AKILLI SAAT TEKNOLOJİSİNE YÖNELİK TÜKETİCİ ALGILARI: MEMNUNİYET VE TEKRAR SATIN ALMA NİYETİ ÜZERİNE BİR ARAŞTIRMA

Yıl 2024, Cilt: 25 Sayı: 3, 267 - 300, 29.09.2024

Fatih Bilici

https://doi.org/10.53443/anadoluibfd.1410970

Öz

Giyilebilir akıllı cihazlardan biri olan akıllı saatler, dokunmatik ekran, sensörler ve kablosuz bağlantı gibi fonksiyonlara sahiptirler. Akıllı saatler, sağlık hizmetlerinde ve spor alanında sıklıkla kullanılmakta ve kalp atış hızı, uyku düzeni, fiziksel aktivite izleme gibi işlevlere sahiptirler. Akıllı saatler, sosyal hayatta moda ve güvenlik amaçlı kullanılabilmektedirler. Bu araştırmada son yıllarda popüler hale gelen akıllı saat teknolojisinin tüketiciler üzerindeki etkileri incelenmiş ve özellikle memnuniyetin tekrar satın alma niyeti üzerindeki etkisi vurgulanmıştır. Araştırma sonuçlarına göre araştırmanın boyutlarından olan algılanan zevkin, memnuniyet üzerindeki etkisi istatistiksel olarak anlamlı değildir. Algılanan kullanım kolaylığının ve algılanan kullanışlılığının memnuniyet üzerindeki etkisi ise istatistiksel olarak anlamlı ve pozitif yönlü bulunmuştur. Kullanıcıların, ürünün kullanım kolaylığı ve kullanışlılığına dair algıları, memnuniyet düzeyini belirleyici bir faktör olarak öne çıkmaktadır. Son olarak, memnuniyetin satın alma niyeti üzerindeki etkisi güçlü, istatistiksel olarak anlamlı ve pozitif bulunmuştur.

Anahtar Kelimeler

Akıllı Saat, Tüketici Memnuniyeti, Yapısal Eşitlik Modeli

Etik Beyan

Bu çalışma bilimsel araştırma ve yayın etiği kurallarına uygun olarak hazırlanmıştır.

CONSUMERS PERCEPTIONS OF SMARTWATCHES: A RESEARCH ON SATISFACTION AND REPURCHASE INTENTION

Öz

Smartwatches are wearable smart devices and have functions such as touch screens, sensors, and wireless connections. Smartwatches are frequently used in healthcare and sports and have functions such as monitoring heart rate, sleep patterns, and physical activity. It can also be used in social life for fashion and security purposes. In this research, the effects of smartwatch technology, which has become popular in recent years, on consumers are examined, especially the relationship between satisfaction and repurchase intention. According to the research results, the relationship between perceived enjoyment and satisfaction is not statistically significant. The effect of perceived ease of use and perceived usefulness on satisfaction was statistically significant. Users' perceptions of the product's ease of use and usefulness stand out as a determining factor in the level of satisfaction. Finally, the effect of satisfaction on purchase intention was found to be substantial and statistically significant.

