Trust versus Privacy: Using Connected Car Data in Peer-to-Peer Carsharing
Abstract
Trust is the lubricant of the sharing economy. This is true especially in peer-to-peer carsharing, in which one leaves a highly valuable good to a stranger in the hope of getting it back unscathed. Nowadays, ratings of other users are major mechanisms for establishing trust. To foster uptake of peer-to-peer carsharing, connected car technology opens new possibilities to support trust-building, e.g., by adding driving behavior statistics to users' profiles. However, collecting such data intrudes into rentees' privacy. To explore the tension between the need for trust and privacy demands, we conducted three focus group and eight individual interviews. Our results show that connected car technologies can increase trust for car owners and rentees not only before but also during and after rentals. The design of such systems must allow a differentiation between information in terms of type, the context, and the negotiability of information disclosure.
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Telematics can enable new forms of trust building by enabling assessments based on quantitative real-time data on driving behavior.
“Telematics can enable new forms of trust building (Bossauer et al., 2020) by, e.g., enabling assessments based on quantitative real-time data on driving behavior.”
From Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems (2024) · cites this paper as Bossauer et al., 2020
A lack of trust in other buyers or sellers is one of the most frequent reasons for rejecting P2P sharing platforms.
“However, a lack of trust in other 'buyers' or 'sellers' is stated as one of the most frequent reasons for rejecting P2P sharing platforms (Bossauer et al., 2020; Pakusch et al., 2018).”
From Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems (2024) · cites this paper as Bossauer et al., 2020
There is a trade-off between trust-building and privacy when using car telematics.
“As Bossauer et al. (Bossauer et al., 2020) pointed out, there is a trade-off between trust-building and privacy by using car telematics.”
From Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems (2024) · cites this paper as Bossauer et al., 2020
A score can only be applied if an added value is given, if data utilization is comprehensible, or at least drivers cannot be disadvantaged.
“Such a score can only be applied if an added value is given, if the utilization of the data is comprehensible, or at least drivers can not be disadvantaged. (Athey et al., 2019; Bossauer et al., 2020).”
From Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems (2024) · cites this paper as Bossauer et al., 2020
Cites
What trust means in e-commerce customer relationships: an interdisciplinary conceptual typology
McKnight, D. H., & Chervany, N. L. (2001) · International Journal of Electronic Commerce · doi:10.1080/10864415.2001.11044235 · Cited by 2,282 · see citing works
Antecedents of trust in the sharing economy: A systematic review
ter Huurne, M., Ronteltap, A., Corten, R., & Buskens, V. (2017) · Journal of Consumer Behaviour · doi:10.1002/cb.1667 · Cited by 456 · see citing works
Hawlitschek, F., Teubner, T., & Weinhardt, C. (2016) · Die Unternehmung · doi:10.5771/0042-059x-2016-1-26 · Cited by 217 · see citing works
A Trust Based Secure and Privacy Aware Framework for Efficient Taxi and Car Sharing System
Olakanmi, O., & Oluwaseun, S. (2018) · International Journal of Vehicular Telematics and Infotainment Systems · doi:10.4018/ijvtis.2018010103 · Cited by 3 · see citing works
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Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems
Neifer, T., Bossauer, P., Pakusch, C., Boehm, L., & Lawo, D. (2024) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-022-09461-4 · Cited by 6 · see citing works
“Telematics can enable new forms of trust building (Bossauer et al., 2020) by, e.g., enabling assessments based on quantitative real-time data on driving behavior.”
InfluentialSelf-citation
- Authors
- Bossauer, P., Neifer, T., Stevens, G., & Pakusch, C.
- Affiliations
- University of Siegen (DE)
- Hochschule Bonn-Rhein-Sieg (DE)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-13
- OpenAlex
- W3032294355
- Cited by
- 17
- References
- 4
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 6 · 2023: 3 · 2022: 1 · 2021: 5
- FWCI
- 6.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Publication Server of Bonn-Rhein-Sieg University of Applied Sciences (Bonn-Rhein-Sieg University of Applied Sciences) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Sharing Economy and Platforms
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Keywords
- peer-to-peer car sharing, connected vehicles, trust, privacy, sharing economy, user ratings, information disclosure, car rental, user profiles
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