Per piece or per kilogram? Default-unit effects in retailing

Journal of Retailing and Consumer Services 53 (2020) 101956

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Per piece or per kilogram? Default-unit effects in retailing Andr�e Fecher *, Thomas Robbert, Stefan Roth Department of Business Studies and Economics, TU Kaiserslautern, PO Box 3049, 67663, Kaiserslautern, Germany

A R T I C L E I N F O

A B S T R A C T

Keywords: Default units Price measure Pricing strategy Weight information Consumer product choice Grocery shopping

Retailers offer a variety of products either per unit or per weight. Depending on the product category, consumers may find either one of these pricing strategies typical and the default. Especially online retailers are increasingly using unit-based prices, which is the non-default for many produce categories. So far, consequences resulting from non-default pricing strategies are unclear. This study addresses the questions of whether and how pricing strategies affect consumer behavior. In a series of four experiments, we show that default pricing strategies exist in the marketplace and that consumers prefer products that retailers offer using default pricing strategies. We also demonstrate that this behavior is due to uncertainty issues when assessing prices in non-default pricing strategies. Furthermore, we elaborate on the influence of weight expectations and explicitly stated weight in­ formation on this default-unit effect. The findings suggest that retailers can mitigate negative effects resulting from non-default pricing strategies by providing weight information.

1. Introduction Unpackaged groceries such as vegetables and fruits are typically sold either per weight or per unit depending on the product category. For example, retailers in Germany typically sell apples per kilogram but offer cucumbers per piece. Yet some produce, such as zucchini, are sold both per piece and per kilogram, oftentimes in the same store. In the following, we refer to weight-based pricing strategies when retailers offer their products per weight (e.g., per kg) and to unit-based pricing strategies when retailers offer their products per unit (e.g., per piece). Consumers often find one of these two pricing strategies typical because they have become familiar with it over time and find it to be the default (Lembregts and Pandelaere, 2013). However, in many situations, retailers, especially those using different retail channels, deviate from these typically used pricing strategies and employ non-default pricing strategies. For example, online grocery retailers offer most produce on a per piece basis (e.g., REWE, FreshDirect). The reason is the different shopping and packaging processes. When consumers buy unpackaged fruits and vegetables offline, they can easily add or unbag the desired quantity from their shopping basket to arrive at a specific weight or number of items. Online retailers try to avoid the costly weighting process. By offering the products per piece, they also overcome the problem of exactly matching consumers’ weight requirements. Yet, in doing so, they present consumers with prices that may be untypical for a specific product category.

Another problem in online retailing is that consumers are not able to see, touch, or feel the groceries. Thus, they face a great deal of uncer­ tainty not only about the quality but also about the weight of a per piece item. Consequently, they are also uncertain about the price per weight. To address this problem, some online retailers provide information about the approximate weight of their unpackaged products on offer (e. g., FreshDirect, Walmart), though many others do not (e.g., Amazon Fresh, Tesco). Despite its high relevance, only little is known about the pricing of groceries online in general (Fedoseeva et al., 2017) and the influence of different pricing strategies in particular. This is surprising because on­ line food retailing is on the rise around the world (Halzack, 2015), with 14% of consumers already regularly buying groceries online and another 30% considering online grocery shopping in the near future (Nielsen, 2017). Insight into how the underlying pricing strategy affects consumer purchase decisions is therefore highly relevant not only but especially for online retailers. Retailers need to understand the influence of different pricing strategies on preference and choice. In addition, they need to know if and how uncertainty arises and how this affects choice and subsequent retailer evaluations. Finally, they need to understand the differences between weight expectations and explicitly given weight information to better understand consumers’ decision making. Academic discussion has well established that the unit in which quantitative information is expressed can influence judgments and

* Corresponding author. E-mail addresses: [email protected] (A. Fecher), [email protected] (T. Robbert), [email protected] (S. Roth). https://doi.org/10.1016/j.jretconser.2019.101956 Received 16 April 2019; Received in revised form 12 September 2019; Accepted 23 September 2019 Available online 4 November 2019 0969-6989/© 2019 Elsevier Ltd. All rights reserved.

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decisions (e.g., Ülkümen and Thomas, 2013; Zhang and Schwarz, 2012). However, the specific notion of typical units has received only limited attention (Lembregts and Pandelaere, 2013). In this paper, we enrich previous research on units by investigating the effect of default pricing strategies on consumers’ product choices and evaluations while shop­ ping for unpackaged produce. In particular, we find answers to two research questions: (1) How do pricing strategies for groceries affect consumers’ preference and choice? and (2) How do expectations of or explicitly given information about a product’s weight influence con­ sumer behavior? To answer these questions, we present a series of grocery shopping experiments and offer three important findings. First, we show that default pricing strategies exist in the marketplace for unpackaged pro­ duce. We also demonstrate that consumers buy fruits and vegetables more often when retailers employ default pricing strategies rather than non-default pricing strategies. Second, we show that this default-unit effect still exists when consumers’ weight expectations indicate that the product offered in a non-default pricing strategy is cheaper. Only when explicit weight information is given is the effect significantly attenuated. Third, we demonstrate that consumers can better evaluate the prices when information about product weight is explicitly stated. We also show that price comparisons are easier and that consumers are more likely to compare prices to identify the best buy when weight in­ formation is stated. Consequently, our research contributes to emerging literature on numerosity effects (e.g., Burson et al., 2009; Zhang and Schwarz, 2012). We show that in the retailing contex, choice is driven by the perceived default price measure (i.e., default pricing strategy) rather than by numerosity. In addition, we add to research on default units (Lembregts and Pandelaere, 2013) by demonstrating that the default-unit effect also holds when attribute information in default units is specified in larger numbers. Furthermore, our findings add to research on unitosity (e.g., Monga and Bagchi, 2012; Ülkümen and Thomas, 2013) by showing that additional information, in our context weight information, helps con­ sumers transform one unit into another. Finally, in contrast with pre­ vious research, we investigate situations in which the pricing strategies are not the default for the products. Retailers need to be aware of what customers perceive as the default pricing strategy for each product category and the resulting consequences. This is especially the case for multinational retailers, because default pricing strategies may vary across regions. The remainder of this article is as follows: First, we discuss the relevant literature on the role of measurement units and develop our hypotheses. Second, we present the results of four experiments on the existence and boundary conditions of the default-unit effect and its consequences for consumer behavior. Finally, we discuss the implica­ tions of our findings and present limitations of this research.

