THE EFFECT OF ADVANTAGES, DISADVANTAGES AND PERCEIVED VALUE ON THE INTENTION TO USE SERVICE ROBOTS IN THE HEALTH SECTOR

Year 2024, , 927 - 952, 31.12.2024

Ali Yıldırım , Ertuğrul Çavdar

https://doi.org/10.17130/ijmeb.1412528

Abstract

In addition to technological developments, these robots are increasingly entering our daily lives
with the acceptance perception of society towards service robots. Robots, used in the entire service sector,
have a wide range of usage opportunities in the health sector. The health sector is one of the sectors with
high customer sensitivity. Service robots’ perceived disadvantages and benefits affect the perspective on
using service robots in the health sector. This study investigated the effect of perceived advantages and
disadvantages and perceived value of service robots on the intention to use service robots in the healthcare sector. For this purpose, data were collected from 394 patients with a questionnaire. After analyzing
the validity and reliability of the scales used, the effect of perceived advantage and disadvantage and
perceived value on the intention to use service robots was analyzed with structural equation modeling.
As a result of the analysis, it was found that perceived advantage and perceived value have a significant
positive effect on the intention to use service robots. Perceived disadvantage, on the other hand, was
found to have no significant effect on intention to use. The study also made recommendations to the sector
and researchers.

Keywords

Service robots, Healthcare robots, Intention to use

AVANTAJ, DEZAVANTAJ VE ALGILANAN DEĞERİN SAĞLIK SEKTÖRÜNDE HİZMET ROBOTU KULLANIM NİYETİNE ETKİSİ

Abstract

Üretim sektöründen sonra hizmet sektöründe de kullanılmaya başlayan robotlar hem hizmet
sunanların işlerini kolaylaştırmakta hem de hizmetlerin standardizasyonuna katkı sağlamaktadır.
Teknolojik gelişmelerin yanı sıra toplumun hizmet robotlarına yönelik kabul algısı ile birlikte bu robotlar
her geçen gün giderek daha fazla günlük hayatımıza girmektedir. Hizmet sektörünün tamamında kullanım
alanı bulunan robotlar sağlık sektöründe de geniş kullanım imkânına sahiptir. Sağlık sektörü müşteri
hassasiyetinin yüksek olduğu sektörlerden biridir. Hizmet robotlarının faydalarının yanı sıra algılanan
dezavantajları, sağlık sektöründe hizmet robotu kullanımına bakış açısını etkilemektedir. Bu çalışmada
sağlık sektöründe hizmet robotları ile ilgili algılanan avantaj ve dezavantaj ile algılanan değerin hizmet
robotlarını kullanım niyeti üzerindeki etkisi araştırılmıştır. Bu amaçla 394 hastadan anket ile veri
toplanmıştır. Kullanılan ölçeklerin geçerlilik ve güvenilirliğe yönelik analizler yapıldıktan sonra algılanan
avantaj ve dezavantaj ile algılanan değerin hizmet robotlarını kullanım niyeti üzerindeki etkisi yapısal
eşitlik modeli ile analiz edilmiştir. Gerçekleştirilen analizler sonucunda algılanan avantaj ve algılanan
değerin hizmet robotları kullanım niyetini pozitif yönde anlamlı olarak etkilediği bulgusuna ulaşılmıştır.
Algılanan dezavantajın ise kullanım niyeti üzerinde anlamlı bir etkisi olmadığı tespit edilmiştir. Çalışmada
ayrıca sektöre ve araştırmacılara önerilerde bulunulmuştur.

Keywords

Hizmet robotları, Sağlık robotları, Kullanım niyeti

Ethical Statement

Çalışmada için Kastamonu Üniversitesi Sosyal ve Beşeri Bilimler Etik Kurulu’ndan Etik izin alınmıştır.

