title: "Concealing Prices: How Delayed Price Disclosure Influences Consumer Purchase Decisions" authors: "Felipe M. Affonso, Amin Shiri, Diego Aparicio, Minzhe Xu, Xiang Wang, Chris Janiszewski, Marco Bertini" journal: "Journal of Consumer Research" year: 2025 doi: "10.1093/jcr/ucaf051" citation: "Affonso, Felipe M., Amin Shiri, Diego Aparicio, Minzhe Xu, Xiang Wang, Chris Janiszewski, and Marco Bertini (2025), \"Concealing Prices: How Delayed Price Disclosure Influences Consumer Purchase Decisions,\" Journal of Consumer Research." > **Disclaimer:** This is a machine-readable conversion of the published paper for use with AI tools. It may contain conversion errors in formatting, tables, or equations. Always verify against the [published version](https://doi.org/10.1093/jcr/ucaf051). Concealing Prices: How Delayed Price Disclosure Influences Consumer Purchase Decisions Felipe M. Affonso Amin Shiri Diego Aparicio Minzhe Xu Xiang Wang Chris Janiszewski Marco Bertini Felipe M. Affonso () is Assistant Professor of Marketing, Spears School of Business, Oklahoma State University, USA. Amin Shiri () is a doctoral student in Marketing, Mays Business School, Texas A&M University, USA. Diego Aparicio () is Assistant Professor of Marketing, IESE Business School, University of Navarra, Spain. Minzhe Xu () is Assistant Professor of Marketing, Ivy College of Business, Iowa State University, USA. Xiang Wang () is Assistant Professor of Marketing, Lingnan University, China. Chris Janiszewski () is the Russell Berrie Eminent Scholar Chair and Professor of Marketing, University of Florida, USA. Marco Bertini () is Professor of Marketing, Esade, Universitat Ramon Llull, Spain. The first three authors contributed equally to this research. Supplementary materials are included in the Web Appendix accompanying the online version of this article. Concealing Prices: How Delayed Price Disclosure Influences Consumer Purchase Decisions Felipe M. Affonso Amin Shiri Diego Aparicio Minzhe Xu Xiang Wang Chris Janiszewski Marco Bertini Forthcoming, *Journal of Consumer Research* Felipe M. Affonso () is Assistant Professor of Marketing, Spears School of Business, Oklahoma State University, USA. Amin Shiri () is a doctoral student in Marketing, Mays Business School, Texas A&M University, USA. Diego Aparicio () is Assistant Professor of Marketing, IESE Business School, University of Navarra, Spain. Minzhe Xu () is Assistant Professor of Marketing, Ivy College of Business, Iowa State University, USA. Xiang Wang () is Assistant Professor of Marketing, Lingnan University, China. Chris Janiszewski () is the Russell Berrie Eminent Scholar Chair and Professor of Marketing, University of Florida, USA. Marco Bertini () is Professor of Marketing, Esade, Universitat Ramon Llull, Spain. The first three authors contributed equally to this research. Supplementary materials are included in the Web Appendix accompanying the online version of this article. **ABSTRACT** This article presents the first systematic empirical investigation into a longstanding question in retail: Is it better to display prices upfront or reveal them later in the purchase process? Two large-scale field studies demonstrate that delayed price disclosure can either increase or decrease sales. Supporting lab studies reveal that one plausible explanation is that a price delay allows price beliefs to shift consumers' internal reference prices upward or downward, creating either positive or negative price expectation disconfirmations when prices are revealed. When consumers anticipate prices should be expensive (e.g., from premium brands or upscale stores), a price delay allows price beliefs to shift price expectations upward, making purchases more likely when prices are revealed. Conversely, when consumers anticipate prices should be inexpensive (e.g., sales events or discount stores), a price delay allows price beliefs to shift price expectations downward, making purchases less likely when prices are revealed. Our findings offer retailers actionable insights on when to reveal prices to customers. In doing so, the authors contribute to the literature on price obfuscation and challenge the conventional wisdom that shopping experiences should always minimize friction. (184 words) *Keywords*: Price disclosure, price obfuscation, consumer expectations, pricing, retailing. A classic retail tactic is to withhold prices from consumers. Window displays have long invited passersby to "ask inside for prices." Print catalogs prompted readers to "contact us for prices." More recently, e-commerce websites urge shoppers to "add to cart to see price." Despite its persistence, businesses continue to debate whether "obfuscating" pricesdefined as "actions that firms take to make price search more costly" (Ellison 2016, p. 288)enhances or harms sales performance. Some proponents argue that "it is downright stupid to show your prices" alongside products (Silva 2018), claiming consumers must first internalize a product's value before being exposed to its cost. Conversely, opponents insist that retailers should be forthcoming and "own" the price conversation (Hubspot 2017), reasoning that delaying price disclosure creates confusion, eroding trust and drawing unwanted attention to cost. Prior research has found that obfuscating a part of the price can be beneficial for retailers, especially when enough consumers in a market are unwilling or unable to determine what they should pay. For instance, advertising a low base price while obscuring ancillary charges (e.g., delivery costs, service fees, taxes) can lead to increased sales compared to disclosing the total cost upfront (Chiles 2021). This occurs because some consumers commit to the purchase before seeing the additional charges, while others disregard the charges once revealed. However, academic research has largely overlooked the impact of concealing the entire price of a product. We propose that delaying price disclosure influences sales by amplifying the impact of price beliefs on expected prices. Consumers hold pre-existing price beliefs based on product type (e.g., discount, luxury), quality level (e.g., low, high), retailer (e.g., discount, premium), or the economic environment (e.g., high inflation). When the disclosure of a price is delayed, consumers rely more heavily on these pre-existing beliefs to form price expectations. Consequently, expensive-price beliefs (inexpensive-price beliefs) shift price expectations upward (downward), making an offer price seem more (less) appealing once the price is revealed, thus increasing (decreasing) the likelihood of purchase. Consistent with these hypotheses, we show that delayed (vs. immediate) price disclosure increases purchase intentions when consumers hold expensive-price beliefs. We demonstrate this effect in a field study at the online store of a mainstream retailer (study 1), and in lab experiments where the product is premium (study 2) or sold by a premium retailer (study 5). We also show that delayed (vs. immediate) price disclosure decreases purchase intentions when consumers hold inexpensive-price beliefs. We demonstrate this effect in a field study investigating the sales promotions of a mainstream retailer (study 3), and in lab experiments where the product is on deal (study 4) or sold by a discount retailer (study 5). We also test a practical boundary condition of our effects: the disclosure of a minimum advertised price (MAP) policy as a reason for delaying the price, which moderates the effect of a delayed price (study 6). Our research offers three key contributions. First, our research offers retailers insights into optimal price disclosure timing, moving beyond the reliance on anecdotes and post-hoc justifications that have previously dominated the debate. Second, it extends the price obfuscation literature by examining the understudied tactic of full price concealment and revealing its unique effect on shifting price expectations. Contrary to the common belief that obfuscation is universally beneficial, our findings demonstrate predictable situations where it is detrimental. Finally, our findings engage with the broader management conversation about friction in the customer journey. Contrary to the prevailing advice to pursue "zero-friction" experiencesas exemplified by Facebook's (2023) vision for retailour studies show that introducing specific forms of friction (delaying price disclosure) can be positive. **PRICE OBFUSCATION** Obfuscating Ancillary Charges Price obfuscation has been studied through both theoretical and empirical research. Theoretically, hidden surcharges can be used to exploit 'vulnerable' consumers who may intentionally or unintentionally underestimate the actual cost of a product. This exploitation arises from several factors: consumers within a market exhibit varying degrees of sensitivity to price differences (Ellison 2005); some demonstrate either myopia or sophistication regarding future expenses (Carlin 2009; Gabaix and Laibson 2006); individual differences in search costs exist (Ellison and Wolitzky 2012); and some consumers may simply be confused by obfuscation (Chioveanu and Zhou 2013). Despite the differing approaches to modeling consumer behavior, these studies consistently conclude that firms benefit from concealing prices. Similarly, empirical studies on price obfuscation have consistently shown that 'dripping' ancillary charges later in the purchase process, rather than disclosing them upfront, increases demand (Blake et al. 2021; Brown, Hossain, and Morgan 2010; Chetty, Looney, and Kroft 2009; Goldin and Homonoff 2013; Hossain and Morgan 2006; Morwitz, Greenleaf, and Johnson 1998). Specifically, in online shopping contexts, research indicates that starting eBay auctions with low opening prices and high shipping fees generates more bidders and higher revenue compared to bundling the base price and shipping fees (Hossain and Morgan 2006). The authors suggest that obfuscation exploits consumers' tendency to downplay surcharges. Further, eBay sellers have been shown to capture more revenue by shrouding shipping fees, but not when they are disclosed upfront (Brown et al. 2010). Similarly, a field experiment on StubHub, a secondary marketplace for event tickets, showed that consumers were more likely to select and purchase more expensive tickets when mandatory fees were initially hidden (Blake et al. 2021). Analysis of click-stream data by the same authors also indicated that price shrouding hindered price comparisons. Beyond the digital world, in physical retail settings, research indicates that disclosing a product's sales tax on the shelf, rather than at the cashier, reduces sales by approximately 8% (Chetty et al. 2009). This reduction was attributed to increased salience of the sales tax, rather than consumer ignorance about taxes. This finding has been replicated and further explored, revealing that low-income consumers pay closer attention to taxes levied at the register compared to high-income consumers (Goldin and Homonoff 2013). In laboratory settings, studies have shown that separating a buyer's premium from the product price increases demand (Morwitz et al. 1998). This is partly due to consumers anchoring on the base price and insufficiently adjusting for the premium when calculating the total price.[^1] Obfuscating an Entire Price To our knowledge, there are only two articles that study the obfuscation of an entire price. First, Lynch and Ariely (2000) manipulated price "usability" such that participants in the high usability condition saw prices upfront and could sort products according to this information, while those in the low usability condition saw prices at a later stage (and, therefore, had no sorting option). They found that "increasing price usability had no reliable effect" on price sensitivity (Lynch and Ariely 2000, p. 100), but this conclusion relied on one laboratory experiment with a sample of 72 participants for an eight-cell between-subjects design. Second, Karmarkar, Shiv, and Knutson (2015) used functional magnetic resonance imaging to study the relationship between product primacy (product information before price information) or price primacy (price information before product information) and brain activity. While the authors found that product primacy shifted the focus of participants from assessing monetary worth to assessing product liking, the effect on purchase intentions was unclear. In addition, although price primacy has been demonstrated in laboratory settings, it is uncommon in real-world contextsunlike the obfuscation of an entire price, which is frequently observed in practice. Research Opportunity In summary, the price obfuscation debatewhether businesses should reveal prices upfront or later in the purchase processspans various sectors and retail formats, generates conflicting opinions, yet lacks substantial empirical evidence. The existing literature on price obfuscation highlights two key avenues for new research. First, while all obfuscation strategies inherently delay price discovery, the practice of delaying the disclosure of an entire price is sufficiently common to merit focused investigation. Second, if delayed price disclosure indeed alters purchase behavior, current explanations within the price obfuscation literature may be insufficient. When businesses withhold the full price, it is unlikely that consumers pre-commit to a purchase or misrepresent price information once revealed. Therefore, a deeper examination of this tactic is warranted. **PRICE EXPECTATIONS** Price expectations, also known as internal reference prices, serve as benchmarks against which consumers evaluate offer prices (Kalwani et al. 1990; Monroe 1973; Winer 1986). These expectations are shaped by three primary factors: (1) knowledge of the product's price history, (2) product attributes, and (3) price beliefs. Knowledge about the price history of the product helps a consumer understand the value a seller, and the marketplace, assign to a product as well as price trends (Kalyanaram and Winer 1995; Monroe 1973; Winer 1986). Product attribute levels help a consumer form price expectations when price history is unavailable (Shirai and Meyer 1997). For example, a first-time home buyer knows that the neighborhood, square footage, and quality of internal features (e.g., flooring, cabinets, fixtures) influence price. Price beliefs influence the construction of, or adjustments to, price expectations (Hamilton 2024). In this discussion, we focus on how price beliefs influence price expectations. Price Beliefs Price beliefs are typically directional, in that a consumer can believe a product is going to be inexpensive or expensive. There are three primary sources of price beliefs. First, there are beliefs about a retailer's price image (Hamilton and Chernev 2013). Consumers can use the retailer's non-price characteristics (e.g., ambiance, décor, and location) and pricing policy (e.g., price advertising, price-points, promotional history) to form beliefs that the retailer has inexpensive (e.g., Walmart) or expensive (e.g., Nordstrom) prices. Second, there are price beliefs about categories or subcategories of products (Cheng and Monroe 2013; Mazumdar, Raj, and Sinha 2005). For example, premium product categories (e.g., diamonds, caviar, yachts) should be more expensive than value product categories (e.g., ramen noodles, dish rags, cotton swabs). Likewise, premium products in a category (e.g., designer purse, haute couture) should be more expensive than basic products in the same product category (e.g., affordable handbag, fast fashion). Subcategory price beliefs are based on differences in attributes or attributes levels between sub-categories. For example, espresso machines with programable settings (versus not) belong to the premium subcategory. Similarly, espresso machines with a dual boiler system, versus a single boiler system, belong to the premium category. Consumers aggregate product features and represent beliefs at the subcategory level, because is it more cognitively efficient, though it is possible for consumers to use beliefs about individual attributes to inform price beliefs. Third, there are non-attribute events that are used to make inferences about a product's price (e.g., advertising, external reference prices, general economic conditions) (Biswas and Blair 1991; Cheng and Monroe 2013). Regardless of the source, price beliefs can be used to construct price expectations when limited information is available or to adjust price expectations that are initially based on product attributes. If price beliefs are shared by consumers in a target market, these beliefs will shift the distribution of price expectations for the target market (see figure 1) (Hamilton and Chernev 2013). Manufacturers and retailers can proactively influence price beliefs and alter consumers' expectations about price, independent of the product itself. For example, premium retailers design their spaces (a store's layout, fixtures, window displays, etc.) to convey an image of exclusivity, luxury, or meticulous quality. These actions can prompt shoppers to anticipate even higher prices (i.e., distribution D in figure 1), which will result in more unit sales at a current offer price or similar sales at a higher offer price (Vigneron and Johnson 1999; Wiedmann, Hennings, and Siebels 2009). Similarly, the product mix offered by a retailer plays a crucial role in creating and reinforcing price beliefs. An assortment dominated by premium products signals that the retailer caters predominantly to an upmarket clientele, reinforcing the expectation of higher prices (Allard and Griffin 2017; Simonson 1999). **FIGURE 1.** DISTRIBUTIONS OF CUSTOMER PRICE EXPECTATIONS [The figure displays three overlapping bell-curve (normal) distributions plotted on a horizontal axis labeled "Expected Price" running from "Low" (left) to "High" (right), and a vertical axis labeled "Number of Customers with E($)." The three distributions are identical in shape but shifted horizontally. Distribution B (leftmost, outlined in black) is labeled "Adjustment for inexpensive-price belief" and represents the downward shift in price expectations when consumers hold inexpensive-price beliefs. Distribution C (center, outlined in black) is labeled "Price expectation based on price history and product attributes" and represents the baseline distribution. Distribution D (rightmost, outlined in black) is labeled "Adjustment for expensive-price belief" and represents the upward shift when consumers hold expensive-price beliefs. Each distribution's peak is marked with a vertical dashed line descending from the peak to the horizontal axis, labeled B, C, and D respectively. The three curves overlap partially, illustrating how price beliefs shift the entire distribution of expected prices within a target market rather than changing its shape.] *Note: Figure described by AI (Claude Opus) from published PDF. Values cross-referenced against paper text.* NOTE. Curve C illustrates the baseline distribution of a target market's price expectations, derived from price history and product attributes. Curve B (D) represents adjustments to price expectations owing to the target market's belief that prices will be inexpensive (expensive). Discount retailers are positioned to encourage consumers to believe they offer lower prices (i.e., distribution B in figure 1). Discount retailers design their spaces to convey an image of affordability, frugality, or essential quality to attract customers who desire or require low price points (Chang and Wang 2014; Grace and O'Cass 2005). Similarly, low-end retailers offer product assortments comprised primarily of "value" or "economy" alternatives to signal that they cater to a budget-conscious clientele (Simonson 1999). These actions encourage and reinforce inexpensive-price beliefs. Price Delays, Price Beliefs, Price Expectations, and Purchase Behavior Price expectations are a function of a weighted average of a product's price history, product attributes, and price beliefs. Price beliefs have a stronger influence on price expectations when there is uncertainty about how to assign value to product attributes, such as when there is a limited history of purchasing and/or using a product (Shirai and Meyer 1997). Price beliefs can influence the anticipated performance of individual attributes (Plassmann et al. 2008; Rao 2005) or exert a general, halo-like influence on the expected price (Hamilton 2024). Price beliefs can also exert a stronger influence on price expectations when contextual factors make these beliefs more salient, as is the case when a price is absent (i.e., delayed) and consumers must infer pricing information. When the price is delayed, as opposed to disclosed immediately, price beliefs can exert a greater influence in shaping price expectations. In other words, a customer's belief about whether a price will be inexpensive or expensive has a significantly larger impact on their price expectations when the price is delayed, rather than disclosed immediately. This occurs because the absence of price information creates a knowledge gap that consumers fill by relying on their pre-existing beliefs about what the price should be. The predicted influence of price beliefs and price delays on price expectations is illustrated in figure 2. The top panel shows a situation where the belief is that prices will be expensive. This belief shifts the distribution of consumers' price expectations from distribution C (i.e., baseline price expectation based on price history and product attributes) to distribution D (i.e., adjustment for expensive-price belief). A price delay amplifies the relative weight of the expensive-price belief in the price expectation judgment, further shifting consumer price expectations to distribution E (i.e., amplification of the expensive-price belief). The bottom panel of figure 2 shows the opposite effect. When the belief is that prices will be inexpensive, the distribution of consumers' price expectations shifts from distribution C (i.e., baseline price expectation based on price history and product attributes) to distribution B (i.e., adjustment for inexpensive-price belief). A price delay amplifies the relative weight of the inexpensive-price belief in the price expectation judgment, so that the price expectation shifts from distribution B to distribution A (i.e., amplification of the inexpensive-price belief). Shifts in price expectations should influence consumer purchase behavior and, by extension, the overall sales in a target market. To illustrate, consider the top panel of figure 2. When an expensive-price belief shifts consumers' price expectations from distribution C to distribution D, more consumers will have expectations exceeding the offer price (OP). When considering the offer price, these consumers should experience a favorable price expectation disconfirmation (i.e., \[E(\$)~D~ OP\] is positive) because the offer price is below their expected price (Kalyanaram and Winer 1995). Consumers with a favorable price expectation disconfirmation should be more willing to purchase the product because the offer price is better than expected. A price delay, which further shifts price expectations to distribution E, should increase the number of consumers with a favorable expectation disconfirmation even further, leading to higher sales. Of course, this prediction assumes homogeneity in consumer price beliefsspecifically, that all consumers in the target market share expensive-price beliefs, as consumers with inexpensive price beliefs would shift their price expectations downward. Similar predictions can be made in the lower panel of figure 2: inexpensive-price beliefs should shift price expectations downward (distribution B), and even more so with a price delay (distribution A). This results in fewer consumers willing to purchase the product, as more of them will experience an unfavorable price expectation disconfirmation. Again, heterogeneity in price beliefs will weaken this effect. **FIGURE 2.