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Abstract

Consumer information sharing is considered an effective strategy to attract consumers, yet certain high-end retailers, such as Bergdorf Goodman and Farfetch, tend to hinder consumers from sharing information through online reviews. We study a retailer’s strategy for consumer information sharing in a supply chain. We find that a retailer’s information sharing strategy can prevent manufacturers from extracting excessive profit when consumers are heterogeneous in their valuations of the selling product. Specifically, a retailer can achieve a higher profit margin by targeting all consumer segments. By strategically choosing the information sharing strategy to influence consumer beliefs, the retailer can induce the manufacturer to conform to the retailer’s preferred targeting segment through a low wholesale price. Thus, a high-end retailer, whose consumers have a high ex-ante quality belief, favors hindering information sharing among consumers because it enables the retailer to target all consumer segments. Interestingly, deterring consumers from learning about the product quality may generate more consumer surplus. Our main results are robust under extensions such as consumer search behavior, consumer waiting, and multiple product selling. When selling multiple products, a retailer with a large quality variation is better off hindering consumers from sharing information. Our work shows that strategically choosing a consumer information sharing strategy enables retailers to enhance profit margins in their interactions with upstream manufacturers.

Keywords

Consumer information sharing supply chain target consumer segment online reviews pricing

1. Introduction

Consumer information sharing, whether through word-of-mouth (WOM) marketing or online reviews, has been popular in practice and played a crucial role in influencing consumer behavior. One of the primary functions of consumer information sharing is to assist consumers in making informed purchase decisions by providing additional insights into product quality. Previous research has demonstrated the impact of such information sharing on consumer choices. For instance, as discussed by Godes (2017), books with higher ratings are more likely to be purchased (Chevalier and Mayzlin, 2006), and more positive comments attract more consumers to attend a movie (Chintagunta et al., 2010).

Existing studies have predominantly examined the impact of online reviews on a retailer’s pricing and product design decisions (Kwark et al., 2014; Jiang and Yang, 2018; Liu and Feng, 2021), but have not directly addressed the fundamental question of whether retailers should support online reviews. While it is appealing to believe that high-end retailers, known for their superior product quality, would prefer consumers to share information about product quality, empirical observations reveal a different reality. Notably, several high-end retailers, such as Bergdorf Goodman and Farfetch, have opted not to support online product ratings. Figure 1 presents the sample product pages on BergdorfGoodman.com (Figure 1(a)) and JCPenney.com (Figure 1(b)), where Bergdorf Goodman does not provide any product ratings, while JCPenney.com allows consumers to review products and provides overall ratings as well as attribute-specific ratings such as comfort level, fit, and style. To gain broader insights, we surveyed 29 online retailers listed by Stratten (2019) and Store Support (2019) and found that 16 out of 22 high-end retailers neither provide rating information nor comment space, whereas all seven out of seven low-end retailers offer rating information (Table 1). These observations prompt an intriguing exploration of the underlying factors driving retailers to adopt different consumer information sharing strategies.

Figure 1.

Review space on (a) BergdorfGoodman.com and (b) JCPenney.com.

Table 1.

Survey of high-end and low-end retailers’ consumer information sharing strategy in practice.

	Rating
Retailer type	No	Yes	Retailers in survey
High-end	16/22	6/22	Aesop, Alexander McQueen, Away, Balenciaga, Balmain, Bergdorf Goodman,
			Burberry, Canada Goose, En, Escada, Farfetch, Glossier, Goop Lab, Gucci Garden,
			Leica, Mulberry, Neiman Marcus, Reformation, Saks Fifth Avenue, Stella McCartney,
			The Maiyet Collective Concept Store, and Venus ET Fleur
Low-end	0/7	7/7	Dollar Tree, Forever 21, JCPenny, Miniso, Sears, Target, and Walmart

Effective management of consumer communication (e.g., WOM) plays a vital role in customer relationship management (CRM), which administers a company’s interactions and relationships with its customers (Musalem and Joshi, 2009; Akbari et al., 2016). Given that CRM is an integral aspect of supply chain management, it is critical to evaluate how efficient communication with customers influences supply chain strategies (CRM Blog Team, 2022). Specifically, prior research has demonstrated that the uncertainty of product quality directly affects the effectiveness of WOM communication, which, in turn, impacts price decisions in the upstream supply chain (Kwark et al., 2014). However, existing research has yet to adopt a holistic view that considers the broader impact of interactions among supply chain players on CRM.

To gain insights into the consumer information sharing strategies adopted by retailers and their implications for the supply chain, we adopt an analytical model to study a supply chain in which a retailer procures a new product from a manufacturer and sells the product to consumers over a two-period selling season. We focus on a common scenario where the manufacturer lacks a prominent brand presence. This is because most manufacturers distribute their products through retailers, who have more direct interactions with consumers. For example, brands like Benefit, Mary Kay, Revlon, Sephora, E.L.F., and Laura Gellar source cosmetic items such as cosmetic brushes and eye masks from Beauty Yaurient Cosmetics Accessories Co. Ltd. in China. Similarly, retailers like Saks Fifth Avenue, Lord and Taylor, Bass Pro Shop, Kmart, and Primark obtain various apparel such as t-shirts, pants, and dresses from Norp Knit Industries in Bangladesh (Hamrick, 2022). Nevertheless, most consumers are not aware of the manufacturers when making purchasing decisions from these brands. Thus, the retailers’ closer proximity to consumers often results in greater consumer familiarity with retailers as opposed to manufacturers (Sung et al., 2023).

The examples discussed above also indicate that a supplier can provide products of similar quality to both high-end and low-end lines. Therefore, it is not always true that products from a high-end retailer are inherently superior in quality compared to those from a low-end retailer. However, as will be shown later in our article, it is more likely for a high-end retailer to offer a high-quality product compared to a low-end retailer.

We consider a consumer base that exhibits heterogeneity in their willingness to pay (WTP) for the product, comprising both high-type and low-type consumers, with the former having a higher WTP than the latter. Consumers arriving in the first period make purchase decisions based on the retailer’s brand image. (i.e., the ex-ante belief of the expected product quality). The retailer has the option to either facilitate or hinder consumer information sharing. When the retailer facilitates information sharing, consumers arriving in the second period will learn about the actual product quality through the information shared by the first-period consumers. When the retailer hinders information sharing, the second-period consumers will not acquire knowledge about the product quality from their predecessors. Thus, they will rely on the retailer’s brand image, much like the first-period consumers, to make purchase decisions. In addition, the retailer may target different consumer segments based on the manufacturer’s wholesale price and consumers’ valuation of the product. The interplay between the manufacturer’s wholesale price and the retailer’s target consumer segments, in turn, influences the retailer’s decisions regarding pricing and consumer information sharing strategies.

In addition, note that in certain circumstances, price could serve as a signal of product quality. However, we focus on cases where price fails to effectively signal product quality, and consumers are unable to update their expected quality belief based on price alone. Instead, the retailer’s brand and customer reviews serve as indicators of product quality and impact consumer purchase decisions.

Our study aims to address the following research questions: (1) How does a retailer’s information sharing strategy affect retail prices, target consumer segment, and the retailer’s profit? (2) When should a retailer choose to facilitate or hinder consumer information sharing? (3) What is the impact of a retailer’s consumer information sharing strategy on manufacturer, supply chain, and consumer surplus? (4) How do consumer behaviors, such as strategic waiting, consumer search, and risk-aversion, influence the retailer’s decision regarding the information sharing strategy?

