You Should Try These Together: Combinatory Recommendations Signal Expertise and Improve Product Attitudes

Abstract

This work introduces a novel cue that consumption advisers, like stylists and interior designers, can use to signal expertise: combinatory recommendations. In a combinatory recommendation, a person offers an opinion about compatibility among multiple products intended for joint usage. Across nine studies conducted in the lab and field, the authors find that offering a combinatory recommendation signals greater expertise (Study 1a, Study 2a) and, specifically, greater depth of knowledge (Study 1b), compared with other types of recommendations involving the same number of products. This effect does not depend on the helpfulness of the adviser (Study 2b) but is qualified by features of the recommendation itself (Study 3a) as well as the type of combination recommended (Study 3b). Importantly, the authors find this effect to have important downstream consequences, as the increased perceptions of expertise that follow a combinatory recommendation improve consumers’ attitudes both toward products included in the recommendation and toward subsequent recommendations made by the adviser (Study 4, Study 5). The real-world persuasive value of combinatory recommendations is also tested in a field study (Study 6) that explores the effect of combinatory recommendations on click-through rates of Instagram advertisements.

Keywords

signaling expertise combinations complements recommendations

In recent years, brands have made significant investments in enlisting consumption advisers to recommend their products to consumers (Gorin 2018; Howland 2018). These advisers exist in a breadth of product domains, ranging from stylists who facilitate consumers’ shopping experiences (Bobb 2019; Davenport et al. 2020) to social media influencers who recommend products to their followers (Libai, Muller, and Peres 2013; Valsesia, Proserpio, and Nunes 2020). Even at the height of the COVID-19 pandemic, retailers doubled down on the usage of these advisers. For example, retailers like Neiman Marcus and John Lewis launched services whereby consumers could interact with online stylists and interior designers (Coker 2020; Wilson 2020). Further, in 2020, most U.S. brands increased their influencer spending despite cutting a variety of other marketing expenses (Glenday 2020).

Although consumption advisers come in many forms, they are related in their motivation to signal expertise to gain consumers’ trust and increase their persuasiveness (Alba and Hutchinson 1987), thereby differentiating their services in the ever-growing marketplace of online and offline advising services. In this work, we introduce a novel cue advisers can use to signal expertise: combinatory recommendations. In a combinatory recommendation, a person offers an opinion about compatibility among multiple products considered for joint usage (i.e., whether or not these products go well together) within a single consumption occasion. For instance, a stylist might discuss how a certain tie pairs well with a dress shirt to curate an outfit; an interior designer might suggest hanging different pieces of artwork together to create a gallery wall; a beauty influencer might suggest combining multiple skin-care products to form a skin-care routine. Combinatory recommendations may also provide opinions about what does not go well together. For example, a chef might warn against pairing sparkling wine with spicy fish.

This research focuses on situations where an adviser offers a consumer an unsolicited combinatory recommendation. We propose that such combinatory recommendations reflect an ability to process how multiple products will interact with one another, which consumers perceive as a demonstration of the adviser’s depth of knowledge in a product category. Thus, this adviser signals expertise in the category. Moreover, their recommendation may garner more favorable attitudes from consumers, consistent with findings that experts are more influential and persuasive (Alba and Hutchinson 1987; Gershoff, Broniarczyk, and West 2001).

The literature shows that consumers rely on a variety of cues to infer others’ expertise, including the individual's appearance (Bellezza, Gino, and Keinan 2014), choices (Sela et al. 2019), and demeanor (Price and Stone 2004; Sniezek and Buckley 1995), as well as the consistency between the individual's opinion and other external sources of information (Palmeira 2020). These cues can be used strategically to signal expertise. Our research extends the literature on signaling and persuasion by identifying combinatory recommendations as a novel cue that signals expertise and increases persuasiveness. In doing so, we also contribute to the literature on consumer knowledge and expertise. Prior work generally categorizes depth of knowledge as understanding the details of a product’s attributes in isolation (Alba and Hutchinson 1987). We show that displaying an understanding of interactions across the attributes of different products also contributes to the perception that an individual has depth of knowledge in a product category, resulting in greater perceived expertise.

Combinatory recommendations relate to two important constructs: curation and bundling. The nascent marketing literature on curation uses the term to refer to the selection, organization, or presentation of a combination of objects (Babin and Hulland 2019). Thus, curation does not necessarily imply the presence of an explicit recommendation nor the inclusion of items intended for joint usage. In this sense, a combinatory recommendation could be seen as a particular type of curation. Little is known about consumer responses to those in charge of curation and to the curated selection of products. We suggest that exhibiting an ability to curate products for joint usage can be an effective signal of expertise.

Bundling, in contrast, refers to “marketing two or more products and/or services in a single package for a special price” (Guiltinan 1987, p. 74). Our research encompasses a broader lens, including situations where products are not packaged together by marketers nor sold at a special price. Nonetheless, a bundle of complementary products can be characterized as one type of combinatory recommendation. For this reason, our work also contributes to the bundling literature. Prior work largely focuses on consumers’ evaluations of the items included in a bundle (Karataş and Gürhan-Canli 2020; Rahinel and Redden 2013) rather than on consumers’ perceptions of the bundle curator. Our work is consistent with findings that consumers tend to favorably evaluate complementary product bundles over other types of bundles (Popkowski Leszczyc and Haubl 2010; Zhao and Xia 2020) and offers a contributory explanation driving these favorable evaluations: perceived expertise of the bundle curator.

In addition to these theoretical contributions, we make a substantive contribution by introducing the use of combinatory recommendations as a tactic advisers can implement to signal their expertise to consumers and differentiate themselves in an increasingly crowded marketplace.

Theoretical Framework

Prior literature highlights how consumers can enjoy products more when consuming them in combination, especially if these products appear to be made for each other, provide a novel consumption experience, or appease intricate preferences (Hildebrand, Häubl, and Herrmann 2014; Rahinel and Redden 2013; Spence 2020a). With a marketplace surge in customization opportunities, consumers now have many opportunities to combine products for themselves (D’Angelo, Diehl, and Cavanaugh 2019). Yet, products typically have and continue to be combined by the marketer (Karataş and Gürhan-Canli 2020; Tellis 1986), alluding to the notion that lay consumers may lack an understanding of compatibility (i.e., which products go well together), at least in some product categories.

This lack of understanding may stem from the cognitive demand consumers encounter when simultaneously evaluating multiple products (Aribarg and Foutz 2009) and the difficulties in predicting the outcome of product combinations (Deng, Hui, and Wesley Hutchinson 2010; Petre, Sharp, and Johnson 2006). Many things could happen when products interact. Indeed, the outcome of a product's attributes can differ depending on what it is combined with, as these attributes could be enhanced, emphasized, or suppressed (Smith and Redden 2020). The outcome, in some cases, may even be nonmonotonic, changing over the course of the consumption occasion. For example, when combining salt and tonic water, the mixture first tastes sweet but becomes noticeably saltier over time (Spence 2020b). We thus argue that consumers perceive an individual who seemingly demonstrates an understanding of product interactions via a combinatory recommendation as having expertise in a product category.

