طراحی مدل داده‌بنیاد پذیرش اجتماعی فناوری خانه هوشمند در میان مصرف‌کنندگان عراقی

نوع مقاله : مقاله پژوهشی( کیفی )

نویسندگان

گروه مدیریت بازرگانی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران.

چکیده
هدف این پژوهش طراحی مدل داده‌بنیاد پذیرش اجتماعی فناوری خانه هوشمند در میان مصرف‌کنندگان عراقی می‌باشد. این پژوهش مبتنی بر پارادایم تفسیری و با رویکردی کیفی انجام گرفت. جامعه آماری پژوهش شامل 15 نفر از متخصصان فناوری خانه هوشمند و اساتید دانشگاه با تخصص بازاریابی می‌باشند که به روش گلوله برفی انتخاب شدند و در پاسخگویی به سؤالات مصاحبه‌ها مشارکت کردند. ابزار گردآوری اطلاعات مصاحبه نیمه ساختاریافته می‌باشد. داده‌های جمع‌آوری شده با تکیه بر تاکتیک تحلیل محتوای پنهان در نرم‌افزار MAXQDA2024 در سه مرحله کدگذاری باز، محوری و انتخابی مورد تجزیه و تحلیل قرار گرفت. یافته‌ها حاکی از تولید مفهوم مقوله‌های پذیرش فناوری خانه هوشمند، ادراک اعتماد و امنیت، رفتارهای ادغام سبک زندگی، تصمیم گیری اقتصادی و پاسخ به تأثیر اجتماعی بود که 7 فرضیه اصلی و جدید میان آن‌ها برقرار شد. نتایج نشان داد که مقوله محوری «پذیرش اجتماعی فناوری خانه هوشمند»، یک سازه چندوجهی را در بر می‌گیرد که تحت تأثیر ابعاد شناختی، اجتماعی و نوآورانه قرار دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Designing a Grounded Model of the Social Acceptance of Smart Home Technology among Iraqi Consumers

نویسندگان English

Abdullah Abdulkareem Abbas Al-saadi
Ghasem Zarei
Mohammad Bashekouh Ajirloo
Naser Seifollahi
Department of Business Administration, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
چکیده English

