Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales

JBR-09319; No of Pages 8 Journal of Business Research xxx (2017) xxx–xxx

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Journal of Business Research

Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales☆ Brian Wansink a,⁎, Dilip Soman b, Kenneth C. Herbst c a b c

Charles H. Dyson School of Applied Economics and Management, Cornell University, 475 Warren Hall, Ithaca, NY, United States Rotman School of Business, University of Toronto, 105 S. George Street, Toronto, Ontario M5S 3E6, Canada School of Business, Wake Forest University, 214 Farrell Hall, Building 60, Winston-Salem, NC 27109, United States

a r t i c l e

i n f o

Article history: Received 1 September 2015 Received in revised form 1 December 2015 Accepted 1 June 2016 Available online xxxx Keywords: Fruits and vegetables Half-cart Healthy shopping Produce Grocery retailers Shopping carts Social norms Partitioning Part-carts Sectioned shopping trolley

a b s t r a c t Before food portions are determined at home, they are determined at the supermarket. Building on the notion of implied social norms, this research proposes that allocating or partitioning a section of a shopping cart for fruits and vegetables (produce) may increase their sales. First, a concept test for on-line shopping (Study 1) shows that a large produce partition led people to believe that purchasing larger amounts of produce was normal. Next, an in-store study in a supermarket (Study 2) shows that the amount of produce a shopper purchased was in proportion to the size of this partition – the larger the partition, the larger the purchases (especially in a nutritionreinforced environment). Using partitioned or divided shopping carts (such as half-carts) could be useful to retailers who want to sell more high-margin produce, but they could also be useful to consumers who can simply divide their own shopping cart in half with their jacket, purse, or briefcase. Divided shopping carts may lead to healthier shoppers and to healthier profits. © 2017 Elsevier Inc. All rights reserved.

1. Introduction When considering portion size, the best and worst habits begin in the supermarket. Doctors, dieticians, and the Department of Agriculture endorse the adage “Healthy eating begins at home” (Koh, 2011). Yet what – and how much – is eaten at home is determined by the food that consumers put in their shopping cart at the supermarket. Before healthy eating can occur at home, healthy eating needs to start with healthier purchases in the supermarket. Grocery shopping occurs in a stimulus-rich and often timeconstrained context, and healthy options are often obscured. Consequently, most Americans consistently buy foods that are too high in fat, calories, and sodium, and they buy less than half (24.1% vs. 50%) of the amount of fruits and vegetables recommended by U.S. Dietary ☆ Thanks to Ailing Chua, Neuri Park, Adrian Lo, Jason Yu, and Jon Elias for assisting with the research. Thanks to John Brand for helping with data collection and with data entry for Study 1. In addition, thanks to Huy Tran for analyzing the Study 1 data. Special thanks to Collin Payne (New Mexico State University) for his analysis and for his editorial help on related versions of this research. ⁎ Corresponding author. E-mail addresses: [email protected] (B. Wansink), [email protected] (D. Soman), [email protected] (K.C. Herbst).

Guidelines (French, Wall, Mitchell, Shimotsu, & Welsh, 2009). Increasing the purchase dollars allocated to healthy foods could contribute to healthier shoppers and to healthier profits. When prompted in a lab, many consumers can categorize food as healthy or less healthy subjectively, or as a virtue or a vice (Chernev & Gal, 2010; Rozin, Ashmore, & Markwith, 1996; Rozin & Vollmecke, 1986). Yet when people shop for groceries, whether they actively think in terms of separate categories, such as “healthy” versus “unhealthy” unless goal-directed or unless prompted to do so by an external stimulus is unclear (Miller, 1998). Additionally, such categories may not be salient, consistently used, well-defined, or even remembered during a shopping trip (Wansink & Kranz, 2013). One way in which stores could help shoppers consider separate categories of food would be to partition a shopping cart. For instance, a visual cue could suggest that half of the shopping cart be allocated to “fruits and vegetables”, and the other half be allocated to “everything else”. Making consumers categorize their choices has been shown to alter the allocations in other contexts (Fox, Ratner, & Lieb, 2005; Morwitz, Greenleaf, & Johnson, 1998), including plating and personal food serving decisions (Riis & Ratner, 2010; Wansink, 2014). Two primary questions follow: 1) Can partitioned shopping carts influence purchase and assortment allocations when grocery shopping?

http://dx.doi.org/10.1016/j.jbusres.2016.06.023 0148-2963/© 2017 Elsevier Inc. All rights reserved.

Please cite this article as: Wansink, B., et al., Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.06.023

