinsky and Gladstone (2019) found that couples who pooled finances in a joint bank account were more likely to choose utilitarian products when spending from that joint account than when spending from a separate account, due to the need to justify the decision. Members of DMUs also have individual, relational, and collective identities that ebb and flow over time (Epp and Price 2008). Hence, individual preferences may change when members consider their collective identity as a part of the DMU. For example, looking at joint decisions relating to self-control (e.g., spending, healthy eating), Dzhogleva and Lamberton (2014) found that dyads with a member low in self-control tended to make decisions reflecting reduced self-control because the member high in self-control relinquished that self-control to maintain harmony within the dyad.

Emergent research areas

Social others have a significant impact on how consumers formulate and evaluate consideration sets based on both who they are and what information they provide. Much is known about this process, but key questions remain. For example, how does social distance affect the evaluation and weighting of information on product attributes? As is the case with the entirety of the customer journey, conventional wisdom may suggest that those closest to the consumer have the greatest influence, but the familiarity of close others may make perceived biases, particularly those relevant to evaluation, more transparent to the consumer. This is especially relevant in the presence of conflicting opinions about product alternatives. When would a more credible, but distal, social other fare better against a less credible but proximal one? Furthermore, one’s close social group may hold a contrary evaluation of a product compared with the wisdom of thousands of reviewers on Amazon. In such instances, people must grapple with clear trade-offs between going with the views of their smaller and more proximal in-group or those of a larger, but less known and more distal group of social others. Consumers may well develop heuristics to deal with such situations, and understanding precisely what these heuristics are deserves further attention.

As highlighted by Hughes, Swaminathan, and Brooks (2019), new technology has moved the power of endorsements from traditional celebrities to social media influencers. We see significant opportunities to examine how credibility, trustworthiness, likability, and attractiveness are established for these influencers, and how the intent, content, and platform all combine to drive the effectiveness of these sources. Appel et al. (2020) view characteristics of influencers as key variables in how brands will choose to use them for conveying their messages. Relatedly, how do consumers apply persuasion knowledge (Friestad and Wright 1994) when evaluating the potential usefulness or truthfulness of other consumers’ articulated experiences or opinions, and does social distance affect the level of scrutiny?

We also see opportunities for research related to the role of AI-based recommendation agents in this stage of the social customer journey (Kumar et al. 2016). These AI agents are designed to learn from the customer’s expressed preferences in concert with aggregated data on choice patterns. Such algorithms should improve with usage and, ideally, be able to accurately predict preferences. What might be the optimal consideration set size provided by an AI agent? Could the set consist of just one option? The better an algorithm performs, the more likely it is to serve as a substitute rather than a complement to human social influence. Thus, the reverse may also be possible: the more a consumer sees an AI agent as a traveling companion, the more likely the consumer is to rely exclusively on its evaluations. The effect of social closeness of AI agents on advice acceptance is a fascinating area for future investigation as it will likely affect perceptions of credibility and trustworthiness and differ across contexts; for example, within health care, patients are concerned with how AI agents account for their unique circumstances (Longoni, Bonezzi, and Morewedge 2019), but in other product categories, such as entertainment or music, advice from the AI agent may be more readily accepted.

Physical presence of social others

Much research has examined how the physical presence of social others at the point of purchase can affect the outcome. Such presence makes their influence more proximal and may affect consumers because of self-presentational or other concerns. For instance, Kurt, Inman, and Argo (2011) found that male consumers tend to spend more when they shop with a friend than when they shop alone, and Ashworth, Darke, and Schaller (2005) showed that the likelihood of using coupons decreased in the presence of others. In both cases, the physical presence of others activated a desire to avoid appearing cheap. Impression management at the point of purchase is not limited to situations involving known others. Even the mere presence of others can affect the experience of various emotions, including embarrassment, at the point of purchase (Argo, Dahl, and Manchanda 2005; Dahl, Argo, and Manchanda 2001). More generally, from a marketer’s perspective, the crowding or social density of one’s surroundings can have both positive (e.g., increased brand attachment; Huang, Huang, and Wyer 2018) and negative (e.g., decreased purchase intentions; Zhang et al. 2014) effects on consumers. The presence of others may also be relevant at the time of purchase as social others may provide valuable product information or express their own preferences. Such “moment of truth” social inputs may well override prior inputs gathered in the predecision stages because of their proximity to the purchase. For example, the presence of others during a grocery shopping trip is associated with increased in-store decision making (Inman, Winer, and Ferraro 2009) and a more positive shopping experience (Lindsey-Mullikin and Munger 2011).