Dimensions of Expertise Signaled by Combinatory Recommendations

Experts differ from novices in both the amount and organization of their domain knowledge, and domain knowledge is usually measured to assess expertise (for a discussion, see Mitchell and Dacin [1996]). Two knowledge dimensions contribute to expertise: breadth and depth of knowledge. Breadth refers to the diverse knowledge of various options in a category, whereas depth refers to a focused understanding of each individual option (Clarkson, Janiszewski, and Cinelli 2013; Mannucci and Yong 2018; Yang, Jin, and Sheng 2017). When evaluating someone's expertise, consumers seek cues indicating that the individual has a breadth and/or a depth of knowledge in the category (Sela et al. 2019). We posit that a combinatory recommendation signals expertise to consumers because it is perceived as a demonstration of depth of knowledge.

Prior work focuses on depth of knowledge of individual product attributes (e.g., Clarkson, Janiszewski, and Cinelli 2013) and discusses how the ability to analyze attributes is a manifestation of knowledge depth (Alba and Hutchinson 1987). We build on this work and argue that the ability to analyze interactions of attributes among multiple products is a unique form of knowledge depth. This notion is supported by work in psychology suggesting that some further factor must be included in cognition when one engages in integrative thinking about how elements interact (Kallio 2011). For example, when evaluating the compatibility of paprika and cumin for joint usage, one considers not only the individual spices’ attributes but also how these attributes will interact (e.g., determining whether the combination will result in an overpowering amount of spiciness). Thus, using combinatory recommendations, where one seemingly demonstrates the ability to understand outcomes of product interactions, may reflect depth of knowledge and thereby signal expertise. Formally:

H_1a: Keeping the number of products constant, providing recommendations about the joint (vs. nonjoint) usage of these products signals greater expertise.

H_1b: Keeping the number of products constant, providing recommendations about the joint (vs. nonjoint) usage of these products reflects greater depth (but not breadth) of knowledge.

Instead of recommending multiple products for joint usage, an adviser might make recommendations that do not discuss joint usage (i.e., using products during separate consumption occasions or as substitutes). According to prior literature, which typically dichotomizes products that share some relation as either complements or substitutes (Diehl, Van Herpen, and Lamberton 2015; Huh, Vosgerau, and Morewedge 2016; Karataş and Gürhan-Canli 2020; Shocker, Bayus, and Kim 2004), a natural comparison to combinatory recommendations would be substitute recommendations. By our theoretical account, when evaluating two products intended for use in separate consumption occasions or when two products’ substitutability is assessed, an understanding of product interactions is not needed, thus signaling less expertise than combinatory recommendations signal.

Note that expertise cues are based on lay beliefs of how an expert should behave but may not necessarily reflect real expertise (Packard and Wooten 2013). Therefore, although compatibility can be prescribed by objective organizing principles (e.g., extent of harmony among the products; Spence 2020a), we argue that merely recommending products for joint usage, even if the recommendation is not based on objective principles, may signal expertise to consumers.

Factors That Influence the Signaling Effectiveness of Combinatory Recommendations

Joint usage could manifest through various means, such as a blending, fusion, or pairing of multiple, discrete products (Spence 2020a). Our theoretical account can apply to these various forms of joint usage, which we assess empirically. Nonetheless, the extent to which an adviser signals expertise when providing a combinatory recommendation should depend on the extent to which the recommendation highlights the interaction among product attributes. Cue utilization theory suggests that the extent to which a signal is used by consumers to form an evaluation varies as a function of its ambiguity (Slovic and Lichtenstein 1971). It is therefore reasonable to expect that an explicit combinatory recommendation, in which an adviser speaks about the interaction, will more clearly demonstrate depth of knowledge compared with an implicit combinatory recommendation.

H₂: The same combinatory recommendation leads to greater expertise perceptions if it explicitly addresses product attribute interactions.

Moreover, not all combinatory recommendations imply the same level of interaction between product attributes (Spence 2020a). Think, for instance, of recommending two products for a meal that are to be consumed simultaneously (two food items served on the same plate) versus sequentially (two food items served on different plates, one after the other). Although both could be framed as a combinatory recommendation, the temporal distance between individual consumption acts (eating a food item) within the single consumption occasion (the meal) is longer in the latter case. Similarly, the physical proximity of the products recommended for joint usage (pairing a tie with a shirt vs. pairing a tie with socks) affects the spatial distance between products. We reason that the greater the distance between products or consumption acts, the lower the expected interaction between product attributes. For this reason:

H₃: The effect of a combinatory recommendation on perceptions of expertise is reduced the greater the distance (either temporal or spatial) between individual consumption acts.

The Persuasive Effect of Combinatory Recommendations

Perceived expertise is a peripheral cue known to affect persuasive outcomes (Petty and Cacioppo 1986). Therefore, in line with the literature on expertise (Alba and Hutchinson 1987; Gershoff, Broniarczyk, and West 2001), we predict that when an adviser signals expertise by using a combinatory recommendation, they become more persuasive. As such, that adviser should garner more favorable consumer attitudes toward their recommendations. This includes products mentioned in the combinatory recommendation as well as subsequent, unrelated product recommendations.

H₄: An individual who uses a combinatory recommendation (vs. not) is more persuasive because a combinatory recommendation signals expertise.

This research tests our predictions across nine studies conducted in the lab and field. We generalize our findings across a breadth of product categories, including categories where the products can be experienced (e.g., wall art) and cannot be experienced (e.g., spices) as the recommendation is being made. We also generalize our findings across several types of advisers (e.g., subscription box curator, store associate, interior designer, influencer). Study 1a finds that recommending two products that go well together (i.e., a combinatory recommendation) signals greater expertise than recommending two products that could be used as substitutes or recommending a single product (H_1a). Study 1b digs deeper into the type of expertise signaled and finds that a combinatory recommendation signals greater depth (but not breadth of knowledge, H_1b). Studies 2a and 2b address potential alternative accounts for the signaling value of combinatory recommendations. Study 2a finds that combinatory recommendations that (1) suggest which products go well together or (2) warn of which products do not go well together are equally effective in signaling expertise, thus showing that the effect of combinatory recommendations on perceived expertise does not depend on the utility consumers can extract from the purchase. Study 2b shows that the effect does not depend on whether the recommender is addressing a specific consumer need. Studies 3a and 3b address the moderating role of explicitness of the combinatory recommendation (H₂) and of the distance between recommended consumption acts (H₃).

The next studies explore the downstream consequences of the effect of combinatory recommendations. Study 4 finds that a combinatory recommendation improves consumers’ attitudes toward a product from that recommendation and provides mediation evidence of perceived expertise of the adviser as the underlying driver of this effect (H₄). Study 5 finds that, following an adviser's combinatory recommendations, consumers’ attitudes toward subsequent, unrelated product recommendations also improve. Finally, Study 6 uses an Instagram ad field study to demonstrate the real-world persuasive value of combinatory recommendations.