We summarize several interesting findings in this article. First, irrespective of the brand image, a retailer always obtains a higher profit when targeting all consumers than when targeting only the high-type consumers. This is because a manufacturer charges a low (high) wholesale price when inducing the retailer to target all (only the high-type) consumers. By strategically selecting an information sharing strategy, the retailer gains the ability to influence consumer beliefs, thereby achieving its desired target segment and a higher profit.

Second, a high-end retailer favors hindering consumer information sharing. Under a hindering (facilitating) strategy, a high-end (low-end) retailer is weakly better off targeting all consumers compared to targeting only the high-type consumers. This divergence arises from the high-quality belief held by consumers of high-end (low-end) retailers when information sharing is hindered (facilitated). Consequently, a high-end retailer chooses to hinder information sharing as a means to prevent excessive profit extraction by manufacturers. This result highlights the effectiveness of a retailer’s information sharing strategy as a lever to safeguard its profit.

Third, our results show that the retailer’s dominant consumer information sharing strategy benefits the supply chain as a whole. This is because the retailer and the manufacturer are both better off under the consumer information sharing strategy which leads to a higher level of consumer quality belief.

Fourth, we find that hindering consumer information sharing may generate greater consumer surplus, that is, consumers may benefit from having limited access to information. This is because hindering consumer information sharing induces a high-end retailer to target all consumers, and the retailer charges a lower retail price in such a case than when targeting only the high-type consumers.

Fifth, when consumers incorporate both the expected quality belief and quality variation into their purchasing decisions, our main result that a high-end retailer opts for hindering consumer information sharing remains robust. Additionally, a higher variation in quality leads to the hindering (facilitating) strategy when the low-type consumers’ WTP is low (high).

Moreover, when the retailer sells multiple products, a low variation in product quality motivates the retailer to facilitate consumer information sharing, whereas a high variation prompts the retailer to hinder information sharing. This is because the retailer’s marginal profit decreases with the quality variation under the facilitating strategy, but stays constant under the hindering strategy.

Lastly, we consider the case that consumers may search for quality information from other sources by incurring a search cost when product quality information is not accessible to consumers on the retailer’s website. We find that consumer search makes the hindering strategy even more favorable for a retailer. Similarly, when consumers display strategic behavior, facilitating the sharing of consumer information could lead to consumers postponing their purchasing decisions to a later period, anticipating potential price reductions. Consequently, the retailer is once again inclined towards adopting a strategy that hinders information sharing.

The rest of this article is organized as follows. We review the relevant literature and highlight our main contributions in each research area in Section 2. We present our model to capture the supply chain dynamics in Section 3. Subsequently, we analyze the supply chain players’ decisions under the single-product setting in Section 4. To validate the robustness of our theoretical findings, we extend the analysis to several alternative settings in Section 5. We conclude the article and suggest directions for future research in Section 6. All technical proofs are presented in the Online Appendices.

2. Literature Review

Our article contributes to the following two streams of literature: information sharing and brand position. First, the literature on information sharing can be divided into two categories: upward information sharing and downward information sharing. The literature on upward information sharing has focused on the information sharing between manufacturers and retailers from the perspective of supply chain coordination in various contexts, including monopolistic supply chains consisting of one retailer and one manufacturer (Lee et al., 2000; Cachon and Lariviere, 2001), competitive retailers (Li, 2002; Zhang, 2002; Kong et al., 2013; Wang et al., 2022), competitive manufacturers (Shang et al., 2016; Ha et al., 2023), and competitive supply chains where each supply chain consists of one retailer and one manufacturer (Ha and Tong, 2008; Ha et al., 2017). Our work distinguishes from the above literature by focusing on the information sharing between a retailer and consumers.

The literature on downward information sharing has focused on the information sharing between retailers and consumers. There are empirical studies exploring the value of consumer information sharing (Ryu and Feick, 2007; Godes and Mayzlin, 2009; Iyengar et al., 2011; Seiler et al., 2017). Notably, Godes and Mayzlin (2009) demonstrate the effectiveness of WOM communication in promoting product sales. In addition, there are theoretical studies that investigate a retailer’s information disclosure decision and its impact on consumer behavior and other decisions (Mayzlin, 2006; Godes, 2017; Liu et al., 2017; Cui and Shin, 2018; Kwark et al., 2014; Jiang and Yang, 2018; Chen et al., 2022; Zhao et al., 2022; Yang and Zhang, 2022). For instance, Mayzlin (2006) finds that online reviews that enable consumers to learn product quality may benefit consumers in a market consisting of an incumbent and an entrant. In contrast, our findings indicate that online reviews can reduce consumer surplus when considering target consumer segments and supply chain interactions. Godes (2017) suggests that more WOM leads to a higher price, but we find that price can be lower under a facilitating strategy due to strategic decisions related to the target consumer segment. Jiang and Yang (2018) analyze the impact of consumer information sharing on a firm’s product design strategy. Kwark et al. (2014) investigate the impact of online reviews on a retailer’s and manufacturers’ pricing decisions and show that manufacturers should strategically respond to unfavorable reviews. Our article is distinct from Jiang and Yang (2018) and Kwark et al. (2014) as we endogenize the retailer’s consumer information sharing strategy and find that the retailer can use information sharing strategy as leverage to prevent manufacturers from extracting excessive profit. Moreover, while the above-mentioned literature, except for Kwark et al. (2014), primarily focuses on the interaction between a firm and its consumers, our study considers the strategic interactions among manufacturers, retailers, and consumers in the entire supply chain.

Our article is also related to the literature on brand position and brand value. One stream of research assumes that consumers consider brand as a form of personal statement (Kuksov, 2007; Rayo, 2013; Amaldoss and Jain, 2015; Gao et al., 2017; Sebald and Vikander, 2019). Another stream of research treats brand as signals that convey information about product quality when a firm’s product quality is not readily observable (Mailath and Samuelson, 2006). Consistent with this latter perspective, we assume that when a retailer hinders consumers from sharing information, consumers will rely on the retailer’s brand image to infer product quality. On the other hand, when the retailer facilitates consumer information sharing, consumers can learn about the true product quality from the information shared by previous consumers. We contribute to the literature by demonstrating the significant role played by a retailer’s brand image in determining the consumer information sharing strategy.

3. Model

We consider a supply chain consisting of an online retailer and a manufacturer. The manufacturer supplies a product to the online retailer, who then sells it to consumers over two consecutive periods. In this section, we first outline the model of consumer demand and then describe the manufacturer’s and the retailer’s profit functions and decisions.

Consumers

A consumer visits the online retailer and purchases at most one unit of the product in a given period $t$ , where $t \in {1, 2}$ . We normalize the number of arriving consumers in each period to one. Consumers are heterogeneous in their WTP for the product. Specifically, there are two types of consumers: H-type and L-type.¹ A proportion $λ \in (0, 1)$ of consumers are H-type with a valuation, denoted by $v_{t}$ ; the remaining $1 - λ$ of consumers are L-type with a valuation of $θ v_{t}$ , where $θ \in (0, 1)$ denotes the L-type’s WTP. Note that for the H-type consumers, the valuation $v_{t}$ is consistent with the consumer quality belief in period $t$ . To ensure a meaningful analysis where both the manufacturer and the retailer can target both types of consumers, we assume $θ > λ + λ (1 - λ)$ . Otherwise, the manufacturer or the retailer would only target the H-type consumers, rendering the analysis trivial. Given a retail price $p_{t}$ , an H-type consumer has a net utility of $v_{t} - p_{t}$ , and an L-type consumer has a net utility of $θ v_{t} - p_{t}$ . Let $D_{t}$ denote the demand for the product in period $t$ . Based on the net utility functions, when the retail price is sufficiently low (i.e., $p_{t} \leq θ v_{t}$ ), both types of consumers will purchase the product, that is, $D_{t} = 1$ . When the retail price is moderate (i.e., $θ v_{t} < p_{t} \leq v_{t}$ ), only the H-type consumers will purchase the product, so $D_{t} = λ$ . Finally, when the retail price is exorbitantly high (i.e., $p_{t} > v_{t}$ ), no consumers will purchase the product, that is, $D_{t} = 0$ .