** PRICE BELIEFS AND PRICE DELAY INFLUENCE PRICE EXPECTATIONS [The figure contains two stacked panels, each showing overlapping bell-curve distributions on axes with "Number of Customers with E($)" (vertical) and Expected Price (horizontal). Top panel, labeled "Expensive Price Belief Effect": Three overlapping bell-curve distributions are shown, labeled C (leftmost), D (middle), and E (rightmost). Their means are marked as E($)_C, E($)_D, and E($)_E on the horizontal axis. A vertical dashed line marks the Offer Price (OP), positioned between E($)_C and E($)_D. A horizontal arrow labeled "Delay" spans from distribution D to distribution E, illustrating how the price delay amplifies the rightward shift in expectations. Below the horizontal axis, a colored bar is divided at the OP line: the left portion is labeled "Unfavorable E($) Disconfirmation" and the right portion is labeled "Favorable E($) Disconfirmation." Because distributions D and E are shifted to the right of OP, more consumers in these distributions fall in the favorable disconfirmation zone. Bottom panel, labeled "Inexpensive Price Belief Effect": Three overlapping bell-curve distributions are shown, labeled A (leftmost), B (middle), and C (rightmost). Their means are marked as E($)_A, E($)_B, and E($)_C on the horizontal axis. The Offer Price (OP) is positioned to the right of E($)_C. A horizontal arrow labeled "Delay" spans from distribution B to distribution A, illustrating how the price delay amplifies the leftward shift in expectations. Below the horizontal axis, the same colored bar appears: the left portion is labeled "Unfavorable E($) Disconfirmation" and the right portion is labeled "Favorable E($) Disconfirmation." Because distributions A and B are shifted well to the left of OP, more consumers in these distributions fall in the unfavorable disconfirmation zone.] *Note: Figure described by AI (Claude Opus) from published PDF. Values cross-referenced against paper text.* NOTE. In the top panel of the figure, E(\$)~C~ is the mean expected price of all target market consumers in the absence of price beliefs. E(\$)~D~ is the mean expected price of all target market consumers in the presence of expensive-price beliefs. E(\$)~E~ is the mean expected price, of all target market consumers, in the presence of expensive-price beliefs and a price delay. OP is the offer price. Consumers with price expectations to the right (left) of the offer price should experience a favorable (unfavorable) price expectation disconfirmation and be more (less) likely to purchase the product. The bottom panel shows corresponding effects for target market consumers with inexpensive-price beliefs. In summary, when the dependent variable is purchase intention, it should increase (decrease) as favorable (unfavorable) price expectation disconfirmation becomes more extreme. The hypotheses represented in figure 2 are summarized below. > **H1**: When consumers believe a price will be expensive, a delayed price (compared to an immediate price): a. will increase purchase intention and/or sales; b. because of a more favorable price expectation disconfirmation (mediator). > **H2**: When consumers believe a price will be inexpensive, a delayed price (compared to an immediate price): a. will decrease purchase intention and/or sales; b. because of a more unfavorable price expectation disconfirmation (mediator). **OVERVIEW OF STUDIES** We test the hypotheses as follows. Study 1 is a field study that tests H1a (i.e., a price delay increases sales) at the on-line store of a mainstream consumer retailer. A post-test is used to support the assumption that shoppers had expensive-price beliefs. Study 2 is a lab study that shows H1b is one plausible explanation for the effects observed in the field. When consumers have expensive-price beliefs, a price delay increases purchase intent because consumers experience a more favorable price expectation disconfirmation. Study 3 is a field study that tests H2a (i.e., a price delay decreases sales) using responses to a national retailer's e-mail promotional appeals. A post-test is used to support the assumption that shoppers had inexpensive-price beliefs. Study 4 is a lab study that shows H2b is one plausible explanation for the effects observed in the field. When consumers have inexpensive-price beliefs, a price delay decreases purchase intent because consumers experience a more unfavorable price expectation disconfirmation. Study 5 is a lab study that manipulates price beliefs via store image and shows how a price delay increases (decreases) sales when people have expensive (inexpensive) price beliefs. Study 6 is a lab study showing how the disclosure of the reasons for a price delay moderates the influences of the price delay on expectation disconfirmation and purchase intention. Data and code for all studies are available for download at the OSF repository ([https://osf.io/xt42w/?view_only=f6169fde8e7b4578be228f18abf79e19](https://osf.io/xt42w/?view_only=f6169fde8e7b4578be228f18abf79e19)). The Web Appendix provides full materials and additional information for all studies. **STUDY 1: DELAYING PRICE IN AN ONLINE STORE INCREASES SALES** Study 1 tests H1a that when consumers believe a product will be expensive, a delayed price (versus an immediate price) will increase sales. This field study was conducted in the online store of Fravega, an Argentinian retailer.[^2] Fravega is a mid-tier retailer of household appliances and technology products, similar to Best Buy in the United States. Privately held Fravega had estimated sales of \$2.3B in 2024 (\$188M in e-commerce). Fravega established its online channel in 2014 and routinely conducts up to five online A/B tests per month. This partnership provided us with a real-world setting to test our hypothesis. In 2020, the year these data were collected, the inflation rate in Argentina was 42%. Thus, consumers' price beliefs associated with Fravega (i.e., mid-tier retailer), the product category and product line (see below), and the economic environment, were likely expensive-price beliefs. Method *Design and Data*. The field study had two conditions: (1) immediate price disclosure (henceforth immediate-PD), with prices displayed alongside the products, and (2) delayed price disclosure (henceforth delayed-PD), with prices displayed only after a query was made (i.e., after a product was clicked on). In collaboration with the retailer, the experiment was conducted over an 8-week period (56 days) during the spring of 2020. The data and code are available on OSF. *Procedure*. Study 1 involved electric kettles (26 options) and espresso coffee machines (18 options). The procedure is illustrated in figure 3 using English language materials for espresso machines. Prospective customers who browsed either category saw a "thumbnail" for each product featuring an image and short description of each product. In the immediate-PD condition, prices appeared in the thumbnails (see the four products on the left side of the display in figure 3). In the delayed-PD condition, the pictures of the products and descriptions were identical, but the prices were omitted (see the four products in the center of the display in figure 3). If shoppers clicked on a product, in either condition, they were directed to the retailer's standard product page, which provided additional details on features and attributes, provided the price, and displayed an "add to cart" button (see the right side of the display in figure 3). Thus, all participants had to advance to the standard product page before they could add a product to their cart. Purchase behavior was recorded. **FIGURE 3.** ILLUSTRATION OF CONDITIONS IN STUDY 1 [The figure illustrates the two-step shopping process used in Study 1 (Fravega online store, espresso machines). The layout is divided into "Step 1. Condition:" on the left and "Step 2. Product page:" on the right, connected by a large red arrow. Step 1 shows two side-by-side product listing grids. The left grid, labeled "Immediate-PD," displays four espresso machine thumbnails arranged in a 2x2 layout. Each thumbnail contains a product photo, a short description (brand and model name), star ratings, and a visible price in green text (e.g., $99.95, $148.85, $181.00, $119.99). The center grid, labeled "Delayed-PD," shows the same four products with identical photos and descriptions, but all prices are omitted. A yellow "VS." label separates the two conditions. Step 2 (right side) shows the standard product detail page that consumers in both conditions reach after clicking a product. It displays a single espresso machine (DeLonghi EC155 15 Bar Espresso and Cappuccino Machine, Black) with a large product image, the price $99.95 displayed prominently, product specifications, and an "Add to Cart" button. This page is identical across conditions, meaning that all participants see the price at this stage regardless of their initial condition assignment.] *Note: Figure described by AI (Claude Opus) from published PDF. Values cross-referenced against paper text.* The retailer was responsible for implementing the A/B test. The retailer uses the VTEX platform to manage its e-commerce business. The VTEX platform supports A/B testing and can direct web traffic, at random, to one of the two experimental conditions. The platform has the following advantages: (1) random assignment to condition, (2) the ability to store experimental assignment information in browser cookies, so a returning shopper using the same device can be reassigned to the same experimental condition, and (3) an ability to record customer behaviors during the shopping journey. A disadvantage of the platform is it uses simple random assignment (i.e., each participant who enters an A/B test has a 50/50 chance of being assigned to a condition), hence there will be unequal sample sizes across conditions. A second disadvantage is it cannot retain experimental condition information if a consumer switches devices (e.g., the first visit was on a cell phone and the second visit was on a laptop). *Retailer Data Organization*. The data were organized by day and experimental condition, collapsed over products and product categories. Thus, the data set consisted of sales metrics from two experimental conditions over 8 weeks (n = 112). The retailer also recorded data on the volume of web visits. The average number of unique daily visitors was 267 to either of the product categories in the immediate-PD condition and 280 to either of the product categories in the delayed-PD condition. There was no information on whether any customers visited both product categories on a given day, but the retailer believed this was very unlikely. *Measures*. In order to assess the robustness of the effect of the price delay manipulation, three unique shopping behaviors were analyzed. The first dependent measure was a *binary daily purchase indicator*, a useful metric for a retailer that operates in a high inflation environment. This measure assessed whether a sale occurred in either product category on that day. The binary daily purchase indicator was set to 1 if a purchase in either category took place on date *t*, or to 0 otherwise. The second measure was *daily units sold* (log transformed). Daily units sold refers to the total number of units purchased in an experimental condition on a given day. The third measure was *daily sales revenue* (log transformed). Daily sales revenue refers to the sum of the sale prices (in Argentinian pesos) for the units sold in a condition on a day. In addition, we calculated the bounce rate (the percentage of sessions ending after a single page view with no clicks to a standard product page) to check whether the delayed-PD was more interesting to shoppers (i.e., more shoppers clicked-through to standard product page). Results We estimate the model *DM~i,t~ = α + βPD + δ~t~ + ε~i,t\ ~* for each dependent measure; where *DM~i,t~* denotes the dependent measure in experimental condition *i* on day *t*, *PD* is an indicator variable for the manipulation of price disclosure equal to 0 in the immediate-PD condition and 1 in the delayed-PD condition, and *δ~t~* denotes time fixed effects.[^3] Fixed-time effects help control for the error associated with the high variability of a metric across days. Thus, the fixed-time effect reduces the error term without influencing the beta coefficient representing the effect of the price delay. Table 1 summarizes the results. Given that the daily unit sales and daily revenue are log transformed, as required by their distribution, we report the statistics for these dependent measures using the model discussed above. However, we also report the means of conditions from the raw data to aid interpretability. *Binary Daily Purchase Indicator*. Relative to the immediate-PD, the delayed-PD increased the percentage of days at least one customer made a purchase (*M*~immediate~ = .804 or 45 days, *M*~delayed~ = .947 or 53 days, *β* = .143, *SE* = .054, *t*(55) = 2.66, *p* = .010, *r*^2^ = .114). *Daily Log Units Sold*. Relative to the immediate-PD, the delayed-PD increased the daily log units sold (*M*~immediate\ log~ = 1.111, *M*~delayed\ log~ = 1.259; *β* = .148, *SE* = .082, *t*(55) = 1.82, *p* = .075, *r*^2^ = .057; *M*~immediate\ raw~ = 2.911 units, *M*~delayed\ raw~ = 3.143 units).[^4] *Daily Log Sales Revenue (pesos)*. Relative to the immediate-PD, the delayed-PD increased the daily log sales revenue (*M*~immediate\ log~ = 7.806, *M*~delayed\ log~ = 9.054, *β* = 1.248, *SE* = .514, *t*(55) = 2.43, *p* = .018, *r*^2^ = .097; *M*~immediate\ raw~ = 21893.77 pesos, *M*~delayed\ raw~ = 29109.71 pesos).[^5]^,^[^6] **TABLE 1.** ONLINE STORE STUDY SUMMARY OF RESULTS ++::+::+::+::+::+::+::+::+ |   | Probability Daily Purchase | Log Daily Units | Log Daily Revenue | Bounce Rate (%) | ++++++++++ | Delay Prices | .143 | \*\* | .1480 | ^†^ | 1.248 | \* | 3.001 | \*\* | ++++++++++ | | (.054) | | (.082) | | (.514) | | (.560) | | ++++++++++ | Constant | .804 | \*\* | 1.111 | \*\* | 7.806 | \*\* | 20.868 | \*\* | ++++++++++ | | (.038) | | (.058) | | (.363) | | (.340) | | ++++++++++ | Observations | 112 | 112 | 112 |   | 112 | +++++++ > NOTE.The dependent variables are computed daily by experimental condition and collapsed across the two product categories. Units sold and sales revenue are log-transformed. Standard errors are in parentheses. ^†^*p* \< .10. \**p* \< .05. \*\**p* \< .01. *Daily Bounce Rate*. The daily bounce rate was higher in the delayed-PD condition (23.869%) than the immediate-PD condition (20.868%; β = 3.001, *SE* = .560, *t*(55) = 5.36, *p* \< .001, *r*^2^ = .344). Note that higher bounce rates decrease, rather than increase, the likelihood that a sale will occur in the delayed-PD condition, making the hypothesis test conservative. *Robustness Checks.* In Web Appendix A, we report the estimation of a non-linear model for the binary daily purchase indicator, which yields similar results. *Post-Test: Expensive-Price Beliefs.* We recruited Prolific participants who were interested in electric kettles (n = 78) and espresso machines (n = 79) and tested the assumption that consumers had expensive-price beliefs in the shopping situation. We told participants: "In this scenario, you are browsing through a variety of electric kettles \[espresso machines\] on a store website. Your search leads to a website displaying multiple electric kettles \[espresso machines\]. The website shows pictures of the products and provides short descriptions, but there are no prices displayed - you have to click on the picture of the product to advance to a page that shows more information on the product, including the price. See below." We used Fravega's website category pages for electric kettles and espresso machines to create thumbnails similar to those used in the delayed-PD condition of the field study. Then, we translated the product information into English, while keeping the exact layout used in the field study for ecological validity (Web Appendix A). We asked people: "Does the fact that you are not seeing the price lead you to believe that..." 1 = "The price is so low, they do not want to initially show it," to 7 = "The price is so high, they do not want to initially show it." We found that the mean score was higher than the midpoint for electric kettles (*M* = 6.08, *t*(77) = 16.82, *p* \< .001) and espresso machines (*M* = 6.29, *t*(78) = 21.15, *p* \< .001), suggesting that consumers in this setting likely held expensive-price beliefs. Our post-tests, in this and subsequent studies, validate that price delays increase the salience of directional price beliefs. *Alternative Explanations.* It is possible the withholding prices encouraged consumers to make favorable inferences about product quality or the retailer. Using a procedure similar to the post-test described above, we asked 75 (87) people interested in electric kettles (espresso machines) whether the hidden price decreased/increased perceived product quality, their trust in the retailer, and customer service expectations (see Web Appendix A for details). The mean scores for all measures in both categories did not differ from the midpoint (all *p*'s \> .227). Discussion Consistent with H1a, study 1 provides evidence that delayed price exposure can increase the sales of electric water kettles and espresso machines. The process account of the effect cannot be determined because the field study was on an active retail website where consumers could not be interviewed. Yet, there are three pieces of information that are consistent with customers having expensive-price beliefs. First, the retailer is a major department store chain that targets the middle class. Second, the data were collected in a high inflation period. Third, a post-test showed that a price delay resulted in people expecting higher prices than was the norm. **STUDY 2: DELAYING PRICE FOR A PREMIUM PRODUCT INCREASES PURCHASE INTENTIONS** Study 2 (preregistration: [https://aspredicted.org/wj3c-c2ws.pdf](https://aspredicted.org/wj3c-c2ws.pdf)) is a lab study that conceptually replicates study 1. We sought to do a conceptual replication because we could not create a website, with multiple SKUs, which manipulated the timing of prices. Instead, we created a situation that encouraged expensive-price beliefs for a single product. The study used a premium espresso machine that was representative of the premium espresso machines used in study 1. We expected that delayed prices, relative to immediate prices, would increase purchase intentions (H1a) and favorable price expectation disconfirmation (H1b). We expected participants to have expensive-price beliefs in this shopping situation (confirmed in a post-test). Thus, study 2 could offer a plausible explanation for the price delay effect observed in study 1. Method *Participants and Design.* The study had two between-subjects price disclosure conditions: immediate-PD vs. delayed-PD. Following our preregistration, we initially recruited 300 Prolific participants who were then pre-screened based on their interest in espresso machines. Specifically, we asked about people's interest in espresso machines on a 5-point scale (1 = no interest at all, 2 = low interest, 3 = neutral, 4 = moderate interest, and 5 = a lot of interest). Those who answered 1, 2, or 3 were screened out and paid for their time. Those interested in espresso machines (4 or 5) continued the survey. Our final sample size had 229 participants (60.3% female, *M*~age~ = 37.38), who were randomly assigned to the conditions, and paid a fixed nominal payment. *Procedure.* Participants imagined they were interested in buying an espresso machine. In the scenario, participants imagined they were browsing through espresso machines on a store website and found an espresso machine that interested them. Participants in the immediate-PD condition saw a picture of the espresso machine, information about attributes (brand, automation level, bars of pressure, integrated grinder, customer ratings, preheat time), and price, then added it to their shopping basket. Participants in the delayed-PD condition saw the same espresso machine and information about attributes, but not the price, and then added it to their shopping basket, which is when they finally saw the price. The price (\$142.50) was the median price in a pretest (see below for details). See Web Appendix B for stimuli. Next, all participants indicated their likelihood of purchasing the espresso machine ("How likely would you be to buy this espresso machine?" 0 = extremely unlikely, 50 = somewhat likely, 100 = extremely likely) and price expectation disconfirmation ("Before seeing the actual price of the espresso machine ... 1 = "I expected a much lower price than its current price," to 4 = "I expected a price equal or similar to its current price," to 7 = "I expected a much higher price than its current price". The purchase intention and expected price disconfirmation measures were counterbalanced. There were no main effects or interactions involving the dependent variable and mediator measurement order (Web Appendix B). Lastly, participants reported their gender and age. *Pretest*. A pretest was used to set the price of the espresso machine and verify expensive-price beliefs for the espresso machine. Prolific participants (n = 82) interested in espresso machines were asked to look at the espresso machine and estimate its price in an open-ended response (*M* = \$167.68, *SD* = 117.51). The median price (\$142.50) was used to set the price in the study. Then, participants responded to a price belief question: "Relative to a typical espresso machine, I expected the price of this espresso machine to be..." (1 = much less expensive than a typical espresso machine, 50 = neither as inexpensive or expensive as a typical espresso machine, and 100 = much more expensive than a typical espresso machine)". The mean (*M* = 58.79, *SD* = 19.38) was significantly above the midpoint of the scale (*t*(81) = 4.11, p \< .001). Results *Purchase Likelihood.* We predicted that delayed-PD would increase purchase likelihood. Consistent with our prediction, a one-way ANOVA revealed a higher likelihood of buying the espresso machine in the delayed-PD condition (*M* = 62.80, *SD* = 24.60) than in the immediate-PD condition (*M* = 51.86, *SD* = 27.97; *F*(1, 227) = 9.88, *p* = .002, $\eta_{p}^{2}$ *=* .042). *Price Expectation Disconfirmation.* According to H1b, delayed-PD should make price expectation disconfirmation more favorable. Consistent with our prediction, a one-way ANOVA revealed a more favorable price expectation disconfirmation in the delayed-PD condition (*M*~delayed~ = 4.33, *SD* = 1.60; *M*~immediate~ = 3.75, *SD* = 1.38; *F*(1, 227) = 8.76, *p* = .003, $\eta_{p}^{2}$ *=* .037). *Mediation.* We ran a mediation analysis (PROCESS model 4; Hayes 2022) with 5,000 bootstrapped samples, using price disclosure (0 = immediate, 1 = delayed) as the independent variable, the likelihood of purchasing the espresso machine as the dependent variable, and price expectation disconfirmation as the mediator. Consistent with H1b, the indirect effect via price expectation disconfirmation (β = 5.86, *SE* = 1.98, 95% CI = \[2.00, 9.81\]) was significant. *Post-Test: Expensive-Price Beliefs.* A post-test similar to that of Study 1 was used to confirm delayed prices led to an expectation of higher prices. We showed 89 Prolific participants interested in espresso machines the same scenario and product from Study 2's delayed-PD condition. We told participants: "Imagine you are shopping online and interested in this espresso machine. You can't see the price yetthis happens in situations where the price tag is missing and you have to ask for more information, or when you have to add the product to the shopping cart to see the price. Does the fact that you are not seeing the price lead you to believe that..." 1 = "The price is so low, they do not want to initially show it," to 7 = "The price is so high, they do not want to initially show it." We found that the mean score was higher than the midpoint (*M* = 5.83, *t*(88) = 16.82, *p* \< .001), suggesting that expensive-price beliefs influenced price expectations in the delayed-PD condition. *Alternative Explanations.