Study 1a: Combinatory Recommendations Signal Expertise

Study 1a tests our core prediction that an adviser who provides a combinatory recommendation signals expertise. We compare a combinatory recommendation with a substitute recommendation involving the same number of products (i.e., two). We posit that determining compatibility (compared with substitutability) requires a more complex processing of interactions among products, and thus a combinatory recommendation should be a stronger signal of depth of knowledge. Finding that a combinatory recommendation has a stronger effect on signaling expertise would support our theorizing. Note that although we include the same quantity of information in the substitute and combinatory recommendations (see the Web Appendix for a pilot study), these recommendations should be perceived as qualitatively different, as a combinatory recommendation should signal greater expertise.

Method

A total of 300 participants from Amazon Mechanical Turk (MTurk) completed the study for a nominal payment.¹ Participants on MTurk are known to misrepresent their characteristics to meet study eligibility (Chandler and Paolacci 2017). Therefore, MTurk participants were only allowed to complete the study if they successfully passed a U.S. cultural check question and an attention check question. Full details of these questions are reported in the Web Appendix. We also allowed only one response per MTurk ID. If participants attempted the cultural check or the attention check questions multiple times, or entered the study twice for other reasons, only their first answer, even if incomplete, was considered valid. Subsequent responses were excluded from the analysis, resulting in a final sample of 286 responses (M_age = 39 years, 44% female). The same exclusion criteria were used across all our studies conducted online.

Participants were randomly assigned to one of three recommendation conditions (combinatory, substitute, single) using a between-subjects design. Participants imagined signing up for PantryBox, a monthly subscription box company that delivers a selection of spices and other cooking ingredients. The subscription box included a note from Chef Taylor, the curator of the subscription box. The chef's note highlighted some of the products included in the box. The number of products participants were told were included in the box did not differ across conditions, although the number of products highlighted in the note did differ (see the Web Appendix for full descriptions of stimuli for all studies).

Specifically, in the combinatory condition the chef's note described two spices, stating, “You have to try our cardamom. It has an addicting, tangy and herby freshness to it. Its flavor profile pairs beautifully with the sweetness of fenugreek seeds. Together, they make a balanced combination. Using these two complimentary spices together will add great flavors to your dish.”

The substitute condition's note also described two spices. The note read, “You have to try our cardamom. It has an addicting, tangy and herby freshness to it. If you are not looking for this flavor profile, try the sweetness of fenugreek seeds. They are both delicious spices. Using either one of these spices will add great flavors to your dish.”

Finally, in the single condition, the chef only described the flavor profile of one spice and how that spice would add great flavor to a dish. The one spice described in this condition was counterbalanced as either cardamom or fenugreek seeds. We included this condition as a baseline for this first study. Subsequent studies focus exclusively on comparing situations where the number of products recommended is the same, in line with our theorizing.

To measure to what extent Chef Taylor's recommendation signaled category expertise, participants rated the expertise of the chef using a three-item measure (person seems like an expert on food, person seems knowledgeable about food, person knows what they are talking about) on a nine-point scale (1 = “Not at all,” and 9 = “Very much”). The items were averaged into one composite measure of perceived expertise (α = .96). For all studies, all dependent measures taken are reported (see the Web Appendix for full wording of all measures taken).

Results

In the single condition, there was no significant difference in perceived expertise, regardless of whether the chef described cardamom or fenugreek seeds (F < 1); thus, responses were collapsed into one condition.

A one-way analysis of variance (ANOVA) revealed a significant effect of recommendation on perceived expertise (F(2, 283) = 26.62, p < .001, $ω_{p}^{2}$ = .152). Compared with the single condition (M = 5.72), perceptions of expertise were greater in both the combinatory condition (M = 7.37; F(1, 283) = 53.23, p < .001, $ω_{p}^{2}$ = .155) and the substitute condition (M = 6.57, F(1, 283) = 13.47, p < .001, $ω_{p}^{2}$ = .042). Importantly, as predicted, the combinatory recommendation also signaled significantly more expertise than the substitute recommendation (F(1, 283) = 12.45, p < .001, $ω_{p}^{2}$ = .039).

Discussion

Study 1a shows that providing a combinatory recommendation signals greater expertise than recommending a single product, perhaps by demonstrating a greater breadth of knowledge of products in the category. More pertinent to our theorizing, and in support of H_1b, we find that providing a combinatory recommendation signals greater expertise than recommending the same number of products as substitutes. Given that the number of items recommended in these recommendations is the same, the breadth of knowledge displayed is presumably similar. We suggest that the difference in expertise perceptions may come from a combinatory recommendation displaying one's understanding of the outcome of interactions among products and thus demonstrating depth of knowledge. In the next study, we examine breadth and depth of knowledge to better understand how combinatory recommendations affect these components of expertise.

Study 1b: Combinatory Recommendations Affect Depth, but Not Breadth, of Knowledge Perceptions

We compare a combinatory recommendation with a substitute recommendation involving the same number of products and examine the extent to which each cues breadth and depth of knowledge. We expect combinatory and substitute recommendations to reflect similar levels of breadth of knowledge. However, providing a combinatory recommendation suggests that a person can process the outcome of interactions among products, which we suggest is a stronger cue of depth of knowledge.

Method

Participants from MTurk (N = 199) completed the study for a nominal payment. One response from a duplicate ID was excluded, leaving a sample of 198 responses (M_age = 38 years, 57% female).

Participants were randomly assigned to one of two recommendation conditions (combinatory vs. substitute) using a between-subjects design. They imagined that while they were browsing wall art at a furniture store, the store's interior design adviser provided them with an unsolicited recommendation. In both conditions, the adviser discussed two pieces of wall art, one depicting the Isle of Skye and one depicting the Botallack Tin Mine. Both pieces of art were shown to the participant.

In the combinatory recommendation condition, the adviser noted how the Botallack Tin Mine art would be a good complement to the Isle of Skye art. In contrast, in the substitute condition, the adviser noted how the Botallack Tin Mine art would be a good alternative to the Isle of Skye art. After reading the recommendation, participants rated the depth of knowledge possessed by the interior designer using a three-item measure (person understands a lot about these pieces of wall art, person seems to know many details about these pieces of wall art, person has a deep understanding of these pieces of wall art) on a seven-point scale (1 = “Strongly disagree,” and 7 = “Strongly agree”). Participants then rated the breadth of knowledge possessed by the interior designer using a three-item measure (person is familiar with many wall art options, person seems to know at least a little bit about lots of different types of wall art, person has some knowledge about a great number of wall art pieces) on a seven-point scale (1 = “Strongly disagree,” and 7 = “Strongly agree”). We validated these depth and breadth items as separate factors in a pilot study reported in the Web Appendix.

Results

The Fornell and Larcker (1981) criterion for discriminant validity requires the average variance extracted (AVE) of both constructs to be greater than the squared correlation between the two constructs. In this case, the depth AVE is .86, the breadth AVE is .88, and the squared correlation between the two variables is .38, meeting this criterion and suggesting discriminant validity between breadth and depth of knowledge. Thus, the three depth-related items were averaged into one composite measure of depth of knowledge (α = .94) and the three breadth-related items were averaged into one composite measure of breadth of knowledge (α = .95).