Felt presence of social others

Just as technology has changed the role of social influence in the predecision stages, it has also enhanced the felt presence of social others at the point of purchase. As noted by Roggeveen, Grewal, and Schweiger (2020) in discussing social factors in retail contexts, consumers may connect virtually through FaceTime or other technologies to get live insights from others who are not physically present with them. In the online context, firms use tactics that explicitly highlight felt social presence by noting other consumers’ interest in a product (e.g., “30 other customers have booked this today,” “5 people are viewing this right now!”). Such tactics may trigger scarcity concerns or make salient that a product is highly desirable (He and Oppewal 2018). Technology has also enabled a subtler use of social influence as firms leverage information about the decisions of social others (e.g., electricity usage of neighbors; Allcott and Rogers 2014) to influence decision making and short-circuit the “last mile problem” in a customer’s journey, whereby the final transition from evaluation to action fails (Soman 2015). Furthermore, we suggest that AI agents can be viewed as a form of felt social presence at the point of purchase, particularly in online shopping environments.

Decisions in joint journeys

In a joint journey, the decision is influenced by both the individual and joint utilities of the DMU members. The more closely members of the DMU have traveled together along the predecision phases of the journey, the more straightforward the decision phase will likely be. However, because the decision is the stage of ultimate commitment, it might also be the stage where disagreements or misaligned motives and preferences come to a head. Indeed, different members of the DMU may use different evaluation models (e.g., one spouse uses a lexicographic model, while the other spouse uses a weighted-additive model), and these differences would only become evident at this stage. If so, negotiations might cause the DMU to return to any of the previous stages of the journey.

Furthermore, the point of the actual purchase has special significance in many joint journeys given that such decisions often have significant financial or other implications, so decisions about the timing and mechanism of purchase also become important. Joint decision journeys may be especially likely to evoke risk aversion at the decision stage, relative to solo customer journeys. The old B2B sales aphorism, “Nobody ever got fired for buying IBM,” speaks to the perceived importance of sticking with “safe” decision options—the status quo option or the dominant brand—when employees know they will eventually have to justify their decisions to more powerful members of the DMU.

Emergent research areas

An excellent review by Argo and Dahl (2020) synthesizes findings of social influences in retail contexts. They draw important distinctions between active and passive social influences and then further distinguish (within passive influences) those aimed at a focal recipient versus those merely witnessed by other customers. The authors provide keen insights about future research directions that fit primarily within the decision stage of our social customer journey. We also note opportunities to focus on other specific decision contexts wherein social influences are likely to be powerful, including decisions about experiences, financial decision making, and decisions in health care settings. Furthermore, given that much of the research about the impact of present social others is in physical contexts, we see opportunities for understanding the impact of present and virtual traveling companions in online consumption contexts.

The decision may also be influenced by anticipation of others’ responses to the choice. More proximal traveling companions might naturally be expected to have a greater influence on the current decision than more distal others—even those who may have played an important role at earlier stages in the journey, as closer others are more likely to see the consumer using the product. Yet clearly the breadth of the audience that can be made aware of one’s decision has expanded through the same technologies discussed throughout. Thus, once the decision has been made and executed, traveling companions will play a key role in how the focal consumer uses and evaluates the product.

