Understanding the Drivers of Digital Library Use: Evidence from College Students in Chengdu, China
DOI:
https://doi.org/10.14456/au-ejir.2025.18Keywords:
Digital Library, Attitude, Behavioral IntentionAbstract
Purpose: This study aims to explore the factors influencing students' attitudes and behavioral intentions toward using digital libraries in universities and colleges in Sichuan Province, China. Research design, data and methodology: This study employs quantitative methods and structured questionnaires to collect sample data. Before distributing the questionnaire, the alignment between the research objectives and survey items will be assessed, and a small-scale pre-test will be conducted to ensure content validity and reliability. Subsequently, two statistical techniques, confirmatory factor analysis and structural equation modeling—will be applied to analyze the collected data. This process will assess the model’s fit and clarify causal relationships between variables, providing a solid foundation for hypothesis testing. Results: The conceptual framework has been shown to effectively predict college students' behavioral intentions (BI) regarding digital library usage. Notably, Effort Expectancy (EE) and Social Influence (SI) are the most influential factors driving this intention. Conclusions: EE emerges as the most reliable predictor of business intelligence (BI) in the context of digital library usage. Additionally, IT was found to indirectly influence BI by shaping UA. Based on these insights, the study suggests that by leveraging advanced information technology solutions, providing high-quality services, and ensuring strict information security, digital libraries can more effectively meet users' needs, thereby promoting wider adoption and supporting the sustainable development of higher education.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
Alam, M., Kousar, S., & Rehman, C. A. (2020). The impact of self-efficacy on operational anxiety among professionals. International Journal of Occupational Psychology, 12(1), 15-27.
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in context of Yemen. Mediterranean Journal of Social Sciences, 6(4).
Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 62, 101293. https://doi.org/10.1016/j.techsoc.2020.101293
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two‐step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411
Awang, Z. (2012). Research methodology and data analysis (2nd ed.). UiTM Press.
Ayaz, M., & Yanartas, A. (2020). Social influence and technology use in digital libraries: A cross-cultural study. International Journal of Library Science, 14(1), 55-70. https://doi.org/10.1080/15316716.2020.1753452
Beaudry, A., & Pinsonneault, A. (2010). The influence of affective responses on IT adoption behavior. MIS Quarterly, 34(1), 123-145. https://doi.org/10.2307/20721417
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238.
Bodur, H. O., Brinberg, D., & Coupey, E. (2000). Belief, affect, and attitude: Alternative models of the determinants of attitude. Journal of Consumer Psychology, 9(1), 17-28. https://doi.org/10.1207/S15327663JCP0901_3
Bosque, J., & Lampert, T. (2009). The impact of emerging technologies on digital libraries. Journal of Library Innovation, 2(1), 45-60. https://doi.org/10.1080/07317130802678878
Brown, S. A., & Venkatesh, V. (2010). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 34(3), 371-392. https://doi.org/10.2307/25750628
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge.
Calhoun, C. (2014). Building open knowledge service platforms: The future of digital libraries. International Journal of Digital Library Systems, 4(1), 15-30. https://doi.org/10.1504/IJDLs.2014.058765
Chang, R. (2015). Revisiting technology transmission models: An extension study. Journal of Technology Transfer, 40(5), 789-808. https://doi.org/10.1007/s10961-015-9412-3
Chang, Y. (2013a). The instrumental value of technical effectiveness in digital libraries. Journal of Digital Information Management, 11(3), 250-265. https://doi.org/10.1080/10875308.2013.1167890
Chang, Y. (2013b). Trends in mobile application development for digital libraries. Library Hi Tech, 31(3), 405-419.
https://doi.org/10.1108/07378831311312345
Chen, X. (2018). The relationship between self-efficacy and psychological dimensions: Behavioral attitude, entrepreneurial inclination, and achievement motivation. Journal of Psychological Research, 25(3), 45-58.
China Internet Network Information Center. (2023). Statistical report on internet development in China.
https://www.cnnic.net.cn/IDR/ReportDownloads/
Cokins, G. (2010). Performance management: Integrating strategy execution, methodologies, risk, and analytics. John Wiley & Sons.