Anahtar Kelimeler

Smartwatch, Consumer Satisfaction, Structural Equation Modeling

Kaynakça

• Aaron, P. L., & Bonni, S. (2020). An evaluation of wearable technological advancement in medical practices. J. Med. Image Comput, 58-65. doi: 10.46532/jmic.20200902
• Ada, A., & Aksoy, R. (2020). Giyilebilir teknolojik ürünlerde tüketicilerin algıladıkları risklerin farklılaşması: Akıllı saat kullanıcılarına dönük bir araştırma. Herkes için Spor ve Rekreasyon Dergisi, 2(1), 50-61.
• Adekunle, S., & Ejechi, J. (2018). Modeling repurchase intention among smartphone users in Nigeria. Journal of Modelling in Management, 13(4), 794-814. doi: 10.1108/jm2-12-2017-0138
• Akbar, N., & Nurmahdi, D. (2019). Analysis of perceived usefulness, perceived ease of use, and service quality on user satisfaction in using snap communication application in Ignatius Slamet Riyadh Karawang Elementary School. Saudi Journal of Business and Management Studies, 04(11), 849-855. doi: 10.36348/sjbms.2019.v04i11.005
• Ali, F., Amin, M., & Çobanoğlu, 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.
• Almuraqab, N. (2021). Determinants that influence consumers' intention to purchase smartwatches in the UAE: a case of university students. Advances in Science Technology and Engineering Systems Journal, 6(1), 1249-1256. doi: 10.25046/aj0601142
• Amhag, L. (2016). Mobile technologies for student centered learning in a distance higher education program. J. Holland (Ed.) Wearable technology and mobile innovations for next-generation education (s. 184-199) içinde. IGI Global. doi: 10.4018/978-1-5225-0069-8
• Ananda, A., Mugiono, M., & Hussein, A. (2021). The influence of store image on repurchase intention: The mediation role of perceived value and customer satisfaction. International Journal of Research in Business and Social Science, 10(4), 17-27. Doi: 10.20525/ijrbs.v10i4.1209
• Attallah, B., & Agure, Z. (2019). Evaluating the affordances of wearable technology in education. International Journal of Grid and Utility Computing, 10(1), 22. Doi: 10.1504/ijguc.2019.097227
• Ayunda, O., Yasri, Y., & Evanita, S. (2019). Effect of perceived ease of use, service quality, price fairness, and trust on go-car user satisfaction in padang city. In 2nd Padang International Conference on Education, Economics, Business and Accounting (PICEEBA-2 2018) (ss. 586-592). Atlantis Press. Doi: 10.2991/piceeba2-18.2019.77
• Benbunan-Fich, R. (2020). User satisfaction with wearables. AIS Transactions on Human-Computer Interaction, 12(1), 1-27. doi: 10.17705/1thci.00126
• Bianchi, C., Tuzovic, S., & Kuppelwieser, V. (2022). Investigating the drivers of wearable technology adoption for healthcare in South America. Information Technology and People, 36(2), 916-939. doi: 10.1108/itp-01-2021-0049
• Bieber, G., Haescher, M., & Vahl, M. (2013, May). Sensor requirements for activity recognition on smart watches. In Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments (ss. 1-6). doi: 10.1145/2504335.2504407
• Casaló, L., Flavián, C., & Ibáñez‐Sánchez, S. (2017). Antecedents of consumer intention to follow and recommend an Instagram account. Online Information Review, 41(7), 1046-1063. doi: 10.1108/oir-09-2016-0253
• Chau, K., Lam, M., Cheung, M., Tso, E., Flint, S., Broom, D., & Lee, K. (2019). Smart technology for healthcare: Exploring the antecedents of adoption intention of healthcare wearable technology. Health Psychology Research, 7(1). doi: 10.4081/hpr.2019.8099
• Cheung, M., Chau, K., Lam, M., Tse, G., Ho, K., Flint, S., & Lee, K. (2019). Examining consumers' adoption of wearable healthcare technology: the role of health attributes. International Journal of Environmental Research and Public Health, 16(13), 2257. doi: 10.3390/ijerph16132257
