ABSTRACT
This study explores the state of human-robot collaboration (HRC) in a Thai hospital at multiple levels, using qualitative research methods. The case study was undertaken at the only hospital in Thailand to have deployed three robots to deliver patient documents between units. The objectives of this study were to investigate stakeholders’ opinions on using service robots in the hospital, explore organizational context related to robot deployment, and identify the success factors of robot deployment in the hospital. The participants in this study were a hospital executive, human resources staff, nurses, IT officer, and hospital clients. Findings revealed that the hospital stakeholders accepted the robots; the main advantages were that they were attractive and reduced the work burden on the staff; however, their lack of human sense was the main disadvantage. The organizational context in both human and system issues is significant. The key success factors of HRC were found in human, robot, and organizational dimensions. In terms of human dimension, user acceptance and user ability were the key success factors. Robot capability and robot company were very crucial in the robot dimension, while leadership, competent IT officer, and well-planned robot deployment were success factors in the organizational dimension.
- R. Gervasi, L. Mastrogiacomo, and F. Franceschini. 2020. A conceptual framework to evaluate human-robot collaboration. Int J Adv Manuf Tech, 108, 841–865. https://doi.org/10.1007/s00170-020-05363-1Google ScholarCross Ref
- C. Bröhl, J. Nelles, C. Brandl, A. Mertens, and V. Nitsch. 2019. Human–robot collaboration acceptance model: Development and comparison for Germany, Japan, China and the USA, Int J Soc Robot, 11, 709–726. https://doi.org/10.1007/s12369-019-00593-0Google ScholarCross Ref
- J. Manyika, M. Chui, J. Bughin, R. Dobbs, P. Bisson and A. Marrs, 2013. Disruptive Technologies: Advances that will Transform Life, Business, and the Global Economy. McKinsey Global Institute, San FranciscoGoogle Scholar
- Y. Weng, C. Chen, and C. Sun. 2009. Toward the human–robot co-existence society: on safety intelligence for next generation robots. Int J Soc Robot, 1, 267. https://doi.org/10.1007/s12369-009-0019-1Google ScholarCross Ref
- M. Destephe, M. Brandao, T. Kishi, M. Zecca, K. Hashimoto, and A. Takanishi. 2015. Walking in the uncanny valley: importance of the attractiveness on the acceptance of a robot as a working partner. Front Psychol, 6(FEB), 204. https://doi.org/10.3389/fpsyg.2015.00204Google ScholarCross Ref
- A. Alaiad, and L. N. Zhou. 2014. The determinants of home healthcare robots adoption: an empirical investigation. Int J Med Inf 83, 11, 825–840. https://doi.org/10.1016/j.ijmedinf.2014.07.003Google ScholarCross Ref
- M. Pfadenhauer, and C. Dukat. 2015. Robot caregiver or robot - supported caregiving? Int J Soc Robot, 7, 393–406. https://doi.org/10.1007/s12369-015-0284-0Google ScholarCross Ref
- K. Wasen. 2010. Replacement of highly educated surgical assistants by robot technology in working life: paradigm shift in the service sector. Int J Soc Robot, 2, 431–438. https://doi.org/10.1007/s12369-010-0062-yGoogle ScholarCross Ref
- R-M. Johansson‑Pajala, K. Thommes, J. A. Hoppe, O. Tuisku, L. Hennala, S. Pekkarinen, H. Melkas, and C. Gustafsson. 2020. Care robot orientation: what, who and how? Potential users’ perceptions. Int J Soc Robot, 12, 1103–1117. https://doi.org/10.1007/s12369-020-00619-yGoogle ScholarCross Ref
- J.A. Mann, B.A. Macdonald, I. Kuo, X. Li, and E. Broadbent. 2015. People respond better to robots than computer tablets delivering healthcare instructions. Comput Hum Behav, 43, 112–117. https://doi.org/10.1016/j.chb.2014.10.029Google ScholarDigital Library
- L.L. Flynn, T.R. Bush, Sikorskii, A., Mukherjee, R., and Wyatt, G. 2011. Understanding the role of stimulation in reflexology: development and testing of a robotic device. Eur J Cancer Care 20, 5, 686–696. https://doi.org/10.1111/j.1365-2354.2011.01268.xGoogle ScholarCross Ref