References

• Ahn, J., & Lee, H. (2019). The effect of consumers’ perceived value on acceptance of an internet-only bank service. Sustainability, 11(17), 1-9.
• Alaiad, A & Zhou, L. (2014). The determinants of home healthcare robots adoption: An empirical investigation,International Journal of Medical Informatics, 83/11, 825-840, https://doi. org/10.1016/j.ijmedinf.2014.07.003.
• Asgharian, P., Panchea, A. M., & Ferland, F. (2022). A review on the use of mobile service robots in elderly care. Robotics, 11(6), 1-27.
• Awad, A., Trenfield, J., Pollard, T. D., Ong, J. J., Elbadawi, M., McCoubrey, L. E., Goyanes, A., … Basit, A. W. (2021). Connected healthcare: Improving patient care using digital health technologies. Advanced Drug Delivery Reviews, 178, 1-20.
• Ayyıldız, A. Y., Baykal, M., & Koç, E. (2022). Attitudes of hotel customers towards the use of service robots in hospitality service encounters. Technology in Society, 70, 1-10.
• Bartneck, C., & Forlizzi, J. (2004, September). A design-centred framework for social human-robot interaction. 13th IEEE International Workshop on Robot and Human Interactive Communication’da sunulmuş bildiri, (591-594), Japan.
• Başer, S.H. & Bakırtaş, H. (2023). Hizmet sektöründe insansı robot kullanımı üzerine bir literatür incelemesi, Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 16(1), 207–223.
• Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020). Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success. Journal of Service Management, 31(2), 267-289.
• Betriana, F., Tanioka, R., Gunawan, J., & Locsin, R. C. (2022). Healthcare robots and human generations: Consequences for nursing and healthcare. Collegian, 29(5), 767-773.
• Bowen, J., & Morosan, C. (2018). Beware hospitality industry: The robots are coming. Worldwide Hospitality and Tourism Themes, 10(6), 726-733.
• Brinkman, W.P. (2009). Design of a questionnaire instrument, İçinde S. Love (Ed). Handbook of Mobile Technology Research Methods, (s. 31-57). Nova Publisher.
• Broadbent, E., Stafford, R., & MacDonald, B. (2009). Acceptance of healthcare robots for the older population: Review and future directions. International Journal of Social Robotics, 1, 319-330.
• Burton, A., Chiou, E. K., & Gutzwiller, R. S. (2020). A brief literature review on human perceptions of service robots with a focus on healthcare. Human Factors and Ergonomics Society Annual Meeting’de sunulmuş bildiri. 64, 117-121.
• Cheng, E.W. (2001). SEM being more effective than multiple regression in parsimonious model testing for management development research. J. Manag. Dev., 20, 650–667.
• Cheng, M., Li, X., & Xu, J. (2022). Promoting healthcare workers’ adoption ıntention of artificial-ıntelligence- assisted diagnosis and treatment: The chain mediation of social ınfluence and human–computer trust. International Journal of Environmental Research and Public Health, 19(20), 1-19.
• Chi, O. H., Jia, S., Li, Y., & Gursoy, D. (2021). Developing a formative scale to measure consumers’ trust toward interaction with artificially intelligent (AI) social robots in service delivery. Computers in Human Behavior, 118, 1-17.
• Chiang, A. H., & Trimi, S., (2020). Impacts of service robots on service quality. Service Business, 14(3), 439-459.
• Chuah, H. W., Aw, E. C. X., & Yee, D. (2021). Unveiling the complexity of consumers’ intention to use service robots: An fsQCA approach. Computers in Human Behavior, 123, 1-13.
• Cui, J., & Zhong, J. (2023). The effect of robot anthropomorphism on revisit intentions after service failure: A moderated serial mediation model. Asia Pacific Journal of Marketing and Logistics. 35(11), 2621-2644. https: //doi.org/10.1108/APJML-10-2022-0862
• de Kervenoael, R., Hasan, R., Schwob, A., & 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, https://doi. org/10.1016/j.tourman.2019.104042.
• Dino, M. J. S., Davidson, P. M., Dion, K. W., Szanton, L., & Ong, I. L. (2022). Nursing and humancomputer interaction in healthcare robots for older people: An integrative review. International Journal of Nursing Studies Advances, 4, 1-23.
• Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
• Fosch-Villaronga, E., & Mahler, T. (2021). Cybersecurity, safety and robots: Strengthening the link between cybersecurity and safety in the context of care robots. Computer Law & Security Review, 41, 1-13.
• Fusté-Forné, F., & Jamal, T. (2021). Co-creating new directions for service robots in hospitality and tourism. Tourism and Hospitality, 2(1), 43-61.
• Gani, M. O., Rahman, M. S., Bag, S., & Mia, M. P. (2023). Examining behavioural intention of using smart health care technology among females: dynamics of social influence and perceived usefulness. Benchmarking: An International Journal, 31(2), 330-352. https: //doi.org/10.1108/BIJ-09- 2022-0585.
