Many of today’s most popular firms are selling products that exhibit network effects; that is, the value created by the product increases with the number of users adopting it. Examples include social media platforms (e.g., Twitter and LinkedIn), information technology providers (e.g., Apple and Huawei), and video game companies. Firms selling these network products can use new tools, such as machine learning, to predict individual consumers’ willingness to pay and then use those data to adopt personalized pricing strategies. For instance, firms can deliver a customized price in the form of a discount to a universal posted price via a mobile application or other channel.
Meanwhile, a growing number of firms are making corporate social responsibility (CSR) an integral part of their business strategies. Among the dimensions of CSR, social responsibility toward consumers is seen as particularly important. For example, Huawei, the leading provider of information and communication technology infrastructure, has actively launched a technology (RuralStar Pro) aimed at providing mobile connections to previously uncovered rural areas at optimal cost and fast speed. Likewise, Riot Games, the company behind the massively popular multiplayer game League of Legends, has matched millions in donations made by their global eSports community.
In our recent working paper, we provide a unified conceptual framework to understand these seemingly unrelated trends. In brief, we show that when offering network goods, even a profit-maximizing firm would find it optimal to commit to being socially responsible toward consumers, i.e., caring about consumer surplus. This result provides a novel perspective for understanding CSR in the digital economy.
To illustrate, consider a simplified setting in which a firm sells a network good to two consumers. Because the good exhibits network effects, when making purchase decisions, one consumer cares about whether the other consumer also purchases the good: Only if both consumers purchase the good can they derive the network utility. As a real-world example, LinkedIn generates more value for its users—i.e., produces more content, information, and services—as more users become active on the platform. Thus, the customer base is a crucial factor for a consumer deciding whether to join LinkedIn.
In addition to the network effect, a key feature of our setting is that the firm adopts personalized pricing; that is, the two consumers might be charged different prices for the same good, and each observes only her own price. Personalized pricing has become widespread in the digital era. Big data has lowered the costs of collecting customer-level information, making it easier for firms to identify new customer segments to target with customized marketing and pricing plans. For instance, it is now possible to track users’ locations, browser and search histories, and “likes” on social networks (such as LinkedIn). This massive volume of data, combined with the power of machine learning, has given rise to a wide and varied range of personalized services (e.g., news content, advertising), as well as personalized pricing.
Although firms adopt personalized pricing in the hope of increasing profits, it may actually create friction in the presence of network effects. To see this, we examine one consumer’s purchase decision. Given that the consumption utility depends on whether the other consumer also joins the product network, the consumer will base her purchase decision not only on the price offered to her by the firm but also on the expected purchase behavior of the other consumer. The consumer will be concerned that if the firm charges a high price to the other consumer, the product’s customer base will be too small to generate high network value. This concern is valid since the other consumer’s price is unobservable and the firm is unable to commit to low prices. The same reasoning applies to the other consumer. As a result, the consumers become reluctant to make purchases, which lowers the firm’s profit.
To overcome this friction, CSR can kick in as a device that commits the firm to charging low prices, resulting in higher consumer demand and optimal firm profits. By committing to being socially responsible toward consumers (i.e., caring about consumer surplus), the firm assures the consumer that the product will also be provided to the other consumer at a low price. This alleviates the consumer’s concern about the customer base and incentivizes her to make the purchase, which ultimately improves firm profits. In practice, firms make credible commitments regarding social responsibility by publicly announcing initiatives, such as Huawei’s official launch of RuralStar Pro and Xiaomi’s promise to cap its profit margin at 5%.
Overall, instead of being a scheme that is realized at the expense of firm profits, CSR is optimally chosen by the firm to increase its selling profits. Our analysis thus provides a justification for the notion of “doing well by doing good.” That is, a firm’s profit maximization and socially responsible awareness can be well aligned.
Our results yield novel implications for firms’ CSR practices. First, our theory links CSR to firms’ product characteristics. Our analysis predicts that firms or industries that offer products with high network value will devote considerable resources to consumer-oriented CSR practices. This prediction is borne out by casual observations in the cases of Huawei and LinkedIn and in the gaming industry. In addition, our channel crucially hinges on consumers being well aware of the CSR practices adopted by firms. This suggests that firms or industries that offer products with high network value are likely to actively disclose relevant CSR activities. Consistent with this prediction, a survey conducted by KPMG in 2020 shows that the technology, media, and telecommunications (TMT) sector has the highest sustainability reporting rate.
Second, we show that CSR is closely related to technological developments that enable firms to adopt personalized pricing. This is broadly consistent with the recent widespread use of data and artificial intelligence algorithms in business and firms’ increasing focus on social responsibility.
Yan Xiong is an Assistant Professor of Finance at the Hong Kong University of Science and Technology.
Liyan Yangis a Professor of Finance and Professor of Economics
Peter L. Mitchelson/SIT Investment Associates Foundation Chair in Investment Strategy at the University of Toronto.
This post was adapted from their paper, “A Product-Based Theory of Corporate Social Responsibility,” available on SSRN.
