Key Factors Influencing Male Undergraduate Students’ Behavioral Intentions Towards Mobile Library Platforms in Chengdu, China
Keywords:
Mobile Library Behavioral Intention, Attitude, Information Technology, Social InfluenceAbstract
Purpose: This study aimed to examine the primary factors that influence the behavioral intention of male undergraduate students toward mobile library platforms (m-library) in private universities in Chengdu, China. The key variables are system quality, perceived ease of use, perceived interaction, perceived usefulness, use attitude, information technology, social influence, and behavior intention. Research design, data, and methodology: The study adopted a quantitative technique, utilizing a questionnaire to acquire data from the sample group. The questionnaire's content validity and reliability were evaluated via IOC and pilot testing before distribution. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to analyze the data, evaluate the model's adequacy, and construct a causal relationship between variables to test the hypothesis. Results: The study's findings indicate that the conceptual model effectively forecasted private college students' behavioral intention to use MLPs. Information technology, perceived usefulness, and attitude towards use are significant factors that influence the behavioral intention to use MLP. Conclusions: Behavioral intention predictions are most directly influenced by attitudes. Therefore, this study suggests that MLP developers in private colleges and universities be focused on using attitudes targeting female students to encourage usage patterns and behavioral intentions.
References
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies?. Decision sciences, 30(2), 361-391. https://doi.org/10.1111/j.1540-5915.1999.tb01614.x
Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 268-273. https://doi.org/10.5901/mjss.2015.v6n4s1p268
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
Atabek, O. (2020). Alternative certification candidates’ attitudes towards using technology in education and use of social networking services: a comparison of sports sciences and foreign language graduates. World Journal on Educational Technology: Current Issues, 12(1), 1-12. https://doi.org/10.18844/wjet.v12i1.4433
Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA
Ayaz, A., & Yanartaş, M. (2020). An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS). Computers in Human Behavior Reports, 2, 100032. https://doi.org/10.1016/j.chbr.2020.100032
Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67-102.
Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2014). Trust transfer in the continued usage of public e-services. Information & Management, 51(6), 627-640. https://doi.org/10.1016/j.im.2014.05.016
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037//0033-2909.107.2.238
Boslaugh, S. (2008). Agent Orange: A Personal Requiem. Disability Studies Quarterly, 28(4). https://doi.org/10.18061/dsq.v28i4.163
Brown, R. (2012). Preliminary findings from a survey of student acceptance and use of e-textbooks in higher education. Allied Academies International Conference, Academy of Educational Leadership. Proceedings, 17(2), 1.
Celik, H. (2008). What determines Turkish customers' acceptance of internet banking?. International journal of bank marketing, 26(5), 353-370. https://doi.org/10.1108/02652320810894406
Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Computers & Education, 60(1), 148-158. https://doi.org/10.1016/j.compedu.2012.06.008
Chang, T.-K. (2014). A Secure Operational Model for Mobile Payments. The Scientific World Journal, 2014, 1-14.
https://doi.org/10.1155/2014/626243
Chen, S.-J., & Chen, S.-M. (2007). Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Applied Intelligence, 26(1), 1-11. https://doi.org/10.1007/s10489-006-0003-5
Chen, T. (2018). Empirical Support for the Development of Mobile Library User Portraits in Higher Education Institutions. Library and Information Work, 62(7), 38-46.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information systems research, 3(1), 60-95. https://doi.org/10.1287/isre.3.1.60
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems, 19(4), 9-30.
Drennan, J., Kennedy, J., & Pisarski, A. (2005). Factors Affecting Student Attitudes Toward Flexible Online Learning in Management Education. The Journal of Educational Research, 98(6), 331-338. https://doi.org/10.3200/joer.98.6.331-338
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/10.1177/002224378101800104
George, D., & Mallery, P. (2003). Using SPSS for Windows Step by Step: A Simple Guide and Reference (4th ed.). Pearson Education.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate Data Analysis (6th ed.). Prentice Hall
Heskett, J. L., Sasser, W. E., & Hart, C. W. L. (1990). Service Breakthroughs: Changing the Rules of the Game. The Free Press.
Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational technology research and development, 55(3), 223-252.
https://doi.org/10.1007/s11423-006-9022-5
Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision-making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16-24. https://doi.org/10.1016/j.ejor.2009.05.009
Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853-868. https://doi.org/10.1016/j.im.2003.08.014
Hu, J., & Zhang, Y. (2016). Chinese students’ behavior intention to use mobile library apps and effects of education level and discipline. Library Hi Tech, 34(4), 639-656. https://doi.org/10.1108/lht-06-2016-0061
Huang, Y.-M., Pu, Y.-H., Chen, T.-S., & Chiu, P.-S. (2015). Development and evaluation of the mobile library service system success model: A case study of Taiwan. The Electronic Library, 33(6), 1174-1192. https://doi.org/10.1108/el-06-2014-0094
Hung, R., Lee, S., & Bennett, J. W. (2015). Fungal volatile organic compounds and their role in ecosystems. Applied microbiology and biotechnology, 99(8), 3395-3405. https://doi.org/10.1007/s00253-015-6494-4
iResearch Institution. (2020). Product Marketing Insights Report on Digital Reading in China Shanghai, China.
https://www.iresearch.com.cn/Detail/report?id=3664&isfree=0
Jia, D., & Dong, W. (2014). Exploratory research on college mobile library users' cognitive structure. Library and Information Service, 58(24), 37-44.
