2 Technological Functionality of Paywall Systems
3 Configuration of Paywall Solutions
3.1 Access Restriction Options
3.2 Pricing Options
4 Use of Machine Learning in Paywall Solution Configurations
5 Prior Research and Avenues for Future Work
- The impact of paywall solution’s configuration on consumer behavior in pre-conversion (acquisition) and post-conversion stages (engagement and retention) Researchers could empirically examine this relationship by conducting experiments or analyzing secondary data obtained from content providers. Additionally, research could focus on important and relevant moderators and mediators of this relationship, for instance by examining content, consumers, markets, and additional paywall characteristics to generate insights on optimal paywall configurations.
- The effect of online content monetization strategies across media products and their online and offline channels While studies in the context of news content exist, IS research could examine the applicability of digital paywalls beyond this type of content. For example, the German video streaming platform Joyn recently implemented a usage-independent access restriction for specific serials and movies on their website. Additionally, IS research could focus on content providers’ multi-channel activities, as well as on bundling the offerings of one company or between companies, which aim to increase the value of the content providers’ paid content. Moreover, platform characteristics of media companies’ websites need to be further examined to optimize content monetization strategies within this multi-sided market.
- The impact of technological innovations and their applicability on paywall systems and resulting paywall solutions’ configuration Current trends in ML or blockchain technology influence digital paywalls by providing new system functionalities and related architecture designs. Recent general developments in the media industry include the personalization and recommendation of content, as well as the automation of content creation (Newman 2019). Accordingly, questions arise regarding consumers’ attitudes toward automated processes, privacy concerns based on content providers’ data usage, and threats to freedom of opinion and the content providers’ role of informing the public (e.g., filter bubbles) as consequences of the use of ML.