SERVICE ROBOT INTEGRATION WILLINGNESS (SRIW) SCALE: ADAPTATION TO TURKISH, VALIDATION AND RELIABILITY STUDY
- Service Robot Integration Willingness Scale,
- Scale Adaptation,
- Robotic Systems,
- Artificial Intelligence
- Hizmet Robotu Entegrasyon İsteklilik Ölçeği,
- Ölçek Uyarlama,
- Robotik Sistemler,
- Yapay Zeka
How to Cite
1.1 RESEARCH SUBJECT
Mechanization in human life has been continuing rapidly since the industrial revolution. With Industry 4.0, this process has accelerated, and machines have formed an essential part of human life. Accordingly, artificial intelligence has begun to be used at a high level, and the era of robotic systems, smart machines and robots has begun. In addition to the workforce, many tasks based on robotic processes have begun to be defined with more mechanization in all departments in businesses. These technologies are not only valid for mechanical works such as production and logistics, but are also used extensively in other departments such as personnel tracking, marketing and decision making. Therefore, interest in high-level artificial intelligence and robotics has increased, laboratory environments have been created, many types of research have been published, conferences, congresses and panels have started to be organized. For example, in a study conducted by Frey and Osborne (2015) on 702 professions in the USA; It has been stated that about half of the professions can be automated. The most significant source of this situation is, of course, the mighty footsteps of artificial intelligence. According to Eberl (2019; 20), in this industry 4.0 period where there is a fundamental transformation in all living spaces of people; It is a matter of curiosity to what extent the investments for the future of artificial intelligence will affect people and what direction the willingness and readiness to have for this effect.
1.2. RESEARCH PURPOSE AND IMPORTANCE
The purpose of this study is to introduce the concept of Service Robot Integration Willingness developed by Lu, Chi and Gursoy (2019) and to adapt the Service Robot Integration Willingness (SRIW) scale to Turkish.
1.3. CONTRIBUTION of the ARTICLE to the LITERATURE
Although the scale is used by many researchers abroad, there has not been any study conducted in our country to adapt this scale to Turkish culture. In this study, the Service Robot Integration Willingness (SRIW) scale is adapted to the Turkish language and culture that contributes to the literature.
2. DESIGN AND METHOD
2.1. RESEARCH TYPE
The study is a quantitative method, and the data were collected by questionnaire.
2.2. RESEARCH PROBLEMS
Although the scale is used by many researchers abroad, there has not been any study conducted in our country to adapt this scale to Turkish culture
2.3. DATA COLLECTION METHOD
The Service Robot Integration Willingness scale originally consisted of 36 items and 6 factors. In this context, first of all, the meaning integrity of the Turkish translations of the scale, which includes 36 items, was reviewed. Service Robot Integration Willingness scale was adapted according to the adaptation method suggested by Brislin (1980). This method is a model that includes five necessary steps:
- Translating the scale into the target language to be used,
- Evaluation of the translation made to the target language,
- Re-translation into the source language,
- Evaluating the repeat translation to the source language,
- Final evaluation by experts.
2.4. QUANTITATIVE / QUALITATIVE ANALYSIS
Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed.
3. FINDINGS AND DISCUSSION
Findings show that the factors in this study are interrelated and that there is only one factor that includes all factors. In this study, the model with acceptable goodness of fit values "second-order multifactorial model" is presented in Figure 1 (Δχ² = 750.059, sd = 489 χ² / sd = 1.53, NFI = 0.91, CFI = 0.94 GFI. = 0.92, AGFI = 0.90, RMR = 0.03 RMSEA = 0.04, and p = .000).
3.1. DISCUSSING the FINDINGS with the LITERATURE
In order to be able to plan and understand the futures of businesses in every aspect, it is thought that the Service Robot Integration Willingness Scale will benefit both academically and on a sectoral basis. The scale initially consists of 36 items and 6 factors. Data obtained from three samples voluntarily participated in 673 employees operating in different service sectors were analyzed. A 6-factor structure that explains 73.01% of the total variance and as in the original was obtained.