Manufacturer

The manufacturer produces and sells the product with quality $q$ , where $q$ follows a uniform distribution on $[q_{c} - Δ, q_{c} + Δ]$ . We consider the case where product quality is exogenous. In practice, a manufacturer may provide the same product to multiple retailers, and the product quality is unaffected by retailers’ information sharing strategies. The manufacturer incurs a quadratic production cost $k q^{2}$ , which is a common assumption in the literature (see, e.g., Shi et al., 2013; Jiang and Yang, 2018). To examine the robustness of our results, we also consider the case where $q$ is endogenous in Online Appendix EC.10, with the production cost coefficient $k$ following a uniform distribution on $[k_{l}, k_{u}]$ .

The manufacturer sets a wholesale price $w_{t}$ in period $t$ . The manufacturer’s profit in period $t$ is given by $π_{m t} = (w_{t} - k q^{2}) \cdot D_{t}$ , and the total profit across the two selling periods is $Π_{m} = \sum_{t = 1}^{2} π_{m t}$ . We extend the analysis to the case where the retailer sells multiple products in Section 5.6.

Retailer

The retailer has two key decisions to make. First, it needs to determine which type(s) of consumers to target in each period by setting a retail price $p_{t}$ in period $t$ . That is, the retailer has the option to charge a low price to target both types of consumers or a high price to solely focus on the H-type consumers. Moreover, the retailer must choose one of the following two consumer information sharing strategies: facilitating consumer sharing of information, denoted by Strategy $F$ , or hindering consumer sharing of information, denoted by Strategy $N$ . The retailer’s profit in period $t$ is given by $π_{r t} = (p_{t} - w_{t}) \cdot D_{t}$ , and the total profit across the two selling periods is denoted as $Π_{r} = \sum_{t = 1}^{2} π_{r t}$ .

Consumer information sharing strategies affect the demand $D_{t}$ by influencing consumers’ beliefs about the product quality $v_{t}$ . In the first period, as the product is newly introduced to the market, consumers lack information regarding its true quality $q$ , irrespective of the consumer information sharing strategy. This setting is consistent with our focus on the scenario where the manufacturer lacks a prominent brand presence. Consequently, consumers arriving in the first period base their purchase decisions on their ex-ante quality belief, $E [q]$ , which is aligned with the retailer’s brand image $q_{c}$ . That is, $v_{1} = q_{c} = E [q]$ . This assumption is consistent with Dodds et al. (1991), who show that positive brand information enhances consumers’ perceptions of quality and value, thereby increasing the likelihood of a purchase. In this context, we refer to a retailer with a relatively high (low) ex-ante belief, $q_{c}$ , as a high-end (low-end) retailer. As shown in Lemma EC.1.1 in Online Appendix EC.1, a high-end retailer is more likely to provide a high-quality product. This higher likelihood comes from the fact that a high-end retailer possesses a stronger brand image, which influences consumers’ initial expectations of product quality.

In the second period, if the retailer allows consumers to share information (Strategy $F$ ), the second-period consumers learn the actual product quality $q$ through online reviews. Consequently, $v_{2} = q$ . On the other hand, if the retailer hinders consumers from sharing information (Strategy $N$ ), the second-period consumers rely on the same quality belief as the first-period consumers when making purchase decisions; that is, $v_{2} = q_{c}$ .

In the main model, we consider the case that consumers cannot access product quality information elsewhere. We analyze the case where consumers can access quality information at a search cost even if the retailer hinders information sharing in Section 5.1.

Sequence of events

We summarize the sequence of events as follows:

(1)
The retailer chooses a consumer information sharing strategy before the selling season.
(2)
With knowledge of the retailer’s consumer information sharing strategy, the manufacturer produces the product with a particular quality level $q$ at a unit cost of $k q^{2}$ .
(3)
At the beginning of each period $t$ , the manufacturer charges the retailer a wholesale price $w_{t}$ , and the retailer sets a retail price $p_{t}$ .
(4)
Consumers arrive in each period $t$ and make purchase decisions based on their valuation, their beliefs about the product quality, and the prevailing retail price.
The retailer’s brand image $q_{c}$ is common knowledge, but the true product quality $q$ is only known to the manufacturer and the retailer. In Section 5.5, we consider the alternative case, where the retailer does not know the product quality before making the information sharing strategy decision.
4. Analysis and Results

In this section, we examine the retailer’s target consumer segment in Section 4.1 and present the equilibrium results under a given consumer information sharing strategy in Section 4.2. We then analyze the retailer’s dominant consumer information sharing strategy in Section 4.3 and investigate the impact of the retailer’s consumer information sharing strategy on supply chain performance and consumer surplus in Sections 4.4 and 4.5, respectively.

4.1. Target Consumer Segment

We first analyze the retailer’s target consumer segment. In each period, the retailer chooses to target either the H-type consumers only (denoted by segment $H$ ) or both types of consumers (denoted by segment $V$ ). Let $β = \frac{θ - λ - λ (1 - λ)}{k (1 - λ)^{2}}$ . The retailer’s choice of target consumer segment based on consumer quality belief is described inLemma 1.

Lemma 1 (Target Consumer Segment)

(1) When $v_{t} > \frac{q^{2}}{β}$ , the manufacturer sets $w_{t}^{*} = \frac{θ - λ}{1 - λ} v_{t}$ to induce the retailer to target segment $V$ ; the retailer sets $p_{t}^{*} = θ v_{t}$ and earns a positive profit. (2) When $v_{t} \leq \frac{q^{2}}{β}$ , the manufacturer sets $w_{t}^{*} = v_{t}$ to induce the retailer to target segment $H$ ; the retailer sets $p_{t}^{*} = v_{t}$ and earns zero profit.

Lemma 1 shows that, given the consumer quality belief $v_{t}$ , the manufacturer induces the retailer to target both types of consumers (segment V) when $v_{t}$ is high and only the H-type consumers (segment H) when $v_{t}$ is low. When the target segment is $V$ , the retailer achieves a positive profit, as $w_{t} = \frac{θ - λ}{1 - λ} v_{t} < p_{t}$ . When the target segment is $H$ , the retailer’s profit becomes zero, as $w_{t} = v_{t} = p_{t}$ . Consequently, when targeting both consumer segments, the retailer attains a higher profit margin (i.e., $p_{t} - w_{t}$ ), which weakly increases with the consumer quality belief $v_{t}$ . By strategically influencing consumer belief $v_{t}$ , the retailer could achieve its preferred target segment, that is, segment V. In the following, we describe how the retailer strategically chooses its information sharing strategy to influence $v_{t}$ .

Figure 2.

Target consumer segments under (a) Strategy $N$ and (b) Strategy $F$ .

4.2. Equilibrium Results Under a Given Consumer Information Sharing Strategy

In this section, we explore the equilibrium outcomes under the consumer information sharing Strategy $N$ in Section 4.2.1 and under Strategy $F$ in Section 4.2.2.

