CONSUMER INNOVATIVENESS AND AI-ENABLED EASE OF USE: EXAMINING PURCHASE INTENTION IN DIGITAL FASHION RETAIL

Raja Muneeb Anwar, Syeda Fatima Hussain, Zia Rasheed, Muhammad Shoaib, Haseeb Anwar, Ebrahim Mollik

Abstract


The increasing integration of artificial intelligence (AI) in digital fashion retail has transformed how consumers interact with online shopping platforms. While prior research has predominantly focused on technological attributes such as perceived usefulness, limited attention has been given to the role of individual consumer traits in shaping AI adoption outcomes. Addressing this gap, the present study examines the influence of consumer innovativeness on purchase intention in AI-enabled digital fashion retail, with AI-enabled ease of use proposed as a mediating mechanism. Drawing on the Technology Acceptance Model (TAM) and consumer innovativeness theory, a quantitative, cross-sectional research design was employed. Data were collected through an online survey of 204 UK fashion consumers and analysed using regression-based mediation analysis. The results indicate that consumer innovativeness has a significant positive effect on purchase intention and on perceptions of AI-enabled ease of use. Furthermore, AI-enabled ease of use partially mediates the relationship between consumer innovativeness and purchase intention, highlighting usability as a key mechanism through which innovative tendencies translate into behavioural outcomes. These findings extend TAM by positioning consumer innovativeness as an antecedent of perceived ease of use in AI-driven retail contexts. Practically, the study underscores the importance of intuitive AI design, consumer segmentation based on innovativeness, and strategic onboarding in digital fashion retail. Overall, this research contributes to a more consumer-centric understanding of AI adoption and offers actionable insights for fashion retailers and AI developers.

 

JEL: M31, O33, D91, L81

 

Article visualizations:

Hit counter


Keywords


consumer innovativeness, artificial intelligence (AI), AI-enabled ease of use, purchase intention, digital fashion retail

Full Text:

PDF

References


Akar, E. & Nasir, V.A. (2015). A review of literature on consumers’ online purchase intentions, Journal of Customer Behaviour, 14(3), pp. 215–233. https://doi.org/10.1362/147539215X14441363630837

Ashraf, A.R., Thongpapanl, N. & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts, Journal of International Marketing, 22(3), pp. 68–93. https://doi.org/10.1509/jim.14.0065

Batool, S. & Mou, Y. (2024). Artificial intelligence in online retail: A review of consumer adoption, Journal of Retailing and Consumer Services, 74, 103465.

Davenport, T.H., Guha, A., Grewal, D. & Bressgott, T. (2019). How artificial intelligence will change the future of marketing, Journal of the Academy of Marketing Science, 48(1), pp. 24–42. https://doi.org/10.1007/s11747-019-00696-0

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13(3), pp. 319–340. https://doi.org/10.2307/249008

Dillman, D.A., Smyth, J.D. & Christian, L.M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method. 4th edn. Hoboken: Wiley. Retrieved from https://onlinelibrary.wiley.com/doi/book/10.1002/9781394260645

Dwianto, A., Nugraha, Y.E., Handayani, P.W. & Azzahro, F. (2024). Understanding consumer adoption of AI-based retail applications, Technological Forecasting and Social Change, 197, 122886.

Esfahani, M.S. & Reynolds, N. (2021). Consumer innovativeness and technology adoption behaviour, Journal of Consumer Behaviour, 20(2), pp. 256–270.

Eryigit, C. (2020). Opinion leadership and innovativeness in consumer behaviour, Journal of Retailing and Consumer Services, 54. Retrieved from https://essuir.sumdu.edu.ua/server/api/core/bitstreams/123795d3-430c-4f3c-8c8e-ba5d997a776f/content

Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2019). Multivariate data analysis. 8th edn. Harlow: Pearson Education. Retrieved from https://books.google.ro/books/about/Multivariate_Data_Analysis.html?id=VvXZnQEACAAJ&redir_esc=y

Hur, W.M., Yoo, J.J. & Chung, T.L. (2012). The consumption values and consumer innovativeness on convergence products, Industrial Management & Data Systems, 112(5), pp. 688–706. Retrieved from https://doi.org/10.1108/02635571211232271

Istiqomah, N. and Alfansi, L. (2023). Consumer acceptance of artificial intelligence in fashion e-commerce, Journal of Fashion Marketing and Management, 27(3), pp. 489–507.

Kashive, N., Powale, S. & Kashive, K. (2020). Understanding user perception toward artificial intelligence in customer service, Journal of Retailing and Consumer Services, 53. https://doi.org/10.1108/IJILT-05-2020-0090

Lebo, M.J. and Weber, C. (2014). An effective approach to the repeated cross-section design, American Journal of Political Science, 59(1), pp. 242–258. https://doi.org/10.1111/ajps.12095

Lowe, B. & Alpert, F. (2015). Forecasting consumer perception of innovativeness, Technovation, 45–46, pp. 1–14. https://doi.org/10.1016/j.technovation.2015.02.001

Malhan, S., Mewafarosh, M. & Agnihotri, R. (2023). AI interface usability and consumer engagement, Journal of Business Research, 155, 113418.

