Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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The Effect of Characteristics of Social Intelligence Robots on Satisfaction and Intention to Use: Focused on User of Single Person Households

소셜 지능로봇의 특성이 만족과 사용의도에 미치는 영향: 1인 가구 소셜 지능로봇 사용자를 중심으로

  • Jeon, Gyuri (Department of Business Administartion, Graduate School of Soongsil University) ;
  • Lee, Chaehyun (SOARIT Co., Ltd) ;
  • Jung, Sungmi (Department of IT Policy and Management, Graduate School of Soongsil University) ;
  • Choi, Jeongil (College of Business Administration, Soongsil University)
  • 전규리 (숭실대학교 대학원 경영학과) ;
  • 이채현 (주식회사 소올아이티) ;
  • 정성미 (숭실대학교 대학원 IT정책경영학과) ;
  • 최정일 (숭실대학교 경영학부)
  • Received : 2024.02.07
  • Accepted : 2024.03.11
  • Published : 2024.03.31
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Abstract

Purpose: This study focused on the societal changes associated with the entry into an ultra-aged society and the increase in single-person households. The core objective of this research is to investigate how social intelligent robots can bring about positive changes in the lives of individuals in single-person households and how such changes influence user satisfaction and the intention to use these robots. Methods: The study employed a cross-sectional analysis using a structural equation model. A survey designed to assess the impact of social intelligent robots' characteristics, such as perceived encouragement, empathy, presence, appearance, and attachment, on user satisfaction and usage intentions was conducted. Data were collected from a total of 335 users and analyzed using the structural equation model. Results: In the characteristics of social intelligent robots for single-person households, it was found that empathy, presence, and attachment significantly influenced satisfaction, while perceived encouragement, empathy, and attachment significantly influenced usage intentions. The research results indicate differences between enhancing user satisfaction and increasing the intention to use social intelligent robots. The findings suggest the essential need for a user-centric approach in the design and development of social intelligent robots. Additionally, it was observed that emotional support plays a crucial role in users' experiences with social intelligent robots. Conclusion: This study verified the impact of social intelligent robots on satisfaction and usage intentions based on users' experiences. It examined the influence of linguistic, visual, and personal characteristics of robots on user experiences, providing insights into how technological and human aspects of social intelligent robots interact to shape user satisfaction and usage intentions. Consequently, the study confirmed that social intelligent robots can bring positive changes to human life, emphasizing the necessity for the advancement of robot technology in a human-centric direction.

Keywords

References

  1. Abdollahi, H., Mahoor, M. H., Zandie, R., Siewierski, J., and Qualls, S. H. 2023. Artificial emotional intelligence in socially assistive robots for older adults: A pilot study. IEEE Transactions on Affective Computing 14(3):2020-2032. https://doi.org/10.1109/TAFFC.2022.3143803
  2. Ashfaq, M., Yun, J., Yu, S., and Loureiro, S. M. C. 2020. I, chatbot: Modeling the determinants of users' satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics 54:101473.
  3. Azizaha, Y. N., Rijalb, M. K., Rumainurc, U. N. R., Pranajayae, S. A., Ngiuf, Z., Mufidg, A., Purwantoh, A., and Maui, D. H. 2020. Transformational or transactional leadership style: Which affects work satisfaction and performance of Islamic university lecturers during COVID-19 pandemic. Systematic Reviews in Pharmacy 11(7):577-588.
  4. Babin, B., and Griffin, M. 1998. The nature of satisfaction: An updated examination and analysis. Journal of Business Research 41(2):127-136.
  5. Belanche, D., Casalo, L. V., Schepers, J., and Flavian, C. 2021. Examining the effects of robots' physical appearance, warmth, and competence in frontline services: The humanness-value-loyalty model Psychology & Marketing 38(12):2357-2376. https://doi.org/10.1002/mar.21532
  6. Bhargava, A., Bester, M., and Bolton, L. 2021. Employees' perceptions of the implementation of robotics, artificial intelligence, and automation(RAIA) on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science 6(1):106-113. https://doi.org/10.1007/s41347-020-00153-8
  7. Bilquise, G., Ibrahim, S., and Shaalan, K. 2022. Emotionally intelligent chatbots: A systematic literature review. Human Behavior and Emerging Technologies 2022:9601630.
  8. Birmingham, C., Perez, A., and Mataric, M. 2022. Perceptions of cognitive and affective empathetic statements by socially assistive robots. 2022 17th ACM /IEEE International Conference on Human -Robot Interaction, Sapporo, Japan, 323-331, doi: 10.1109/HRI53351.2022.9889386.