• Ching, K., & Singh, M. (2016). Wearable technology devices security and privacy vulnerability analysis. International Journal of Network Security & Its Applications, 8(3), 19–30. doi: 10.5121/ijnsa.2016.8302
• Choi, Y., Hua, W., Chen, M., & Yang, F. (2021). Sustainable determinants influencing habit formation among mobile short-video platform users. Sustainability, 13(6), 3216. doi: 10.3390/su13063216
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2. Baskı). USA: Lawrence Erlbaum Associates.
• Connor, E. (2016). Wearable technology: Smartwatches to Google Glass for libraries. Journal of the Medical Library Association, 104(2), 180–180.
• Daga, D., Saikia, H., Bhattacharjee, S., & Saha, B. (2021). A conceptual design approach for women safety through better communication design. International Journal of Electronics Engineering and Applications, IX(III), s. 01. doi: 10.30696/ijeea.ix.iii.2021.01-11
• Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Mis Quarterly, 13(3), 319. doi: 10.2307/249008
• Davis, F., Bagozzi, R., & Warshaw, P. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. doi: 10.1111/j.1559-1816.1992.tb00945.x
• Dinh-Le, C., Chuang, R., Chokshi, S., & Mann, D. (2019). Wearable health technology and electronic health record integration: Scoping review and future directions. Jmir Mhealth and Uhealth, 7(9), e12861. doi: 10.2196/12861
• Dockx, K., Alcock, L., Bekkers, E., Ginis, P., Reelick, M., Pelosin, E., & Nieuwboer, A. (2017). Fall-prone older people's attitudes towards the use of virtual reality technology for fall prevention. Gerontology, 63(6), 590-598. doi: 10.1159/000479085
• Domingos, C., Costa, P., Santos, N., & Pêgo, J. (2022). Usability, acceptability, and satisfaction of a wearable activity tracker in older adults: An observational study in a real-life context in northern Portugal. Journal of Medical Internet Research, 24(1), e26652. doi: 10.2196/26652
• Düking, P., Hotho, A., Holmberg, H., Fuss, F., & Sperlich, B. (2016). Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies. Frontiers in Physiology, 7. doi: 10.3389/fphys.2016.00071
• Düking, P., Stammel, C., Sperlich, B., Sutehall, S., Muniz-Pardos, B., Lima, G., & Pitsiladis, Y. (2018). Necessary steps to accelerate the integration of wearable sensors into recreation and competitive sports. Current Sports Medicine Reports, 17(6), 178-182. doi: 10.1249/jsr.0000000000000495
• Echegaray, F. (2016). Consumers' reactions to product obsolescence in emerging markets: The case of Brazil. Journal of Cleaner Production, 134, 191-203.
• Elbeltagi, I., & Agag, G. (2016). E-retailing ethics and its impact on customer satisfaction and repurchase intention. Internet Research, 26(1), 288-310. doi: 10.1108/intr-10-2014-0244
• Eldrandaly, K. A., Naguib, S. M., & Hassan, M. M. (2015). A model for measuring geographic information systems success. Journal of Geographic Information System, 7(4), 328.
• El-Masri, M., Al-Yafi, K., & Kamal, M. (2022). A task-technology-identity fit model of smartwatch utilization and user satisfaction: a hybrid sem-neural network approach. Information Systems Frontiers. doi: 10.1007/s10796-022-10256-7
• Esquivel, K., Gillespie, J., Kelly, D., Condell, J., Davies, R., McHugh, C., & Nordström, A. (2023). Factors influencing continued wearable device use in older adult populations: Quantitative study. Jmir Aging, 6. doi: 10.2196/36807
• Fernández‐Caramés, T., & Fraga‐Lamas, P. (2018). Towards the internet-of-smart-clothing: a review on iot wearables and garments for creating intelligent connected e-textiles. Electronics, 7(12), 405. doi: 10.3390/electronics7120405
• 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.
• Fu, Z., Shu, K., & Zhang, H. (2019). Ping pong motion recognition based on smartwatch. 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019) (ss. 617-625). Atlantis Press. doi: 10.2991/icmeit-19.2019.99