Abstract
The objective of this research is to design a grounded theory model for the social acceptance of smart home technology (SHT) among Iraqi consumers. This study was conducted based on an interpretive paradigm through a qualitative approach. The statistical population consists of 15 smart home technology experts and university professors specializing in marketing, selected through the snowball sampling method, and participated in semi-structured interviews. The data were analyzed by latent content analysis tactics in MAXQDA 2024 software through three stages: open, axial, and selective coding. The findings led to the conceptualization of several categories, including “acceptance of smart home technology,” “perceptions of trust and security,” “lifestyle integration behaviors,” “economic decision-making,” and “response to social influence,” with seven main and original hypotheses established among them. The results revealed that the core category, “Social Acceptance of Smart Home Technology,” encompasses a multi-faceted construct influenced by cognitive, social, and innovative dimensions.
Introduction
In recent years, Iraq has faced significant challenges in the energy sector due to international sanctions and the occurrence of multiple wars (Al‑Ghabera et al., 2024). Efficient energy consumption in this country has become highly important and has been introduced by the government as a national priority (Al‑Yozbaky et al., 2022). Smart home technology represents the convergence of the Internet of Things (IoT), artificial intelligence (AI), sensors, and automation systems to create intelligent and responsive living environments that enhance residents’ comfort, security, energy efficiency, and sustainability (Yeoh Lui et al., 2025). In Iraq, where persistent challenges such as frequent power outages, security concerns, water shortages, and rapid urbanization exist, smart home systems offer an appropriate solution for optimizing resource use and adapting to local lifestyles (Mustafa et al., 2025).
On the other hand, an examination of Iraq’s demographic composition indicates that the majority of the country’s population consists of young people, many of whom pay particular attention to innovation in their purchasing decisions and tend to prefer products that possess a superior mental image compared with competing alternatives (Ahmed et al., 2020). Furthermore, according to reports from Iraq’s National Housing Sector, approximately 75 percent of Iraqi households are interested in adopting modern and innovative architectural approaches in order to improve their quality of life (Al‑Khafaji & Kamaran, 2019).
A cultural analysis of Iraq based on Hofstede’s cultural dimensions shows that the country is characterized by high power distance, a collectivist culture, masculinity, and a high level of uncertainty avoidance (Sardar, 2015). Analytical studies indicate that societies with high power distance often demonstrate lower resistance to smart home technologies, as many households in such cultures are willing to adopt innovative architectural ideas to enhance comfort and welfare (Bao et al., 2014; Wang et al., 2024). Regarding collectivism, researchers argue that positive social attitudes toward smart home technology spread rapidly within society, and once the technology gains collective acceptance, resistance declines and consumers quickly move from early adoption to more rational and widespread use (Liu et al., 2021; Mouzaidis et al., 2023).
Analysis of the masculinity–femininity dimension suggests that in more feminine cultures there is a greater tendency to adopt practical and modern technologies such as remote-control systems, graphical user interfaces, and smart electricity technologies (Jeong et al., 2013). Finally, studies on uncertainty avoidance indicate that in such cultures consumers often view new technologies with caution and tend to accept smart home technologies only after they become widely adopted within society (Furszyfer Del Rio et al., 2021; Chang & Chiu, 2025). Accordingly, the present study seeks to answer the following main research question: What is the grounded theory model of social acceptance of smart home technology among Iraqi consumers?
Theoretical Framework
Social Acceptance of Smart Home Technology
Social acceptance refers to the processes through which individuals, groups, and societies approve, adopt, or integrate innovations, technologies, or social phenomena into their norms, behaviors, and institutional structures (Moeremans & Dooms, 2025; Shuhaiber & Mashal, 2019). The theory of social acceptance is not a single unified framework; rather, it is a multidimensional concept that has evolved across different disciplines, particularly in social psychology, energy transition studies, and innovation research. It encompasses psychological needs for belonging, the social validation of innovations, and the institutional transformations required for systemic change (Ji & Chan, 2020; Alshammari & Alkhwaldi, 2025).
Norouzzadeh et al. (2025) examined the role of the Internet of Things (IoT) in residential smart home technologies. Their findings indicated that social influence was not a significant factor in the adoption of IoT-based smart home technologies. They attributed this result to habit-related barriers and the level of effort required from users within the studied sample, suggesting that technology acceptance depends more on individual usability than on social norms.
Moghbal Naseri et al. (2023) investigated human behavior based on sleep–wake patterns using smart home sensors. In this study, smart home technologies were used to monitor the behavior of elderly individuals who faced physical or psychological challenges and lived independently or with pets. Using data collected from smart home sensors, the daily behaviors of individuals during waking and sleeping periods were predicted and analyzed through computational approaches. The researchers developed an algorithm capable of modeling sleep and wake behavior patterns and enabling behavioral prediction. By analyzing data from the CASAS smart home dataset, this approach was presented as an effective method for improving the ability of individuals with physical or mental limitations to live independently.
Research Methodology
This study was conducted based on the interpretive paradigm through a qualitative research approach. The statistical population consisted of 15 experts in smart home technology and university professors specializing in marketing. Participants were selected through the snowball sampling method and took part in responding to the interview questions. The data collection tool was semi‑structured interviews.
Research Findings
The collected data were analyzed by latent content analysis techniques in MAXQDA 2024 software through three stages: open coding, axial coding, and selective coding. The findings led to the identification and development of several key categories, including smart home technology acceptance, perceived trust and security, lifestyle integration behaviors, economic decision‑making, and responses to social influence. Seven main and novel hypotheses were established among these categories. The results indicated that the core category, “Social Acceptance of Smart Home Technology,” represents a multidimensional construct influenced by cognitive, social, and innovation‑related dimensions.
Conclusion
The present study aimed to design a grounded theory model for the social acceptance of smart home technology among Iraqi consumers. The findings of this research are consistent with previous studies conducted by Moghbal Naseri et al. (2023), Nakhalet al‑Jannah and Ghaffarian (2022), Abbasi (2022), Pirzada et al. (2022), Farah Hussin et al. (2022), Arar et al. (2021), Pal et al. (2021), Gross et al. (2020), Mashal et al. (2020), and Ko et al. (2020).
Pal et al. (2021) found that user characteristics, particularly user skills, have the greatest influence on privacy concerns, followed by legal and policy aspects and technological dimensions. In addition, physical and psychological privacy were considered more important than concerns related to informational privacy.
Based on the results of the study, the following recommendation is proposed. Managers and stakeholders should recognize the lack of a significant relationship between economic decision‑making and perceptions of trust and security. This insight should encourage them to separate financial incentives from security‑focused campaigns. Instead, they should develop independent educational initiatives that address cost–benefit analysis separately, ensuring that trust‑building efforts are not influenced by perceptions of economic trade‑offs.

