Research Article
BibTex RIS Cite

Year 2025, Volume: 9 Issue: 1, 314 - 338, 30.06.2025
https://doi.org/10.26650/acin.1515409

Abstract

References

  • Al-Husamiyah, A. & Al-Bashayreh, M. (2022). A comprehensive acceptance model for smart home services. International Journal of Data and Network Science, 6(1), 45-58. https://doi.org/10.5267/j.ijdns.2021.10.005 google scholar
  • Almusaed, A., Yitmen, İ., & Almssad, A. (2023). Enhancing smart home design with ai models: a case study of living spaces implementation review. Energies, 16(6), 2636. doi:10.3390/en16062636 google scholar
  • Arar, M., Jung, C., Awad, J., & Chohan, A. (2021). Analysis of smart home technology acceptance and preference for the elderly in Dubai, google scholar
  • UAE. Designs, 5(4), 70. doi:10.3390/designs5040070 google scholar
  • Baek, J., Na, S., Lee, H., Jung, H., Lee, E., Jo, M., … & Jang, I. (2022). Implementation of an integrated home internet of things system for vulnerable older adults using a frailty-centered approach. Scientific Reports, 12(1). doi:10.1038/s41598-022-05963-9 google scholar
  • Baudier, P., Ammi, C., & Deboeuf-Rouchon, M. (2020). Smart home: Highly-educated students' acceptance. Technological Forecasting and Social Change, 153. https://doi.org/10.1016/j.techfore.2018.06.043. google scholar
  • Brucks, M., Zeithaml, V. A. & Naylor, G. (2000). Price and brand name as indicators of quality dimensions for durable goods. Journal of the Academy of Marketing Science, 28(3), 359–374. google scholar
  • Büyüköztürk, Ş. (2011). Sosyal Bilimler için Veri Analizi El Kitabı. Pegem Akademi. google scholar
  • Camacho, J., Aguirre, B., Ponce, P., Anthony, B., & Molina, A. (2024). Leveraging artificial intelligence to bolster the energy sector in smart cities: a literature review. Energies, 17(2), 353. doi:10.3390/en17020353 google scholar
  • Cannizzaro, S., Procter, R., Ma, S., & Maple, C. (2020). Trust in the smart home: findings from a nationally representative survey in the uk. Plos One, 15(5), e0231615. https://doi.org/10.1371/journal.pone.0231615 google scholar
  • Chin, C., Wong, W., Cham, T., Thong, J., & Ling, J. (2023). Exploring the usage intention of ai-powered devices in smart homes among millennials and zillennials: the moderating role of trust. Young Consumers Insight and Ideas for Responsible Marketers, 25(1), 1-27. https://doi.org/10.1108/yc-05-2023-1752 google scholar
  • Cho, Y., & Choi, A. (2020). Application of affordance factors for user-centered smart homes: a case study approach. Sustainability, 12(7), 3053. https://doi.org/10.3390/su12073053 google scholar
  • Cyr, D., Head, M., & Ivanov, A. (2006). Design esthetics leading to m-loyalty in mobile commerce. Information & Management, 43(8), 950–963. google scholar
  • Demiris, G., Rantz, M., Aud, M., Marek, K., Tyrer, H., Skubic, M., & Hussam A. (2004). Older adults' attitudes toward and perceptions of "smart home" technologies: a pilot study. Med Inform Internet Med,;29(2), 87-94. google scholar
  • Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-service adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. google scholar
  • Field, A. (2009). Discovering Statistics Using SPSS. SAGE. google scholar
  • Gazieva, L., Aygumov, T., & Zaripova, R. (2023). The role of smart technologies in the development of cost-effective and sustainable energy. E3s Web of Conferences, 451, 01007. doi:10.1051/e3sconf/202345101007 google scholar
  • George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Boston: Allyn and Bacon. google scholar
  • George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson. google scholar
  • Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505 google scholar
  • Gu, W., Bao, P., Hao, W., & Kim, J. (2019). Empirical examination of the intention to continue using smart home services. Sustainability, 11(19), 5213. https://doi.org/10.3390/su11195213. google scholar
  • Hassan, Q., Sameen, A., Salman, H., Al‐Jiboory, A., & Jaszczur, M. (2023). The role of renewable energy and artificial intelligence toward environmental sustainability and net zero. https://doi.org/10.21203/rs.3.rs-2970234/v1 google scholar
  • He, F., Wu, Y., Jiao, Y., Chen, K., Xie, J., Tuersun, Y., … & Chen, J. (2022). Chinese adult segmentation according to health skills and analysis of their use for smart home: a cross-sectional national survey. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-08126-8 google scholar
  • Hong, S.-J., & Tam, K. Y. (2006). Understanding the adoption of multipurpose information appliances: The case of mobile data services. Information Systems Research, 17(2), 162–179. google scholar
  • Hsu, C.-L., & Lin, J. C.-C. (2016). Empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior, 62, 516–527. google scholar
  • Hubert, M., Blut, M., Brock, C., Zhang, R., Koch, V., & Riedl, R. (2019). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing, 53(6), 1073-1098. https://doi.org/10.1108/ejm-12-2016-0794 google scholar
  • Hubert, M., Carugati, A., Brock, C., & Obel, B. (2020). Take it personally—the role of consumers’ perceived value of personalization on cross-category use in a smart home ecosystem. Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020. doi:10.24251/hicss.2020.144. google scholar
  • Jacobsson, A., Boldt, M., & Carlsson, B. (2016). A risk analysis of a smart home automation system. Future Generation Computer Systems, 56, 719-733. https://doi.org/10.1016/j.future.2015.09.003 google scholar
  • Jain, P., Pendyala, S., Etu, E., Zhang, Z., Shah, M., Larot, J., … & Huang, G. (2024). Exploring attitudes toward smart home technology through focus groups: comparing older adults with and without health conditions. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 68(1), 3-8. doi: 10.1177/10711813241276479 google scholar
  • Katuk, N., Ku‐Mahamud, K., Zakaria, N., & Maarof, M. (2018). Implementation and recent progress in cloud-based smart home automation systems., 71-77. https://doi.org/10.1109/iscaie.2018.8405447 google scholar