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2) If partitioned shopping carts influence purchase, is the reason partially because they alter perceptions of purchase norms? The answer to these questions would be of interest to a wide range of stakeholders: • Shoppers. A simple “half-cart” rule-of-thumb could subtly emphasize the tradeoffs between healthy and less healthy foods while grocery shopping. • Supermarkets. Using modified shopping carts could shift the distribution of sales to higher-margin foods (such as perishable produce), increasing overall sales and perhaps increasing the supermarket's overall profit. • Public policy officials. Partitioned shopping carts could be championed and more quickly accepted by supermarkets than common policy proposals that focus on nutrition information, taxation, or subsidies. • On-line retailers. The notion of partitioning a shopping cart may also hold for partitioning a blank on-line shopping form. Having separate areas for separate types of products could increase sales or alter the on-line retail distribution of sales to higher-margin items. To investigate how partitioned carts may influence shopping behavior, Study 1 uses a lab study that suggests that partitioning is effective because of the purchase norms that partitioning implies. Study 2 then takes this to a supermarket and consistently demonstrates that the simple act of partitioning a cart can increase the amount of healthy food purchased in relation to the size of the partition. In this paper, synergistic recommendations for shoppers, supermarkets, and public policy officials are discussed along with new opportunities for researchers who want to examine how environmental cues can be used to guide shoppers toward healthier behavior. 1.1. The social norms of shopping Starting with the U.S. Dietary Guidelines, nutrition education has been dominated by an information processing approach which emphasizes that nutrition knowledge is nutrition power (Nestle, 2007). Yet, this approach presupposes a high level of motivation and engagement that might not reflect a typical shopper's state of mind (Cobb & Hoyer, 1986; Kuenzel & Musters, 2007). Instead of potentially wrongly assuming that shoppers have a strong motivation to process nutrition information (Andrews, Burton, & Kees, 2011), assortment allocation cues – such as a partitioned shopping cart – might make healthy shopping decisions easier without requiring a strong health-related motivation. In ambiguous allocation contexts, perceived social norms can powerfully influence a wide range of consumer behaviors (Goldstein, Cialdini, & Griskevicius, 2008; Schultz, Nolan, Cialdini, Goldstein, & Griskevicius, 2007), including many related to food (Herman, Roth, & Polivy, 2003; Robinson, Thomas, Aveyard, & Higgs, 2014b). This has been shown with a wide range of food choices (Herman & Polivy, 2005) including dieting (Stroebe, Mensink, Aarts, Schut, & Kruglanski, 2008; Woody, Costanzo, Liefer, & Conger, 1981), healthy versus unhealthy eating (Robinson, Benwell, & Higgs, 2013; Robinson & Higgs, 2013), serving size (Wansink & van Ittersum, 2007), the timing of meals (de Castro, Bellisle, Feunekes, Dalix, & De Graaf, 1997), and one's need for social acceptance (Robinson, Fleming, & Higgs, 2014a; Robinson et al., 2014b). In these areas, even gentle suggestions of what might be a general consumption norm can alter what or how much a person consumes (Robinson et al., 2014a). For instance, consider the “Half-Plate Rule” (Wansink & Tran, 2017 (working paper)) explored in a recent study in which diners were told that half of their dinner plate needed to be reserved for fruit, vegetables, or salad. The diners' reported serving of fruits and vegetables increased, and their serving of meat and grain items significantly decreased. Not only did the half-plate provide a visual benchmark, but the half-plate may have also provided an implied social consumption norm (Wansink & Kranz, 2013).

When shopping for groceries, how much of a healthy food – such as fruits or vegetables – is the right amount to buy is unclear. This amount is variable, subjective, and situation-specific. Yet similar to the “HalfPlate Rule”, any implied suggestion of what is normal might also alter how much shoppers may otherwise purchase (Tran et al., under review). In the case of fruits and vegetables, only 24.1% of the items purchased in a typical U.S. shopping trip are fruits and vegetables (French et al., 2009). Signs that suggest or imply social norms have been effective when explicitly stating a norm (Robinson et al., 2014a; Robinson et al., 2014b). Similarly, if a shopping cart reserved and labeled 50% of the shopping cart's area for fruits and vegetables, a social norm might be implicitly suggested. This may lead shoppers to consider purchasing more fruits and vegetables than if only 25% of the shopping cart was explicitly reserved and labeled for fruits and vegetables. Although this reserved space would not be binding or physically restrictive, such a partition could continuously suggest that grocery shoppers who generally buy less than these amounts should consider at least offsetting or balancing their less healthy food purchases with healthier ones. This may not only increase the amount of healthy foods purchased, but this may also decrease the amount of less healthy foods purchased. In such a case, social norms would become purchase norms. Partitions could be used to differentiate any distinctions between target foods that can be easily made by consumers – snack foods versus meal foods; processed versus non-processed – but the more clear the distinction, the more effective the partitioning might be. This research is focused on target foods that are healthy. A wide range of healthy foods exist that a grocery store could encourage shoppers to purchase, including fruit, vegetables, lean meat, dairy, and whole grains. Because some debate exists about what constitutes a healthy percentage of fat or whole grain, fruits and vegetables will be the categories of food used throughout this paper as generally representative of a larger class of “healthy foods”. Supposing that partitioning will be used to focus on the norms of purchasing fruits and vegetables, the following is hypothesized: H1. A partitioned shopping cart will alter the number of fruits and vegetables purchased. If partitioned carts influence the number of fruits and vegetables purchased, then perhaps the reason is because they suggest a shopping norm. Such a norm would serve as an aspirational quality that might lead a shopper to be continually more motivated to balance the allocation of items between the partitions. Just as this is believed to be what motivates more balanced food serving decisions with divided (partitioned) plates at mealtime (Wansink & van Ittersum, 2007), this might lead to more balance in one's purchase decisions. If the size of a partition changes, then this change might also change the target of how much a person believes is appropriate to buy. Although this relationship may not be proportional, the size of the partition should be positively related to the amount one purchases. H2. The size of a partition will alter the number of fruits and vegetables purchased. A partition could take a variety for forms. In a traditional retail context, a shopping cart or a basket could simply be divided with metal, plastic, or a visual divider. In an on-line context, a shopping basket could have a divided line on the order form, or the shopping basket could be explicitly labeled with distinct categories for products. Key differences exist between traditional retail shopping and on-line shopping in terms of fatigue, processing involvement, timing, and impulsivity (Childers, Carr, Peck, & Carson, 2002). Being able to examine the role of partitioning in both instances would provide confidence on whether partitioning's impact can be generalized. Because of this, the hypotheses in this paper are explored in a field study involving a simulated on-line grocery store concept and in an in-store supermarket field study. In Study 1, a simulated on-line shopping study suggests that partitions