Joo, S., & Choi, N. (2015). Factors affecting undergraduates’ selection of online library resources in academic tasks: Usefulness, ease-of-use, resource quality, and individual differences. Library Hi Tech. 33(2), 272-291.
https://doi.org/10.1108/lht-01-2015-0008
Killingsworth, B., Xue, Y., & Liu, Y. (2016). Factors influencing knowledge sharing among global virtual teams. Team Performance Management, 22(5/6), 284-300. https://doi.org/10.1108/tpm-10-2015-0042
Kim, J., & Park, H.-A. (2012). Development of a Health Information Technology Acceptance Model Using Consumers' Health Behavior Intention. Journal of Medical Internet Research, 14(5), e133. https://doi.org/10.2196/jmir.2143
Kim, R., Olfman, L., Ryan, T., & Eryilmaz, E. (2014). Leveraging a personalized system to improve self-directed learning in online educational environments. Computers & Education, 70, 150-160. https://doi.org/10.1016/j.compedu.2013.08.006
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & management, 43(6), 740-755. https://doi.org/10.1016/j.im.2006.05.003
Klobas, J. E. (1995). Beyond information quality: Fitness for purpose and electronic information resource use. Journal of Information Science, 21(2), 95-114. https://doi.org/10.1177/016555159502100204
Lam, P., Lam, S. L., Lam, J., & McNaught, C. (2009). Usability and usefulness of eBooks on PPCs: How students' opinions vary over time. Australasian journal of educational technology, 25(1), 30-44. https://doi.org/10.14742/ajet.1179
Leary, M. R. (1995). Introduction to Behavioral Research Method (2nd ed.). Brooks/Cole Publishing.
Lin, H.-F. (2007). Knowledge sharing and firm innovation capability: an empirical study. International Journal of Manpower, 28(3/4), 315-332. https://doi.org/10.1108/01437720710755272
Liu, C., & Forsythe, S. (2010). Sustaining Online Shopping: Moderating Role of Online Shopping Motives. Journal of Internet Commerce, 9(2), 83-103.
Liu, H., & Zhou, L. (2012). Predicting young Chinese consumers’ mobile viral attitudes, intents, and behavior. Asia Pacific Journal of Marketing and Logistics, 24(1), 59-77.
Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education, 54(2), 600-610. https://doi.org/10.1016/j.compedu.2009.09.009
Lucas, H. C., & Spitler, V. K. (1999). Technology Use and Performance: A Field Study of Broker Workstations. Decision Sciences, 30(2), 291-311. https://doi.org/10.1111/j.1540-5915.1999.tb01611.x
Mazursky, D., & Vinitzky, G. (2005). Modifying consumer search processes in enhanced on-line interfaces. Journal of Business Research, 58(10), 1299-1309. https://doi.org/10.1016/j.jbusres.2005.01.003
McMillan, S. J., & Hwang, J.-S. (2002). Measures of Perceived Interactivity: An Exploration of the Role of Direction of Communication, User Control, and Time in Shaping Perceptions of Interactivity. Journal of Advertising, 31(3), 29-42. https://doi.org/10.1080/00913367.2002.10673674
Mtebe, J. S., & Raisamo, R. (2014). Investigating perceived barriers to the use of open educational resources in higher education in Tanzania. International Review of Research in Open and Distributed Learning, 15(2), 43-66.
https://doi.org/10.19173/irrodl.v15i2.1803
Nagy, J., Oláh, J., Erdei, E., Máté, D., & Popp, J. (2018). The role and impact of Industry 4.0 and the internet of things on the business strategy of the value chain—the case of Hungary. Sustainability, 10(10), 3491.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376-385. https://doi.org/10.1016/j.tele.2013.11.008
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Perry, S. E., Hockenberry, M. J., Lowdermilk, D. L., Wilson, D., Alden, K. R., & Cashion, M. C. (2017). Maternal child nursing care (6th ed.). Mosby.
Rafaeli, S., & Sudweeks, F. (1997). Networked interactivity. Journal of computer-mediated communication, 2(4), 243.