* It is possible the withholding prices encouraged consumers to make favorable inferences about product quality or the retailer. Using a procedure similar to Study 2's (condition: immediate-PD vs. delayed-PD), we asked 235 people interested in espresso machines about perceived product quality and customer service expectations (see Web Appendix B for details). There was no effect of condition on perceived product quality (*F*(1, 233) = .915, *p* = .340) and customer service expectations (*F*(1, 233) = .354, *p* = .552). Discussion Study 2 provided evidence for H1a and H1b. In a setting where information about the product and the context of the purchase prompted consumers to believe prices would be expensive, delayed-PD increased consumers' likelihood of buying the product. The effect was mediated by favorable price expectation disconfirmation, suggesting that the expensive-price belief exerted a stronger influence on expected prices when the price was delayed (see distributions D and E in figure 2). **STUDY 3: DELAYING PRICE IN A SALES FLYER REDUCES SALES** Study 3 tested H2a that when consumers have inexpensive-price beliefs, a delayed price (vs. an immediate price) will decrease sales. Study 3 was conducted in cooperation with Fravega in December 2021. In this instance, the retailer e-mailed a daily sales flyer (on seven consecutive days) to a database of over 700,000 current (high potential customers in the customer database) and prospective customers. Similar to many e-mail sales flyers, the flyer featured a limited selection of products (from 8 to 12 unique products each day, changing each day over the seven-day week, resulting in 70 unique advertised products) offered at a discount. To help communicate the opportunity for a deal, the first word of the e-mail subject line included the word "Deals" or similar terminology. Thus, the price beliefs associated with the purchase opportunity were likely inexpensive. The influence of inexpensive-price beliefs on price expectations, when there are price delays, will be verified in a post-test. Method *Procedure.* The field study was conducted using a sales flyer that the retailer regularly e-mailed. Management had historical evidence that the flyer was a powerful tool for driving engagement and promoting transactions. On this occasion, the retailer e-mailed the sales flyer on seven consecutive days to 771,583 selected addresses in its database. The subject line of the seven email flyers included the word "Deals" or similar terminology, indicating a promotional shopping context. Each of the seven flyers advertised between 8 and 12 products from various categories (electric kettles, video consoles, headphones, microwaves, blenders, speakers, etc.), all offered at a promotional price. The e-mails contained thumbnail product pictures with a format similar to that used in study 1. Examples of the flyers are shown in web appendix C. E-mail recipients were randomly assigned to the immediate-PD condition, in which products and prices appeared together on the thumbnail in the sales flyer, or to the delayed-PD condition, in which prices were omitted from the thumbnail in the sales flyer. In both conditions, a click on the thumbnail in the flyer sent the participant to the standard product page that provided details on features and attributes, listed the price, and displayed an "add to cart" button. In this experiment, 53.7% of consumers clicked on at least one product in at least one email, resulting in 4.096% of links clicked (i.e., on average, people clicked on 2.867 of the 70 advertised products). *Measures*. The measures were similar to those used in study 1, except that they were (1) at the individual level and (2) aggregated over the seven-day period. The *binary weekly purchase indicator* was set to 1 if a consumer made at least one purchase during the week, or to 0 if the consumer made no purchase during the week. Thus, it is the percentage of consumers who made a purchase. The *weekly units sold* (log transformed) measured how many total units were sold to a customer during the week. The *weekly sales revenue* (log transformed) measured the sales (in Argentinian pesos) revenue from the items purchased by a consumer during the week. *Data*. The experiment was run during December 2021, and the data refers to e-mail newsletters sent to 771,583 customers. The data and analyses code are posted at OSF. Results We estimate the model *DM~i~ = α + βPD + ε~i~*; where *DM~i~* denotes the dependent measure of consumer *i*, and *PD* is an indicator variable for the manipulation of price disclosure equal to 0 in the immediate-PD condition and 1 in the delayed-PD condition. Table 2 summarizes the results. Precision is shown at four digits because the parameter estimates are small. **TABLE 2.** E-MAIL STUDY SUMMARY OF RESULTS ++::+::+::+::+::+::+ |   | Probability (%) Weekly Purchase | Per-User Log Weekly Units | Per-User Log Weekly Revenue | ++++++++ | Delay Prices | -.2974 | \*\* | -.0022 | \*\* | -.0306 | \*\* | ++++++++ | | (.0352) | | (.0003) | | (.0035) | | ++++++++ | Constant | 2.6075 | \*\* | .0186 | \*\* | .2549 | \*\* | ++++++++ | | (.0257) | | (.0002) | | (.0025) | | ++++++++ | Observations | 771,583 |   | 771,583 |   | 771,583 |   | ++++++++ > NOTE.The dependent variables are computed weekly by participant (i.e., collapsed across all 70 products). Weekly units sold and weekly sales revenue are log-transformed. Standard errors are in parentheses. ^†^*p* \< .10. \**p* \< .05. \*\**p* \< .01. *Binary Weekly Purchase Indicator*. Relative to the immediate-PD, the delayed-PD decreased the percentage of customers who made a purchase during the week (*M*~immediate~ = 2.6075%, *M*~delayed~ = 2.3101%, *β* = -.2977, *SE* = .0352, *t*(771581) = -8.43, *p* \< .001, *r*^2^ = .0001). *Weekly Log Units Sold*. Relative to the immediate-PD, the delayed-PD decreased the weekly log units sold (*M*~immediate\ log~ = .0186, *M*~delayed\ log~ = .0164; *β* = -.0022, *SE* = .0003, *t*(771581) = -8.54, *p* \< .001, *r*^2^ = .0001; *M*~immediate\ raw~ = .0277 units, *M*~delayed\ raw~ = .0243 units). *Weekly Log Sales Revenue*. Relative to the immediate-PD, the delayed-PD decreased the daily log sales revenue (*M*~immediate\ log~ = .2549, *M*~delayed\ log~ = -.2243, *β* = -.0306, *SE* = .0035, *t*(771581) = -8.84, *p* \< .001, *r*^2^ = .0001; *M*~immediate\ raw~ = 900.86 pesos, *M*~delayed\ raw~ = 768.31 pesos)[^7]^,^[^8]. *Click-Through Rate*. One concern is that the click-through rate in the delayed-PD condition may have been lower than in the immediate-PD condition, and this explains the lower sales in the delayed-PD condition. The click-through rate for the delayed-PD condition (*M* = 2.92 out of 70 products) was actually higher than the click-through rate in immediate-PD condition (*M* = 2.81 out of 70 products, *t*(771581) = -11.32, *p* \< .001, *r*^2^ = .0001). *Robustness Checks.* Robustness checks (reported in Web Appendix C) show comparable negative results when we estimate a non-linear model. In addition, we find similar results when restricting the analysis to customers who clicked on the e-mail at least once, which suggests that the findings are not driven by differences in the design of the sales flyers across conditions. *Post-Test: Inexpensive-Price Beliefs.* A post-test was used to confirm the assumption that consumers had inexpensive-price beliefs in the sales flyer context. We created a sales flyer that mimicked the delayed-PD sales flyer in terms of number of products (nine), claimed discounts (e.g., 45% off), financing (e.g., flexible payment plan), and layout (see Web Appendix C). We recruited 100 Prolific participants and told them to imagine they received a sales flyer via email. We asked participants: "Does the fact that you are not seeing the price lead you to believe that..." 1 = "The price is so low, they do not want to initially show it," to 7 = "The price is so high, they do not want to initially show it." We found that the mean score was lower than the midpoint (*M* = 3.29, *t*(99) = -3.29, *p* = .001), suggesting that, on average, consumers held inexpensive-price beliefs in the sales flyer situation. *Alternative Explanations.* It is possible the withholding prices encouraged consumers to make unfavorable inferences about product quality or the retailer. Using a procedure similar to the post-test, we asked 100 Prolific participants whether the hidden price decreased/increased perceived product quality, their trust in the retailer, and customer service expectations (see Web Appendix C). The mean scores did not differ from the midpoint (all *p*'s \> .363). Discussion Consistent with H2a, study 3 provides evidence that delayed price exposure can decrease the sales of products offered via an e-mail sales flyer. Similar to study 1, the process account of the effect cannot be determined because the field study recorded sales on an active retail website where consumers could not be interviewed. Yet, there is evidence consistent with a majority of the customers having an inexpensive-price belief. A post-test showed people expected prices to be lower than the norm when they were not listed in a sales flyer. Still, the effects were much weaker than in study 1. One explanation may be that there was price belief heterogeneity, both across and within consumers. For example, if the positioning of the retailer, product categories, and economic environment encouraged expensive-price beliefs and the sales flyer encouraged inexpensive-price beliefs, then the relative weight of the competing beliefs will determine if an individual consumer's price expectations increase or decrease when prices are delayed. **STUDY 4:** **DELAYING PRICE FOR A DISCOUNTED PRODUCT REDUCES PURCHASE INTENTIONS** Study 4 (preregistration: ) is a conceptual replication of study 3. We sought to do a conceptual replication because we could not mimic the e-mail sales flyer procedure in the lab. Instead, we designed a procedure where all participants were encouraged to have inexpensive-price beliefs. The study used an espresso machine that was representative of the basic espresso machines seen in the sales flyers of study 3. Participants were told the espresso machine was on sale. We expected that a delayed price, relative to an immediate price, would decrease purchase intentions (H2a) because of an unfavorable price expectation disconfirmation (H2b). We expected participants to have inexpensive-price beliefs (confirmed in a post-test). Thus, study 4 could offer a plausible explanation for the price delay effect observed in study 3. Method *Participants and Design.* The study had two between-subjects price disclosure conditions: immediate-PD vs. delayed-PD. Following our preregistration, we initially recruited 300 Prolific participants, which were then pre-screened with the same criterion used in Study 2. Our final sample size had 223 participants (57.8% female, *M*~age~ = 36.30), which were randomly assigned to the conditions, and paid a fixed nominal payment. *Procedure.* Participants imagined they were interested in buying an espresso machine. In the scenario, participants were checking their email when they saw a subject line that caught their attention: "Best Deals on Espresso Machines: Up to 30% Off." Then, they opened the email and saw a sales flyer for an espresso machine. The sales flyer was labeled "Starts today: 30% off select Breville espresso machines", with a picture of the espresso machine with a large "SALE: 30% OFF" red label on it. Below the sales flyer, participants in the immediate-PD condition saw: "SALE \$100.09 ~~REG. \$142.99~~ + FREE SHIPPING". Participants in the delayed-PD condition saw: "SALE \$? (Click to see deal) ~~REG. \$?~~ + FREE SHIPPING". Participants also read: "After some consideration, you decide to click the promotion to see more information about the product." The next page showed the espresso machine, information about attributes, and the price. Whereas those in the immediate-PD condition knew the price, those in the delayed-PD condition saw the price for the first time. The price (\$100.09) was near the median price in a pretest (see below for details). See Web Appendix D for stimuli. Next, all participants indicated their likelihood of purchasing the espresso machine ("How likely would you be to buy this espresso machine?" 0 = extremely unlikely, 50 = somewhat likely, 100 = extremely likely) and expected price disconfirmation ("Before seeing the actual price of the espresso machine ..." 1 = "I expected a much lower price than its current price," to 4 = "I expected a price equal or similar to its current price," to 7 = "I expected a much higher price than its current price". The purchase intention and expected price disconfirmation measures were counterbalanced. There were no main effects or interactions involving the dependent variable and mediator measurement order (Web Appendix D). Lastly, participants reported their gender and age. *Pretest*. A pretest was used to set the price of the espresso machine and verify inexpensive-price beliefs for the espresso machine. Prolific participants (n = 76) interested in espresso machines were asked to look at the espresso machine (which had a 30% off discount, just as in the main study), and estimate its final (i.e., after the 30% OFF discount) price in an open-ended response. The median price (\$100.00) was used to set the price in the study (\$100.09). We added 9 cents to the discounted price to make the regular price before the 30% OFF discount (\$142.99) end in .99 (\$100.09/0.7 = \$142.99), which has greater ecological validity. Then, participants responded to a price belief question: "Relative to a typical espresso machine, I expected the price of this espresso machine to be ..." (1 = much less expensive than a typical espresso machine, 50 = neither as inexpensive or expensive as a typical espresso machine, and 100 = much more expensive than a typical espresso machine). The mean (*M* = 44.03, *SD* = 18.53) was significantly below the midpoint of the scale (*t*(75) = -2.81, *p* = .006). Results *Purchase Likelihood.* We predicted delayed-PD would decrease purchase likelihood. Consistent with our prediction, a one-way ANOVA revealed a lower likelihood of buying the espresso machine in the delayed-PD condition (*M* = 51.71, *SD* = 24.52) than in the immediate-PD condition (*M* = 63.17, *SD* = 21.06; *F*(1, 221) = 14.02, *p* \< .001, $\eta_{p}^{2}$ *=* .060). *Price Expectation Disconfirmation.* According to H2b, delayed-PD should make price expectation disconfirmation more unfavorable. Consistent with our prediction, a one-way ANOVA revealed a more unfavorable price expectation disconfirmation in the delayed-PD condition (*M*~delayed~ = 3.83, *SD* = 1.29; *M*~immediate~ = 4.36, *SD* = 1.33; *F*(1, 221) = 9.11, *p* = .003, $\eta_{p}^{2}$ *=* .040). *Mediation.* We ran a mediation analysis (PROCESS model 4; Hayes 2022) with 5,000 bootstrapped samples, using price disclosure (0 = immediate, 1 = delayed) as the independent variable, the likelihood of purchasing the espresso machine as the dependent variable, and price expectation disconfirmation as the mediator. Consistent with H2b, the indirect effect via price expectation disconfirmation (β = -3.34, *SE* = 1.42, 95% CI = \[-6.40, -.95\]) was significant. *Post-Test: Inexpensive-Price Beliefs.* A post-test similar to those in prior studies was used to confirm delayed prices led to an expectation of lower prices. We showed 82 Prolific participants interested in espresso machines the same scenario from Study 4's delayed-PD condition. We then asked participants: "Imagine you receive an email advertising this espresso machine on sale. The e-mail advertising does not show the priceyou have to click to see the deal. Does the fact that you are not seeing the price lead you to believe that..." (1 = "The price is so low, they do not want to initially show it," to 7 = "The price is so high, they do not want to initially show it.") We found that the mean score was lower than the midpoint (*M* = 3.32, *t*(81) = -3.06, *p* = .003), suggesting that inexpensive-price beliefs influenced price expectations in the delayed-PD condition. *Alternative Explanations.* It is possible that withholding prices encouraged consumers to make unfavorable inferences about product quality or the retailer. Using a procedure similar to Study 4's (condition: immediate-PD vs. delayed-PD), we asked 256 people interested in espresso machines their perceived product quality and customer service expectations (see Web Appendix D for additional details). There was no effect of condition on perceived product quality (*F*(1, 254) = .829, *p* = .364) and customer service expectations (*F*(1, 254) = .420, *p* = .517). Discussion Study 4 provided evidence consistent with H2a and H2b. In a setting where information about the product and the context of the purchase prompted consumers to believe prices would be inexpensive, delayed-PD decreased consumers' intention to buy the product. The effect was mediated by unfavorable price expectation disconfirmation, suggesting that the inexpensive-price belief exerted a stronger influence on expected prices when the price was delayed (see distributions A and B in figure 2). **STUDY 5: DELAYING PRICES WITH DIFFERENT RETAILER PRICE IMAGE BELIEFS** Studies 1 through 4 used post-tests to provide evidence for the influence of price beliefs on price expectations and purchase behavior. In study 5, we manipulated price beliefs associated with store image, while trying to keep other sources of price beliefs constant. There is robust literature showing that price beliefs related to store image create strong and consistent influences on price expectations (Hamilton and Chernev 2013). Thus, we manipulated whether prices were delayed or immediately displayed in a store known for high prices (i.e., a premium retailer) versus low prices (a discount retailer). We anticipated delayed-PD at a premium (discount) retailer would result in increased (decreased) purchase intention owing to more favorable (unfavorable) price expectation disconfirmation, as hypothesized in H1a and H1b (H2a and H2b) We also included a national retailer condition that was meant to be comparable to the department store used in studies 1 and 3. Given we have argued that the mid-tier price positioning of Fravega contributed to expensive-price beliefs in study 1, it was appropriate to investigate this claim by including a "national retailer" condition and showing support for H1a and H1b. These predictions were preregistered at [https://aspredicted.org/gfsy-6shc.pdf](https://aspredicted.org/gfsy-6shc.pdf). Method *Participants and Design.* Study 5 had a 2 (Price Disclosure: Immediate-PD vs. Delayed-PD) × 3 (Store Image: Premium vs. National vs. Discount) between-subjects design. Following our preregistration, we initially recruited 900 participants, which were then pre-screened with the same criterion used in Studies 2 and 4. Our final sample size had 726 Prolific participants (56.5% female, *M*~age~ = 39.63), which were randomly assigned to the conditions, and paid a fixed nominal payment. Web Appendix E provides details of the stimuli, including images of how the products were presented across conditions. *Procedure.* The first step was to elicit an internal reference price (IRP) from each participant. Specifically, we asked "Considering the variety of espresso machines available on the market, how much do you estimate the average price for a typical espresso machine to be? Please base your estimate on your general knowledge and understanding of the market, without looking up any prices (there are no right or wrong answers!). Use numbers only." We also reminded participants of the difference between espresso machines and regular coffee makers. In the second step, we described an on-line retailer that sold espresso machines. In the National retailer condition, participants read that *Whole Latte Love*, founded in 1997, was the largest online retailer of coffee and espresso equipment. Further information on size, staff, and customer service reinforced the national retailer image. Participants in the Premium retailer condition were told *Whole Latte Love* specialized in products for discerning aficionados. The retailer only sold machines that passed extensive quality control guidelines. Additional statements reinforced the Premium retailer image. Participants in the Discount retailer condition were told *Whole Latte Love* offered unbeatable deals and was committed to providing great coffee experiences for less. Additional statements reinforced the Discount retailer image (see Web Appendix E). In the third and final step, participants shopped. We strove to make the product display and shopping experience as ecologically valid as possible. We displayed the 12 espresso machines randomly in a 4 × 3 grid. For each espresso machine, we provided a picture and information on the brand, automation level, bars of pressure, integrated grinder, customer ratings, and preheat time. Some attributes were negatively correlated, so that choosing between options required making tradeoffs. We instructed participants to search for their favorite option and place it in the shopping cart. We manipulated Price Disclosure such that participants in the immediate-PD condition saw the price of each espresso machine alongside the other attribute information, and participants in the delayed-PD condition only saw the caption "Add to cart to see price." This procedure was similar to how customers shopped in Study 1. Importantly, to ensure that prices were relevant to each participant but within a fixed range, we used the IRP of each participant to generate prices ranging from 85% to 115% (in 2.5% increments) of a given IRP, adding \$0.99 in each case for realism. For example, a participant who believed that the average price of a typical espresso machine is \$300 saw 12 options priced at \$255.99 (85% of \$300 plus \$0.99), \$263.49 (87.5% of \$300 plus \$0.99), \$270.99 (90% of \$300 plus \$0.99), and so on up to \$345.99 (115% of \$300 plus \$0.99).[^9] Figure 4 reproduces three of the 12 espresso machines this participant would have encountered in the immediate-PD (Panel A) or delayed-PD (Panel B) condition. The prices shown correspond to 95%, 107.5%, and 110% of the \$300 IRP, respectively. **FIGURE 4.** MANIPULATION OF PRICE DISCLOSURE [The figure shows two panels (A and B) illustrating the price disclosure manipulation used in Study 5. Panel A, labeled "Immediate Price Disclosure Condition," displays three espresso machine product cards side by side. Each card contains a product photo at the top and a specification table below with rows for Brand, Automation, Bars of Pressure, Integrated Grinder, Customer Ratings, Preheat Time, and Price. Left card: GE Profile, Semi-automatic, 15 Bars of Pressure, Integrated Grinder: Yes, Customer Ratings: 4.1/5 (32 Reviews), Preheat Time: 80 seconds, Price: $285.99. Center card: De'Longhi, Manual, 15 Bars of Pressure, Integrated Grinder: No, Customer Ratings: 4.4/5 (1,150 Reviews), Preheat Time: 45 seconds, Price: $323.99. Right card: CAFE, Automatic, 20 Bars of Pressure, Integrated Grinder: Yes, Customer Ratings: 4.2/5 (251 Reviews), Preheat Time: 30 seconds, Price: $330.99. The prices correspond to 95%, 107.5%, and 110% of a $300 internal reference price, plus $0.99. Panel B, labeled "Delayed Price Disclosure Condition," displays the same three espresso machine product cards with identical photos and attribute specifications, except the Price row for all three machines reads "Add to cart to see price" instead of displaying dollar amounts. The visual layout, product images, and all non-price attributes are identical to Panel A.] *Note: Figure described by AI (Claude Opus) from published PDF. Values cross-referenced against paper text.