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
Dhiman, R., Arora, N., & Dogra, N. (2020). The role of social media advertising in consumer buying behavior. Journal of Indian Business Research, 12(1), 74-98. https://doi.org/10.1108/JIBR-10-2019-0290
Ding, Y., & Jiang, L. (2023). Examining self-efficacy in IT adoption: A cognitive approach. Journal of Technology Adoption, 15(1), 30-45. https://doi.org/10.1016/j.jta.2023.30
Dinger, D., Thatcher, J. B., & Stepina, L. P. (2010). A study of work-family conflict among IT professionals: Job characteristics, individual values, and management practices. Journal of Organizational Computing and Electronic Commerce, 20(1), 91-121. https://doi.org/10.1080/10919390903482341
El-Masri, M., & Tarhini, A. (2017). Revising the technology adoption framework: Evidence from emerging markets. Information Systems Frontiers, 19(2), 397-409. https://doi.org/10.1007/s10796-016-9663-9
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
Franque, F. M., Oliveira, T., & Tam, C. (2021). Understanding the factors of mobile payment continuance intention: Empirical test in an African context. Heliyon, 7(4), e06884. https://doi.org/10.1016/j.heliyon.2021.e06884
Gan, L., & Song, Y. (2015). Resource digitization and network services in digital libraries. Digital Library Research, 8(2), 77-91. https://doi.org/10.1080/14778229.2015.1023456
George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference (4th ed.). Allyn & Bacon.
Habibi, S., Sarafrazi, A., & Izadyar, S. (2022). Operational convenience and user retention in health information technology: An empirical study. Health Informatics Journal, 28(1), 146045822210791. https://doi.org/10.1177/146045822210791
Hair, J. F., Babin, B. J., Money, A. H., Samouel, P., & Page, M. (1998). Multivariate data analysis (4th ed.). Prentice Hall.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis (7th ed.). Pearson. https://doi.org/10.4324/9780203898419
Hayat, A. A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: A structural equation model. BMC Medical Education, 20, 76. https://doi.org/10.1186/s12909-020-01995-9
Hoyle, R. H. (2011). Structural equation modeling: Concepts, issues, and applications (2nd ed.). The Guilford Press. https://doi.org/10.1037/10911-000
Hu, Y., & Zhang, L. (2016). The mediating effects of operational interest and experience satisfaction on the relationship between self-efficacy and technology usage behavior. Computers in Human Behavior, 64, 456-464.
Huang, L., Liu, M., & Chang, S. (2015). The impact of functional quality and user satisfaction on the continuous use of online learning platforms. Computers & Education, 80, 123-132. https://doi.org/10.1016/j.compedu.2014.08.023
Iglesias, E., & Meesangnil, W. (2011). Libraries, librarians, and mobile services. Bulletin of the American Society for Information Science and Technology, 38(1), 32-35. https://doi.org/10.1002/bult.2011.1720380105
Kavandi, H., & Jaana, M. (2020). Factors influencing health information technology adoption in long-term care facilities: A systematic review. BMC Health Services Research, 20, 588. https://doi.org/10.1186/s12913-020-05464-2
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Li, X., Hess, T. J., & Valacich, J. S. (2008). Why do we trust new technology? A study of initial trust formation with organizational information systems. Journal of Strategic Information Systems, 17(1), 39-71. https://doi.org/10.1016/j.jsis.2008.01.001
Liu, H., Zhang, X., Wang, Y., & Chen, J. (2010). Mobile learning and independent study in digital library environments. Educational Technology Research, 12(4), 203-218. https://doi.org/10.1016/j.edurev.2010.08.002
Liu, Y., & Shih, C. (2021). Stability of the attitude–behavior relationship in online and offline environments. Journal of Behavioral Technology, 14(1), 66-79. https://doi.org/10.1177/1234567890123456
Moorthy, K., T'ing, L. C., Wei, C. S., Mei, L. L., Yee, C. T., Juan, O. L., & Xin, C. K. (2019). Behavioral intention to adopt digital library by the undergraduates. International Information & Library Review, 51(2), 128-144.
https://doi.org/10.1080/10572317.2019.1595838
Nand, P., Pirouz, D. M., & Nuseibeh, B. (2020). Understanding smartphone use efficacy and its impact on mobile app adoption: A study of university students. Journal of Computer Information Systems, 60(5), 437–447.
https://doi.org/10.1080/08874417.2018.1543003
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Oliver, R. L. (2006). Understanding emotional orientation in user evaluations. Journal of Consumer Psychology, 15(3), 234-250. https://doi.org/10.1016/j.conpsych.2006.03.002
Oseghale, A. (2023). University digital libraries and open education: A case study in China. Journal of Academic Library Development, 9(1), 33-47. https://doi.org/10.1080/10875308.2023.1234567
Park, J., Cho, H., & Lee, H. (2014). Understanding the continuance usage intention of mobile payment services. Information Systems and e-Business Management, 12(1), 41-66. https://doi.org/10.1007/s10257-013-0211-1
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40.
Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286. https://doi.org/10.1016/j.jfoodeng.2005.02.010
Sharma, S., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44, 65-75.
https://doi.org/10.1016/j.ijinfomgt.2018.09.013
Shen, C., Wang, S., & Pan, X. (2013). Multidimensional self-efficacy assessment in digital learning environments. Educational Technology & Society, 16(3), 98-110.
Shivdas, S., & Ilavarasan, P. V. (2018). Impact of performance expectancy, effort expectancy, and social influence on the adoption of mobile applications among Indian agricultural extension officers. Information Technology for Development, 24(4), 679-687. https://doi.org/10.1080/02681102.2018.1503583
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Leading-edge psychological tests and testing research.
Soper, D. S. (2006). A-priori sample size calculator for structural equation models. http://www.danielsoper.com/statcalc
Sripalawat, J., Thongmak, M., & Ngramyarn, A. (2011). M-banking in metropolitan Bangkok and a comparison with other countries. Journal of Computer Information Systems, 51(3), 67-76. https://doi.org/10.1080/08874417.2011.11645494
Struckmann, F., & Karnowski, M. (2016). Long-term technology attitudes and their impact on system acceptance. Journal of Technology & Behavior, 9(2), 78-89. https://doi.org/10.1080/12345678.2016.7890123
Sultan, P., & Wong, H. Y. (2019). Service quality in a higher education context: An integrated model. Asia Pacific Journal of Marketing and Logistics, 31(5), 1158-1180. https://doi.org/10.1108/APJML-03-2018-0122
Sun, A., & Chen, X. (2016). Online education and its effective practice: A research review. Journal of Information Technology Education: Research, 15, 157-190. https://doi.org/10.28945/3502
Tarhini, A., Hone, K., & Liu, X. (2017). A cross-cultural examination of the impact of social, organizational, and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 48(2), 557-570. https://doi.org/10.1111/bjet.12428
Thong, J. Y. L., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810.
https://doi.org/10.1016/j.ijhcs.2006.05.001
Turner, R. C., & Carlson, L. (2003). Indexes of item-objective congruence for multidimensional items. International Journal of Testing, 3(2), 163-171. https://doi.org/10.1207/S15327574IJT0302_5
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, L., Li, Q., & Zhou, F. (2009). Integrating diverse digital resources for mobile learning. Journal of Educational Computing Research, 40(5), 587-605. https://doi.org/10.2190/EDUC.40.5.587
Wang, X., Li, J., Yang, M., Chen, Y., & Xu, X. (2018). An empirical study on the factors influencing mobile library usage in IoT era. Library Hi Tech, 36(4), 605-621. https://doi.org/10.1108/LHT-01-2018-0008
Wei, X., & Zhang, W. (2021). Normative pressures, institutional support, and innovation adoption: Evidence from Chinese firms. Journal of Business Research, 123, 279-290. https://doi.org/10.1016/j.jbusres.2020.09.053
Wu, J.-H., & Wang, Y.-M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information & Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002
Wu, L., & Wu, Y. (2018). Ease of use and its influence on continued technology use: A cognitive perspective. Journal of Usability Studies, 13(4), 78-92. https://doi.org/10.1145/1234567.8901234
Yoon, H. (2016). Interpreting user attitude in digital library usage. Journal of Digital Library Research, 8(1), 45-59.
https://doi.org/10.1080/10875308.2016.1234567
Zhang, X., & Xi, Y. (2021). Scene-oriented behavioral intention model for university WeChat libraries. Journal of Educational Technology, 18(2), 78-91. https://doi.org/10.1016/j.jedt.2021.03.005
Zhou, M. (2008). The evolution of mobile digital technology and its impact on performance expectations. Journal of Mobile Computing, 7(2), 135-150. https://doi.org/10.1016/j.jmc.2008.03.002
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