ESP-4347

Published

2026-01-14

Issue

Vol. 11 No. 1 (2026): Publishing

Section

Research Articles

License

Copyright (c) 2026 Shen Qinjie*, Nainapas Injoungjirakit, Sombat Teekasap, Prapai Sridama

Creative Commons License

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

The journal adopts the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which means that anyone can reuse and redistribute the materials for non-commercial purposes as long as you follow the license terms and the original source is properly cited.

Author(s) shall retain the copyright of their work and grant the Journal/Publisher rights for the first publication with the work concurrently licensed since 2023 Vol.8 No.2.

Under this license, author(s) will allow third parties to download, reuse, reprint, modify, distribute and/or copy the content under the condition that the authors are given credit. No permission is required from the authors or the publisher.

This broad license intends to facilitate free access, as well as the unrestricted use of original works of all types. This ensures that the published work is freely and openly available in perpetuity.

By providing open access, the following benefits are brought about:

  • Higher Visibility, Availability and Citations-free and unlimited accessibility of the publication over the internet without any restrictions increases citation of the article.
  • Ease of search-publications are easily searchable in search engines and indexing databases.
  • Rapid Publication – accepted papers are immediately published online.
  • Available for free download immediately after publication at https://esp.as-pub.com/index.php/ESP

 

Copyright Statement

1.The authors certify that the submitted manuscripts are original works, do not infringe the rights of others, are free from academic misconduct and confidentiality issues, and that there are no disputes over the authorship scheme of the collaborative articles. In case of infringement, academic misconduct and confidentiality issues, as well as disputes over the authorship scheme, all responsibilities will be borne by the authors.

2. The author agrees to grant the Editorial Office of Environment and Social Psychology a licence to use the reproduction right, distribution right, information network dissemination right, performance right, translation right, and compilation right of the submitted manuscript, including the work as a whole, as well as the diagrams, tables, abstracts, and any other parts that can be extracted from the work and used in accordance with the characteristics of the journal. The Editorial Board of Environment and Social Psychology has the right to use and sub-licence the above mentioned works for wide dissemination in print, electronic and online versions, and, in accordance with the characteristics of the periodical, for the period of legal protection of the property right of the copyright in the work, and for the territorial scope of the work throughout the world.

3. The authors are entitled to the copyright of their works under the relevant laws of Singapore, provided that they do not exercise their rights in a manner prejudicial to the interests of the Journal.

About Licence

Environment and Social Psychology is an open access journal and all published work is available under the Creative Commons Licence, Authors shall retain copyright of their work and grant the journal/publisher the right of first publication, and their work shall be licensed under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

Under this licence, the author grants permission to third parties to download, reuse, reprint, modify, distribute and/or copy the content with attribution to the author. No permission from the author or publisher is required.

This broad licence is intended to facilitate free access to and unrestricted use of original works of all kinds. This ensures that published works remain free and accessible in perpetuity. Submitted manuscripts, once accepted, are immediately available to the public and permanently accessible free of charge on the journal’s official website (https://esp.as-pub.com/index.php/ESP). Allowing users to read, download, copy, print, search for or link to the full text of the article, or use it for other legal purposes. However, the use of the work must retain the author's signature, be limited to non-commercial purposes, and not be interpretative.

Click to download <Agreement on the Licence for the Use of Copyright on Environmental and Social Psychology>.

How to Cite

Shen Qinjie, Nainapas Injoungjirakit, Sombat Teekasap, & Prapai Sridama. (2026). Adoption of campus IoT in Shanghai higher education: A mixed-methods UTAUT study with infrastructure and privacy extensions. Environment and Social Psychology, 11(1), ESP-4347. https://doi.org/10.59429/esp.v11i1.4347

Adoption of campus IoT in Shanghai higher education: A mixed-methods UTAUT study with infrastructure and privacy extensions

Shen Qinjie

Graduate School, Bansomdejchaopraya Rajabhat University, Bangkok, 10600, Thailand

Nainapas Injoungjirakit

Graduate School, Bansomdejchaopraya Rajabhat University, Bangkok, 10600, Thailand

Sombat Teekasap

Graduate School, Bansomdejchaopraya Rajabhat University, Bangkok, 10600, Thailand

Prapai Sridama

Graduate School, Bansomdejchaopraya Rajabhat University, Bangkok, 10600, Thailand


DOI: https://doi.org/10.59429/esp.v11i1.4347


Keywords: internet of things; higher education; UTAUT; infrastructure readiness; privacy; behavioral intention

Abstract

 

Smart-campus initiatives in Shanghai have expanded rapidly, yet evidence on students’ adoption of campus Internet of Things (IoT) services remains mixed. This study integrates the Unified Theory of Acceptance and Use of Technology with two contextually salient antecedents, reliable Internet connection and security or privacy concern, to explain intention to use campus IoT in higher education. We employed a cross-sectional student survey and complementary tutor interviews. The quantitative strand tested separate bivariate models for key predictors, and the qualitative strand used thematic analysis to contextualize mechanisms and barriers.

Findings indicate an infrastructure-first pathway. When campus connectivity is stable and low-friction, students treat IoT as an ambient utility, and intention to use increases, while traditional cognition-centric predictors play a smaller role. Perceived usefulness remains a consistent positive driver; privacy concerns can be addressed through clear policies and visible safeguards; and brief onboarding helps novices move from trial to routine use. The study contributes a pragmatic extension of technology-acceptance work by specifying infrastructure readiness and privacy governance as first-order antecedents of adoption in higher education. Practical recommendations include campus-level connectivity targets, streamlined authentication, plain-language data-use messaging, and micro-orientations at the start of courses. Limitations include a single-city scope and a cross-sectional design; future research should validate the infrastructure-first thesis using multivariable models and multi-site samples.  