Supporting H_1b, a one-way ANOVA revealed that perceived depth of knowledge was greater in the combinatory recommendation condition (M = 5.19), compared with the substitute recommendation condition (M = 4.71, F(1, 196) = 5.52, p = .020, $ω_{p}^{2}$ = .022). Importantly, breadth of knowledge did not differ between the combinatory (M = 4.43) and substitute conditions (M = 4.58; F < 1).

Discussion

Study 1b demonstrates that the depth, but not breadth, component of expertise is heightened when comparing a combinatory recommendation with a substitute recommendation.

Yet, there may be some alternative accounts for why a combinatory recommendation signals greater expertise. For one, the bundling literature suggests that complementary items have superadditive utilities, whereas substitute items do not. That is, the utility that consumers extract from a bundle with complementary items is greater than the sum of utilities provided by each item in isolation (Guiltinan 1987; Karataş and Gürhan-Canli 2020). Thus, it is possible that when one provides a combinatory recommendation, one may be demonstrating an ability to create superadditive utility of a purchase. One's ability to create superadditive utility, in turn, may signal expertise.

Another alternative explanation for the results of Study 1a may be that, while it was not explicitly mentioned, respondents might have believed their goal when getting a subscription box was to purchase multiple products for joint usage, and a combinatory recommendation helps achieve that goal more so than a substitute recommendation. If so, respondents in that study may have perceived the adviser as being more helpful, which, in turn, drove perceptions of expertise. Although it is possible that, in some situations, these elements would contribute to combinatory recommendations’ ability to signal expertise, our next two studies show that our findings from Study 1a hold in situations where combinatory recommendations do not provide superadditive utility and are not helpful to the consumer.

Study 2a: Both Positive and Negative Combinatory Recommendations Signal Expertise

In the previous studies, we focused on a positive combinatory recommendation, where the products recommended were described as compatible. Study 2a tests whether a positive combinatory recommendation differs in signaling expertise compared with a negative combinatory recommendation, where products are described as incompatible. Both types of combinatory recommendations could demonstrate depth of knowledge (i.e., one is able to determine whether or not the interactions among the products would lead to positive consumption outcomes). Thus, we predict that both types of combinatory recommendations can signal greater expertise compared with a substitute recommendation. If, however, expertise is signaled because of one's ability to create superadditive utility, one should signal expertise with a positive combinatory recommendation but not with a negative combinatory recommendation.

Method

Undergraduate students from two universities (N = 337, M_age = 20 years, 47% female) completed an online study within a one-week period. Sample size was determined by the number of students who signed up to take the study for course credit. There were no significant differences across the two universities; therefore, results were collapsed across universities.

Participants were randomly assigned to one of three recommendation conditions (positive combinatory, negative combinatory, substitute) using a between-subjects design. As in Study 1a, participants read Chef Taylor's note describing two spices. Across conditions, the note's description of the individual characteristics of cardamom and fenugreek seeds was held constant. However, in the positive combinatory condition, the note also described how the characteristics of the two spices paired beautifully and recommended using the spices together in a dish. In the negative combinatory condition, the note instead described how the characteristics of the two spices did not pair well together and recommended not using these spices together in a dish. In the substitute condition, the note described how the characteristics of the two spices were equally delicious and recommended using either one in a dish. Participants completed the same perceived expertise measure used in Study 1a (α = .94).

Results

A one-way ANOVA revealed a significant effect of recommendation on perceived expertise (F(2, 334) = 5.01, p = .007, $ω_{p}^{2}$ = .023). A substitute recommendation (M = 6.25) signaled significantly less expertise than both a positive combinatory (M = 6.77; F(1, 334) = 7.05, p = .008, $ω_{p}^{2}$ = .018) and negative combinatory (M = 6.81; F(1, 334) = 8.02, p = .005, $ω_{p}^{2}$ = .020) recommendation. Importantly, positive and negative combinatory recommendations signaled similar levels of expertise (F < 1).

Discussion

Study 2a's findings suggest that the display of one's ability to process both what is and what is not compatible can equally signal expertise more so than a substitute recommendation. Thus, even when one's combinatory recommendation does not demonstrate an ability to create superadditive utility, as was the case with the negative combinatory recommendation, one can still use the recommendation to signal expertise.

For the remaining studies, we focus on positive combinatory recommendations, as marketers may be inclined to discuss the positive aspects of their products and encourage, rather than dissuade, consumers from purchasing multiple products.

Study 2b: Combinatory Recommendations Signal Greater Expertise Even When They Are Less Helpful Than Substitute Recommendations

Study 2b tests whether expertise is contingent on the helpfulness of the recommendation. When providing a combinatory recommendation, we expect that an adviser should be able to demonstrate their depth of knowledge of the products in the recommendation, even if the information provided is not particularly helpful to the consumer. Thus, we predict that a combinatory recommendation can signal greater expertise compared with a substitute recommendation independent of how helpful the recommendation is to the consumer.

Method

Undergraduate students (N = 262; M_age = 20 years, 47% female) completed a lab study within a two-week period. Sample size was determined by the number of students who signed up to take the study for course credit. Participants were randomly assigned to one of two recommendation conditions (combinatory vs. substitute) using a between-subjects design.

All participants were asked to imagine having some empty wall space in their bedroom that they wanted to fill with a piece of art. This empty wall space could accommodate, at most, one 18″ × 24″ piece of art. Participants then imagined shopping at a store to find a piece of 18″ × 24″ art. While they were shopping, the store's interior design adviser provided them with a recommendation. Similar to Study 1b, in both conditions, the adviser discussed two pieces of wall art, one depicting the Isle of Skye and one depicting the Botallack Tin Mine. Both pieces of art measured 18″ × 24″. Thus, the participant would only be able to fit one of these pieces in their bedroom. In the combinatory recommendation condition, the adviser noted how the two pieces should be hung together. In contrast, in the substitute condition, the adviser noted that the participant could hang either of these pieces on their empty wall.

Participants then rated their perceived expertise of the interior designer using the three-item scale used in our previous studies (α = .94). In addition, they rated how helpful the interior designer was in finding art to fill the participant's empty wall space (1 = “Not at all,” and 9 = “Very”).

Results and Discussion

Consistent with our previous studies, the interior designer signaled significantly more expertise when providing a combinatory recommendation (M = 6.69) than when providing a substitute recommendation (M = 5.59; F(1, 260) = 30.17, p < .001, $ω_{p}^{2}$ = .100). This was in spite of the interior designer being perceived as significantly less helpful in the combinatory recommendation condition (M = 6.31) than in the substitute condition (M = 7.32; F(1, 260) = 21.77, p < .001, $ω_{p}^{2}$ = .073). Thus, even in situations where an adviser is less helpful in achieving the consumer's goal, the use of combinatory recommendations can signal greater expertise.

