Research Article: A pricing model for group buying based on network effects

Date Published: January 24, 2019

Publisher: Public Library of Science

Author(s): Guanqun Ni, Yongli Li.


Group buying (GB) is a popular business model in e-commerce. With the rise of online social media, the positive network effect of buying with others is more important than price discount for consumers to choose GB. However, the negative network effect of GB is also significant for some consumers. In this paper, we classify consumers into two segments considering both positive and negative network effects, and three possible sales strategies as well as their optimal decisions on price are presented. We find that GB strategy dominates individual buying (IB) strategy when the positive network effect is sufficiently high or the proportion of consumers with low valuation is relatively large. We also find that MIX strategy offering both IB and GB is always better than IB, while the relationship between MIX and GB is depending on actual market situations. Some other managerial insights are also discussed.

Partial Text

Group buying (GB) is a form of selling under which consumers are encouraged to buy together and discounts are offered based on consumers’ aggregated purchasing quantity [1]. This practice is observed in a variety of product categories, ranging from consumer electronics and furniture to dental services and museum visits [2]. For consumers, the most compelling reason to participate in GB is financial, specifically getting low price due to discounts negotiated between GB consumers and sellers. Low price, however, is not GB’s only goal. Extant research has shown that shopping with someone enhances the overall shopping experience and that the presence of other persons in a GB situation is likely to have an influence on the decision to make a purchase [3–5]. Also, pointed out by Wen et al. [6, 7], the decision-makers’ risk preference and thus decisions on purchase are more likely to be influenced under uncertain environment. One of the most distinctive features of online GB is that online shoppers cannot directly observe and experience a product and can only acquire product information from the pictures and text posted on the retailer’s website. Thus, a gap sometimes occurs between the actual product and consumers’ expectation, which in turn leads to the uncertainty risk of product quality [8, 9]. Under such a situation, the presence of other buyers in shopping instances can reduce a consumer’s uncertainty risk of product quality and thus enhance a consumer’s perceived quality of product [10]. In the field of GB, this kind of impact of other buyers’ presence on one’s perceived quality of product is called the positive “network effect” in the literature [11]. A positive network effect also arises for consumers in a GB option when buying with friends and family because of information exchange, affirmation of choice, and lower cognitive load when deciding what to buy [5]. With the rise of online social media and social networks, the positive network effect is even more important than price discount.

Consider a market served by a monopoly seller who sells a unique product. To focus on the influence of network effects, we assume that the market is in complete information symmetry and consists of two consumer segments, denoted by H and L respectively. Proportion m of consumers belong to H segment and they derive a valuation vH for the product, which is uniformly distributed between c and b + c. Proportion 1 − m of consumers belong to L segment whose valuations are systematically lower than those of consumers in H segment, and thus we assume that consumers in L segment derive a valuation vL, which is uniformly distributed between 0 and b, for the product. Here, c captures the difference of valuations between the two populations. Without loss of generality, we assume that ach consumer is interested in buying a single unit of product. In order to avoid trivial results, we additionally assume 0 < c < b throughout this paper. In this section, we will analyze the optimal prices and revenues for the seller if it offers individual buying (IB), group buying (GB), and buying through both mechanisms (MIX). Some useful insights are also derived by comparative analysis in this section. In this section we present several numerical examples to illustrate the results of our model as well as some managerial insights. Note that, we normalize the scale of market to 1 in all numerical examples. GB is becoming more and more popular in e-commerce. In this paper, we derive the utility consumers obtain from GB by recognizing both positive network effect and negative network effect buying with others. According to their valuations for the product and attitudes to network effects, consumers are classified into two segments. From the perspective of sellers, we compare three possible strategies, i.e., offering only IB option, offering only GB option, and offering both options, and derive the optimal decisions on price and quantity for each strategy. Our result shows that both the network effect and consumer heterogeneity play a significant role in choosing optimal strategy. In particular, offering only GB dominates offering only IB when the positive network effect is sufficiently high or the proportion of low valuation segment is relatively large. We also find that offering both options simultaneously can improve the seller’s revenue compared to offering only IB option, while the seller has to decide offering only GB or offering GB along with IB based on actual market situations.   Source:


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