No abstract

Firms nowadays are increasingly proactive in trying to strategically capitalize on consumer networks and social interactions. In this paper, we complement an emerging body of research on the engineering of wordof-mouth effects by exploring a different angle through which firms can strategically exploit the value-generation potential of the user network. Namely, we consider how software firms should optimize the strength of network effects at utility level by adjusting the level of embedded social media features in tandem with the right market seeding and pricing strategies in the presence of seeding disutility. We explore two opposing seeding cost models where seeding-induced disutility can be either positively or negatively correlated with customer type. We consider both complete and incomplete information scenarios for the firm. Under complete information, we uncover a complementarity relationship between seeding and building social media features that holds for both disutility models. When the cost of any of these actions increases, rather than compensating by a stronger action on the other dimension to restore the overall level of network effects, the firm will actually scale back on the other initiative as well. Under incomplete information, this complementarity holds when seeding disutility is negatively correlated with customer type but may not always hold in the other disutility model, potentially leading to fundamentally different optimal strategies. We also discuss how our insights apply to asymmetric networks.

A repeated challenge in launching a two-sided market platform is how to ignite the cross-side network effects to jump-start adoption. This research note studies “piggybacking”—expanding the focal market to recruit exclusive users from external networks—as a new and nonpricing control to launch platforms in conjunction with pricing controls. We first consider consumer-side piggybacking. Our results provide a rich set of novel insights into strategies that platforms use to monetize exclusive access to external users with nontrivial characterizations of the interplay among piggybacking, cross-side network effects, and price competition. We identify conditions when piggybacking is profit improving and when it leads to a prisoner’s dilemma, depending on the piggybacking cost and strengths of cross-side network effects. Among others, we show that piggybacking may intensify rather than ease price competition. We then consider provider-side piggybacking, and we show that the insights are qualitatively the same as consumer-side piggybacking except that the prisoner’s dilemma disappears if piggybacking providers multihome.

Should a monopolistic vendor adopt the selling model or the leasing model for information goods or services? We study this question in the context of consumer valuation depreciation. Using a two-period game-theoretic model, we consider two types of consumer-side valuation depreciation for information goods or services: vintage depreciation and individual depreciation. Vintage depreciation assumes that a good or service loses some of its appeal to consumers as it becomes dated, and this effect persists independent of usage. Individual depreciation instead assumes that valuation depreciation happens only for consumers who have consumed or experienced the good or service. We identify conditions under which each pricing model is preferred. For vintage depreciation information goods, the leasing model dominates the selling model in vendor profit. For individual depreciation information goods, the selling model dominates the leasing model as long as the magnitude of individual depreciation exceeds a certain threshold; otherwise, leasing dominates selling. We consider several model extensions such as when network effects are present. Furthermore, we show a negative interaction effect between vintage depreciation and network effects in vendor profit; in contrast, the interaction effect between individual depreciation and network effects can be either negative or positive depending on the magnitude of individual depreciation. Managerial implications are also discussed.

WeChat, an instant messaging app, is considered a mega app because of its dominance in terms of use among Chinese smartphone users. A major concern for such an app is whether its increasing usage would crowd out the usage of other apps in the broader app market. This study estimates the spillover effects of WeChat on the other top 50 most frequently used apps in China, using users’ weekly app usage data. Given the challenge of determining causal inference from observational data, this study borrows a machine-learning method and integrates it with econometrics. We find that WeChat does not crowd out the other apps’ use. In fact, only two other apps, Tencent News and Taobao, are affected by WeChat usage causally, and interestingly, both receive positive spillover effects as a result of the complementary effects. This finding alleviates concerns about the threat by WeChat and indicates that WeChat’s success is not in exchange for squeezing out the use of other major apps. Instead, WeChat might contribute to the platform by transforming users’ nonmobile activities to the mobile setting and by exhibiting some positive spillovers. In addition, this study shows the strength of graphical models in identifying causal relationships from observational data.