* *Measures*. The shopping cart displayed the espresso machine chosen by each participant and a subtotal with the price. At this moment, we asked "If you were to purchase an espresso machine, how likely would you be to purchase the one you selected?" (1 = "Not likely at all" to 7 = "Extremely likely"). Next, we measured the price expectation disconfirmation: "Before seeing the actual price of the espresso machine ..." 1 = "I expected a much lower price than its current price," to 4 = "I expected a price equal or similar to its current price," to 7 = "I expected a much higher price than its current price". Lastly, participants reported their gender and age. Results *Purchase Likelihood.* As predicted, a 3 (Store Image) × 2 (Price Disclosure) ANOVA on purchase likelihood showed a significant interaction (*F*(2, 720) = 13.40, *p* \< .001, $\eta_{p}^{2}$ *=* .036). As shown in figure 5, in the Premium condition, participants in the delayed-PD condition were more likely to purchase (*M* = 6.08, *SD* = .98) than their counterparts in the immediate-PD condition (*M* = 5.79, *SD* = .78; *F*(1, 720) = 6.66, *p* = .010, $\eta_{p}^{2}$ *=* .009). In the National condition, participants who faced delayed-PD were more likely to purchase (*M* = 6.06, *SD* = .93) than participants who faced immediate-PD (*M* = 5.72, *SD* = .75; *F*(1, 720) = 9.25, *p* = .002, $\eta_{p}^{2}$ *=* .013). However, in the Discount condition participants in the delayed-PD condition were less likely to purchase (*M* = 5.40, *SD* = .98) than their counterparts in the immediate-PD condition (*M* = 5.79, *SD* = .78; *F*(1, 720) = 12.34, *p* \< .001, $\eta_{p}^{2}$ *=* .017). *Price Expectation Disconfirmation*. There was a Store Image by Price Disclosure interaction (*F*(2, 720) = 15.08, *p* \< .001, $\eta_{p}^{2}$ *=* .040). In the Premium and National conditions, participants in the delayed-PD condition provided more favorable price expectation disconfirmation scores (Premium: *M* = 4.83, *SD* = 1.18; National: *M* = 4.81, *SD* = 1.27) than participants in the immediate-PD condition (Premium: *M* = 4.35, *SD* = 1.11; *F*(1, 720) = 12.40, *p* \< .001, $\eta_{p}^{2}$ *=* .017; National: *M* = 4.33, *SD* = .98; *F*(1, 720) = 12.22, *p* \< .001, $\eta_{p}^{2}$ *=* .017). In the Discount condition, however, participants in the delayed-PD reported less favorable price expectation disconfirmation scores (*M* = 3.97, *SD* = .89) than participants in the immediate-PD condition (*M* = 4.41, *SD* = .89; *F*(1, 720) = 10.37, *p* = .001, $\eta_{p}^{2}$ *=* .014). **FIGURE 5.** PURCHASE LIKELIHOOD ACROSS PRICE DISCLOSURE AND STORE IMAGE CONDITIONS [Grouped bar chart with purchase likelihood on the y-axis (scale from 1 to 7) and three Store Image conditions on the x-axis: Premium, National, and Discount. Each condition has two bars: a white bar for "Immediate Price Disclosure" and a dark/shaded bar for "Delayed Price Disclosure." Error bars representing +/- 1 standard error are shown on each bar. Premium condition: The Immediate-PD bar reaches 5.79 and the Delayed-PD bar reaches 6.08. The Delayed-PD bar is taller, indicating that delayed price disclosure increased purchase likelihood. A significance bracket with a single asterisk (*) marks the difference (p = .010). National condition: The Immediate-PD bar reaches 5.72 and the Delayed-PD bar reaches 6.06. Again, the Delayed-PD bar is taller. A significance bracket with double asterisks (**) marks the difference (p = .002). Discount condition: The Immediate-PD bar reaches 5.79 and the Delayed-PD bar reaches 5.40. Here the pattern reverses: the Delayed-PD bar is shorter, indicating that delayed price disclosure decreased purchase likelihood. A significance bracket with double asterisks (**) marks the difference (p < .001). The visual pattern is clear: for Premium and National store images, delayed price disclosure lifts purchase likelihood above immediate disclosure, while for Discount store images, it depresses purchase likelihood below immediate disclosure. The crossover interaction is the key result of Study 5.] *Note: Figure described by AI (Claude Opus) from published PDF. Bar chart values verified against Results section: Premium DPD = 6.08, IPD = 5.79; National DPD = 6.06, IPD = 5.72; Discount DPD = 5.40, IPD = 5.79. All values match.* > NOTE. Error bars = +/− 1 SE. ^†^*p* \< .10. \**p* \< .05. \*\**p* \< .01. *Mediation.* Accordingly, we conducted a moderated mediation analysis (PROCESS Model 8, Hayes 2022; 5,000 bootstrapped samples) with Price Disclosure as the independent variable (0 = Immediate-PD, 1 = Delayed-PD), Store Image as the moderator, price expectation disconfirmation as the mediator, and purchase likelihood as the dependent variable. The index of moderated mediation is significant when comparing the Discount condition to the Premium (*Index* = .17; 95% CI: \[.09 to .27\]) and National (*Index* = .17; 95% CI: \[.09 to .28\]) conditions. In the Premium condition, the pathway to purchase likelihood through price expectation disconfirmation is positive and significant (*β* = .09, *SE* = .03, 95% CI: \[.03 to .15\]). In the National condition, the same pathway is also positive and significant (*β* = .09, *SE* = .03, 95% CI: \[.03 to .15\]). In the Discount condition, however, the pathway to purchase likelihood through expectation disconfirmation is negative and significant (*β* = -.08, *SE* = .03, 95% CI: \[-.14 to -.04\]). This pattern indicates that participants expected a higher (lower) price for their chosen option in the Premium and National (Discount) store image condition(s), which in turn increased (decreased) purchase likelihood. *Evidence for Assumptions about Expected Prices*. The process explanation for the delayed price effect is that price beliefs shift expected prices to a greater extent when prices are delayed and this results in greater price expectation disconfirmation once the price is revealed. Study 5 measured price expectation disconfirmation (mediator), but it did not measure the expected price. The reason for this decision relates to the immediate-PD condition ̶ it is unnatural for a participant to report an expected price on a product after its price is disclosed. To address this issue, we repeated study 5 with two changes. First, we measured price expectations about the product category, rather than the product the participant selected. This should address the awkwardness in the immediate price condition. Second, we measured category price expectations (henceforth category IRP) at the beginning of the study (e.g., "Considering the variety of espresso machines available on the market, how much do you estimate the price for a typical espresso machine to be? \_\_") and after the product had been placed in the shopping cart, but before the price had been disclosed in the delayed-PD condition. Detailed information is in Web Appendix F. The crucial dependent measure was the second category IRP minus the first category IRP. As expected, an ANOVA on the category IRP differential found a significant interaction between Store Information and Price Disclosure (*F*(2, 591) = 15.46, *p* \< .001, $\eta_{p}^{2}$ *=* .050). In the Premium and National conditions, the category IRP differential is positive and larger for participants in the delayed-PD condition (*M*~premium~ = \$67.91, *SD* = \$159.97; *M*~national~ = \$61.94, *SD* = \$144.69) than for participants in the immediate-PD (*M*~premium~ = \$2.63, *SD* = \$36.03; *F*(1, 591) = 21.83, *p* \< .001, $\eta_{p}^{2}$ *=* .036; *M*~national~ = \$5.73, *SD* = \$24.76; *F*(1, 591) = 16.19, *p* \< .001, $\eta_{p}^{2}$ *=* .027). Conversely, in the Discount condition, the category IRP differential is negative and larger for participants in the delayed-PD condition (*M* = -\$26.96, *SD* = \$99.17) than for participants in the immediate-PD condition (*M* = \$7.61, *SD* = \$17.25; *F*(1, 591) = 6.03, *p* = .014, $\eta_{p}^{2}$ *=* .010). Thus, price beliefs did influence category price expectations in the delayed-PD condition. *Alternative Explanations.* An analysis using the price of the chosen option found no main effect of Price Disclosure (*F*(1, 720) = .156, *p* = .693), a marginally significant effect of Store Information (*F*(2, 720) = 2.368, *p* = .094), and most importantly, no interaction effect (*F*(1, 720) = .628, *p* = .534). This is evidence against the alternative hypothesis that purchase likelihood was influenced by participants in the immediate price condition selecting higher (in premium and national condition) or lower (in discount condition) price-point products than people in the delayed price condition. In addition, we reran the same procedure from Study 5 (N = 776 valid responses), and once participants had the product in their shopping cart, we asked the same product quality (α = .92) and customer service expectations (α = .88) questions used in Studies 2 and 4's alternative explanation post-tests (see Web Appendix F). There were no main effects or interaction effect on perceived product quality (*F*s \< 1). In addition, there was no main effect of price disclosure or an interaction with store image (*F*s \< 1). There was, not unexpectedly, a main effect of store image (*F*(2, 770) = 3.34, *p* = .036, $\eta_{p}^{2}$ *=* .009), such that expectations of customer service were lower in the Discount condition (*M* = 6.07, *SD* = 1.09) than in the National (*M* = 6.28, *SD* = .88; *F*(1, 770) = 6.22, *p* = .012, $\eta_{p}^{2}$ *=* .008) and Premium (*M* = 6.23, *SD* = .96; *F*(1, 770) = 3.25, *p* = .074, $\eta_{p}^{2}$ *=* .004) conditions. Thus, our effects appear to be driven by price expectations rather than changes in perceptions about the product and/or the retailer. Discussion Overall, study 5 provides evidence that, relative to immediate-PD, delayed-PD amplifies price expectations, generating expectation disconfirmations that then influence purchase intentions. Web Appendix G reports a successful replication where participants considered purchasing a Bluetooth keyboard, Store Image had only two conditions (Premium and Discount), and prices were pre-determined rather than a function of IRPs. This study (and its conceptual replication) manipulated price beliefs through store image. However, there are additional ways to manipulate price beliefs that could have meaningful consequences for retailers. For example, when consumers have expensive-price beliefs, disclosing that a price is hidden because it is a bargain can encourage a shift to inexpensive-price beliefs. A practical, e-commerce example is when a retailer discloses a price is hidden because it violates a Minimum Advertised Price (MAP) policy (i.e., an agreement between a manufacturer and a retailer that stipulates the lowest price that can be advertised for a product). We investigated this practical moderator in Study 6. **STUDY 6: THE MODERATING ROLE OF MINIMUM ADVERTISED PRICE POLICIES** A Minimum Advertised Price (MAP) policy is an agreement between a manufacturer and a retailer that stipulates the lowest price that can be advertised for a product. Retailers retain the right to sell at a price below the MAP, but they can only reveal it once consumers advance to the shopping cartwhere the price is considered part of the sales agreement rather than a promotional hook. Consequently, many retailers that delay price disclosure because of a MAP policy prompt consumers to "Add to cart to see the price." Whereas some U.S. retailers in this situation simply delay price disclosure, other retailers, including Academy Sports + Outdoors and Walmart, explain "Price is too low to show" and attribute it directly to the MAP policy, providing reasons for the price delay. (See web appendix H for examples.) The goal of this experiment was to test the possibility that attributing delayed-PD to a MAP policy moderates its effect on purchase intentions. When consumers have expensive-price beliefs, an assumption that will be confirmed in a post-test to be discussed later, a price delay increases purchase intention (H1a) owing to a favorable price expectation disconfirmation (H1b). Yet, attributing the delayed disclosure to a MAP policy may signal to consumers that the price is likely low, effectively replacing an expensive-price belief with an inexpensive-price belief. When consumers have inexpensive-price beliefs, a price delay decreases purchase intention (H2a) owing to an unfavorable price expectation disconfirmation (H2b). Thus, attributing a price delay to a MAP policy can make the beneficial effects of a price delay (when the MAP policy is not disclosed) become detrimental (when the MAP policy is disclosed). Method *Participants and Design*. We recruited 322 Prolific participants. We randomly assigned participants to one of three Price Disclosure conditions (Immediate-PD, Delayed-PD, Delayed-PD + MAP Policy). In line with the preregistration (), we removed 22 participants who failed the attention check, resulting in an effective sample of 300 (45.7% female, *M~age~* = 38.36). *Procedure*. We explained "In this survey, we want you to imagine you want to purchase a robot vacuum cleaner. In this scenario, you are browsing through a variety of robot vacuum cleaners on a website. At some point, you found a robot vacuum cleaner that interested you. Click the arrow button to continue." Web appendix H provides details of the stimuli. On the next page, participants previewed the product "iRobot Roomba Combo i5+ Self-Emptying Robot Vacuum & Mop," which included the number of reviews and shipping date. Here, we randomly assigned participants to one of three Price Disclosure conditions. Participants in the immediate-PD (IPD) condition saw the product's price (\$349.99) and an "Add to cart" button. In this study, the product's price was based on the real product's price at the time the experiment was run rather than a pretest. Participants in the delayed-PD (DPD) condition did not see the price, and the button read "See price in cart." Finally, participants in the delayed-PD with MAP Policy (DPD-MAP) did not see its price, saw the "See price in cart" button, and saw a mouseover element in the shape of a question mark that read: "Please add this item to your cart to see the price. Because the price is below the manufacturer's minimum advertised price, we are unable to show it here". We then told all participants "You decided to see more information about the product. Click the Next button to continue." On the following page, we provided additional product information. This information, which included a highlight of the main features, a one-paragraph description, and zoomed product pictures, was taken directly from Best Buy. Participants in the two delayed-PD conditions still did not receive the price. After processing the additional details, participants returned to the product previews and read "After some consideration, you decide to add the product to your shopping cart. Please click the next button to see the product in your shopping cart." Then, on the final page, participants viewed the shopping cart containing the product, its price, and the cart subtotal. This was the first time that participants in the delayed-PD conditions saw the price. Below the shopping cart, we asked "If you were in this scenario, how likely would you be to purchase the option in your shopping cart (i.e., checkout)?" (1 = "Not likely at all" to 7 = "Extremely likely"). To measure price expectation disconfirmation, we then asked "Consider your shopping experience, the product, and its price (\$349.99). How would you describe your price expectations relative to its current price?" (1 = "I expected a much lower price than its current price," to 4 = "I expected a price equal or similar to its current price," to 7 = "I expected a much higher price than its current price"). Results *Purchase Likelihood.* First, a three-cell ANOVA on purchase likelihood shows a main effect of Price Disclosure (*F*(2, 297) = 17.07, *p \<* .001, $\eta_{p}^{2}$ *=* .103). Specifically, participants were more likely to buy the vacuum cleaner in the delayed-PD condition (*M* = 5.01, *SD* = 1.27) than in the immediate-PD (*M* = 4.43, *SD* = 1.70; *F*(1, 297) = 6.36, *p* = .012, $\eta_{p}^{2}$ *=* .021) or delayed-PD MAP (*M* = 3.67, *SD* = 1.85; *F*(1, 297) = 33.94, *p* \< .001, $\eta_{p}^{2}$ *=* .103) conditions (figure 6). In contrast, participants were less likely to buy the vacuum cleaner in the delayed-PD MAP condition than in the immediate-PD condition (*F*(1, 297) = 10.92, *p* \< .001, $\eta_{p}^{2}$ *=* .035). *Price Expectation Disconfirmation.* Second, a similar ANOVA on price expectation disconfirmation reveals a main effect of Price Disclosure (*F*(2, 297) = 18.76, *p \<* .001, $\eta_{p}^{2}$ *=* .112). Specifically, participants reported higher scores in the delayed-PD condition (*M* = 4.52, *SD* = 1.34) than in the immediate-PD (*M* = 4.02, *SD* = 1.20; *F*(1, 297) = 6.93, *p* = .009, $\eta_{p}^{2}$ *=* .023) or delayed-PD MAP (*M* = 3.36, *SD* = 1.47; *F*(1, 297) = 37.28, *p* \< .001, $\eta_{p}^{2}$ *=* .112) conditions. In contrast, participants reported lower scores in the delayed-PD MAP than in the immediate-PD condition (*F*(1, 297) = 12.07, *p* \< .001, $\eta_{p}^{2}$ *=* .039). *Mediation.* Accordingly, we conducted a mediation analysis (PROCESS Model 4, Hayes 2022; 5,000 bootstrapped samples) with Price Disclosure as the independent variable (multi-categorical), expectation disconfirmation as the mediator, and purchase likelihood as the dependent variable. The pathway to purchase likelihood through expectation disconfirmation is positive and significant when contrasting the delayed-PD condition to the immediate-PD (*β* = .23, *SE* = .09, 95% CI: \[.06 to .42\]) and delayed-PD MAP (*β* = .54, *SE* = .12, 95% CI: \[.32 to .79\]) conditions. In addition, the same pathway is negative and significant when contrasting the delayed-PD MAP and immediate-PD (*β* = -.31, *SE* = .10, 95% CI: \[-.53 to -.12\]) conditions. **FIGURE 6.** PURCHASE LIKELIHOOD ACROSS PRICE DISCLOSURE CONDITIONS [Bar chart with purchase likelihood on the y-axis (scale from 1 to 7) and three Price Disclosure conditions on the x-axis. Each condition is represented by a single bar with error bars (+/- 1 standard error). The x-axis labels from left to right are: "Delayed Price Disclosure" (leftmost), "Delayed Price Disclosure with MAP" (center), and "Immediate Price Disclosure" (rightmost). The Delayed-PD bar (leftmost) is the tallest, reaching a mean of 5.01. The Immediate-PD bar (rightmost) reaches a mean of 4.43. The Delayed-PD with MAP bar (center) is the shortest, reaching a mean of 3.67. A horizontal bracket with "**" connects the Delayed-PD and Immediate-PD bars (p = .012). Another bracket with "**" connects the Delayed-PD and Delayed-PD MAP bars (p < .001). A third bracket with "**" connects the Immediate-PD and Delayed-PD MAP bars (p < .001). The pattern shows that for a robot vacuum cleaner (where consumers hold expensive-price beliefs by default), delayed price disclosure increases purchase likelihood relative to immediate disclosure. However, when the delay is attributed to a Minimum Advertised Price (MAP) policy, which signals that the price is low, purchase likelihood drops below even the immediate disclosure condition. This demonstrates that disclosing the reason for a price delay can shift consumers from expensive-price beliefs to inexpensive-price beliefs, reversing the beneficial effect.] *Note: Figure described by AI (Claude Opus) from published PDF. Bar chart values verified against Results section: IPD = 4.43, DPD = 5.01, DPD-MAP = 3.67. All values match.* > NOTE. Error bars = +/− 1 SE. MAP, minimum advertised price. ^†^*p* \< .10. \**p* \< .05. \*\**p* \< .01. *Post-Test: Price Beliefs.* A post-test similar to those used in prior studies was used to confirm delayed prices (vs. delayed prices with MAP policy attribution) led to an expectation of higher (lower) prices. We showed 79 (vs. 80) Prolific participants interested in robot vacuum cleaners the same scenario from Study 6's delayed-PD (vs. delayed-PD with MAP attribution). We then asked participants: "Imagine you are shopping online and interested in this robot vacuum cleaner. You can't see the price yetthis happens in situations where the price tag is missing and you have to ask for more information, or when you have to add the product to the shopping cart to see the price. Does the fact that you are not seeing the price lead you to believe that..." (1 = "The price is so low, they do not want to initially show it," to 7 = "The price is so high, they do not want to initially show it."). We found that in the delayed-PD condition, the mean score was higher than the midpoint (*M* = 5.35, *t*(78) = 7.97, *p \<* .001), suggesting that expensive-price beliefs influenced price expectations in this condition. Likewise, in the delayed-PD with MAP condition, the mean score was lower than the midpoint (*M* = 3.29, *t*(79) = -3.12, *p* = .003), suggesting that inexpensive-price beliefs influenced price expectations in this condition. Web Appendix H provides additional details. *Alternative Explanations.