So far, we focused on comparing combinatory recommendations with substitute recommendations. However, to further test our theorizing and demonstrate boundary conditions of combinatory recommendations, the next two studies examine other comparison conditions beyond substitutes. First, to test H₂, we focus specifically on combinatory recommendations and vary how the recommendation is formulated.

Study 3a: Explicit Combinatory Recommendations Signal Greater Expertise Than Implicit Combinatory Recommendations

Study 3a examines what happens when an adviser makes (or does not make) their knowledge about interactions explicitly known. In other words, the combination of products recommended remains the same, but the emphasis put on the product interactions varies. We expect that emphasizing product interactions in a combinatory recommendation will make the recommendation a more diagnostic signal. In other words, explicitly demonstrating an understanding of relationships among multiple products will signal greater expertise.

Method

Participants from MTurk (N = 301) completed the study for a nominal payment. Nine responses from duplicate IDs were excluded, leaving a sample of 292 responses (M_age = 41 years, 52% female). Participants were randomly assigned to one of two combinatory recommendation conditions (explicit vs. implicit) using a between-subjects design.

Participants imagined dining at a restaurant, where their waiter offered a three-course meal recommendation. In the implicit condition, the waiter emphasized each dish's individual features, recommending the spring salad, followed by the king salmon, and finally a chocolate and strawberry crepe dessert. In the explicit condition, the waiter made the same recommendation but also explicitly mentioned that the three dishes go well together, putting emphasis on the interactions across the dishes. Thus, although both conditions offered the same combinatory recommendation, resulting in the same meal experience, the waiter in the explicit condition exhibited more knowledge about the relationships among the dishes. Participants then rated the waiter's expertise using the three-item scale used in our previous studies (α = .94).

Results

A one-way ANOVA revealed a significant effect of the combinatory recommendation on perceived expertise, where perceptions of expertise were greater in the explicit condition (M = 6.90) than in the implicit condition (M = 5.90; F(1, 290) = 34.56, p < .001, $ω_{p}^{2}$ = .103).

Discussion

In support of H₂, we find that even in situations where multiple products are expected to be consumed together, explicitly acknowledging the compatibility of these products can be more effective in signaling expertise. In the next study, we test H₃ and establish a boundary condition for when a combinatory recommendation signals expertise.

Study 3b: Greater Temporal Distance Between Consumption Events Reduces Perceptions of Expertise

Although products in a combinatory recommendation are expected to be jointly used during a single consumption occasion, what qualifies as a consumption occasion might vary across different situations. We expect this to affect the nature of the interaction between product attributes and, ultimately, the effectiveness of a combinatory recommendation. For instance, the time between when each individual product is consumed (i.e., the individual consumption act) within the consumption occasion can vary. On one extreme, the products recommended could be consumed with the individual acts spread out in time. On the other, the products could be consumed jointly or in close succession. Study 3b tests whether the temporal distance between the individual consumption acts of the products included in a combinatory recommendation affects perceptions of expertise. We reason that the shorter the distance between consumption acts, the more consumers expect interactions between product attributes. Thus, we predict that the shorter the temporal intervals suggested in a combinatory recommendation, the more an adviser signals expertise.

Method

Participants from MTurk (N = 301) completed the study for a nominal payment. Four responses from duplicate IDs were excluded, leaving a sample of 297 responses (M_age = 41 years, 52% female). Participants were randomly assigned to one of three recommendation conditions (close joint usage, distant joint usage, substitute) using a between-subjects design.

Participants imagined striking up a conversation with a DJ who offered a recommendation about what music to play to fill downtime at an event. In both joint usage conditions, the DJ recommended three contemporary songs to play at an event because they have rhythms that complement one another. However, in the close joint consumption condition, the DJ stated that the transitions between the songs (i.e., the end of one song and the beginning of the next) could be blended. Thus, the three songs would be consumed immediately after one another as a combined entity. In contrast, in the distant joint usage condition, the DJ stated that each song could be played at different points of downtime at the event. Therefore, each song would be consumed separately, but within the same overall consumption occasion (i.e., the event). In the substitute condition, the DJ recommended playing any one of the three contemporary songs during downtime because the rhythms of the songs were good alternatives.

Participants then rated their perceived expertise of the DJ using the three-item scale used in our previous studies (α = .96). They also rated the extent to which they expected that the songs in the recommendation would interact with one another (1 = “Not at all,” and 9 = “Very much”).

Results

The songs in the close joint usage condition (M = 7.81) were perceived as interacting significantly more than those in the distant joint usage condition (M = 7.01; F(1, 294) = 10.05, p = .002, $ω_{p}^{2}$ = .030) and substitute condition (M = 6.29; F(1, 294) = 36.88, p < .001, $ω_{p}^{2}$ = .108), confirming the effectiveness of our manipulation.

A one-way ANOVA revealed a significant effect of recommendation on perceived expertise (F(2, 294) = 5.40, p = .005, $ω_{p}^{2}$ = .029). The close joint usage recommendation (M = 7.45) signaled significantly more expertise than a distant joint usage recommendation (M = 6.76; F(1, 294) = 10.70, p = .001, $ω_{p}^{2}$ = .032). Thus, recommending shorter temporal intervals between the songs signaled greater expertise.

Consistent with our previous findings, the close joint usage recommendation also signaled marginally more expertise than the substitute recommendation (M = 7.06; F(1, 294) = 3.58, p = .059, $ω_{p}^{2}$ = .009). Interestingly, there was no significant difference between the distant joint usage and substitute recommendation conditions (F = 1.94, p = .165). It thus appears that recommending the joint consumption of products where the consumption acts are significantly spaced apart might function as a boundary of combinatory recommendations’ advantage in signaling expertise. This may be because there is more ambiguity about the advisers’ understanding of interactions or because the depth of knowledge required to understand product interactions in distant joint usage occasions is modest.

Discussion

In support of H₃, we find that temporal distance between consumption acts in a combinatory recommendation affects consumers’ perceived expertise of the adviser. We conceptually replicate our findings by manipulating spatial distance (see Appendix for full study write-up). An adviser's combinatory recommendation that pairs products consumed close in spatial distance (i.e., pairing a pindot tie with an oxford shirt) signals greater expertise than one that pairs products farther in spatial distance (i.e., pairing a pindot tie with a pair of oxford socks; F(1, 195) = 6.97, p = .009, $ω_{p}^{2}$ = .029).

Notably, in this study we also introduce a boundary condition for combinatory recommendations, where larger temporal intervals between consumption acts may attenuate a combinatory recommendation's ability to signal expertise above and beyond a substitute recommendation.

The next studies investigate the practical importance of a combinatory recommendation by examining its impact on consumers’ product attitudes and behavior.

Study 4: Combinatory Recommendations Improve Product Attitudes via Expertise Signals

Study 4 explores several important questions. First, we test whether a combinatory recommendation affects consumers’ attitudes toward products included in the recommendation. Second, we examine whether the effect of combinatory recommendations on product attitudes is driven by perceptions of expertise or other alternative explanations, namely perceptions that the recommender innately has good taste (rather than knowledge-based expertise) or that making a combinatory recommendation highlights the versatility of the products included in the recommendation. Finally, this study examines whether one's own expertise plays a role in the effect of combinatory recommendations on product attitudes. Although consumers may often perceive expertise from an individual's combinatory recommendation, this expertise signal may be weakened if consumers themselves have knowledge of product interactions. For these consumers, an individual's combinatory recommendation could be considered less diagnostic of their unique expertise.