Nonhuman entities are increasingly entering the retail space, changing the nature of what social presence means. Wang et al. (2007) found that the use of social cues on online retail sites led consumers to rate the websites as more helpful and intelligent, and Mende et al. (2019) found increased food consumption in response to the discomfort felt from being served by a robot. This raises questions about whether and how robots and other technological devices will serve as surrogates for humans in the retail space. We expect that these devices will tend to be perceived as distal “others”; however, this will likely change as their physical proximity and ubiquity—in our homes, on our wrists—are likely to shift perceptions, making them seem more proximal. Important questions emerge about how and when these technologies will serve as substitutes or complements to the influences of human others. It seems likely that consumers will begin to substitute artificial social others for the flesh-and-blood variety for purchases that are especially embarrassing, that are ephemeral or low-stakes, or that are beyond the technical expertise of the customer to evaluate.

Satisfaction from consuming with others

Prior research has demonstrated how joint consumption, even when the decisions are made independently, affects evaluation of experiences (Lowe and Haws 2014). For example, experiencing something in the presence of others can lead to emotional contagion, whereby individuals share more consonant emotions than when experiencing the same thing independently, leading to enhanced enjoyment or dissatisfaction (Ramanathan and McGill 2007). On the other hand, recent research suggests that consumers overestimate how much they will enjoy an activity engaged in with another person versus alone. Specifically, Ratner and Hamilton (2015) demonstrated that consumers often feel inhibited from engaging in hedonic activities alone, especially when these activities are observable by others, as they anticipate that others will make negative inferences about them. As it turns out, consumers enjoy the activity just as much whether it is undertaken alone or with another person.

Satisfaction based on inputs from others

Even when consumers do not consume a product jointly, others may have a significant influence on usage and satisfaction. Consumers care about why others choose the same option that they did and even experience reduced confidence in their own decisions when others arrive at the same decision for different reasons (Lamberton, Naylor, and Haws 2013). Furthermore, social others help consumers to more fully experience the goods and services they have chosen. For products that require learning, the role of traveling companions in this learning process is crucial in influencing usage and satisfaction. A consumer who purchases an Instant Pot cooker on the basis of a friend’s recommendation may ask that friend for usage tips and recipes, or the consumer may look to social media (i.e., distal others) for that information. How-to videos garnered more than 42 billion views in 2018 alone (Marshall 2019.) Technology has likely made acquiring usage information from distal others less costly in terms of time and effort than acquiring the information from proximal others. Furthermore, information from distal others may be more useful as they may offer more varied and possibly less biased inputs on usage.

Satisfaction in joint journeys

In the case of joint journeys, the decision and often the consumption are experienced as a joint DMU. Yet the members’ individual assessments of satisfaction may vary. These satisfaction assessments could pertain to both the outcome and the process: dissatisfaction with the process or even with the other members of the DMU may well manifest itself in the individual’s level of satisfaction with the product or service and/or even in unrelated future journeys embarked on by the DMU. Furthermore, each member may obtain usage information and tips from different social sources or may invest varying amounts of time and effort in using a product, thereby leading to different experiences.

Emergent research areas

The many ways traveling companions can affect postdecision satisfaction remain largely open for investigation. When does consumption influenced by distal (vs. proximal) others result in greater satisfaction? This question would depend on whether expectations are set differently depending on who is observing the consumption and also on whether feedback about the experience would be shared or not. In some contexts, the act of consumption, not just the feedback, is shared. How does “virtual” joint consumption, where independent experiences feel shared because the individuals know that others are concurrently having similar experiences, affect satisfaction? And when does shared consumption enhance versus detract from satisfaction? Much of the extant research on joint consumption has focused on food, but opportunities are available for research in new settings such as online gaming or online exercise classes, where joint consumption occurs virtually. Likewise, technology has changed what were previously solitary experiences (e.g., watching a sporting event at home) into things that can be jointly experienced and evaluated via real-time online discussions, leading to the question of the circumstances in which consumers seek shared consumption versus individual experiences. The rise of access-based products, for which a consumer pays per usage, in an increasing number of industries (e.g., clothing, boats) raises new questions about satisfaction with products that have been used by many others. Some research points to psychological benefits of consuming previously owned and used products (e.g., Sarial-Abi et al. 2017), but there are likely cases where the opposite occurs, and further research could shed light on both the costs and benefits.