 

References

[1]. 1.Min-Allah, N., & Alrashed, S. (2020). Smart campus—A sketch. Sustainable Cities and Society, 59, 102231. https://doi.org/10.1016/j.scs.2020.102231

[2]. 2.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

[3]. 3.Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412

[4]. 4.Fernández-Batanero, J. M., Montenegro-Rueda, M., Fernández-Cerero, J., & López Menéses, E. (2024). Adoption of the Internet of Things in higher education: opportunities and challenges. Interactive Technology and Smart Education, 21(2), 292-307. https://doi.org/10.1108/ITSE-01-2023-0025

[5]. 5.Madni, S. H. H., Ali, J., Husnain, H. A., Masum, M. H., Mustafa, S., Shuja, J., ... & Hosseini, S. (2022). Factors influencing the adoption of IoT for E-learning in higher educational institutes in developing countries. Frontiers in Psychology, 13, 915596. https://doi.org/10.3389/fpsyg.2022.915596

[6]. 6.Korkmaz, Ö., Erer, E., & Erer, D. (2022). Internet access and its role on educational inequality during the COVID-19 pandemic. Telecommunications Policy, 46(5), 102353. https://doi.org/10.1016/j.telpol.2022.102353

[7]. 7.Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509–514. https://doi.org/10.1126/science.aaa1465

[8]. 8.Bélanger, F., & Carter, L. (2008). Trust and risk in e-government adoption. Journal of Strategic Information Systems, 17(2), 165–176. https://doi.org/10.1016/j.jsis.2007.12.002

[9]. 9.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(4), 987–1005. https://doi.org/10.1007/s10796-020-10007-6

[10]. 10.Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008

[11]. 11.Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50. https://doi.org/10.1016/j.iheduc.2013.10.001

[12]. 12.Valks, B., Arkesteijn, M. H., Koutamanis, A., & den Heijer, A. C. (2021). Towards a smart campus: supporting campus decisions with Internet of Things applications. Building Research & Information, 49(1), 1-20. https://doi.org/10.1080/09613218.2020.1784702

[13]. 13.Trkman, M., Popovič, A., & Trkman, P. (2023). The roles of privacy concerns and trust in voluntary use of governmental proximity tracing applications. Government Information Quarterly, 40(1), 101787. https://doi.org/10.1016/j.giq.2022.101787

[14]. 14.Rindfleisch, A., Malter, A. J., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing Research, 45(3), 261–279. https://doi.org/10.1509/jmkr.45.3.261

[15]. 15.Dearnley, C. (2005). A reflection on the use of semi-structured interviews. Nurse Researcher, 13(1), 19–28. https://doi.org/10.7748/nr2005.07.13.1.19.c5997

[16]. 16.Saunders, M. N., & Thornhill, A. (2011). Researching sensitively without sensitizing: Using a card sort in a concurrent mixed methods design to research trust and distrust. International Journal of Multiple Research Approaches, 5(3), 334–350. https://doi.org/10.5172/mra.2011.5.3.334

[17]. 17.Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students (5th ed.). Pearson.

[18]. 18.Andrews, D., Nonnecke, B., & Preece, J. (2007). Conducting research on the internet: Online survey design, development and implementation guidelines. https://auspace.athabascau.ca/handle/2149/1336

[19]. 19.Creswell, J. W., & Creswell, J. D. (2005). Mixed methods research: Developments, debates, and dilemmas. Research in organizations: Foundations and methods of inquiry, 2, 315-326.

[20]. 20.Flick, U. (2015). Introducing research methodology: A beginner's guide to doing a research project. Sage.

[21]. 21.Connolly, P. (2007). Quantitative data analysis in education: A critical introduction using SPSS (1st ed.). Routledge. https://doi.org/10.4324/9780203946985

[22]. 22.Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press.

[23]. 23.Mertens, W., Pugliese, A., & Recker, J. (2017). Quantitative data analysis: A companion for accounting and information systems research. Springer. https://doi.org/10.1007/978-3-319-42700-3

[24]. 24.Sarstedt, M., & Mooi, E. (2018). Regression analysis. In A concise guide to market research: The process, data, and methods using IBM SPSS Statistics (pp. 209-256). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-56707-4_7

[25]. 25.Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428

[26]. 26.Domínguez-Bolaño, T., Barral, V., Escudero, C. J., & García-Naya, J. A. (2024). An IoT system for a smart campus: Challenges and solutions illustrated over several real-world use cases. Internet of Things, 25, 101099. https://doi.org/10.1016/j.iot.2024.101099

[27]. 27.Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory of Acceptance and Use of Technology (UTAUT) in higher education: A systematic review. SAGE Open, 14(1). https://doi.org/10.1177/21582440241229570

[28]. 28.Popescu, M., Baruh, L., & Sudhakar, S. (2024). Role-based privacy cynicism and local privacy activism: How data stewards navigate privacy in higher education. Big Data & Society, 11(2). https://doi.org/10.1177/20539517241240664

[29]. 29.Mexhuani, B. (2025). Adopting digital tools in higher education: Opportunities, challenges and theoretical insights. European Journal of Education, 60(1), e12819. https://doi.org/10.1111/ejed.12819



21 Woodlands Close #02-10, Primz Bizhub,Postal 737854, Singapore

Email:editorial_office@as-pub.com