  9. Breazeal, C. 2004. Social interactions in HRI: The robot view. IEEE Transactions on Systems, Man, and Cybernetics, Part C(Applications and Reviews) 34(2):181-186. https://doi.org/10.1109/TSMCC.2004.826268
  10. Brown, L. N., and Howard, A. M. 2014. The positive effects of verbal encouragement in mathematics education using a social robot. 2014 IEEE Integrated STEM Education Conference, Princeton, NJ, USA, 2014, 1-5, doi: 10.1109/ISECon.2014.6891009.
  11. Caviggioli, F., Lamberti, L., Landoni, P., and Meola, P. 2020. Technology adoption news and corporate reputation: Sentiment analysis about the introduction of bitcoin. Journal of Product & Brand Management 29(7):877-897. https://doi.org/10.1108/JPBM-03-2018-1774
  12. Chatzoglou, P. D., Lazaraki, V., Apostolidis, S. D., and Gasteratos, A. C. 2023. Factors affecting acceptance of social robots among prospective users. International Journal of Social Robotics 151(2022):104033.
  13. Chin, W. W. 1998. The partial least squares approach to structural equation modeling in modern methods for business research. In G. A. Marcoulides (Ed.), Modern methods for business research. Methodology for business and management, Hillsdale, NJ:Lawrence Erlbaum Associates Publishers. 295-336.
  14. Chita-Tegmark, M., and Scheutz, M. 2021. Assistive robots for the social management of health: A framework for robot design and human-robot interaction research. International Journal of Social Robotics 13(2):197-217. https://doi.org/10.1007/s12369-020-00634-z
  15. Choi, M. S. 2022. A study on social robot emotional expression face for emotional empathy. Design, Crafts and Industrial Design majors, PhD diss, Graduate School of Hongik University.
  16. Czepiel, J., and Rosenberg, L. 1977. Customer satisfaction: Concept and measurement. Journal of Academy of Marketing Science 5(4):403-411. https://doi.org/10.1007/BF02722070
  17. Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3):319-340. https://doi.org/10.2307/249008
  18. Davison, D. P., Wijnen, F. M., Charisi, V., Meij, J., Reidsma, D., and Evers, V. 2021. Words of encouragement: How praise delivered by a social robot changes children's mindset for learning. Journal on Multimodal User Inerfaces 15:61-76. https://doi.org/10.1007/s12193-020-00353-9
  19. De Graaf, M. M., and Allouch, S. B. 2013. Exploring influencing variables for the acceptance of social robots. Robotics and Autonomous Systems 61(12):1476-1486. https://doi.org/10.1016/j.robot.2013.07.007
  20. Deng, T., Kanthawala, S., Meng, J., Peng, W., Kononova, A., Hao, Q., Zhang, Q., and David, P. 2019. Measuring smartphone usage and task switching with log tracking and self-reports. Mobile Media & Communication 7(1):3-23. https://doi.org/10.1177/2050157918761491
  21. Dziergwa, M., Kaczmarek, M., Kaczmarek, P., Kedzierski, J., and Wadas-Szydlowska, K. 2018. Long-term cohabitation with a social robot: A case study of the influence of human attachment patterns. International Journal of Social Robotics, 10(1):163-176. https://doi.org/10.1007/s12369-017-0439-2
  22. Ebadi, S., and Amini, A. 2022. Examining the roles of social presence and human-likeness on iranian EFL learners' motivation using artificial intelligence technology: a case of CSIEC chatbot. Interactive Learning Environments. 1-19.
  23. Gatzioufa, P., and Saprikis, V. 2022. A literature review on users' behavioral intention toward chatbots' adoption. Applied Computing and Informatics 2022(5):1-14.
  24. Gebhard, P., Aylett, R., Higashinaka, R., Jokinen, K., Tanaka, H., and Yoshino, K. 2021. Modeling trust and empathy for socially interactive robots. In Multimodal agents for ageing and multicultural societies: Communications of NII Shonan Meetings(pp.21-60). 10.1007/978-981-16-3476-5_2.
  25. Hair Jr, J. F., Sarstedt, M., Hopkins, L., and Kuppelwieser, V. G. 2014. Partial least squares structural equation modeling(PLS-SEM). European Business Review 26(2):106-121. https://doi.org/10.1108/EBR-10-2013-0128
  26. Henschel, A., Laban, G., and Cross, E. S. 2021. What makes a robot social? A review of social robots from science fiction to a home or hospital near you. Current Robotics Reports 2(1):9-19. https://doi.org/10.1007/s43154-020-00035-0
  27. Homburg, R. S. 2022. Survey of emotions in human-robot interactions: Perspectives from robotic psychology on 20 years of research. International Journal of Social Robotics 14(2):389-411. https://doi.org/10.1007/s12369-021-00778-6
  28. Hwang, Y. H., and Park, J. W. 2023. A study of the effects of an apartment community's servicescape on resident satisfaction and quality of life: A focus on new hope town. Journal of Korean Society for Quality Management, 51(3):329-346. https://doi.org/10.7469/JKSQM.2023.51.3.329
  29. Jo, S, J. 2023. The effect of food tech based robot barista cafe experiential value on customer satisfaction and revisit intention - Focused on moderating role of the recommendation intentions. Journal of The Korea Contents Association, 23(5):193-204.