Please cite this article as: Wansink, B., et al., Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.06.023

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may be effective because they suggest purchase norms. In Study 2, a field study in a supermarket reinforces the potential power of partitioned carts by showing that most shoppers purchased fruits and vegetables in quantities relative to the size of their allocated partition within a shopping cart. 2. Methods and results 2.1. Study 1: does partitioning alter purchase norms and buying intentions? In Study 1, the aim was to determine whether partitioning a shopping cart biases purchase in a controlled environment. Whereas trying to simulate grocery shopping – and a divided cart – in this more controlled environment would be difficult, this does lend itself to the increasingly common practice of ordering groceries (as well as other items (e.g., books and recordings)) on-line. In working with a retailer that was exploring the feasibility of on-line ordering and delivery, a determination was made that partitioning could be accomplished by subdividing the order blank that a person would see on a simulated computer screen. Without having to subdivide a cart physically, this procedure would allow implementation flexibility and less potential for demand effects. 2.1.1. Method Study 1 involved weekly or bi-weekly shoppers who were responsible for their own cooking and who lived in an apartment or home with full kitchen facilities. They were recruited through flyers and emails that requested their involvement in a 30-minute study about “off-campus living and shopping”. In exchange for their participation, they received $10 USD and a boxed lunch. The study was conducted over a six-day period (Tuesday to Thursday on two consecutive weeks) with 17 to 24 people in each session. Of the 118 people who showed up for the study, eleven were not allowed to participate in the study because they did not fit the recruiting criteria. For instance, they did not do their own cooking (they had a spouse or roommate cook for them), or they dined out four or more times a week. In total, 107 people completed the study (63% women; average age 25.3 years). The session began with an unrelated decoy task involving the participants describing how they located their off-campus apartment and home. They were then asked to provide five pieces of advice they would give to others when moving to this particular town, including where to shop. Following this, they were told that on-line grocery

Control Condition

3

shopping is becoming a service that some supermarkets were beginning to offer. They were told that these shopping experiences took a variety of interactive designs that are suited differently for computers, tablets, and smart phones. They were told that some of these designs included pull-down menus, “type-and-match” lists (in which the computer provides auto-fill suggestions), or a basic typed “e-shopping list”. Participants were asked to indicate which day of the week they typically did their major shopping trip and then to indicate when they thought they were most likely to make their next shopping trip. Participants were told that the research would examine their general shopping patterns and what they thought they were most likely to buy during their next major shopping trip. They were then given a stiff cardboard sheet that was designed in the same shape and color as an iPad and which was printed on heavy cardboard stock paper. The order portion of the screen was in the form of a rectangular box on the “screen” which noted “Fruits and Vegetables” at the top of the box and “All Other Grocery Items” at the bottom of the box. This rectangular box was shaped in proportion to the length and width of the dimensions of the standard-sized shopping cart. The rectangle was partitioned in one of three ways (see Fig. 1). First, in the Control condition, the rectangle had no partition. The rectangle in the Control condition simply said “Fruits and Vegetables” at the top of the box and “All Other Grocery Items” at the bottom of the box. Second, the 33% Partition condition had a partition drawn approximately onethird the way down the box, leaving the top third of the box for “Fruits and Vegetables” and the bottom two-thirds for “All Other Grocery Items”. Third, the 66% Partition condition had a partition drawn approximately two-thirds of the way down the box leaving the top two-thirds of the box for “Fruits and Vegetables” and the bottom third for “All Other Grocery Items”. In addition to being asked to indicate what they would buy, participants were also asked to indicate how much they would buy if they were purchasing more than one (e.g., two bags of apples, three boxes of cereal). After people had written down what they would order and the quantities, they were asked to turn the cardboard sheet over and to answer the questions on the back. Recall the hypothesis that dividing, sectioning, or partitioning a cart might influence consumers if the dividing, sectioning, or partitioning suggested a higher purchase norm for fruits and vegetables. Two key questions were asked to assess this. First, participants were asked to write down the amount of money they believed the average person spent on fruits and vegetables when making a similar shopping trip. Second, participants were asked to estimate what

One-Third Partition

Two-Thirds Partition

Fig. 1. Sample order forms for different-size partitions in Study 1.

