Start Date
11-12-2016 12:00 AM
Description
Users of digital media platforms often confront uncertainty about two aspects of product quality: the general quality and the taste-match quality. Existing approaches in IS usually address this uncertainty issue separately, by either learning users’ preferences to recommend taste-matched items or using pricing and branding strategies to suggest the general quality level of items. This paper presents a new theory-based model to solve the dual quality uncertainties simultaneously without using any users’ preference data or pricing/branding strategies. The model is innovative in that it identifies and captures new quality signals using aggregate level data. Empirical testing demonstrates the effectiveness of the model. The model not only helps users infer the dual qualities simultaneously but also offers actionable knowledge to the platform and content developers on signal device design to build their audiences. This study thus has important managerial implications for platform managers, content developers, content users, and marketers.
Recommended Citation
Ding, Amy Wenxuan, "A Signal Model to Understand Users’ Engagement Behavior on Digital Literature Platforms" (2016). ICIS 2016 Proceedings. 4.
https://aisel.aisnet.org/icis2016/Human-ComputerInteraction/Presentations/4
A Signal Model to Understand Users’ Engagement Behavior on Digital Literature Platforms
Users of digital media platforms often confront uncertainty about two aspects of product quality: the general quality and the taste-match quality. Existing approaches in IS usually address this uncertainty issue separately, by either learning users’ preferences to recommend taste-matched items or using pricing and branding strategies to suggest the general quality level of items. This paper presents a new theory-based model to solve the dual quality uncertainties simultaneously without using any users’ preference data or pricing/branding strategies. The model is innovative in that it identifies and captures new quality signals using aggregate level data. Empirical testing demonstrates the effectiveness of the model. The model not only helps users infer the dual qualities simultaneously but also offers actionable knowledge to the platform and content developers on signal device design to build their audiences. This study thus has important managerial implications for platform managers, content developers, content users, and marketers.