consumers perceive the shipping time of 72 h as longer than three days (Siddiqui et al., 2018). Research streams such as temporal reframing (Bambauer-Sachse and Mangold, 2009; Gourville, 1998), currency ef­ fects (Gamble et al., 2005; Raghubir and Srivastava, 2002; Wertenbroch et al., 2007), and the ratio bias (Denes-Raj and Epstein, 1994; Denes-Raj et al., 1995) report similar results (for an overview on the role of numerosity, see Bagchi and Davis, 2016). In the context of retail pricing, research shows that consumers perceive prices as higher when numbers are high because of large units of measure (e.g., 3.40€ per kilogram vs. 0.34€ per 100 g) (Fecher et al., 2019; Roth and Himbert, 2015). 2.2. Unitosity Monga and Bagchi (2012) shift the focus from numbers to units and emphasize that not only the numerosity but also the unit of measure itself evokes consumer reactions. Similarly, Zhang and Schwarz (2012) also argue that this number-focused perspective would benefit from the consideration of the interplay of numbers and units in context. Research shows that the effects resulting from numerosity disappear when units are salient (Shen and Urminsky, 2013; Shrivastava et al., 2017). For examples, numerosity effects vanish when consumers are made aware that the same information could also be described with a different unit (Pandelaere et al., 2011). Shen and Urminsky (2013) also show that numerosity effects diminish when the font size of measurement units is larger than the numbers and thus units are salient. 2.3. Default units

A unit of measurement mostly accompanies quantitative information and specifies the numerical value. The price of 0.79€ for a cucumber weighing 500 g contains information about the numerical value (i.e., 0.79) and the price measure (i.e., € per piece). The same cucumber could also be sold for 1.58€ per kilogram. The price in both situations is the same; however, the numerical value and the price measure differ. Research on measurement units addresses effects arising from numerical values, referred to as numerosity effects (Adaval, 2013; Lembregts and Van den Bergh, 2018), or from the unit, referred to as unitosity effects (Monga and Bagchi, 2012).

In the previous examples, people most likely do not prefer either expression of the underlying units of measure (e.g., per kilogram vs. per 100g). However, in other cases, specific units are preferred to others to express information (Lembregts and Pandelaere, 2013). That is, people have default units “in which they prefer attribute information to be expressed.” (Lembregts and Pandelaere, 2013, p. 1278). Such default units optimally account for aspects of accuracy and cognitive efficiency. Cognitive efficiency can be improved through the use of small numbers because individuals find such numbers easier to distinguish (Viarouge et al., 2010) and calculate (Ashcraft, 1992). Thus, research shows that individuals use small numbers more often than larger ones (Dehaene and Mehler, 1992; Jansen and Pollmann, 2001). However, in many cases, small numbers are imprecise and limit discrimination among objects (Lembregts and Pandelaere, 2013). Therefore, default units are always characterized by some tradeoff between accuracy and efficiency. When numerical values grow too large, people change to larger units and, thus, lower numbers. Furthermore, default units evolve from frequent use of and encounters with a particular unit (Lembregts and Pandelaere, 2013). A repeating encounter with a stimulus results in fa­ miliarity (Hoyer, 1984). Consequently, default units are also units with which consumers are familiar. Although research (e.g., Lembregts and Pandelaere, 2013) considers several different product attributes across different units of measure, the price measure has received only limited attention (e.g., Fecher et al., 2019). Retailers’ leeway to choose price measures is not limited to unpackaged produce. For example, as noted previously, German re­ tailers typically sell apples per kilogram and cucumbers per piece. In this case, the default pricing strategy is weight-based for apples and the default pricing strategy is unit-based for cucumbers. However, with the rise of online grocery shopping, some retailers now employ different pricing strategies. REWE, for example, uses a weight-based pricing strategy offline for apples but a unit-based pricing strategy online.

2.1. Numerosity

3. Hypotheses development

Pandelaere et al. (2011) argue that consumers often neglect mea­ surement units and focus on the sheer numbers. Consequently, evalua­ tions are more strongly driven by numbers than by units. For example,

Repeated exposure and familiarity can affect stimuli evaluations, even when individuals are not aware of having been exposed to the stimuli (Harmon-Jones and Allen, 2001; Zajonc, 1968). Stimuli that are

2. Role of measurement units

2

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repeatedly encountered enhance the ease of processing (i.e., processing fluency) (Labroo and Pocheptsova, 2016; Schwarz, 2004; Shapiro and Nielsen, 2013). Similarly, typicality of a stimulus also triggers feelings of processing fluency (Landwehr et al., 2011; Landwehr et al., 2013; Lembregts and Pandelaere, 2013). Such feelings lead to a wide array of positive outcomes, such as judgments of truth (Reber and Schwarz, 1999; Sundar et al., 2015), liking (King and Janiszewski, 2011; Lee and Labroo, 2004; Mosteller et al., 2014), and risk perceptions (Park et al., 2016; Song and Schwarz, 2009). Consequently, typically used price measures (i.e., default pricing strategies) trigger feelings of ease because consumers are familiar with using such measures during their grocery shopping. By contrast, untypical price measures (i.e., non-default pric­ ing strategies) lead to difficulties in processing. Thus:

Thirty-seven respondents took part in the online survey. Respondents saw product pictures with authentic price tags and rated the typicality of the pricing strategies. We adapted a two-item seven-point semantic differential scale (1 ¼ “untypical/unfamiliar with,” 7 ¼ “typical/ familiar with”) from prior research (Coulter and Roggeveen, 2014; Lembregts and Pandelaere, 2013). The two typicality items were highly correlated (r ¼ .915). A mixed analysis of variance (ANOVA) with both experimental factors and typicality as the dependent variable yielded a significant interaction (F(8, 35) ¼ 16.705, p < .001), showing that default pricing strategies are different for the product categories. Of the nine product categories, we chose three categories that clearly fulfilled our requirements based on the typicality rating (see Table 1): one category with a weight-based pricing strategy as default (apples), one with a unit-based pricing strategy as default (cucumbers), and one with ambiguous default pricing strategies (zucchini).