Please cite this article as: Wansink, B., et al., Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.06.023

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percentage of their shopping budget was allocated toward fruits and vegetables. Following this, they were asked a number of controlrelated questions (including demographics). On a separate piece of paper, following an unrelated set of questions related to commuting and transportation, participants were asked about what they thought this particular study was. Most believed the study was about the feasibility of on-line grocery shopping. While a handful of participants said the study was “about fruits and vegetables”, no one explicitly mentioned the dividing lines on the order form. Note that participants were asked to answer the two purchase norm questions after completing their order form and after turning over the order form. So, their order form and the division lines on the order form could not be seen. This was done to try further to separate any potential demand effect by removing the immediate salience of what they bought and the position of the line from their estimate of what the typical person bought. 2.1.2. Results Partitioning had a significant impact on how many fruits and vegetables shoppers selected. As shown in Table 1, when comparing the three conditions – the Control, the 33% Partition, and the 66% Partition – the influence of the cart respectively increased the total number of fruits and vegetables selected (5.61, 8.26, and 12.51; F(2, 104) = 12.93, p b 0.001) and the different types of fruits and vegetables selected (3.83, 5.38, and 6.95; F(2, 104) = 13.83, p b 0.001). When examining the impact of partitioned carts on the total number of non-fruit and non-vegetable items selected (e.g., chips, crackers, frozen dinners), the partitioned carts marginally decreased the total number of these items selected (10.81, 11.29, and 8.11; F(2, 104) = 3.06, p = 0.051). The contrast between the Control condition and the 66% Partition showed that the partition more than doubled the total number of fruits and vegetables selected (from 5.61 to 12.51; p b 0.001). This partition nearly did the same for the different types of fruits and vegetables selected (from 3.83 to 6.95; p b 0.001), and this partition marginally decreased the total number of non-fruit and non-vegetable items selected (from 10.81 to 8.11; p = 0.053). Compared to the Control condition, the 33% Partition condition also increased the total number of fruits and vegetables selected (from 5.61 to 8.26; p b 0.05). This partition increased the different types of fruits and vegetables selected (from 3.83 to 5.38; p b 0.05), and this partition had no impact on the total number of non-fruit and non-vegetable items selected (10.81 vs. 11.29; p = 0.73). Interestingly, just as 66% vs. 33% Partition increased both the total number of fruits and vegetables selected (p b 0.01) and the different types of fruits and vegetables selected (p b 0.05), this partition had the opposite impact on the total number of non-fruit and non-vegetable

items selected. A 66% vs. 33% Partition decreased the total number of non-fruit and non-vegetable items selected (11.29 vs. 8.11, p b 0.05), and this partition decreased the different types of non-fruit and nonvegetable items selected (8.32 to 6.51, p b 0.05). To examine specifically the effectiveness of the shopping norm explanation, people's estimation of how much money they believed the average shopper spends on fruits and vegetables on a shopping trip was analyzed. Although assuredly noisy, one might expect estimates to be higher in the 66% Partition condition than in the Control condition. Looking across the three conditions – Control, 33% Partition, and 66% Partition – an upward trend was present on how much they believed others spend on fruits and vegetables ($21.52, $22.12, and $29.28; F(2, 104) = 4.08, p b 0.05). The paired comparison between the 66% Partition and the 33% Partition was significant (p b 0.05). Similarly, when people were asked to indicate the average percentage other shoppers spend on fruits and vegetables across the three conditions – Control, 33% Partition, and 66% Partition, an upward trend was also present (13.72%, 21.69%, and 30.69%; F(2, 104) = 9.66, p b 0.001). The paired comparison between the Control and the 66% Partition was significant (p b 0.001), as was the difference between the 33% Partition and the 66% Partition (p b 0.05). As depicted in Fig. 2, a process mediation analysis (Preacher & Hayes, 2008) revealed that the average perceived level of spending by others on fruits and vegetables partially mediates the effect of partition on total fruits and vegetables. As Fig. 2 (Model B) illustrates (examining the direct effect), the effect of partition influenced the total fruits and vegetables (β = 0.11; p b 0.001). As Fig. 2 (Model A) illustrates (checking for mediation), partitioning influenced the average perceived level of spending by others on fruits and vegetables (β = 0.12; p b 0.05). The mediation analysis revealed that the indirect effect of the average perceived level of spending by others on fruits and vegetables on total fruits and vegetables was marginally significant (β = 0.11; p = 0.09). This indirect effect was significant at a 95% confidence interval with both the lower and upper limits excluding zero (0.0023 to 0.0353). This indicates that partitioning partially influences total fruits and vegetables because partitioning may lead shoppers to believe that buying more fruits and vegetables is normal or typical because that is what others are perceived to be doing. 2.1.3. Discussion Study 1 illustrates the potential promise that partitioning has in altering choice. Study 1 suggests that implicit social norms may be contributing to this shopping behavior in addition to the heightened salience that the partition provides. Studies that involve lowinvolvement behaviors (such as this one) can be prone to demand effects. Care was taken so that no person witnessed another person

Table 1 Grocery cart partitions influence fruit and vegetable (F&V) selection (standard deviations in parentheses) in Study 1.