Method

Participants from MTurk (N = 200) completed the study for a nominal payment. Eight responses from duplicate IDs were excluded, leaving a sample of 192 responses (M_age = 41 years, 60% female).

Participants were randomly assigned to one of two recommendation conditions (combinatory vs. substitute) using a between-subjects design. We used the same stimuli from Study 1b but collected different measures. Participants rated their attitudes toward the Isle of Skye art using three items (how positive are your impressions of, how much do you like, and how likely are you to try out the Isle of Skye art) on a nine-point scale (1 = “Not at all,” and 9 = “Very much”). The three items were averaged into one composite measure of product attitude (α = .90). Participants also rated their perceived expertise of the interior designer using the three-item scale used in our previous studies (α = .97). The perceived expertise AVE is .86, the product attitude AVE is .79, and the squared correlation between the two variables is .33, meeting the Fornell and Larcker (1981) criterion for discriminant validity.

Participants then responded to measures of potential alternative drivers of product attitudes. They rated to what extent the interior design adviser has good taste when it comes to art on a seven-point scale (1 = “Not at all,” and 7 = “Very much”) and to what extent they perceived the art to be versatile on a seven-point scale (1 = “Not at all,” and 7 = “Very much”). Finally, participants rated to what extent they would describe themselves as an interior design expert (1 = “Not at all,” and 7 = “Very much”).

Results

The Isle of Skye wall art was rated more positively in the combinatory recommendation condition (M = 7.35) than in the substitute recommendation condition (M = 6.80; F(1, 190) = 7.86, p = .006, $ω_{p}^{2}$ = .034). The interior designer in the combinatory recommendation condition was perceived as having greater expertise (M = 7.75) than in the substitute condition (M = 6.55; F(1, 190) = 30.69, p < .001, $ω_{p}^{2}$ = .134). There was no difference between conditions for taste (F(1, 190) = 1.09, p = .297) and versatility (F < 1).

Importantly, a mediation analysis (10,000 bootstraps; PROCESS Model 4; Hayes 2017) was conducted, testing expertise, taste, and versatility as potential drivers of product attitudes. The analysis revealed that the effect of the combinatory recommendation on wall art attitude was driven by perceived expertise of the adviser (indirect effect: b = .23, SE = .09, 95% confidence interval [CI]: [.07, .41])² but not taste (indirect effect: b = .11, SE = .11, 95% CI: [−.09, .34]) or versatility (indirect effect: b = .00, SE = .02, 95% CI: [−.04, .04]). Thus, using a combinatory recommendation increased perceptions of expertise of the adviser, which in turn, increased attitudes toward the wall art included in the combinatory recommendation.

We also analyzed whether the effect of combinatory recommendations on perceived expertise of the adviser depended on one's own self-perceived expertise in interior design (10,000 bootstraps; PROCESS Model 7; Hayes 2017). The interaction of recommendation and self-perceived expertise on perceived expertise of the adviser was marginally significant (F(1, 188) = 3.24, p = .073). Furthermore, the Johnson–Neyman technique (Spiller et al. 2013) revealed that a combinatory recommendation significantly increased perceptions of the adviser's expertise for values of self-perceived expertise less than 4.32 (b_JN = .70, SE = .35, t = 1.97, p = .05), which represents 90% of our sample. That is, the combinatory recommendation had a stronger effect on perceptions of the adviser's expertise among those who had less expertise in interior design. The index of moderated mediation was not significant at the 95% confidence level (index = −.143, 95% CI: [−.32, .02]) but was significant at the 90% confidence level (90% CI: [−.29, −.01]).

Discussion

In Study 4, we conceptually replicate our finding that a combinatory recommendation signals expertise. Furthermore, we find that this signal of expertise, in turn, can increase consumers’ attitudes toward the products included in the combinatory recommendation, supporting H₄. At the same time, we address alternative explanations of taste and product versatility. It appears that combinatory recommendations do not affect perceptions that the recommender innately has good taste nor change how versatile the products included in the recommendation are expected to be.

Although these results do not reach full statistical significance, we also find that the effect of combinatory recommendations on expertise is somewhat weaker among those who perceive themselves to have expertise in that category. It may be that those who have prior knowledge in a category are less likely to consider external cues when making judgments (Miller and Curry 2013).

In the next study, we extend the findings of Study 4 to another important type of adviser, social media influencers, and show that the persuasive effects of combinatory recommendations extend beyond consumers’ attitudes toward the products included in the recommendation.

Study 5: Expertise Signals from Combinatory Recommendations Improve Subsequent Product Recommendations

Although we rule out the role of versatility in Study 4, in Study 5 we tackle the broader question of whether the effect of combinatory recommendations on product attitudes depends on consumers making different inferences about the qualities of the items recommended. If consumers’ product attitudes improve because combinatory recommendations affect perceptions of a product's qualities, a combinatory recommendation should not improve consumers’ attitudes toward products not included in the recommendation. However, if expertise signals are indeed a driver of improved product attitudes, consumers’ attitudes toward subsequent recommendations not included in the original combinatory recommendation should improve. That is, once a person has established category expertise via a combinatory recommendation, their subsequent product recommendations in that category, even those not included in a combinatory recommendation, should be viewed more favorably. This study tests this prediction.

Method

We recruited Instagram users from MTurk who completed the study for a nominal payment. Following a preregistered procedure (https://aspredicted.org/7XX_NGZ), we collected responses from unique IDs until we reached our desired sample of 500 complete responses (M_age = 36 years, 56% female). Participants were randomly assigned to one of two recommendation conditions (combinatory vs. substitute).

In both conditions, participants read three Instagram posts from a food influencer. Each post recommended two food-related items. In the combinatory condition, the influencer recommended that these two items be paired together (e.g., one post recommended pairing apples and kiwifruit for a healthy snack; see the Web Appendix for all posts). In the substitute condition, the influencer recommended that these items could be good alternatives (e.g., one post recommended eating apples or kiwifruit for a healthy snack).

Participants rated the influencer's food expertise using the same three item-measure from Study 1a (α = .96). Next, participants across conditions viewed the same fourth post from the influencer, where the influencer recommended a type of cookie with no recommendation of a compatible or substitute item. Thus, there was no cue of additional qualities of this cookie in either condition. Participants then rated their attitude toward this brand of cookie using the same product attitude measure used in Study 4 (α = .90). The perceived expertise AVE is .83, the product attitude AVE is .75, and the squared correlation between the two variables is .38, suggesting discriminant validity.

Results

The influencer signaled greater expertise when using a combinatory recommendation (M = 6.93) than when using a substitute recommendation (M = 5.44; F(1, 498) = 100.15, p < .001, $ω_{p}^{2}$ = .17).