  • Khan, M., Seo, J., & Kim, D. (2020). Toward energy efficient home automation: a deep learning approach. Sensors, 20(24), 7187. https:// doi.org/10.3390/s20247187 google scholar
  • Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126. google scholar
  • Lashkari, B., Chen, Y., & Musıı́lek, P. (2019). Energy management for smart homes—state of the art. Applied Sciences, 9(17), 3459. doi:10.3390/app9173459 google scholar
  • Lau, J., Lau, J., & Harun, Z. (2022). Factors influencing the intention to adopt smart home technology among households in johor bahru, malaysia. International Journal of Academic Research in Business and Social Sciences, 12(12). https://doi.org/10.6007/ijarbss/v12-i12/16016 google scholar
  • Li, J. (2022). Research on the impact of smart home service innovation on consumers' willingness to use from the perspective of privacy concerns. BCP Business & Management, 29, 204-217. doi:10.54691/bcpbm.v29i.2272 google scholar
  • Li, W., Yiğitcanlar, T., Erol, I., & Liu, A. (2021). Motivations, barriers and risks of smart home adoption: from systematic literature review to conceptual framework. Energy Research & Social Science, 80, 102211. doi:10.1016/j.erss.2021.102211 google scholar
  • Lin, K.-Y., & Lu, H.-P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152–1161. google scholar
  • 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. https://doi.org/10.3390/s21062037 google scholar
  • Luor, T., Lu, H., Yu, H., & Lu, Y. (2015). Exploring the critical quality attributes and models of smart homes. Maturitas, 82(4), 377-386. https://doi.org/10.1016/j.maturitas.2015.07.025 google scholar
  • Mamonov, S., & Benbunan‐Fich, R. (2020). Unlocking the smart home: exploring key factors affecting the smart lock adoption intention. Information Technology and People, 34(2), 835-861. https://doi.org/10.1108/itp-07-2019-0357 google scholar
  • Marikyan, D., Papagiannidis, S., & Alamanos, E. (2019). Smart home technology acceptance: an empirical investigation., 305-315. https:// doi.org/10.1007/978-3-030-29374-1_25 google scholar
  • Maswadi, K., Ghani, N., & Hamid, S. (2022). Factors influencing the elderly’s behavioral intention to use smart home technologies in Saudi Arabia. Plos One, 17(8), e0272525. https://doi.org/10.1371/journal.pone.0272525 google scholar
  • Maznah, M., Najwa, M., Kamaliah, M., Jeffery, L., Sahithi, A. & Preece, C. (2021). Sustainable townships and sustainable homes: public perceptions. Journal of the Society of Automotive Engineers Malaysia, 5(3), 331-347. https://doi.org/10.56381/jsaem.v5i3.176 google scholar
  • Mennicken, S. & Huang, E. (2012). Hacking the natural habitat: an in-the-wild study of smart homes, their development, and the people who live in them., 143-160. doi:10.1007/978-3-642-31205-2_10 google scholar
  • Nascimento, D., Tortorella, G., & Fettermann, D. (2022). Association between the benefits and barriers perceived by the users in smart home services implementation. Kybernetes, 52(12), 6179-6202. https://doi.org/10.1108/k-02-2022-0232 google scholar
  • Nepomuceno, M. V., Laroche, M., & Richard, M.-O. (2014). How to reduce perceived risk when buying online: The interactions among intangibility, product knowledge, brand familiarity, privacy and security concerns. Journal of Retailing and Consumer Services, 21(4), 619–629. google scholar
  • Nikou, S. (2019). Factors driving the adoption of smart home technology: an empirical assessment. Telematics and Informatics, 45, 101283. https://doi.org/10.1016/j.tele.2019.101283 google scholar
  • Orlov, A., Saxena, A., Mittal, A., Ranjan, R., Singh, B., & Yellanki, V. (2024). User satisfaction and technology adoption in smart homes: a user experience test. Bio Web of Conferences, 86, 01087. https://doi.org/10.1051/bioconf/20248601087 google scholar
  • Paetz, A., Dütschke, E., & Fïchtner, W. (2011). Smart homes as a means to sustainable energy consumption: a study of consumer perceptions. Journal of Consumer Policy, 35(1), 23-41. doi:10.1007/s10603-011-9177-2 google scholar
  • Pal, D., Funilkul, S., Vanijja, V., & Papasratorn, B. (2018). Analyzing the Elderly Users’ Adoption of Smart-Home Services. in IEEE Access, 6, 51238-51252. DOI: 10.1109/ACCESS.2018.2869599 google scholar
  • Patskanick, T., Cerino, L., Ashebir, S., FakhrHosseini, S., D’Ambrosio, L. & Coughlin, J. (2024). Aging in a smart home? the oldest olds’ attitudes toward technology for aging-in-place. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 68(1), 9-12. https://doi.org/10.1177/10711813241260397 google scholar
  • Phan, L. & Kim, T. (2020). Breaking down the compatibility problem in smart homes: a dynamically updatable gateway platform. Sensors, 20(10), 2783. https://doi.org/10.3390/s20102783 google scholar
  • Pliatsikas, P. & Economides, A. (2022). Factors influencing the intention of Greek consumers to use smart home technology. Applied System Innovation, 5(1), 26. https://doi.org/10.3390/asi5010026 google scholar
  • Qiu, M., Najm, E., Sharrock, R., & Traverson, B. (2022). Reinforcement learning-based architectures for dynamic generation of smart home services., 7-14. https://doi.org/10.1109/icmla55696.2022.00010 google scholar
  • Rumsey, D. J. (2016). Statistics for dummies (2nd ed.). Wiley. google scholar
  • Sepasgozar, S., Karimi, R., Farahzadi, L., Moezzi, F., Shirowzhan, S., Ebrahimzadeh, S., … & Aye, L. (2020). A systematic content review of artificial intelligence and the internet of thing applications in the smart home. Applied Sciences, 10(9), 3074. https://doi.org/10. 3390/app10093074 google scholar
  • Sequeiros, H., Oliveira, T., & Thomas, M. (2021). The impact of iot smart home services on psychological well-being. Information Systems Frontiers, 24(3), 1009-1026. doi:10.1007/s10796-021-10118-8 google scholar
  • Șerban, A. & Lytras, M. (2020). Artificial intelligence for the smart renewable energy sector in europe—smart energy infrastructures for next-generation smart cities. IEEE Access, 8, 77364-77377. https://doi.org/10.1109/access.2020.2990123 google scholar