Total # F&V Diff Types F&V Total # non-F&V Diff Types non-F&V Avg Perceived F&V Purchsd by Others Avg Perceived % Spent on F&V by Others

Control condition - A n = 36

33% FV condition - B n = 34

66% FV condition - C n = 37

F-test (p-value)

Eta squared

5.61 (3.99) 3.83 (1.86) 10.81 (6.07) 8.17 (3.41) $21.52 (10.79) 13.72% (8.48)

8.26 (5.23) 5.38 (2.85) 11.29 (6.95) 8.32 (3.24) $22.12 (9.51) 21.69% (17.02)

12.51 (7.64) 6.95 (2.77) 8.11 (4.53) 6.51 (3.00) $29.28 (12.91) 30.69% (15.28)

12.93⁎⁎⁎ (0.000) 13.83⁎⁎⁎ (0.000) 3.06 (0.051) 3.51⁎

0.199

0.063

(0.033) 4.08⁎

0.096

(0.021) 9.66⁎⁎⁎ (0.000)

0.210 0.056

0.201

Planned contrasts t-test (p-value) A-B

A-C

B-C

2.377⁎ (0.021) 2.562⁎ (0.012) 0.346 (0.730) 0.204 (0.839) 0.189 (0.850) 2.006⁎ (0.048)

4.856⁎⁎⁎ (0.000) 5.258⁎⁎⁎ (0.000) −1.953 (0.053) −2.194⁎

2.752⁎⁎ (0.008) −2.603⁎ (0.011) 2.274⁎ (0.025) 2.367⁎

(0.030) 2.529⁎ (0.013) 4.382⁎⁎⁎ (0.000)

(0.020) −2.360⁎ (0.021) −2.348⁎ (0.021)

⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

Please cite this article as: Wansink, B., et al., Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.06.023

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that a store with healthy or nutrition-reinforced messages (versus a value/cost-savings message) will lead to an increased dollar amount spent on fruits and vegetables when shoppers use a partitioned cart. However, this manipulation of promotional environments was not intended for hypothesis testing as much as for exploring preliminarily the potential generalizability of these findings across different store environments.

Fig. 2. Cart partitions influence perceptions of average spending by others on fruits and vegetables in Study 1. Note. Model A is the mediation model with average perceived level of spending by others on fruits and vegetables as a mediator between Partition condition and total fruits and vegetables. Model B is the direct effect model between Partition condition and total fruits and vegetables. Standardized path coefficients are shown with p-value in parentheses. *p b .05, **p b .001.

completing the study (and therefore potentially being assigned to a different condition). During debriefing, nobody inferred the purpose of the study. Those who did venture a guess thought that the study was a survey on cooking or on where they shopped for groceries. One unexpected issue of generalizability with this study involved the store and the shoppers who were involved in the study. The primary store in which these people indicated they shopped was one that aggressively positioned and promoted itself as being the high-quality, healthy grocery store in the market. The grocery store's focus on quality attracted higher income shoppers, who may have had a predisposition to purchase more fruits and vegetables from the outset. Recall in the introduction of this paper an acknowledgement that the power of cues – such as partitioning – may work differently with highly-motivated (e.g., health-conscious) shoppers than with less motivated shoppers. This will be addressed in Study 2 by varying the focus of shoppers by having them either think about quality or think about value when shopping. 2.2. Study 2: do larger partitions lead to larger sales in grocery stores? In this study, the aim was to assess whether the size of the partition influences sales, and to focus on determining whether partitioning would be generalizable to stores that were not necessarily “quality” positioned retailers with higher income shoppers. That is, Study 1 examined grocery shopping in an on-line simulated context, but Study 1 was also conducted in proximity to a high-quality grocery store that positioned itself as high-quality (versus high-value) and as having a variety of healthy, attractive, high-quality fruits and vegetables that were frequently promoted. Study 2 moves shopping behavior into the store, and uses physical modifications of a cart – partitioning maps – to address the following two questions: 1) Does the size of a partition determine the size of one's sales, and 2) does partitioning work equally well for stores that do not focus as strongly on quality (and on fruits and vegetables) as they do on value. For instance, some supermarket chains and some independent stores choose to use in-store advertising and promotions to emphasize the value/cost-savings that they offer instead of specifically promoting fruits and vegetables, which may be lower in quality and variety than that in larger premium supermarkets. Study 2 explores whether a reinforcing promotional environment enhances the effectiveness of partitions. The general expectation is

2.2.1. Method Study 2 was a field study conducted using shoppers in an independently owned and operated grocery store in a Canadian city. The study utilized a 3 × 2 between-participants design which combined one of three different cart partitioning conditions (Control vs. 35:65 Partition vs. 50:50 Partition) with one of two different store positioning messages (Health/Nutrition vs. Value/Cost-Savings). Please note that these data were collected in 2007. At this time, smartphone penetration and usage were negligible, simplified nutrition labeling was still in infancy, grocery delivery and on-line grocery shopping in this community were minimal, and consumer awareness and trends of healthy eating were not as strong as they are today. Cart partitioning was manipulated in the form of one of three different paper mats which fully covered the bottom of each cart. As depicted in Fig. 3, in the Control condition, a gray mat with nothing written on the mat occupied the bottom of the shopping cart. In the “35-65” Partition condition, 35% of the mat was labeled “Fruits & Veggies”, and 65% of the mat was labeled “Meats & Treats”. In the “50-50” Partition condition, 50% of the mat was labeled “Fruits & Veggies”, and 50% of the mat was labeled “Meats & Treats” (see Fig. 3). Every morning, an attendant placed one of these three mats randomly at the bottom of the carts, numbered them sequentially, and nested them into a cart-garage at the entrance of the store. While the use of the label “fruits and vegetables” is consistent with Study 1, a concern of this retailer was a sign that said “all other grocery items” would stigmatize and otherwise unfairly lower sales of items other than fruits and vegetables. So, the sign was modified to say “Meats & Treats”. The research included a manipulation of store positioning via a flyer given to shoppers as they entered the store. The layout of both versions of the flyer was identical except for the wording. One version had a Health/Nutrition positioning, and the other version had a Value/CostSavings positioning. The flyer for the Health/Nutrition positioning read: “Healthy Food at Great Values. Research shows that eating more fruits and veggies and less meats and treats is good for health! Healthy Food. Great Prices”. The flyer for the Value/Cost-Savings positioning read: “Superior Food at Great Values. Research shows that purchasing high-quality food reduces spoilage and is overall more cost effective! Superior Food. Great Prices!”. As shoppers approached the store, a researcher who was blind to the hypotheses intercepted them, welcomed them to the store, and asked them for their willingness to participate in a study of consumer purchasing. The research assistant then assessed whether the shopper was making a regular weekly shopping trip visit (or a “fill-in” visit). Because the interest was in the impact of shopping carts, eliminating shoppers who were not going to use a shopping cart (shoppers often use hand baskets for fill-in visits) was desired. Shoppers who wanted a cart were given one of the two flyers (from a stack which had been randomly shuffled) and then asked if they would be willing to let a researcher (also blind to the hypotheses) look at their receipt and record some details at the end of their shopping trip. Of the 205 shoppers who were asked to participate, 169 (82.4%) consented. The researcher also affixed a small sticker to the handle of the cart that was color coded to identify which version of the flyer the participant had received. Shoppers retrieved their cart from a cart-garage which had been filled with carts with the three different mat allocation conditions (Control, 35:65 Partition, and 50:50 Partition) in random order. Because the carts were nested within one another, participants retrieving a cart

