TÜKETİCİLERİN AKILLI ROBOT SÜPÜRGELERE YÖNELİK TUTUM VE ROBOT SÜPÜRGELERİ KULLANMA NİYETİ ÜZERİNE BİR ARAŞTIRMA

Öz

Yapay zekâ ve robot teknolojilerindeki ilerlemeler sonucunda ortaya çıkan ve evlerde yaygın olarak kullanılan akıllı robot süpürgeler, kullanıcıların ilgisini çekmektedir. Fakat, bu teknolojilere yönelik tüketici yaklaşımlarını inceleyen nicel araştırmalar literatürde sınırlıdır. Bu çalışma, akıllı robot süpürgelerin tüketiciler tarafından kabul edilip edilmediğini ve bu teknolojinin hangi yönlerinin tüketicilerin ilgisini çektiğini nicel yöntemlerle belirlemeyi amaçlamaktadır. Araştırma verileri, çevrim içi anket yöntemi ve kolayda örnekleme yoluyla toplanmış, analizlerde Smart PLS paket programı ve yapısal eşitlik modellemesi kullanılmıştır. Araştırma sonuçları, algılanan eğlence, algılanan fayda ve algılanan temizliğin, kullanmaya yönelik tutumu anlamlı ve pozitif bir şekilde etkilediğini, kullanmaya yönelik tutumun ise gelecekte kullanma niyetini anlamlı ve pozitif yönde etkilediğini göstermektedir. Bununla birlikte, algılanan kullanım kolaylığının kullanmaya yönelik tutum üzerinde anlamlı bir etkisi bulunamamıştır. Ayrıca çoklu grup analizleri, algılanan eğlence, fayda ve temizliğin kullanmaya yönelik tutum üzerindeki etkilerinde cinsiyet ve medeni durum değişkenlerine göre anlamlı farklılıklar olduğunu ortaya koymuştur. Araştırma sonuçları, akıllı robot süpürgeleri piyasaya süren işletmelerin, tüketicilerin algıladığı eğlence, fayda ve temizlik boyutlarına daha fazla önem vermeleri gerektiğini ve cinsiyet ile medeni durum farklılıklarının tüketici algılarında değişikliklere yol açtığını göstermektedir.

Anahtar Kelimeler

• Akıllı Robot Süpürge
• Yapay Zekâ
• Teknoloji Kabul Modeli

RESEARCH ON CONSUMERS ATTITUDE AND INTENTION TO USE SMART ROBOT VACUUM CLEANER

Abstract

Smart robot vacuum cleaners, which have emerged due to advances in artificial intelligence and robot technologies and are widely used in homes, attract users’ attention. However, the literature has limited quantitative research examining consumer attitudes towards these technologies. This study aims to determine whether consumers accept smart robot vacuum cleaners and which aspects of this technology are of interest to consumers through quantitative methods. The research data were collected through an online survey method and convenience sampling, and the Smart PLS package program and structural equation modelling were used in the analysis. The study results show that perceived enjoyment, usefulness, and cleanliness significantly and positively affect attitudes towards use, and attitudes towards use significantly and positively affect future intention to use. However, perceived ease of use did not significantly affect attitude towards use. In addition, multiple group analyses revealed significant differences in the effects of perceived fun, utility and cleanliness on attitude towards use according to gender and marital status variables. The research results show that businesses that launch smart robot vacuum cleaners should pay more attention to the dimensions of entertainment, utility and cleanliness perceived by consumers and that gender and marital status differences lead to changes in consumer perceptions.

Keywords

• Smart Robot Vacuum Cleaner
• Artificial Intelligence
• Technology Acceptance Model

Kaynakça

1. Aiolfi, S. (2023). How shopping habits change with artificial intelligence: smart speakers’ usage intention. International Journal of Retail & Distribution Management, 51(9/10), 1288-1312.
2. Alali, H. (2020). Efawateercom as an electronic bill payment service: The case of jordan. International Journal on Advanced Science, Engineering and Information Technology, 10(4), 1430-1435. https:// doi.org/10.18517/ijaseit.10.4.12028
3. Ali, F., Amin, M. & Cobanoglu, C. (2016). An integrated model of service experience, emotions, satisfaction, and price acceptance: An empirical analysis in the Chinese hospitality industry. Journal of Hospitality Marketing & Management, 25(4), 449-475.
4. Alshammari, R., Parkes, M., & Adlington, R. (2018). Factors influencing Saudi Arabian preparatory year students’ skills and attitudes in the use of mobile devices in learning English as a Foreign Language. International Journal of Research Studies in Educational Technology, 7(1), 9-27.
5. Arık, A. & Zeren, D. (2023). Tüketicilerin sanal asistanları kullanma niyetinde etkili faktörlerin araştırılması. Alanya Akademik Bakış, 7(1), 27-49.
6. Asafa, T. B., Afonja, T. M., Olaniyan, E. A. & Alade, H. O. (2018). Development of a vacuum cleaner robot. Alexandria Engineering Journal, 57(4), 2911-2920.
7. Avcı, İ. (2022). Akıllı evlerde IoT teknolojileri ve siber güvenlik. Avrupa Bilim ve Teknoloji Dergisi, 34, 226-233.
8. Bendel, O. (2017). Ladybird: The animal-friendly robot vacuum cleaner. In 2017 AAAI Spring Symposium Series.

9. Berg A.J. (1994) A gendered socio-technical construction: the smart house. In: Cockburn C, Furst Dilic R (Eds.) Bringing Technology Home: Gender and Technology in Changing Europe. Buckingham: Open University Press, pp. 165–180.
10. Bergman, J. & Lind, J. (2019). Robot vacuum cleaner (Uzmanlık Tezi). https://www.diva-portal.org/ adresinden erişilmiştir. Erişim tarihi: 10.09.2023
11. Bilici, F., & Özdemir, E. (2019). Tüketicilerin Artırılmış Gerçeklik Teknolojilerini Kullanmaya Yönelik Tutum ve Niyeti Üzerine Bir Araştırma. Business & Management Studies: An International Journal, 7(5), 2011-2033.
12. Burman, V., & Kumar, R. (2020). IoT-Enabled automatic floor cleaning robot. In International Conference on Recent Advancements in Mechanical Engineering (pp. 707–721). Singapore: Springer Nature Singapore.
13. Caleb-Solly, P., Dogramadzi, S., Ellender, D., Fear, T. & Heuvel, H. V. D. (2014,). A mixed-method approach to evoke creative and holistic thinking about robots in a home environment. In Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction (pp. 374-381).
14. Cao, X. & Liu, L. (2020). Use of smart devices: A survey, some research issues, and challenges. In 2020 International Conference on Culture-oriented Science & Technology (ICCST) (pp. 378-382). IEEE.
15. Chawla, D. & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India – an empirical study. International Journal of Bank Marketing, 37(7), 1590–1618.
16. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. 2nd Edition, Lawrence Erlbaum Associates, USA
17. Cremin, C. (2020, Nisan 20). History of robot vacuum cleaner – 2024 Review. Ferguson action. https:// fergusonaction.com/history-of-robot-vacuum-cleaner sayfasından erişilmiştir. Erişim Tarihi: 11.09.2023
18. Çelik, Z., & Aydın, İ. (2021). Tüketicilerin fiziksel mağaza alışverişlerinde artırılmış gerçeklik uygulaması olarak akıllı ayna kullanmasının davranışsal niyete etkisi. İşletme Araştırmaları Dergisi, 13(4), 3573-3585.
19. Çıbuk, M., & Maraşlı, F. (2015). Rfid teknolojisi ve kullanım alanları. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 2, 249-275
20. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results, (Doktora Tezi). https://dspace.mit.edu/handle/1721.1/15192 sayfasından erişilmiştir. Erişim tarihi: 10.09.2023
21. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
22. De Carvalho, R. N., Vidal, H. A., Vieira, P., & Ribeiro, M. I. (1997). Complete coverage path planning and guidance for cleaning robots. In ISIE’97 Proceeding of the IEEE International Symposium on Industrial Electronics (Vol. 2, pp. 677-682). IEEE.
23. Diewald, S. (2011). Implementation of a development toolchain for the simulation of mobile devices using the ros middleware (Doktora Tezi). https://www.researchgate.net/profile/Stefan-Diewald sayfasından erişilmiştir. Erişim Tarihi: 09.09.2023
24. Duane, A., O’Reilly, P., & Andreev, P. (2012). Realising m-payments: modelling consumers’ willingness to m-pay using smartphones. Behavior and Information Technology, 33(4), 318–334. Esen, F. (2020). A technology acceptance-based approach to digital banking use. Bilişim Teknolojileri Dergisi, 13(4), 401-410.
25. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388.
26. Galceran, E., & Carreras, M. (2013). A survey on coverage path planning for robotics. Robotics and Autonomous Systems, 61(12), 1258-1276.
27. Garson, G. D. (2016). Partial least squares. Regression and structural equation models. Statistical Publishing Associates.
28. Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389-400.
29. Gezdirici, A. (2012). Kendi kendini şarj edebilen süpürge robotu (Doktora Tezi). https://polen.itu. edu.tr/items/1959249d-0894-4804-85da-a8e865a3e2e2 sayfasından erişilmiştir. Erişim Tarihi: 09.09.2023
30. Gheitasi, A., Almaliky, A., & Albaqawi, N. (2015, Kasım). Development of an automatic cleaning system for photovoltaic plants. In 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) (pp. 1-4). IEEE.
31. Goebert, C., & Greenhalgh, G. P. (2020). A new reality: fan perceptions of augmented reality readiness in sport marketing. Comput. Hum. Behav. 106, 106231. doi: 10.1016/j.chb.2019.106231
32. Guerreiro, J., Loureiro, S., & Ribeiro, C. (2022). Advertising acceptance via smart speakers. Spanish Journal of Marketing- Esic, 26(3), 286-308.
33. Gunawan, F., Ali, M., & Nugroho, A. (2019). Analysis of the effects of perceived ease of use and perceived usefulness on consumer attitude and their impacts on purchase decision on pt Tokopedia in Jabodetabek. European Journal of Business Management and Research, 4(5), 1-5
34. Gunawan, F., Ali, M. M., & Nugroho, A. (2019). Analysis of the effects of perceived ease of use and perceived usefulness on consumer attitude and their impacts on purchase decision on pt tokopedia in jabodetabek. European Journal of Business and Management Research, 4(5). https://doi. org/10.24018/ejbmr.2019.4.5.100