4. CONCLUSION, RECOMMENDATION AND LIMITATIONS
The findings show that the Turkish form of the Service Robot Integration Willingness Scale is a reliable and valid measurement tool with acceptable values that can be used for institutions and organizations operating in different sectors. The final version of the scale is included in Annex-4.
The limitations of the study can be considered to reach a limited population in Marmara and Aegean Regions, and the number of people evaluated is 673. Our suggestions include evaluating the employees in other regions, evaluating the differences between generations with a distinction, and determining the relationship and impact with different variables.
4.1. RESULTS of the ARTICLE
Service robot integration willingness is an essential factor that characterizes the long-term willingness to integrate AI and service robots into regular service processes. This study aims to adapt the Service Robot Integration Willingness Scale developed by Lu, Chi and Gursoy (2019) into Turkish. The scale initially consists of 36 items and 6 factors. The data were obtained from three samples and analyzed. In order to determine the construct validity of the scale, exploratory and confirmatory factor analyzes were performed first. A 6-factor structure that explains 73.01% of the total variance was obtained as in the original. However, since the goodness of fit values of the three items were not within the accepted value range, they were excluded from the scale one by one, respectively. As a result of the analysis, a scale structure consisting of 33 items and 6 factors as in the original was obtained. The obtained findings It can be said that the Turkish form of the Service Robot Integration Willingness Scale is a reliable and valid measurement tool with acceptable values that can be used for institutions and organizations operating in different sectors.
4.2. SUGGESTIONS BASED on RESULTS
Our suggestions include evaluating the employees in other regions, evaluating the differences between generations with a distinction, and determining the relationship and impact with different variables.
4.3. LIMITATIONS of the ARTICLE
The limitations of the study can be considered to reach a limited population in Marmara and Aegean Regions, and the number of people evaluated is 673.
- Belanche, D., Casaló, L. V, Flavián, C., & Schepers, J. (2020). Service robot implementation: a theoretical framework and researcha genda. The Service Industries Journal, 40(3–4), 203–225. https://doi.org/10.1080/02642069.2019.1672666.
- Brislin, R. W. (1980). Cross-cultural research methods. In Environment and culture (pp. 47-82). Springer, Boston, MA.
- Büyüköztürk, Ş. (2007). Sosyal Bilimler İçin Veri Analizi El Kitabı (Sekizinci Baskı). Ankara: Pegem Akademi.
- Büyüköztürk, Ş. (2018). Sosyal Bilimler İçin Veri Analizi El Kitabı (Yirmi Dördüncü Baskı). Ankara: Pegem Akademi.
- Byrne, B. M. (2010). Structural equation modeling with AMOS: basic concepts, applications, and programming (multi variate applications series). New York: Taylor & Francis Group, 396, 73-84.
- Çelik, Y. (2016). SPSS ile İstatistik, Biyoistatistik ve Modern Bilimsel Araştırma. İstanbul: Hünkar Ofset
- Çolakoğlu, Ö. M., & Büyükekşi, C. (2014). Açımlayıcı faktör analiz sürecini etkileyen unsurların değerlendirilmesi. Karaelmas Eğitim Bilimleri Dergisi, 2(1), 58-64.
- Desmet, P. M. A. & Schifferstein, H. N. J. (2008). Sources of positive and negative emotions in food experience. Appetite, 50(2–3), 290–301.
- DiSalvo, C. F., Gemperle, F., Forlizzi, J., & Kiesler, S. (2002). All Robots Are Not Created Equal: The Design and Perception of Humanoid Robot Heads. Proceedings of the 4th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, 321–326. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/778712.778756
- Duffy, B. R. (2003). Anthropomorphism and the social robot. Robotics and Autonomous Systems, 42(3), 177–190. https://doi.org/10.1016/S0921-8890(02)00374-3
- Durmuş, B., Yurtkoru S. & Çinko M. (2018). Sosyal Bilimlerde SPSS’le Veri Analizi (Yedinci Basım). İstanbul: Beta Basım Yayın
- Eberl, U. (2019). Akıllı Makineler – Yapay Zeka Hayatımızı Nasıl Değiştiriyor (1st ed.; çev: Levent Tayla, ed.). İstanbul: Paloma Yayınevi.