H1. Consumers prefer products that retailers offer using default rather than non-default pricing strategies. H1 predicts that consumers have difficulties in processing prices when retailers employ non-default pricing strategies. We argue that consumers cannot use previous price references in default measures to assess price information in non-default measures. Research on currency effects shows that consumers have difficulties in estimating and are more uncertain about prices in an unfamiliar currency (i.e., a special form of a price measure) (Amado et al., 2007; Dehaene and Marques, 2002; Mussweiler and Englich, 2003). Consequently, we expect that consumers also experience uncertainty when evaluating product prices in retailing when retailers employ non-default pricing strategies; yet they can (re)calculate prices to the default price measure. In the context of grocery retailing, they need to know the product weight to transform a price per unit into a price per weight or vice versa. In some situations, such as online shopping, consumers cannot obtain such information when retailers do not provide it explicitly. Consequently, the proposed default-unit effect weakens when weight information is stated. Thus:

5. Study 1: default-unit effects in grocery retailing In Study 1, we strive to find support that shoppers prefer products offered with a default pricing strategy. To test the default-unit effect, we include products offered with a default pricing strategy and with ambiguous default pricing strategies (i.e., weight-based and unit-based pricing strategies are the default). 5.1. Method 5.1.1. Procedure Consumers faced a hypothetical shopping task in an online survey in which they imagined shopping for different produce. They were told that they needed to buy just one piece in each product category, to control for different consumption amounts. The study comprised one repeated factor (default pricing strategy: weight-based, ambiguous, unit-based). To manipulate default pricing strategies, we used the three product categories we identified in the pilot study (i.e., apples, zucchini, and cucumbers). Respondents saw the product categories in a random­ ized sequence. For each product category, the stimuli displayed two identical products simultaneously, each with an authentic representa­ tion of a price tag, priced either per kilogram (i.e., weight-based) or per piece (i.e., unit-based). Then, respondents needed to choose which of the two products to buy by clicking on the product picture (see Appendix A). Product weights and prices are based on common weights and prices in the marketplace. Table 2 gives details on the price stimuli.

H2. Weight information reduces the preference for products that re­ tailers offer using default rather than non-default pricing strategies. The weight information is an additional information cue that re­ tailers can provide to reduce information asymmetry (Spence, 2002). In general, consumers consider both internal and external cues before making a purchase (Purohit and Srivastava, 2001; Rao and Monroe, 1989). Research indicates that the use of a specific cue increases with its diagnosticity (Purohit and Srivastava, 2001). In an environment with limited product information, consumers tend to use any cue available (He and Oppewal, 2018). The weight cue functions as a signal to reduce uncertainty about the product’s actual size (Connelly et al., 2011; Spence, 2002). Consequently, we expect that consumers can better evaluate product offers when they have more information about the product characteristics. Price comparisons are also easier for consumers and they more likely compare the prices when weight information is stated. Thus: H3a.

Evaluation certainty is higher when weight information is stated.

H3b.

Price comparisons are easier when weight information is stated.

5.1.2. Respondents and measures We recruited 54 respondents online. They were 68.5% female with an average age of 30.66 years (SD ¼ 10.19). Respondents chose the item to purchase, before rating the typicality of the pricing strategies on a single seven-point item (1 ¼ “per weight typical,” 4 ¼ “both typical,” 7 ¼ “per piece typical”). 5.2. Results Our manipulation of the typicality of the pricing strategies was successful (F(2, 106) ¼ 108.27, p < .001). As expected, the default pricing strategy for apples was weight-based (M ¼ 1.91, SD ¼ 1.20), the default pricing strategy for cucumbers was unit-based (M ¼ 6.06, SD ¼ 1.34), and the default pricing strategy for zucchini was ambiguous (M ¼ 3.26, SD ¼ 1.84). Pairwise comparisons showed that all products differed significantly from one another on typicality ratings (ps < .001). To test H1, we first analyzed product choice. Fig. 1 illustrates that consumers were more likely to purchase apples per kilogram (77.8%) and cucumbers per piece (85.2%). Thus, they chose the product offered in the default pricing strategy. When default pricing strategies were ambiguous in a product category, as in the case of zucchini, respondents were indifferent to choosing the product per kilogram (46.3%) or per piece (53.7%).

H3c. Consumers are more likely to compare prices when weight in­ formation is stated. 4. Pilot study: default pricing strategies Our pilot study aims to show that default pricing strategies exist in the marketplace for fruits and vegetables. We conducted this study to identify products offered with a default pricing strategy and those offered with ambiguous default pricing strategies. In this context, ambiguous means that more than one pricing strategy is typical for a product. We focused on nine different produce categories and used a mixed design with pricing strategy (weight-based vs. unit-based) as a between-subjects factor and product category as a repeated measure. 3

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Table 1 Typicality ratings of pricing strategies. Typicality weight-based Typicality unitbased Significance

Pineapple

Apple

Banana

Mango

Eggplant

Avocado

Pepper

Cucumber

Zucchini

3.16 (SD ¼ 1.92) 6.00 (SD ¼ 1.04) p < .001

6.50 (SD ¼ .67) 3.77 (SD ¼ 2.14) p < .001

5.43 (SD ¼2.08) 3.47 (SD ¼ 1.99) p < .001

3.30 (SD ¼ 1.95) 5.83 (1.39)

4.34 (SD ¼ 1.64) 5.30 (SD ¼ 1.33) p ¼ .075

2.86 (SD ¼ 2.42) 6.07 (SD ¼ 1.21) p < .001

4.75 (SD ¼ 1.71) 4.50 (SD ¼ 1.64) p ¼ .668

3.57 (SD ¼ 2.01) 6.33 (SD ¼ 1.36) p < .001

4.84 (SD ¼ 2.29) 4.60 (SD ¼ 1.97) p ¼ .741

p < .001

6.1. Method

Table 2 Study design. Product category

Apples Zucchini Cucumbers

per kg 2.49€ per kg 1.99€ per kg 1.59€

6.1.1. Procedure The experiment again consisted of a hypothetical shopping task implemented in an online survey. The design comprised two betweensubjects factors (pricing strategy: weight-based vs. unit-based; weight information: not stated vs. stated) and one within-subject factor (prod­ uct category: apples, cucumbers). Respondents were randomly assigned to one of the four experimental conditions and faced both product cat­ egories in a randomized order. They were told that they needed to buy just one piece in each product category, to control for different con­ sumption amounts. The stimuli consisted of single product pictures that displayed the product with authentic representations of price tags. Depending on the condition, prices were either per kilogram or per piece (see Appendix B). In addition, half the respondents received information about the approximate weight of the offered products. Unlike in Study 1, we measured consumers’ preferences for the product offer. Table 3 shows the complete study design.

per piece 0.49€ per piece 0.45€ per piece 0.79€

Fig. 1. Product choices.

