Abstract
Although in the last years there has been a growing amount of research in the field of privacy-enhancing technologies (PETs), they are not yet widely adopted in practice. In this paper we discuss the socioeconomical aspects of how users and service providers make decisions about adopting PETs. The analysis is based on our experiences from the deployment of Privacy-respecting Attribute-based Credentials (Privacy-ABCs) in a real-world scenario. In particular, we consider the factors that affect the adoption of Privacy-ABCs as well as the cost and benefit trade-offs involved in their deployment and usage, as perceived by both parties.
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Notes
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m denotes mean value, \(\sigma \) denotes standard deviation.
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Understanding of the Technology factor will be discussed later in Sect. 3.3 for readability reasons.
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E.g., see the Internet Privacy Engineering Network initiative (https://secure.edps.europa.eu/EDPSWEB/edps/EDPS/IPEN).
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http://europa.eu/rapid/press-release_MEMO-14-186_de.htm last visited May 15th, 2015.
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Appendices
A Discussion on the Applicability of UTAUT and UTAUT2
Although UTAUT [34] and UTAUT2 [35] are more successful models than TAM in predicting technology acceptance, we identified TAM as being more suitable in the context of the Privacy-ABC trial.
TAM considers two main factors that influence user adoption: Perceived Usefulness (called Performance Expectancy in UTAUT) and Perceived Ease of Use (called Effort Expectancy in UTAUT). UTAUT extends TAM with one additional factor that directly influences intention to use the technology: Social Influence, which is the degree to which the user perceives that people whose opinion the user values believe that the user should use the technology.
We tested the influence of this factor in the first Privacy-ABC trial [5] and found no relation to the intention to use Privacy-ABCs. Therefore, we decided to drop this factor. We hypothesize that in the trial environment, this factor may not be applicable, as Privacy-ABCs are only known to the fellow students, and the usage in our scenario did not involve peer pressure (as this would be the case, for example, for social media).
These findings are consistent with the UTAUT and UTAUT2 investigations, where Social Influence was not found to be an important adoption factor, especially for younger users with high experience, as in our sample. We note, however, that for application of Privacy-ABCs in other scenarios and with other (older and less experienced) user populations, Social Influence may be considered.
Additionally, UTAUT considers some factors (age, gender, experience, voluntariness of use) that moderate the relation between the intention to use the systems and the main factors. Considering these moderators does not make sense in our case, however, as our sample is very homogeneous in this respect: The students are of very similar age and experience, all of them use the system voluntarily, and the overwhelming majority is male.
Similar non-applicability considerations apply to the UTAUT2 model that considers additional main acceptance factors: hedonic motivation (the user derives fun or pleasure from using the system), price value (the monetary cost of the system usage), and the habit in using the system.
B Measurement Scales for User Acceptance Factors
The constructs considered in this research are presented in Table 2 on page 18.
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Krontiris, I., Benenson, Z., Girard, A., Sabouri, A., Rannenberg, K., Schoo, P. (2016). Privacy-ABCs as a Case for Studying the Adoption of PETs by Users and Service Providers. In: Berendt, B., Engel, T., Ikonomou, D., Le Métayer, D., Schiffner, S. (eds) Privacy Technologies and Policy. APF 2015. Lecture Notes in Computer Science(), vol 9484. Springer, Cham. https://doi.org/10.1007/978-3-319-31456-3_6
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