4.2.1. Strategy $N$

Under Strategy $N$ , the retailer hinders the first-period consumers from sharing product quality information with the second-period consumers. Hence, when making purchase decisions, consumers lack access to product quality information in either period and must rely solely on their ex-ante quality belief (i.e., $q_{c}$ ), which is identical in both periods; that is, $v_{1} = v_{2} = q_{c} = E [q]$ . We represent the retailer’s target consumer segments across both periods as $XY$ , where $X,Y \in {V, H}$ . For example, “ $HV$ ” indicates that the retailer targets only the H-type consumers in the first period and both types of consumers in the second period. Lemma 2, along with Figure 2(a), describes the target consumer segment under Strategy $N$ . The optimal retail and wholesale prices are outlined in the proof of Lemma 2.

Lemma 2
Under Strategy $N$ , when $q_{c} \geq \frac{q^{2}}{β}$ , the retailer targets segment $VV$ and makes a positive profit. Otherwise, the retailer targets segment $HH$ and makes zero profit.

Lemma 2 is derived from Lemma 1 by setting $v_{1} = v_{2} = q_{c}$ . The result can be explained as follows: the manufacturer’s profit increases with $q_{c}$ regardless of the retailer’s target segment. However, the rate of increase is higher when the retailer’s target segment is $V$ compared to $H$ due to the higher demand in the former case. Therefore, when $q_{c}$ is sufficiently high, the manufacturer induces the retailer to target segment $V$ ; otherwise, the manufacturer induces the retailer to target segment $H$ .
4.2.2. Strategy $F$

Under Strategy $F$ , the first-period consumers can share product quality information with the second-period consumers. Therefore, while the first-period consumers still rely on their ex-ante quality belief (i.e., $q_{c} = E [q]$ ) to make purchase decisions, the second-period consumers update their quality belief to the true product quality. That is, $v_{1} = q_{c}$ and $v_{2} = q$ . We derive the optimal target consumer segment under Strategy $F$ in Lemma 3 and illustrate the results in Figure 2(b). The corresponding optimal retail and wholesale prices are presented in the proof of Lemma 3.

Lemma 3
Under Strategy $F$ : (1) When $q \leq min {\sqrt{β q_{c}}, β}$ , the retailer’s target consumer segment is $VV$ . (2) When $min {\sqrt{β q_{c}}, β} < q \leq β$ ,the retailer’s target consumer segment is $HV$ . (3) When $min {\sqrt{β q_{c}}, β} < q \leq \sqrt{β q_{c}}$ , the retailer’s target consumer segment is $VH$ . (4) When $q > max {\sqrt{β q_{c}}, β}$ , the retailer’s target consumer segment is $HH$ .

Lemma 3 is derived from Lemma 1 by setting $v_{1} = q_{c}$ and $v_{2} = q$ . Unlike Strategy $N$ , where the retailer consistently targets the same consumer segment(s) in both periods, Lemma 3 reveals that under Strategy $F$ , the retailer’s target consumer segments can shift from $H$ ( $V$ ) to $V$ ( $H$ ) across the two periods, especially when $q$ is moderate. This shift occurs because Strategy $F$ allows the first- and second-period consumers to hold different quality beliefs, while Strategy $N$ results in identical quality beliefs for both periods.

We summarize the retailer’s optimal target consumer segments under Strategies $N$ and $F$ , respectively, in Table 2. While the target segments under both strategies are always the same in the first period, they may differ in the second period. We present these results in the following lemma.

Table 2.
Target consumer segment under Strategies $N$ and $F$ .

$q_{c} \leq β$ $q_{c} > β$

Strategy $q \leq q_{c}$ $q_{c} < q \leq \sqrt{β q_{c}}$ $\sqrt{β q_{c}} < q \leq β$ $q > β$ $q \leq β$ $β < q \leq \sqrt{β q_{c}}$ $q > \sqrt{β q_{c}}$

$N$ ${VV}^{}$ $VV$ $HH$ ${HH}^{}$ ${VV}^{}$ ${VV}^{}$ ${HH}^{}$

$F$ $VV$ ${VV}^{}$ ${HV}^{}$ ${HH}^{}$ $VV$ $VH$ ${HH}^{}$

Note: We use an asterisk sign () in superscript to indicate the dominant information sharing strategy for the retailer, which is derived in the proof of Proposition 1.

Lemma 4
A retailer with $q_{c} > \frac{q^{2}}{β} > β$ (high-end) targets segment $VV$ under Strategy $N$ but $VH$ under Strategy $F$ . A retailer with $q_{c} < \frac{q^{2}}{β} \leq β$ (low-end) targets segment $HH$ under Strategy $N$ but $HV$ under Strategy $F$ . Otherwise, the target segments are the same under both strategies.

Lemma 4 follows from the fact that the quality belief is higher under $N$ than that under $F$ for a high-end retailer (i.e., $q_{c} > \frac{q^{2}}{β} > β$ ). This higher quality belief enables the high-end retailer to target segment $V$ under $N$ , as indicated by Lemma 1. In contrast, the quality belief is higher under $F$ than that under $N$ for a low-end retailer (i.e., $q_{c} < \frac{q^{2}}{β} \leq β$ ), triggering the low-end retailer to target segment $V$ under $F$ by Lemma 1.

In the following corollary, we compare the second-period retail prices under the hindering information sharing strategy (denoted by $p_{N 2}^{}$ ) with those under the facilitating information sharing strategies (denoted by $p_{F 2}^{}$ ).
Corollary 1 (Price Comparison)

	$q_{c} \leq β$	$q_{c} > β$
$N$	${VV}^{*}$	$VV$	$HH$	${HH}^{*}$	${VV}^{*}$	${VV}^{*}$	${HH}^{*}$
$F$	$VV$	${VV}^{*}$	${HV}^{*}$	${HH}^{*}$	$VV$	$VH$	${HH}^{*}$

The retail price is lower under Strategy $F$ than that under Strategy $N$ (i.e., $p_{F 2}^{*} \leq p_{N 2}^{*}$ ) when $\sqrt{β q_{c}} < q \leq min {β, \frac{q_{c}}{θ}}$ .

Corollary 1 reveals that facilitating information sharing may reduce the retail price, even when the true product quality exceeds the ex-ante consumer quality belief (i.e., $q > q_{c}$ ). Specifically, under Strategy $N$ , the retailer can set a relatively high retail price (i.e., $p_{N 2}^{*} = q_{c}$ ) to serve only the H-type consumers. However, under Strategy $F$ , the retailer opts for a low retail price (i.e., $p_{F 2}^{*} = θ q$ ) to serve both types of consumers. This result challenges the conventional notion that consumer information sharing leads to higher retail prices. The distinction arises from the effect of the target consumer segment captured in our model.

4.3. Retailer’s Optimal Consumer Information Sharing Strategy

We have analyzed the optimal pricing decisions and target consumer segments under a given consumer information sharing strategy (Table 2). As previously shown in Lemma 1, the retailer earns a positive profit when targeting both types of consumers (segment $V$ ) but zero profit when focusing solely on the H-type consumers (segment $H$ ). Thus, the retailer prefers the strategy that leads to target segment $V$ .² Additionally, when the retailer targets segment $V$ under both strategies (e.g., $q_{c} \leq β$ and $q \leq \sqrt{β q_{c}}$ , or $q \leq β \leq q_{c}$ , in columns 2, 3, and 6 in Table 2), the retailer prefers the strategy that leads to a higher quality belief by Lemma 1. As a high-end retailer’s consumer quality belief is relatively high even without consumer information sharing, the retailer is weakly better off under Strategy $N$ . Hence, a retailer’s information sharing strategy can be used as an effective lever to prevent manufacturers from extracting excessive profit. We present the retailer’s dominant consumer information sharing strategy in the following proposition.