35. Gürgöze, G., & Türkoğlu, İ. (2019). Kullanım alanlarına göre robot sistemlerinin sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 31(1), 53-66.
36. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (pp. 1–734). Eng: Pearson Education Limited.
37. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017) A Primer on partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks, CA.
38. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) PLS-SEM: Indeed a silver bullet, Journal of Marketing Theory and Practice, 19(2), 139-152.
39. Hasan, K. M., & Reza, K. J. (2014). Path planning algorithm development for autonomous vacuum cleaner robots. In 2014 International Conference on Informatics, Electronics & Vision (ICIEV) (pp. 1-6). IEEE.
40. Hayat, N., Mamun, A., Salameh, A., Ali, M., Hussain, W., & Zainol, N. (2022). Exploring the smart wearable payment device adoption intention: Using the symmetrical and asymmetrical analysis methods. Frontiers in Psychology, 13.
41. Hendrickson, A. R., Massey, P. D., & Cronan, T. P. (1993). On the test-retest reliability of perceived usefulness and perceived ease of use scales. MIS Quarterly, 17(2), 227–230.
42. Hendriks, B., Meerbeek, B., Boess, S., Pauws, S., & Sonneveld, M. (2011). Robot vacuum cleaner personality and behavior. International Journal of Social Robotics, 3, 187-195.
43. Henseler, J. (2017). Partial least squares path modeling; Advanced Methods for Modeling Markets (ss. 361-381). Springer.
44. Hill, R. J., Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Contemporary Sociology, 6(2), 244.
45. Hofner, C., & Schmidt, G. (1995). Path planning and guidance techniques for an autonomous mobile cleaning robot. Robotics and Autonomous Systems, 14(2-3), 199-212.
46. Elektrikli süpürgelerin icadı ve tarihçesi (2023). http://www.vacuumcleanerhistory.com/ adresinden erişilmiştir. Erişim Tarihi: 11.09.2023.
47. Robot süpürgelerin icadı (2023). http://www.vacuumcleanerhistory.com/vacuum-cleaner-development/ history-of-robotic-vacuum-cleaner/ adresinden erişilmiştir, Erişim Tarihi: 11.09.2023.
48. Huang, F., & Lai, Y. (2016). The acceptance of smart wearable devices through health cognitive. Matec Web of Conferences, 71, 05005.
49. Hwang, C., Chung, T., & Sanders, E. (2016). Attitudes and purchase intentions for smart clothing. Clothing and Textiles Research Journal, 34(3), 207-222.
50. Ichhpujani, P., & Thakur, S. (2018). Smart resources in ophthalmology: Applications and Social Networking. Springer.
51. İslamoğlu, A. H., & Alnıaçık, Ü. (2014). Sosyal bilimlerde araştırma yöntemleri. İstanbul: Beta Yayınevi. Karagül, B. İ., Şeker, M., & Aykut, C. (2021). Investigating students’ digital literacy levels during online education due to covid-19 pandemic. Sustainability, 13(21), 11878. https://doi.org/10.3390/ su132111878
52. Kayabaşı, A., Er, İ., Demirağ, F., & Erçin Yurcu, M. (2022). Hizmet Robotlarına Yönelik Algıların Kullanım Niyetine Etkisi. Tüketici ve Tüketim Araştırmaları Dergisi, 14(2), 433-469.
53. Kim, H., Lee, H., Chung, S., & Kim, C. (2007). User-centered approach to path planning of cleaning robots: Analyzing user’s cleaning behavior. In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, 373–380.
54. Kim, J., Ariza-Montes, A., & Han, H. (2021). The role of expected benefits towards smart hotels in shaping customer behavior: Comparison by age and gender. Sustainability, 13(4), 1698.
55. Kurulgan, M., & Özata, F. (2010). Elektronik kütüphane hizmetlerinin öğretim elemanları tarafından benimsenmesinde etkili olan faktörler: Anadolu Üniversitesi öğretim elemanları üzerinde bir araştırma. Bilgi Dünyası, 11(2), 243-262.
56. Ma, W. W. K., Andersson, R., & Streith, K. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer-Assisted Learning, 21(6), 387-395.
57. Mamonov, S., & Benbunan-Fich, R. (2020). Unlocking the smart home: Exploring key factors affecting the smart lock adoption intention. Information Technology & People, 34(2), 835-861. https://doi. org/10.1108/itp-07-2019-0357
58. Mattern, F. (2003). From smart devices to smart everyday objects. In Proceedings of Smart Objects Conference, 15–16.
59. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12-12.
60. Nor Aziati A.H., Ang, S. H., Abdullah, N. H., Ngadiman, Y., & Ahmad, M. F. (2016). The adoption of 4g mobile network services in klang valley. Proceedings of 2016 the 6th International Workshop on Computer Science and Engineering. https://doi.org/10.18178/wcse.2016.06.042
61. Oreck, S. (2016). A brief history of the vacuum cleaner, Medium https://medium.com/@ShopOreck/abrief- history-of-the-vacuum-cleaner-8d48bef97d79 sayfasından erişilmiştir. Erişim Tarihi: 11.09.2023.
62. Özbek, V., & Tahtacı, İ. (2022). Tüketicilerin kombi tercihlerini etkileyen faktörler: Teknoloji kabul modeli çerçevesinde bir araştırma. Business and Economics Research Journal, 13(1), 159-178.