کلیدواژه‌ها English

Technology
Smart home technology
Social acceptance
Economic decision-making
Iraqi consumers
Abbasi, M. (2021). Secure data aggregation in smart home based on Internet of Things based on ant colony optimization algorithm. Master's thesis, Islamic Azad University, Ardabil Branch, Faculty of Technology and Engineering. (In Persian)
Ahmed, A. M. (2020). Consumer Behavior toward the Use of Credit Cards: The Empirical Evidence from Iraq. Shirkah: Journal of Economics and Business, 5(1), 53. https://doi.org/10.22515/shirkah.v5i1.303.
Al Khafaji, I. A. M., & Kamaran, R. (2019). The Influence of Spatial Flexibility to improve Sustainability of Interior Design by Using Smart Technology (Case study –Future Smart home in Iraq). European Journal of Sustainable Development, 8(4), 438. https://doi.org/10.14207/ejsd.2019.v8n4p438.
Al-Ghabera, H., & Hassan Ahmed, R., & Youssef, M., & Mahmood, A. (2024). Challenges and Opportunities in Implementing Renewable Energy in Iraq. International Journal of Education, Science, Technology, and Engineering (IJESTE), 7(2), 64–74. https://doi.org/10.36079/lamintang.ijeste-0702.731.
Alsamarah, O., & Alshare, K. A., & Lane, P. L. (2023). Determinants of individual’s intention to use the internet of things for smart home technology: a cultural moderating effect. International Journal of Mobile Communications, 21(3), 316. https://doi.org/10.1504/IJMC.2023.129980.
Alshammari, S. H., & Alkhwaldi, A. F. (2025). An integrated approach using social support theory and technology acceptance model to investigate the sustainable use of digital learning technologies. Scientific Reports, 15(1), 342. https://doi.org/10.1038/s41598-024-83450-z.
Al-Yozbaky, O. S. A.-D., & Khalel, S. I. (2022). The Future of Renewable Energy in Iraq: Potential and Challenges. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 10(2). https://doi.org/10.52549/ijeei.v10i2.3756.
Balta-Ozkan, N., & Davidson, R., & Bicket, M., & Whitmarsh, L. (2013). Social barriers to the adoption of smart homes. Energy Policy, 63, 363–374. https://doi.org/10.1016/j.enpol.2013.08.043.
Bao, H., & Chong, A. Y. L., & Ooi, K. B., & Lin, B. (2014). Are Chinese consumers ready to adopt mobile smart home? An empirical analysis. International Journal of Mobile Communications, 12(5), 496. https://doi.org/10.1504/IJMC.2014.064595.
Basarir-Ozel, B., Turker, H. B., & Nasir, V. A. (2022). Identifying the Key Drivers and Barriers of Smart Home Adoption: A Thematic Analysis from the Business Perspective. Sustainability, 14(15), 9053. https://doi.org/10.3390/su14159053.
Buil-Gil, D., & Kemp, S., & Kuenzel, S., & Coventry, L., & Zakhary, S., & Tilley, D., & Nicholson, J. (2023). The digital harms of smart home devices: A systematic literature review. Computers in Human Behavior, 145, 107770. https://doi.org/10.1016/j.chb.2023.107770.
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.
Chan, E. T. H., & Li, T. E., & Schwanen, T. (2025). Societal acceptance of advanced aerial mobility in China’s Greater Bay Area among young- and middle-aged adults. Transportation Research Part F: Traffic Psychology and Behaviour, 110, 88–103. https://doi.org/10.1016/j.trf.2025.02.008.
Chang, C.Y., & Chiu, T.P. (2025). A Study on Cultural Differences and Acceptance of Smart TV Usage Intentions Among the Elderly in Singapore and Taiwan (pp. 152–162). https://doi.org/10.1007/978-3-031-94159-7_16.
Charmaz, K., & Thornberg, R. (2021). The pursuit of quality in grounded theory. Qualitative Research in Psychology, 18(3), 305–327. https://doi.org/10.1080/14780887.2020.1780357.
