Impact of human-robot interaction on user satisfaction with humanoid-based healthcare

  • Authors

    • Ohbyung Kwon
    • Jeonghun Kim
    • Yoonsun Jin
    • Namyeon Lee
    2018-04-03
    https://doi.org/10.14419/ijet.v7i2.12.11038
  • Humanoid, Human-Robot Interaction, Health-Care Robot, User Satisfaction, Heterogeneity.

  • Background/Objectives: The advent of self-service technology (SST) (e.g.,kiosks and Automatic Response System), has made it possible for service providersto make use of non-face-to-face channels to meet users’needs and decrease users’costs and time. On the other hand, however, more complex technology and/or services inhibit users’ satisfaction and,consequently,the intention to adopt SST, because such SST can instill fear in users. Nevertheless, at present, patients and other people who are interested in their own health and well-being are paying great attention to healthcare robots (as a form of SST)and,consequently, it has become crucial to investigate how these healthcare robots can positively influence users’ satisfaction with them. Hence, this study aims to empirically investigate the factors that affect users’ satisfaction with healthcare robots, especially in regard to human-robot interaction (HRI).

    Methods/Statistical analysis: We focused on the theory of heterophily and applied a series of factors identified in previous robot-adoption studies.Uniquely, this study focuses on users’ heterophily with healthcare robots, examining heterophily through three fundamental elements, empathy, professionalism, and personality, which we considered to be suitable fordetermining user satisfaction with HRI-based communication.To prove the validity of our hypotheses, we conducted an empirical testthat involved participants receiving a short health assessment from a robot.

    Findings: The findings of our empirical test supported our hypothesis that the lower the difference in empathy between a user and robot, the higher the level of user satisfaction with the humanoid-style healthcare service. Further, our results also suggest that heterogeneity between a user and healthcare robot is positively associated with user satisfaction.

    Improvements/Applications: First, to increase user satisfaction,robots must be provided with the ability to somehow recognizea user’s personality and adjust their own accordingly before beginning the robot-based healthcare service. Secondly, users’ behavior patterns should be analyzed by the healthcare robot. Overall, our study empirically shows the importance of ensuring thatprofessionalism is present in healthcare-domain-related HRI.

     

     

  • References

    1. [1] Fasola J, Mataric M J, Using Socially Assistive Human–Robot Interaction to Motivate Physical Exercise for Older Adults. Proceedings of the IEEE, 2012, 100(8), pp. 2512-2526.

      [2] Feil-Seifer D, Mataric´ M J, Towards the Integration of Socially Assistive Robots into the Lives of Children with ASD.International Conference on Human-Robot Interaction Workshop on Societal Impact: How Socially Accepted Robots Can be Integrated in our Society, San Diego, 2009.

      [3] Fasola J, Mataric´ M J, Robot Motivator: Increasing User Enjoyment and Performance on a Physical/Cognitive Task.2010 International Conference on Development and Learning, Ann Arbor, 2010, pp. 274-279.

      [4] Yamazaki K, Ueda R, Nozawa S, Kojima M, Okada K, Matsumoto K, Ishikawa M, Shimoyama I, Inaba M, Home-Assistant Robot for an Aging Society, IEEE Journals and Magazines, 2012, 100 (8), pp. 2429-2441.

      [5] Fischinger D, Einramhof P, Wohlkinger W, Papoutsakis K, Mayer P, Panek P, Koertner T, Hofmann S, Argyros A, Vincze M, Weiss A, Gisinger C, Hobbit - The Mutual Care Robot. ASROB-2013 in conjunction with IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, 2013

      [6] Shibata T, Therapeutic Seal Robot as Biofeedback Medical Device: Qualitative and Quantitative Evaluations of Robot Therapy in Dementia Care, IEEE Proceedings, 2012, 100 (8), pp. 2527-2538.

      [7] Goold S D, Lipkin M, The Doctor–Patient Relationship, Journal of General Internal Medicine, 1999, 14 (S1), pp. 26-33.

      [8] Beatty S E, Mayer M, Coleman J E, Ellis Reynolds K, Lee J, Customer-Sales Associate Retail Relationships, Journal of Retailing, 1996, 72 (3), pp. 223-247.

      [9] Bendapudi N, Berry L L, Customers’ Motivations for Maintaining Relationships with Service Providers, Journal of Retailing, 1997, 73 (1), pp. 15-37.

      [10] Macintosh G, Customer Orientation, Relationship Quality, and Relational Benefits to the Firm, Journal of Services Marketing, 2007, 21 (3), pp. 150-159.

      [11] Crosby L A, Evans K R, Cowles D, Relationship Quality in Services Selling: An Interpersonal Influence Perspective, The Journal of Marketing, 1990, 54 (3) pp. 68-81.