6.1.2. Respondents We recruited 179 respondents online. They were 46.6% female with an average age of 26.37 years (SD ¼ 4.19).

To test the statistical significance of our findings, we executed a bi­ nary logistic regression with default pricing strategy on product choice (coded 1 for products per kg and coded 2 for products per piece). The product category with ambiguous default pricing strategies (i.e., zucchini) served as the reference group. The results show that con­ sumers were more likely to choose apples offered per kilogram than in the case for zucchini (b ¼ 1.401, SE ¼ .426, Wald χ2(1) ¼ 10.810, p ¼ .001, odds ratio [OR] ¼ .246). By contrast, consumers were more likely to choose cucumbers offered per piece than in the case for zucchini (b ¼ 1.601, SE ¼ .470, Wald χ2(1) ¼ 11.583, p ¼ .001, OR ¼ 4.957). These results give support to H1.

6.1.3. Measures We measured product preference with two indicators proposed by Coulter and Roggeveen (2014) on seven-point scales: “How much did you like this deal?” and “How much did you like the offer at the listed sale price?” (1 ¼ “dislike extremely,” 7 ¼ “like extremely”). Items were highly correlated (r ¼ .916). We averaged the items into an index, with higher numbers indicating higher preference. We measured typicality of the pricing strategies with the two indicators from the pilot study to test our manipulation (1 ¼ “untypical/unfamiliar with,” 7 ¼ “typical/fami­ liar with”). Items also showed high correlations (r ¼ .974).

5.3. Discussion We find that for several product categories, default pricing strategies exist in the marketplace. Our results reveal that consumers clearly prefer products that retailers offer employing default pricing strategies. Yet retailers offer some product categories using more than one specific pricing strategy. In this case, shoppers are indifferent between product offers. Nevertheless, when a default pricing strategy exists, consumers seem to avoid products that are offered with non-default pricing stra­ tegies. In this study, consumers were uncertain about the prices in the non-default pricing strategies because they had no information about the product weight. Without this weight information, consumers were not able to transform the price information in a non-default measure into information in a default measure. In the following studies, we investi­ gate how weight information affects the default-unit effect.

6.2. Results We tested our manipulation of the typicality of the underlying pricing strategy with a three-way mixed ANOVA. As expected, the sig­ nificant interaction of product category with pricing strategy (F(1, 175) ¼ 392.418, p < .001) suggests that apples are typically priced per weight (M ¼ 6.25, SD ¼ 1.21 vs. M ¼ 2.89, SD ¼ 1.71) and cucumbers are typically priced per unit (M ¼ 5.98, SD ¼ 1.80 vs. M ¼ 3.00, SD ¼ 1.86). To test the effects on product preference, we used a three-way mixed Table 3 Study design.

6. Study 2: additional information as a boundary condition of the default-unit effect

Product category

In Study 2, we aim to investigate the influence of weight information given by retailers. In particular, we examine whether negative effects on product preference resulting from non-default pricing strategies are reduced. In doing so, we introduce a boundary condition of the defaultunit effect. As we are particularly interested in the default-unit effect, we drop the product category with ambiguous price measures. 4

Weight information not stated

Weight information stated

Pricing strategy

Pricing strategy

Weight-based

Unit-based

Weight-based

Unit-based

Apples

2.49€

0.49€

Cucumbers

1.09€

0.49€

2.49€ approx. 200g 1.09€ approx. 450g

0.49€ approx. 200g 0.49€ approx. 450g

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7. Study 3: the role of weight expectations

ANOVA with pricing strategy and weight information as betweensubjects factors and product category as a repeated measure. A signifi­ cant two-way interaction of product category with pricing strategy emerged (F(1, 175) ¼ 25.088, p < .001). Simple effects show that con­ sumers preferred a product offered with a default pricing strategy to a product offered with a non-default pricing strategy (apples: F(1, 175) ¼ 9.013, p < .001; cucumbers: F(1, 175) ¼ 18.773, p < .001), providing further support for H1. In addition, we found a significant three-way interaction of product, pricing strategy, and weight infor­ mation (F(1, 175) ¼ 3.559, p ¼ .061). A simple effects analysis shows that weight information can reduce the difference in product prefer­ ences resulting from pricing strategies. The default-unit effect is clearly stronger without weight information (apples: F(1, 175) ¼ 5.999, p ¼ .015; cucumbers: F(1, 175) ¼ 19.106, p < .001) than with weight information (apples: F(1, 175) ¼ 3.125, p ¼ .079; cucumbers: F(1, 175) ¼ 2.652, p ¼ .105), in support of H2. We also found a significant main effect of product category (F(1, 175) ¼ 31.374, p < .001), indi­ cating that respondents viewed the cucumbers as a better deal than the apples, which is not relevant for our investigation. Fig. 2 illustrates the results.

The aim of Study 3 is fourfold. First, we strive to eliminate a possible interruption by controlling for consumers’ weight expectations that may evoke effects in the same direction as the default-unit effect. Second, we reduce prices of products offered with non-default pricing strategies compared with the same product offered with a default pricing strategy. Thus, the choice of a product offered with default pricing strategies is economically an unwise decision and is a clear indication of the exis­ tence of the default-unit effect. Third, we aim to replicate our previous findings of the default-unit effect with another dependent varia­ ble—namely, purchase choice. Here, we can examine whether con­ sumers actually compare prices and transform the price of non-default pricing strategies to default price information. Fourth, we test whether weight information helps reduce information asymmetry and leads to higher evaluation certainty. We also analyze whether weight informa­ tion helps and motivates compare prices. 7.1. Method 7.1.1. Procedure First, we distinguished between the typicality of the pricing strate­ gies. One condition only addressed products with weight-based pricing strategies as the default (i.e., apples and clementines). The other con­ dition used products with unit-based pricing strategies as the default (i. e., cucumbers and lettuce). We did not employ a full factorial design because product categories differ between the two pricing strategy conditions. Second, we employed a mixed design with one betweensubjects factor (weight information: not stated, stated) and product category as a within-subject factor. We randomly assigned participants to one of the two default pricing strategy conditions and then randomly to one of the two weight condi­ tions. Products appeared in a randomized sequence to control for order effects. The stimuli displayed two identical products with two authentic representations of price tags: price per kilogram or price per piece. Participants needed to decide which of the two products to buy by clicking on the product picture (see Appendix C). In addition, half the participants received information about the approximate weight of the offered products. Participants were told that they needed to buy just one piece in each product category, to control for different consumption amounts. Products offered with non-default pricing strategies were approximately 10% cheaper, so purchase of a product in a default pricing strategy would indicate an economically unwise choice. Table 4 shows the complete study design.