• Gao, S., Zhang, X., & Peng, S. (2016). Understanding the adoption of smart wearable devices to assist healthcare in China. Social Media: The Good, the Bad, and the Ugly: 15th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2016, Swansea, UK, September 13–15, 2016, [Proceedings 15] (ss. 280-291). Springer International Publishing. doi: 10.1007/978-3-319-45234-0_26
• Gao, Y., Li, H., & Yan, L. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723. doi: 10.1108/imds-03-2015-0087
• Garson, G. D. (2016). Partial least squares. Regression and structural equation models. Statistical Publishing Associates.
• Ginting, Y., Miran, I., & Yusriadi, Y. (2023). Repurchase intention of e-commerce customers in Indonesia: An overview of the effect of e-service quality, e-word of mouth, customer trust, and customer satisfaction mediation. International Journal of Data and Network Science, 7(1), 329-340. doi: 10.5267/j.ijdns.2022.10.001
• Gordon, L. (2018). Assessment of smartwatches for management of non-communicable diseases in the aging population: a systematic review. Geriatrics, 3(3), 56. doi: 10.3390/geriatrics3030056
• Gökbulut Özdemir, Ö., & Açıkgöz, O. E. (2023). Yenilikçi tüketim ve yenilikçi tüketime etki eden faktörler: Akıllı saat üzerine bir araştırma. Electronic Journal of Social Sciences, 22(87).
• Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., Ray, S., & Ray, S. (2021). An introduction to structural equation modeling. Partial least squares structural equation modeling (PLS-SEM) using R: A workbook, 1-29.
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis. Eng: Pearson Education Limited.
• Hair, J., Risher, J., Sarstedt, M., & Ringle, C. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. doi: 10.1108/ebr-11-2018-0203
• Hair, J.F., Hult, G.T.M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on partial least squares structural equation modeling (PLS-SEM) (2. Baskı). Sage, Thousand Oaks, CA.
• Hamdan, F., Rahim, N., Othman, A., Mahmud, A., Ibrahim, N., & Zainuddin, A. (2022). The determinants of service quality and customer satisfaction in Malaysian e-hailing services. International Journal of Academic Research in Economics and Management Sciences, 11(2). doi: 10.6007/ijarems/v11-i2/14055
• Hanif, M., Akram, T., Shahzad, A., Khan, M., Tariq, U., Choi, J., & Zulfiqar, Z. (2022). Smart devices-based multisensory approach for complex human activity recognition. Computers Materials & Continua, 70(2), 3221-3234. doi: 10.32604/cmc.2022.019815
• Henseler, J. (2017). Partial least squares path modeling; advanced methods for modeling markets. P. S. H. Leeflang, J.E. Wieringa, T.H.A. Bijmolt & K. H. Pauwels (Ed.). Advanced Methods for Modeling Markets (ss. 361-38) içinde. Springer.
• Higgins J.P, Cerra A. Z., & Apicella M. L. (2022). Smartwatches: Capturing our hearts. Journal of Cardiothoracic Surgery and Therapeutics, 6(1). doi: 10.36959/582/441 Idoga, P., & Adamu, A. (2020). Understanding smart wearable sensors technology: Impact on human health and fitness. Journal of Applied Sciences and Environmental Management, 24(7), 1261-1265. doi: 10.4314/jasem.v24i7.19
• Ioannou, A., & Tussyadiah, I. (2021). Privacy and surveillance attitudes during health crises: acceptance of surveillance and privacy protection behaviors. Technology in Society, s. 67, 101774. doi: 10.1016/j.techsoc.2021.101774
• Jayathilaka, W., Qi, K., Qin, Y., Chinnappan, A., Serrano‐Garcia, W., Baskar, C., & Ramakrishna, S. (2018). Significance of nanomaterials in wearables: a review on wearable actuators and sensors. Advanced Materials, 31(7). doi: 10.1002/adma.201805921
• Jia-li, Z. (2023). Smart sports outward bound training assistant system based on wsns. International Journal of Distributed Systems and Technologies, 14(2), 1–11. doi: 10.4018/ijdst.317939
• Jiang, H., Chen, X., Zhang, S., Zhang, X., Kong, W., & Zhang, T. (2015). Software for wearable devices: challenges and opportunities. 015 IEEE 39th Annual Computer Software and Applications Conference, Taichung, Taiwan (ss. 592-597). doi: 10.1109/compsac.2015.269
• Khan, S., Ling, Y., & Shahzad, S. (2015). An empirical study of perceived factors affecting customer satisfaction to repurchase intention in online stores in China. Journal of Service Science and Management, 08(03), 291-305. doi: 10.4236/jssm.2015.83032
• Khmeleva, G. A., Kurnikova, M. V., Nedelka, E., & Tóth, B. I. (2022). Determinants of sustainable cross-border cooperation: A structural model for the Hungarian context using the PLS-SEM methodology. Sustainability, 14(2), 893.
• Khokhar, A. (2019). What decides women entrepreneurship in India? Journal of Entrepreneurship and Innovation in Emerging Economies, 5(2), 180–197. doi: 10.1177/2393957519862465
• Kim, K., & Shin, D. (2015). An acceptance model for smartwatches. Internet Research, 25(4), 527–541. doi: 10.1108/intr-05-2014-0126
• Kim, K. H., Jeon, M. Y., Lee, J. Y., Jeong, J. H., & Jeong, G. M. (2014). A study on the app development using sensor signals from smartphone and smart watch. Advanced Science and Technology Letters, 62, 66-69. doi: 10.14257/astl.2014.62.17
• Kim, M., Chung, N., Lee, C., & Preis, M. (2013). Motivations and use context in mobile tourism shopping: applying contingency and task-technology fit theories. International Journal of Tourism Research, 17(1), 13-24. doi: 10.1002/jtr.1957
• Kim, S., Yoon, J., & Kim, C. (2019). Exploring the influence of the level of technology and expected functions in product semantic. Proceedings of the Design Society International Conference on Engineering Design, 1(1), 3881-3890. doi: 10.1017/dsi.2019.395
• Kim, T., & Chiu, W. (2019). Consumer acceptance of sports wearable technology: the role of technology readiness. International Journal of Sports Marketing and Sponsorship, 20(1), 109-126. doi: 10.1108/ijsms-06-2017-0050
• Kock, N., & Hadaya, P. (2016). Minimum sample size estimation in pls‐sem: the inverse square root and gamma‐exponential methods. Information Systems Journal, 28(1), 227-261. doi: 10.1111/isj.12131
• Komninos, A., & Dunlop, M. (2014). Text input on a smartwatch. Ieee Pervasive Computing, 13(4), 50-58. doi: 10.1109/mprv.2014.77
• Lee, L. (2015). Risk perceptions for wearable devices. doi: 10.48550/arxiv.1504.05694
• Lee, L., Lee, J., Egelman, S., & Wagner, D. (2016). Information disclosure concerns in the age of wearable computing. doi: 10.14722/usec.2016.23006
• Lee, Y., Gan, C., & Liew, T. (2022). Do e-wallets trigger impulse purchases? An analysis of Malaysian gen-y and gen-z consumers. Journal of Marketing Analytics, 11(2), 244-261. doi: 10.1057/s41270-022-00164-9
• Levay, K., Freese, J., & Druckman, J. (2016). The demographic and political composition of Mechanical Turk samples. Sage Open, 6(1). doi: 10.1177/2158244016636433
• Li, H., & Chundi, H. (2019). Using tactile and multimodal notifications on smartwatches in clinical settings. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, 8(1), 161-163. doi: 10.1177/2327857919081039
• Ling, Y., An, T., Yap, L. W., Zhu, B., Gong, S., & Cheng, W. (2020). Disruptive, soft, wearable sensors. Advanced Materials, 32(18). doi: 10.1002/adma.201904664
• Liu, Y., Gan, Y., Song, Y., & Liu, J. (2021). What influences the perceived trust of a voice-enabled smart home system: an empirical study. Sensors, 21(6), 2037. doi: 10.3390/s21062037
• Lorwongtragool, P., Sowade, E., Watthanawisuth, N., Baumann, R., & Kerdcharoen, T. (2014). A novel wearable electronic nose for healthcare based on flexible printed chemical sensor array. Sensors, 14(10), 19700-19712. doi: 10.3390/s141019700