- I.L. Boman, and A. Bartfai. 2015. The first step in using a robot in brain injury rehabilitation: Patients’ and health-care professionals’ perspective. Disabil Rehabil Assist Techno 10, 5, 365–370. https://doi.org/10.3109/17483107.2014.913712Google ScholarCross Ref
- V.S. Jones, and R.C. Cohen. 2008. Two decades of minimally invasive pediatric surgery-taking stock. J Pediatr Surg 43, 9, 1653–1659. https://doi.org/10.1016/j.jpedsurg.2008.01.006Google ScholarCross Ref
- H.J. Otway, and D. Von Winterfeldt. 1982. Beyond acceptable risk: on the social acceptability of technologies. Policy Sci, 14, 247–256. https://doi.org/10.1007/BF00136399Google ScholarCross Ref
- F.D. Davis. 1993. User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. Int J Man Mach Stud 38, 3, 475–487. https://doi.org/10.1006/imms.1993.1022Google ScholarDigital Library
- F.D. Davis, R.P. Bagozzi, and P.R. Warshaw. 1989. User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 35, 8, 982–1003. https://doi.org/10.1287/mnsc.35.8.982Google ScholarDigital Library
- A. Prakash, and W A. Rogers 2015. Why some humanoid faces are perceived more positively than others: Effects of human-likeness and task. Int J Soc Robot, 7, 309–331. https://doi.org/10.1007/s12369-014-0269-4Google ScholarCross Ref
- L. Willcocks, and S. Lester. 1994. Evaluating the feasibility of information systems investments: recent UK evidence and new approaches. In: L. Willcocks (ed). Information Management. Springer, New York, pp. 49–77.Google Scholar
- T. Turja, and A. Oksanen. 2019. Robot acceptance at work: A multilevel analysis based on 27 EU countries. Int J Soc Robot, 11, 679–689. https://doi.org/10.1007/s12369-019-00526-xGoogle ScholarCross Ref
- Z. Sahatjian, A.A. Martin, and M.R. Buckley. 2018. Working with robots: organizational consideration, Organ Dyn 49, 2. https://doi.org/ 10.1016/j.orgdyn.2018.09.002Google ScholarCross Ref
- A.S. Ghazali, J. Ham, E.I. Barakova, and P. Markopoulos. 2018. Effects of robot facial characteristics and gender in persuasive human–robot interaction. Front Robot AI 5, 73. https://doi.org/10.3389/frobt.2018.00073Google ScholarCross Ref
- J. Złotowski, A. Khalil1, and S. Abdallah. 2019. One robot doesn't fit all: aligning social robot appearance and job suitability from a Middle Eastern perspective. AI & Soc, 35, 485–500. https://doi.org/10.1007/s00146-019-00895-xGoogle ScholarDigital Library
- S. Whelan, K. Murphy, E. Barrett, C. Krusche, A. Santorell, and D. Casey. 2018. Factors affecting the acceptability of social robots by Older Adults including people with dementia or cognitive impairment: A literature review. Int J Soc Robot, 10, 643–668. https://doi.org/10.1007/s12369-018-0471-xGoogle ScholarCross Ref
- W. Moyle, M. Cooke, C. Jones, S. O'Dwyer, and B. Sung. 2013. Assistive technologies as a means of connecting people with dementia. Int Psychogeriatr, 25, S21–22Google Scholar
- S. Frennert, H. Eftring, and B. Östlund. 2013. Older people's involvement in the development of a social assistive robot. In G. Herrmann M.J. Pearson, A. Lenz, P. Bremner, A. Spiers, and U. Leonards (eds) Social robotics. ICSR 2013. Lecture Notes in Computer Science, vol 8239. Springer, Cham. https://doi.org/10.1007/978-3-319-02675-6_2Google ScholarDigital Library
- P. Salovey, and J. D. Mayer. 1990. Emotional intelligence. Imagin Cogn Personal 9, 3, 185–211. https://doi.org/10.2190/DUGG-P24E-52WK-6CDGGoogle ScholarCross Ref
- C.D. Martin. 1997. Book Review: The media equation: how people treat computers, television and new media like real people and places by B. Reeves and C. Nass, Cambridge University Press, 1997. Spectrum 34, 3, 9–10. https://doi.org/10.1109/MSPEC.1997.576013.Google ScholarDigital Library
- E. I. Barakova, M. De Haas, W. Kuijpers, N. Irigoyen, and A. Betancourt. 2018. Socially grounded game strategy enhances bonding and perceived smartness of a humanoid robot. Connect Sci, 30, 81–98. https://doi.org/10.1080/09540091.2017.1350938Google ScholarCross Ref
- C. Urquhart. 2013. Grounded Theory for Qualitative Research. SAGE Publications.Google Scholar
- M. Q. Patton. 1990. Qualitative Evaluation and Research Method, 2nd ed. SAGE Publications.Google Scholar