* We reran the same procedure from Study 6 (N = 300 valid responses), and once participants had the robot vacuum cleaner in their shopping cart, we asked the same product quality (α = .95) and expectations about customer service (α = .88) questions used in previous post-tests. There was no effect of price disclosure on perceived product quality (*F* \< 1) or expectations about customer service (*F* \< 1). Thus, our effects appear to be driven by price expectations rather than changes in perceptions about the product and/or the retailer. See Web Appendix H for details. Discussion The results support our intuition that attributing delayed price disclosure to a MAP policy can moderate the effect of the tactic. Specifically, relative to the immediate-PD and delayed-PD conditions, the delayed-PD MAP condition prompted participants to expect a low price, which resulted in disappointment once the price was revealed and reduced purchase intent. **GENERAL DISCUSSION** Withholding prices has long been a staple tactic in retail, yet its effectiveness remains a subject of enduring debate among businesses. Surprisingly, despite its prevalence, academic research offers limited empirical guidance to resolve these conflicting perspectives. This article addresses this long-standing disconnect. A central finding of this research is that delayed price disclosure exerts a significant influence on consumer purchase behavior. However, the direction of this influencewhether it increases or decreases purchase likelihoodis contingent upon consumers' pre-existing price beliefs and their influence on price expectations. Holding back prices exacerbates the influence of price beliefs on price expectations (see figure 2), increasing the likelihood that shoppers experience a positive (if they have expensive-price beliefs) or negative (if they have inexpensive-price beliefs) disconfirmation once prices are revealed. This disconfirmation then carries through to purchase decisions. Contributions First, our research provides guidance to retailers questioning or exploring the right moment to post a price. We show that a determining factor is the relationship between price beliefs and price expectations. By understanding this relationship, retailers can assess factors influencing price beliefs (e.g., store image, product category, economic conditions) and predict whether a price delay will boost or hinder sales. Second, our research extends and enriches the established literature on price obfuscation in economics and marketing. This body of work has predominantly focused on firms' strategies to complicate price discovery by advertising a low base price and concealing supplementary charges for add-ons, additional services, taxes, and related expenses. In such scenarios, it is plausible to assume that some consumers will opt to minimize search costs or commit computational errors, leading to the general finding that price obfuscation tends to inflate the final sales price. In contrast, our investigation examines the more fundamental phenomenon of shrouding the entire product price. When consumers are temporarily prevented from accessing any price information, the arguments centered on search costs and computational errors become less compelling. Instead, we propose and test an alternative explanatory framework for the impact of price obfuscation, one that yields a more nuanced understanding of when withholding prices proves advantageous versus detrimental. Specifically, we demonstrate that consumers' pre-existing price beliefs shape their price expectations, which in turn influence their purchase decisions through the mechanism of price expectation disconfirmation. Finally, our research offers valuable insights into the broader managerial debate surrounding the role of friction, or perceived hassle, in shopping experiences. Conventional wisdom often dictates the pursuit of frictionless purchase journeys, particularly when it comes to pricing and payment processes. However, our studies demonstrate that not all friction is inherently detrimental. Indeed, it may seem counterintuitive that a retailer could strategically benefit from introducing friction, yet this is precisely what delayed price disclosure accomplishes. This finding suggests a promising avenue for companies to explore: the implementation of an 'adaptive' approach to price disclosure, wherein the timing of price presentation is personalized based on individual shoppers' price beliefs. Such an approach could strike a balance between enhancing the shopping experience and achieving sales outcomes an optimal balance between friction and flow in the purchase journey (Bertini, Aparicio, and Aydinli 2024). Limitations and Future Research To the best of our knowledge, this article reports the first empirical test of the effect of delayed price disclosure on consumer behavior. In this initial investigation, we do not address heterogeneity in price beliefs among consumers. Heterogeneity occurs when one portion of a consumer population has expensive-price beliefs and another portion of the consumer population has inexpensive-price beliefs. The relative size of these two groups determines the degree and direction of the price expectation shift, and corresponding impact on sales, when there is price delay. This may be why the positive effect of the price delay was strong in study 1 (i.e., a large majority of customers had expensive-price beliefs) and the negative effect of the price delay was weak in study 3 (i.e., a small majority of customers had inexpensive-price beliefs). Identifying indicators of the direction and strength of price beliefs, at the individual customer level, would be a useful tool for price discrimination. Moreover, while our research emphasizes the role of price expectations as a key explanatory mechanism, we acknowledge that this is likely one of several contributing processes. To illustrate, consider research showing that price promotions can cause reward-seeking and impatience (Shaddy and Lee 2020), making consumers more present oriented. Consumers who are impatient might find delayed price disclosure frustrating, as it prevents them from quickly obtaining all the information they need to decide, leading to negative affect and/or abandonment of the purchase process. While this explanation is admittedly simplified, it offers a plausible alternative account for the effects observed in Study 3. Future research could investigate this and other possibilities. Beyond the role of price expectations, another critical moderator to consider is price importancethe relative weight consumers assign to price compared to other product benefits. When price is deemed less important in a purchase decision, delaying its disclosure may paradoxically enhance product appeal. In such instances, consumers are likely to focus more intently on the immediately available product features and advantages. Conversely, when price is a primary driver of the decision, delaying its disclosure could shift attention disproportionately to the missing information, potentially diminishing the perceived value of other product benefits. Consequently, understanding how and when price importance moderates the impact of the price delay effect represents a logical next step in this research stream. Our studies adopt a price delay duration that we think is meaningful to consumers, but conservative given that it extends only to the next logical step in the purchase process. Longer price delay durations are certainly possible, all the way to the moment of payment. Varying the duration of the price delay is an interesting extension of our work, one that would allow researchers to contrast the possibility of an escalation of beneficial shifts in price expectations with the risk of making the shopping experience less realistic or less natural to consumers. Another promising avenue for future research lies in exploring whether delayed price disclosure (delayed-PD) can function as a behavioral intervention, empowering consumers to select products based on genuine preferences rather than mere cost justification. By withholding the price, consumers may be prompted to focus more intently on the product's inherent features and benefits, thereby aligning their decisions more closely with their true desires rather than being predominantly driven by price considerations. This perspective expands upon the notion of positive expectation disconfirmation, suggesting that delayed-PD has the potential to enhance the congruence between consumer preferences and their purchase decisions. Furthermore, it would be interesting to extend our research to consider competition. If prices are disclosed not only to consumers, but also to competitors, then is it better for a retailer to hide them from the more "public" places of the purchase process (a store shelf, a landing page, etc.)? The answer to this question probably depends on several factors, including the underlying pricing policy of the retailer and its competitors. For instance, a company with a policy of matching the lowest price might benefit from posting prices immediately because consumers who compare prices will have less friction in the shopping experience. Finally, identifying potential moderators of the price delay effect warrants further investigation. Individual differences, such as dispositional optimism or pessimism, could influence a consumer's propensity to utilize price beliefs when updating price expectations. Similarly, price knowledge and confidence may mitigate the effects of delayed price disclosure, as more knowledgeable consumers are likely less susceptible to revising their expectations during the delay. This implies price delay effects are less likely to manifest for fast-moving consumer goods, where consumers possess extensive price histories. Additionally, when a price delay is perceived as non-normativedeviating from standard practice in a given retail contextconsumers may be less inclined to allow their price beliefs to sway their expectations, potentially attenuating the effects observed in our studies. **DATA COLLECTION STATEMENT** Data from Study 1 were provided by Fravega, an Argentinian retailer of household and tech appliances, to the third author. Collected in spring 2020 by the company under the third author's supervision, these data were analyzed by the third author with input from the first and sixth authors. The posttest and alternative explanations tests for Study 1 were conducted in February 2025 by the first author, who also analyzed the data. Study 2's main study and pretest were conducted via Prolific in February 2025 by the first and second authors. The posttest and alternative explanations tests were conducted online in March 2025 by the same authors. All Study 2 data were analyzed by the first and second authors. Data for Study 3 were again provided by Fravega to the third author. Collected in December 2021 under the third author's supervision, these data were analyzed by the third author with input from the first and sixth authors. The posttest and alternative explanations tests for Study 3 were conducted in February 2025 by the first author, who also analyzed the data. Study 4's main study and pretest were conducted via Prolific in February 2025 by the first and second authors. The posttest and alternative explanations tests were conducted in March 2025 by the same authors, who analyzed all tests and study data. Study 5's main study and alternative explanations test were conducted via Prolific in March 2025 by the first author and analyzed by the first and second authors. The Web Appendix F study (i.e., Study 5 procedure with category-level price expectations) was conducted in April 2024 by the first author and analyzed by the first and second authors. The Web Appendix G study (a conceptual replication of Study 5) was also conducted in April 2024 by the first author and analyzed by the first and second authors. Data for Study 6 were collected via Prolific in August 2024 by the first author and analyzed by the first and second authors with input from all other authors. The posttest and alternative explanations tests for Study 6 were conducted in March 2025 by the first author and analyzed by the first and second authors. 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(1986), "A Reference Price Model of Brand Choice for Frequently Purchased Products," *Journal of Consumer Research*, 13 (2), 2506. **HEADINGS LIST** **1) PRICE OBFUSCATION** 2\) Obfuscating Ancillary Charges 2\) Obfuscating an Entire Price 2\) Research Opportunity **1) PRICE EXPECTATIONS** 2\) Price Beliefs 2\) Price Delays, Price Beliefs, Price Expectations, and Purchase Behavior **1) OVERVIEW OF STUDIES** **1) STUDY 1: DELAYING PRICE IN AN ONLINE STORE INCREASES SALES** 2\) Method *3) Design and Data* *3) Procedure* *3) Retailer Data Organization* *3) Measures* 2\) Results *3) Binary Daily Purchase Indicator* *3) Daily Log Units Sold* *3) Daily Log Sales Revenue (pesos)* *3) Daily Bounce Rate* *3) Robustness Checks* *3) Post-Test: Expensive-Price Beliefs* *3) Alternative Explanations* 2\) Discussion **1) STUDY 2: DELAYING PRICE FOR A PREMIUM PRODUCT INCREASES PURCHASE INTENTIONS** 2\) Method *3) Participants and Design* *3) Procedure* *3) Pretest* 2\) Results *3) Purchase Likelihood* *3) Price Expectation Disconfirmation* *3) Mediation* *3) Post-Test: Expensive-Price Beliefs* *3) Alternative Explanations* 2\) Discussion **1) STUDY 3: DELAYING PRICE IN A SALES FLYER REDUCES SALES** 2\) Method *3) Procedure* *3) Measures* *3) Data* 2\) Results *3) Binary Weekly Purchase Indicator* *3) Weekly Log Units Sold* *3) Weekly Log Sales Revenue* *3) Click-Through Rate* *3) Robustness Checks* *3) Post-Test: Inexpensive-Price Beliefs* *3) Alternative Explanations* 2\) Discussion **1) STUDY 4: DELAYING PRICE FOR A DISCOUNTED PRODUCT REDUCES PURCHASE INTENTIONS** 2\) Method *3) Participants and Design* *3) Procedure* *3) Pretest* 2\) Results *3) Purchase Likelihood* *3) Price Expectation Disconfirmation* *3) Mediation* *3) Post-Test: Inexpensive-Price Beliefs* *3) Alternative Explanations* 2\) Discussion **1) STUDY 5: DELAYING PRICES WITH DIFFERENT RETAILER PRICE IMAGE BELIEFS** 2\) Method *3) Participants and Design* *3) Procedure* 2\) Results *3) Purchase Likelihood* *3) Price Expectation Disconfirmation* *3) Mediation* *3) Evidence for Assumptions about Expected Prices* *3) Alternative Explanations* 2\) Discussion **1) STUDY 6: THE MODERATING ROLE OF MINIMUM ADVERTISED PRICE POLICIES** 2\) Method *3) Participants and Design* *3) Procedure* 2\) Results *3) Purchase Likelihood* *3) Price Expectation Disconfirmation* *3) Mediation* *3) Post-Test: Price Beliefs* *3) Alternative Explanations* 2\) Discussion **1) GENERAL DISCUSSION** 2\) Contributions 2\) Limitations and Future Research [^1]: There is far less research on the long-run effect of price obfuscation. In one article, Santana, Dallas, and Morwitz (2020) find that self-justification and mistaken perceptions about additional search costs, or potential gains from switching sellers, made initial decisions excessively sticky. Yet, Chiles (2021) argues that reputational concerns counteract the incentive to companies to obfuscate prices especially if consumers can punish deceptive behavior. Specifically, Chiles (2021) finds that shrouding resort fees lowers guest ratings in the U.S. hotel industry. [^2]: Website: https://www.fravega.com/ [^3]: There are 112 observations in the data (2 conditions × 56 days). With 55 control variables for days, one degree of freedom (df) for the constant, and one df for the treatment, there are therefore 55 df remaining for tests. Applying OLS to a binary outcome is a Linear Probability Model (LPM). A LPM has implementation and interpretation advantages that make it more appealing than logistic regression. In LPM, parameters represent mean marginal effects that are easier to interpret than log odds ratios. Because LPM and logistic regression usually yield similar results (i.e., the impact of violating assumptions about the distribution of the dependent measure is minimal), econometric researchers tend to favor LPM for estimating treatment effects (Deke 2014). [^4]: We conducted a two-stage analysis decomposing units sold and revenue conditional on a sale being made on the day (i.e., the binary daily purchase indicator). The results showed the increased units sold (revenue) occurred because the price delay increased the likelihood of a sale being made on a day, but not because there were more units sold (revenue) on days when there was a sale. See Web Appendix A. [^5]: The Argentinian peso means represent the experimental condition means before the data were log transformed. The official exchange rate on March 2, 2020 was 64 pesos to the dollar. The unofficial exchange rate was 83.86 pesos to the dollar. Thus, the peso amounts should be used to understand magnitudes of sales by condition rather than to assess dollar revenue to the firm during the test. [^6]: The delayed price condition (n = 280 per day) averaged 4.86% more visitors per day than the immediate price condition (n = 267). This could have impacted the dependent measures in a hypothesis consistent direction. [^7]: The Argentinian peso means represent the experimental condition means before the data were log transformed. The official exchange rate on December 2, 2021 was 106.25 pesos to the dollar. The unofficial exchange rate was 175.81 pesos to the dollar. [^8]: Due to the high proportion of zero purchases (97.4% in control, 97.7% in treatment) and non-normal residual distributions, we conducted comprehensive alternative specifications including negative binomial, zero-inflated Poisson, hurdle models, Gamma GLM, and two-stage analyses to address potential distributional concerns. All specifications confirm consistent negative treatment effects. See Web Appendix C for complete details. [^9]: While the prices of the espresso machines varied from participant to participant depending on their IRP, each option had a fixed percentage relative to IRPthat is, one option from the set was always priced at 85% of the IRP, another option was always priced at 87.5% of the IRP, and so on. The presentation order was randomized. # Web Appendix WEB APPENDIX Concealing Prices: How Delayed Price Disclosure Influences Consumer Purchase Decisions Felipe M. Affonso Amin Shiri Diego Aparicio Minzhe Xu Xiang Wang Chris Janiszewski Marco Bertini TABLE OF CONTENTS [WEB APPENDIX A: ADDITIONAL RESULTS AND METHODOLOGICAL DETAILS STUDY 1 [5](#web-appendix-a-additional-results-and-methodological-details-study-1)](#web-appendix-a-additional-results-and-methodological-details-study-1) [MODEL ROBUSTNESS CHECK [5](#model-robustness-check)](#model-robustness-check) [TWO-STAGE ANALYSIS DECOMPOSING REVENUE AND UNITS CONDITIONAL ON SALES [6](#two-stage-analysis-decomposing-revenue-and-units-conditional-on-sales)](#two-stage-analysis-decomposing-revenue-and-units-conditional-on-sales) [POST-TEST: EXPENSIVE-PRICE BELIEFS [6](#post-test-expensive-price-beliefs)](#post-test-expensive-price-beliefs) [ALTERNATIVE EXPLANATIONS TEST PROCEDURE AND RESULTS [10](#alternative-explanations-test-procedure-and-results)](#alternative-explanations-test-procedure-and-results) [WEB APPENDIX B: ADDITIONAL RESULTS AND METHODOLOGICAL DETAILS STUDY 2 [12](#web-appendix-b-additional-results-and-methodological-details-study-2)](#web-appendix-b-additional-results-and-methodological-details-study-2) [MATERIALS MAIN STUDY [12](#materials-main-study)](#materials-main-study) [MATERIALS PRE-TEST PROCEDURE [16](#materials-pre-test-procedure)](#materials-pre-test-procedure) [MATERIALS POST-TEST PROCEDURE [18](#materials-post-test-procedure)](#materials-post-test-procedure) [RESULTS COUNTERBALANCED MEDIATOR AND DEPENDENT VARIABLE [20](#results-counterbalanced-mediator-and-dependent-variable)](#results-counterbalanced-mediator-and-dependent-variable) [ALTERNATIVE EXPLANATIONS TEST PROCEDURE AND RESULTS [21](#alternative-explanations-test-procedure-and-results-1)](#alternative-explanations-test-procedure-and-results-1) [WEB APPENDIX C: ADDITIONAL RESULTS AND METHODOLOGICAL DETAILS STUDY 3 [23](#web-appendix-c-additional-results-and-methodological-details-study-3)](#web-appendix-c-additional-results-and-methodological-details-study-3) [EXAMPLES OF SALES FLYERS FROM FRÁVEGA [23](#examples-of-sales-flyers-from-frávega)](#examples-of-sales-flyers-from-frávega) [DISTRIBUTIONAL ANALYSIS AND ALTERNATIVE SPECIFICATIONS FOR STUDY 3 [32](#distributional-analysis-and-alternative-specifications-for-study-3)](#distributional-analysis-and-alternative-specifications-for-study-3) [MODEL ROBUSTNESS CHECK [34](#model-robustness-check-1)](#model-robustness-check-1) [POST-TEST: INEXPENSIVE-PRICE BELIEFS [36](#post-test-inexpensive-price-beliefs)](#post-test-inexpensive-price-beliefs) [ALTERNATIVE EXPLANATIONS TEST PROCEDURE AND RESULTS [38](#alternative-explanations-test-procedure-and-results-2)](#alternative-explanations-test-procedure-and-results-2) [WEB APPENDIX D: ADDITIONAL RESULTS AND METHODOLOGICAL DETAILS STUDY 4 [39](#web-appendix-d-additional-results-and-methodological-details-study-4)](#web-appendix-d-additional-results-and-methodological-details-study-4) [MATERIALS MAIN STUDY [39](#materials-main-study-1)](#materials-main-study-1) [MATERIALS PRE-TEST PROCEDURE [43](#materials-pre-test-procedure-1)](#materials-pre-test-procedure-1) [MATERIALS POST-TEST PROCEDURE [45](#materials-post-test-procedure-1)](#materials-post-test-procedure-1) [RESULTS COUNTERBALANCED MEDIATOR AND DEPENDENT VARIABLE [47](#results-counterbalanced-mediator-and-dependent-variable-1)](#results-counterbalanced-mediator-and-dependent-variable-1) [ALTERNATIVE EXPLANATIONS TEST PROCEDURE AND RESULTS [48](#alternative-explanations-test-procedure-and-results-3)](#alternative-explanations-test-procedure-and-results-3) [WEB APPENDIX E: ADDITIONAL RESULTS AND METHODOLOGICAL DETAILS FOR STUDY 5 [50](#web-appendix-e-additional-results-and-methodological-details-for-study-5)](#web-appendix-e-additional-results-and-methodological-details-for-study-5) [MATERIALS MAIN STUDY [50](#materials-main-study-2)](#materials-main-study-2) [ALTERNATIVE EXPLANATIONS TEST PROCEDURE AND RESULTS [63](#alternative-explanations-test-procedure-and-results-4)](#alternative-explanations-test-procedure-and-results-4) [WEB APPENDIX F: STUDY 5 PROCEDURE WITH PRETEST AND POST-TEST OF CATEGORY EXPECTED PRICE [65](#web-appendix-f-study-5-procedure-with-pretest-and-post-test-of-category-expected-price)](#web-appendix-f-study-5-procedure-with-pretest-and-post-test-of-category-expected-price) [PROCEDURE AND RESULTS [65](#procedure-and-results)](#procedure-and-results) [MATERIALS [68](#materials)](#materials) [WEB APPENDIX G: CONCEPTUAL REPLICATION OF STUDY 5 - ERGONOMIC KEYBOARD [81](#web-appendix-g-conceptual-replication-of-study-5ergonomic-keyboard)](#web-appendix-g-conceptual-replication-of-study-5ergonomic-keyboard) [PROCEDURE AND RESULTS [81](#procedure-and-results-1)](#procedure-and-results-1) [MATERIALS [86](#materials-1)](#materials-1) [WEB APPENDIX H: ADDITIONAL RESULTS AND METHODOLOGICAL DETAILS STUDY 6 [91](#web-appendix-h-additional-results-and-methodological-details-study-6)](#web-appendix-h-additional-results-and-methodological-details-study-6) [EXAMPLES OF MAP POLICY DISCLOSURE IN THE MARKETPLACE [91](#examples-of-map-policy-disclosure-in-the-marketplace)](#examples-of-map-policy-disclosure-in-the-marketplace) [MATERIALS [92](#materials-2)](#materials-2) [POST-TEST: EXPENSIVE AND INEXPENSIVE-PRICE BELIEFS [100](#post-test-expensive-and-inexpensive-price-beliefs)](#post-test-expensive-and-inexpensive-price-beliefs) [ALTERNATIVE EXPLANATIONS TEST PROCEDURE [102](#alternative-explanations-test-procedure)](#alternative-explanations-test-procedure) # WEB APPENDIX A: ADDITIONAL RESULTS AND METHODOLOGICAL DETAILS STUDY 1 ## Model Robustness Check As a robustness check, we estimate the model *Purchase~i,t~* = *α* + *βPD* + ε*~i,t~* using a Logistic regression. As mentioned in the main text, *Purchase~i,t~* denotes a binary daily purchase indicator set to 1 if a purchase took place on date *t*, or set to 0 otherwise; *PD* is an indicator variable for the manipulation of Price Disclosure equal to 1 in the Delayed (DPD) condition and 0 in the Immediate (IPD) condition. The results are shown in Table WA-1. The results are similar to those in the main text. That is, the DPD condition increases the probability of a sale, compared to the IPD condition. **TABLE WA-1:** ONLINE STORE EXPERIMENTLOGISTIC REGRESSION +++ |   | **Purchase Likelihood** | +===============+=============:+=============:+ | DPD | 1.463 | \* | ++++ | | (.685) | | ++++ | Constant | 1.409 | \*\* | ++++ | | (.338) | | ++++ | Observations | 112 | +++ NotesStandard errors in parentheses.