• Garcia-Haro, J. M., Oña, E. D., Hernandez-Vicen, J., Martinez, S., & Balaguer, C. (2020). Service robots in catering applications: A review and future challenges. Electronics, 10(1), 1-22.
• George, D., & Mallery, P, (2016). IBM SPSS Statistics 23 Step by Step A Simple Guide and Reference; (Fourteenth edition) New York: Routledge.
• Ghali, Z., Garrouch, K., & Aljasser, A. (2023). Drivers of patients’ behavioral intention toward public and private clinics’ services. Healthcare, 11(16), 1-19.
• Gonzalez-Aguirre, J. A., Osorio-Oliveros, R., Rodríguez-Hernández, K. L., Lizárraga-Iturralde, J., Morales Menendez, R., Ramírez-Mendoza, R. A., … Lozoya-Santos, J. D. J. (2021). Service robots: Trends and technology. Applied Sciences, 11(22), 1-22.
• Gürdin, B. (2020). Türkiye’de robonomi: Z kuşağı gençlerin hastanelerde potansiyel hizmet robotu kullanımına yönelik tutumları. Artuklu Kaime Uluslararası İktisadi ve İdari Araştırmalar Dergisi, 3(1), 41-55.
• Holland, J., Kingston, L., McCarthy, C., Armstrong, E., O’Dwyer, P., Merz, F., & McConnell, M. (2021). Service robots in the healthcare sector. Robotics, 10(1), 1-47.
• Holthöwer, J., & van Doorn, J. (2023). Robots do not judge: Service robots can alleviate embarrassment in service encounters. Journal of the Academy of Marketing Science, 51(4), 767-784.
• Hsu, C. L., & Lin, J. C. C. (2016). Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention. Technological Forecasting and Social Change, 108, 42-53.
• Huang, D., Chen, Q., Huang, S. S., & Liu, X. (2023). Consumer intention to use service robots: A cognitive– affective–conative framework. International Journal of Contemporary Hospitality Management, 36(6), 1893-1913. https: //doi.org/10.1108/IJCHM-12-2022-1528.
• Ikumapayi, O. M., Afolalu, A., Ogedengbe, T. S., Kazeem, R. A., & Akinlabi, E. T. (2023). Human-robot co-working ımprovement via revolutionary automation and robotic technologies–an overview. Procedia Computer Science, 217, 1345-1353.
• Jagatheesaperumal, K., Mishra, P., Moustafa, N., & Chauhan, R. (2022). A holistic survey on the use of emerging technologies to provision secure healthcare solutions. Computers and Electrical Engineering, 99, 1-16. Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2021). Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics, 1, 58-75.
• Jeng, M. Y., Pai, F. Y., & Yeh, T. M. (2022). Antecedents for older adults’ intention to use smart health wearable devices-technology anxiety as a moderator. Behavioral Sciences, 12(4), 1-16.
• Kalisz, D. E., Khelladi, I., Castellano, S., & Sorio, R. (2021). The adoption, diffusion & categorical ambiguity trifecta of social robots in e-health–Insights from healthcare professionals. Futures, 129, 1-12. Kandemir, F., Azizoğlu, F. & Terzi, B. (2023). Hemşirelikte yapay zekâ ve robot teknolojilerinin kullanımı, Yoğun Bakım Hemşireliği Dergisi, 27(2), 118-127.
• Kayabaşi, A., Er, İ., Demirağ, F., & Erçin Yurcu, M. (2022). Hizmet robotlarına yönelik algıların kullanım niyetine etkisi. Tüketici ve Tüketim Arastirmalari Dergisi, 14(2), 433-469.
• Khan, Z. H., Siddique, A., & Lee, C. W. (2020). Robotics utilization for healthcare digitization in global COVID-19 management. International Journal of Environmental Research and Public Health, 17(11), 1-21.
• Kim, Y., & Lee, H.S. (2014). Quality, perceived usefulness, user satisfaction, and ıntention to use: an empirical study of ubiquitous personal robot service. Asian Social Science, 10, 1.
• Kim, Y., Kim, J. U., & Park, C. (2017). The effects of perceived value, website trust and hotel trust on online hotel booking intention. Sustainability, 9(12), 1-14.
• Kim, H. L., & Hyun, S. (2019). The relationships among perceived value, intention to use hashtags, eWOM, and brand loyalty of air travelers. Sustainability, 11(22), 1-12.
• Kwak, M. K., Lee, J., & Cha, S. (2021). Senior consumer motivations and perceived value of robot service restaurants in Korea. Sustainability, 13(5), 1-15.
• Lee, I. (2021). Service robots: A systematic literature review. Electronics, 10(21), 1-29.
• Lei, C., Hossain, M. S., & Wong, E. (2023). Determinants of repurchase ıntentions of hospitality services delivered by artificially ıntelligent (AI) service robots. Sustainability, 15(6), 1-17.
• Liu, X. S., Yi, X. S., & 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, 1-13.
• Liu, X., He, X., Wang, M., & Shen, H. (2022b). What influences patients’ continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics. Technology in Society, 70, 1-12.
