Exploring factors influencing Contrimetric adoption and word-of-mouth advocacy in academic libraries
DOI:
https://doi.org/10.22452/mjlis.vol30no2.2Keywords:
Perceived usefulness, Perceived ease of use, Social influence, Facilitating conditions, Behavioral intention to use, Word of mouthAbstract
Contrimetric is an AI-powered bibliometric plugin designed to provide real-time citation tracking, personalised content recommendations, reference validation, and dynamic research impact dashboards. This study investigates the factors influencing the adoption of Contrimetric, an emerging AI-powered bibliometric tool, in academic libraries across China. Drawing on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the research examines how perceived ease of use, perceived usefulness, social influence, and facilitating conditions affect behavioural intention, which in turn drives word-of-mouth (WOM) advocacy among academic peers. Data were collected through a structured survey administered to librarians and researchers in Chinese universities, yielding 400 valid responses representing diverse academic disciplines and institutional settings. The results confirm that perceived ease of use, perceived usefulness, social influence, and facilitating conditions all have significant positive effects on behavioural intention, and behavioural intention positively influences WOM. Mediation analysis further indicates that behavioural intention mediates the relationships between perceived usefulness, social influence, facilitating conditions, and WOM, while the indirect path for perceived ease of use is marginally significant at the 10% level. These findings highlight the critical role of institutional support and peer influence in fostering advocacy, such as recommending Contrimetric to colleagues and encouraging its adoption in academic environments. The study contributes to the technology adoption literature by extending TAM and UTAUT beyond initial adoption, incorporating post-adoption WOM behaviours as a critical outcome, and offers practical implications for policymakers and library administrators seeking to enhance research visibility through AI-enabled services.
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References
Abid, H. (2021). Uses of blockchain technologies in library services. Library Hi Tech News, 38(8) 9–11. https://doi.org/10.1108/lhtn-08-2020-0079.
Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management system use: An extension of UTAUT2. Information Development, 32(5), 1306–1321. https://doi.org/10.1177/0266666915597546.
Al Halbusi, H., Al-Sulaiti, K., Abbas, J., & Al-Sulaiti, I. (2022). Assessing factors influencing technology adoption for online purchasing amid COVID-19 in Qatar: Moderating role of word of mouth. Frontiers in Environmental Science, 10, 942527. https://doi.org/10.3389/fenvs.2022.942527.
Asante, I. K., Ametepey, E. T. K., Ocran, J. K., Omega, S., & Annor-Frempong, F. (2024). Factors influencing the behaviour of extension agents towards the adoption of digital technologies in agricultural extension: A theory of planned behaviour perspective. International Journal of Agricultural Extension, 12(3), 361–378. https://doi.org/10.33687/ijae.012.003.5340.
Caputo, A., & Kargina, M. (2022). A user-friendly method to merge Scopus and Web of Science data during bibliometric analysis. Journal of Marketing Analytics, 10(1), 82–88. https://doi.org/10.1057/s41270-021-00142-7.
Chen, C.-C., Liu, C.-C., Chiu, T.-H., Lee, Y.-W., & Wu, K.-C. (2022). Role of perceived ease of use for augmented reality app designed to help children navigate smart libraries. International Journal of Human-Computer Interaction, 39(13), 2606–2623. https://doi.org/10.1080/10447318.2022.2082017.
Chen, G.-H., Fan, J.-M., & Azam, M. (2024). Exploring artificial intelligence (AI) chatbots adoption among research scholars using unified theory of acceptance and use of technology (UTAUT). Journal of Librarianship and Information Science. Advance online publication. https://doi.org/10.1177/09610006241269189.
Chou, C.-M., Shen, T.-C., Shen, T.-C., & Shen, C.-H. (2025). Teachers’ adoption of AI-supported teaching behavior and its influencing factors: Using structural equation modeling. Journal of Computers in Education, 12(3), 853-896. https://doi.org/10.1007/s40692-024-00332-z.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587.
Contrimetric. (2025). What’s the Contrimetric’s plug-in? https://contrimetric.com/plugin
Dattolo, A., & Corbatto, M. (2022). Assisting researchers in bibliographic tasks: A new usable, real-time tool for analyzing bibliographies. Journal of the Association for Information Science and Technology, 73(6), 757–776. https://doi.org/10.1002/asi.24578.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008.
Farzin, M., Sadeghi, M., Yahyayi Kharkeshi, F., Ruholahpur, H., & Fattahi, M. (2021). Extending UTAUT2 in M-banking adoption and actual use behavior: Does WOM communication matter? Asian Journal of Economics and Banking, 5(2), 136–157. https://doi.org/10.1108/ajeb-10-2020-0085.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203.
Harnadi, B., Prasetya, F. H., & Widiantoro, A. D. (2022). Understanding behavioral intention to use social media technology: Two comparing model, TAM and UTAUT. In 2022 6th International Conference on Information Technology (InCIT) (pp. 352–357). IEEE. 10.1109/InCIT56086.2022.10067645.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11757-014-0403-8.