Chun Tie, Y., & Birks, M., & Francis, K. (2019). Grounded theory research: A design framework for novice researchers. SAGE Open Medicine, 7. https://doi.org/10.1177/2050312118822927.
Furszyfer Del Rio, D. D., & Sovacool, B. K., & Griffiths, S. (2021). Culture, energy and climate sustainability, and smart home technologies: A mixed methods comparison of four countries. Energy and Climate Change, 2, 100035. https://doi.org/10.1016/j.egycc.2021.100035.
Gaul, S., & Ziefle, M. (2009). Smart Home Technologies: Insights into Generation-Specific Acceptance Motives (pp. 312–332). https://doi.org/10.1007/978-3-642-10308-7_22.
Glaser, B. G. (2016). Open Coding Descriptions. Grounded Theory Review, 15(02), 108–110. Retrieved from https://groundedtheoryreview.org/index.php/gtr/article/view/239.
Guo, M., & Xiao, S. (2023). An empirical analysis of the factors driving customers’ purchase intention of green smart home products. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1272889.
Jeong, K.A., & Salvendy, G., & Proctor, R. W. (2010). Smart home design and operation preferences of Americans and Koreans. Ergonomics, 53(5), 636–660. https://doi.org/10.1080/00140130903581623.
Ji, W., & Chan, E. H. W. (2020). Between users, functions, and evaluations: Exploring the social acceptance of smart energy homes in China. Energy Research & Social Science, 69, 101637. https://doi.org/10.1016/j.erss.2020.101637.
Klobas, J. E., & McGill, T., & Wang, X. (2019). How perceived security risk affects intention to use smart home devices: A reasoned action explanation. Computers & Security, 87, 101571. https://doi.org/10.1016/j.cose.2019.101571.
Larionova, Y. v., & Sharma, D., & Nijhawan, G., & Kumari, N., & Devi, S. (2024). The Economic Viability of Smart Home Investments: A Cost-Benefit Analysis. BIO Web of Conferences, 86, 01086. https://doi.org/10.1051/bioconf/20248601086.
Li, W., & Yigitcanlar, 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. https://doi.org/10.1016/j.erss.2021.102211.
Lindvall, D., & Sörqvist, P., & Lindeberg, S., & Barthel, S. (2025). The polarization of energy preferences – A study on social acceptance of wind and nuclear power in Sweden. Energy Policy, 198, 114492. https://doi.org/10.1016/j.enpol.2024.114492.
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.
Milchram, C., & van de Kaa, G., & Doorn, N., & Künneke, R. (2018). Moral Values as Factors for Social Acceptance of Smart Grid Technologies. Sustainability, 10(8), 2703. https://doi.org/10.3390/su10082703.
Moeremans, B., & Dooms, M. (2025). Social license to operate: Factors determining social acceptance among local port community stakeholders. Maritime Economics & Logistics, 27(1), 183–210. https://doi.org/10.1057/s41278-024-00297-x.
Mouzaidis, P., & Tsatiris, M., & Damalas, C., & Tsantopoulos, G., & Katsileros, A., & Milis, C. (2023). Investigation of the attitudes of Greek consumers towards the biofuel’s consumption using social acceptance theory. Biofuels, 14(1), 109–118. https://doi.org/10.1080/17597269.2022.2122147.
Mustafa, M. H., & Al-Saatee, H. H., & Al-Bakry, H. M. J., & Sabu, A. I., & Al-Helli, M., & Al-Graiti, A. A. A., & Mohammed, A. A., & Ismael, Z. A., & Mohson, Z. H., & Jassim, A. K. (2025). The Importance of Empowering the Smart City in Iraq: A Case Study of Baghdad Municipalities. International Journal of Sustainable Development and Planning, 20(3), 1133–1141. https://doi.org/10.18280/ijsdp.200319.