6.3. Discussion We find additional support that consumers prefer products offered with default pricing strategies. Yet our results also provide evidence that retailers can mitigate this negative effect when they use non-default pricing strategies by reducing product information asymmetry. Cues such as the weight information reduce or even eliminate the default-unit effect. Therefore, retailers should provide additional information when employing non-default pricing strategies. We further argue that the additional information helps consumers make better-informed decisions when facing prices in non-default measures. However, we cannot disentangle the default-unit effect from other effects. First, consumers may also choose the product offered with a default pricing strategy because it is economically the better choice based on their expectations of the product weight. When consumers under-estimate product weights, prices per weight appear cheaper than prices per piece. When they over-estimate product weights, it is the other way around. Second, with equal prices for both offers, there is no need for consumers to buy a product that is offered with non-default pricing strategies, as economically there is no difference. However, purchase is economically sub-optimal when a product offered with a default pricing strategy is more expensive than an offer with a nondefault pricing strategy. In such situations, the choice of a product in a default price measure is a clear indication of the default-unit effect.

7.1.2. Participants We conducted the experiment at a computer lab of a midsized Table 4 Study design.

Weightbased is default

Unit-based is default

Fig. 2. Ratings of product preference. 5

Product category Apples

Weight information not stated per kg per piece 2.75€ 0.49€

Clementines

per kg 2.09€

per piece 0.19€

Cucumbers

per kg 1.35€

per piece 0.75€

Lettuce

per kg 1.45€

per piece 0.79€

Weight information stated per kg per piece 2.75€ 0.49€ approx. approx. 200g 200g per kg per piece 2.09€ 0.19€ approx. approx. 100g 100g per kg per piece 1.35€ 0.75€ approx. approx. 500g 500g per kg per piece 1.45€ 0.79€ approx. approx. 500g 500g

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German university with 183 participants. Participants were 36.1% fe­ male with an average age of 23.43 years (SD ¼ 2.76). Participants received 5€ compensation for taking part in the study.

Table 5 Best choice based on weight estimations in Study 3. Best choice

7.1.3. Measures Before making their purchase decisions, participants needed to indicate their weight estimate of the displayed products in three openended questions about the minimum, average, and maximum weight of each product. The physical sizes of the product stimuli were the same as later in the purchase decision to control for weight effects. We thereby control whether participants made their product choice because it is economically the better choice based on their expectations of the product weight or because it is offered with a default rather than nondefault pricing strategy. Participants made their selection of the item to purchase, before rating the typicality of both pricing strategies separately (1 ¼ “untypical,” 7 ¼ “typical”). We also asked participants to indicate which of the two product offers was economically the better deal (per kilogram was cheaper, per piece was cheaper, or equal prices). To assess the impact of weight information on evaluation certainty, we used three indicators (Zielke, 2010): “I can assess this store very well regarding the prices,” “I cannot assess this store at all regarding the prices” (reverse coded), and “I find it difficult to assess the prices in this shopping establishment.” Cronbach’s alpha for evaluation certainty was 0.911. To test the effect of weight information on price comparisons, we employed two constructs. First, we adapted the difficulty scale of Motyka et al. (2016) to measure the ease of price comparison with five indicators. Cronbach’s alpha for ease of price comparison was .917. Second, we used the three indicators from Putrevu and Ratchford (1997) to assess whether participants actually compared prices. Cronbach’s alpha for comparing prices was .773.

Default pricing strategy Non-default pricing strategy Total

Product category Apples

Clementines

Cucumbers

Lettuce

Total

60 (65.2%) 32 (34.8%)

50 (54.3%) 42 (45.7%)

8 (8.8%) 83 (91.2%)

16 (17.6%) 75 (82.4%)

134 (36.6%) 232 (63.4%)

92 (100.0%

92 (100.0%

91 (100.0%

91 (100.0%

366 (100.0%)

choice for 48.9% of consumers. However, 44.4% of these customers still chose the offer in the default pricing strategy. When the offer in the default pricing strategy was the best choice, which was the case for the other 51.1%, the majority (78.7%) chose the offer in the default pricing strategy (χ2 ¼ 11.461, p < .001). In total, 32.6% of the participants made an economically unwise decision. When retailers provide weight infor­ mation, consumers’ prior weight expectations are irrelevant because they now know the actual product weight. Only 12.0% of consumers made an economically sub-optimal decision. Consumers seemed to recognize the better deal and purchased the actual cheaper product offer, even though it was priced per non-default pricing strategy. Table 6 displays the statistics. To offer further support that consumers use weight information, we analyzed participants’ answers to the question about which deal they believed was better. Participants explicitly indicated that the weightbased offer was cheaper when weight information was not stated (60.9%) but that the unit-based offer was cheaper when weight infor­ mation was stated (78.3%; χ2 ¼ 45.273, p < .001). Thus, weight infor­ mation clearly helps consumers identify best buys when retailers employ non-default pricing strategies, in further support of H2. We executed regression-based analyses using Model 4 of the PRO­ CESS macro with 5000 bootstrapped samples to test for mediation ef­ fects of weight information on product choice through ease of price comparisons (a proxy for processing fluency) (Hayes, 2018). The indi­ rect effect through ease of price comparison (i.e., processing fluency) on product choice is marginally significant (a � b ¼ .324, 90% confidence interval [CI] ¼ .039 to 0.631). Thus, we conclude that weight infor­ mation makes price comparisons easier (i.e., enhances processing fluency), which consequently leads to better informed purchases.