Finally, as consumers seek out how to use new products, how do others affect that learning and satisfaction? The degree of collaboration in expectation setting, perceived togetherness in consumption, and the extent to which the feedback is shared publicly versus experienced privately should all influence satisfaction with joint consumption experiences. As consumers evaluate their satisfaction with products in the context of the social influences discussed previously, they are likely also to be developing a sense of how willing they might be to share their experiences with others, raising interesting questions such as when consumption, influenced by others, is more likely to lead to postdecision sharing.

Reasons for sharing

Prior research suggests that the reasons for sharing product experiences and the consequences thereof are wide-ranging. We suggest that social distance may influence the likelihood that product experience will be conveyed and the form that this sharing will take. In general, consumers are more likely to share experiences for which they have stronger attitudes, positive or negative (Akhtar and Wheeler 2016); however, this likelihood may depend on social distance (e.g., Chen 2017). One reason that consumers share consumption experiences is that sharing serves as a mechanism for identity signaling. Before social media, consumers conveyed their identity through readily observable material purchases, such as clothing or a car. Now, people convey identity through what they post, including their tastes in music, books, and food, as well as via the brands and influencers that they follow. Unlike the offline context, where product choices are limited by personal (e.g., income) or contextual (e.g., audience size) factors, online sharing allows consumers more freedom to carefully curate the identities they convey through discussion or display of products and experiences, whether or not they actually own or use them. In this way, technology has brought distal others closer and given them the opportunity to be included in a focal customer’s journey. Ironically, this has happened even though many people use social media to express themselves at a distance, not communicating directly with targeted recipients (Buechel and Berger 2018).

Consistent with the increased ease of sharing of experiences and opinions is an increased potential for some of this shared information to be less objective or sincere. In research conducted prior to mass use of social media, Sengupta, Dahl, and Gorn (2002) found that consumers may conceal that they purchased a product at a regular price when it is important to be perceived as a smart shopper. Concomitantly, technology has facilitated the phenomenon of humblebragging (Sezer, Gino, and Norton 2018), a form of insincere sharing that is common on social media. Clearly, one’s motives for sharing affect what is shared and to whom the sharing is targeted.

Audiences for sharing

Consumers also share their product experiences to affiliate with others, and who these others are affects the nature of the sharing. For example, consumers are more likely to share negative product experiences with friends to connect emotionally, whereas they are more likely to share positive product experiences with strangers to give a better impression of themselves (Chen 2017). Furthermore, the nature of what is shared changes with audience size: people are more other-focused and share more useful content with one person (i.e., narrowcasting) but are more concerned with not looking bad when sharing with larger audiences (i.e., broadcasting; Barasch and Berger 2014). Sharing may have additional unintended consequences as well. For example, Motyka et al. (2018) demonstrate that consumers providing reviews feel an emotional boost from the enhanced social connectedness they experience that then leads them to buy impulsively.

Another form of postdecision sharing is done within brand communities, in which people bond over their common affiliation with a brand (Muniz and O’Guinn 2001). It is likely that the more socially close one feels to a community of brand loyalists, the more likely one is to become an advocate for the brand, as strong relationships with community members enhance identification with the community, leading to further engagement. Yet negative effects of brand community membership have been identified, as communities exert pressure to conform to rules and practices (Algesheimer, Dholakia, and Herrmann 2005). Zhu et al. (2012) found that participation in a brand community can lead to riskier financial decision making because members perceive that others in the community will help and support them if the decision turns out poorly. Furthermore, reputations are built within the community and affect how community members engage with each other (Hanson, Jiang, and Dahl 2019). In sum, technology has significantly increased the number of potential audiences and communities with which consumers can share their consumption experiences.

