People

Firms increasingly use a combination of image and text description when displaying products and engaging consumers. Existing research has examined consumers’ response to text and image stimuli separately but has yet to systematically consider how the semantic relationship between image and text impacts consumer choice. In this research, we conduct a series of multimethod empirical studies to examine the congruence between image- and text-based product representation. First, we propose a deep-learning approach to measure image-text congruence by building a state-of-the-art two-branch neural network model based on wide residual networks and bidirectional encoder representations from transformers. Next, we apply our method to data from an online reading platform and discover a U-shaped effect of image-text congruence: Consumers’ preference toward a product is higher when the congruence between the image and text representation is either high or low than when the congruence is at the medium level. We then conduct experiments to establish the causal effect of this finding and explore the underlying mechanisms. We further explore the generalizability of the proposed deep-learning model and our substantive finding in two additional settings. Our research contributes to the literature on consumer information processing and generates managerial implications for practitioners on how to strategically pair images and text on digital platforms.

Non-advertising-based mobile apps face several critical challenges when trying to monetize their free services—among them, the choice of pricing strategies (hard landing vs. soft landing; i.e., a “pay or churn” paywall vs. continuing to offer limited free services to existing users after monetization) and aspects of product design (whether to provide exclusive secondary offerings to paying users). The authors implemented a large-scale randomized field experiment with an app firm to test the causal effects of such pricing and product design strategies. Results show that both soft landing and exclusive secondary offerings decrease existing app users’ willingness to subscribe, but there is a positive interaction between these two strategies on subscriptions. The authors propose a theoretical framework, discuss potential mechanisms that might be at play, and conduct robustness checks to rule out several alternative explanations. A customer survey by the firm and an experiment on Prolific provide further support for the theoretical mechanism. To assess generalizability, the authors conducted a second field experiment and obtained consistent results. They also report the results from the actual implementation of the best-performing strategy by the firm. This research provides guidance on possible theoretical underpinnings of users’ responses and important managerial implications for app monetization.