IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0291870.html
   My bibliography  Save this article

Understanding consumers’ intentions to purchase smart clothing using PLS-SEM and fsQCA

Author

Listed:
  • Shucong Chen
  • Jing Ye

Abstract

With the advancement of artificial intelligence (AI) and the Internet of Things (IoT), smart clothing, which has enormous growth potential, has developed to suit consumers’ individualized demands in various areas. This paper aims to construct a model that integrates that technology acceptance model (TAM) and functionality-expressiveness-aesthetics (FEA) model to explore the key factors influencing consumers’ smart clothing purchase intentions (PIs). Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the data, complemented by fuzzy-set qualitative comparative analysis (fsQCA). The PLS-SEM results identified that the characteristics of functionality (FUN), expressiveness (EXP), and aesthetics (AES) positively and significantly affect perceived ease of use (PEOU), and only EXP affects perceived usefulness (PU). PU and PEOU positively impact consumers’ attitudes (ATTs). Subsequently, PU and consumers’ ATTs positively influence PIs. fsQCA revealed the nonlinear and complex interaction effects of the factors influencing consumers’ smart clothing purchase behaviors and uncovered five necessary and six sufficient conditions for consumers’ PIs. This paper furthers theoretical understanding by integrating the FEA model into the TAM. Additionally, on a practical level, it provides significant insights into consumers’ intentions to purchase smart clothing. These findings serve as valuable tools for corporations and designers in strategizing the design and promotion of smart clothing. The results validate theoretical conceptions about smart clothing PIs and provide useful insights and marketing suggestions for smart clothing implementation and development. Moreover, this study is the first to explain smart clothing PIs using symmetric (PLS-SEM) and asymmetric (fsQCA) methods.