  • Shih, T. (2013). Determinates of consumer adoption attitudes. International Journal of E-Adoption, 5(2), 40-56. https://doi.org/10.4018/ jea.2013040104 google scholar
  • Shin, J., Park, Y., & Lee, D. (2018). Who will be the smart home users? An analysis of adoption and diffusion of smart homes. Technological Forecasting and Social Change, 134, 246-253, https://doi.org/10.1016/j.techfore.2018.06.029. google scholar
  • Singh, M. & Dhablia, A. (2023). Smart home automation using iot: prototyping and integration of home devices. RJCSE, 4(2), 130-143. https://doi.org/10.52710/rjcse.83 google scholar
  • Statista (2022). Smart home device ownership in Turkey in 2022. https://www.statista.com/forecasts/1003015/smart-home-device-ownership-in-Turkey. AD: 25.02.2023. google scholar
  • Stecuła, K., Wolniak, R., & Grebski, W. (2023). Ai-driven urban energy solutions—from individuals to society: a review. Energies, 16(24), 7988. https://doi.org/10.3390/en16247988 google scholar
  • Strzelecki, A., Kolny, B., & Kucia, M. (2024). Smart homes as catalysts for sustainable consumption: a digital economy perspective. Sustainability, 16(11), 4676. doi:10.3390/su16114676 google scholar
  • Tang, R. & Inoue, Y. (2021). Services on platform ecosystems in the smart home 2.0 era: elements influencing consumers’ value perception of smart home products. Sensors, 21(21), 7391. https://doi.org/10.3390/s21217391 google scholar
  • Tarhouni, M. & Aloui, I. (2024). Toward smart home automation based on containerization. https://doi.org/10.21203/rs.3.rs-4761820/v1 google scholar
  • Teng, W. & Lu, H.-P. (2009). Consumer adoption of PDA phones in Taiwan. International Journal of Mobile Communications, 8(1), 1–20. google scholar
  • Tiersen, F., Batey, P., Harrison, M., Naar, L., Serban, A., Daniels, S., … & Calvo, R. (2021). Smart home sensing and monitoring in households with dementia: a user-centered design approach. Jmir Aging, 4(3), e27047. https://doi.org/10.2196/27047 google scholar
  • Tuna, F. (2016). Sosyal Bilimler İçin İstatistik. Pegem Akademi, google scholar
  • Tural, E., Lu, D., & Cole, D. (2021). Safely and actively aging in place: older adults’ attitudes and intentions toward smart home technologies. Gerontology and Geriatric Medicine, 7. https://doi.org/10.1177/23337214211017340 google scholar
  • TÜİK. (2022). TÜİK Veri Portalı. https://data.tuik.gov.tr. Last Access: 01.01.2025. google scholar
  • Türkyılmaz, S. & Altındağ, E. (2022). Analysis of smart home systems in the context of the internet of things in terms of consumer experience. International Review of Management and Marketing, 12(1), 19-31. doi:10.32479/irmm.12709 google scholar
  • Uddin, M., Kim, T., & Kim, J. (2010). Video-based indoor human gait recognition using depth imaging and hidden markov model: a smart system for smart home. Indoor and Built Environment, 20(1), 120-128. https://doi.org/10.1177/1420326x10391140 google scholar
  • Van der Heijden, H. (2004). User acceptance of the hedonic information systems. MIS Quarterly, 28(4), 695–704. google scholar
  • Van Dijk, J. (2020). The Digital Divide: Bridging the Gap. Cambridge University Press. google scholar
  • Wang, Y., Zhang, R., Zhang, X., & Zhang, Y. (2023). Privacy risk assessment of a smart home system based on the stpa–fmea method. Sensors, 23(10), 4664. https://doi.org/10.3390/s23104664 google scholar
  • Wei, N., Baharudin, A., Hussein, L., & Hilmi, M. (2019). Factors affecting user’s intention to adopt smart home in malaysia. International Journal of Interactive Mobile Technologies (IJIM), 13(12), 39. https://doi.org/10.3991/ijim.v13i12.11083 google scholar
  • Wei, W., Gong, X., Li, J., Tian, K., & Xing, K. (2023). A study on community older people’s willingness to use the smart home—an extended technology acceptance model with intergenerational relationships. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh. 2023.1139667 google scholar
  • Wong, J. & Leung, J. (2016). Modeling factors influencing the adoption of smart-home technologies. Facilities, 34(13/14), 906-923. https:// doi.org/10.1108/f-05-2016-0048 google scholar
  • Wright, D. & Shank, D. (2019). Smart home technology diffusion in a living laboratory. Journal of Technical Writing and Communication, 50(1), 56-90. https://doi.org/10.1177/0047281619847205 google scholar
  • Yaldaie, A., Porras, J., & Drögehorn, O. (2023). This state of home automation: a systematic literature review. International Journal of Hybrid Innovation Technologies, 3(1), 23-46. https://doi.org/10.21742/ijhit.2653-309x.2022.2.1.03 google scholar
  • Yang, H., Lee, H. & Zo, H. (2017). User acceptance of smart home services: an extension of the theory of planned behavior. Industrial Management & Data Systems, 117(1), 68-89. https://doi.org/10.1108/IMDS-01-2016-0017 google scholar
  • Yang, Y., Liu, Y., Li, H. & Yu, B. (2015). Understanding the perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253–269. google scholar
  • Yazıcıoğlu, Y., & Erdoğan, S. (2014). SPSS Uygulamalı Bilimsel Araştırma Yöntemleri. Detay Yayıncılık. google scholar
  • Yu, J., Lee, H., Ha, I. & Zo, H. (2017). User acceptance of media tablets: An empirical examination of perceived value. Telematics and Informatics, 34(4), 206–223. google scholar
  • Zhang, N., Wang, S., & Li, H. (2024). Improving user satisfaction by analyzing users’ subjective cognitive types in smart home systems. Universal Access in the Information Society. doi:10.1007/s10209-024-01105-2 google scholar
  • Zhang, Q., Li, M., & Wu, Y. (2020). Smart home for elderly care: development and challenges in China. BMC Geriatrics, 20(1). https://doi. org/10.1186/s12877-020-01737-y google scholar
  • Zhou, C., Qian, Y., & Kaner, J. (2024). A study on the smart home use intention of elderly consumers based on technology acceptance models. Plos One, 19(3), e0300574. https://doi.org/10.1371/journal.pone.0300574 google scholar
  • Ziamou, P. L. & Ratneshwar, S. (2002). Promoting consumer adoption of high-technology products: Is more information always better? Journal of Consumer Psychology, 12(4), 341–351. google scholar

Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective

Year 2025, Volume: 9 Issue: 1, 314 - 338, 30.06.2025
https://doi.org/10.26650/acin.1515409

Abstract

The purpose of this research is to determine the factors influencing consumers’ adoption of smart home systems. Within this context, the relationships between factors such as perceived benefit, perceived risk, functionality, and price and their effects on the adoption of smart home systems were investigated. Additionally, whether there were significant differences in the mentioned factors based on gender, age group, education level, marital status, and income level was investigated. The population of the study consists of individuals living in Turkey who use smart home systems, while the sample comprises 556 individuals selected from this population. The data for the study were collected through a survey and evaluated using quantitative analysis methods. The statistical analyses were conducted using the SPSS 25 software, applying descriptive statistical methods, Pearson correlation analysis, independent samples t-test, and one-way variance analysis. The analyses reveal that consumers’ overall levels of adoption of smart home systems are slightly above average. However, it is observed that the first three factors significantly influencing the adoption of smart home systems are price, perceived benefit, and function ality, respectively. Furthermore, strong and very strong positive and significant relationships were found among all factors related to the adoption of smart homes. Furthermore, it is concluded that the approach to smart homes varies significantly according to gender, age group, education level, marital status, and income level.

References

  • Al-Husamiyah, A. & Al-Bashayreh, M. (2022). A comprehensive acceptance model for smart home services. International Journal of Data and Network Science, 6(1), 45-58. https://doi.org/10.5267/j.ijdns.2021.10.005 google scholar
  • Almusaed, A., Yitmen, İ., & Almssad, A. (2023). Enhancing smart home design with ai models: a case study of living spaces implementation review. Energies, 16(6), 2636. doi:10.3390/en16062636 google scholar
  • Arar, M., Jung, C., Awad, J., & Chohan, A. (2021). Analysis of smart home technology acceptance and preference for the elderly in Dubai, google scholar
  • UAE. Designs, 5(4), 70. doi:10.3390/designs5040070 google scholar
  • Baek, J., Na, S., Lee, H., Jung, H., Lee, E., Jo, M., … & Jang, I. (2022). Implementation of an integrated home internet of things system for vulnerable older adults using a frailty-centered approach. Scientific Reports, 12(1). doi:10.1038/s41598-022-05963-9 google scholar
  • Baudier, P., Ammi, C., & Deboeuf-Rouchon, M. (2020). Smart home: Highly-educated students' acceptance. Technological Forecasting and Social Change, 153. https://doi.org/10.1016/j.techfore.2018.06.043. google scholar
  • Brucks, M., Zeithaml, V. A. & Naylor, G. (2000). Price and brand name as indicators of quality dimensions for durable goods. Journal of the Academy of Marketing Science, 28(3), 359–374. google scholar
  • Büyüköztürk, Ş. (2011). Sosyal Bilimler için Veri Analizi El Kitabı. Pegem Akademi. google scholar
  • Camacho, J., Aguirre, B., Ponce, P., Anthony, B., & Molina, A. (2024). Leveraging artificial intelligence to bolster the energy sector in smart cities: a literature review. Energies, 17(2), 353. doi:10.3390/en17020353 google scholar
  • Cannizzaro, S., Procter, R., Ma, S., & Maple, C. (2020). Trust in the smart home: findings from a nationally representative survey in the uk. Plos One, 15(5), e0231615. https://doi.org/10.1371/journal.pone.0231615 google scholar
  • Chin, C., Wong, W., Cham, T., Thong, J., & Ling, J. (2023). Exploring the usage intention of ai-powered devices in smart homes among millennials and zillennials: the moderating role of trust. Young Consumers Insight and Ideas for Responsible Marketers, 25(1), 1-27. https://doi.org/10.1108/yc-05-2023-1752 google scholar
  • Cho, Y., & Choi, A. (2020). Application of affordance factors for user-centered smart homes: a case study approach. Sustainability, 12(7), 3053. https://doi.org/10.3390/su12073053 google scholar
  • Cyr, D., Head, M., & Ivanov, A. (2006). Design esthetics leading to m-loyalty in mobile commerce. Information & Management, 43(8), 950–963. google scholar
  • Demiris, G., Rantz, M., Aud, M., Marek, K., Tyrer, H., Skubic, M., & Hussam A. (2004). Older adults' attitudes toward and perceptions of "smart home" technologies: a pilot study. Med Inform Internet Med,;29(2), 87-94. google scholar
  • Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-service adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. google scholar
  • Field, A. (2009). Discovering Statistics Using SPSS. SAGE. google scholar
  • Gazieva, L., Aygumov, T., & Zaripova, R. (2023). The role of smart technologies in the development of cost-effective and sustainable energy. E3s Web of Conferences, 451, 01007. doi:10.1051/e3sconf/202345101007 google scholar
