Intertemporal Price Discrimination with Complementary Products: E-Books and E-Readers

Abstract

Firms jointly sell durable primary hardware and complementary software in many industries, but it remains unclear how they can coordinate the two products to conduct intertemporal price discrimination (IPD). In addition to the harvesting (i.e., pricecutting over time) incentive in the traditional single-product IPD literature, firms selling complementary products have another investing (i.e., price-raising over time) incentive to penetrate the market early and earn from subsequent complementary product sales. We study the optimal pricing strategy and profitability of joint IPD in the context of e-readers and e-books. A forward-looking monopolist firm makes dynamic pricing decisions for both e-readers and e-books, and forward-looking consumers make dynamic e-reader and book purchase decisions anticipating future price changes. We structurally estimate the demand system using individual-level transaction data of e-readers and books from 2008 to 2012. The estimation reveals two consumer types, "avid readers" and "general readers," who self-select into buying e-readers based on their unobserved heterogeneous tastes for books. Given the demand estimates, we numerically solve for the optimal joint IPD policy and find that harvesting on e-readers and investing in e-books is optimal. In particular, the optimal pricing policies are functions of the composition of consumer types. The monopolist should harvest on e-readers and invest in e-books for avid readers, while it should invest in e-readers and harvest on e-books for general readers. We explain this difference by comparing the relative demand elasticities between e-readers and e-books for avid and general readers. The joint IPD policy provides a better screening device for the monopolist because different price path combinations can induce different consumer types to purchase. It also limits consumers' ability to arbitrage on price changes over time. The profitability of the joint IPD policy depends on the composition of heterogeneous consumers in the initial market. We find that conducting IPD on both products may hurt firm profits if the fraction of avid readers is too low.

Investigating Antecedents of Adoption Intention for Audiobook Applications

“…Interestingly, facilitating conditions, within the PBC, is not found to affect intention, a departure from extant works 24,25 From the SCT perspective, while procedural switching cost (PSC) was found to weakly affect adoption intention, financial switching cost (FSC) was not, a novel finding in this context 27 . Finally, the audiobook genre was found to positively affect the relationship between PEOU/PU, in agreement with the views by Lee 81 and Li 82 for the adoption of e-books. This paper offers practical implications for developers to design audiobook apps that are not only easy to use and have practical utility, contingent on the book genre, but also enhance the user's self-efficacy and have lower PSC to enable switching from traditional reading behaviour.…”

“…4,23,41 Even for behaviour-disruptive technologies, the importance of these two variables, despite the presence of other antecedents, for forming favourable attitudes and adoption intentions, remains intact. 81,82 Further, the study also reveals the importance of self-efficacy, in conjunction with PEOU and PU, a novel combination, in enabling the migration of users to new technologies, like audiobooks. 86 Next, the weak effect of PSC on adoption implies that the time and effort risks involved in purchasing, setting-up, evaluating, and learning its capabilities, can deter adoption, a finding aligned with existing works.…”

Differential pricing strategies of ride‐sharing platforms: choosing customers or drivers?

“…This management cost is composed of two parts. The first is the cost of preventing arbitrage (Li, 2019). For example, users who obtain a regular price or wage could create fake accounts to take advantage of coupons or subsidies (CNET, 2019).…”

Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment

“…In raw tapstream data, books are preclassified into 247 types by the app company. We group these types into three genres: fiction, casual, and practical (Li 2015). Types within the same genre have similar reading purposes.…”