- Nakhalet al-Jannah, N. A., & Ghaffarian, H. (2017). Estimating and presenting smart home behaviors using artificial intelligence. Master's thesis, Arak University, Faculty of Engineering. (In Persian)
Norouzzadeh, A. M., & Toufighi, S. P., & Vang, J., & Edalatipour, A. (2025). Adoption of internet of things in residential smart homes: A structural equation modeling approach. Sustainable Futures, 9, 100665. https://doi.org/10.1016/j.sftr.2025.100665.
Pirzada, P., & Wilde, A., & Doherty, G. H., & Harris-Birtill, D. (2022). Ethics and acceptance of smart homes for older adults. Informatics for Health and Social Care, 47(1), 10–37. https://doi.org/10.1080/17538157.2021.1923500.
Radha, R. K. (2020). Smart home design flexibility as an enabler of sustainability and the reception in Sulaiymaniah, Kurdistan Region-Iraq. Journal of Building Pathology and Rehabilitation, 5(1), 29. https://doi.org/10.1007/s41024-020-00094-y.
Sardar, H. (2015). The influence of national culture on international marketing and consumer behavior in Iraqi Kurdistan, focusing on Hofstede’s model. British Journal of Marketing Studies, 3(8), 50–78.
Shevchuk, N., & Oinas-Kukkonen, H., & Benson, V. (2020). Risk and social influence in sustainable smart home technologies. In Cyber Influence and Cognitive Threats (pp. 185–216). Elsevier. https://doi.org/10.1016/B978-0-12-819204-7.00010-5.
Shuhaiber, A., & Mashal, I. (2019). Understanding users’ acceptance of smart homes. Technology in Society, 58, 101110. https://doi.org/10.1016/j.techsoc.2019.01.003.
Shuhaiber, A., & Alkarbi, W., & Almansoori, S. (2023). Trust in Smart Homes: The Power of Social Influences and Perceived Risks (pp. 305–315). https://doi.org/10.1007/978-981-19-7660-5_27.
Taebi, B. (2017). Bridging the Gap between Social Acceptance and Ethical Acceptability. Risk Analysis, 37(10), 1817–1827. https://doi.org/10.1111/risa.12734.
Tak, A. N., & Becerik-Gerber, B., & Soibelman, L., & Lucas, G. (2023). A framework for investigating the acceptance of smart home technologies: Findings for residential smart HVAC systems. Building and Environment, 245, 110935. https://doi.org/10.1016/j.buildenv.2023.110935.
Valencia-Arias, A., & Cardona-Acevedo, S., & Gómez-Molina, S., & Gonzalez-Ruiz, J. D., & Valencia, J. (2023). Smart home adoption factors: A systematic literature review and research agenda. PLOS ONE, 18(10), e0292558. https://doi.org/10.1371/journal.pone.0292558.
Vrain, E., & Wilson, C., & Kerr, L., & Wilson, M. (2022). Social influence in the adoption of digital consumer innovations for climate change. Energy Policy, 162, 112800. https://doi.org/10.1016/j.enpol.2022.112800.
Wang, J., & Yu, Y., & Lu, C. (2024). Switching Intention of Potential Smart Home Users: The Co-action of Innovation Diffusion Theory and Switching Costs. International Journal of Human–Computer Interaction, 1–18. https://doi.org/10.1080/10447318.2024.2338658.
Wüstenhagen, R., & Wolsink, M., & Bürer, M. J. (2007). Social acceptance of renewable energy innovation: An introduction to the concept. Energy Policy, 35(5), 2683–2691. https://doi.org/10.1016/j.enpol.2006.12.001.
Yeoh Lui, C. X., & Yang, N., & Tang, A., & Tam, W. W. S. (2025). Effectiveness Evaluation of Smart Home Technology in Preventing and Detecting Falls in Community and Residential Care Settings for Older Adults: A Systematic Review and Meta-Analysis. Journal of the American Medical Directors Association, 26(1), 105347. https://doi.org/10.1016/j.jamda.2024.105347.

  • تاریخ دریافت 11 دی 1404
  • تاریخ بازنگری 20 اسفند 1404
  • تاریخ پذیرش 24 اردیبهشت 1405