Suggested Citation

  • Shucong Chen & Jing Ye, 2023. "Understanding consumers’ intentions to purchase smart clothing using PLS-SEM and fsQCA," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-25, September.
    • Handle: RePEc:plo:pone00:0291870
    DOI: 10.1371/journal.pone.0291870
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0291870
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0291870&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0291870?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sang M. Lee & DonHee Lee, 2020. "Correction to: Healthcare wearable devices: an analysis of key factors for continuous use intention," Service Business, Springer;Pan-Pacific Business Association, vol. 14(4), pages 577-577, December.
    2. Natarajan, Thamaraiselvan & Balasubramanian, Senthil Arasu & Kasilingam, Dharun Lingam, 2018. "The moderating role of device type and age of users on the intention to use mobile shopping applications," Technology in Society, Elsevier, vol. 53(C), pages 79-90.
    3. Kasilingam, Dharun Lingam, 2020. "Understanding the attitude and intention to use smartphone chatbots for shopping," Technology in Society, Elsevier, vol. 62(C).
    4. Sang M. Lee & DonHee Lee, 2020. "Healthcare wearable devices: an analysis of key factors for continuous use intention," Service Business, Springer;Pan-Pacific Business Association, vol. 14(4), pages 503-531, December.
    5. Soyoung An & Thomas Eck & Huirang Yim, 2023. "Understanding Consumers’ Acceptance Intention to Use Mobile Food Delivery Applications through an Extended Technology Acceptance Model," Sustainability, MDPI, vol. 15(1), pages 1-14, January.
    6. McLeay, Fraser & Olya, Hossein & Liu, Hongfei & Jayawardhena, Chanaka & Dennis, Charles, 2022. "A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    7. Khin Shoon Lei Thant Zin & Seieun Kim & Hak-Seon Kim & Israel Fisseha Feyissa, 2023. "A Study on Technology Acceptance of Digital Healthcare among Older Korean Adults Using Extended Tam (Extended Technology Acceptance Model)," Administrative Sciences, MDPI, vol. 13(2), pages 1-18, February.
    8. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang & Lee, Cheng fang, 2022. "Adoption model of healthcare wearable devices," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    9. Xue, Zhebin & Li, Qing & Zeng, Xianyi, 2023. "Social media user behavior analysis applied to the fashion and apparel industry in the big data era," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hoffmann, Stefan & Lasarov, Wassili & Reimers, Hanna, 2022. "Carbon footprint tracking apps. What drives consumers' adoption intention?," Technology in Society, Elsevier, vol. 69(C).
    2. Hayat, Naeem & Salameh, Anas A. & Malik, Haider Ali & Yaacob, Mohd Rafi, 2022. "Exploring the adoption of wearable healthcare devices among the Pakistani adults with dual analysis techniques," Technology in Society, Elsevier, vol. 70(C).
    3. Yuhyung Shin & Won-Moo Hur & Hansol Hwang, 2022. "Impacts of customer incivility and abusive supervision on employee performance: a comparative study of the pre- and post-COVID-19 periods," Service Business, Springer;Pan-Pacific Business Association, vol. 16(2), pages 309-330, June.
    4. Siwalak Jierasup & Adisorn Leelasantitham, 2024. "A Change from Negative to Positive of Later Adoption Using the Innovation Decision Process to Imply Sustainability for HR Chatbots of Private Companies in Thailand," Sustainability, MDPI, vol. 16(13), pages 1-29, July.
    5. Uzir, Md Uzir Hossain & Al Halbusi, Hussam & Lim, Rodney & Jerin, Ishraq & Abdul Hamid, Abu Bakar & Ramayah, Thurasamy & Haque, Ahasanul, 2021. "Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19," Technology in Society, Elsevier, vol. 67(C).
    6. Shah, Sayed Kifayat & Zhongjun, Tang, 2021. "Elaborating on the consumer’s intention–behavior gap regarding 5G technology: The moderating role of the product market-creation ability," Technology in Society, Elsevier, vol. 66(C).
    7. Seckin Damar & Gulsah Hancerliogullari Koksalmis, 2024. "A bibliometric analysis of metaverse technologies in healthcare services," Service Business, Springer;Pan-Pacific Business Association, vol. 18(2), pages 223-254, June.
    8. Santos-Vijande, María Leticia & Gómez-Rico, Mar & Molina-Collado, Arturo & Davison, Robert M., 2022. "Building user engagement to mhealth apps from a learning perspective: Relationships among functional, emotional and social drivers of user value," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    9. Chong Li & Yingqi Li, 2023. "Factors Influencing Public Risk Perception of Emerging Technologies: A Meta-Analysis," Sustainability, MDPI, vol. 15(5), pages 1-37, February.
    10. Mingyu Park & Youngjung Geum, 2021. "On the data-driven generation of new service idea: integrated approach of morphological analysis and text mining," Service Business, Springer;Pan-Pacific Business Association, vol. 15(3), pages 539-561, September.
    11. Marinko Skare & Domingo Riberio Soriano, 2022. "Explaining COVID-19 shock wave mechanism in the European service industry using convergence clubs analysis," Service Business, Springer;Pan-Pacific Business Association, vol. 16(2), pages 283-307, June.
    12. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang & Lee, Cheng fang, 2022. "Adoption model of healthcare wearable devices," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    13. Mahmoud Al-shami & Rawad Abdulghafor & Abdulaziz Aborujilah & Abubakar Yagoub & Sherzod Turaev & Mohammed A. H. Ali, 2023. "COVID-19 Tracking Applications Acceptance among General Populace: An Overview in Malaysia," Sustainability, MDPI, vol. 15(5), pages 1-13, February.
    14. K Akdim & Luis V. Casaló, 2023. "Perceived value of AI-based recommendations service: the case of voice assistants," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 81-112, March.
    15. Jesús J. Cambra-Fierro & María Fuentes-Blasco & Rocío Huerta-Álvarez & Ana Olavarría, 2021. "Customer-based brand equity and customer engagement in experiential services: insights from an emerging economy," Service Business, Springer;Pan-Pacific Business Association, vol. 15(3), pages 467-491, September.
    16. Sang-Heui Lee & Christine E. Fogliasso & Seunghye Jung & M. Minsuk Shin & Connie Shum, 2023. "User psychological states and enterprise information systems adoption: evidence from the United States, Zimbabwe, South Korea, and Paraguay," Service Business, Springer;Pan-Pacific Business Association, vol. 17(2), pages 477-497, June.
    17. Khan, Asad Ullah & Ma, Zhiqiang & Li, Mingxing & Hu, Weijun & Khan, Muhammad Naeem & Sohu, Jan Muhammad & Aziz, Fazeelat, 2024. "Beyond bookshelves, how 5/6G technology will reshape libraries: Two-stage SEM and SF-AHP analysis," Technology in Society, Elsevier, vol. 78(C).
    18. Jorge Serrano-Malebran & Cristian Vidal-Silva & Iván Veas-González, 2023. "Social Media Marketing as a Segmentation Tool," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    19. Mostafa Bigdeli & Mahsa Akbari, 2024. "Machine-learning-based Classification of Customers’ Behavioural Model in Instagram," Paradigm, , vol. 28(2), pages 223-240, December.
    20. Cliff R. Kikawa & Charity Kiconco & Moses Agaba & Dimas Ntirampeba & Amos Ssematimba & Billy M. Kalema, 2022. "Social Media Marketing for Small and Medium Enterprise Performance in Uganda: A Structural Equation Model," Sustainability, MDPI, vol. 14(21), pages 1-20, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0291870. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.