Free for you and me? Exploring the value users gain from their seemingly free apps
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Notice bibliographique
Résumé
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle