Consumer-consumption-need theoretical model for smart clothing: Construction and empirical study for Chinese silver-haired population
Vol 8, Issue 8, 2024
VIEWS - 149 (Abstract) 73 (PDF)
Abstract
In the era of artificial intelligence, smart clothing, as a product of the interaction between fashion clothing and intelligent technology, has increasingly attracted the attention and affection of enterprises and consumers. However, to date, there is a lack of focus on the demand of silver-haired population’s consumers for smart clothing. To adapt to the rapidly aging modern society, this paper explores the influencing factors of silver-haired population’s demand for smart clothing and proposes a corresponding consumer-consumption-need theoretical model (CCNTM) to further promote the development of the smart clothing industry. Based on literature and theoretical research, using the technology acceptance model (TAM) and functional-expressive-aesthetic consumer needs model (FEAM) as the foundation, and introducing interactivity and risk perception as new external variables, a consumer-consumption-need theoretical model containing nine variables including perceived usefulness, perceived ease of use, functionality, expressiveness, aesthetics, interactivity, risk perception, purchase attitude, and purchase intention was constructed. A questionnaire survey was conducted among the Chinese silver-haired population aged 55–65 using the Questionnaire Star platform, with a total of 560 questionnaires issued. The results show that the functionality, expressiveness, interactivity, and perceived ease of use of smart clothing significantly positively affect perceived usefulness (P < 0.01); perceived usefulness, perceived ease of use, aesthetics, and interactivity significantly positively affect the purchase attitude of the silver-haired population (P < 0.01); perceived usefulness, aesthetics, interactivity, and purchase attitude significantly positively affect the purchase intention of the silver-haired population (P < 0.01); functionality and expressiveness significantly positively affect perceived ease of use (P < 0.01); risk perception significantly negatively affects purchase attitude (P < 0.01). Through the construction and empirical study of the smart clothing consumer-consumption-need theoretical model, this paper hopes to stimulate the purchasing behavior of silver-haired population’s consumers towards smart clothing and enable them to enjoy the benefits brought by scientific and technological advancements, which to live out their golden years in comfort, also, promote the rapid development of the smart clothing industry.
Keywords
Full Text:
PDFReferences
Alattar, A. E., & Mohsen, S. (2023). A survey on smart wearable devices for healthcare applications. Wireless Personal Communications, 132(1), 775–783. https://doi.org/10.1007/s11277-023-10639-2
An, S., Eck, T., & Yim, H. (2023). Understanding consumers’ acceptance intention to use mobile food delivery applications through an extended technology acceptance model. Sustainability, 15(1), 832. https://doi. org/10.3390/su15010832
Ates, H. C., Yetisen, A. K., Güder, F., & Dincer, C. (2021). Wearable devices for the detection of COVID-19. Nature Electrons, 4, 13–14. https://doi.org/10.1038/s41928-020-00533-1
Bailey, D. R., Almusharraf, N., & Almusharraf, A. (2022). Video conferencing in the e-learning context: explaining learning outcome with the technology acceptance model. Education and Information Technologies, 27(1), 7679–7698. https://doi.org/10.1007/s10639-022-10949-1
Bakhshian, S., & Lee, Y. A. (2022). Social acceptability and product attributes of smart apparel: Their effects on consumers’ attitude and use intention. The Journal of The Textile Institute, 113(1), 671–680. https://doi.org/10.1080/00405000.2021.1898138
Bianchi, C., Tuzovic, S., & Kuppelwieser, V. G. (2023). Investigating the drivers of wearable technology adoption for healthcare in South America. Information Technology & People, 36(2), 916–939. https://doi.org/10.1108/ITP-01-2021-0049
Bu, F., Wang, N., Jiang, B., & Jiang, Q. (2021). Motivating information system engineers’ acceptance of privacy by design in China: An extended UTAUT model. International Journal of Information Management, 60(6), 102358. https://doi.org/10.1016/j.ijinfomgt.2021.102358
Bunn, J. A., Navalta, J. W., Fountaine, C. J., & Reece, J. D. (2018). Current state of commercial wearable technology in physical activity monitoring 2015–2017. International Journal of Exercise Science, 11(7), 503–515.
Caird, S. (1994). How important is the innovator for the commercial success of innovative products in SMEs? Technovation, 14(2), 71–83. https://doi.org/10.1016/0166-4972(94)90097-3
Chae, J. M. (2009). Consumer acceptance model of smart clothing according to innovation. International Journal of Human Ecology, 10(2), 23–33.
Chae, M., & Evenson, S. (2014). Prototype development of golf wear for mature women. International Journal of Fashion Design, Technology and Education, 7(1), 2–9. https://doi.org/10.1080/17543266.2013.837966
Channa, A., Popescu, N., Skibinska, J., & Burget, R. (2021). The rise of wearable devices during the COVID-19 pandemic: A systematic review. Sensors, 21(17), 5787. https://doi.org/10.3390/s21175787
Chattaraman, V., & Rudd, N. A. (2006). Preferences for Aesthetic Attributes in Clothing as a Function of Body Image, Body Cathexis and Body Size. Clothing and Textiles Research Journal, 24(1), 46–61. https://doi.org/10.1177/0887302x0602400104
Chen, M., Ma, Y., Song, J., et al. (2016). Smart clothing: Connecting human with clouds and big data for sustainable health monitoring. Mobile Networks and Applications, 21(5), 825–845. https://doi.org/10.1007/s11036-016-0745-1
Chen, S., & Ye, J. (2023). Understanding consumers’ intentions to purchase smart clothing using PLS-SEM and fsQCA. PLOS ONE, 18(9), e0291870. https://doi.org/10.1371/journal. pone.0291870
China Development Research Foundation (CDRF). (2020). China development report 2020: Development trends and policies of population aging in China. China Development Press.
Cho, G., Lee, S., & Cho, J. (2009). Review and reappraisal of smart clothing. International journal of human computer interactions, 25(6), 582–617. https://doi.org/10.1080/10447310902997744
Chuah, S. H. W., Rauschnabe,l P. A., Krey, N., et al. (2016). Wearable technologies: The role of usefulness and visibility in smartwatch adoption. Computers in Human Behavior, 65, 276–284. https://doi.org/10.1016/j.chb.2016.07.047
Cui, T., Chattaraman, V., & Sun, L. (2021). Examining consumers’ perceptions of a 3D printing integrated apparel: a functional, expressive and aesthetic (FEA) perspective. Journal of Fashion Marketing and Management: An International Journal, 26(2), 266–288. https://doi.org/10.1108/jfmm-02-2021-0036
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
de Oliveira, C. R. S., da Silva Júnior, A. H., Immich, A. P. S., et al. (2022). Use of advanced materials in smart textile manufacturing. Materials Letters, 316, 132047. https://doi.org/10.1016/j.matlet.2022.132047
Dizon-Paradis, R., Kalavakonda, R. R., Chakraborty, P., et al. (2024). Pasteables: A Flexible and Smart “Stick-and-Peel Wearable Platform for Fitness & Athletics.” IEEE Consumer Electronics Magazine, 1. https://doi.org/10.1109/mce.2022.3158044
Duell, N., & Steinberg, L. (2019). Positive risk taking in adolescence. Child Development Perspectives, 13(1), 48–52. https://doi.org/10.1111/cdep.12310
Dunne, L. E., Ashdown, S. P., & Smyth, B. (2005). Expanding garment functionality through embedded electronic technology. Journal of Textile and Apparel Technology and Management, 4(3), 1–11.
Eckman, M., Damhorst, M. L., & Kadolph, S. J. (1990). Toward a Model of the In-Store Purchase Decision Process: Consumer Use of Criteria for Evaluating Women’s Apparel. Clothing and Textiles Research Journal, 8(2), 13–22. https://doi.org/10.1177/0887302x9000800202
Fengfan, J. (2017). Present Situation and Future Development Trend of Smart Clothing. Journal of Arts and Humanities, 6(8), 54. https://doi.org/10.18533/journal.v6i8.1232
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. https://doi.org/10.3390/electronics7120405
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley.
Ge, Y., Qi, H., & Qu, W. (2023). The factors impacting the use of navigation systems: A study based on the technology acceptance model. Transportation Research Part F: Traffic Psychology and Behavior, 93, 106–117. https://doi.org/10.1016/j.trf.2023.01.005
Geng, R., & Lu, X. (2023). Investigating older adults’ willingness to use AI in sport and the factors influencing it. Journal of Liaoning Institute of Science and Technology, 25(1), 87–91.
Hwang, C., Chung, T. L., & Sanders, E. A. (2016). Attitudes and Purchase Intentions for Smart Clothing. Clothing and Textiles Research Journal, 34(3), 207–222. https://doi.org/10.1177/0887302x16646447
Imbesi, S., & Scataglini, S. (2021). A user centered methodology for the design of smart apparel for older users. Sensors, 21(8), 2804. https://doi.org/10.3390/s21082804
Jeong, J., Kim, Y., & Roh, T. (2021). Do Consumers Care About Aesthetics and Compatibility? The Intention to Use Wearable Devices in Health Care. SAGE Open, 11(3), 215824402110400. https://doi.org/10.1177/21582440211040070
Jeon, M. (2017). Emotions and Affect in Human Factors and Human-Computer Interaction: Taxonomy, Theories, Approaches, and Methods. Emotions and Affect in Human Factors and Human-Computer Interaction, 3–26. https://doi.org/10.1016/b978-0-12-801851-4.00001-x
Ji, J., & Dai, L. (2024). The silver-haired economy: Conceptual evolution, policy context, and realistic challenges. China Development Observation, 1, 43–52.
Ju, N., & Lee, K. H. (2020). Consumer resistance to innovation: Smart clothing. Fashion and Textiles, 7(1). https://doi.org/10.1186/s40691-020-00210-z
Ju, N., & Lee, K. H. (2021). Perceptions and Resistance to Accept Smart Clothing: Moderating Effect of Consumer Innovativeness. Applied Sciences, 11(7), 3211. https://doi.org/10.3390/app11073211
Ko, E., Sung, H., & Yoon, H. R. (2008). The effect of attributes of innovation and perceived risk on product attitudes and intention to adopt smart wear. Journal of Global Scholars of Marketing Science, 18(2), 89–93. https://doi.org/10.1080/12297119.2008.9707246
Ko, E., Sung, H., & Yun, H. (2009). Comparative analysis of purchase intentions toward smart clothing between Korean and us consumers. Clothing and Textiles Research Journal, 27(4), 259–273. https://doi.org/10.1177/0887302X08327086
Lamb, J. M., & Kallal, M. J. (1992). A conceptual framework for apparel design. Clothing and Textiles Research Journal, 10(2), 42–47. https://doi.org/10.1177/0887302X9201000207
Lee, H. H., Fiore, A. M., & Kim, J. (2006). The role of the technology acceptance model in explaining effects of image interactivity technology on consumer responses. International Journal of Retail & Distribution Management, 34(8), 621–644. https://doi.org/10.1108/09590550610675949
Lee, S., Rho, S. H., Lee, S., et al. (2021). Implementation of an Automated Manufacturing Process for Smart Clothing: The Case Study of a Smart Sports Bra. Processes, 9(2), 289. https://doi.org/10.3390/pr9020289
Li, Q., Xue, Z., Wu, Y., & Zeng, X. (2022). The status quo and prospect of sustainable development of smart clothing. Sustainability, 14(2), 990. https://doi.org/10.3390/su14020990
McCann, J. (2009). End-user based design of innovative smart clothing. Smart Clothes and Wearable Technology, 45–69. https://doi.org/10.1533/9781845695668.1.45
Nam, C., & Lee, Y. A. (2020). Validation of the wearable acceptability range scale for smart apparel. Fashion Text, 7(1), 13. https://doi.org/10.1186/s40691-019-0203-3
Noh, M., Li, Q., & Park, H. (2016). An integration model for innovative products in Korea and China: Bio-based smart clothing. International Journal of Product Development, 21(1), 59–78. https://doi.org/10.1504/IJPD.2016.076933
Orzada, B. T., & Kallal, M. J. (2016). FEA consumer needs model: Looking forward, looking back. In: Proceedings of the International Textile and Apparel Association Proceedings; Vancouver, British Columbia. https://doi.org/10.31274/ITAA_PROCEEDINGS-180814-1406
Orzada, B. T., & Kallal, M. J. (2021). FEA consumer needs model: 25 years later. Clothing and Textiles Research Journal, 39(1), 24–38. https://doi.org/10.1177/0887302X19881211
Pan, Y., & Stolterman, E. (2015). What if HCI becomes a fashion driven discipline? In: Proceedings of the CHI 2015 33rd annual ACM conference on human factors in computing systems; Seoul, Republic of Korea. https://doi.org/10.1145/2702123.2702544
Park, J., Lennon, S. J., & Stoel, L. (2005). On-line product presentation: Effects on mood, perceived risk, and purchase intention. Psychology and Marketing, 22(9), 695–719. https://doi.org/10.1002/mar.20080
Park, Y., & Chen, J. V. (2007). Acceptance and adoption of the innovative use of smartphone. Industrial Management & Data Systems, 107(9), 1349–1365. https://doi.org/10.1108/02635570710834009
Pink, D. H. (2006). A whole new mind: Why right-brainers will rule the future. Riverhead Books.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., et al. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Rahaman, M. A., Hassan, H. M. K., Asheq, A. A., & Islam, K. M. A. (2022). The interplay between eWOM information and purchase intention on social media: Through the lens of IAM and TAM theory. PLOS ONE, 17(9), e0272926. https://doi.org/10.1371/journal.pone.0272926
Rogers, E. M. (2003). Diffusion of innovations. The Free Press.
Runkle, J., Sugg, M., Boase, D., et al. (2019). Use of wearable sensors for pregnancy health and environmental monitoring: Descriptive findings from the perspective of patients and providers. Digital Health, 5, 205520761982822. https://doi.org/10.1177/2055207619828220
Saleem, A., Aslam, J., Kim, Y. B., et al. (2022). Motives towards e-Shopping Adoption among Pakistani Consumers: An Application of the Technology Acceptance Model and Theory of Reasoned Action. Sustainability, 14(7), 4180. https://doi.org/10.3390/su14074180
Schiffman, L. G., & Kanuk, L. L. (2000). Consumer behavior, 7th ed. Prentice Hall.
Shakeriaski, F., Ghodrat, M., Rashidi, M., & Samali, B. (2022). Smart coating in protective clothing for firefighters: An overview and recent improvements. Journal of Industrial Textiles, 51(5), 7428S–7454S. https://doi. org/10.1177/15280837221101213
Shen, L., & Sun, T. (2023). Intelligent wearable research status and its development trend. Journal of Clothing Research, 8(2), 125–133.
Sjöberg, L. (2000). Factors in risk perception. Risk Analysis, 20(1), 1–12. https://doi.org/10.1111/0272-4332.00001
Sonderegger, A., & Sauer, J. (2010). The influence of design aesthetics in usability testing: Effects on user performance and perceived usability. Applied Ergonomics, 41(3), 403–410. https://doi.org/10.1016/j.apergo.2009. 09.002
Stokes, B., & Black, C. (2012). Application of the functional, expressive and aesthetic consumer needs model: Assessing the clothing needs of adolescent girls with disabilities. International Journal of Fashion Design, Technology and Education, 5(3), 179–186. https://doi.org/10.1080/17543266.2012.700735
Suh, M., Carroll, K. E., & Cassill, N. L. (2010). Critical review on smart clothing product development. Journal of Textile and Apparel, Technology and Management, 6(4), 1–18.
Sun, J., Zhang, H. X. (2012). The effect of brand name suggestiveness on consumer decision making: The moderating roles of consumer need for cognition and expertise. Acta Psychologica Sinica, 44(5), 698–710.
Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2021). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers, 23, 987–1005. https://doi.org/10.1007/s10796-020-10007-6
Textile Institute. (2001). Smart fibers, fabrics and clothing. CRC Press.
Thompson, W. R. (2016). Worldwide Survey of Fitness Trends for 2017. ACSM’S Health & Fitness Journal, 20(6), 8–17. https://doi.org/10.1249/fit.0000000000000252
Tsai, T. H., Lin, W. Y., Chang, Y. S., et al. (2020). Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults. PLOS ONE, 15(1), e0227270. https://doi.org/10.1371/journal.pone.0227270
Turhan, G. (2013). An assessment towards the acceptance of wearable technology to consumers in Turkey: the application to smart bra and T-shirt products. Journal of the Textile Institute, 104(4), 375–395. https://doi.org/10.1080/00405000.2012.736191
UN. (2022). World population prospects 2022: Summary of results. Available online: https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of_results.pdf (accessed on 20 April 2024).
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wang, W., Fang, Y., Nagai, Y., et al. (2019). Integrating Interactive Clothing and Cyber-Physical Systems: A Humanistic Design Perspective. Sensors, 20(1), 127. https://doi.org/10.3390/s20010127
Wang, W., & Wang, S. (2021). Toward parent-child smart clothing: Purchase intention and design elements. Journal of Engineered Fibers and Fabrics, 16, 1–13. https://doi.org/10.1177/1558925021991843
Weizman, Y., Tan, A. M., & Fuss, F. K. (2020). Use of wearable technology to enhance response to the Coronavirus (COVID-19) pandemic. Public Health, 185, 221–222. https://doi.org/10.1016/j.puhe.2020.06.048
Wu, L., & Sheng, Q. (2022). On the design of intelligent wearable medical application based on technology acceptance model. Design Research, 12(3), 109–113.
Yang, J., You, J., & Xiong, Y. (2024). Herding or boldness? The uncertainty and the analysts’ herding behavior from a risk perception perspective. Management Review, 36(3), 17–29. https://doi.org/10.14120/j.cnki.cn11-5057/f.2024.03.016
Yang, L., Lu, K., Diaz-Olivares, J. A., et al. (2018). Towards smart work clothing for automatic risk assessment of physical workload. IEEE Access, 6, 40059–40072. https://doi.org/10.1109/ACCESS.2018.2855719
Ye, J., Qiu, Y., Chen, T., & Fan, X. (2022). An empirical study on the influence mechanism of smart clothing purchase intentions. Journal of Silk, 59(5), 77–84. https://doi.org/10.3969/j.issn.1001-7003.2022.05.011
Yi, P., & Xu, Y. (2023). Report on the development of aging society (2022). Social Science Literature Press.
Zhang, X., & Chang, M. (2023). Applying the extended technology acceptance model to explore Taiwan’s generation Z’s behavioral intentions toward using electric motorcycles. Sustainability, 15(4), 3787. https://doi.org/10.3390/su15043787
Zhao, S. (2024). The dilemma and solution of global artificial intelligence governance. Contemporary International Relations, 4, 116–137.
Zhou, H., Song, X., Fang, L., et al. (2022). How empowering leadership influences medical workers’ work-family conflict in the post-pandemic era: A moderated mediation model of leadership “black box.” Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.870753
Zin, K. S. L. T., Kim, S., Kim, H. S., & Feyissa, I. F. (2023). A study on technology acceptance of digital healthcare among older Korean adults using extended TAM (extended technology acceptance model). Administrative Sciences, 13(2), 42. https://doi.org/10.3390/admsci13020042
Zhukov, P. (2022). Impact factors of the digital economy on economic growth. In: Proceedings of the International Conference Engineering Innovations and Sustainable Development. https://doi.org/10.1007/978-3-030-90843-0
DOI: https://doi.org/10.24294/jipd.v8i8.6230
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Zhe Li, Xinping Song, Meitong Guo, Laitan Fang
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License.