Free for you and me? Exploring the value users gain from their seemingly free apps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Personal data is today recognized as an asset in the digital economy, generating billion-dollar annual revenues for many companies. But how much value do users derive from their seemingly free apps (zero-price services), and what user costs are associated with this value exchange? By adopting a human-centric lens, this article scrutinizes the complex trade-offs users face trying to capture the benefits and unperceived costs that such usage entails. Design/methodology/approach Using a mixed-method research design, this study is anchored in empirical survey data from 196 participants in Linköping, Sweden. The authors investigate users’ willingness to pay for these services in relation to different types of costs. Findings The results indicate that users can derive significant value from the use of free services, which can be interpreted as a win-win situation between users and companies. Regarding costs, this research shows that the most significant costs for users are associated with procrastination, sleep deprivation and reduced focus, which can be challenging to identify and evaluate from the users’ perspective. Research limitations/implications This study shows that zero-price services provide significant benefits like enhancing social connectivity and offering a wide variety of content. Significant drawbacks, such as increased procrastination and sleep disturbances, highlight the psychological effects of these platforms. These impacts include behavioral changes, emphasizing the influence of online platforms on user engagement. Furthermore, a trend toward single-purchase preferences over free services suggests changing consumer attitudes toward digital payment models. This underscores the need for further research on non-monetary aspects in zero-price markets for better understanding and regulation of the digital economy. Practical implications This study shows that users appreciate the accessibility and potential of zero-price services but are wary of privacy concerns. It underscores the need for companies to balance profit objectives with user experiences and privacy requirements. Offering a range of ad-free premium services to meet diverse customer needs can be effective. Users’ high valuation of privacy and transparency suggests businesses should focus on human-centric, privacy-respecting strategies. Increased transparency in data usage and giving users greater data control could enhance the user experience and foster sustainable customer relationships. Social implications The study calls for policymakers to focus on non-monetary risks of zero-price services, such as behavioral changes and digital well-being impacts. They should consider implementing regulations to protect users, especially children, from manipulative designs such as “dark patterns”. Policymakers must balance user protection with innovation, leading to a sustainable zero-price economy. For zero-price service users, awareness of non-monetary costs, like procrastination and sleep deprivation, is vital. Understanding that “free” services have hidden costs is important, especially for younger generations. Managing privacy settings and selective service choices can protect privacy and well-being. Originality/value This research shifts the focus from simply valuing personal data based on market prices to assessing the worth of free services themselves. By listing various hidden costs, it underscores the need for increased user awareness and greater corporate transparency. Uniquely, it finds that users prefer making one-time purchases over using zero-price services, extending prior assumptions in the field. Additionally, it also characterizes the zero-price economy ecosystem, highlighting differences between market types and provides a deeper understanding of the zero-price market and its related concepts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it