\ †p \< .10. \*p \< .05. \*\*p \< .01 ## Two-Stage Analysis Decomposing Revenue and Units Conditional on Sales We implemented a two-stage decomposition to address potential conflation of probability and amount mechanisms. Stage 1 analyzes all 112 observations (56 days × 2 conditions) and replicates our published binary purchase indicator (β = 0.143, SE = 0.054, *t*(55) = 2.66, *p* = 0.010), confirming that treatment significantly increases the probability of daily sales. In the immediate-PD condition, sales occurred on 45 of 56 days (80.4%), while in the delayed-PD condition, sales occurred on 53 of 56 days (94.7%). Stage 2 examines amounts conditional on sales occurring, analyzing the 98 days where at least one sale occurred (45 immediate-PD days + 53 delayed-PD days). This analysis reveals nonsignificant effects on both units (β = -0.015, SE = 0.109, *t*(43) = -0.14, *p* = 0.891) and revenue (β = -0.062, SE = 0.241, *t*(43) = -0.26, *p* = 0.797) that support the notion that treatment effects on units and revenues occur due to increased likelihood of sales rather than changes in purchase amounts on days when sales occur. ## Post-test: Expensive-Price Beliefs Post-Test Stimuli Electric Kettle Section of Retailer's Website with Delayed Price Disclosure and English Translation [Screenshot of the Fravega retailer website showing the "Electric kettles" category page. The page displays a grid of approximately 20 electric kettle products with thumbnail images, product names, and short descriptions in Spanish. No prices are visible on any product, consistent with the delayed price disclosure condition. The Fravega logo appears at the top.] Post-Test Stimuli Espresso Machine Section of Retailer's Website with Delayed Price Disclosure and English Translation [Screenshot of the Fravega retailer website showing the "Espresso machines" category page. The page displays approximately 18 espresso machine and coffee maker products with thumbnail images and descriptions in Spanish. No prices are visible, consistent with the delayed price disclosure condition.] Post-Test Procedure (for Electric Kettles) (Verifying Delayed Prices Led to an Expectation of Higher Prices) [Survey screenshot. Question: "How interested are you in **electric kettles**?" Scale: 1 = no interest at all, 2 = low interest, 3 = neutral, 4 = moderate interest, 5 = a lot of interest. Radio buttons.] \[Participants who didn't choose 4 or 5 were screened out. They were paid regardless, consistent with Prolific's policies\] -page break- In this scenario, you were browsing through a variety of electric kettles on a store website. Your search leads to a website displaying multiple electric kettles. The website shows pictures of the products and provides short descriptions, but there are no prices displayed - you have to click on the picture of the product in order to advance to a page that shows more information on the product, including the price. See below: (Stimuli from previous pages was displayed participants were only shown the website section with delayed price disclosure) [Survey screenshot. Question: "Does the fact that you are not seeing the price lead you to believe that..." Scale: 1 = "the price is so **low**, they do not want to initially show it" to 7 = "the price is so **high**, they do not want to initially show it," with unlabeled points 2-6. Radio buttons.] \*The Procedure for Espresso Machines was similar except for the product category ## Alternative Explanations Test Procedure and Results \*Similar as the Post-Test Procedure, but with different measures. [Survey screenshot. "Please indicate how the hidden price influences your perceptions:" Scale: 1 = "the hidden price makes me distrust this retailer," 4 = "the hidden price does not influence my trust in this retailer," 7 = "the hidden price makes me trust this retailer." Radio buttons.] [Survey screenshot. "Please indicate how the hidden price influences your perceptions:" Scale: 1 = "the hidden price suggests this product is low quality," 4 = "the hidden price does not influence my perception of product quality," 7 = "the hidden price suggests this product is high quality." Radio buttons.] [Survey screenshot. "Please indicate how the hidden price influences your perceptions:" Scale: 1 = "the hidden price suggests I would receive poor customer service," 4 = "the hidden price does not influence my expectations about customer service," 7 = "the hidden price suggests I would receive excellent customer service." Radio buttons.] \*The Procedure for Espresso Machines was similar except for the product category *Alternative Explanations Report.* It is possible the withholding prices encouraged consumers to make favorable inferences about product quality or the retailer. Using the post-test described above, we asked people interested in electric kettles (n = 75) and espresso machines (n = 87) whether the hidden price decreased/increased perceived product quality, their trust in the retailer, and customer service expectations. In the electric kettles post-test, there were no differences from the midpoint 4 on perceptions of product quality (*M* = 3.88, *SD* = .854 vs. 4; *t*(74) = -1.22, *p* = .227), trust in the retailer (*M* = 4.09, *SD* = .791 vs. 4; *t*(74) = 1.02, *p* = .310), and customer service expectations (*M* = 3.99, *SD* = .932 vs. 4; *t*(74) = -.125, *p* = .901). In the espresso machines post-test, there were no differences from the midpoint 4 on perceptions of product quality (*M* = 3.90, *SD* = 1.15 vs. 4; *t*(86) = -.838, *p* = .404), trust in the retailer (*M* = 4.13, *SD* = 1.03 vs. 4; *t*(86) = 1.14, *p* = .256), and customer service expectations (*M* = 4.01, *SD* = 1.11 vs. 4; *t*(86) = .097, *p* = .923). # Web Appendix B: Additional Results and Methodological Details Study 2 ## Materials Main Study [Survey screenshot. "How interested are you in **espresso coffee machines**?" Scale: 1 = no interest at all, 2 = low interest, 3 = neutral, 4 = moderate interest, 5 = a lot of interest. Radio buttons.]\[Consistent with the preregistration, participants who did not choose a 4 or 5 were screened out and did not complete the study. They were paid regardless, consistent with Prolific's policies.\] -page break- **CONSUMER DECISION MAKING** In this survey, we want you to imagine you are interested in buying an espresso machine. In this scenario, you were browsing through a variety of espresso machines on a store website. At some point, you found an espresso machine that got you interested. Click the arrow button to continue. -page break- This is the espresso machine you are interested in: **\[immediate-PD condition\]** [Product card: Breville semi-automatic espresso machine (silver/stainless steel). Specs: Brand: Breville, Automation Level: Semi-automatic, Bars of Pressure: 15, Integrated Grinder: No, Customer Ratings: 4.5/5 (217 Reviews), Preheat Time: 3 seconds, Price: $142.50.] **\[delayed-PD condition\]** [Same Breville espresso machine product card with identical specs, except Price reads "$?? (Add to cart to see price)."] After some consideration, you decide to add the product to your shopping cart. Please click the next button to see the product in your shopping cart. -page break- **DV (Counterbalanced with mediator):** **YOUR SHOPPING CART:** [Smaller product card: Breville espresso machine shown in cart.] **SUBTOTAL (1 ITEM): \$142.50** [Survey screenshot. "If you were in this scenario, how likely would you be to purchase the option in your shopping cart (i.e., checkout)?" Slider: 0 = extremely unlikely, 50 = somewhat likely, 100 = extremely likely.] -page break- **Mediator (Counterbalanced with DV):** **YOUR SHOPPING CART:** [Smaller product card: Breville espresso machine shown in cart.] **SUBTOTAL (1 ITEM): \$142.50** [Survey screenshot. "Before seeing the actual price of the espresso machine..." Scale: 1 = "I expected a much lower price than its current price," 4 = "I expected a price equal or similar to its current price," 7 = "I expected a much higher price than its current price." Radio buttons.] [Demographics: "What is your age?" (text box) and "Gender" (Female / Male / Other or prefer not to specify). Radio buttons.] ## Materials Pre-Test Procedure This pre-test was conducted to set the stimulus price and verify expensive-price beliefs about the espresso machine. [Survey screenshot. "How interested are you in **espresso coffee machines**?" Scale: 1 = no interest at all, 2 = low interest, 3 = neutral, 4 = moderate interest, 5 = a lot of interest. Radio buttons.]\[Consistent with preregistration for the main study, participants who didn't choose 4 or 5 were screened out. They were paid regardless, consistent with Prolific's policies\] -page break- **CONSUMER DECISION MAKING** In this survey, we want you to imagine you are interested in buying an espresso machine. In this scenario, you were browsing through a variety of espresso machines on a store website. At some point, you found an espresso machine that got you interested. Click the arrow button to continue. -page break- This is the espresso machine you are interested in: [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- This is the espresso machine you are interested in: [Breville espresso machine card (delayed-PD version)][Survey. "Relative to a typical espresso machine, I expect the price of this espresso machine to be..." Slider: 1 = much less expensive than a typical espresso machine, 50 = neither inexpensive or expensive as a typical espresso machine, 100 = much more expensive than a typical espresso machine.] -page break- Gender and age ## Materials Post-Test Procedure This pre-test was conducted to verify expensive-price beliefs about the delayed price disclosure [Survey. "How interested are you in **espresso coffee machines**?" Scale: 1 = no interest at all to 5 = a lot of interest. Radio buttons.]\[Consistent with the preregistration of the main study, participants who didn't choose 4 or 5 were screened out. They were paid regardless, consistent with Prolific's policies\] -page break- **CONSUMER DECISION MAKING** In this survey, we want you to imagine you are interested in buying an espresso machine. In this scenario, you were browsing through a variety of espresso machines on a store website. At some point, you found an espresso machine that got you interested. Click the arrow button to continue. -page break- This is the espresso machine you are interested in: [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- Gender and age ## Results Counterbalanced Mediator and Dependent Variable *Purchase Likelihood.* A 2 (condition) × 2 (measurement order) ANOVA revealed neither a main effect of mediator and dependent variable measurement order (*F*(1, 225) = 2.45, *p* = .119), nor an interaction effect (*F*(1, 225) = .155, *p* = .695). The only significant effect was the main effect of condition (immediate-PD vs. delayed-PD: *F*(1, 225) = 9.85, *p* = .002, $\eta_{p}^{2}$ *=* .042), which is reported in the main text (collapsed across the measurement order conditions). *Price Expectation Disconfirmation.* A 2 (condition) × 2 (measurement order) ANOVA revealed neither a main effect of mediator and dependent variable measurement order (*F*(1, 225) = .004, *p* = .951), nor an interaction effect (*F*(1, 225) = .540, *p* = .463). The only significant effect was the main effect of condition (immediate-PD vs. delayed-PD: *F*(1, 225) = 8.69, *p* = .004, $\eta_{p}^{2}$ *=* .037), which is reported in the main text (collapsed across the measurement order conditions). ## Alternative Explanations Test Procedure and Results \*Same as the main study up to the shopping cart page: **YOUR SHOPPING CART:** [Breville espresso machine shown in cart.] **SUBTOTAL (1 ITEM): \$142.50** [Survey. "Given this scenario, please indicate your perceptions about the product: I expect this product to..." Semantic differential scale (1-7): Be Unreliable / Be Reliable, Be Poor Quality / Be Good Quality, Not Be Dependable / Be Dependable.] [Survey. "Given this scenario, please indicate your perceptions about the retailer: I expect this retailer to provide..." Semantic differential scale (1-7): Poor Customer Service / Excellent Customer Service, Not to Ship on Time / Ship on Time, Make Product Returns Difficult / Make Product Returns Easy.] *Alternative Explanations Report.* One could argue that delaying the disclosure of a price could generate inferences about the product and/or retailer beyond price expectations. To alleviate this concern, we reran the same procedure from Study 2 (N = 235 valid responses) and asked the following questions once participants had the espresso machine in their shopping cart: perceived product quality - "I expect this product to..." \[1 = be unreliable vs. 7 = be reliable, 1 = be poor quality vs. 7 = be good quality, 1 = not be dependable vs. 7 = be dependable; α = .91; Dodds, Monroe, and Grewal (1991)), and expectations about customer service "I expect this retailer to provide..." \[1 = poor customer service vs. 7 = excellent customer service, 1 = not to ship on time vs. 7 = ship on time, 1 = make product returns difficult vs. 7 = make product returns easy; α = .88). There were no effects of delayed-PD (vs. immediate-PD) on perceived product quality (*M*~delayed~ = 6.14, *SD* = .93; *M*~immediate~ = 6.03, *SD* = .86; *F*(1, 233) = .915, *p* = .340) and expectations about customer service (*M*~delayed~ = 5.88, *SD* = 1.31; *M*~immediate~ = 5.80, *SD* = .95; *F*(1, 233) = .354, *p* = .552). Thus, our effects appear to be driven by price expectations rather than changes in perceptions about the product and/or the retailer. **Reference Used in this Section:** Dodds, William B., Kent B. Monroe, and Dhruv Grewal (1991), "Effects of Price, Brand, and Store Information on Buyers' Product Evaluations," *Journal of Marketing Research*, 30719. # Web Appendix C: Additional Results and Methodological Details Study 3 ## Examples of Sales Flyers from Frávega Note: below we show part of two flyers used in the actual e-mail experiment (this specific flyer was sent on December 18, 2021). [Left: Fravega "Especial Navidad" (Christmas Special) email flyer showing products without prices Philips Lumea IPL, LG XBoom speaker, foam puzzle mat, and trampoline with "VER +" (See More) buttons but no prices. Right: Same Fravega Christmas flyer with prices visible showing 24% OFF $33,999, 9% OFF $72,999, 13% OFF $3,699, 26% OFF $31,990 with original prices crossed out.] Note: the sales flyers below are from 2024 / 2025 but represent those in the field experiment. [Fravega promotional email: "Aprovecha las ofertas destacadas!" (Take advantage of featured deals!) showing fishing combo, gazebo, and beach chair with prices and discounts. "Descuentos semanales" (Weekly discounts) section below.] [Fravega promotional email: "Hasta 45% OFF" (Up to 45% OFF) with household products griddle, washing machine, Samsung A15 phone, TCL TV, cookware set, mattress. "Liquidacion semanal" (Weekly clearance) for bazaar, chairs, mattresses categories.] [Fravega promotional email: "Super ofertas en aires" (Super deals on air conditioning) showing pool equipment, air conditioners, and summer products. "Ofertas y descuentos" section below.] [Fravega "Mi Credito Fravega" financing flyer: Samsung Crystal UHD TV at 18 installments of $69,871, Samsung Galaxy A25 phone, Philco air conditioner, Whirlpool refrigerator with 10% off.] [Fravega "Mi Credito Fravega" financing flyer: Samsung Galaxy A25 5G phone at 12 installments of $49,999, PS5 Slim, Admiral air conditioner, Electrolux refrigerator with 12% off.] [Fravega "Mi Credito Fravega" financing flyer: PS5 Slim at 18 installments of $179,157, Samsung Galaxy A06, Electra air conditioner, Whirlpool refrigerator.] [Fravega "Cyber Monday" promotional email: Categories for refrigerators, furniture, small electronics, bazaar, bicycles, household, and technology with various discounts (up to 50% OFF).] [Fravega "Black Friday" promotional email: Categories for Christmas, toys, refrigerators, pools, mattresses, beauty, small electronics, home furnishing with discounts up to 45% OFF.] ## Distributional Analysis and Alternative Specifications for Study 3 Study 3\'s user-level data (N = 771,583) exhibits severe distributional challenges that require comprehensive alternative modeling approaches. The data shows extreme zero-inflation: 97.4% of control group participants and 97.7% of treatment group participants made zero purchases during the seven-day period. Both units sold residuals (p \< 0.001) and revenue residuals (p \< 0.001) exhibit severe departures from normality, violating standard OLS assumptions. **Alternative Specification Results** Given these distributional concerns, we implemented multiple alternative specifications designed for count data with excess zeros, following best practices for such distributional challenges. *Poisson Models.* Poisson regression for count data yields consistent negative treatment effects: units sold (β = -0.1311, SE = 0.0141, p \< 0.001) and revenue (β = -0.1592, SE = 0.0001, p \< 0.001). *Negative Binomial Models.* Negative binomial models, which account for overdispersion in count data, confirm negative treatment effects: units sold (β = -0.1311, SE = 0.0148, p \< 0.001) and revenue (β = -0.1592, SE = 0.0518, p = 0.002). *Zero-Inflated Poisson Models.* Zero-inflated Poisson models separately estimate count and zero-inflation components. Count components show negative treatment effects for units (β = -0.1819, SE = 0.0607, p = 0.003) and revenue (β = -0.0381, SE = 0.0001, p \< 0.001). Zero-inflation components show non-significant effects for units (β = -0.0662, SE = 0.0772, p = 0.391) and significant positive effects for revenue (β = 0.1228, SE = 0.0147, p \< 0.001). *Hurdle Models.* Hurdle models separately model the probability of non-zero outcomes and counts conditional on being non-zero. Count components yield identical results to ZIP models: units (β = -0.1819, SE = 0.0607, p = 0.003) and revenue (β = -0.0381, SE = 0.0001, p \< 0.001). Zero hurdle components show significant negative effects for both units (β = -0.1241, SE = 0.0147, p \< 0.001) and revenue (β = -0.1241, SE = 0.0147, p \< 0.001). *Gamma GLM for Positive Revenue.* For the subset of participants with positive revenue (n = 18,971), Gamma GLM regression appropriate for continuous positive data shows consistent directional effects (β = -0.0381, SE = 0.0351, p = 0.278), though not statistically significant. *Two-Stage Analysis.* Following the approach used in Study 1, we decomposed treatment effects into extensive and intensive margins. Stage 1 analyzes purchase probability across all 771,583 observations, showing that treatment significantly reduces the probability of purchase (β = -0.003, SE = 0.0004, p \< 0.001). Stage 2 examines amounts conditional on purchase among the 18,971 participants who made purchases. Treatment effects are negative for units (β = -0.0031, SE = 0.0025, p = 0.208) and significantly negative for revenue (β = -0.0656, SE = 0.0169, p \< 0.001). **Summary** The consistency of negative treatment effects across all specificationsOLS, Poisson, negative binomial, zero-inflated Poisson, hurdle models, Gamma GLM, and two-stage approachesprovides evidence that our substantive conclusions about negative treatment effects in the email context are robust to distributional assumptions. ## Model Robustness Check As a robustness check, we estimate the model *Purchase~i~* = *α* + *βPD* + ε*~i,t~* using a Logistic regression. As mentioned in the main text, *Purchase~i~* denotes a binary daily purchase indicator set to 1 if customer *i* made a purchase, or set to 0 otherwise; *PD* is an indicator variable equal to 1 in the DPD condition and 0 in the IPD condition. The results are shown in Table WA-2. The results are similar to those in the main text. In particular, the DPD condition decreases the probability of a sale, compared to the IPD condition. **TABLE WA-2:** SALES FLYER EXPERIMENTLOGISTIC REGRESSION +++ |   | **Purchase Likelihood** | +===============+=============:+=============:+ | DPD | -.124 | \*\* | ++++ | | (.015) | | ++++ | Constant | -3.620 | \*\* | ++++ | | (.010) | | ++++ | Observations | 771,583 |   | ++++ NotesStandard errors in parentheses.\ †p \< .10. \*p \< .05. \*\*p \< .01 Additionally, as another robustness check, we estimate the regression models (i.e., same three DVs) while conditioning the sample on customers who have clicked the email at least once. The results, reported in Table WA-3, are similar. The data show lower sales across all three DVs in the DPD condition, compared to the IPD condition. **TABLE WA-3:** SALES FLYER EXPERIMENTCONDITIONAL ON CLICKING +++++ |   | **Purchase Likelihood** | **Units Sold** | **Sales Revenue** | +===============+=============:+=============:+========:+========:+==========:+==========:+ | DPD | -.537 | \*\* | -.0039 | \*\* | -.054 | \*\* | ++++++++ | | (.052) | | (.0004) | | (.005) | | ++++++++ | Constant | 3.158 | \*\* | .0225 | \*\* | .309 | \*\* | ++++++++ | | (.038) | | (.0002) | | (.004) | | ++++++++ | Observations | 414,414 |   | 414,414 |   | 414,414 |   | ++++++++ NotesStandard errors in parentheses. †p \< .10. \*p \< .05. \*\*p \< .01 ## Post-test: Inexpensive-Price Beliefs Post-Test Stimuli Sales Flyer Similar to Those Used in the Field Study with English Translation [Survey. "How interested are you in **espresso coffee machines**?" Scale: 1 = no interest at all to 5 = a lot of interest. Radio buttons.] This post-test was conducted to verify delayed prices led to an expectation of lower prices [Survey. "How interested are you in **espresso coffee machines**?" Scale: 1 = no interest at all to 5 = a lot of interest. Radio buttons.] -page break- [Survey text: "When you open the e-mail, you see a sales flyer from a store you are familiar with. The sales flyer does not show the price you have to click to see the deals."]*(Stimulus from the previous page was displayed participants were shown the website sales flyer delayed price disclosure) [Fravega promotional sales flyer (English translation provided alongside Spanish original).] ## Alternative Explanations Test Procedure and Results \*Similar as the Post-Test Procedure, but with different measures. [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] *Alternative Explanations Report.* It is possible the withholding prices encouraged consumers to make unfavorable inferences about product quality or the retailer. Using a procedure similar to the post-test, we asked 100 Prolific participants whether the hidden price decreased/increased perceived product quality, their trust in the retailer, and customer service expectations. There were no differences from the midpoint 4 on perceptions of product quality (*M* = 3.91, *SD* = .986 vs. 4; *t*(99) = -.913, *p* = .363), trust in the retailer (*M* = 4.05, *SD* = .783 vs. 4; *t*(99) = .638, *p* = .525), and customer service expectations (*M* = 3.97, *SD* = .979 vs. 4; *t*(99) = -.306, *p* = .760). # Web Appendix D: Additional Results and Methodological Details Study 4 ## Materials Main Study [Survey. "Does the fact that you are not seeing the price lead you to believe that..." Scale: 1 = "the price is so **low**, they do not want to initially show it" to 7 = "the price is so **high**, they do not want to initially show it." Radio buttons.]\[Participants who didn't choose 4 or 5 were screened out. They were paid regardless, consistent with Prolific's policies\] -page break- [Survey text/instruction continuing from previous page.]-page break- When you open the e-mail, you see a promotion for a Breville espresso machine: **\[immediate-PD condition\]** [Demographics: gender (Male / Female / Other) and age (text box).] **\[delayed-PD condition\]** [Demographics: gender (Male / Female / Other) and age (text box).] After some consideration, you decide to click the promotion to see more information about the product. Click the next button to continue. -page break- **DV (Counterbalanced with mediator):** **This is the espresso machine advertised in the e-mail:** [Espresso machine product card or survey element from study materials.] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- **Mediator (Counterbalanced with DV):** **This is the espresso machine advertised in the e-mail:** [Espresso machine product card or survey element from study materials.] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Survey. "Relative to a typical espresso machine, I expect the price of this espresso machine to be..." Slider: 1 = much less expensive, 50 = neither inexpensive or expensive, 100 = much more expensive.] ## Materials Pre-Test Procedure This pre-test was conducted to set the price and verify inexpensive-price beliefs about the espresso machine. [Survey. "Does the fact that you are not seeing the price lead you to believe that..." Scale: 1 = "the price is so **low**, they do not want to initially show it" to 7 = "the price is so **high**, they do not want to initially show it." Radio buttons.]\[Consistent with the main study preregistration, participants who didn't choose 4 or 5 were screened out. They were paid regardless, consistent with Prolific's policies\] -page break- [Demographics: gender (Male / Female / Other) and age (text box).] -page break- When you open the e-mail, you see a promotion for a Breville espresso machine: [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- When you open the e-mail, you see a promotion for a Breville espresso machine: [Espresso machine product card or survey element from study materials.][Espresso machine product card or survey element from study materials.] -page break- Gender and age ## Materials Post-Test Procedure This post-test was conducted to verify inexpensive-price beliefs about the delayed price disclosure [Survey. "Does the fact that you are not seeing the price lead you to believe that..." Scale: 1 = "the price is so **low**, they do not want to initially show it" to 7 = "the price is so **high**, they do not want to initially show it." Radio buttons.]