Please cite this article as: Wansink, B., et al., Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.06.023

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Fig. 3. Paper mats used to partition carts in Study 2.

were likely unaware of what was at the bottom of any of the remaining carts. After the shoppers paid for their purchases, they were intercepted by a researcher with a clipboard and a calculator who a) identified which version of the flyer the shopped had read by looking at the sticker on the handle, and b) recorded data from their shopping receipt. Specifically, the researcher calculated the total (pretax) dollars spent on “fruits and vegetables” and the total amount spent on “meats and treats”. Items that did not fall under these categories were not recorded. This procedure generally took less than 2 min. The researchers had been previously provided with a complete list of items that the store carried in both the “fruits and vegetables” category and the “meats and treats” category. After being asked to read through and familiarize themselves with lists, the researchers were trained to look at any grocery receipt and to identify quickly any items that belonged in these two categories. After returning the receipts, the researcher asked the shoppers if they had a chance to look at the flyer prior to when they started shopping. Each shopper answered “yes”. Shoppers were then thanked and given a gift as a token of appreciation. 2.2.2. Results Data on dollars spent on fruits and vegetables [FV] and data on dollars spent on meats and treats [MT] were collected from each shopper. As hypothesized, both the partitioning of the shopping carts and the positioning of the store (the Health/Nutrition versus Value/Cost-Savings flyer) had main effect influences on the amount spent on fruits and vegetables (see Fig. 4). A significant increase in dollars spent on fruits and vegetables was present when comparing the control cart, the 35%

17.54

$ Spent

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2.2.3. Discussion Simply partitioning a shopping cart in different ways led to a significant sales increase in a nutrition-reinforced environment. In the case of the 50% Partition cart, the purchase amount spent on fruits and Table 2 Dollars spent on fruits and vegetables increase with partition size in a health/nutrition-reinforced environment (standard deviations in parentheses) in Study 2.

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Partition cart, and the 50% Partition cart ($10.36, $11.85, $13.40; F(2, 163) = 10.15, p b 0.01). Additionally, a significant increase in dollars spent on fruits and vegetables was present when the Health/Nutrition flyer was used compared to when the Value/Cost-Savings flyer was used ($14.42 vs. $9.18; F(1, 163) = 72.66, p b 0.01). Recall that, in Study 1, partitioning significantly increased fruit and vegetable selection. In Study 2, an interaction was present between cart partitioning and store positioning in that partitioned carts were most impactful at influencing dollars spent on fruits and vegetables when the Health/Nutrition flyer was used (F(2, 163) = 4.94, p b 0.01). As Table 2 indicates, when shoppers were exposed to a healthy/nutrition positioning, the shopping cart significantly influenced how much they spent on fruits and vegetables when comparing the control cart, the 35% Partition cart, and the 50% Partition cart ($11.61, $14.97, and $17.54; all ps of simple effects tests b 0.05). Among those exposed to the Value/Cost-Savings flyer, partitioned shopping carts did not significantly increase the dollars spent on fruits and vegetables (8.77, 8.72, and 9.92; all ps of simple effects tests N0.05). Interestingly, an ANOVA on the spending on meats and treats [MT] revealed no significant effects (all ps N 0.50). As the data in Table 2 suggest, spending on meats and treats was not affected by the cart positioning, the store partitioning, or by the interaction between the two.

Average $ spent on fruits and vegetables [FV]

14.97

11.61 9.92 8.77

8.72

Nutrition Flyer Value Flyer

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4

0 Control (No Partition)

35-65 Partition

50-50 Partition

Cart Type Fig. 4. Dollars spent in Study 2 on fruits and vegetables (combined) increase with partition size when health/nutrition is reinforced in the environment.