Proposition 1 (Optimal Consumer Information Sharing Strategy)

(1) A high-end retailer (i.e., $q_{c} > β$ ) is weakly better off under Strategy $N$ . (2) A low-end retailer (i.e., $q_{c} \leq β$ ) is strictly better off under Strategy $F$ when the product quality is low (i.e., $q < q_{c}$ ); otherwise, the low-end retailer is weakly better off under Strategy $F$ .

Proposition 1 highlights an important insight: high-end retailers tend to favor hindering consumer sharing of information, while low-end retailers may lean towards facilitating it. This finding aligns with the example presented in Section 1, where JCPenney.com, which targets a broader consumer base, offers detailed online reviews. In contrast, Bergdorf Goodman, as a high-end retailer, chooses to restrict consumer information sharing.

Figure 3 illustrates the retailer’s dominant consumer information sharing strategy and the corresponding target consumer segment. On the one hand, as the ex-ante quality belief $q_{c}$ (vertical axis) increases from $q_{c} \leq q$ to $q_{c} > q$ , the consumer quality belief under Strategy $N$ (i.e., $q_{c}$ ) becomes higher than that under Strategy $F$ (i.e., $q$ ), and the retailer’s target segment shifts from $H$ to $V$ . This is consistent with the results in Table 2 (column 7), which shows that a high-end retailer prefers targeting both types of consumers under Strategy $N$ . Although this may seem counterintuitive, it is supported by practical observations that both high-end and low-end retailers sometimes offer the same brands and products at comparable price points, as detailed in Online Appendix EC.2. For example, Bergdorf Goodman and Macy’s carry products from more than 45 brands in common. They carry shapewear products from SPANX at identical prices, as shown in Figure EC.1. Additionally, at the time of this research, while Bergdorf Goodman sells ZOE LEV 14k gold 0.10ct Diamond Huggie Earrings for $390, Macy’s offers the same earrings at $495 (Figure EC.2). Bergdorf Goodman, as a high-end retailer, does not allow consumers to post reviews, in contrast to Macy’s, which provides opportunities for consumers to post reviews and share photos. These examples underscore the potential for retailers with higher brand images to target a broader consumer base by offering comparable quality products at competitive prices, though these high-end retailers tend to restrict consumer information sharing more than their low-end counterparts.

Figure 3.

Equilibrium of consumer information sharing strategy and the corresponding target segment.Note: We label each region in the form of $Z : XY$ , where $Z \in {N, F}$ denotes the dominant consumer information sharing strategy, $XY$ denotes the retailer’s optimal target segment under Strategy $Z$ , where $X,Y \in {H, V}$ . The notation $N / F$ indicates that the retailer is indifferent to Strategies $N$ and $F$ .

On the other hand, as the true product quality $q$ (horizontal axis) increases from $q_{c} > q$ to $q_{c} \leq q$ , the consumer quality belief under Strategy $F$ (i.e., $q$ ) exceeds that under Strategy $N$ (i.e., $q_{c}$ ), and the retailer becomes better off targeting segment $H$ . This is because a high $q$ is associated with a higher production cost. Thus, the manufacturer sets a high wholesale price to induce the retailer to target solely the H-type consumers via a high retail price.

Based on Proposition 1, we derive the following corollary to examine the impact of consumer mix on the retailer’s consumer information sharing strategy.

Corollary 2 (Impact of Consumer Mix)

The retailer is better off under Strategy $N$ if and only if $λ < 1 - \sqrt{\frac{(1 - θ) q_{c}}{q_{c} - k q^{2}}}$ and $q_{c} > q$ .

Corollary 2 shows that a high-end retailer (i.e., $q_{c} > q$ ) benefits from hindering consumer information sharing when there are few H-type consumers in the market. When $λ$ is small, the manufacturer prefers the retailer to target both types of consumers (segment $V$ ). In such a case, as per Lemma 1, the retailer’s profit increases with the consumer quality belief, which is higher under Strategy $N$ when $q_{c} > q$ .

4.4. Supply Chain Performance

We investigate the impact of consumer information sharing strategy on the performance of the supply chain. The supply chain’s profit in period $t$ is the combined profit of the manufacturer and the retailer, defined as $Π_{t} = π_{m t} + π_{r t} = (p_{t} - k q^{2}) \cdot D_{t}$ . The total profit of the supply chain over the two periods is $Π = \sum_{t = 1}^{2} Π_{t}$ . We assume that the retailer chooses the Pareto optimal strategy, which is the dominant strategy for the supply chain when the retailer is indifferent to the two consumer information sharing strategies. In the following proposition, we compare the dominant information sharing strategies for the manufacturer, the supply chain, and the retailer.

Proposition 2
The dominant consumer information sharing strategies for the retailer, the manufacturer, and the supply chain are aligned.

The retailer, the manufacturer, and the supply chain are all better off under the strategy that leads to a higher level of consumer quality belief, which translates to a higher marginal profit (i.e., a higher retail price). Consequently, their dominant information sharing strategies are aligned.
4.5. Consumer Surplus

According to conventional wisdom, consumers are better off when they can make more informed purchasing decisions. In this section, we show that the opposite may be true. That is, consumer surplus can be greater when consumers are exposed to less product quality information.

Proposition 3 (Impact on Consumer Surplus)

Hindering consumer sharing of information leads to more consumer surplus when $θ \in (θ_{1}, \tilde{θ})$ , $q_{c} > \frac{(2 - λ) (\sqrt{(10 - λ) (2 - λ)} + 2 + λ)}{8 (1 - λ)} Δ$ , and $k < K$ , where $θ_{1}$ and $K$ can be found in the proof in Online Appendix EC.2.

Recall from Lemma 1 that the retailer either charges $p_{t} = v_{t}$ to serve only the H-type consumers or charges $p_{t} = θ v_{t}$ to serve both types of consumers. Consequently, the H-type consumers have zero surplus when the target consumer segment is $H$ and have a positive surplus when the target consumer segment is $V$ . Moreover, our results in Table 2 indicate that the retailer with a high $q_{c}$ (i.e., $q_{c} > β$ ) targets segment $V$ under Strategy $N$ in a larger range of $q$ than that under Strategy $F$ . Thus, hindering consumers from sharing information can lead to more consumer surplus.

5. Extensions

We extend our analysis to several additional realistic scenarios and verify that our main findings still hold. In Sections 5.1 and 5.2, we consider two types of consumer behavior: search and strategic waiting, respectively. In Section 5.3, we focus on the scenario where consumer utility depends on both expected quality belief and quality variation. In Section 5.4, we explore the retailer’s strategy when it has significant bargaining power and enforces a minimum markup. In Section 5.5, we analyze how the retailer determines its consumer information sharing strategy without prior knowledge of the actual product quality. Finally, we examine the scenario involving multiple products in Section 5.6.

5.1. Consumer Search Behavior

Online consumers are presented with a plethora of opportunities to investigate the quality of a product. Recent research highlights that in 2017, 40% of YouTube users utilized the platform to learn more about a product before making a purchase (Andersonn, 2018). Additionally, GE Capital (2013) reports that about 81% of consumers engage in online research prior to committing to a major purchase. In this section, we study the impact of consumer search on the retailer’s consumer information sharing strategy. Specifically, while consumers in the first period are unable to obtain the product quality information as the product is new to the market, consumers in the second period can learn the product quality through the first-period reviews without any search cost under Strategy $F$ , or from other sources with a search cost $c$ under Strategy $N$ . Because consumer search does not affect the retailer’s strategy and target segment in the first period or under Strategy $F$ , we only need to focus on the second period under Strategy $N$ .