Sharing in joint journeys

As with information search, it is likely that members of a joint DMU will engage in postdecision sharing as individuals rather than as a pluralized DMU. For example, presumably most reviews are composed by single individuals, even if the decision was the result of a joint journey. It is also likely that sharing behaviors in joint journeys are less tightly coupled within the DMU than predecision cognitions and behaviors are. Although a joint DMU needs to reach some form of consensus before a decision is made, members of the DMU may go their separate ways in evaluating the experience and sharing those opinions. Alternatively, some joint journeys might culminate in social pressure to align in terms of evaluations and sharing, potentially reducing the likelihood of sharing widely. Consider a family vacation wherein multiple members of the family participated in the motivation, information gathering, decision, and consumption phases of the trip. They may feel subsequent pressure to come to a consensus evaluation of the vacation, and to share stories that are consistent and complementary across the family DMU.

Emergent research areas

Many interesting questions emerge regarding postdecision sharing, including how consumers curate their consumption experiences for others. Identity signaling depends on the audience (Berger and Ward 2010), and thus many opportunities are available to examine the distance between the focal consumer and the audience, and how that distance affects sharing. How does a consumer’s likelihood of sharing and the type of information shared vary with the stage of the listener’s customer journey? What level of detail might consumers provide about experiences to different types of traveling companion? When do consumers refrain from sharing even the best or worst experiences because they fear a negative response from others? When and with whom might consumers be more likely to share less certain evaluations? Related questions concern the venue or medium through which consumers share. Different platforms facilitate different types of communication. Consumers writing a review on Amazon, calling out a brand on Twitter, or telling a customer experience story on Reddit will be operating in environments with different norms of communication and different capabilities facilitated by the platform. Recent research has found that the interface through which consumers write reviews (smartphone vs. computer) affects the emotionality of the content (Melumad, Inman, and Pham 2019) such that the more constrained nature of smartphones leads to shorter but more emotional reviews. Parallel research investigating how customers’ postconsumption sharing is influenced by where they choose to share is likely to uncover similarly interesting findings.

Questions arise as to whether postdecision sharing enhances or hurts consumers’ well-being. For example, postdecision sharing can bolster affiliation and deepening of relationships—Appel et al. (2020) identify social media as a vehicle for combating loneliness—but social media usage can also trigger feelings of envy and isolation (Lin et al. 2016). Additional questions relate to how consumers curate their consumption experiences for impression management. Interestingly, consumers may, at times, desire to elicit negative responses from others. For example, consumers may share their consumption experiences to offend others, allowing them to enhance a normatively negative self-identity (Liu et al. 2019). The topics of lying and humblebragging about purchases also lead to interesting research questions, such as whether the propensity to engage in these actions varies with social distance. If consumers suspect misrepresentation by social others, this suspicion could reduce the persuasiveness that one’s postdecision sharing has on others’ information search and evaluation. Today’s virtual communities are places where consumers discuss not just brands but all aspects of life (e.g., healthy eating, raising kids). This raises interesting questions about how community members bring brands into community discussions. Finally, bringing the discussion full circle, how is a consumer’s sharing motivated by a personal motivation to trigger a customer journey for a traveling companion?

Nature of social influence

Our focus has been on the role that the social distance between the focal consumer and traveling companions plays in the way that those traveling companions affect the customer journey. We recognize that an important additional layer for examining the role of social influence lies in considering the nature of the social influence. Social influence will vary in ways beyond social distance, some of which we have touched on already. For example, influence exerted by a social other may be normative or informational. Moreover, the influence attempt may be intended or unintended, whereby the consumer either is specifically targeted or is an incidental recipient of persuasive information from social others. Influence may be direct, with the social other being in physical or virtual proximity to the customer, or indirect, operating through third parties. Indeed, the influence may even be implicit, in that it is perceived by the focal customer without any intention to influence on the part of the social other (see Argo and Dahl 2020 for similar distinctions). These important differences in the very nature of the social influence may change the path of a journey as the consumer moves forward or turns back to revisit prior stages. These distinctions also raise questions about whether consumers in collectivistic cultures are more interested in exerting, or are more susceptible to, social influence, than consumers in individualistic cultures. Each of these considerations adds nuance to the research questions highlighted throughout.