Manning, K.C., Bearden, W.O. & Madden, T.J. (1995). Consumer innovativeness and the adoption process, Journal of Consumer Psychology, 4(4), pp. 329–345. https://doi.org/10.1207/s15327663jcp0404_02

Marangunić, N. & Granić, A. (2014). Technology acceptance model: A literature review, Universal Access in the Information Society, 14(1), pp. 81–95. https://doi.org/10.1007/s10209-014-0348-1

Nunnally, J.C. and Bernstein, I.H. (1994) Psychometric theory. 3rd edn. New York: McGraw-Hill. Retrieved from https://books.google.ro/books/about/Psychometric_Theory.html?id=r0fuAAAAMAAJ&redir_esc=y

Panagoulias, A., Virvou, M. & Tsihrintzis, G.A. (2023). Explainable AI and user trust in recommender systems, Expert Systems with Applications, 213.

Pantano, E. & Pizzi, G. (2020). Forecasting artificial intelligence on online customer assistance, Journal of Retailing and Consumer Services, 55. https://doi.org/10.1016/j.jretconser.2020.102096

Pavlou, P.A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk, International Journal of Electronic Commerce, 7(3), pp. 101–134. https://doi.org/10.1080/10864415.2003.11044275

Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A. & Siqueira-Junior, J.R. (2020). Purchase intention and purchase behavior online, Journal of Business Research, 113, pp. 166–176. https://doi.org/10.1016/j.heliyon.2020.e04284

Ritchie, J. & Lewis, J. (2003). Qualitative research practice. London: Sage. Retrieved from https://books.google.ro/books/about/Qualitative_Research_Practice.html?id=EQSIAwAAQBAJ&redir_esc=y

Roehrich, G. (2004). Consumer innovativeness: Concepts and measurements, Journal of Business Research, 57(6), pp. 671–677. https://doi.org/10.1016/S0148-2963(02)00311-9

Saunders, M., Lewis, P. & Thornhill, A. (2019). Research methods for business students. 8th edn. Harlow: Pearson Education. Retrieved from https://www.pearson.com/se/Nordics-Higher-Education/subject-catalogue/business-and-management/Research-methods-for-business-students-8e-saunders.html

Taherdoost, H. (2021). Data collection methods and tools, International Journal of Academic Research in Management, 10(1), pp. 10–38. Retrieved from https://www.researchgate.net/publication/359596426_Data_Collection_Methods_and_Tools_for_Research_A_Step-by-Step_Guide_to_Choose_Data_Collection_Technique_for_Academic_and_Business_Research_Projects

Vandecasteele, B. & Geuens, M. (2010). Motivated consumer innovativeness, International Journal of Research in Marketing, 27(4), pp. 308–318. Retrieved from https://ideas.repec.org/a/eee/ijrema/v27y2010i4p308-318.html

Wang, Y., Cao, Y. & Ameen, N. (2022). Artificial intelligence adoption in online retail, Information Technology & People, 35(7), pp. 2230–2255.

Wang, Y., Lin, H. and Yuen, K.F. (2023). Perceived ease of use and AI adoption, Technological Forecasting and Social Change, 189.

Younus, S., Rasheed, F. & Zia, A. (2015). Identifying the factors affecting customer purchase intention, Global Journal of Management and Business Research, 15(2), pp. 1–8. Retrieved from https://globaljournals.org/GJMBR_Volume15/2-Identifying-the-Factors-Affecting.pdf




DOI: http://dx.doi.org/10.46827/ejhrms.v10i1.2145

Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 European Journal of Human Resource Management Studies

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

The research works published in this journal are free to be accessed. They can be shared (copied and redistributed in any medium or format) and\or adapted (remixed, transformed, and built upon the material for any purpose, commercially and\or not commercially) under the following terms: attribution (appropriate credit must be given indicating original authors, research work name and publication name mentioning if changes were made) and without adding additional restrictions (without restricting others from doing anything the actual license permits). Authors retain the full copyright of their published research works and cannot revoke these freedoms as long as the license terms are followed.

Copyright © 2017-2026. European Journal Of Human Resource Management Studies (ISSN 2601-1972) is a registered trademark. All rights reserved.

This journal is a serial publication uniquely identified by an International Standard Serial Number (ISSN) serial number certificate issued by Romanian National Library. All the research works are uniquely identified by a CrossRef DOI digital object identifier supplied by indexing and repository platforms. All the research works published on this journal are meeting the Open Access Publishing requirements and standards formulated by Budapest Open Access Initiative (2002), the Bethesda Statement on Open Access Publishing (2003) and  Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities (2003) and can be freely accessed, shared, modified, distributed and used in educational, commercial and non-commercial purposes under a Creative Commons Attribution 4.0 International License. Copyrights of the published research works are retained by authors.