Under Strategy $N$ , consumers will engage in a search only if the net utility obtained from conducting the search is higher than that without the search. Based on this argument, we describe consumers’ search decisions in Table EC.2 and Figure EC.5 in Online Appendix EC.3. When the retailer faces consumers who can search for product information from external sources, similar to Proposition 1, we find that a retailer with a high $q_{c}$ (i.e., a high-end retailer) benefits more from hindering consumer information sharing, whereas a retailer with a low $q_{c}$ (i.e., a low-end retailer) is better served by facilitating it (see Lemma EC.3.1 in Online Appendix EC.3).

Overall, compared to the case of no consumer search, the retailer can be better off hindering consumers from sharing quality information when consumers can search for product quality information elsewhere. The following proposition shows the impact of consumer search on the retailer’s information sharing strategy. We illustrate the results of the proposition in Figure 4.

Figure 4.

Optimal consumer information sharing strategy with or without consumer search behavior.Note: We label each region in the form of $C : Z$ , where $C \in {No Search, Search}$ represents the presence of consumer search and $Z \in {N, F}$ denotes the retailer’s optimal strategy with $Z$ in the case of $C$ .

Proposition 4 (Impact of the Consumer Search)

When $q_{c} < q \leq min {q^{'}, β}$ (the gray region in Figure 4), the retailer chooses Strategy $F$ without considering consumer search behavior but Strategy $N$ when considering consumer search behavior. The expression of $q^{'}$ can be found in the proof in Online Appendix EC.3.

Figure 5.

Consumer information sharing strategy comparison when consumers are (a) myopic and (b) strategic.

Recall that under Strategy $N$ , the second-period consumers will search for product quality information when the search cost is low. Following the search, consumers will only purchase products with high quality. By eliminating the possibility of purchasing a low-quality product after a search, the expected utility of consumers who make a purchase increases. By Lemma 1, this increased expected utility enables the manufacturer to induce the retailer to target both types of consumers. Hence, the retailer benefits from the resulting higher demand and earns a higher profit margin under Strategy $N$ than Strategy $F$ .

5.2. Consumer Strategic Waiting Behavior

When customers expect a price drop in the future selling period, they may delay their purchase to the later period. According to Elliot (2018), 64% of online customers delay their purchases until items are discounted, and 22% of consumers exclusively buy from their preferred brands when there is a discount. The effect of consumer waiting behavior has also been explored in previous studies, such as Cachon and Swinney (2009), Lai et al. (2010), Briceño-Arias et al. (2017). When consumers are strategic, we consider that the first-period consumers can decide whether to purchase the product immediately with a net utility of $v_{1} - p_{1}$ , or to delay their purchase until the second period with a net utility of $v_{2} - p_{2}$ . We assume that consumers will not delay their purchase if the net utilities associated with purchasing in both periods arethe same.

Under Strategy $N$ , the retailer consistently targets the same consumer segment (i.e., $VV$ or $HH$ ) in both periods, and the first-period consumers cannot obtain a higher net utility by delaying their purchase decisions to the second period. All results remain unchanged regardless of whether consumers exhibit strategic behavior or not.

Under Strategy $F$ , the first-period consumers will delay their purchase decisions only when the target segment is HV. In this case, all consumers defer their decisions to the second period, resulting in no effective information sharing. Thus, all consumers rely solely on their ex-ante quality belief $q_{c}$ to make purchase decisions in the second period.

We investigate the impact of strategic consumers on the retailer’s consumer information sharing strategy. Similar to Proposition 1, we find that a retailer with a high $q_{c}$ (i.e., $q_{c} > q$ , a high-end retailer) is weakly better off under Strategy $N$ , while a retailer a low $q_{c}$ (i.e., $q_{c} \leq q$ , a low-end retailer) is weakly better off under Strategy $F$ (see Lemma EC.4.1 in Online Appendix EC.4).

We next compare the results in the presence and absence of strategic waiting. Proposition 5 investigates the impact of consumers’ strategic waiting behavior on the retailer’s information sharing decision.

Proposition 5 (Impact of Strategic Waiting)

When $q_{c} \leq \frac{2 q^{2}}{β} - q$ and $q \leq β$ , the retailer is better off under Strategy $F$ if consumers are myopic and is indifferent to $N$ and $F$ if consumers are strategic.

Figure 5 illustrates the difference between the equilibrium results with myopic and strategic consumers. In Figure 5(b), the light gray region depicts a scenario where the retailer, when facing myopic consumers, would opt to target $HV$ and employ Strategy $F$ based on Proposition 1. However, consumers would postpone their purchasing to the second period if they are strategic. Due to the relatively high product quality in this region, the manufacturer instead induces the retailer to target segment HH regardless of the consumer information sharing strategy. As a result, the retailer becomes indifferent to Strategies $N$ and $F$ when facing strategic consumers, as both strategies yield zero profit for the retailer.

5.3. Consumer Utility Dependent on Quality Variation

In the base model, consumers make their purchase decisions based on the expected quality belief $q_{c}$ , which is independent of quality variation $Δ$ . To investigate the impact of quality variation on consumers’ purchase decisions and hence on the retailer’s consumer information sharing strategy, we consider the case that consumers are risk-averse and their utilities depend on both the expectation and variance of the product quality. As in the main model, we assume the actual quality $q$ follows a uniform distribution on $[q_{c} - Δ, q_{c} + Δ]$ . For ease of illustration, we assume that $Δ$ is linear in $q_{c}$ , that is, $Δ = Δ_{q} q_{c}$ .³ Following Desai and Padmanabhan (2004) and Jiang and Zhang (2011), when the consumer utility function exhibits constant absolute risk aversion (CARA), the H-type consumers derive a net utility of $q_{c} - p - r \frac{q_{c}^{2} Δ_{q}^{2}}{3}$ , where $q_{c} - p$ is the expected utility for a risk-neutral consumer and $r \frac{q_{c}^{2} Δ_{q}^{2}}{3}$ is the disutility due to the risk. Herein, $r$ is the consumers’ coefficient of absolute risk aversion, and $\frac{q_{c}^{2} Δ_{q}^{2}}{3}$ captures the variance of the consumer’s utility due to the variation of the product quality. Similarly, L-type consumers derive a net utility of $θ q_{c} - p - r \frac{θ^{2} q_{c}^{2} Δ_{q}^{2}}{3}$ . We examine both the expectation (i.e., $q_{c}$ ) and variance (i.e., $Δ_{q}$ ) of the product quality on the retailer’s optimal consumer information sharing. We defer the theoretical analysis to Online Appendix EC.5 and illustrate the main results in Figure 6.

Figure 6.

Impact of quality variation degree on the retailer’s optimal consumer information sharing strategy: (a) high WTP ( $θ > \sqrt{λ (2 - λ)})$ ; and (b) low WTP $(θ \leq \sqrt{λ (2 - λ)})$ .

Figure 6 first shows that an increase in the consumer quality expectation (i.e., $q_{c}$ ) will lead to the retailer’s dominant information sharing strategy switching from Strategy $F$ to $N$ , which is consistent with the base model.

Figure 6 also illustrates that an increase in the quality variation (i.e., $Δ_{q}$ ) will lead to the retailer’s dominant information sharing strategy switching from Strategy $N$ to $F$ ( $F$ to $N$ ) when the L-type consumers’ WTP is high (low). Note that consumers under Strategy $N$ suffer from the risk loss from the quality variation. When $θ$ is high (i.e., $θ > \sqrt{λ (2 - λ)}$ in Figure 6(a)), L-type consumers are more sensitive to the quality variation $Δ_{q}$ , making the manufacturer target only the H-type consumers, yielding zero profit for the retailer. Therefore, the retailer is better off adopting Strategy $F$ ( $N$ ) when $Δ_{q}$ is high (low) due to the independence of the quality variation under Strategy $F$ . In contrast, when $θ$ is low (i.e., $θ \leq \sqrt{λ (2 - λ)}$ in Figure 6(b)), L-type consumers are less sensitive to the quality variation $Δ_{q}$ , leading to outcomes that are opposite to those observed under the high $θ$ conditions.

