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Pricing through health apps generated data-Digital dividend as a game changer: Discrete choice experiment
Alexandra Heidel1, Christian Hagist1, Christian Schlereth2
1WHU -Otto Beisheim School of Management, Chair of Economic and Social Policy, Vallendar, GER.
Plos One
|July 26, 2021
Summary
Users may accept a digital dividend to share health data, with transparency and fair compensation being key. This incentive could drive large-scale data collection for research, but societal views on data selling are divided.
Area of Science:
- Health Informatics
- Behavioral Economics
- Data Privacy
Background:
- Wearable devices and health apps generate vast amounts of self-tracked health data.
- Sharing this data for research is crucial but faces challenges related to user privacy and compensation.
- Existing data donation models have limited success, necessitating new incentive structures.
Purpose of the Study:
- To investigate the conditions under which users would accept monetary compensation, termed a digital dividend, for sharing their self-tracked health data.
- To quantify user willingness to accept compensation for health data sharing with various stakeholders.
- To understand the impact of data type, data sales to third parties, and stakeholder identity on user acceptance.
Main Methods:
- A discrete choice experiment with a separated adaptive dual response was employed to reduce extreme responses and measure willingness to accept.
- Four key attributes were validated: monthly bonus payment, data stakeholder, data type, and data sales to third parties.
- A random utility framework was used to analyze individual choice preferences, with respondents randomly assigned to different price ranges to test for robustness.
Main Results:
- Transparency in data processing and prohibition of third-party data sales were critical decision factors for users.
- High average digital dividends were expected, with pharmaceutical and medical device companies requiring approximately €237.30/month for all types of patient-generated health data.
- An anchor effect was observed, indicating that price expectations were formed during the experiment rather than beforehand. A bimodal distribution of price expectations highlights societal division on personal data selling.
Conclusions:
- Current data donation models are unlikely to succeed without significant improvements in transparency and trust.
- An adequate digital dividend, coupled with transparent data processing, can serve as an effective incentive for diverse populations to share high-quality health data.
- Future research should consider potential shifts in price expectations post-COVID-19 due to increased awareness of big data's importance in public health.
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