7.2. Results The results of a two-factorial ANOVA on typicality of the weightbased pricing strategy reveal that the default price measure for apples and clementines was per kilogram (M ¼ 5.75, SD ¼ 1.60 vs. M ¼ 2.70, SD ¼ 1.61; F(1, 362) ¼ 332.526, p < .001). Another two-factorial ANOVA on typicality of the unit-based pricing strategy showed that the default price measure for cucumbers and lettuce was per piece (M ¼ 5.73, SD ¼ 1.41 vs. M ¼ 2.66, SD ¼ 1.72; F(1, 362) ¼ 347.848, p < .001). 7.2.1. Weight-based pricing strategies as default To test the effects of the weight information, we submitted product choice (1 for products per kg and 2 for products per piece) to a binary logistic regression as a function of weight information (1 with weight information and 0 without weight information, serving as a betweensubjects factor) and product category as a repeated measure. As ex­ pected, when weight information was stated, participants were more likely to choose the lower-priced product, even though it was offered with a non-default pricing strategy (b ¼ 2.733, SE ¼ .566, Wald χ2(1) ¼ 23.321, p < .001, OR ¼ 15.375), in support of H2. All other ef­ fects were non-significant. To test whether participants buy the cheapest product based on their weight estimation, we calculated dummy variables for each product category indicating which product choice (per kg or per piece) was economically the best buy. For the calculation, we used the mean weight estimations and the kilogram price that was actually displayed. The results reveal that consumers under-estimated the weights of the apples and clementines. Consequently, for most consumers, apples per kilo­ gram (default pricing strategy) and clementines per kilogram (default pricing strategy) were the best offer based on their weight estimations. Table 5 gives an overview of the best choices based on weight estimations. We first analyzed choices of participants without weight information. The offer in a non-default pricing strategy was the best

7.2.2. Unit-based pricing strategies as default To test the effects of the weight information for products with unitbased pricing strategies as the default, we executed a binary logistic regression on product choice (1 for products per kg and 2 for products per piece) with weight information serving as a between-subjects factor (1 with weight information and 0 without weight information) and product category as a repeated measure. As expected, participants were more likely to choose the lower-priced product when weight informa­ tion was stated, even though it was offered with a non-default pricing strategy (b ¼ 1.344, SE ¼ .469, Wald χ2(1) ¼ 8.220, p ¼ .004, OR ¼ 0.261), in support of H2. All other effects were non-significant. We again calculated dummy variables on the basis of weight esti­ mations for each product indicating which product choice was economically the best buy. The offer per kilogram (non-default pricing strategy) was the best choice for both categories based on the average weight estimations (see Table 5). We first analyzed choices of participants without weight information. The offer in the non-default pricing strategy was the best choice for 86.5% of consumers. However, 49.4% of these customers still chose the offer in the default pricing strategy. When the offer in the default pricing strategy was the best choice, which was the case for the other 13.5%, the majority (91.7%) chose the offer in the default pricing strategy (χ2 ¼ 4.513, p ¼ .01). In total, 43.8% of the participants made 6

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Journal of Retailing and Consumer Services 53 (2020) 101956

Table 6 Best and actual product choices. Best choice

Weight information not stated (N ¼ 92)

Weight information stated (N ¼ 92)

Actual choice Default pricing strategy Non-default pricing strategy Total

Default pricing strategy

Non-default pricing strategy

Total

37 (78.7%) 20 (44.4%) 57 (62.0%)

10 (21.3%) 25 (55.6%) 35 (38.0%)

47 (51.1%) 45 (48.9%) 92 (100.0%)

Default pricing strategy

Non-default pricing strategy

Total

Not applicable

11 (12.0%)

81 (88.0%)

92 (100.0%)

Note: Unwise decisions are in bold.

an economically unwise decision. When retailers provide weight infor­ mation, consumers’ prior weight expectations are irrelevant because they now know the actual product weight. Only 26.7% made an economically sub-optimal purchase. Consumers seemed to recognize the better deal and purchased the actual cheaper product offer, even though it was priced per the non-default pricing strategy. Table 7 gives details on the statistics. When explicitly asked for the better deal, 55.6% of the participants indicated that the offer per kilogram was cheaper when retailers did not provide weight information. This rate significantly increased to 81.5% (χ2 ¼ 14.547, p < .001) when weight information was stated. Conse­ quently, weight information clearly helps consumers identify best buys when retailers employ non-default pricing strategies. Furthermore, when weight information was not stated, participants estimated the product per kilogram cheaper but still selected the products offered in a default pricing strategy. We executed regression-based analyses using Model 4 of the PRO­ CESS macro with 5000 bootstrapped samples to test for mediation ef­ fects of weight information on product choice through ease of price comparisons (a proxy for processing fluency) (Hayes, 2018). The indi­ rect effect through ease of price comparison (i.e., processing fluency) on product choice is marginally significant (a � b ¼ 0.132, 90% CI ¼ 0.312 to 0.008). Thus, we conclude that weight information makes price comparisons easier (i.e., enhances processing fluency), which consequently leads to better informed purchases.

between-subjects factors and ease of price comparison as the dependent variable also reveals only one significant main effect of the weight in­ formation; all other effects were non-significant (ps > .231). Price comparisons were easier when weight information was stated (F(1, 179) ¼ 18.423, p < .001), in support of H3b. A third two-way ANOVA with default pricing strategy (weight-based vs. unit-based) and weight information (stated vs. not stated) as between-subjects factors and ease of price comparison as the dependent variable showed that participants were more likely to compare prices when weight information was stated (F(1, 179) ¼ 21.078, p < .001); all other effects were non-significant (ps > .105). These results give support to H3c. Fig. 3 illustrates the effects of weight information on evaluation certainty and price comparison. 7.3. Discussion When unit-based pricing strategies are the default, we show that consumers choose products offered in default pricing strategies, even though they expect them to be more expensive than the same product using non-default pricing strategies. However, the design of this study prevented us from separating the default-unit effect from weight ex­ pectations for products for which retailers typically employ weightbased pricing strategies. This is because participants were more likely to choose the product that was offered per kilogram (i.e., the default pricing strategy), though this behavior is also consistent with partici­ pants’ weight estimations and evaluations of the offer. When consumers under-estimated product weights, prices per kilogram were cheaper than prices per piece. This may explain why we were unable to disen­ tangle the default-unit effect from weight expectations when weightbased pricing strategies were the default. In our study, participants ex­ pected apples to weigh around 150 g, clearly lower than common weights in the marketplace for apples and what we used to calculate the price offers. Therefore, we again test the default-unit effect in Study 4 with product weight adjusted to participants’ estimates to clearly disentangle the default-unit effect from weight effects. Consumers choose the cheaper product when weight information helps them transform the non-default price information into a default price information. Our results indicate that they can better identify the