\[Participants who didn't choose 4 or 5 were screened out. They were paid regardless, consistent with Prolific's policies\] -page break- **CONSUMER DECISION MAKING** [Survey text/instruction continuing from previous page.]-page break- When you open the e-mail, you see a promotion for a Breville espresso machine: [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- Gender and age ## Results Counterbalanced Mediator and Dependent Variable *Purchase Likelihood.* A 2 (condition) × 2 (measurement order) ANOVA revealed neither a main effect of mediator and dependent variable measurement order (*F*(1, 219) = 2.95, *p* = .087), nor an interaction effect (*F*(1, 219) = 1.32, *p* = .251). The only significant effect was the main effect of condition (immediate-PD vs. delayed-PD: *F*(1, 219) = 14.15, *p* \< .001, $\eta_{p}^{2}$ *=* .061), which is reported in the main text (collapsed across the measurement order conditions). *Price Expectation Disconfirmation.* A 2 (condition) × 2 (measurement order) ANOVA revealed neither a main effect of mediator and dependent variable measurement order (*F*(1, 219) = .303, *p* = .583), nor an interaction effect (*F*(1, 219) = .450, *p* = .503). The only significant effect was the main effect of condition (immediate-PD vs. delayed-PD: *F*(1, 219) = 9.06, *p* = .003, $\eta_{p}^{2}$ *=* .040), which is reported in the main text (collapsed across the measurement order conditions). ## Alternative Explanations Test Procedure and Results \*Same as the main study up to the page where participants see more information about the product: **This is the espresso machine advertised in the e-mail:** [Espresso machine product card or survey element from study materials.] [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] *Alternative Explanations Report.* It is possible the withholding prices encouraged consumers to make unfavorable inferences about product quality or the retailer. To alleviate this concern, we reran the same procedure from Study 4 (N = 256 valid responses), and once participants had the espresso machine in their shopping cart, we asked the same product quality (α = .92) and expectations about customer service (α = .86) questions used in Study 2's alternative explanation post-test. There were no effects of delayed-PD (vs. immediate-PD) on perceived product quality (*M*~delayed~ = 5.88, *SD* = 1.09; *M*~immediate~ = 5.75, *SD* = 1.07; *F*(1, 255) = .829, *p* = .364) or customer service expectations (*M*~delayed~ = 5.63, *SD* = 1.12; *M*~immediate~ = 5.54, *SD* = 1.13; *F*(1, 254) = .420, *p* = .517). Thus, our effects appear to be driven by price expectations rather than changes in perceptions about the product and/or the retailer. # Web Appendix E: Additional RESULTS AND Methodological Details for Study 5 ## Materials Main Study [Survey. "How interested are you in **espresso coffee machines**?" Scale: 1 = no interest at all to 5 = a lot of interest. Radio buttons.]\[Consistent with the preregistration of the main study, participants who didn't choose 4 or 5 were screened out. They were paid regardless, consistent with Prolific's policies\] -page break- Considering the variety of **espresso machines **available on the market, how much do you estimate the **average price** for a typical **espresso machine** to be?\ Please base your estimate on your general knowledge and understanding of the market, **without looking up any prices **(there are no right or wrong answers!). Use numbers only.\ Note: an espresso machine brews coffee by forcing pressurized water near boiling point through ground coffee. It\'s NOT the same product as a regular, drip brewed coffee maker. (Open-ended text box) \[Note: the participant's provided IRP was used as a parameter to price all options in the study\] -page break- **CONSUMER DECISION MAKING**   We are investigating consumers\' personal preferences. Please click the arrow button to continue. In this survey, we want you to imagine you want to purchase an **espresso machine.** -page break- On the next page, you will see a variety of **espresso machines**. These machines are brought to you by \"Whole Latte Love,\" your go-to place for everything coffee.\ There are 12 (twelve) espresso machines. Please evaluate them as if you were really considering to purchase one of them - just as you would in real life. First, you will read a description of the retailer. Then, you will proceed to see the options. -page break- Please read the retailer description below. The next button will be available after 20 seconds. **\[store image: national\]** [Demographics: gender (Male / Female / Other) and age (text box).] Whole Latte Love, founded in 1997, operates as an online retailer specializing in espresso machines. The company\'s growth into the largest online retailer in its category is attributed to its focus on coffee and espresso equipment. With its headquarters encompassing 40,000 square feet, Whole Latte Love features a dedicated technical staff to support its operations and customer service efforts. The company aims to provide a wide range of espresso machines to its customers, supported by its resources and technical expertise. \ On the next page, you will see a selection of twelve **espresso machines.** **\[store image: premium\]** [Demographics: gender (Male / Female / Other) and age (text box).] Discover an elite selection of espresso machines at Whole Latte Love, where we merge two decades of passion for coffee with unparalleled expertise. Our curated collection showcases only the pinnacle of espresso craftsmanship, chosen for discerning aficionados like you. Each machine is a testament to our rigorous standards, having passed extensive quality control to ensure it meets the zenith of performance and elegance. Whole Latte Love is not just a retailerit\'s a destination for those who see coffee as more than a drink, but as a luxurious experience. Here, expect nothing but the exceptional, a reflection of our commitment to bringing you the very essence of premium coffee culture.\ On the next page, you will see a selection of twelve **premium espresso machines.**  **\[store image: discount\]** [Demographics: gender (Male / Female / Other) and age (text box).] Welcome to Whole Latte Love, where amazing espresso machines meet unbeatable deals! Our selection is all about bringing you great coffee experiences for less. We've handpicked machines that deliver on taste, without the hefty price tag. With our constantly updated deals, you\'re sure to find the perfect espresso maker to fit even the tightest budget. Here, it\'s all about great coffee, great prices, and no compromise on quality. Ready for your next coffee adventure? Check out our offers and enjoy your favorite brew without overspending.\ On the next page, you will see a selection of twelve **espresso machines that are being sold at a discounted price**. All prices are final, and already include the discount. -page break- **\[store image: national\]** [Demographics: gender (Male / Female / Other) and age (text box).] Whole Latte Love, founded in 1997, operates as an online retailer specializing in espresso machines.\ Please select your preferred espresso machine. There are twelve **espresso machines**. Please evaluate the options as you would do in real life. The next button will only appear after 30 seconds. **\[store image: premium\]** [Demographics: gender (Male / Female / Other) and age (text box).] Discover an elite selection of espresso machines at Whole Latte Love, where we merge two decades of passion for coffee with unparalleled expertise.\ Please select your preferred espresso machine. There are twelve **premium espresso machines.** Please evaluate the options as you would do in real life. The next button will only appear after 30 seconds. **\[store image: discount\]** ["Whole Latte Love" logo on black background premium retailer image.] Welcome to Whole Latte Love, where amazing espresso machines meet unbeatable deals! \ Please select your preferred espresso machine. There are twelve **espresso machines that are being sold at a discounted price**. All prices are final, and already include the discount. Please evaluate the options as you would do in real life. The next button will only appear after 30 seconds. **\[price disclosure: delayed condition\]** In this condition, participants read "Add to cart to see price" instead of the price. **\[price disclosure: immediate condition\]** In this condition, prices were immediately available. **\[Note for all conditions\]** Before showing the stimuli, we will explain how options were priced. This rule applies to both price disclosure conditions. The prices were determined by participant's internal reference prices, which were provided on the first question of the survey. We priced the options in such a way that the prices ranged from 85% to 115% of participants' own internal reference prices (provided at the beginning of the survey), in 2.5% increments, plus 99 cents to make the prices realistic. This was programmed by calculating the following embedded data variables: [Espresso machine product card or survey element from study materials.][Espresso machine product card or survey element from study materials.] With "q://QID2/ChoiceTextEntryValue" being participant's internal reference price (i.e., the answer providing an average market price for an espresso machine). To illustrate, consider a participant who indicated \$200 as the average price for a typical espresso machine. This participant would see the following prices distributed across the twelve options: \$170.99 (85% of \$200 + 99 cents), \$175.99 (87.5% of \$200 + 99 cents), ..., \$225.99 (112.5% of \$200 + 99 cents), and \$230.99 (115% of \$200 + 99 cents). Each of the 12 options were assigned to one of the {price1, price2, ..., price12} variables. Below, we will show how each option was priced. If the price shows "\$\${e://Field/price1", this means this option was always priced at 85% of the IRP + 99 cents. Likewise, "\$\${e://Field/price6" represents 115% of the IRP + 99 cents (see embedded data above). [Grid of 12 espresso machine product cards displayed in a 4x3 layout. Each card shows a product photo, brand name, and specifications. Brands include: Bella Pro Series, Breville, De'Longhi, CAFE, Nespresso by Breville, Philips, SMEG, GE Profile, and Mr. Coffee. In the immediate-PD condition, prices are shown (ranging from ~$255 to ~$345 based on each participant's IRP). In the delayed-PD condition, prices read "Add to cart to see price."] **\[price disclosure: delayed condition\]** \*note: the 12 options were displayed in a random order, with 4 rows and 3 columns. Although participants couldn't see the prices for all options during the choice stage, all prices were calculated in the background because participants would see the price of their chosen option (subsequent to their choice). The options are displayed below. **ESPRESSO MACHINES** [Espresso machine product card or survey element from study materials.][Espresso machine product card or survey element from study materials.] [Espresso machine product card or survey element from study materials.][Espresso machine product card or survey element from study materials.] **\ \[price disclosure: immediate condition\]** \*note: the example below shows a choice set based on an internal reference price of 300. Participants saw the 12 options in a random order displayed in a 4x3 grid. **ESPRESSO MACHINES** [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- **YOUR SHOPPING CART** \*The survey used a display logic question that retrieved the participants' espresso machine choice and displayed it again with all its attributes, including the price. Those in the immediate disclosure condition saw the price again, whereas those in the delayed disclosure condition saw the price for the first time. [Demographics: gender (Male / Female / Other) and age (text box).] -page break- \*The selected espresso machine was displayed again [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Qualtrics survey flow screenshot showing embedded data fields and display logic for the experimental conditions.] ## Alternative Explanations Test Procedure and Results \*Same as the main study up to the shopping cart page: \*The survey used a display logic question that retrieved the participants' espresso machine choice and displayed it again with all its attributes, including the price. Those in the immediate disclosure condition saw the price again, whereas those in the delayed disclosure condition saw the price for the first time. [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Qualtrics survey flow screenshot showing embedded data fields and display logic for the experimental conditions.] *Alternative Explanations Results.* We reran the same procedure from Study 5 (N = 776 valid responses), and once participants had the espresso machine in their shopping cart, we asked the same product quality (α = .92) and expectations about customer service (α = .88) questions used in Study 2's and Study 4's alternative explanation post-tests. There was no main effect of store information condition on perceived product quality (*M*~premium~ = 6.42, *SD* = .76; *M*~national~ = 6.42, *SD* = .73; *M*~discount~ = 6.36, *SD* = .94; *F*(2, 770) = .436, *p* = .647). In addition, there was no main effect of price disclosure on perceived product quality (*M*~immediate~ = 6.43 *SD* = .79; *M*~delayed~ = 6.37, *SD* = .84; *F*(1, 770) = 1.18, *p* = .277). Finally, there was no interaction between store information and price disclosure on price quality (*F*(2, 770) = .981, *p* = .375). Further, there was a main effect of store information condition on customer service expectations (*F*(2, 770) = 3.34, *p* = .036), such that expectations of customer service were lower in the Discount condition (*M* = 6.07, *SD* = 1.09) than in the National (*M* = 6.28, *SD* = .88; *F*(1, 770) = 6.22; *p* = .012) and Premium (*M* = 6.23, *SD* = .96; *F*(1, 770) = 3.25; *p* = .074) conditions. There was no main effect of price disclosure on customer service expectations (*M*~immediate~ = 6.24, *SD* = .91; *M*~delayed~ = 6.15, *SD* = 1.03; *F*(1, 770) = 2.06, *p* = .152). Finally, there was no interaction between store information and price disclosure on customer service expectations (*F*(2, 770) = .511, *p* = .600). Thus, our effects appear to be driven by price expectations rather than changes in perceptions about the product and/or the retailer. # Web Appendix F: Study 5 Procedure with Pretest and Post-test of Category Expected Price ## Procedure and Results An implicit assumption of the studies we have presented thus far is that delayed-PD influences expected prices during the delay which, in turn, influences price expectation disconfirmation. The post-tests after studies 1 and 3 and the mediation tests in studies 2, 4, and 6 are consistent with this assumption. Yet, one could level the criticism that the experimental data (studies 2, 4, 6) use a procedure that could allow the mediator and dependent measure to cross-contaminate. That is, participants could infer price expectation disconfirmation from their purchase intention when purchase intention is measured first and vice versa. There are two ways to address this concern: (1) measure expected prices for the product category rather than a specific product and (2) use a pretest (i.e., measure product category price expectations before seeing products) posttest (i.e., measure product category price expectations after selecting a product) design. If the change in the product category expected price (posttest - pretest) is larger in the delayed-PD, then it would be evidence that price beliefs influenced expected prices to a greater extent when price disclosure is delayed and that this happens as a function of considering the product, not committing to purchase a specific product. That is, the procedure controls for the possibility that putting a product in the shopping cart creates a commitment to purchase that magnifies a price expectation disconfirmation. *Participants and Design.* We randomly assigned 597 Prolific participants (51.9% female, *M~age~*= 40.86, *SD* = 13.81) to conditions in a 2 (Price Disclosure: Immediate vs. Delayed) × 3 (Store Image: Premium vs. National vs. Discount) between-subjects design. No participants failed the attention check. *Procedure.* The procedure was identical to that used in study 5 except for one change. First, similar to study 5, a product category internal reference price was measured prior to disclosing any information about the retailer or the products: "Considering the variety of espresso machines available on the market, how much do you estimate the price for a typical espresso machine to be? Please base your estimate on your general knowledge and understanding of the market, without looking up any prices (there are no right or wrong answers!). Use numbers only." Second, similar to study 5, participants experienced the procedure up to the point the product was placed in the shopping cart. Third, different from study 5, participants responded to a second product category internal reference price measure: "Now, we would like to ask you again about espresso machine prices. We are interested in your opinion after having browsed through the twelve espresso machine options. Considering the variety of espresso machines available on the market, how much do you estimate the price for a typical espresso machine to be?" *Results.* The data show no effects across conditions on the first product category IRP (all *p*'s \> .196, *M* = \$220.55, *SD* = \$190.38). This was expected given random assignment and no exposure to stimulus materials prior to the measure of the first category IRP. The crucial dependent measure was the second category IRP minus the first category IRP. As expected, an ANOVA on the category IRP differential found a significant interaction between Store Information and Price Disclosure (*F*(2, 591) = 15.46, *p* \< .001, $\eta_{p}^{2}$ *=* .050). In the Premium and National conditions, the IRP differential is positive and larger for participants in the delayed-PD condition (Premium: *M* = \$67.91, *SD* = \$159.97; National: *M* = \$61.94, *SD* = \$144.69) than for participants in the immediate-PD condition (Premium: *M* = \$2.63, *SD* = \$36.03; *F*(1, 591) = 21.83, *p* \< .001, $\eta_{p}^{2}$ *=* .036; National: *M* = \$5.73, *SD* = \$24.76; *F*(1, 591) = 16.19, *p* \< .001, $\eta_{p}^{2}$ *=* .027;). Conversely, in the Discount condition, the category IRP differential is negative and larger for participants in the delayed-PD condition (*M* = -\$26.96, *SD* = \$99.17) than for participants in the immediate-PD condition (*M* = \$7.61, *SD* = \$17.25; *F*(1, 591) = 6.03, *p* = .014, $\eta_{p}^{2}$ *=* .010). *Discussion.* The results show that, when there was delayed-PD, price beliefs influenced the IRP for the product category. This suggests price beliefs have a greater influence on expected prices, when there is a price delay, because people are considering the value of available products. This result occurred before a product was placed in the shopping cart, suggesting that a commitment to purchase the product was not necessary to observe price expectation disconfirmation. There is one anomaly in the data. Price beliefs did not influence price expectations on the posttest IRP measure in the immediate conditions. We believe this was the case because participants stated price expectations in the pretest IRP and the immediate-PD was consistent with these expectations (i.e., prices were -15% to +15% for the participant's pretest IRP). The potential effects of price beliefs were countered when prices matched stated expectations. ## Materials [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Survey text/instruction continuing from previous page.]-page break- Please read the retailer description below. The next button will be available after 20 seconds. **\[store image: national\]** [Demographics: gender (Male / Female / Other) and age (text box).] Whole Latte Love, founded in 1997, operates as an online retailer specializing in espresso machines. The company\'s growth into the largest online retailer in its category is attributed to its focus on coffee and espresso equipment. With its headquarters encompassing 40,000 square feet, Whole Latte Love features a dedicated technical staff to support its operations and customer service efforts. The company aims to provide a wide range of espresso machines to its customers, supported by its resources and technical expertise. \ \ On the next page, you will see a selection of twelve **espresso machines.** **\[store image: premium\]** [Demographics: gender (Male / Female / Other) and age (text box).] Discover an elite selection of espresso machines at Whole Latte Love, where we merge two decades of passion for coffee with unparalleled expertise. Our curated collection showcases only the pinnacle of espresso craftsmanship, chosen for discerning aficionados like you. Each machine is a testament to our rigorous standards, having passed extensive quality control to ensure it meets the zenith of performance and elegance. Whole Latte Love is not just a retailerit\'s a destination for those who see coffee as more than a drink, but as a luxurious experience. Here, expect nothing but the exceptional, a reflection of our commitment to bringing you the very essence of premium coffee culture.\ On the next page, you will see a selection of twelve **premium espresso machines.**  **\[store image: discount\]** [Demographics: gender (Male / Female / Other) and age (text box).] Welcome to Whole Latte Love, where amazing espresso machines meet unbeatable deals! Our selection is all about bringing you great coffee experiences for less. We've handpicked machines that deliver on taste, without the hefty price tag. With our constantly updated deals, you\'re sure to find the perfect espresso maker to fit even the tightest budget. Here, it\'s all about great coffee, great prices, and no compromise on quality. Ready for your next coffee adventure? Check out our offers and enjoy your favorite brew without overspending.\ \ On the next page, you will see a selection of twelve **espresso machines that are being sold at a discounted price**. All prices are final, and already include the discount. -page break- **\[store image: national\]** [Demographics: gender (Male / Female / Other) and age (text box).] Whole Latte Love, founded in 1997, operates as an online retailer specializing in espresso machines.\ Please select your preferred espresso machine. There are twelve **espresso machines**. Please evaluate the options as you would do in real life. The next button will only appear after 30 seconds. **\[store image: premium\]** [Demographics: gender (Male / Female / Other) and age (text box).] Discover an elite selection of espresso machines at Whole Latte Love, where we merge two decades of passion for coffee with unparalleled expertise.\ Please select your preferred espresso machine. There are twelve **premium espresso machines.