Average $ spent on meats and treats [MT]

Health/Nutrition positioning flyers No Partition $11.61 (n = 34) ($3.64) 35-65 Partition $14.97 (n = 26) ($4.42) 50-50 Partition $17.54 (n = 26) ($4.19)

$17.13 ($4.08) $17.51 ($4.48) $14.60 ($4.79)

Value positioning flyers No Partition (n = 26) 35-65 Partition (n = 26) 50-50 Partition (n = 31)

$16.35 ($5.04) $16.09 ($4.60) $16.63 ($5.00)

$8.77 ($4.39) $8.72 ($4.42) $9.92 ($5.22)

Please cite this article as: Wansink, B., et al., Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.06.023

B. Wansink et al. / Journal of Business Research xxx (2017) xxx–xxx

vegetables increased from $9.92 (when the Value/Cost-Savings flyer was used) to $17.54 (when the Health/Nutrition flyer was used). This exceeds most promotional efforts to increase dollars spent on fruits and vegetables (which are considered to be a success if they increase sales by 5% and they are typically done with costly margin-cutting price promotions (Food Marketing Institute, 2005)). Simply altering the size of the partition from 35% to 50% in the Health/Nutrition store positioning condition increased the dollars spent on fruits and vegetables from $14.97 to $17.54. This is consistent with a purchase norm explanation. Changing the size of the partition proportionately altered the amount spent on fruits and vegetables. Interestingly, changing this mix should have had a similar influence on meats and treats. In this study, the difference was not significant perhaps because one may need real inducement to purchase more meats and treats (as long as they have enough money), and this lack of effect may be due to a ceiling effect. Different stores have different positions in the market to attract different types of shoppers. These results suggest that nudges that are oriented to help customers shop more nutritiously might have the biggest impact when combined with other consistent messaging and marketing efforts (Wansink, 2017, forthcoming). While an assumption might have earlier been that healthy nudges are more effective in some stores than in others because the customers are different, these results suggest that the marketing efforts make the difference more than the customers. In this study, the basic demographics of the customers were the same from day to day. In this study, a store-wide renovation to develop a full-store Health/ Nutrition positioning versus a Value/Cost-Savings positioning would have not been possible. The range of the quality and variation of fruits and vegetables being offered could not be changed. In addition, the promotional environment throughout the store could not be changed. Instead, store positioning in the form of in-store flyers was manipulated. In spite of this minimal exploratory intervention, encouraging synergy was present when the partitioned carts were used in a health/ nutrition-reinforced environment. This is additionally encouraging because the majority of supermarket chains in the U.S. actively and widely embrace institutionalized health/nutrition-reinforced programs in their produce sections. These are either developed in-house by an industry trade association or by the government. For instance, Safeway, Wegmans, Giant, HEB, Hannaford, Hy-Vee, Schnucks, TOPS, and others promote freshness and nutrition by embracing in-store promotional programs such as “More Matters” (Fresh Fruit and Vegetable Alliance), “Five-a-Day” (National Grocers Association), and “Partner with MyPyramid” (U.S. Department of Agriculture). Given the challenges inherent in field studies, Study 2 had a number of limitations. No demographic data were collected, and the possibility exists that shoppers may have modified their shopping if they remained aware that they had to provide their receipt after shopping. In particular, if shoppers saw a mat in their cart which specifically mentioned fruits and vegetables (in combination with the flyer), then they could be more sensitized to shopping more carefully in the produce aisle. 3. General discussion Many people in developed countries are overfed but nutritionally starved (Hedley et al., 2004). These two studies showed that partitioning a shopping cart led customers to spend more money on fruits and vegetables. In these nonrestrictive shopping contexts, shoppers appear to be malleable to healthy suggestions. What is purchased at supermarkets has a sizable impact on one's daily caloric intake. Improving the food purchase decisions made in supermarkets is a promising step in helping change the foods eaten and the weight gained in one's home. Improving the food purchase decisions made in supermarkets would be empowering to all those involved and would show consumers that small changes can have big effects. Improving the food purchase decisions made in supermarkets might also lead to positive

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changes in other parts of a consumer's life. For supermarket managers, this research reveals that small, innovative in-store changes can have a win-win impact on shoppers and sales. For public policy officials, this research demonstrates that important options are present for addressing public health which are more behavioral than regulatory. 3.1. Implications for retailers and shoppers Healthy perishable foods are profitable ones for supermarkets to sell. Often, higher profit margins exist for these foods, and higher spoilage costs exist for not quickly selling them. Yet, the impact of partitioning is not only relevant to produce. A retailer could just as easily use partitioning to suggest another categorization scheme, such as natural foods versus processed foods. Yet in the same way that grocers can use partitioned carts to help disinterested shoppers buy healthier food, interested shoppers can partition their own cart in whatever way they believe will help them shop healthier. For instance, a person could easily and visibly “partition” or divide a cart by creating their own partitions. In Canada, many shoppers use “green boxes” to carry their groceries home. These boxes are sized such that two of them fit perfectly into a standard shopping cart, and many shoppers use this arrangement as a partition. They could then allocate the targeted partition to whatever general category of foods they wished to purchase in greater quantities. A shopper with children might want to be nudged to buy more fruits and vegetables, and a shopper with high blood pressure might want to buy more low-sodium foods. A dieter might want to be nudged to buy more low-fat foods, and a diabetic might want to buy more foods with a low glycemic index. In addition, other operationalizations – such as a partitioned on-line order form – could also be effective. That is, the notion of partitioning a shopping cart may also hold for partitioning an order form on an on-line shopping cart. Having sectioned areas for separate types of products (such as books versus DVDs) could either increase sales or alter the distribution of sales to higher-margin items. Much of the initial research on partitioning allocations was conducted with paper-and-pencil tasks or with computer tasks. Having a segmented order form for on-line grocery shopping is not unlike what is often found in high-traffic delis in which order forms clearly break out several categories (such as sandwich, chips, drink, dessert, and fruit) not all of which might have otherwise been considered. 3.2. Future research and limitations The general interest for grocers is in what will increase the dollars spent in their store. Because the interest was in the purchases of healthy foods, the focus was on the dollars that shoppers spent on fruits and vegetables (which are representative of a larger class of “healthy foods”). While partitioning increased the sales of fruits and vegetables, how partitioning might have influenced the sales of other foods is less clear. In this paper, the focus was on how partitions imply how much fruits and vegetables are normally purchased by other people and on how this contributes to shoppers buying more on their own. Partitions might also have an influence because they increase the salience of fruits and vegetables by calling them out. Such an explanation has been investigated in earlier pilot studies, but this only proved to be moderately significant (Wansink, Payne, Herbst, & Soman, 2013), possibly with highly involved shoppers. As with many interventions, how effective partitioning would be over time is unclear. This may depend on whether partitioning generates a lower-involvement approach to shopping or a higherinvolvement approach to shopping. If partitioning generates a lowerinvolvement approach to shopping, then the effect may be more persistent over time. A purchasing norm explanation would suggest a lowerinvolvement approach. This is similar to how smaller plates suggest a smaller serving norm and are effective in changing serving size and consumption in field contexts in which people are not aware that they are