      [12] Gremler D D, Brown S W, Service Loyalty: Antecedents, Components, and Outcomes. American Marketing Association. Conference Proceedings, 1998, 9, pp. 165-166.

      [13] Yoo H, Kwon O, Lee N, Human Likeness: Cognitive and Affective Factors Affecting Adoption of Robot-Assisted Learning Systems. New Review of Hypermedia and Multimedia, 2016, 22 (3), pp. 169-188.

      [14] McCroskey J C, Hamilton P R, Weiner AN, The effect of Interaction Behavior on Source Credibility, Homophily, and Interpersonal Attraction. Human Communication Research, 1974, 1(1), 42-52.

      [15] Mollica K A, Gray B, Trevino L K, Racial Homophily and Its Persistence in Newcomers’ Social Networks, Organization Science, 2003, 14 (2), pp. 123–136.

      [16] McPherson M, Smith-Lovin L, Cook J M, Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 2001, 27 (1), pp. 415–444.

      [17] Rogers E M, Bhowmik D K, Homophily-Heterophily: Relational Concepts for Communication Research, Public Opinion Quarterly, 1970, 34(4), pp. 523-538.

      [18] Nowak KL, RauhC, Examining the Perception Process of Avatar Anthropomorphism, Credibility and Androgyny in Static and Chat Context, Computers in Human Behavior, 2008, 24(4), pp. 1473-1493.

      [19] Hou J, Lee K M, Effects of Self-Conscious Emotions on Affective and Behavioral Responses in HCI and CMC.29th ACM International Conference on Design of Communication,Pisa, 2011, pp. 151-156.

      [20] Streukens S, Andreassen T W, Customer Preferences for Frontline Employee Traits: Homophily and Heterophily Effects, Psychology & Marketing, 2013, 30(12), pp. 1043-1052.

      [21] Saiki D, DeLong M R, Professionals' Relationships with Clients in the Apparel Industry, Qualitative Market Research: An International Journal, 2006, 9(3), pp. 266-281.

      [22] Robinson H, MacDonald B, Broadbent E,The Role of Healthcare Robots for Older People at Home: A Review, International Journal of Social Robotics, 2014, 6(4), pp. 575-591.

      [23] Butter M, Rensma A, Boxsel J V, Kalisingh S, Schoone M, Leis M, Gelderblom GJ, Cremers G, Wilt MD, Kortekaas W, Thielmaan A, Robotics for Healthcare: Final Report, eHealth, 2008 (Retrieved from https://www.scribd.com/document/10269005/Robotics-for-Healthcare)

      [24] EllenbeckerCH, SamiaL, CushmanM J,Alster K, Patient Safety and Quality in Home Health Care, Patient Safety and Quality: An Evidence-Based Handbook for Nurses, AHRQ Publication: Rockville, MD, 2008.

      [25] HeerinkM, KröseB, WielingaB,EversV, Enjoyment Intention to Use and Actual Use of a Conversational Robot by Elderly People. 3rd ACM/IEEE International Conference on Human Robot Interaction, Amsterdam, 2008, pp. 113-120.

      [26] Broadbent E, Tamagawa R, Patience A, Knock B, Kerse N, Day K, MacDonald B A, Attitudes Towards Healthâ€Care Robots in a Retirement Village. Australasian Journal on Ageing, 2012, 31(2), pp. 115-120.

      [27] BroadbentE, StaffordR, MacDonaldB, Acceptance of Healthcare Robots for the Older Population: Review and Future Directions. International Journal of Social Robotics, 2009. 1(4), pp. 319-330.

      [28] Alaiad A, Zhou L, Patients’ Behavioral Intention toward using Healthcare Robots. Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, IL, 2013.

      [29] Alaiad A,ZhouL, The Determinants of Home Healthcare Robots Adoption: An Empirical Investigation, International Journal of Medical Informatics, 2014, 83(11), pp. 825-840.

      [30] Baron-Cohen S, The Extreme Male Brain Theory of Autism. Trends in Cognitive Sciences, 2002, 6(6), pp. 248-254.

      [31] Davis M H, Measuring Individual Differences in Empathy: Evidence for a Multidimensional Approach, Journal of Personality and Social Psychology, 1983, 44(1), pp. 113-126.

      [32] ParasuramanA, ZeithamlV A, BerryLL, SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality, Journal of Retailing, 1988, 64(1), 12-40.

      [33] Ladhari R, A Review of Twenty Years of SERVQUAL Research, International Journal of Quality and Service Sciences, 2009, 1(2), pp. 172-198.

      [34] Boshoff C,GrayB, The Relationships between Service Quality, Customer Satisfaction and Buying Intentions in the Private Hospital Industry, South African Journal of Business Management, 2004, 35(4), pp. 27-37.

      [35] Lee N, KimJ, Kim E, KwonO, The Influence of Politeness Behavior on User Compliance with Social Robots in a Healthcare Service Setting, International Journal of Social Robotics, 2017,pp. 1-17.