  • George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Boston: Allyn and Bacon. google scholar
  • George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson. google scholar
  • Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505 google scholar
  • Gu, W., Bao, P., Hao, W., & Kim, J. (2019). Empirical examination of the intention to continue using smart home services. Sustainability, 11(19), 5213. https://doi.org/10.3390/su11195213. google scholar
  • Hassan, Q., Sameen, A., Salman, H., Al‐Jiboory, A., & Jaszczur, M. (2023). The role of renewable energy and artificial intelligence toward environmental sustainability and net zero. https://doi.org/10.21203/rs.3.rs-2970234/v1 google scholar
  • He, F., Wu, Y., Jiao, Y., Chen, K., Xie, J., Tuersun, Y., … & Chen, J. (2022). Chinese adult segmentation according to health skills and analysis of their use for smart home: a cross-sectional national survey. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-08126-8 google scholar
  • Hong, S.-J., & Tam, K. Y. (2006). Understanding the adoption of multipurpose information appliances: The case of mobile data services. Information Systems Research, 17(2), 162–179. google scholar
  • Hsu, C.-L., & Lin, J. C.-C. (2016). Empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior, 62, 516–527. google scholar
  • Hubert, M., Blut, M., Brock, C., Zhang, R., Koch, V., & Riedl, R. (2019). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing, 53(6), 1073-1098. https://doi.org/10.1108/ejm-12-2016-0794 google scholar
  • Hubert, M., Carugati, A., Brock, C., & Obel, B. (2020). Take it personally—the role of consumers’ perceived value of personalization on cross-category use in a smart home ecosystem. Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020. doi:10.24251/hicss.2020.144. google scholar
  • Jacobsson, A., Boldt, M., & Carlsson, B. (2016). A risk analysis of a smart home automation system. Future Generation Computer Systems, 56, 719-733. https://doi.org/10.1016/j.future.2015.09.003 google scholar
  • Jain, P., Pendyala, S., Etu, E., Zhang, Z., Shah, M., Larot, J., … & Huang, G. (2024). Exploring attitudes toward smart home technology through focus groups: comparing older adults with and without health conditions. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 68(1), 3-8. doi: 10.1177/10711813241276479 google scholar
  • Katuk, N., Ku‐Mahamud, K., Zakaria, N., & Maarof, M. (2018). Implementation and recent progress in cloud-based smart home automation systems., 71-77. https://doi.org/10.1109/iscaie.2018.8405447 google scholar
  • Khan, M., Seo, J., & Kim, D. (2020). Toward energy efficient home automation: a deep learning approach. Sensors, 20(24), 7187. https:// doi.org/10.3390/s20247187 google scholar
  • Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126. google scholar
  • Lashkari, B., Chen, Y., & Musıı́lek, P. (2019). Energy management for smart homes—state of the art. Applied Sciences, 9(17), 3459. doi:10.3390/app9173459 google scholar
  • Lau, J., Lau, J., & Harun, Z. (2022). Factors influencing the intention to adopt smart home technology among households in johor bahru, malaysia. International Journal of Academic Research in Business and Social Sciences, 12(12). https://doi.org/10.6007/ijarbss/v12-i12/16016 google scholar
  • Li, J. (2022). Research on the impact of smart home service innovation on consumers' willingness to use from the perspective of privacy concerns. BCP Business & Management, 29, 204-217. doi:10.54691/bcpbm.v29i.2272 google scholar
  • Li, W., Yiğitcanlar, T., Erol, I., & Liu, A. (2021). Motivations, barriers and risks of smart home adoption: from systematic literature review to conceptual framework. Energy Research & Social Science, 80, 102211. doi:10.1016/j.erss.2021.102211 google scholar
  • Lin, K.-Y., & Lu, H.-P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152–1161. google scholar
  • 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. https://doi.org/10.3390/s21062037 google scholar
  • Luor, T., Lu, H., Yu, H., & Lu, Y. (2015). Exploring the critical quality attributes and models of smart homes. Maturitas, 82(4), 377-386. https://doi.org/10.1016/j.maturitas.2015.07.025 google scholar
  • Mamonov, S., & Benbunan‐Fich, R. (2020). Unlocking the smart home: exploring key factors affecting the smart lock adoption intention. Information Technology and People, 34(2), 835-861. https://doi.org/10.1108/itp-07-2019-0357 google scholar
  • Marikyan, D., Papagiannidis, S., & Alamanos, E. (2019). Smart home technology acceptance: an empirical investigation., 305-315. https:// doi.org/10.1007/978-3-030-29374-1_25 google scholar
  • Maswadi, K., Ghani, N., & Hamid, S. (2022). Factors influencing the elderly’s behavioral intention to use smart home technologies in Saudi Arabia. Plos One, 17(8), e0272525. https://doi.org/10.1371/journal.pone.0272525 google scholar