- Esen, M.,& Büyük, K. (2014). Teknoloji kabul modeli bağlamında elektronik belge yönetim sisteminin incelenmesi: yükseköğretim kurulu örneği. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (42), 313–326. Retrieved from https://earsiv.anadolu.edu.tr/xmlui/bitstream/handle/11421/11217/11217.pdf?sequence=1&isAllowed=y
- Frey, C. B.,& Osborne, M. (2015). Technology at Work: The Future of Innovation and Employment. In Manufacturing Engineer. Oxford, England. https://doi.org/10.1049/me:19900029
- Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
- Gürbüz S. & Şahin F. (2018). Sosyal Bilimlerde Araştırma Yöntemleri (Beşinci Baskı). Ankara:Seçkin Akademik ve Mesleki Yayınlar
- Huang, M.-H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
- Jones, J. L. (2006). Robots at the tipping point: the road to iRobot Roomba. IEEE Robotics & Automation Magazine, 13(1), 76–78. https://doi.org/10.1109/MRA.2006.1598056
- Karagöz, Y. (2016). SPSS 23 ve AMOS 23 uygulamalı istatistiksel analizler. Nobel Akademik Yayıncılık.
- Klíma, I. (2001). Karel Čapek: Life and work. Catbird Press. Retrieved from https://books.google.com/books?hl=tr&lr=&id=l4i09o0QkCgC&oi=fnd&pg=PR7&ots=M3Qs400OVd&sig=qQCN6kWT-NrKZDuuBsW-mKQ81z8
- Kuo, C.-M., Li-Cheng, C., & Tseng, C.-Y. (2017). Investigating an innovative service with hospitality robots. International Journal of Contemporary Hospitality Management, 29(5), 1305–1321. https://doi.org/10.1108/IJCHM-08-2015-0414
- Lemon, O., & Pietquin, O. (2012). Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces. Springer New York. Retrieved from https://books.google.com.tr/books?id=d9VmX_zZuSAC
- Lin, H., Chi, O. H., & Gursoy, D. (2020). Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services. Journal of Hospitality Marketing & Management, 29(5), 530–549. https://doi.org/10.1080/19368623.2020.1685053
- Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51. https://doi.org/10.1016/j.ijhm.2019.01.005
- McCarthy, J. (2007). What is artificial intelligence? Basic Questions. Computer Science Department, Stanford University. https://stanford.io/2lSo373.
- McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. E. (2006). A Proposal for the Dartmouth Summer Research Project on Artifcial Intelligence. AI Magazine, 27.
- Meydan, C. H. & Şeşen, H. (2015). Yapısal eşitlik modellemesi AMOS uygulamaları. Detay Yayıncılık. 2.Baskı
- Oistad, B. C., Sembroski, C. E., Gates, K. A., Krupp, M. M., Fraune, M. R., & Šabanović, S. (2016). Colleague or Tool? Interactivity Increases Positive Perceptions of and Willingness to Interactwith a Robotic Co-worker BT -SocialRobotics (A. Agah, J.-J. Cabibihan, A. M. Howard, M. A. Salichs, & H. He, eds.). Cham: Springer International Publishing.
- Oudeyer, P. Y., Kaplan, F.,& Hafner, V. V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11(2), 265–286.
- Parisi, G. I., Kemker, R., Part, J. L., Kanan, C.,& Wermter, S. (2019). Continual life long learning with neural networks: A review. Neural Networks, 113, 54–71.
- Pinillos, R., Marcos, S., Feliz, R., Zalama, E., & Gómez-García-Bermejo, J. (2016). Long-term assessment of a service robot in a hotel environment. Robotics and Autonomous Systems, 79, 40–57. https://doi.org/10.1016/j.robot.2016.01.014
- Prentice, C.,&Nguyen, M. (2020). Engaging and retaining customers with AI and employee service. Journal of Retailing and Consumer Services, 56, 102186. https://doi.org/10.1016/j.jretconser.2020.102186
- Rashotte, L. (2007). Social Influence. The Blackwell Encyclopedia of Sociology. https://doi.org/doi:10.1002/9781405165518.wbeoss154
- Sentker, A. (2015). Mist an Bauer: Muss Aufs Feld. Werackert, erzeugt Daten. Und werdiesezulesenversteht, bekommtdiedickeren Kartoffeln. Die Zeit, 44, 35-36.