• Majeed, S., & Kim, W. G. (2022). Toward understanding healthcare hospitality and the antecedents and outcomes of patient-guest hospital-hotel choice decisions: A scoping review. International Journal of Hospitality Management, 112,1-14.
• Mbunge, E., Muchemwa, B., & Batani, J. (2021). Sensors and healthcare 5.0: Transformative shift in virtual care through emerging digital health technologies. Global Health Journal, 5(4), 169-177.
• McAllister, M., Kellenbourn, K., & Wood, D. (2021). The robots are here, but are nurse educators prepared?. Collegian, 28(2), 230-235.
• Meidute-Kavaliauskiene, I., Yıldız, B., Çiğdem, Ş., & Činčikaitė, R. (2021a). The effect of COVID-19 on airline transportation services: A study on service robot usage intention. Sustainability, 13(22), 1-19.
• Meidute-Kavaliauskiene, I., Çiğdem, Ş., Yıldız, B., & Davidavicius: (2021b). The effect of perceptions on service robot usage intention: A survey study in the service sector. Sustainability, 13(17), 1-18.
• Murillo-Zegarra, M., Ruiz-Mafe, C., & Sanz-Blas, S. (2020). The effects of mobile advertising alerts and perceived value on continuance intention for branded mobile apps. Sustainability, 12(17), 1-20.
• Niculescu, M., Honțaru, O. S., Popescu, G., Sterian, A. G., & Dobra, M. (2023). Challenges of integrating new technologies for orthopedic doctors to face up to difficulties during the pandemic era. In Healthcare. 11(11), 1-15.
• Parvez, M. O., Arasli, H., Ozturen, A., Lodhi, R. N., & Ongsakul, V. (2022). Antecedents of human-robot collaboration: theoretical extension of the technology acceptance model. Journal of Hospitality and Tourism Technology, 13(2), 240-263.
• Pham, Q. T., Tran, X. P., Misra, S., Maskeliūnas, R., & Damaševičius, R. (2018). Relationship between convenience, perceived value, and repurchase intention in online shopping in Vietnam. Sustainability, 10(1), 1-14.
• Prentice, C., & Nguyen, M. (2021). Robotic service quality–Scale development and validation. Journal of Retailing and Consumer Services, 62, 1-7.
• Rasheed, H. M. W., He, Y., Khizar, H. M. U., & Abbas, H. S. M. (2023). Exploring consumer-robot interaction in the hospitality sector: Unpacking the reasons for adoption (or resistance) to artificial intelligence. Technological Forecasting and Social Change, 192, 1-8.
• Samarakoon, B. P., Muthugala, M. V. J., & Jayasekara, A. B. P. (2022). A review on human–robot proxemics. Electronics, 11(16), 1-21.
• Sarker, S., Jamal, L., Ahmed, F., & Irtisam, N. (2021). Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Robotics and Autonomous Systems, 146, 1-18.
• Shah, T. R., Kautish, P., & Mehmood, K. (2023). Influence of robots service quality on customers’ acceptance in restaurants. Asia Pacific Journal of Marketing and Logistics. 35(12), 3117-3137. https: // doi.org/10.1108/APJML-09-2022-0780.
• Shin, H. (2022). A critical review of robot research and future research opportunities: Adopting a service ecosystem perspective. International Journal of Contemporary Hospitality Management, 34(6), 2337-2358.
• Sinha, N., Singh, P., Gupta, M., & Singh, P. (2020). Robotics at workplace: An integrated Twitter analytics– SEM based approach for behavioral intention to accept. International Journal of Information Management, 55, 1-17.
• Stahle, B. C., & Coeckelbergh, M. (2016). Ethics of healthcare robotics: Towards responsible research and innovation. Robotics and Autonomous Systems, 86, 152-161.
• van Kemenade, M.A.M., Hoorn, J.F., & Konijn, E.A. (2019). Do you care for robots that care? Exploring the opinions of vocational care students on the use of healthcare robots. Robotics, 8, 22. https:// doi.org/10.3390/robotics8010022.
• Watjatrakul, B. (2020). Intention to adopt online learning: The effects of perceived value and moderating roles of personality traits. The International Journal of Information and Learning Technology, 37(1/2), 46-65.
• Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., … Martins, A. (2018). Brave new world: service robots in the frontline. Journal of Service Management, 29(5), 907-931.
• Yıldız, B., Çavdar, E., & Kütahyalı, D. N. (2022). Service robots, the innovation of our era: A qualitative research in the tourism sector. Journal of Tourism Intelligence and Smartness, 5(2), 184-198.
• Zeqiri, J., Ramadani, V., & Aloulou, W. J. (2023). The effect of perceived convenience and perceived value on intention to repurchase in online shopping: the mediating effect of e-WOM and trust. Economic Research-Ekonomska Istraživanja, 36(3), 1-22.
• Zhang, M., Cui, J., & Zhong, J. (2023). How consumers react differently toward humanoid vs. nonhumanoid robots after service failures: A moderated chain mediation model. International Journal of Emerging Markets. 19(11), 4306-4326.https: //doi.org/10.1108/IJOEM-06-2022-1023.