  • Maznah, M., Najwa, M., Kamaliah, M., Jeffery, L., Sahithi, A. & Preece, C. (2021). Sustainable townships and sustainable homes: public perceptions. Journal of the Society of Automotive Engineers Malaysia, 5(3), 331-347. https://doi.org/10.56381/jsaem.v5i3.176 google scholar
  • Mennicken, S. & Huang, E. (2012). Hacking the natural habitat: an in-the-wild study of smart homes, their development, and the people who live in them., 143-160. doi:10.1007/978-3-642-31205-2_10 google scholar
  • Nascimento, D., Tortorella, G., & Fettermann, D. (2022). Association between the benefits and barriers perceived by the users in smart home services implementation. Kybernetes, 52(12), 6179-6202. https://doi.org/10.1108/k-02-2022-0232 google scholar
  • Nepomuceno, M. V., Laroche, M., & Richard, M.-O. (2014). How to reduce perceived risk when buying online: The interactions among intangibility, product knowledge, brand familiarity, privacy and security concerns. Journal of Retailing and Consumer Services, 21(4), 619–629. google scholar
  • Nikou, S. (2019). Factors driving the adoption of smart home technology: an empirical assessment. Telematics and Informatics, 45, 101283. https://doi.org/10.1016/j.tele.2019.101283 google scholar
  • Orlov, A., Saxena, A., Mittal, A., Ranjan, R., Singh, B., & Yellanki, V. (2024). User satisfaction and technology adoption in smart homes: a user experience test. Bio Web of Conferences, 86, 01087. https://doi.org/10.1051/bioconf/20248601087 google scholar
  • Paetz, A., Dütschke, E., & Fïchtner, W. (2011). Smart homes as a means to sustainable energy consumption: a study of consumer perceptions. Journal of Consumer Policy, 35(1), 23-41. doi:10.1007/s10603-011-9177-2 google scholar
  • Pal, D., Funilkul, S., Vanijja, V., & Papasratorn, B. (2018). Analyzing the Elderly Users’ Adoption of Smart-Home Services. in IEEE Access, 6, 51238-51252. DOI: 10.1109/ACCESS.2018.2869599 google scholar
  • Patskanick, T., Cerino, L., Ashebir, S., FakhrHosseini, S., D’Ambrosio, L. & Coughlin, J. (2024). Aging in a smart home? the oldest olds’ attitudes toward technology for aging-in-place. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 68(1), 9-12. https://doi.org/10.1177/10711813241260397 google scholar
  • Phan, L. & Kim, T. (2020). Breaking down the compatibility problem in smart homes: a dynamically updatable gateway platform. Sensors, 20(10), 2783. https://doi.org/10.3390/s20102783 google scholar
  • Pliatsikas, P. & Economides, A. (2022). Factors influencing the intention of Greek consumers to use smart home technology. Applied System Innovation, 5(1), 26. https://doi.org/10.3390/asi5010026 google scholar
  • Qiu, M., Najm, E., Sharrock, R., & Traverson, B. (2022). Reinforcement learning-based architectures for dynamic generation of smart home services., 7-14. https://doi.org/10.1109/icmla55696.2022.00010 google scholar
  • Rumsey, D. J. (2016). Statistics for dummies (2nd ed.). Wiley. google scholar
  • Sepasgozar, S., Karimi, R., Farahzadi, L., Moezzi, F., Shirowzhan, S., Ebrahimzadeh, S., … & Aye, L. (2020). A systematic content review of artificial intelligence and the internet of thing applications in the smart home. Applied Sciences, 10(9), 3074. https://doi.org/10. 3390/app10093074 google scholar
  • Sequeiros, H., Oliveira, T., & Thomas, M. (2021). The impact of iot smart home services on psychological well-being. Information Systems Frontiers, 24(3), 1009-1026. doi:10.1007/s10796-021-10118-8 google scholar
  • Șerban, A. & Lytras, M. (2020). Artificial intelligence for the smart renewable energy sector in europe—smart energy infrastructures for next-generation smart cities. IEEE Access, 8, 77364-77377. https://doi.org/10.1109/access.2020.2990123 google scholar
  • Shih, T. (2013). Determinates of consumer adoption attitudes. International Journal of E-Adoption, 5(2), 40-56. https://doi.org/10.4018/ jea.2013040104 google scholar
  • Shin, J., Park, Y., & Lee, D. (2018). Who will be the smart home users? An analysis of adoption and diffusion of smart homes. Technological Forecasting and Social Change, 134, 246-253, https://doi.org/10.1016/j.techfore.2018.06.029. google scholar
  • Singh, M. & Dhablia, A. (2023). Smart home automation using iot: prototyping and integration of home devices. RJCSE, 4(2), 130-143. https://doi.org/10.52710/rjcse.83 google scholar
  • Statista (2022). Smart home device ownership in Turkey in 2022. https://www.statista.com/forecasts/1003015/smart-home-device-ownership-in-Turkey. AD: 25.02.2023. google scholar
  • Stecuła, K., Wolniak, R., & Grebski, W. (2023). Ai-driven urban energy solutions—from individuals to society: a review. Energies, 16(24), 7988. https://doi.org/10.3390/en16247988 google scholar
  • Strzelecki, A., Kolny, B., & Kucia, M. (2024). Smart homes as catalysts for sustainable consumption: a digital economy perspective. Sustainability, 16(11), 4676. doi:10.3390/su16114676 google scholar