Rotchanakitumnuai, S., & Speece, M. (2009). Modeling electronic service acceptance of an e‐securities trading system. Industrial Management & Data Systems, 109(8), 1069-1084. https://doi.org/10.1108/02635570910991300
Selim, H. M. (2003). An empirical investigation of student acceptance of course websites. Computers & Education, 40(4), 343-360. https://doi.org/10.1016/s0360-1315(02)00142-2
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
Sheikhshoaei, F., & Oloumi, T. (2011). Applying the technology acceptance model to Iranian engineering faculty libraries. The Electronic Library, 29(3), 367-378. https://doi.org/10.1108/02640471111141106
Shelley, M. C. (2006). Structural Equation Modeling. Encyclopedia of Educational Leadership and Administration.
Shih, H.-P. (2004). An empirical study on predicting user acceptance of e-shopping on the Web. Information & Management, 41(3), 351-368. https://doi.org/10.1016/s0378-7206(03)00079-x
Shih, Y.-Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet Research, 14(3), 213-223. https://doi.org/10.1108/10662240410542643
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading - Edge Psychological Tests and Testing Research (pp. 27-50). Nova
Soper, D. (2006, January). Statistics calculator: A-priori sample size calculator for multiple regression.
Struckmann, S., & Karnowski, V. (2016). News consumption in a changing media ecology: An MESM-study on mobile news. Telematics and informatics, 33(2), 309-319. https://doi.org/10.1016/j.tele.2015.08.012
Sundar, S. S., & Kim, J. (2005). Interactivity and persuasion: Influencing attitudes with information and involvement. Journal of interactive advertising, 5(2), 5-18. https://doi.org/10.1080/15252019.2005.10722097
Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA. Thomson/Brooks/Cole.
Taylor, D. G., & Strutton, D. (2010). Has e-marketing come of age? Modeling historical influences on post-adoption era Internet consumer behaviors. Journal of Business Research, 63(9-10), 950-956. https://doi.org/10.1016/j.jbusres.2009.01.018
Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International journal of research in marketing, 12(2), 137-155. https://doi.org/10.1016/0167-8116(94)00019-k
Tschannen‐Moran, M., Bankole, R. A., Mitchell, R. M., & Moore, D. M. (2013). Student academic optimism: A confirmatory factor analysis. Journal of Educational Administration, 51(2), 150-175. https://doi.org/10.1108/09578231311304689
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x
Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 24(1), 115-139. https://doi.org/10.2307/3250981
Vermeir, I., & Verbeke, W. (2006). Sustainable food consumption: Exploring the consumer “attitude–behavioral intention” gap. Journal of Agricultural and Environmental ethics, 19, 169-194. https://doi.org/10.1007/s10806-005-5485-3
Vongurai, R. (2022). Key Drivers of Operational Performance of E-commerce Distribution Service Providers in Thailand. Journal of Distribution Science, 20(12), 89-98. https://doi.org/10.15722/jds.20.12.202212.89
Vululleh, P. (2018). Determinants of student’ e-learning acceptance in developing countries: An approach based on Structural Equation Modeling (SEM). International Journal of Education and Development using ICT, 14(1), 141-151.
Wang, X., Yang, M., Li, J., & Wang, N. (2018). Factors of mobile library user behavioral intention from the perspective of information ecology. The Electronic Library, 36(4), 705-720. https://doi.org/10.1108/el-03-2017-0046
Wang, Y.-S., Wang, Y.-M., Lin, H.-H., & Tang, T.-I. (2003). Determinants of User Acceptance of Internet Banking: An Empirical Study. International Journal of Service Industry Management, 14(5), 501-519. https://doi.org/10.1108/09564230310500192
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
Yang, K., & Jolly, L. D. (2008). Age cohort analysis in adoption of mobile data services: gen Xers versus baby boomers. Journal of consumer marketing, 25(5), 272-280. https://doi.org/10.1108/07363760810890507
Yen, D. C., Wu, C. S., Cheng, F. F., & Huang, Y. W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906-915.mhttps://doi.org/10.1016/j.chb.2010.02.005
Yoo, Y., Boland, R. J., Lyytinen, K., & Majchrzak, A. (2012). Organizing for Innovation in the Digitized World. Organization Science, 23(5), 1398-1408. https://doi.org/10.1287/orsc.1120.0771
Yun, E. K. (2008). Development and Testing for a Model of Consumer's Health Information Seeking Behavior on the Internet [Unpublished Doctoral Dissertation]. Seoul National University.
Zhang, M., Shen, X., Zhu, M., & Yang, J. (2016). Which platform should I choose? Factors influencing consumers’ channel transfer intention from web-based to mobile library service. Library hi tech, 34(1), 2-20. https://doi.org/10.1108/lht-06-2015-0065
Zhou, X., & Fu, S. (2013). Reading Behavior of College Students in the Mobile Network Era Impact on Mobile Library Construction. Journal of Modem Information, 33(10), 92-95.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ying Xin

This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.