Bidirectional social influence effects

The bidirectional arrows between the focal consumer and the traveling companions in Figure 1 are intended to emphasize that as consumers experience social influence, they simultaneously influence the customer journeys of their companions. This bidirectional influence may take many forms, including a simple back-and-forth conversation about a product between friends that initiates customer journeys for both or the posting of a review by one customer at the end of a journey intended to help distal others during the early stages of their own journeys.

The bidirectionality of social influence throughout the customer journey can also be seen in the way individuals react to distal, macro-level social forces, which in turn affect the contributions individual consumers make to their social networks and society as a whole. For example, Walasek, Bhatia, and Brown (2018) found that income inequality was related to the sharing stage of the customer journey, with luxury brand mentions on Twitter more likely in geographic regions with high inequality, potentially motivating customer journeys in those regions and reinforcing the cycle. Arguably, the burgeoning sharing economy (Price and Belk 2016) is further evidence of the bidirectionality of social influence in the customer journey. The move from ownership to consumer-to-consumer rental transactions breaks down the traditional roles of buyer and seller, facilitating two-way social influence throughout the customer journey. We view these as interesting examples, but many questions regarding how macro-level social factors, such as inequality, corruption, and aging populations, interact with individual customer journeys remain.

The bidirectional nature of social influence might be further extended and used to understand the creation of consumption norms for groups of people and even entire societies. Consider how norms regarding single-use straws have changed over time, with consumers moving away from usage of nonbiodegradable, single-use straws to usage of reusable or biodegradable straws or to abandoning the category entirely (Madrigal 2018). These norms have changed as the result of the decision journeys of many individuals influencing other individuals and groups until, ultimately, a shift in society’s path is detectable. We propose that one may treat shifts in norms as the “decision” a cultural group has reached after a societal-level journey through motivation, information search, and evaluation to a collective decision. From this perspective, just as social others can affect the journey of individuals, so too do the aggregated journeys of individuals affect the journey of societies in the establishment of new social norms, practices, and laws.

Cross-stage influences

As mentioned, traveling companions may influence one or multiple phases of the customer journey. For example, the same person(s) or group(s) may enter one’s journey at just one stage, play a role in the entire journey, or stroll in and out of the journey at various stages, all while separate companions do likewise. Numerous emerging questions are based on how either the same or different sources of social influence at the various stages of the customer journey merge to influence the decision process. For instance, will a customer motivated by general social trends (i.e., distal others) be more likely to advocate for a product after purchase than if the motivation had come from a single, proximal other? How does sharing with traveling companions affect the time until one starts another, similar journey, motivated in part by the desire to share again? Furthermore, when does the motivation to eventually share the outcome initiate a journey? When consumption satisfaction is low, do consumers change the way in which they attend to their traveling companions in the next journey?

Another cross-stage insight afforded by the social lens is that traveling companions may catalyze a customer’s progress from one stage of a journey to the next. We highlighted several of these possibilities in the discussion of the individual stages, and we present additional questions probing these cross-stage influences in Table 2. A traveling companion may provide a tipping point in moving the focal customer from information search to evaluation of the options or by pressuring the focal customer to instead slow down and reconsider the motivations for the journey and the information gathered thus far. A business leader’s initial motivation to investigate some particular technology or strategy could be based on the social influence of friends or competitors, but these social influences could also be the impetus to move from information gathering to more serious evaluation of the options, or from evaluation to purchase. Interesting questions relate to the form of these tipping points and how they are affected by the social distance of others as well as companions’ desire to play either a transient or more pervasive role in the customer journey. Other interesting questions relate to how expectations of others’ reactions at the sharing stage affect how the focal customer goes about gathering information and evaluating the alternatives.