  30. Joo, Y. J., So, H.-J., and Kim, N. H. 2018. Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education 122:260-272. https://doi.org/10.1016/j.compedu.2018.01.003
  31. Jung, K, H., and Kim, Y, M. 2022. Prediction of the elderly's usage intention of wearable robots: Focusing on the technology acceptance model. Journal of Korea Society for Wellness 17(2):271-278. https://doi.org/10.21097/ksw.2022.5.17.2.271
  32. Kervenoael, R., Hasan, R., Schwob, A., and Goh, E. 2020. Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors' intentions to use social robots. Tourism Management 78:104042.
  33. Kim, D, S., and Kim, H, I. 2022. A study on the effect of customer contact service in dessert cafe on customer satisfaction and behavioral intention. Journal of Foodservice Management 25(4):7-21.
  34. Kim, G, J., Park, S, J., and Lee, H, J. 2019. dentifying the role of "Fun" and "Data Security" on intention to use incheon airport's AI service robot. Korea Logistics Review 29(6):25-36.
  35. Kim, H, S., and Lim, H, C. 2023. The effects of the service quality and innovativeness of unmanned robot cafes on customer satisfaction and the intention to revisit:Test for the moderating effects of the user characteristics. Journal of Foodservice Management Society of Korea 26(6):49-71. https://doi.org/10.47584/jfm.2023.26.6.49
  36. Kim, M, H. 2023. Structural relationship analysis between human-animal interaction, empathic ability, self-comfort ability. Interpersonal Stress, and Interpersonal Relationship of Companion Animal Caregivers. Department of Educational Consulting, PhD diss, Graduate School of Pukyong National University.
  37. Kim, Y., and Lee, H. S. 2014. Quality, perceived usefulness, user satisfaction, and intention to use: An empirical study of ubiquitous personal robot service. Asian Social Science 10(11):1.
  38. Ko, Y, M., and Lee, I, J. 2022. The effects of perceived quality and perceived risk of automated robot cafe on intention to use: Consumer innovativeness as a moderating variable. Korean Journal of Hospitality and Tourism 31(5):75-93.
  39. Krueger, F., Mitchell, K. C., Deshpande, G., and Katz, J. S. 2021. Human-dog relationships as a working framework for exploring human-robot attachment: a multidisciplinary review. Animal Cognition 24:371-385. https://doi.org/10.1007/s10071-021-01472-w
  40. Kwon, Y, M., and Song, J, H. 2023. Exploring the effects of relationships between consumers and AI speakers on consumers' control, satisfaction toward the AI speaker, and intention to use. Information Society and Media 24(1):151-181. https://doi.org/10.52558/ISM.2023.04.24.1.151
  41. Lee, B., Xu, J., and Howard, A. 2017. Does appearance matter? Validating engagement in therapy protocols with socially interactive humanoid robots. 2017 IEEE Symposium Series on Computational Intelligence, Honolulu USA, 2017, 1-6, doi: 10.1109/SSCI.2017.8285303.
  42. Lee, J. Y. 2023. The effects of technology acceptance factors and information search types on perceived risk, attitude and intention to use of cryptocurrency. Journal of the Korean Entrepreneurship Society 18(5):895-925.
  43. Liu, X. S., Yi, X. S., and Wan, L. C. 2022. Friendly or competent? The effects of perception of robot appearance and service context on usage intention. Annals of Tourism Research 92:103324.
  44. Mai, L. W., and Ness, M. R. 1999. Canonical correlation analysis of customer satisfaction and future purchase of mail-order speciality food. British Food Journal 101(11):857-870. https://doi.org/10.1108/00070709910301373
  45. Magazine Hankyung. 2019. A 'social robot' that wants to be a human partner. ttps://magazine.hankyung.com/business/article/202102184811b
  46. Naneva, S., Sarda Gou, M., Webb, T. L., and Prescott, T. J. 2020. A systematic review of attitudes, anxiety, acceptance, and trust towards social robots. International Journal of Social Robotics 12(6):1179-1201. https://doi.org/10.1007/s12369-020-00659-4
  47. National Statistical Office. 2023. A single-person househod based on statistics. http://kostat.go.kr.
  48. Nguyen, M. T., and Nguyen, T. N. 2023. A comprehensive review: Interaction of appearance and behavior, artificial skin, and humanoid robot. Journal of Robotics 2023:5589845.