Huang, Y.-H. (2024). Exploring the implementation of artificial intelligence applications among academic libraries in Taiwan. Library Hi Tech, 42(3), 885–905. https://doi.org/10.1108/lht-03-2022-0159.
Hussain, A., & Khan, A. (2025). Librarian acceptance of artificial intelligence in academic libraries of Islamabad: An application of the technology acceptance model. Technical Services Quarterly, 0(0), 1–21. https://doi.org/10.1080/07317131.2025.2512280.
Jannah, F. A. Z., & Susila, I. (2023). The role of TAM and E-WOM on customer purchase intention in E-Commerce which is influenced by trust. Proceedings of the 3rd International Conference on Innovations in Social Sciences Education and Engineering (ICoISSEE-3), 3, Article 109. https://www.conference.loupiasconference.org/index.php/icoissee3/article/view/356.
Kang, M.-H., Liew, B. Y. T., Lim, H.-J., Jang, J.-E., & Lee, S.-Y. (2015). Investigating the determinants of mobile learning acceptance in Korea using UTAUT2. In G. Chen, V. Kumar, Kinshuk, R.-H. Huang, & S. C. Kong, (eds.), Emerging issues in smart learning (pp. 209–216). Lecture notes in educational technology (LNET). Springer. https://doi.org/10.1007/978-3-662-44188-6_29.
Kaushik, M. K., & Agrawal, D. (2021). Influence of technology readiness in adoption of e-learning. International Journal of Educational Management, 35(2), 483–495. https://doi.org/10.1108/ijem-04-2020-0216.
Kim, S.-H., Park, S.-O., Park, J.-W., & Oh, Y.-J. (2022). A study on the intention to use the AI-related educational content recommendation system in the university library: Focusing on the perceptions of university students and librarians. Journal of Korean Library and Information Science Society, 53(1), 231–263. https://doi.org/10.16981/kliss.53.1.202203.231.
Kuo, K. (2023). Contrimetric: Article citation contribution indicator for paper impact evaluation. CNS Spectrums, 28(S2), S144–S145. https://doi.org/10.1017/S1092852923006181.
Lakulu, M. M., Izkair, A. S., Abdul Muttalib, M. F., & Zainuddin, N. A. (2025). Understanding AI and mobile learning adoption in Malaysian universities: A UTAUT-based model. International Journal of Interactive Mobile Technologies (iJIM), 19(11), 80-111. https://doi.org/10.3991/ijim.v19i11.52977.
Li, W. (2025). A study on factors influencing designers’ behavioral intention in using AI-generated content for assisted design: Perceived anxiety, perceived risk, and UTAUT. International Journal of Human-Computer Interaction, 41(2), 1064-1077. https://doi.org/10.1080/10447318.2024.2310354.
Liao, Y.-K., Wu, W.-Y., Le, T. Q., & Phung, T. T. T. (2022). The integration of the technology acceptance model and value-based adoption model to study the adoption of e-learning: The moderating role of e-WOM. Sustainability, 14(2) 815. https://doi.org/10.3390/su14020815.
Mailizar, M., Almanthari, A.-S., & Maulina, S. (2021). Examining teachers’ behavioral intention to use e-learning in teaching of mathematics: An extended TAM model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709.
Masrek, M. N., & Hussein, A. (2021). Intention to adopt mobile applications services: A study among Pakistani academic librarians. International Journal of Innovation, Creativity, and Change, 15(2), 38–54.
Miller, J., & Khera, O. (2010). Digital library adoption and the technology acceptance model: A cross-country analysis. The Electronic Journal of Information Systems in Developing Countries, 40(1). 1–19. https://doi.org/10.1002/j.1681-4835.2010.tb00288.x.
Mogaji, E., Viglia, G., Srivastava, P., & Dwivedi, Y. K. (2024). Is it the end of the technology acceptance model in the era of generative artificial intelligence? International Journal of Contemporary Hospitality Management, 36(10), 3324–3339. https://doi.org/10.1108/ijchm-08-2023-1271.
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies, 25(5), 4139–4155. https://doi.org/10.1007/s10639-020-10157-9.
Orubebe, E. D., Oloniruha, E. A., & Oladokun, B. D. (2025). Adoption and utilization of artificial intelligence in academic libraries: Challenges and opportunities in developed and developing nations. International Journal of Knowledge Content Development & Technology, 15(3), 23-35.
Pinho, C., Franco, M., & Mendes, L. (2021). Application of innovation diffusion theory to the e-learning process: Higher education context. Education and Information Technologies, 26(1), 421–440. https://doi.org/10.1007/s10639-020-10269-2.
Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732. https://doi.org/10.1016/j.compedu.2019.103732.
Raihan, N. (2024). CiteClick: A browser extension for real-time scholar citation tracking. arXiv preprint, arXiv:2410.16211. https://doi.org/10.48550/arXiv.2410.16211Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219–238. https://doi.org/10.1108/itse-11-2018-0092.