5.4. Retailer’s Markup

In our base model, the manufacturer leverages the wholesale price to extract the retailer’s profit. This can result in a low marginal profit for the retailer, potentially even zero, particularly when the retailer solely targets the H-type consumers. In this section, we consider the case where the retailer, having significant bargaining power, enforces a minimum markup (i.e., $η > 0$ ) for selling each unit of the product due to the outside option. We further assume that $η \leq min {q, q_{c}, q - k q^{2}, q_{c} - k q^{2}}$ to avoid trivial cases where the retailer’s markup is exactly $η$ under both Strategies $N$ and $F$ and hence obtains identical profit.

Given $η$ , we describe the optimal information sharing strategy for consumer information sharing in the following proposition.

Lemma 5
When the retailer’s markup is at least $η$ , a high-end retailer (i.e., $q_{c} > {\bar{q}}_{m}$ ) is weakly better off under Strategy $N$ ; a low-end retailer (i.e., $q_{c} \leq {\bar{q}}_{m}$ ) is strictly better off under Strategy $N$ when the product quality is low (i.e., $q \leq {\underline{q}}_{m}$ ). Otherwise, the low-end retailer is weakly better off under Strategy $F$ . The expressions of ${\bar{q}}_{m}$ and ${\underline{q}}_{m}$ can be found in the proof in Online Appendix EC.6.

Lemma 5 shows that a retailer with a higher $q_{c}$ (i.e., $q_{c} > {\bar{q}}_{m}$ ) prefers Strategy $N$ , which is consistent with the findings in Proposition 1 under the base model. Note that the base model is equivalent to the special case where $η = 0$ .

Figure 7 illustrates how the retailer’s minimum markup affects the optimal consumer information sharing strategy. When $q_{c} \geq q$ , a moderate $η$ induces the retailer to choose Strategy $N$ . Otherwise, the retailer is better off under Strategy $F$ . Note that $q_{c} \geq q$ implies that the consumer quality belief is higher under Strategy $N$ than that under Strategy $F$ . By Lemma EC.6.1 in Online Appendix EC.6, when $η$ is moderate, the retailer is induced to target segment $V$ . Thus, both the demand and the consumer quality belief are high under Strategy $N$ , leading to a higher profit for the retailer compared to that under Strategy $F$ . More details of the analysis are deferred to Proposition EC.6.1 in Online Appendix EC.6.

Figure 7.
Impact of retailer’s markup on the optimal consumer information sharing strategy.Note: The expressions of ${\underline{η}}_{N}$ and ${\bar{η}}_{N}$ are shown in the proof of Lemma EC.6.1
5.5. Alternative Sequence With Uninformed Retailer

In our main model, we assume that the retailer is aware of the product quality before setting the consumer information sharing strategy. We refer to this sequence of events as the base sequence. In this section, we explore a scenario where the retailer decides on this strategy before ascertaining the product’s actual quality. The lack of knowledge about the true quality introduces quality uncertainty for the retailer, prompting it to rely on expected profits to identify the optimal consumer information sharing strategy. We refer to this sequence of events as the alternative sequence.

Recall that the product quality $q$ follows a uniform distribution on $[q_{c} - Δ, q_{c} + Δ]$ , we focus on the range of $q_{c} \geq Δ$ to avoid the trivial cases where the product quality is negative. Lemma EC.7.1 in Online Appendix EC.7 shows that our main insight still holds under the alternative sequence: A high-end retailer (with a high $q_{c}$ , i.e., $q_{c} > q_{c p}$ ) opts for Strategy $N$ , while a low-end retailer (with a low $q_{c}$ , i.e., $q_{c} \leq q_{c p}$ ) opts for Strategy $F$ .

Next, we investigate the impact of the alternative sequence on the retailer’s optimal consumer information sharing strategy. Under the base sequence, Proposition 1 indicates that a retailer with a high consumer quality belief (i.e., $q_{c} > q$ ) opts for Strategy $N$ and that with a low consumer quality belief (i.e., $q_{c} \leq min {β, q}$ ) opts for Strategy $F$ . In contrast, under the alternative sequence, the retailer is uninformed of the true product quality and makes the information sharing strategy decision based on the expected profit. By comparing the results in the base sequence (i.e., Proposition 1) with that in the alternative sequence (i.e., Online Lemma EC.7.1), we have the following lemma.

Lemma 6 (Impact of Alternative Sequence on Information Sharing Strategy)

The retailer’s consumer information sharing strategies are consistent under the base and alternative sequences except (1) when $Δ < 2 β$ and $q_{c p} < q_{c} \leq min {β, q}$ , the retailer chooses Strategy $F$ under the base sequence but Strategy $N$ under the alternative sequence, and (2) when $q < q_{c} \leq q_{c p}$ , the retailer chooses Strategy $N$ under the base sequence but Strategy $F$ under the alternative sequence.

The following proposition explores how the alternative sequence affects the manufacturer and the supply chain.

Proposition 6
The alternative sequence weakly hurts the manufacturer and the supply chain.

Proposition 6 indicates that preventing the retailer from accessing accurate product quality information negatively affects both the manufacturer and the supply chain. This is because a retailer informed of the true product quality (i.e., under the base sequence) tends to choose Strategy $F$ for a high-quality product and $N$ for a low-quality product. This decision allows consumers to recognize the product’s superior quality when the product quality is high, thereby increasing the expected consumer quality belief. The increased quality belief further induces the retailer to target both types of consumers, resulting in increased demand by Lemma 1. As a result, both the higher consumer quality belief and the increased demand benefit the manufacturer and the supply chain under the base sequence.
5.6. Multiple Products

We extend our analysis to a multiple product setting to examine whether the findings under the single product setting in Section 4 still hold. In this setting, the retailer (e.g., Walmart and Saks Fifth Avenue) sells more than one product. We assume that the exogenous product quality follows a uniform distribution on $[q_{c} - Δ, q_{c} + Δ]$ , where a higher value of $Δ$ indicates a larger quality variation among the retailer’s products. Recall that the retailer’s first-period profit is the same under both Strategy $N$ and Strategy $F$ . Thus, it suffices to consider the second-period profit functions, denoted by $π_{r 2 | N}$ and $π_{r 2 | F}$ (derived in Lemmas 2 and 3), respectively. We first investigate the retailer’s optimal consumer information sharing strategy by comparing the retailer’s total profits under the two strategies in the next proposition. Let ${\hat{q}}_{c}$ be the solution to $π_{r 2 | F} = π_{r 2 | N}$ , where ${\hat{q}}_{c} \in (max {β - Δ, Δ}, β + Δ)$ .

Proposition 7 (Consumer Information Sharing With Multiple Products)

When (i) $q_{c} \leq β - Δ$ or (ii) $β - Δ < q_{c} \leq min {{\hat{q}}_{c}, β}$ and $β > \sqrt[3]{4} Δ$ , the retailer is better off choosing Strategy $F$ . Otherwise, the retailer is better off choosing Strategy $N$ .

Consistent with Proposition 1 in the single-product setting, Proposition 7 shows that in the multi-product setting, a retailer with a high $q_{c}$ (i.e., a high-end retailer) is still better off hindering consumers from sharing information.