Please cite this article as: Wansink, B., et al., Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.06.023

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B. Wansink et al. / Journal of Business Research xxx (2017) xxx–xxx

being observed (Holden, Zlatevska, & Dubelaar, 2016; Zlatevska, Dubelaar, & Holden, 2014). If the salience explanation is more dominant in a shopper, then the effect may not persist to the same degree (cf., Hayes, 2013). As the effect becomes more expected and more familiar, the effect may become less strong. In retail field studies, intervening without some possibility of demand effects is difficult to avoid (Toft, Winkler, Eriksson, Mikkelsen, & Glumer, working paper; Winkler et al., 2016). If people see a sign or if they are intercepted, then they could behave differently than they otherwise might. Study 1 addresses demand effects through debriefings, but this was not done in Study 2 because of contamination concerns – one shopper telling another shopper what the study involves. Despite this limitation with Study 2, that the results are consistent with Study 1 and with predictions is encouraging. Another limitation of Study 2 was that researchers hand-coded the sales of items. While the research assistants were not blind to the conditions, the condition upon which they were most focused was the positioning condition (value vs. health) as opposed to the partitioning condition. They did not know the purpose or the hypotheses of the studies. Again, the fact that these results are consistent with those in Study 1 is encouraging. Underscoring that dividing a cart could be done with any product is important. Because of the win-win importance of selling more fruits and vegetables, this was the focus of this paper. Using a demarcation such as this could just as easily be done with, among other things, store brands, fresh foods, and meats and treats. The success associated with dividing a cart, however, may be dependent on the ease with which a person can categorize the food on the critical dimension. For the purposes of this research, fruits and vegetables seemed fairly clear. Study 1 included an examination of fruits and vegetables versus everything else. Study 2 included an examination of fruits and vegetables versus meats and treats. Although the phrase “meats and treats” may have encouraged more indulgence, the results were consistent with Study 1 and with predictions. Future studies can push the boundaries and implementation of these ideas to other categories and to other methods of partitioning. 4. Conclusion Although the impact of partitioning appears to be strong in these studies, a number of unanswered questions exist about how robust these findings would be across time and across different types of supermarkets. As one-period studies, whether the increases in sales are shortterm, or whether they resulted in the forward-buying of produce is unclear. If so, the novelty of partitioned carts over time could wear off, and their impact could decay. Still, given the increasing reinforcement from other areas (such as the USDAs MyPlate.gov message to make half of your plate available for fruits and vegetables), partitioned carts could be a tool that leads to healthier shopping. For some supermarkets, the next step could be a cautious one that involves controlled randomtrial studies across a number of units in the chain. This could initially be accomplished on a test basis to gauge the acceptance by shoppers and staff. Much of the incremental improvement in lifespan and in quality of life is likely to come increasingly from behavioral lifestyle changes (Wansink, working paper). Well-intentioned marketers may be suited to help lead the movement effectively toward behavioral change. References Andrews, J. C., Burton, S., & Kees, J. (2011). Is simpler always better? Consumer evaluations of front-of-package nutrition symbols. Journal of Public Policy & Marketing, 30, 175–190. de Castro, J. M., Bellisle, F., Feunekes, G. I. J., Dalix, A. M., & De Graaf, C. (1997). Culture and meal patterns: A comparison of the food intake of free-living American, Dutch, and French students. Nutrition Research, 17, 807–829. Chernev, A., & Gal, D. (2010). Categorization effects in value judgments: Averaging bias in evaluating combinations of vices and virtues. Journal of Marketing Research, 47, 738–747. Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2002). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77, 511–535.

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Please cite this article as: Wansink, B., et al., Larger partitions lead to larger sales: Divided grocery carts alter purchase norms and increase sales, Journal of Business Research (2017), http://dx.doi.org/10.1016/j.jbusres.2016.06.023