** Please evaluate the options as you would do in real life. The next button will only appear after 30 seconds. **\[store image: discount\]** ["Whole Latte Love" logo on black background premium retailer image.] Welcome to Whole Latte Love, where amazing espresso machines meet unbeatable deals! \ Please select your preferred espresso machine. There are twelve **espresso machines that are being sold at a discounted price**. All prices are final, and already include the discount. Please evaluate the options as you would do in real life. The next button will only appear after 30 seconds. **\[price disclosure: delayed condition\]** In this condition, participants read "Add to cart to see price" instead of the price. **\[price disclosure: immediate condition\]** In this condition, prices were immediately available. **\[Note for all conditions\]** Before showing the stimuli, we will explain how options were priced. This rule applies to both price disclosure conditions. The prices were determined by participant's internal reference prices, which were provided on the first question of the survey. We priced the options in such a way that the prices ranged from 85% to 115% of participants' own internal reference prices (provided at the beginning of the survey), in 2.5% increments, plus 99 cents to make the prices realistic. This was programmed by calculating the following embedded data variables: [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] With "q://QID2/ChoiceTextEntryValue" being participant's internal reference price (i.e., the answer providing an average market price for an espresso machine). To illustrate, consider a participant who indicated \$200 as the average price for a typical espresso machine. This participant would see the following prices distributed across the twelve options: \$170.99 (85% of \$200 + 99 cents), \$175.99 (87.5% of \$200 + 99 cents), ..., \$225.99 (112.5% of \$200 + 99 cents), and \$230.99 (115% of \$200 + 99 cents). Each of the 12 options were assigned to one of the {price1, price2, ..., price12} variables. Below, we will show how each option was priced. If the price shows "\$\${e://Field/price1", this means this option was always priced at 85% of the IRP + 99 cents. Likewise, "\$\${e://Field/price6" represents 115% of the IRP + 99 cents (see embedded data above). [Grid of 12 espresso machine product cards displayed in a 4x3 layout. Each card shows a product photo, brand name, and specifications. Brands include: Bella Pro Series, Breville, De'Longhi, CAFE, Nespresso by Breville, Philips, SMEG, GE Profile, and Mr. Coffee. In the immediate-PD condition, prices are shown (ranging from ~$255 to ~$345 based on each participant's IRP). In the delayed-PD condition, prices read "Add to cart to see price."] **\[price disclosure: delayed condition\]** \*note: the 12 options were displayed in a random order, with 4 rows and 3 columns. Although participants couldn't see the prices for all options during the choice stage, all prices were calculated in the background because participants would see the price of their chosen option (subsequent to their choice). The options are displayed below. **ESPRESSO MACHINES** [Espresso machine product card or survey element from study materials.][Espresso machine product card or survey element from study materials.] [Espresso machine product card or survey element from study materials.][Espresso machine product card or survey element from study materials.] **\ \[price disclosure: immediate condition\]** \*note: the example below shows a choice set based on an internal reference price of 300. Participants saw the 12 options in a random order displayed in a 4x3 grid. **ESPRESSO MACHINES** [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- Right after participants made their choice: [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Qualtrics survey flow screenshot showing embedded data fields and display logic for the experimental conditions.] # Web Appendix g: Conceptual Replication of Study 5 - Ergonomic Keyboard ## Procedure and Results The study (preregistration at ) was designed to generalize the results of Study 5. We tested whether delayed price disclosure (vs. immediate price disclosure) influenced price expectations, and product evaluations depending on the contextual information. However, instead of internal reference prices setting the prices of the products, we used real prices observed in the market. In addition, we used two contextual information conditions: expensive-price beliefs and inexpensive-price beliefs. *Method.* We recruited 431 U.S. participants from Prolific in exchange for a nominal payment. As preregistered, we removed thirty-one participants who failed an attention check at the outset of the survey, resulting in an effective sample of 400 (47.0% female; 18 to 78 years, *M* = 44.64, *SD* = 13.34). This experiment adopted a 2 (Price Disclosure: immediate-PD vs. delayed-PD) × 3 (Store Information: Premium vs. Discount) between-subjects design. We explained to participants we were interested in consumers' personal preferences and opinions. Participants read: "In this survey, we want you to imagine you want to purchase an ergonomic keyboard. On the next pages, you will see a variety of ergonomic keyboards. These keyboards are brought to you by Uplift, a non-existent retailer. Please evaluate the keyboards as if you were really considering purchasing one of them - just as you would in real life. First, you will read a description of the retailer. Then, you will proceed to see the options." Then, on the next page, participants read a retailer description. We developed the stimuli in a way that resembled a company's "About Us" page. All participants saw a logo by the brand Uplift, and read "We are committed to designing, manufacturing, and shipping the highest quality ergonomic office furniture. We offer a complete ergonomic ecosystem, ranging from desks and tables to keyboard trays, mice, and keyboards." We manipulated contextual information based on the information we provided about the retailer. Participants in the premium store information condition read: "Every UPLIFT Desk product is built to last and offer the latest office furniture innovations. Each is designed by a world-class team of a dozen engineers that includes our founder and CEO Jon Paulsen, a Certified Professional Ergonomist (CPE) and mechanical engineer himself." Participants in the discount store information condition read: "Clearance Sale (Big Savings!). Uplift is currently undergoing the Spring Office Essentials Sale. All store 20% OFF, plus coupons. For a limited time, you can score incredible deals storewide until our stock levels return to normal. Backed by the same unbeatable 15-year warranty included with all UPLIFT products. Hurry - this offer won\'t last forever. Sale ends 4/9, 3 PM Central. While supplies last. All advertised prices are the final prices (that is, the discounts have been already applied)." Participants in the Discount condition also read information about different coupons. Then, we showed participants six ergonomic computer keyboards, in a randomized display, and asked them to place their choice in the shopping cart. In addition to a product picture, the keyboards were described based on the following attributes: brand, wireless (yes or no), palm-rest (yes or no), Amazon rating (1 to 5), numeric keypad (yes or no), and price. The attributes were negatively correlated, such that choosing between one option over the others involved making tradeoffs between attributes. The materials section of this Web Appendix demonstrates the procedure. Participants in the immediate-PD condition could see the price for all options upfront. The prices ranged from \$59.99 to \$159.99. Participants in the delayed-PD condition read "Add to cart to see price" instead of seeing the price. Once participants selected their preferred option, they clicked "next" to proceed to the shopping cart, which displayed their chosen option. At this point, participants in the immediate-PD condition saw their selected option again and a subtotal highlighting the price. However, those in the delayed-PD condition saw the price and respective subtotal for the first time. *Measures.* The shopping cart displayed the option selected by each participant and a subtotal with the price. At this moment, we asked: "Please provide your overall evaluation for the keyboard you selected" (1 = "very bad" and 9 = "very good"; 1 = "very negative" and 9 = "very positive"; 1 = "very unfavorable" and 9 = "very favorable") (α = .95). Next, we measured (price) expectation disconfirmationby asking participants the following: "Please look again at the option you selected and consider its price. How would you evaluate its price compared to how much you expect a similar ergonomic keyboard to cost? In other words: is the price of the option you selected cheaper, about the same, or more expensive than what you would expect it to cost?" (1 = "the price is much lower than what I would expect it to cost," 4 = "the price is about the same as what I would expect it to cost," 7 = "the price is much higher than what I would expect it to cost"). Note that, in this study, we measured price expectation disconfirmation in a slight different way compared to the studies in the main text: participants evaluated the price relative to their expectations (rather than indicated what they expectations were). Thus, lower (higher) scores indicate stronger positive (negative) price expectation disconfirmation. *Results.* First, a 2 (Store Information) × 2 (Price Disclosure) ANOVA on product evaluation revealed no main effect of price expectations (*F*(1, 396) = 2.30, *p* = .130), and no main effect of price disclosure (*F*(1, 396) = .014, *p* = .907). Consistent with our predictions, there was a significant interaction (*F*(1, 396) = 11.12, *p* \< .001, $\eta_{p}^{2}$ *=* .027). As shown in figure WA-G.1, in the Premium condition, participants evaluated the keyboard more positively in the delayed-PD condition (*M* = 7.23, *SD* = 1.02) than in the immediate-PD condition (*M* = 6.82, *SD* = 1.33; *F*(1, 396) = 5.18, *p* = .023, $\eta_{p}^{2}$ *=* .013). In the Discount condition, however, participants evaluated the keyboard more negatively in the delayed-PD condition (*M* = 7.00, *SD* = 1.54) than in the immediate-PD condition (*M* = 7.44, *SD* = 1.20; *F*(1, 396) = 5.96, *p* = .015, $\eta_{p}^{2}$ *=* .015). **FIGURE WA-G.1:** STUDY WA-G EVALUATION OF A KEYBOARD [Survey. "Before seeing the actual price of the espresso machine..." Scale: 1 = "I expected a much lower price than its current price," 4 = "I expected a price equal or similar to its current price," 7 = "I expected a much higher price than its current price." Radio buttons.] NOTE. Error bars = +/− 1 SE. †p \< .10. \*p \< .05. \*\*p \< .01 Second, we examine expectation disconfirmation*.* A 2 (Store Information) × 2 (Price Disclosure) ANOVA on price expectation shift revealed a main effect of contextual information (*F*(1, 396) = 18.61, *p* \< .001, $\eta_{p}^{2}$ *=* .034), such that participants in the Discount condition reported the price of their chosen option to be lower than expected (*M* = 4.55, *SD* = 1.07) compared to participants in Premium condition (*M* = 4.98, *SD* = .99). There was no main effect of Price Disclosure (*F* \< 1). The effect of price expectations was qualified by a significant interaction (*F*(1, 396) = 16.94, *p* \< .001, $\eta_{p}^{2}$ *=* .041). In the Premium condition, participants rated the price of their chosen option as lower than expected in the delayed-PD condition (*M* = 4.74, *SD* = .90) than in the immediate-PD condition (*M* = 5.23, *SD* = 1.02; *F*(1, 396) = 11.81, *p* \< .001, $\eta_{p}^{2}$ *=* .029). In the Discount condition, however, participants rated the price of their chosen option as higher than expected in the delayed-PD condition (*M* = 4.72, *SD* = 1.10) than in the immediate-PD condition (*M* = 4.38, *SD* = 1.00; *F*(1, 396) = 5.68, *p* = .018, $\eta_{p}^{2}$ *=* .014). Accordingly, we proceeded with a moderated mediation analysis using PROCESS Model 8 (Hayes 2022) with price disclosure as the independent variable (0 = immediate-PD, 1 = delayed-PD), store information as the moderator, price expectation discrepancy as the mediator, and product evaluation the dependent variable (5000 bootstrapped samples). The index of moderated mediation was significant (*Index* = .14; 95% CI: \[.01 to .31\]). In the Premium condition, the pathway to evaluation through price expectation discrepancy was positive and significant (β = .09, *SE* = .05, 95% CI: \[.01 to .31\]). In the Discount condition, the pathway to evaluation through price expectation discrepancy was negative and significant (β = -.06, *SE* = -.15, 95% CI: \[-.15 to -.01\]). Overall, Study WA-G provides evidence of generalizability and robustness for our effect, showing that that, relative to immediate price disclosure, delayed price disclosure increased (vs. decreased) product evaluations when the store information was premium (vs. discount). These effects were driven by price expectation disconfirmations, which occurred as a function of the contextual information when the price disclosure was delayed (vs. immediate). ## Materials **Background and Employment**   During this survey you will answer questions using your judgment. Background experiences and employment can be an important factor for these types of tasks. Please select your occupation from the menu below. In order to demonstrate that you have read these instructions, please select farming, fishery and forestry as your answer to the question below. Failure to do so will disqualify you from taking this survey. Please be assured that we will keep your answer confidential. Thank you for your participation. Please select the category that best represents your occupation. - Management, professional, and related - Service - Sales and office - Astronomy - Farming, fishing, and forestry - Construction, extraction, and maintenance - Production, transportation, and material moving - Government - Retired - Unemployed - Other \[Participants who didn't choose "Farming, fishing, and forestry" couldn't take the study.\] -page break- **CONSUMER DECISION MAKING**   We are investigating consumers\' personal preferences and opinions. Please click the arrow button to continue. \[The next button showed up after participants spent two seconds on this page.\] -page break- In this survey, we want you to imagine you want to purchase an ergonomic keyboard. On the next pages, you will see a variety of ergonomic keyboards. These keyboards are brought to you by Uplift. Please evaluate the keyboards as if you were really considering purchasing one of them - just as you would in real life. First, you will read a description of the retailer. Then, you will proceed to see the options. -page break- Please read the retailer description below. The next button will be available after 20 seconds. **\[contextual information: premium\]** **Uplift. Work better. Live healthier.** ["UPLIFT" logo large black text with a light blue horizontal line underneath.] We are committed to designing, manufacturing, and shipping the highest quality ergonomic office furniture. Every UPLIFT Desk product is built to last and offer the latest office furniture innovations. Each is designed by a world-class team of a dozen engineers that includes our founder and CEO Jon Paulsen, a Certified Professional Ergonomist (CPE) and mechanical engineer himself.\ \ We offer a complete ergonomic ecosystem, ranging from desks and tables to keyboard trays, mice, and keyboards. **\[contextual information: discount\]** **Uplift\'s CLEARANCE SALE (BIG SAVINGS!)** [Ergonomic keyboard product display grid showing 6 keyboards from brands including Logitech, Microsoft, and Cloud Nine, with specifications and prices (or "Add to cart to see price" in delayed-PD condition).] We are committed to designing, manufacturing, and shipping the highest quality ergonomic office furniture. We offer a complete ergonomic ecosystem, ranging from desks and tables to keyboard trays, mice, and keyboards.   **Uplift is currently undergoing the Spring Office Essentials Sale. All store 20% OFF, plus coupons. **   For a limited time, you can score incredible deals storewide until our stock levels return to normal. Backed by the same unbeatable 15 year warranty included with all UPLIFT products. Hurry - this offer won\'t last forever.   Sale ends 4/9, 3 PM Central. While supplies last. All advertised prices are the final prices (that is, the discounts have been already applied).\ \ [Survey element or stimulus from study materials.]     -page break- \*Participants viewed the retailer information again, along with the following: Please select your preferred ergonomic keyboard to add it to your shopping cart. Your shopping cart will appear on the next page. The next button will appear after 30 seconds. **\[price disclosure: immediate\]** [Demographics: gender (Male / Female / Other) and age (text box).] **\[price disclosure: delayed\]** [Demographics: gender (Male / Female / Other) and age (text box).] -page break- **YOUR SHOPPING CART** \*The survey used a display logic question that retrieved the participants' keyboard choice and displayed it again with all its attributes, including the price. Those in the immediate disclosure condition saw the price again, whereas those in the delayed disclosure condition saw the price for the first time. [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Demographics: gender (Male / Female / Other) and age (text box).] [Survey element or stimulus from study materials.] # Web Appendix h: ADDITIONAL RESULTS AND METHODOLOGICAL DETAILS STUDY 6 ## Examples of MAP Policy Disclosure in the Marketplace [Screenshots from Reddit r/assholedesign: (1) "I can't see the price of this shirt until I add it to my cart and checkout" showing a product listing with hidden price. (2) "Walmart won't let me see the price until I add the item to my cart" showing a Walmart product with "See price in cart" displayed instead of the actual price.] ## Materials **Background and Employment**   During this survey you will answer questions using your judgment. Background experiences and employment can be an important factor for these types of tasks. Please select your occupation from the menu below. In order to demonstrate that you have read these instructions, please select farming, fishery and forestry as your answer to the question below. Failure to do so will disqualify you from taking this survey. Please be assured that we will keep your answer confidential. Thank you for your participation. Please select the category that best represents your occupation. - Management, professional, and related - Service - Sales and office - Astronomy - Farming, fishing, and forestry - Construction, extraction, and maintenance - Production, transportation, and material moving - Government - Retired - Unemployed - Other \[Participants who didn't choose "Farming, fishing, and forestry" couldn't take the study.\] -page break- **CONSUMER DECISION MAKING**   We are investigating consumers\' personal preferences. Please click the arrow button to continue. -page break- In this survey, we want you to imagine you want to purchase a robot vacuum cleaner. In this scenario, you are browsing through a variety of robot vacuum cleaners on a website. At some point, you found a robot vacuum cleaner that got you interested. Click the arrow button to continue. -page break- This is the robot vacuum stick that you got interested in. The following information is what you see before entering the product page. **\[price disclosure: immediate\]** [Demographics: gender (Male / Female / Other) and age (text box).] **\[price disclosure: delayed\]** [Demographics: gender (Male / Female / Other) and age (text box).] **\[price disclosure: delayed with MAP policy disclosure\]** [Demographics: gender (Male / Female / Other) and age (text box).] You decided to see more information about the product. Click the next button to continue. -page break- Please read the product information below. [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] When you are done reading the product information, please click the next button. -page break- You are considering whether to add this product to your shopping cart or not. **\[price disclosure: immediate\]** [Demographics: gender (Male / Female / Other) and age (text box).] **\[price disclosure: delayed\]** [Demographics: gender (Male / Female / Other) and age (text box).] **\[price disclosure: delayed with MAP policy disclosure\]** [Demographics: gender (Male / Female / Other) and age (text box).] After some consideration, you decide to add the product to your shopping cart. Please click the next button to see the product in your shopping cart. -page break- **(Dependent Variable Page)** [Demographics: gender (Male / Female / Other) and age (text box).] **SUBTOTAL (1 ITEM): \$349.99** [Demographics: gender (Male / Female / Other) and age (text box).] -page break- **(Mediator Page)** [Demographics: gender (Male / Female / Other) and age (text box).] **SUBTOTAL (1 ITEM): \$349.99** [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Qualtrics survey flow screenshot showing embedded data fields and display logic for the experimental conditions.] ## Post-Test: Expensive and Inexpensive-Price Beliefs [Demographics: gender (Male / Female / Other) and age (text box).] \[Participants who didn't choose 4 or 5 were screened out. They were paid regardless, consistent with Prolific's policies\] -page break- [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Demographics: gender (Male / Female / Other) and age (text box).] **\[price disclosure: delayed condition\]** [Demographics: gender (Male / Female / Other) and age (text box).] **\[price disclosure: delayed with MAP policy disclosure condition\]** [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Qualtrics survey flow screenshot showing embedded data fields and display logic for the experimental conditions.] ## Alternative Explanations Test Procedure \*Same as the main study up to the shopping cart page: [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] [Demographics: gender (Male / Female / Other) and age (text box).] -page break- [Qualtrics survey flow screenshot showing embedded data fields and display logic for the experimental conditions.] # Data Collection Information **DATA COLLECTION INFORMATION** Data from Study 1 were provided by Fravega, an Argentinian retailer of household and tech appliances, to the third author. Collected in spring 2020 by the company under the third author's supervision, these data were analyzed by the third author with input from the first and sixth authors. The posttest and alternative explanations tests for Study 1 were conducted in February 2025 by the first author, who also analyzed the data. Study 2's main study and pretest were conducted via Prolific in February 2025 by the first and second authors. The posttest and alternative explanations tests were conducted online in March 2025 by the same authors. All Study 2 data were analyzed by the first and second authors. Data for Study 3 were again provided by Fravega to the third author. Collected in December 2021 under the third author's supervision, these data were analyzed by the third author with input from the first and sixth authors. The posttest and alternative explanations tests for Study 3 were conducted in February 2025 by the first author, who also analyzed the data. Study 4's main study and pretest were conducted via Prolific in February 2025 by the first and second authors. The posttest and alternative explanations tests were conducted in March 2025 by the same authors, who analyzed all tests and study data. Study 5's main study and alternative explanations test were conducted via Prolific in March 2025 by the first author and analyzed by the first and second authors. The Web Appendix F study (i.e., Study 5 procedure with category-level price expectations) was conducted in April 2024 by the first author and analyzed by the first and second authors. The Web Appendix G study (a conceptual replication of Study 5) was also conducted in April 2024 by the first author and analyzed by the first and second authors. Data for Study 6 were collected via Prolific in August 2024 by the first author and analyzed by the first and second authors with input from all other authors. The posttest and alternative explanations tests for Study 6 were conducted in March 2025 by the first author and analyzed by the first and second authors. The raw data for all studies, code (SPSS for lab studies and STATA/R for field studies), Qualtrics survey files (.qsf), and preregistration documents for the lab surveys and studies are available on the Open Science Framework website: ().