Product attitude toward the cookie was also rated more positively after the influencer used the combinatory recommendation (M = 6.57), compared with the substitute recommendation (M = 5.95; F(1, 498) = 18.87, p < .001, $ω_{p}^{2}$ = .035). Importantly, a mediation analysis (10,000 bootstraps; PROCESS Model 4) revealed that the effect of recommendation on product attitude was driven by perceived expertise of the influencer (indirect effect: b = .86, SE = .10, 95% CI: [.67, 1.06]).³

Discussion

This study provides additional evidence that a combinatory recommendation can signal expertise and that this expertise signal can subsequently improve consumers’ attitudes, not just toward the products included in the combinatory recommendation, but also toward products included in a subsequent recommendation. Next, we use a field study measuring real behavior to demonstrate how combinatory recommendations can benefit firms.

Study 6: Combinatory Recommendations Influence Real Behavior

In Study 6, we partnered with a skin-care company that hosts an online skin-care advice platform. The company specifically asks skin-care advice givers to post recommendations of multiple skin-care products that work well together in a skin-care routine (i.e., provide combinatory recommendations). The company believes it is uniquely positioned in the marketplace with its use of combinatory recommendations since most other beauty-related review platforms only provide recommendations for individual skin-care products. Using an Instagram A/B test, Study 6 examines whether consumers will find skin-care advertising featuring combinatory recommendations from advice givers more appealing than similar advertising where advice givers recommend the same products without combinatory recommendations. From the findings in our previous studies, we reason that skin-care advice givers who use combinatory recommendations signal greater expertise. Thus, we predict that consumers will be more likely to visit the skin-care advice platform knowing that the advice givers on that platform have expertise.

Method

Facebook Ad Manager (which places advertisements on Instagram, Facebook, Facebook Messenger, and other platforms) has an A/B split test feature, which enables marketers to compare the effectiveness of different advertisements. We used Facebook Ad Manager to run ads on Instagram only.

We created advertisements with the following settings: split test on creative; four-day test;⁴ age 18 years and up; women; location: United States; maximum daily budget: $66;⁵ interests: cosmetics, beauty, skin care, self-care; optimization: link clicks; bid strategy: lowest cost; placement: Instagram; charged: cost per click. We chose the carousel format, in which the ad consists of multiple images a consumer can swipe through, because Facebook recommended this format to increase click-through rates (Facebook for Business 2015). These settings hold all elements constant except for the messaging in the advertisement.

Each ad version (combinatory recommendation: present vs. absent) consisted of three carousel cards (i.e., three images). In the ad with the combinatory recommendation present, each card quoted and depicted one skin-care advice giver's recommendation for which products go well together in a skin-care routine. In the ad without the combinatory recommendation, each card quoted and depicted the same skin-care advice giver's product recommendations, but the quote did not mention whether the products go well together in a skin-care routine (see the Web Appendix for ads used).

Furthermore, a caption appeared below the carousel card image. In the ad with the combinatory recommendation present, the caption emphasized how the advice givers on the skin-care platform could help consumers find the best combination of products for their skin. In the ad without the combinatory recommendation, the caption emphasized how the advice givers on the platform could help consumers find the best types of products for their skin.

In both conditions, when an Instagram user clicked on the ad, they were directed to the same front page of the skin-care platform website. Following Paharia (2020), for our statistical analyses, we used the number of impressions for each ad as the N. We examined three distinct dependent variables: click-through rate (CTR), sign-up rate, and cost per click. These variables are described subsequently.

Click-through rate

We measured the number of clicks on each ad and used the CTR (number of ad clicks divided by number of impressions) as the key DV.

Sign-up rate

In each ad, URL parameters were added to allow us to capture how many Instagram users, among those who clicked the ad, ended up signing up for the skin-care advice platform. We used sign-up rate (number of sign-ups divided by number of ad clicks) as another measure of analysis.

Cost per click

Finally, we examined the average cost of an ad click (number of ad clicks divided by total dollar amount spent). A lower cost per click implies a more effective ad campaign.

Results

Click-through rate

A logistic regression predicting CTR revealed that the ad with the combinatory recommendation present led to significantly more clicks (1.52%, n = 43,331) than the ad without the combinatory recommendation (1.26%, n = 36,966; b = −.19, SE = .06, Wald χ²(1) = 9.92, p = .002).

The same statistical conclusions apply for CTR when we used reach, instead of impressions, as our N (see the Web Appendix for supplementary analysis).

Sign-up rate

Among those who clicked on the ad to visit the skin-care website, the sign-up rate was greater among those who came from the ad with the combinatory recommendation (7.12%; 47 sign-ups, n = 660) than among those who came from the ad without the combinatory recommendation (4.51%; 21 sign-ups, n = 466; b = −.49, SE = .27, Wald χ²(1) = 3.24, p = .072). Although this difference is marginally significant, the effect of the combinatory recommendation on the sign-up rate is still notable, as this effect may have been weakened from users in both conditions landing on the same home page featuring combinatory recommendations.

Cost per click

Finally, in terms of cost per click, the ad with the combinatory recommendation (CPC = $.19) outperformed the ad without the combinatory recommendation (CPC = $.28).

Discussion

This Instagram field experiment demonstrates the external validity of our findings, measuring real consumer behavior. We note that given Facebook's proprietary algorithms, the ad optimization strategy remains a black box. Therefore, if a particular advertisement performs better on an A/B test, it is unclear whether the advertisement performed better overall or whether it optimized better (see Hardisty and Weber [2020] for an in-depth discussion). However, our experimental methods from Studies 4 and 5, coupled with this field study, suggest that combinatory recommendations can be effective signals of expertise, which subsequently lead to important downstream consequences, including improved product attitudes and website traffic.

General Discussion

In recent years, retailers have made significant investments in hiring individuals (e.g., stylists, designers, chefs, sponsored social media influencers) to advise consumers on what to purchase across a number of product domains, from food to clothing to interior design. These consumption advisers want to establish themselves as trusted experts in their field, particularly as the number of advisers available to consumers proliferates. This research identifies combinatory recommendations as a novel cue advisers can use to signal category expertise to consumers. Across nine studies, we find that using combinatory recommendations has a positive effect on expertise perceptions. When advisers make a combinatory recommendation, they display their understanding of how product attributes interact and the outcome of such interactions. Thus, keeping the number of items recommended constant, combinatory recommendations can increase perceived depth (but not breadth) of knowledge in a product category.

Signals of advisers’ expertise matter, as these signals improve consumers’ attitudes toward the products recommended by these advisers. This effect includes products in the combinatory recommendation and extends to other, subsequently recommended, products that were not part of a combinatory recommendation.

