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That is, high utility often comes at the cost of lenient privacy settings, allowing the robot to store personal data and to connect to the internet permanently, which brings in associated data security risks. However, to date, it still remains unclear how this dilemma affects attitudes and behavioral intentions towards the respective robot. To shed light on the influence of a social robot\u2019s privacy settings on robot-related attitudes and behavioral intentions, we conducted two online experiments with a total sample of <jats:italic>N<\/jats:italic>\u2009=\u2009320 German university students. We hypothesized that strict privacy settings compared to lenient privacy settings of a social robot would result in more favorable attitudes and behavioral intentions towards the robot in Experiment 1. For Experiment 2, we expected more favorable attitudes and behavioral intentions for choosing independently the robot\u2019s privacy settings in comparison to evaluating preset privacy settings. However, those two manipulations seemed to influence attitudes towards the robot in diverging domains: While strict privacy settings increased trust, decreased subjective ambivalence and increased the willingness to self-disclose compared to lenient privacy settings, the choice of privacy settings seemed to primarily impact robot likeability, contact intentions and the depth of potential self-disclosure. Strict compared to lenient privacy settings might reduce the risk associated with robot contact and thereby also reduce risk-related attitudes and increase trust-dependent behavioral intentions. However, if allowed to choose, people make the robot \u2018their own\u2019, through making a privacy-utility tradeoff. This tradeoff is likely a compromise between full privacy and full utility and thus does not reduce risks of robot-contact as much as strict privacy settings do. Future experiments should replicate these results using real-life human robot interaction and different scenarios to further investigate the psychological mechanisms causing such divergences.<\/jats:p>","DOI":"10.1007\/s12369-023-01043-8","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T13:01:55Z","timestamp":1695387715000},"page":"1487-1505","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Never Trust Anything That Can Think for Itself, if You Can\u2019t Control Its Privacy Settings: The Influence of a Robot\u2019s Privacy Settings on Users\u2019 Attitudes and Willingness to Self-disclose"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1347-9607","authenticated-orcid":false,"given":"Julia G.","family":"Stapels","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9064-762X","authenticated-orcid":false,"given":"Angelika","family":"Penner","sequence":"additional","affiliation":[]},{"given":"Niels","family":"Diekmann","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4978-8922","authenticated-orcid":false,"given":"Friederike","family":"Eyssel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"1043_CR1","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s12369-019-00605-z","volume":"12","author":"E Fosch-Villaronga","year":"2020","unstructured":"Fosch-Villaronga E, Lutz C, Tam\u00f2-Larrieux A (2020) Gathering expert opinions for social robots\u2019 ethical, legal, and societal concerns: findings from four international workshops. 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