- Severinson-Eklundh, K., Green, A., & Hüttenrauch, H. (2003). Social and collaborative aspects of interaction with a service robot. Robotics and Autonomous Systems, 42(3), 223–234. https://doi.org/10.1016/S0921-8890(02)00377-9
- Shi, X., Jason, S., & Mark, A. (2020). How will service robots redefine leadership in hotel management? A Delphi approach. International Journal of Contemporary Hospitality Management, 32(6), 2217–2237. https://doi.org/10.1108/IJCHM-05-2019-0505
- Solomon, R. C., & Stone, L. D. (2002). On “positive” and “negative” emotions. Journal for the Theory of Social Behaviour, 32(4).
- Tabak, A., Kızıloğlu, A. & Türköz, T. (2013). Örtülü liderlik ölçeği geliştirme çalışması. Middle East Technical University Studies in Development, 40(1), 97-138.
- Taylor, S. & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6, 144-176.
- Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal Computing: Toward a Conceptual Model of Utilization. MIS Quaterly, 126-143. https://doi.org/10.2307/249443
- Triandis, H. C. (1980). Values, Attitudes, and Interpersonal Behavior. Nebraska Symposium on Motivation. Nebraska Symposium on Motivation, 27, 195-259.
- Triebel, R., Arras, K., Alami, R., Beyer, L., Breuers, S., Chatila, R., &Zhang, L. (2016). SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports BT - Fieldand Service Robotics: Results of the 10th International Conference (D. S. Wettergreen& T. D. Barfoot, Eds.). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-27702-8_40
- Turing, A. M. (2009). Computing Machinery and Intelligence BT –Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer (R. Epstein, G. Roberts, & G. Beber, Eds.). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-1-4020-6710-5_3
- Tussyadiah, I. (2020). A review of research in to automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism. Annals of Tourism Research, 81, 102883. https://doi.org/10.1016/j.annals.2020.102883
- VanDoorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2016). Domo Arigato Mr. Roboto: Emergence of Automated Social Presence in Organizational Frontlines and Customers’ Service Experiences. Journal of Service Research, 20(1), 43–58. https://doi.org/10.1177/1094670516679272
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
- Venkatesh, V., Thong, J. Y. L., & 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
- Waytz, A., Cacioppo, J., & Epley, N. (2010). Who Sees Human?: The Stability and Importance of Individual Differences in Anthropomorphism. Perspectives on Psychological Science, 5(3), 219–232. https://doi.org/10.1177/1745691610369336
- Widen, S. C., & Russell, J. A. (2010). Descriptive and prescriptive definitions of emotion. Emotion Review, 2(4), 377–378.
- You, S., & Robert Jr., L. P. (2018). Human-Robot Similarity and Willingness to Work with a Robotic Co-Worker. Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 251–260. New York, NY, USA: Associationfor Computing Machinery. https://doi.org/10.1145/3171221.3171281
- İnternet Kaynakları
- Akın Robotics (2020). Erişim adresi: https://www.akinrobotics.com/tr/
- Asimo (2020). Erişim adresi: https://honda.com.tr/asimo
- Beyond Robotics (2020). Erişim adresi: http://beyondrobotics.com.tr/
- Icub (2020). Erişim adresi: https://icub.iit.it/
- International Organization for Standardization (2012). Erişim adresi: https://www.iso.org/obp/ui/#iso:std:iso:8373:ed-2:v1:en
- Kuka (2020). Erişim adresi: https://www.welt.de/139426894b
- Mercedes (2020). Erişim adresi: https://www.mercedes-benz.com/en/innovation/autonomous/the-new-s-class-intelligent-drive-next-level/
- Roboy (2020). Erişim adresi: https://roboy.org/
- Sophia (2020). Erişim adresi: https://www.hansonrobotics.com/sophia/
- The Room Service Robots (2015). Erişim adresi: https://www.pcmag.com/news/the-room-service-robots-have-arrived