- S. Lamnek. 2010. Qualitative Sozialforschung. 5th ed. Beltz, Weinheim.Google Scholar
- M. Heerink, B. Krose, V. Evers, and B. Wielinga. 2007. Observing conversational expressiveness of elderly users interacting with a robot and screen agent. Proceedings of the IEEE 10th International Conference on Rehabilitation Robotics (ICORR '07), pp. 751–756, Noordwijk, The Netherlands. https://doi.org/10.1109/ICORR.2007.4428509.Google ScholarCross Ref
- T.L. Mitzner, T.L. Chen, C.C. Kemp, and W.A. Rogers. 2014. Identifying the potential for robotics to assist older adults in different living environments. Int J Soc Robot 6, 2, 213–227. https://doi.org/10.1007/s12369-013-0218-7.Google ScholarCross Ref
- R. Stafford, B. MacDonald, D. Jayawardena, D. Wegner, and E. Broadbent. 2013. Does the robot have a mind? Mind perception and attitudes towards robots predict use of an elder care robot. Int J Soc Robot 6, 1, 17–32. https://doi.org/10.1007/s12369-013-0186-yGoogle ScholarCross Ref
- V. Venkatesh, and H. Bala. 2008.Technology acceptance model 3 and a research agenda on interventions. DecisSci, 39, 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.xGoogle ScholarCross Ref
- D. Hebesberger, T. Koertner, C. Gisinger, and J. Pripfl. 2017. A long-term autonomous robot at a care hospital: A mixed methods study on social acceptance and experiences of staff and older adults. Int J Soc Robot, 9, 417–429. https://doi.org/10.1007/s12369-016-0391-6Google ScholarCross Ref
- M. Heerink, B. Krose, V. Evers, and B. Wielinga. 2007. Observing conversational expressiveness of elderly users interacting with a robot and screen agent. In: IEEE international conference on rehabilitation robotics (ICORR 2007). IEEE, pp. 751–756. https://doi.org 10.1109/ICORR.2007.4428509Google Scholar
- J. E. Young, R. Hawkins, E. Sharlin, and T. Igarashi. 2009. Toward acceptable domestic robots: applying insights from social psychology. Int J Soc Robot, 1, 95. https://doi.org/10.1007/s12369-008-0006-yGoogle ScholarCross Ref
- J, Goetz, S. Kiesler, and A. Powers. 2003. Matching robot appearance and behavior to tasks to improve human–robot cooperation. In: Robot and Human Interactive Communication. The 12th IEEE International Workshop on Robot and Human Interactive Communication, ROMAN 2003, pp 55–60. https://doi.org/10.1109/ROMAN.2003.1251796Google ScholarCross Ref
- D. Li, P.P. Rau, and Y. Li. 2010. A cross-cultural study: effect of robot appearance and task. Int J Soc Robot, 2, 175–186. https://doi.org/10.1007/s12369-010-0056-9Google ScholarCross Ref
- M. Pino, M. Boulay, F, Jouen, and A-S. Rigaud. 2015. Are we ready for robots that care for us? Attitudes and opinions of older adults toward socially assistive robots. Front Aging Neurosci, 7, 1–15. https://doi.org/10.3389/fnagi.2015.00141Google ScholarCross Ref
- M. Scopelliti, M. V. Giuliani, and F. Fornara. 2004. Robots in a domestic setting: a psychological approach. Univ Access Inf Soc, 4, 146–155. https://doi.org/10.1007/s10209-005-0118-1Google ScholarDigital Library
Recommendations
Friend or Foe? Understanding Assembly Workers’ Acceptance of Human-robot Collaboration
Research NotesDue to rising demands on productivity and flexibility, assembly processes are currently experiencing a substantial transformation. Workstations where humans and robots work closely together are becoming increasingly popular, as they provide major ...
Concurrent Probabilistic Motion Primitives for Obstacle Avoidance and Human-Robot Collaboration
Intelligent Robotics and ApplicationsAbstractThe paper proposed a new method to endow a robot with the ability of human-robot collaboration and online obstacle avoidance simultaneously. In other words, we construct a probabilistic model for human-robot collaboration primitives to learn the ...
Human-Robot Collaboration: an analysis of worker’s performance
AbstractCollaborative robots are an important enabling technology of Industry 4.0. The interaction between humans and robots, called Human-Robot Collaboration (HRC), aims to improve system performance. However, it is necessary to investigate in depth the ...
Comments