Do You Trust Me, Blindly? Factors Influencing Trust Towards a Robot Recommender System

When robots and human users collaborate, trust is essential for user acceptance and engagement. In this paper, we investigated two factors thought to influence user trust towards a robot: preference elicitation (a combination of user involvement and explanation) and embodiment. We set our experiment in the application domain of a restaurant recommender system, assessing trust via user decision making and perceived source credibility. Previous research in this area uses simulated environments and recommender systems that present the user with the best choice from a pool of options. This experiment builds on past work in two ways: first, we strengthened the ecological validity of our experimental paradigm by incorporating perceived risk during decision making; and second, we used a system that recommends a nonoptimal choice to the user. While no effect of embodiment is found for trust, the inclusion of preference elicitation features significantly increases user trust towards the robot recommender system. These findings have implications for marketing and health promotion in relation to Human-Robot Interaction and call for further investigation into the development and maintenance of trust between robot and user.

[1]  David V. Pynadath,et al.  Building Trust in a Human-Robot Team with Automatically Generated Explanations , 2015 .

[2]  Regina A. Pomranky,et al.  The role of trust in automation reliance , 2003, Int. J. Hum. Comput. Stud..

[3]  Kerstin Dautenhahn,et al.  Would You Trust a (Faulty) Robot? Effects of Error, Task Type and Personality on Human-Robot Cooperation and Trust , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[4]  M. König,et al.  Users’ resistance towards radical innovations: The case of the self-driving car , 2017 .

[5]  Li Chen,et al.  A user-centric evaluation framework for recommender systems , 2011, RecSys '11.

[6]  Franca Garzotto,et al.  Investigating the Persuasion Potential of Recommender Systems from a Quality Perspective: An Empirical Study , 2012, TIIS.

[7]  Izak Benbasat,et al.  Evaluating Anthropomorphic Product Recommendation Agents: A Social Relationship Perspective to Designing Information Systems , 2009, J. Manag. Inf. Syst..

[8]  Judith Masthoff,et al.  A Survey of Explanations in Recommender Systems , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[9]  Bilge Mutlu,et al.  In-body experiences: embodiment, control, and trust in robot-mediated communication , 2013, CHI.

[10]  Peter R. Magnusson,et al.  Value in use through service experience , 2008 .

[11]  D. Leutner,et al.  Direct Measurement of Cognitive Load in Multimedia Learning , 2003 .

[12]  Toshiyuki Inagaki,et al.  Laboratory studies of trust between humans and machines in automated systems , 1999 .

[13]  Susan R. Fussell,et al.  Comparing a computer agent with a humanoid robot , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[14]  Jaap Ham,et al.  Combining Robotic Persuasive Strategies: The Persuasive Power of a Storytelling Robot that Uses Gazing and Gestures , 2015, Int. J. Soc. Robotics.

[15]  Tom Ziemke,et al.  Disentangling Notions of Embodiment , 2001 .

[16]  Jodi Forlizzi,et al.  All robots are not created equal: the design and perception of humanoid robot heads , 2002, DIS '02.

[17]  Izak Benbasat,et al.  Trust In and Adoption of Online Recommendation Agents , 2005, J. Assoc. Inf. Syst..

[18]  Hong Joo Lee,et al.  The Influence of Social Presence on Customer Intention to Reuse Online Recommender Systems: The Roles of Personalization and Product Type , 2011, Int. J. Electron. Commer..

[19]  Richard F. Antonak,et al.  Direct and indirect methods to measure attitudes toward persons with disabilities, with an exegesis of the error-choice test method. , 1995 .

[20]  Dan J. Kim,et al.  A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents , 2019 .

[21]  N. Luhmann.  Familiarity, Confidence, Trust: Problems and Alternatives , 2000 .

[22]  D. Bem.  Self-perception: An alternative interpretation of cognitive dissonance phenomena. , 1967, Psychological review.

[23]  Jessie Y. C. Chen,et al.  Antecedents of trust in human-robot collaborations , 2011, 2011 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).

[24]  D. Gefen,et al.  E-commerce: the role of familiarity and trust , 2000 .

[25]  Silvia Rossi,et al.  Recommender Interfaces: The More Human-Like, the More Humans Like , 2016, ICSR.

[26]  Tetsuo Ono,et al.  Can an android persuade you? , 2018, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.

[27]  Katia Sycara,et al.  The role of trust in human-robot interaction , 2018 .

[28]  J. Mccroskey,et al.  Goodwill: A reexamination of the construct and its measurement , 1999 .

[29]  Xun Wang,et al.  Embodiment, Privacy and Social Robots: May I Remember You? , 2017, ICSR.

[30]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[31]  Florian Jentsch,et al.  Determinants of system transparency and its influence on trust in and reliance on unmanned robotic systems , 2014, Defense + Security Symposium.

[32]  N Moray,et al.  Trust, control strategies and allocation of function in human-machine systems. , 1992, Ergonomics.

[33]  Tatsuya Nomura.  Cultural differences in social acceptance of robots , 2017, 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[34]  Rashmi R. Sinha,et al.  Comparing Recommendations Made by Online Systems and Friends , 2001, DELOS.

[35]  Florian Jentsch,et al.  Building Appropriate Trust in Human-Robot Teams , 2013, AAAI Spring Symposium: Trust and Autonomous Systems.

[36]  Edward,et al.  Measuring Trust , 2000 .

[37]  Mary-Anne Williams,et al.  Bon Appetit! Robot Persuasion for Food Recommendation , 2018, HRI.