• Lu, T., Fu, C., Huei‐Ming, M., Fang, C., & Turner, A. (2016). Healthcare applications of smartwatches. Applied Clinical Informatics, 07(03), 850-869. doi: 10.4338/aci-2016-03-r-0042
• Luque-Vílchez, M., Mesa-Pérez, E., Husillos, J., & Larrinaga, C. (2019). The influence of pro-environmental managers' personal values on environmental disclosure. Sustainability Accounting Management and Policy Journal, 10(1), 41-61. doi: 10.1108/sampj-01-2018-0016
• Magno, F., Cassia, F., & Ringle, C. (2022). A brief review of partial least squares structural equation modeling (PLS-SEM) use in quality management studies. The TQM Journal. doi: 10.1108/tqm-06-2022-0197
• Marangoz, M., & Aydın, A. E. (2018). Tüketicilerin giyilebilir teknoloji ürünlerini benimsemesinde etkili olan faktörler: akıllı saatler üzerine bir araştırma, Pazarlama Teorileri ve Uygulamaları Dergisi, 4(1), 1-20.
• Mardonova, M., & Choi, Y. (2018). Review of wearable device technology and its applications to the mining industry. Energies, 11(3), 547. doi: 10.3390/en11030547
• Marinković, V., & Kalinić, Z. (2017). Antecedents of customer satisfaction in mobile commerce. Online Information Review, 41(2), 138-154. doi: 10.1108/oir-11-2015-0364
• Mueller, R. O., & Hancock, G. R. (2008). Best practices in structural equation modeling. In J. W. Osborne (Ed.), Best practices in quantitative methods (s. 488-508) içinde. Thousand Oaks, CA: Sage Publications.
• Nadya, N. (2020). Effect of experiential marketing on customer loyalty: The role of customer satisfaction as a mediator. Journal of Business Studies and Management Review, 4(1), 27–33. doi: 10.22437/jbsmr.v4i1.11909
• Noh, M., Lee, K., Kim, S., & Garrison, G. (2013). Effects of collectivism on actual scommerce use and the moderating effect of price consciousness. Journal of Electronic Commerce Research, 14(3), 244-260
• Oemar, H., Prasetyaningsih, E., Bakar, S., Djamaludin, D., & Septiani, A. (2023). Awareness and intention to register halal certification of micro and small-scale food enterprises. F1000research, 11, 170. doi: 10.12688/f1000research.75968.3
• Otieno, F., Gachohi, J., Gikuma-Njuru, P., Kariuki, P., Oyas, H., Canfield, S., & Blackburn, J. (2021). Modeling the potential future distribution of anthrax outbreaks under multiple climate change scenarios for Kenya. International Journal of Environmental Research and Public Health, 18(8), 4176. doi: 10.3390/ijerph18084176
• Özdemir, E., Kılıç, S., & Çakırer, M., A. (2019). Satış promosyonları ve kredi kartı kullanımının tüketicilerin plansız satın alma ve satın alma sonrası pişmanlık davranışı üzerindeki etkileri. İşletme Araştırmaları Dergisi, 11(4), 2528-2545.
• Paidi, Z., Haliza, H., Zain, N., & Othman, M. (2022). Development of a notification system using a wearable concept that allows hearing-impaired person to detect that their doorbell has been pressed using the nrf24l01 wireless module. International Journal of Academic Research in Business and Social Sciences, 12(12). doi: 10.6007/ijarbss/v12-i12/16069
• Panayiotou, A. G., & Protopapadakis, E. D. (2021). Ethical issues concerning the use of data from commercially available wearable sensors in children. Preprints, 202105.0026/v1. 1-9. doi: 10.20944/preprints202105.0026.v1
• Park, K., & Koh, C. (2014). Effect of change management capability in real-time environment: An information orientation perspective in supply chain management. Behavior and Information Technology, 34(1), 94-104. doi: 10.1080/0144929x.2014.945961
• Peng, C., Xi, N., Hong, Z., & Hamari, J. (2022). Acceptance of wearable technology: A meta-analysis. Proceedings of the 55th Hawaii International Conference on System Sciences, 5101-5110. doi: 10.24251/hicss.2022.621
• Pham, C., Nguyen-Thai, S., Huy, T., Tran, S., Vu, H., Tran, T., & Le, T. (2020). Senscapsnet: Deep neural network for non-obtrusive sensing-based human activity recognition. Ieee Access, 8, 86934-86946. doi: 10.1109/access.2020.2991731