  • Tang, R. & Inoue, Y. (2021). Services on platform ecosystems in the smart home 2.0 era: elements influencing consumers’ value perception of smart home products. Sensors, 21(21), 7391. https://doi.org/10.3390/s21217391 google scholar
  • Tarhouni, M. & Aloui, I. (2024). Toward smart home automation based on containerization. https://doi.org/10.21203/rs.3.rs-4761820/v1 google scholar
  • Teng, W. & Lu, H.-P. (2009). Consumer adoption of PDA phones in Taiwan. International Journal of Mobile Communications, 8(1), 1–20. google scholar
  • Tiersen, F., Batey, P., Harrison, M., Naar, L., Serban, A., Daniels, S., … & Calvo, R. (2021). Smart home sensing and monitoring in households with dementia: a user-centered design approach. Jmir Aging, 4(3), e27047. https://doi.org/10.2196/27047 google scholar
  • Tuna, F. (2016). Sosyal Bilimler İçin İstatistik. Pegem Akademi, google scholar
  • Tural, E., Lu, D., & Cole, D. (2021). Safely and actively aging in place: older adults’ attitudes and intentions toward smart home technologies. Gerontology and Geriatric Medicine, 7. https://doi.org/10.1177/23337214211017340 google scholar
  • TÜİK. (2022). TÜİK Veri Portalı. https://data.tuik.gov.tr. Last Access: 01.01.2025. google scholar
  • Türkyılmaz, S. & Altındağ, E. (2022). Analysis of smart home systems in the context of the internet of things in terms of consumer experience. International Review of Management and Marketing, 12(1), 19-31. doi:10.32479/irmm.12709 google scholar
  • Uddin, M., Kim, T., & Kim, J. (2010). Video-based indoor human gait recognition using depth imaging and hidden markov model: a smart system for smart home. Indoor and Built Environment, 20(1), 120-128. https://doi.org/10.1177/1420326x10391140 google scholar
  • Van der Heijden, H. (2004). User acceptance of the hedonic information systems. MIS Quarterly, 28(4), 695–704. google scholar
  • Van Dijk, J. (2020). The Digital Divide: Bridging the Gap. Cambridge University Press. google scholar
  • Wang, Y., Zhang, R., Zhang, X., & Zhang, Y. (2023). Privacy risk assessment of a smart home system based on the stpa–fmea method. Sensors, 23(10), 4664. https://doi.org/10.3390/s23104664 google scholar
  • Wei, N., Baharudin, A., Hussein, L., & Hilmi, M. (2019). Factors affecting user’s intention to adopt smart home in malaysia. International Journal of Interactive Mobile Technologies (IJIM), 13(12), 39. https://doi.org/10.3991/ijim.v13i12.11083 google scholar
  • Wei, W., Gong, X., Li, J., Tian, K., & Xing, K. (2023). A study on community older people’s willingness to use the smart home—an extended technology acceptance model with intergenerational relationships. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh. 2023.1139667 google scholar
  • Wong, J. & Leung, J. (2016). Modeling factors influencing the adoption of smart-home technologies. Facilities, 34(13/14), 906-923. https:// doi.org/10.1108/f-05-2016-0048 google scholar
  • Wright, D. & Shank, D. (2019). Smart home technology diffusion in a living laboratory. Journal of Technical Writing and Communication, 50(1), 56-90. https://doi.org/10.1177/0047281619847205 google scholar
  • Yaldaie, A., Porras, J., & Drögehorn, O. (2023). This state of home automation: a systematic literature review. International Journal of Hybrid Innovation Technologies, 3(1), 23-46. https://doi.org/10.21742/ijhit.2653-309x.2022.2.1.03 google scholar
  • Yang, H., Lee, H. & Zo, H. (2017). User acceptance of smart home services: an extension of the theory of planned behavior. Industrial Management & Data Systems, 117(1), 68-89. https://doi.org/10.1108/IMDS-01-2016-0017 google scholar
  • Yang, Y., Liu, Y., Li, H. & Yu, B. (2015). Understanding the perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253–269. google scholar
  • Yazıcıoğlu, Y., & Erdoğan, S. (2014). SPSS Uygulamalı Bilimsel Araştırma Yöntemleri. Detay Yayıncılık. google scholar
  • Yu, J., Lee, H., Ha, I. & Zo, H. (2017). User acceptance of media tablets: An empirical examination of perceived value. Telematics and Informatics, 34(4), 206–223. google scholar
  • Zhang, N., Wang, S., & Li, H. (2024). Improving user satisfaction by analyzing users’ subjective cognitive types in smart home systems. Universal Access in the Information Society. doi:10.1007/s10209-024-01105-2 google scholar
  • Zhang, Q., Li, M., & Wu, Y. (2020). Smart home for elderly care: development and challenges in China. BMC Geriatrics, 20(1). https://doi. org/10.1186/s12877-020-01737-y google scholar
  • Zhou, C., Qian, Y., & Kaner, J. (2024). A study on the smart home use intention of elderly consumers based on technology acceptance models. Plos One, 19(3), e0300574. https://doi.org/10.1371/journal.pone.0300574 google scholar
  • Ziamou, P. L. & Ratneshwar, S. (2002). Promoting consumer adoption of high-technology products: Is more information always better? Journal of Consumer Psychology, 12(4), 341–351. google scholar
There are 89 citations in total.