Moreover, Proposition 7 and Figure 8 show that the quality variation has an impact on the retailer’s information sharing strategy under the multi-product setting. In particular, a low variation in product quality (i.e., a low $Δ$ ) leads to Strategy $F$ , whereas a high variation leads to Strategy $N$ . This is because the second-period consumers make purchase decisions based on $q_{c}$ (independent of $Δ$ ) under $N$ but the actual quality $q$ under $F$ . As a result, the retailer’s marginal profit is unaffected by the variation under $N$ but decreases with the variation under $F$ . Eventually, as the variation increases, the retailer finds it more profitable to adopt Strategy $N$ due to the diminishing marginal profit under Strategy $F$ .

Figure 8.

Consumer information sharing strategies for a retailer with multiple products.

6. Conclusion

Consumer information sharing plays a crucial role in informing consumers about product quality. Practical reports, such as Spiegel Research Center (2017) and Vega (2017), emphasize the importance of firms being open about online reviews, as most consumers rely on them when making purchasing decisions. It is commonly assumed that high-end retailers who offer high-quality products would benefit from encouraging consumer information sharing. However, we observe that contrary to the practices of many low-end retailers like JCPenney, high-end retailers such as Neiman Marcus and Bergdorf Goodman opt to hinder consumers from sharing information.

Our article sheds light on retailers’ management of online consumer reviews, emphasizing the significance of considering the consumers’ expected quality belief of the retailer (i.e., the brand image) in this process. While facilitating consumer information sharing tends to benefit low-end retailers, high-end retailers should exercise caution when allowing consumers to share information. Hindering information sharing may benefit a high-end retailer by allowing the retailer to target more consumers, leading to increased marginal profit through strategic interactions with an upstream manufacturer. Thus, consumer information sharing strategies are an effective mechanism for a retailer to prevent upstream manufacturers from extracting excessive profits. In addition, because the retailer, the manufacturer, and the supply chain are all better off under the consumer information sharing strategy that leads to a higher consumer quality belief, the retailer’s dominant consumer information sharing strategy also benefits the supply chain.

We also show that consumers can benefit from less product information. This is because the hindering strategy induces a high-end retailer to target both types of consumers. While the L-type consumers always achieve zero surplus, the H-type consumers can enjoy a positive consumer surplus when the retailer targets both types of consumers.

Lastly, we find that consumers’ strategic search can incentivize the retailer to hinder information sharing. Without information sharing, the search behavior leads to a higher expected quality belief in equilibrium. This high quality belief encourages the manufacturer to induce the retailer to target both types of consumers. As a result, the retailer is better off hindering information sharing. In addition, if consumers may strategically wait when making purchasing decisions, the retailer’s facilitation of information sharing could motivate these consumers to defer their purchases to a later period, expecting potential price drops. In light of this, the retailer prefers to implement a strategy that discourages information sharing. We also demonstrate the robustness of our results under other several extensions: consumer utility influenced by both the expectation and variance of the product quality, scenarios with exogenous retailer markup, retailer’s uncertainty about the true product quality, and the sale of multiple products.

This research can be extended in several directions. For example, future research can examine the implications of consumer information sharing when dealing with competing manufacturers. Additionally, exploring the operational aspects of retail management, such as how sales volumes influence retailers’ decisions to implement consumer review systems, could offer valuable insights. Another interesting extension is the signaling effect of the retailer’s consumer information sharing strategy. Consumers unaware of a product’s true quality may rely on the retailer’s information sharing strategy as a cue to infer the product quality. Our current focus is on the case where consumers’ purchase decisions are influenced by the retailer’s brand, as supported by Dodds et al. (1991), who find that positive brand and store information significantly enhance consumers’ perceptions of quality and value. The signaling role played by the information sharing strategy represents a promising avenue for future research.

Appendix

Notation

$t$

selling period, $t = {1, 2}$

v_{t}

consumer quality belief in period $t$

θ

L-type consumers’ WTP.

λ

proportion of H-type consumers, $λ \in (0, 1)$

q_{c}

retailer’s brand image

q

true product quality

k

production cost coefficient

p_{t}

retail price in period $t$

w_{t}

wholesale price in period $t$

D_{t}

demand in period $t$

π_{m t}

manufacturer’s profit in period $t$

π_{r t}

retailer’s profit in period $t$

Π_{t}

supply chain’s profit in period $t$

Π_{m}

manufacturer’s total profit across two periods

Π_{r}

retailer’s total profit across two periods

Π

supply chain’s profit total profit across two periods

target consumer segment consists of H-type consumers only

target consumer segment consists of both types of consumers

Supplemental Material

sj-pdf-1-pao-10.1177_10591478241256662 - Supplemental material for To Hinder or to Facilitate: Retailers’ Strategy of Consumer Information Sharing

Supplemental material, sj-pdf-1-pao-10.1177_10591478241256662 for To Hinder or to Facilitate: Retailers’ Strategy of Consumer Information Sharing by Buqing Ma, Guang Li and Guangwen Kong in Production and Operations Management

Footnotes

Acknowledgments

We are grateful to the Department Editor, Prof. Michael Pinedo, the anonymous senior editor, and the reviewers for their constructive feedback and valuable suggestions. Their guidance has been instrumental in enhancing the quality of this manuscript.

Declaration of Conflicting Interests

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

Funding

Guang Li is partially supported by the Natural Sciences and Engineering Research Council of Canada (RGPIN-2020-04321).

ORCID iDs

Buqing Ma

Guang Li

Supplemental Material

Supplemental material for this article is available online ().

Notes

How to cite this article

Ma B, Li G and Kong G (2024) To Hinder or to Facilitate: Retailers’ Strategy of Consumer Information Sharing. Production and Operations Management 33(8): 1759–1774.

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Supplementary Material

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	$q_{c} \leq β$				$q_{c} > β$
Strategy	$q \leq q_{c}$	$q_{c} < q \leq \sqrt{β q_{c}}$	$\sqrt{β q_{c}} < q \leq β$	$q > β$	$q \leq β$	$β < q \leq \sqrt{β q_{c}}$	$q > \sqrt{β q_{c}}$
$N$	${VV}^{*}$	$VV$	$HH$	${HH}^{*}$	${VV}^{*}$	${VV}^{*}$	${HH}^{*}$
$F$	$VV$	${VV}^{*}$	${HV}^{*}$	${HH}^{*}$	$VV$	$VH$	${HH}^{*}$

To Hinder or to Facilitate: Retailers’ Strategy of Consumer Information Sharing

Abstract

Keywords

1. Introduction

3. Model

Consumers

Manufacturer

Retailer

Sequence of events

4.1. Target Consumer Segment

Lemma 1 (Target Consumer Segment)

4.2.1. Strategy N

4.3. Retailer’s Optimal Consumer Information Sharing Strategy

Proposition 1 (Optimal Consumer Information Sharing Strategy)

4.4. Supply Chain Performance

Proposition 3 (Impact on Consumer Surplus)

5. Extensions

5.1. Consumer Search Behavior

Proposition 5 (Impact of Strategic Waiting)

5.3. Consumer Utility Dependent on Quality Variation

Lemma 6 (Impact of Alternative Sequence on Information Sharing Strategy)

Proposition 7 (Consumer Information Sharing With Multiple Products)

Appendix

Notation

Supplemental Material

sj-pdf-1-pao-10.1177_10591478241256662 - Supplemental material for To Hinder or to Facilitate: Retailers’ Strategy of Consumer Information Sharing

Footnotes

Acknowledgments

Declaration of Conflicting Interests

Funding

ORCID iDs

Supplemental Material

Notes

How to cite this article

References

Supplementary Material

4.2.1. Strategy $N$