• Piwek, L., Ellis, D., Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: Promises and barriers. Plos Medicine, 13(2), e1001953. doi: 10.1371/journal.pmed.1001953
• Prybutok, G., Ta, A., Liu, X., & Prybutok, V. (2020). An integrated structural equation model of eHealth behavioral intention. International Journal of Healthcare Information Systems and Informatics, 15(1), 20-39. doi: 10.4018/ijhisi.2020010102
• Puri, A., Kim, B., Nguyen, O., Stolee, P., Tung, J., & Lee, J. (2017). User acceptance of wrist-worn activity trackers among community-dwelling older adults: Mixed method study. Jmir Mhealth and Uhealth, 5(11), e173. doi: 10.2196/mhealth.8211
• Puriwat, W., & Tripopsakul, S. (2021). Explaining an adoption and continuance intention to use contactless payment technologies during the COVID-19 pandemic., Emerging Science Journal 5(1), 85-95. doi: 10.28991/esj-2021-01260
• Rakate, A., & Gaikwad, H. (2022). The impact of social media in Islamic studies on consumer behavior towards smartwatches with special reference to the city of Islampur. Religio Education, 2(2), 80-91. doi: 10.17509/re.v2i2.51151
• Ramadhani, F., & Ilona, D. (2018). Determinants of web-user satisfaction: Using technology acceptance model. Matec Web of Conferences, 248, 05009. doi: 10.1051/matecconf/201824805009
• 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.
• Ringle, C. M., Wende, S. & Becker, J.-M. (2022). SmartPLS 4. Oststeinbek: SmartPLS GmbH, http://www.smartpls.com adresinden erişildi.
• Sanders, J., Loveday, A., Pearson, N., Edwardson, C., Yates, T., Biddle, S., & Esliger, D. (2016). Devices for self-monitoring sedentary time or physical activity: a scoping review. Journal of Medical Internet Research, 18(5), e90. doi: 10.2196/jmir.5373
• Sağbaş, E. A., & Ballı, S. (2017). Akıllı saat algılayıcıları ile insan hareketlerinin sınıflandırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21(3), 980-990.
• Savalei, V. (2019). A comparison of several approaches for controlling measurement error in small samples. Psychological Methods, 24(3), 352–370. doi: 10.1037/met0000181
• Sayah, S., & Sarkis, R. (2017, November). Design and analysis of conformal antennas for smartwatch. In 2017 Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL) (ss. 1889-1894). IEEE. doi: 10.1109/piers-fall.2017.8293446
• Shafique, M., Khurshid, M., Rahman, H., Khanna, A., Gupta, D., & Rodrigues, J. (2019). The role of wearable technologies in supply chain collaboration: a case of pharmaceutical industry. Ieee Access, p. 7, 49014–49026. doi: 10.1109/access.2019.2909400
• Shahzad, M., Paracha, K., Naseer, S., Ahmad, S., Malik, M., Farhan, M., & Sharif, A. (2021). An artificial magnetic conductor-backed compact wearable antenna for smartwatch IoT applications. Electronics, 10(23), 2908. doi: 10.3390/electronics10232908
• Shao, C. (2020, January). An empirical study on the identification of driving factors of satisfaction with online learning based on TAM. In 5th international conference on economics, management, law and education (EMLE 2019) (ss. 1067-1073). Atlantis Press. doi: 10.2991/aebmr.k.191225.205
• Siahaan, A., & Thiodore, J. (2022). Analysis influence of consumer behavior to purchase organic foods in Jakarta. In 6th International Conference of Food, Agriculture, and Natural Resource (IC-FANRES 2021) (ss. 57-65). Atlantis Press. doi: 10.2991/absr.k.220101.009
• Sivo, S., Saunders, C., Chang, Q., & Jiang, J. (2006). How low should you go? Low response rates and the validity of inference in I.S. questionnaire research. Journal of the Association for Information Systems, 351- 414
