Familiarity Breeds Trust? The Relationship between Dating App Use and Trust in Dating Algorithms via Algorithm Awareness and Critical Algorithm Perceptions
Previous studies have suggested that dating app use can foster acceptance of the controversial idea of algorithm matchmaking. However, an integrated framework to explain this relationship is lacking. To address this gap, this study proposes a familiarity-breeds-trust hypothesis, which suggests that...
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          | Published in | International journal of human-computer interaction Vol. 40; no. 17; pp. 4596 - 4607 | 
|---|---|
| Main Authors | , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Norwood
          Taylor & Francis
    
        01.09.2024
     Lawrence Erlbaum Associates, Inc  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1044-7318 1532-7590 1044-7318  | 
| DOI | 10.1080/10447318.2023.2217014 | 
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| Abstract | Previous studies have suggested that dating app use can foster acceptance of the controversial idea of algorithm matchmaking. However, an integrated framework to explain this relationship is lacking. To address this gap, this study proposes a familiarity-breeds-trust hypothesis, which suggests that dating app use can increase trust in dating algorithms by raising algorithm awareness and enhancing perceived agency. To test this hypothesis, the current work employed an exploratory sequential mixed-methods design. Study 1 interviewed 19 dating app users and identified four types of critical algorithm perceptions that reflected perceived threats to user agency. Study 2 surveyed 371 users of Tantan-a mainstream Chinese dating app that resembles Tinder-and found a positive relationship between Tantan use and trust in dating algorithms. Additionally, Tantan use was positively related to algorithm awareness, which was negatively related to critical algorithm perceptions. Furthermore, critical algorithm perceptions negatively predicted trust in dating algorithms. The findings provide support for the familiarity-breeds-trust hypothesis. Theoretical implications for future human-algorithm interaction studies and practical suggestions are also discussed. | 
    
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| AbstractList | Previous studies have suggested that dating app use can foster acceptance of the controversial idea of algorithm matchmaking. However, an integrated framework to explain this relationship is lacking. To address this gap, this study proposes a familiarity-breeds-trust hypothesis, which suggests that dating app use can increase trust in dating algorithms by raising algorithm awareness and enhancing perceived agency. To test this hypothesis, the current work employed an exploratory sequential mixed-methods design. Study 1 interviewed 19 dating app users and identified four types of critical algorithm perceptions that reflected perceived threats to user agency. Study 2 surveyed 371 users of Tantan—a mainstream Chinese dating app that resembles Tinder—and found a positive relationship between Tantan use and trust in dating algorithms. Additionally, Tantan use was positively related to algorithm awareness, which was negatively related to critical algorithm perceptions. Furthermore, critical algorithm perceptions negatively predicted trust in dating algorithms. The findings provide support for the familiarity-breeds-trust hypothesis. Theoretical implications for future human-algorithm interaction studies and practical suggestions are also discussed. | 
    
| Author | Hu, Junwen Wang, Rui  | 
    
| Author_xml | – sequence: 1 givenname: Junwen orcidid: 0000-0001-9571-9947 surname: Hu fullname: Hu, Junwen organization: The School of Journalism and Communication, Nanjing University – sequence: 2 givenname: Rui orcidid: 0000-0001-6535-1694 surname: Wang fullname: Wang, Rui organization: Department of Communication, The State University of New York at Buffalo  | 
    
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| SubjectTerms | Algorithm awareness Algorithms dating app Hypotheses perception Perceptions trust  | 
    
| Title | Familiarity Breeds Trust? The Relationship between Dating App Use and Trust in Dating Algorithms via Algorithm Awareness and Critical Algorithm Perceptions | 
    
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