THE PERSONALIZATION PRIVACY PARADOX: THE IMPACT OF PERCEIVED DATA SENSITIVITY ON THE EFFECT OF TRANSPARENCY FEATURES
Kopf, Aaron (2023-01-08)
THE PERSONALIZATION PRIVACY PARADOX: THE IMPACT OF PERCEIVED DATA SENSITIVITY ON THE EFFECT OF TRANSPARENCY FEATURES
Kopf, Aaron
(08.01.2023)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe202301163098
https://urn.fi/URN:NBN:fi-fe202301163098
Tiivistelmä
Personalized services and advertisements are getting more common, as bigger amounts of data are available, which enables possibilities for reshaped business models. Personalization can bring benefits for users, for example by reducing information overload. In contrast, the collection of data, which is necessary for personalization, can increase users’ privacy concerns. The tension between the benefits of personalization and the privacy concerns is called the personalization privacy paradox. Investigating this paradox can be essential for the personalization provider to get a better understanding when to use and when not to use personalization as best for their business.
To gain a better understanding of this paradox, a structured literature review is conducted in this work. It provides a broad overview over the current literature of the personalization privacy paradox. Building on the outcomes of the literature review, a quantitative analysis is conducted to investigate the role of transparency features in different data sensitivity contexts within the personalization privacy paradox. The outcomes indicate that transparency features can have a different impact, depending on the different data sensitivity settings.
To gain a better understanding of this paradox, a structured literature review is conducted in this work. It provides a broad overview over the current literature of the personalization privacy paradox. Building on the outcomes of the literature review, a quantitative analysis is conducted to investigate the role of transparency features in different data sensitivity contexts within the personalization privacy paradox. The outcomes indicate that transparency features can have a different impact, depending on the different data sensitivity settings.
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