Trusting Intentions Towards Robots in Healthcare: A Theoretical Framework

Within the next decade, robots (intelligent agents that are able to perform tasks normally requiring human intelligence) may become more popular when delivering healthcare services to patients. The use of robots in this way may be daunting for some members of the public, who may not understand this technology and deem it untrustworthy. Others may be excited to use and trust robots to support their healthcare needs. It is argued that (1) context plays an integral role in Information Systems (IS) research and (2) technology demonstrating anthropomorphic or system-like features impact the extent to which an individual trusts the technology. Yet, there is little research which integrates these two concepts within one study in healthcare. To address this gap, we develop a theoretical framework that considers trusting intentions towards robots based on the interaction of humans and robots within the contextual landscape of delivering healthcare services. This article presents a theory-based approach to developing effective trustworthy intelligent agents at the intersection of IS and Healthcare.

[1]  Patric R. Spence,et al.  Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter , 2014, Comput. Hum. Behav..

[2]  B. Bahrami,et al.  Individual differences in anthropomorphic attributions and human brain structure. , 2014, Social cognitive and affective neuroscience.

[3]  Michael Wooldridge,et al.  Intelligent Agents: The Key Concepts , 2001, Multi-Agent-Systems and Applications.

[4]  Nancy K. Lankton,et al.  Degrees of Humanness in Technology: What Type of Trust Matters? , 2011, AMCIS.

[5]  Barbara D. Minsky,et al.  Why Faculty Members Use E-Mail: The Role of Individual Differences in Channel Choice , 1999 .

[6]  Jason Bennett Thatcher,et al.  Trust in a specific technology: An investigation of its components and measures , 2011, TMIS.

[7]  Gurpreet Dhillon,et al.  A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research , 2014, Inf. Syst. Res..

[8]  J. Cacioppo,et al.  On seeing human: a three-factor theory of anthropomorphism. , 2007, Psychological review.

[9]  Aneil Mishra,et al.  Trust in physicians and medical institutions: what is it, can it be measured, and does it matter? , 2001, The Milbank quarterly.

[10]  J. M. Digman PERSONALITY STRUCTURE: EMERGENCE OF THE FIVE-FACTOR MODEL , 1990 .

[11]  Wolter Pieters,et al.  Explanation and trust: what to tell the user in security and AI? , 2011, Ethics and Information Technology.

[12]  Javad Dargahi,et al.  Mechatronics in Medicine A Biomedical Engineering Approach , 2011 .

[13]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004, Hum. Factors.

[14]  G. W. Rever Attachment and Loss. Vol. 1. Attachment , 1972 .

[15]  Colin Camerer,et al.  Not So Different After All: A Cross-Discipline View Of Trust , 1998 .

[16]  Bruce A. MacDonald,et al.  The Role of Healthcare Robots for Older People at Home: A Review , 2014, Int. J. Soc. Robotics.

[17]  J. Travaline,et al.  Patient-Physician Communication: Why and How , 2005, The Journal of the American Osteopathic Association.

[18]  M. Becker,et al.  The Health Belief Model: A Decade Later , 1984, Health education quarterly.

[19]  Sangwon Park Multifaceted trust in tourism service robots , 2020 .

[20]  Branislav Borovac,et al.  Pilot corpus of child-robot interaction in therapeutic settings , 2017, 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom).

[21]  C. Mattingly,et al.  The narrative nature of clinical reasoning. , 1991, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[22]  Bram Vanderborght,et al.  A Survey of Expectations About the Role of Robots in Robot-Assisted Therapy for Children with ASD: Ethical Acceptability, Trust, Sociability, Appearance, and Attachment , 2015, Science and Engineering Ethics.

[23]  John W. Creswell,et al.  A Concise Introduction to Mixed Methods Research , 2014 .

[24]  R. Epstein,et al.  Measuring patient-centered communication in patient-physician consultations: theoretical and practical issues. , 2005, Social science & medicine.

[25]  Murray R. Barrick,et al.  THE BIG FIVE PERSONALITY DIMENSIONS AND JOB PERFORMANCE: A META-ANALYSIS , 1991 .

[26]  Milind Tambe,et al.  Intelligent Agents for Interactive Simulation Environments , 1995, AI Mag..

[27]  Jessie Y. C. Chen,et al.  A Model of Human-Robot Trust , 2011 .

[28]  Allison W. Pearson,et al.  Five-factor model personality traits as predictors of perceived and actual usage of technology , 2015, Eur. J. Inf. Syst..

[29]  Ching-Wen Lin,et al.  A technology acceptance model for the perception of restaurant service robots for trust, interactivity, and output quality , 2018, Int. J. Mob. Commun..

[30]  E. Mangina,et al.  Intelligent Optimisation Agents in Supply Networks , 2003 .