• Sonnenschein, S., Stites, M., & Ross, A. (2021). Home learning environments for young children in the U.S. during COVID-19. Early Education and Development, 32(6), 794-811. doi: 10.1080/10409289.2021.1943282
• Stradolini, F., Lavalle, E., De Micheli, G., Motto Ros, P., Demarchi, D., & Carrara, S. (2017). Paradigm-shifting players for IoT: Smart-watches for intensive care monitoring. In P. Perego, E. Mugellini, & P. S. Rodriguez (Eds.), Wireless mobile communication and healthcare: 6th International conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings 6 (ss. 71-78). Springer International Publishing. doi: 10.1007/978-3-319-58877-3_9
• Taber, K. S. (2018). The use of Cronbach's alpha when developing and reporting research instruments in science education. Research in Science Education, p. 48, 1273–1296.
• Tırpan, E., & Semiz, T. (2022). Bibliometric analysis of wearable technology studies in the healthcare industry. Ajit-E Online Academic Journal of Information Technology, 13(50), 107-122. doi: 10.5824/ajite.2022.03.001.x
• 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.
• To, A., & Trinh, T. (2021). Understanding behavioral intention to use mobile wallets in Vietnam: extending the tam model with trust and enjoyment. Cogent Business & Management, 8(1). doi: 10.1080/23311975.2021.1891661
• Turan, A. İ., & Çoban, S. D. (2023). Dijital yaşam teknolojileri bağlamında akıllı saat kullanıcılarının deneyimleri üzerine nitel bir çalışma. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 13(1), 531-551.
• Tsai, H. T., & Huang, H. C. (2007). Determinants of e-repurchase intentions: An integrative model of quadruple retention drivers. Information & Management, 44(3), 231-239.
• Varma, U., & Bharadwaj, A. (2016). Home automation using a smartwatch. International Journal of Computer Applications, 153(11), 5-8. doi: 10.5120/ijca2016912067
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
• Wang, W., Hsieh, J., & Song, B. (2012). Understanding user satisfaction with instant messaging: An empirical survey study. International Journal of Human-Computer Interaction, 28(3), 153-162. doi: 10.1080/10447318.2011.568893
• Watt, A., Swainston, K., & Wilson, G. (2019). Health professionals' attitudes to patients' use of wearable technology. Digital Health, p. 5. doi: 10.1177/2055207619845544
• Westbrook, R. A., & Oliver, R. L. (1981). Developing better measures of consumer satisfaction: Some preliminary results. ACR North American Advances.
• Wile, D., Ranawaya, R., & Kiss, Z. (2014). Smartwatch accelerometry for analysis and diagnosis of tremor. Journal of Neuroscience Methods, 230, 1-4. doi: 10.1016/j.jneumeth.2014.04.021
• Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: an extended perspective of perceived value. Telematics and Informatics, 33(2), 256-269. doi: 10.1016/j.tele.2015.08.007
• Yıldız, B., & Kütahyalı, D. N. (2021). Tüketici yenilikçiliğinin akıllı saat kullanmaya devam etme niyeti üzerindeki etkisinde hedonik ve faydacı değerin aracı rolü. Alanya Akademik Bakış, 5(2), 705-726.
• Yuan, K. (2023). Comments on the article "Marketing or methodology? Exposing the fallacies of PLS with simple demonstrations" and PLS-SEM in general. European Journal of Marketing, 57(6), 1618–1625. doi: 10.1108/ejm-07-2021-0472
• Zboja, J., & Voorhees, C. (2006). The impact of brand trust and satisfaction on retailer repurchase intentions. Journal of Services Marketing, 20(6), 381-390. doi: 10.1108/08876040610691275
• Zhang, W., Leng, X., & Liu, S. (2020). Research on mobile impulse purchase intention from the perspective of system users during COVID-19. Personal and Ubiquitous Computing, 27(3), 665–673. doi: 10.1007/s00779-020-01460-w
• Zhu, Z., Ren, Y., & Duan, P. (2022). Modeling of smart watch and system construction method for the elderly based on big data. Mathematical Problems in Engineering, 2022, 1-9. doi: 10.1155/2022/2606781