7.2.3. Evaluation certainty and price comparison In a next step, we analyzed the effects of weight information on evaluation certainty. We ran a two-way ANOVA with default pricing strategy (weight-based vs. unit-based) and weight information (stated vs. not stated) as between-subjects factors and evaluation certainty as the dependent variable. Only one significant main effect of the weight information emerged for evaluation certainty (F(1, 179) ¼ 27.695, p < .001); all other effects were non-significant (ps > .478). Participants could better evaluate the stated prices at this store when weight infor­ mation was provided. The results give support to H3a. A second two-way ANOVA with default pricing strategy (weightbased vs. unit-based) and weight information (stated vs. not stated) as

Table 7 Best and actual product choices. Best choice

Weight information not stated (N ¼ 89)

Weight information stated (N ¼ 90) Actual choice

Default pricing strategy Non-default pricing strategy Total

Default pricing strategy

Non-default pricing strategy

Total

11 (91.7%) 38 (49.4%) 49 (55.1%)

1 (8.3%) 39 (50.6%) 40 (44.9%)

12 (13.5%)

Default pricing strategy

Non-default pricing strategy

Total

Not applicable

77 (86.5%) 89 (100.0%)

Note: Unwise decisions are in bold. 7

24 (26.7%)

66 (73.3%)

90 (100.0%)

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Journal of Retailing and Consumer Services 53 (2020) 101956

Fig. 3. Effects of weight information on evaluation certainty and price comparison.

on the ratings of typicality of the pricing strategies (t(103) ¼ 13.268, p < .001). Respondents indicated that apples were typically offered per weight (M ¼ 2.11, SD ¼ 1.50) and cucumbers per unit (M ¼ 6.02, SD ¼ 1.52). We again calculated a dummy variable for each product category indicating which product choice was economically the best buy based on weight estimations. This time, the best buy based on average weight estimations for most respondents was the offer per piece (non-default pricing strategy) when shopping for apples. The offer per kilogram (nondefault pricing strategy) was the best choice for most respondents when shopping for cucumbers. Table 9 gives an overview of the best choices based on weight estimations. The offer in the non-default pricing strategy was the best choice for 69.5% of respondents. Of these, 57.5% still chose the offer in the default pricing strategy. When the offer in the default pricing strategy was the best choice, which was only the case for 30.5% of respondents, the majority (84.4%) chose the offer in the default pricing strategy (χ2 ¼ 7.114, p ¼ .013). In total, 44.8% of respondents made an economically unwise decision. The results give support to H1. Table 10 displays the statistics.

cheaper offer when weight information is stated. This finding is also qualified by the results. First, consumers can better evaluate prices when information about product size is explicitly stated. Second, they can more easily compare the prices when the weight information is pro­ vided. That is, as the weight information eases price comparisons (i.e., enhances processing fluency), consumers are more likely to choose the cheaper product. Third, consumers are more likely to compare prices to identify the best buy when weight information is stated. 8. Study 4: disentangling default-unit effects and weight effects In Study 4, we aim to disentangle the default-unit effect from the weight effects. We adjust the underlying product weights to partici­ pants’ expectations of two products used in Study 3 (i.e., apples and cucumbers). 8.1. Method The study comprised only product category as a between-subjects factor. We again used apples as the product category with a weightbased pricing strategy as the default and cucumbers as the product category retailers typically offer using a unit-based pricing strategy. We randomly assigned respondents to one of the two experimental condi­ tions; all else was the same as in Study 3. Respondents were told that they needed to buy just one piece in each product category, to control for different consumption amounts. Note that product prices in the nondefault pricing strategies were again 10% cheaper than product prices in the default pricing strategies. Table 8 shows the study design.

8.3. Discussion First, we replicate the default-unit effect in this study. Second, we clearly disentangle the default-unit effect from weight effects by demonstrating that consumers prefer products offered in default pricing strategies, even though they expect them to be more expensive than the same product retailers offer using non-default pricing strategies.

8.1.1. Respondents We conducted the experiment online and recruited 105 respondents. Respondents were 54.3% female with an average age of 30.62 years (SD ¼ 11.286).

9. General discussion We investigated default pricing strategies for produce across a series of studies. The pilot study shows that the default pricing strategy de­ pends on the product category. We also reveal the existence of some product categories that have ambiguous default pricing strategies. Study 1 establishes the default-unit effect by showing that consumers prefer products offered with default pricing strategies to product offers with non-default pricing strategies. The results also suggest that consumers are indifferent in their choice when default pricing strategies of a product are ambiguous. In Study 2, we introduce explicitly stated weight information about the offered products as a boundary condition. The results show that the default-unit effect vanishes with stated weight information. Study 3 demonstrates that this effect is not due to weight

8.1.2. Measures Before making purchase decisions, respondents needed to estimate the weight of the displayed products with three indicators (minimum, average, and maximum weight). Then, they made their selection of the item to purchase by clicking on the product picture, before rating the typicality (1 ¼ “per weight typical,” 4 ¼ “both typical,” 7 ¼ “per piece typical”). 8.2. Results The results of a t-test show that both products differed significantly

Table 9 Best choice based on weight estimations in Study 4.

Table 8 Study design.