63. Özcan, H., Çelik, Ş., & Özer, A. (2019). Bireysel müşterilerin mobil bankacılık kullanım niyetini etkileyen faktörler. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 149- 180.
64. Özsoy, S. K. (2010). Human-service robot interaction: An ethnographic study on Roomba vacuum cleaner in the domestic environment in Italy (Yüksek Lisans Tezi). https://polen.itu.edu.tr sayfasından erişilmiştir. Erişim Tarihi: 05.09.2023
65. Palm, G. (1986). Warren McCulloch and Walter Pitts: A logical calculus of the ideas immanent in nervous activity. In Brain Theory: Proceedings of the First Trieste Meeting on Brain Theory, October 1–4, 1984, 229–230. Springer Berlin Heidelberg.
66. Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research, Journal of Operations Management, 30 (6), 467-480.
67. Pookulangara, S., Wen, H., & Josiam, B. M. (2022). Consumer attitudes toward ordering from cloud kitchens: a gender and marital status perspective. International Journal of Contemporary Hospitality Management, 35(5), 1859-1879. https://doi.org/10.1108/ijchm-03-2022-0310
68. Prassler, E., Ritter, A., Schaeffer, C., & Fiorini, P. (2000). A short history of cleaning robots. Autonomous Robots, 9, 211–226.
69. Ray, P. P. (2018). A survey on Internet of things architectures. Journal of King Saud University-Computer and Information Sciences, 30(3), 291–319.
70. Rese, A., Schreiber, S., & Baier, D. (2014). Technology acceptance modeling of augmented reality at the point of sale: Can surveys be replaced by an analysis of online reviews? Journal of Retailing and Consumer Services, 21(5), 869-876.
71. Rogers, E. M., & Shoemaker, F. F. (1971). Communication of innovations; A Cross-Cultural Approach. Rosenblueth, A., & Wiener, N. (1945). The role of models in science. Philosophy of Science, 12(4), 316- 321.
72. Rouibah, K., Abbas, H., & Rouibah, S. (2011). Factors affecting camera mobile phone adoption before e-shopping in the Arab world. Technology in Society, 33(3-4), 271-283.
73. Sarstedt, M., Ringle, C. M., & Hair J. F. (2017). Partial least squares structural equation modeling, Handbook of Market Research, Springer International Publishing, 1–40.
74. Silverio-Fernández, M., Renukappa, S., & Suresh, S. (2018). What is a smart device? - A conceptualisation within the paradigm of the Internet of things. Visualization in Engineering, 6(1), 1-10.
75. Srinoi, P., & Thermsuk, S. (2017). Recent patents on floor cleaning robot applications and new development prospects in future. Kasem Bundit Engineering Journal, 7(1), 223-236.
76. Stojkoska, B. L. R., & Trivodaliev, K. V. (2017). A review of Internet of Things for smart home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464.
77. Strengers, Y., Gram-Hanssen, K., Dahlgren, K., & Aagaard, L. K. (2022). Energy, emerging technologies and gender in homes. Buildings and Cities, 3(1), 842-853. https://doi.org/10.5334/bc.273
78. Sürücü, Ç., Ülker, M., & Hassan, A. (2017). Restoranlarda tablet mönü kullanımının tekrar ziyaret etme niyeti üzerindeki etkisi. Journal of Tourism and Gastronomy Studies, 6(3), 178-196.
79. Tiryaki, İ., & Önder, L. G. (2022). Tüketicilerin akıllı giyilebilir nesnelerin kullanımına yönelik davranış niyetlerinin genişletilmiş teknoloji kabul modeli aracılığıyla incelenmesi. İşletme Araştırmaları Dergisi, 14(1), 182-202.
80. Tobin, S., Jayabalasingham, B., Huggett, S., & De Kleijn, M. (2019). A brief historical overview of artificial intelligence research. Information Services & Use, 39(4), 291–296.
81. Tor-Kadıoğlu, C. (2020). Tüketicilerin akıllı robot süpürge kullanımı üzerine bir araştırma. Third Sector Social Economic Review, 55(4), 2515-2537.
82. Turing, A. M. (1950). Computing machinery and intelligence. Mind, LIX (236), 433–460.
83. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
84. Waktola, T. U., & Fekadu, K. (2021). Adoption of coffee shade agroforestry technology and shade tree management in gobu seyo district, east wollega, oromia. Advances in Agriculture, 2021, 1-13. https://doi.org/10.1155/2021/8574214
85. Weizenbaum, J. (1966). ELIZA— A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.
86. Weng, Y. (2021). An empirical study on the influence of the mobile information system on sports and fitness on the choice of tourist destinations. Mobile Inform. Syst. 2021. doi: 10.1155/2021/5303590
87. Yakoubi, M. A., & Laskri, M. T. (2016). The path planning of cleaner robot for coverage region using genetic algorithms. Journal of Innovation in Digital Ecosystems, 3(1), 37-43.
88. Yang, S. X., & Luo, C. (2004). A neural network approach to complete coverage path planning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), 718-724.
89. Yılmaz, A. (2020) Yapay zekâ Eğitim Kitabı. Kodlab Yayın Dağıtım.
90. Zhong, L., Zhang, X., Rong, J., Chan, H. K., Xiao, J., & Kong, H. (2020). Construction and empirical research on acceptance model of service robots applied in hotel industry. Industrial Management & Data Systems, 121(6), 1325–1352.