[31]  Jessie Y. C. Chen,et al.  A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction , 2011, Hum. Factors.

[32]  N. L. Chervany,et al.  Initial Trust Formation in New Organizational Relationships , 1998 .

[33]  Stefan Morana,et al.  The Effect of Anthropomorphism on Investment Decision-Making with Robo-Advisor Chatbots , 2020, ECIS.

[34]  Jaap Ham,et al.  Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots , 2020, Int. J. Soc. Robotics.

[35]  Mohammed-Issa Riad Mousa Jaradat,et al.  The big five personality traits and their relationship with the intensity of using Facebook: a developing country perspective , 2018, Int. J. Bus. Inf. Syst..

[36]  Patrick Ajibade,et al.  Technology Acceptance Model Limitations and Criticisms: Exploring the Practical Applications and Use in Technology-related Studies, Mixed-method, and Qualitative Researches , 2018 .

[37]  Mark Keil,et al.  Theorizing in information systems research: A reflexive analysis of the adaptation of theory in information systems research , 2006, J. Assoc. Inf. Syst..

[38]  T. Tanioka,et al.  Framing the Development of Humanoid Healthcare Robots in Caring Science , 2019, International Journal for Human Caring.

[39]  O. Williamson Calculativeness, Trust, and Economic Organization , 1993, The Journal of Law and Economics.

[40]  J. Schommer,et al.  Effects of anthropomorphic images and narration styles in promotional messages for generic prescription drugs. , 2013, Research in social & administrative pharmacy : RSAP.

[41]  Judy Kay,et al.  Mapping Beyond the Uncanny Valley: A Delphi Study on Aiding Adoption of Realistic Digital Faces , 2019, HICSS.

[42]  James A. Roberts,et al.  I need my smartphone: A hierarchical model of personality and cell-phone addiction , 2015 .

[43]  J. Mowen The 3M Model of Motivation and Personality: Theory and Empirical Applications to Consumer Behavior , 1999 .

[44]  Tom Nadarzynski,et al.  Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study , 2019, Digital health.

[45]  Saini Das,et al.  Would you Trust a Bot for Healthcare Advice? An Empirical Investigation , 2020, PACIS.

[46]  John Fox,et al.  The Knowledge Engineering Review , 1984, The Knowledge Engineering Review.

[47]  Katia P. Sycara,et al.  Distributed Intelligent Agents , 1996, IEEE Expert.

[48]  Nicole C. Krämer,et al.  How design characteristics of robots determine evaluation and uncanny valley related responses , 2014, Comput. Hum. Behav..

[49]  Karl F. MacDorman,et al.  The Uncanny Valley [From the Field] , 2012, IEEE Robotics Autom. Mag..

[50]  Rob Kling,et al.  Human centered systems in the perspective of organizational and social informatics , 1998, CSOC.

[51]  Christian Brock,et al.  Innovative Technologies in Branded-Service Encounters: How Robot Characteristics Affect Brand Trust and Experience , 2018, ICIS.

[52]  Christine Rzepka,et al.  User Interaction with AI-enabled Systems: A Systematic Review of IS Research , 2018, ICIS.

[53]  Maartje M. A. de Graaf,et al.  Sharing a life with Harvey: Exploring the acceptance of and relationship-building with a social robot , 2015, Comput. Hum. Behav..

[55]  L. D. Jones,et al.  Artificial intelligence, machine learning and the evolution of healthcare , 2018, Bone & joint research.

[56]  Simon Kasif,et al.  Human Centered Systems in the Perspective of Organizational and Social Informatics , 2019 .

[57]  H. Wellman,et al.  Robot teachers for children? Young children trust robots depending on their perceived accuracy and agency. , 2020, Developmental psychology.

[58]  Ayse Yasemin Seydim INTELLIGENT AGENTS: A DATA MINING PERSPECTIVE , 2001 .

[59]  Nancy K. Lankton,et al.  USING EXPECTATION DISCONFIRMATION THEORY TO PREDICT TECHNOLOGY TRUST AND USAGE CONTINUANCE INTENTIONS , 2006 .

[60]  Heloir,et al.  The Uncanny Valley , 2019, The Animation Studies Reader.

[61]  Chrisanthi Avgerou,et al.  The significance of context in information systems and organizational change , 2001, Inf. Syst. J..

[62]  Mike Chiasson,et al.  Pushing the contextual envelope: developing and diffusing IS theory for health information systems research , 2004, Inf. Organ..

[63]  Michael Winikoff,et al.  JACKTM Intelligent Agents: An Industrial Strength Platform , 2005, Multi-Agent Programming.

[64]  Nicole C. Krämer,et al.  Investigations on empathy towards humans and robots using fMRI , 2014, Comput. Hum. Behav..

[65]  Elisa Giaccardi,et al.  Technology and More-Than-Human Design , 2020, Design Issues.