Sakib, M. N., Islam, M., Fahlevi, M., Rahman, M. S., Younos, M., & Rahman, M. M. (2025). Factors influencing users’ intention to adopt ChatGPT based on the extended technology acceptance model. Computers in Human Behavior: Artificial Humans, 6, 100204. https://doi.org/10.1016/j.chbah.2025.100204.
Schorr, A. (2023). The technology acceptance model (TAM) and its importance for digitalization research: A review. International Symposium on Technikpsychologie (TecPsy)2023 (pp. 55-65). Technische Universität Darmstadt 2023. https://doi.org/10.2478/9788366675896.
Shahzad, K., & Khan, S. A. (2023). Effects of e-learning technologies on university librarians and libraries: A systematic literature review. The Electronic Library, 41(4), 528–554. https://doi.org/10.1108/el-04-2023-0076.
Shahzad, K., Khan, S. A., & Iqbal, A. (2024). Identifying librarians’ readiness to leverage artificial intelligence for sustainable competence development and smart library services: An empirical investigation from universities’ librarians. Global Knowledge, Memory & Communication. Advance online publication. https://doi.org/10.1108/gkmc-02-2024-0107.
Shal, T., Ghamrawi, N., & Naccache, H. (2024). Leadership styles and AI acceptance in academic libraries in higher education. Journal of Academic Librarianship, 50(2), 102849. https://doi.org/10.1016/j.acalib.2024.102849.
Silva, P. (2015). Chapter 13: Davis’ technology acceptance model (TAM) (1989). In M. N. Al-Suqri, & A. S. Al-Aufi (Eds.), Information seeking behavior and technology adoption: Theories and trends (pp. 205-219). IGI Global. https://doi.org/10.4018/978-1-4666-8156-9.ch013.
Subaveerapandiyan, A. (2023). Application of artificial intelligence (AI) in libraries and its impact on library operations review. Library Philosophy & Practice (e-journal), 7828. https://digitalcommons.unl.edu/libphilprac/7828/.
Szymkowiak, A., & Jeganathan, K. (2022). Predicting user acceptance of peer-to-peer e-learning: An extension of the technology acceptance model. British Journal of Educational Technology, 53(6), 1993–2011. https://doi.org/10.1111/bjet.13229.
Tait, E., & Pierson, C. M. (2022). Artificial intelligence and robots in libraries: Opportunities in LIS curriculum for preparing the librarians of tomorrow. Journal of the Australian Library and Information Association, 71(3) 256–274. https://doi.org/10.1080/24750158.2022.2081111.
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.
Tian, W.-Q., Ge, J.-S., Zhao, Y., & Zheng, X. (2024). AI chatbots in Chinese higher education: Adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models. Frontiers in Psychology, 15, 1268549. https://doi.org/10.3389/fpsyg.2024.1268549.
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.
Venkatesh, V. (2022). Adoption and use of AI tools: A research agenda grounded in UTAUT. Annals of Operations Research, 308(1), 641–652. https://doi.org/10.1007/s10479-020-03918-9.
Wang, Y.-M., Liu, C.-C., & Tu, Y.-F. (2021). Factors affecting the adoption of AI-based applications in higher education: An analysis of teachers' perspectives using structural equation modeling. Educational Technology & Society, 24(3), 116–129.
Wheatley, A., & Hervieux, S. (2019). Artificial intelligence in academic libraries: An environmental scan. Information Services & Use, 39(4), 347–356. https://doi.org/10.3233/isu-190065.
Wu, W.-T., Zhang, B., Li, S.-T., & Liu, H.-H. (2022). Exploring factors of the willingness to accept AI-assisted learning environments: An empirical investigation based on the UTAUT model and perceived risk theory. Frontiers in Psychology, 13, 870777. https://doi.org/10.3389/fpsyg.2022.870777.
Xu, S., Khan, K. I., & Shahzad, M. F. (2024). Examining the influence of technological self-efficacy, perceived trust, security, and electronic word of mouth on ICT usage in the education sector. Scientific Reports, 14(1), 16196. https://doi.org/10.1038/s41598-024-66689-4.
Yoon, J. W., Andrews, J. E., & Ward, H. L. (2022). Perceptions on adopting artificial intelligence and related technologies in libraries: Public and academic librarians in North America. Library Hi Tech, 40(6), 1893–1915. https://doi.org/10.1108/lht-07-2021-0229.
Yu, Y., & Liu, Z.-L. (2022). Article citation contribution indicator: Application in energy and environment. Ecofeminism and Climate Change (EFCC), 3(2), 81-84. https://doi.org/ 10.26480/efcc.02.2022.81.84.
Zhang, H.-B. (2018). The influence mechanism of internet word-of-mouth on consumer behavior: A contrastive study between Japan and China on tablet marketing (Second Version). Scientific Research Publishing, Inc.
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