Although we find that the effects identified generally hold across different types of combinations and interactions (e.g., blended combinations, such as drinks; temporal combinations, such as skin-care routines), the extent to which a combinatory recommendation can be an effective signal of expertise can depend on a variety of factors. Our theorizing suggests that combinatory recommendations work as expertise signals because they highlight one's ability to understand attribute interactions. Thus, both the understanding of the interaction displayed in the recommendation (implicit versus explicit) and nature of the combination recommended (the extent to which products are expected to interact) can affect the effectiveness of combinatory recommendations as signals. We also find some evidence that one's own knowledge in a product category may weaken the adviser's expertise signal. To further explore this possibility, we ran an additional study testing whether recommendation conventionality affects perceived expertise. We find that at high and low levels of conventionality, consumers feel more knowledgeable about how products will interact with one another. Thus, consumers perceive less expertise for these kinds of recommendations. For example, recommending a combination of lemonade with iced tea (also known as an Arnold Palmer), which consumers already know interact well with each other, will not be as strong of a signal of expertise. At the same time, recommending a mixture of lemonade with milk, which consumers anticipate will not interact well with each other, will also not signal expertise. However, at moderate levels of conventionality, consumers feel less certain about the interaction outcome and thus are more likely to see combinatory recommendations as signals of expertise (see the Web Appendix for full study write-up).

Our work makes important contributions to several streams of literature. For one, we add to the literature on signaling by identifying a cue that signals expertise and renders more persuasive recommendations. Importantly, the cue we identify may be more readily actionable than some of the cues identified in prior work. For instance, projecting a confident attitude might be natural to some but might be very hard for others.

Our work also extends the literature on persuasion in two important ways. First, we identify a novel persuasive tactic and explore when and why this tactic is effective. Second, Hardesty, Bearden, and Carlson (2007) find that consumers’ persuasion knowledge may be activated when marketers bundle multiple items together to increase revenue over what would have been obtained had the products been priced separately. Thus, one could reason that consumers perceive an adviser's combinatory recommendation as an upselling tactic, ultimately undermining the persuasiveness of the adviser. Interestingly, we find that this is not the case, as a combinatory recommendation generally improves, rather than undermines, an adviser's persuasiveness.

Importantly, we also contribute to the bundling literature and the nascent literature on curation. Prior work predominantly focuses on evaluating the products included in the bundle, but not the perceptions of the curator of bundle. We offer a new contributory explanation for the well-established finding that bundling complementary products improves consumers’ product attitudes. That is, we show that these improved attitudes may be driven by the bundle curator being perceived as an expert. Further, whereas most work on product complementarity examines the pairing of true complements, like chips and salsa (Huh, Vosgerau, and Morewedge 2016; Karataş and Gürhan-Canli 2020; Rahinel and Redden 2013), we examine products that could be perceived as either complements or substitutes on the basis of the recommendation type. We show that simply framing products as complements can also yield improved product attitudes.

Managerial Implications

Our work also informs both retailers and individual advisers of the effectiveness of using combinatory recommendations to establish one's expertise. Retailers should consider training advisers, be they sponsored influencers or salespeople, to use combinatory recommendations to improve perceptions of expertise and, ultimately, attitudes toward recommended products. At the same time, retailers should emphasize these advisers’ ability to create combinations through their marketing communications. For instance, Nordstrom's personal styling service, Trunk Club, simply lists “Advice putting outfits together” as one of six services offered by its stylists. Nordstrom could consider prioritizing this aspect of the service in its marketing communications as a way of signaling the expertise of its stylists. Similarly, individuals, such as social media influencers trying to break through the clutter, may consider using combinatory recommendations to position themselves as category experts.

These managerial recommendations come with an important caveat. We have learned that not all combinatory recommendations function in the same way, and that both the language used in making the recommendation and the expected interactions between products recommended matter, highlighting that combinatory recommendations actually need to display depth of knowledge in order to be effective signals of expertise.

Our research also bears potentially important managerial insights beyond the type of consumption advice studied in this work. For instance, retailers could consider using direct mail and personalized communication to provide combinatory recommendations. In this vein, fashion retailer Mango sends follow-up emails that contain suggestions of clothing pieces consumers could wear their recent purchase with. Similarly, review platforms could prompt combinatory recommendations from consumers. A clothing retailer could ask consumers to fill out the question “What did you wear this with?” when submitting a review. Online clothing retailers could also encourage consumers to post outfit photos, a practice already implemented by retailers like Rent the Runway and Old Navy. That way, other consumers can get a better idea of which clothing items are compatible with each product. Our findings from Study 6 suggest that these types of recommendations should generate consumer interest and differentiate the online platform.

Future Research

Future research could examine different compositions of combinatory recommendations. In our studies, we intentionally held the number of products in our combinatory and substitute recommendations constant to control for category breadth. However, future research could compare combinatory recommendations involving varying numbers of products. According to our theorizing, a person who can recommend many compatible products may exhibit a deeper ability to process interactions among products. Would this signal greater expertise and lead to other positive outcomes, including greater engagement with the adviser? We find initial evidence to support this prediction from Smart Closet, an online platform where users post clothing pieces that one could wear together. An analysis of the nonduplicate posts displayed on the first page (N = 399) reveals a significant correlation between the number of clothing pieces in the post and the number of likes the post received (r = .14, p = .004). Future research could also explore whether there are limits to the number of compatible items included in a recommendation a consumer finds believable.

Although we examined compatibility among multiple products, another direction for future research might explore combinatory recommendations that offer opinions about compatibility among a product and the traits of the consumer. For example, does recommending whether a skin-care product goes well with oily skin or whether a dress complements a pear-shaped body signal expertise? These types of combinatory recommendations may require a more complex processing of relationships between the nonalignable attributes of the product and the consumer. Thus, one could reason that these types of combinatory recommendations might signal even greater expertise than the combinatory recommendations explored in this research.

One might be concerned that recommending the joint usage of two or more products might be perceived as an upselling technique, triggering consumers’ persuasion knowledge and skepticism (Friestad and Wright 1994) and leading to lower purchase intentions. Interestingly, we find no evidence of these potential negative consequences in our work. It is nonetheless possible that, under certain situations, a combinatory recommendation might indeed trigger a negative reaction in consumers. If and when this could happen may be another potentially interesting question for future research.

Finally, there is growing interest in how consumers interact with artificial intelligence (AI) and the extent to which perceptions of people extend to perceptions of AI agents. Future research might thus explore whether AI recommendation agents can signal expertise using the same tactics as human advisers. These future directions for research will help uncover additional conditions in which combinatory recommendations are most likely to signal expertise.

Supplemental Material

sj-pdf-1-mrj-10.1177_00222437221111344 - Supplemental material for You Should Try These Together: Combinatory Recommendations Signal Expertise and Improve Product Attitudes

Supplemental material, sj-pdf-1-mrj-10.1177_00222437221111344 for You Should Try These Together: Combinatory Recommendations Signal Expertise and Improve Product Attitudes by Jennifer K. D’Angelo and Francesca Valsesia in Journal of Marketing Research

Footnotes

Appendix: Greater Spatial Distance Between Products in a Combinatory Recommendation Reduces Perceptions of Expertise

This study tests whether spatial distance between the products in a combinatory recommendation affects perceptions of expertise.

Associate Editor

James Bettman

Author Note

The authors contributed equally and are listed in alphabetical order.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Online supplement:

Notes

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