[66]  R. C. Fraley,et al.  Attachment and Loss , 2018 .

[67]  J. G. Holmes,et al.  Trust in close relationships. , 1985 .

[68]  Fred D. Davis,et al.  Trusting Humans and Avatars: A Brain Imaging Study Based on Evolution Theory , 2014, J. Manag. Inf. Syst..

[69]  Carlos Llopis-Albert,et al.  A review of mobile robots: Concepts, methods, theoretical framework, and applications , 2019, International Journal of Advanced Robotic Systems.

[70]  Horst-Michael Groß,et al.  Robot companion for domestic health assistance: Implementation, test and case study under everyday conditions in private apartments , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[71]  D. Wiegmann,et al.  Similarities and differences between human–human and human–automation trust: an integrative review , 2007 .

[72]  J. Kim Clinical Reasoning: , 2009, Neurology.

[73]  Serkan Erebak,et al.  Caregivers’ attitudes toward potential robot coworkers in elder care , 2018, Cognition, Technology & Work.

[74]  Frank E. Ritter,et al.  Designs for explaining intelligent agents , 2009, Int. J. Hum. Comput. Stud..

[75]  Arthur C. Graesser,et al.  Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents , 1996, ATAL.

[76]  A. Ali,et al.  The use of robotics in surgery: a review , 2014, International journal of clinical practice.

[77]  Atreyi Kankanhalli,et al.  Studying users' computer security behavior: A health belief perspective , 2009, Decis. Support Syst..

[78]  David Carmel,et al.  Learning Models of Intelligent Agents , 1996, AAAI/IAAI, Vol. 1.

[79]  Alex John London,et al.  Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. , 2019, The Hastings Center report.

[80]  Thomas Magedanz,et al.  Intelligent agents: an emerging technology for next generation telecommunications? , 1996, Proceedings of IEEE INFOCOM '96. Conference on Computer Communications.

[81]  George A. Bekey,et al.  On autonomous robots , 1998, The Knowledge Engineering Review.

[82]  Iis P. Tussyadiah,et al.  Do travelers trust intelligent service robots? , 2020 .

[83]  Mark R. Lehto,et al.  Foundations for an Empirically Determined Scale of Trust in Automated Systems , 2000 .

[84]  Karl F. MacDorman,et al.  Measuring the Uncanny Valley Effect , 2017, Int. J. Soc. Robotics.

[85]  Sang Hoon Lee,et al.  Biomedical Engineering: Frontier Research and Converging Technologies , 2016 .

[86]  I. Fras Identity: Youth and Crisis , 1968 .

[87]  Samuel S. Monfort,et al.  Almost human: Anthropomorphism increases trust resilience in cognitive agents. , 2016, Journal of experimental psychology. Applied.

[88]  Gerald Matthews,et al.  Individual Differences in Trust in Autonomous Robots: Implications for Transparency , 2020, IEEE Transactions on Human-Machine Systems.

[89]  Laurel D. Riek,et al.  Robotics Technology in Mental Health Care , 2015, ArXiv.

[90]  F. Jouen,et al.  “Are we ready for robots that care for us?” Attitudes and opinions of older adults toward socially assistive robots , 2015, Front. Aging Neurosci..

[91]  Jung Kim,et al.  Robotics for Healthcare , 2016 .

[92]  Christian Rauh,et al.  The Influence of the Avatar on Online Perceptions of Anthropomorphism, Androgyny, Credibility, Homophily, and Attraction , 2005, J. Comput. Mediat. Commun..

[93]  S. Shapiro The Social Control of Impersonal Trust , 1987, American Journal of Sociology.

[94]  Nancy K. Lankton,et al.  Technology, Humanness, and Trust: Rethinking Trust in Technology , 2015, J. Assoc. Inf. Syst..

[95]  C. Todd,et al.  Moving beyond ‘safety’ versus ‘autonomy’: a qualitative exploration of the ethics of using monitoring technologies in long-term dementia care , 2019, BMC Geriatrics.

[96]  B. Malle,et al.  A multidimensional conception and measure of human-robot trust , 2021, Trust in Human-Robot Interaction.

[97]  J. H. Davis,et al.  An Integrative Model Of Organizational Trust , 1995 .

[98]  L. Zucker Production of trust: Institutional sources of economic structure, 1840–1920. , 1986 .

[99]  Charles J. Kacmar,et al.  The impact of initial consumer trust on intentions to transact with a web site: a trust building model , 2002, J. Strateg. Inf. Syst..

[100]  S. Levy-Tzedek,et al.  Neuroscience and Biobehavioral Reviews , 2022 .

[101]  Antonio Moreno,et al.  A Systematic Literature Review of Agents Applied in Healthcare , 2016, Journal of Medical Systems.