Details

Primary Language English
Subjects Human-Computer Interaction
Journal Section Research Article
Authors

Onur Muço 0000-0001-6830-9897

Üstün Özen 0000-0002-7595-4306

Publication Date June 30, 2025
Submission Date July 12, 2024
Acceptance Date May 9, 2025
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Muço, O., & Özen, Ü. (2025). Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective. Acta Infologica, 9(1), 314-338. https://doi.org/10.26650/acin.1515409
AMA Muço O, Özen Ü. Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective. ACIN. June 2025;9(1):314-338. doi:10.26650/acin.1515409
Chicago Muço, Onur, and Üstün Özen. “Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective”. Acta Infologica 9, no. 1 (June 2025): 314-38. https://doi.org/10.26650/acin.1515409.
EndNote Muço O, Özen Ü (June 1, 2025) Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective. Acta Infologica 9 1 314–338.
IEEE O. Muço and Ü. Özen, “Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective”, ACIN, vol. 9, no. 1, pp. 314–338, 2025, doi: 10.26650/acin.1515409.
ISNAD Muço, Onur - Özen, Üstün. “Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective”. Acta Infologica 9/1 (June2025), 314-338. https://doi.org/10.26650/acin.1515409.
JAMA Muço O, Özen Ü. Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective. ACIN. 2025;9:314–338.
MLA Muço, Onur and Üstün Özen. “Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective”. Acta Infologica, vol. 9, no. 1, 2025, pp. 314-38, doi:10.26650/acin.1515409.
Vancouver Muço O, Özen Ü. Factors Influencing Consumer Adoption of Smart Home Systems: A Socio-Demographic Perspective. ACIN. 2025;9(1):314-38.