Best choice

Product category

Price information

Apple Cucumber

per kg 2.65€ per kg 1.65€

Default pricing strategy Non-default pricing strategy Total

per piece 0.39€ per piece 0.79€

8

Product category Apples

Cucumbers

Total

23 (43.4%) 30 (56.6%) 53 (100.0%)

9 (17.3%) 43 (82.7%) 52 (100.0)

32 (30.5%) 73 (69.5%) 105 (100.0%)

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Journal of Retailing and Consumer Services 53 (2020) 101956

that consumers consider the default when they are employing nondefault strategies to reduce the negative effects. Moreover, the addi­ tional weight information leads to higher evaluation certainty. Previous research shows that evaluation certainty is also an important dimension of retailer price image, which is a substantive driver of shopping in­ tentions at a store (Zielke, 2010). In addition, the results contribute to the debate surrounding con­ sumer policy. Especially in online grocery stores, consumers are not able to assess product weights. First, consumers cannot assess the true value of a product in a non-default pricing strategy without knowing its weight; only the additional weight information allows them to transform this price information into their reference price information. Conse­ quently, consumers face a high degree of uncertainty when retailers do not provide detailed information about their product offers when employing non-default pricing strategies. Second, consumers do not know about the unit price per weight when purchasing a product per unit. Consequently, price comparisons between products offered with different pricing strategies are only possible when retailers provide weight information; only then can consumers transform the price per unit into a price per weight to compare the prices. This problem of price comparisons is not only the case with unpackaged products but also when comparing unpackaged and pre-packaged products. Pre-packaged products are offered with a unit price in many countries to help con­ sumers identify cheaper products (Weeks et al., 2016; Yao and Oppewal, 2016). However, in most cases retailers state unit prices per weight. Again, consumers can only compare prices when retailers provide weight information. Regulations on unit pricing widely neglect unpackaged products.

Table 10 Best and actual product choices. Best choice

Default pricing strategy Non-default pricing strategy Total

Actual choice Default pricing strategy

Non-default pricing strategy

Total

27 (84.4%) 42 (57.5%) 69 (65.7%)

5 (15.6%) 31 (42.5%) 36 (34.3%)

32 (30.5%) 73 (69.5%) 105 (100.0%)

Note: Unwise decisions are in bold.

expectations. Surprisingly, we find that consumers still prefer default pricing strategies, even when their weight expectations indicate that the product offered in a non-default pricing strategy is an economically better choice for them. Further supporting the findings, Study 4 adjusts product weights to customers’ estimations and shows that the defaultunit effect exists regardless of consumers’ weight expectations. The re­ sults also suggest that weight information given by retailers helps con­ sumers identify the economically better choice. With this information, they not only choose the cheaper product offer but also have higher evaluation certainty. In addition, weight information eases price com­ parisons and motivates consumers to compare prices. 9.1. Theoretical and managerial implications The presented studies have meaningful implications for research. In contrast with previous research (e.g., Lembregts and Pandelaere, 2013; Zhang and Schwarz, 2012), we investigate negative attributes because consumers tend to prefer lower values (i.e., a lower price) to higher values. In this context, we are able to disentangle the numerosity effect from the default-unit effect by showing that preference is driven by default units rather than by numerosity. Thus, we demonstrate that the default-unit effect also holds when attribute information in default units is specified in larger numbers. In addition, our findings add to research on unitosity (e.g., Monga and Bagchi, 2012; Ülkümen and Thomas, 2013) by showing how addi­ tional information helps consumers transform one unit into another. The weight information allows consumers to compare prices when different pricing strategies are employed. Our results indicate that consumers indeed compare prices when this information is provided. Consequently, the additional information mitigates the effect arising from the pricing strategy itself. The weight information also enhances processing fluency, and therefore consumers are more likely to choose the cheaper product. Furthermore, we investigate a situation in which products are offered in price measures that are atypical for the product category. In particular, we were interested in unit-based prices for produce that are commonly offered with a weight-based price but that are increasingly used in online retailing. Thus, our research responds to Lembregts and Pandelaere’s (2013) call by investigating a transition period in which one unit becomes increasingly important. In product categories that retailers offer with ambiguous pricing strategies, the default-unit effect does not occur when two strategies are similarly typical. In such situa­ tions, consumers are indifferent between two offers in different pricing strategies. Our findings also have important implications for retailers. Retailers need to be aware of the default-unit effect, especially online retailers, which often employ non-default pricing strategies. Consumers evaluate product offers in non-default pricing strategies poorly and seem to avoid these offers. Thus, retailers should try to attain a price premium for products sold in default measures. Yet, if they use non-default pricing strategies, they can reduce or even eliminate the negative effects by providing weight information to allow consumers to transform different price measures. Retailers should also try to change the pricing strategy

9.2. Limitations and future research Our findings highlight the importance of price measures and pricing strategies in price evaluations. Retailers that offer products in different price measures (i.e., employ different pricing strategies) complicate price comparisons for consumers because consumers must re-calculate prices to compare them. Unit pricing was introduced to simplify price comparisons and to reduce mathematical operations, but regulations so far focus only on pre-packaged products. Future research might further elaborate on situations when consumers compare only unpackaged or unpackaged and pre-packaged products and their consequences for unit pricing. All our studies took place in Germany and resemble the market sit­ uation in that country. Retailers in other countries may use different pricing strategies, and thus the default may differ from those in our experiments. Furthermore, in different types of marketplaces (e.g., su­ permarkets, farmers markets) default pricing strategies may also vary. Consequently, retailers need to know what customers believe is the default pricing strategy for each category in their market to understand the ramifications of a change of pricing strategies. This paper focused only on the role of weight information for unpackaged and single-offered produce. For pre-packaged produce offered with weight-based prices, unit information may also help over­ come uncertainty when shopping for groceries online. Consumers are not able to see the groceries in this context and consequently face problems in assessing the product size. A bag of apples offered with a weight-based price and the additional information on the numbers of apples (i.e., unit information) may help them better assess the product offer. Future research might address the effects arising from unit infor­ mation in this context. Moreover, we only assessed the role of price measures and pricing strategies in the specific context of grocery retailing (i.e., fruits and vegetables). Yet, in many other marketing areas, companies introduce new price measures in their price schemes, especially in industrial markets (Essegaier et al., 2002; Hypko et al., 2010). It would be worthwhile to investigate whether the identified effects occur in other contexts as well. 9

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Journal of Retailing and Consumer Services 53 (2020) 101956

Acknowledgements

agencies in the public, commercial, or not-for-profit sectors.

This research did not receive any specific grant from funding

Appendix A. Sample stimuli in Study 1

Appendix B. Sample stimuli in Study 2

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Journal of Retailing and Consumer Services 53 (2020) 101956

Appendix C. Sample stimuli in Study 3

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