  49. Ni, A., and Cheung, A. 2023. Understanding secondary students' continuance intention to adopt AI-powered intelligent tutoring system for English learning. Education and Information Technologies 28(3):3191-3216. https://doi.org/10.1007/s10639-022-11305-z
  50. Nunnally, J. C. and Bernstein, I. H. 1994. Psychometric theory. 3rd Edition. New York, NY: McGraw-Hill Education.
  51. Oliver. R. L. 1980. A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research 17(4):460-469. https://doi.org/10.1177/002224378001700405
  52. Parasuraman, A. 2000. Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research 2(4):307-321. https://doi.org/10.1177/109467050024001
  53. Park, S., and Whang, M. 2022. Empathy in human-robot interaction: Designing for social robots. International Journal of Environmental Research and Public Health 19(3):1889. https://doi.org/10.3390/ijerph19031889.
  54. Park, Y. J. 2022. 4-to, 5-year-old Children's perception of robots: Focusing inference of life phenomena, vitality, appearance, and roles. Interpersonal Stress, and Interpersonal Relationship of Companion Animal Caregivers. Department of Early Childhood Education, PhD diss, Graduate School of Wonkwang University.
  55. Pelau, C., Dabija, D-C., and Ene, I. 2021. What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior 122:106855.
  56. Pnevmatikos, D., Christodoulou, P., and Fachantidis, N. 2022. Designing a socially assistive robot for education through a participatory design approach: Pivotal principles for the developers. International Journal of Social Robotics 14(3):763-788.
  57. Rabb, N., Law, T., Chita-Tegmark, M., and Scheutz, M. 2022. An attachment framework for human-robot Interaction. International Journal of Social Robotics 14(2):539-559. https://doi.org/10.1007/s12369-021-00802-9
  58. Rasouli, S., Gupta, G., Nilsen, E., and Dautenhahn, K. 2022. Potential applications of social robots in robot-assisted interventions for social anxiety. International Journal of Social Robotics 14(5):1-32.
  59. Robinson, R., and Clifford, C. 2012. Authenticity and festival food service experiences. Annals of Tourism Research 39(2):571-600. https://doi.org/10.1016/j.annals.2011.06.007
  60. Rogers, E. M. 2003. Diffusion of innovations(5th ed.). New York: Free Press.
  61. Rossi, S., Conti, D., Garramone, F., Santangelo, G., Staffa, M., Varrasi, S., and Di Nuovo, A. 2020. The role of personality factors and empathy in the acceptance and performance of a social robot for psychometric evaluations. Robotics 9(2):39.
  62. Sanchez, G. 2013. PLS Modeling with R, Trowchez Editions, Berkeley, http://www.gastonsanchez.com/PLS_Path_Modeling_with_R.pdf. Abdollahi.
  63. Seo, J., An, S., and Choi, J. I. 2022. A study on factors affecting intention to use online collaboration tools for the non-face-to-face educational environment. Journal of Korean Society for Quality Management 50(3):571-591.
  64. Shin, D.-H., and Choo, H. 2011. Modeling the acceptance of socially interactive robotics: Social presence in human-robot interaction. Interaction Studies 12(3):430-460. https://doi.org/10.1075/is.12.3.04shi
  65. Statistics Korea. 2023. A single-person household based on 2023 statistics. 2023.12.12. Press release.
  66. Stock, R. M., Oliveira, P., and Hippel, E. V. 2015. Impacts of hedonic and utilitarian user motives on the innovativeness of user-developed solutions. Journal of Product Innovation Management 32(3):389-403. https://doi.org/10.1111/jpim.12201
  67. Sung, Y. B., and Noh, Y. 2023. The effect of scuba diving resort service quality on customer satisfaction and revisit intention: Focusing on the moderating effect of perceived risk attributes. Journal of Korean Society for Quality Management 51(4):589-606.
  68. Tsai, W-H. S., Liu, Y., and Chuan, C-H. 2021. How chatbots' social presence communication enhances consumer engagement: the mediating role of parasocial interaction and dialogue. Journal of Research in Interactive Marketing 15(3):460-482.
  69. Venkatesh, V., Thong, J. Y. L,. and Xu, X. 2012. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 36(1):157-178. https://doi.org/10.2307/41410412
  70. Yang, Y. K. 2022. The structural relationships among self-encouragement, social support, desire for re-employment, and job preparation behavior of career interrupted women. Department of Agricultural Economics and Education, PhD diss, Graduate School of Suncheon University.
  71. Yeo, S.-K., Lee, J.-H., Choi, W.-J., and Kim, K.-H. 2023. Analysis of the covid-19 pandemic-driven effect changes of quality factors on customer satisfaction in korean police civil affairs service. Journal of Korean Society for Quality Management 51(1):67-78.