      [36] DaggerTS, SweeneyJ C, JohnsonLW, A Hierarchical Model of Health Service Quality: Scale Development and Investigation of an Integrated Model. Journal of Service Research, 2007, 10(2), pp. 123-142.

      [37] DiMATTEOMR, Expectations in the Physician-Patient Relationship: Implications for Patient Adherence to Medical Treatment Recommendations. In P. D. Blanck, Interpersonal Expectations: Theory, Research, and Applications, Cambridge University Press: Cambridge, pp.296-315.

      [38] EgolfDB, Using Robots as Companions for the Elderly: Research and Controversy. 23rd Annual International Conference on Technology and Persons with Disabilities, Los Angeles, CA, 2008.

      [39] Pereira A, Leite I, MascarenhasS, MartinhoC,PaivaA, Using Empathy to Improve Human-Robot Relationships. In International Conference on Human-Robot Personal Relationship. Springer Berlin Heidelberg, Leiden, 2010, pp. 130-138.

      [40] LooijeR, CnossenF, NeerincxMA, Incorporating Guidelines for Health Assistance into a Socially Intelligent Robot. The 15th IEEE International Symposium on Robot and Human Interactive Communication, Hatfield, 2006, pp. 515-520.

      [41] Cheshire JR WP, The Robot Will See You Now: Can Medical Technology Be Professional?, Ethics & Medicine, 2016, 32(3), pp. 135.

      [42] Pilling B K,ErogluS, An Empirical Examination of the Impact of Salesperson Empathy and Professionalism and Merchandise Salability on Retail Buyers' Evaluations, Journal of Personal Selling & Sales Management, 1994, 14(1), pp. 45-58.

      [43] KeillorB D, Parker R S, Pettijohn C E, Sales Force Performance Satisfaction and Aspects of Relational Selling: Implications for Sales Managers, Journal of Marketing Theory and Practice, 1999, 7(1), pp. 101-115.

      [44] MohamedB,AzizanNA, Perceived Service Quality’s Effect on Patient Satisfaction and Behavioural Compliance, International Journal of Health Care Quality Assurance, 2015, 28(3), pp. 300-314.

      [45] PurcăreaVL, Gheorghe I R, PetrescuCM, The Assessment of Perceived Service Quality of Public Health Care Services in Romania Using the SERVQUAL Scale, Procedia Economics and Finance, 2013, 6, pp. 573-585.

      [46] SureshchandarGS, Rajendran C,AnantharamanRN, The Relationship Between Service Quality and Customer Satisfaction-A Factor Specific Approach, Journal of Services Marketing, 2002, 16(4), pp. 363-379.

      [47] DigmanJM, Personality Structure: Emergence of the Five-Factor Model, Annual Review of Psychology, 1990, 41(1), pp. 417-440.

      [48] Tapus A, MataricMJ, Socially Assistive Robots: The Link between Personality, Empathy, Physiological Signals, and Task Performance. In AAAI spring symposium: emotion, personality, and social behavior, 2008, pp. 133-140.

      [49] Lee N, Kim J, Kim E, Kwon O, The Influence of Politeness Behavior on User Compliance with Social Robots in a Healthcare Service Setting, International Journal of Social Robotics, 2017, pp. 1-17.

      [50] Barrick MR, Mount MK, The Big Five Personality Dimensions and Job Performance: A Metaâ€Analysis, Personnel Psychology, 1991, 44(1), pp. 1-26.

      [51] AlyA, Tapus A, Towards an Intelligent System for Generating an Adapted Verbal and Nonverbal Combined Behavior in Human–Robot Interaction, Autonomous Robots, 2016, 40(2), pp. 193-209.

      [52] PrattR,ChudobaK, Is Extraversion the Next Predictor of System Adoption? Effects of Personality Traits on System Acceptance, In Academy of Management Meeting, Atlanta, 2006.

      [53] GoetzJ,KieslerS, Cooperation with a Robotic Assistant, In CHI'02 Extended Abstracts on Human Factors in Computing Systems, 2002, pp. 578-579.

      [54] Nass C,LeeKM, Does Computer-Synthesized Speech Manifest Personality? Experimental Tests of Recognition, Similarity-Attraction, and Consistency-Attraction, Journal of Experimental Psychology,2001, 7(3), pp. 171-181.

      [55] Straub D, Boudreau MC, Gefen D, Validation Guidelines for IS Positivist Research, The Communications of the Association for Information Systems, 2004, 13 (1), pp.1-70.

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    Kwon, O., Kim, J., Jin, Y., & Lee, N. (2018). Impact of human-robot interaction on user satisfaction with humanoid-based healthcare. International Journal of Engineering & Technology, 7(2.12), 68-75. https://doi.org/10.14419/ijet.v7i2.12.11038