Skip to main content

Advertisement

SpringerLink
Log in

No more excuses, learn English for free: Factors affecting L2 learners intention to use online technology for informal English learning

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

There is a wealth of research investigating language learners’ adoption of digital technology in higher education. To date, however, research primarily focuses on the implementation of technology in formal learning either inside or outside of the classroom, and the associations between factors influencing learners’ informal English learning in online contexts are not adequately discussed. Therefore, the present study aims to identify and test the main factors related to students’ informal online English learning (IOEL) that could predict their usage intention. To better understand the drivers of technology use for informal L2 learning among university students, this study proposed a model based on the Technology Acceptance Model (TAM) to investigate students’ behavioural intention toward IOEL with particular reference to Malaysian L2 learners. The partial least square approach of the structural equation modelling (PLS-SEM) was applied to test the proposed research model and the corresponding hypotheses. The results indicate that nine out of ten of the hypotheses were supported, which implies that the extended model can effectively predict L2 learners’ IOEL technology adoption in online contexts. The results also showed that perceived interaction and perceived ease of use were the two main predictors influencing learner’s technology acceptance for IOEL.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90.

    Google Scholar 

  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.

    Google Scholar 

  • Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314.

    Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

    Google Scholar 

  • Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100–110.

    Google Scholar 

  • Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148.

    Google Scholar 

  • AL-Subari, S. N., Zabri, S. M., & Ahmad, K. (2018). Factors influencing online banking adoption: the case of academicians in Malaysian Technical University network (MTUN). Advanced Science Letters, 24(5), 3193–3197.

    Google Scholar 

  • Anderson, T. (2003). Modes of interaction in distance education: recent developments and research questions. Handbook of Distance Education, 129–44.

  • Balouchi, M., Aziz, Y. A., Hasangholipour, T., Khanlari, A., Rahman, A. A., & Raja-Yusof, R. N. (2017). Explaining and predicting online tourists’ behavioural intention in accepting consumer generated contents. Journal of Hospitality and Tourism Technology, 8(2), 168–189.

    Google Scholar 

  • Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359–373. https://doi.org/10.1521/jscp.1986.4.3.359.

    Article  Google Scholar 

  • Blake, R.J. (2017). Technologies for teaching and learning L2 speaking. The Handbook of Technology and Second Language Teaching and Learning (pp. 107–17).

  • Bolliger, D. U., & Halupa, C. (2018). Online student perceptions of engagement, transactional distance, and outcomes. Distance Education, 39(3), 299–316.

    Google Scholar 

  • Buabeng-Andoh, C., Yaokumah, W., & Tarhini, A. (2019). Investigating students’ intentions to use ICT: a comparison of theoretical models. Education and Information Technologies, 24(1), 643–660.

    Google Scholar 

  • Cabanac, M. (1992). Pleasure: the common currency. Journal of Theoretical Biology, 155(2), 173–200.

    Google Scholar 

  • Cao, J, Shang, Y., Mok, Q., & Lai, I.K.-W. (2019). The Impact of Personal Innovativeness on the Intention to Use Cloud Classroom: An Empirical Study in China. In International Conference on Technology in Education, 179–88. Springer.

  • Chang, C.-T., Hajiyev, J., & Chia-Rong, S. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143.

    Google Scholar 

  • Chen, N.-H. (2019). Extending a TAM–TTF model with perceptions toward telematics adoption. Asia Pacific Journal of Marketing and Logistics.

  • Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE Bulletin, 3, 7.

    Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

    Google Scholar 

  • Chiu, C.-K., Lin, C.-P., Chen, K.-J., Liu, C.-M., & Ma, H.-C. (2019). Modeling continuance intention towards Mobile travel service system (MTSS): a theoretical perspective of motivation and dependency. Review of Managerial Science, 13(4), 749–769.

    Google Scholar 

  • Darbyshire, P., & Burgess, S. (2009). The internet and tertiary education. In Encyclopedia of Information Science and Technology, Second Edition, 2189–94. IGI Global.

  • Davis, F.D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.

  • Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487.

    Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111–1132.

    Google Scholar 

  • Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the use of social Media in Higher Education Institutions (HEIs) using the technology acceptance model. International Journal of Educational Technology in Higher Education, 14(1), 5.

    Google Scholar 

  • Eom, S. B., & Ashill, N. (2016). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An update’, Decision Sciences Journal of Innovative Education. Wiley Online Library, 14(2), 185–215.

  • Feng, X., Shenglan, F., & Qin, J. (2016). Determinants of consumers’ attitudes toward Mobile advertising: The mediating roles of intrinsic and extrinsic motivations. Computers in Human Behavior, 63, 334–341.

    Google Scholar 

  • Fishbein, M., & Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.

  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 1–22.

    Google Scholar 

  • Galikyan, I., & Admiraal, W. (2019). Students’ engagement in asynchronous online discussion: the relationship between cognitive presence, learner prominence, and academic performance. The Internet and Higher Education, 43, 100692.

    Google Scholar 

  • Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: a systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593.

    Google Scholar 

  • Hair, J. R., Joseph, F., Tomas, G., Hult, M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

  • Hair, J. R., Joseph, F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. Sage Publications.

  • Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for Mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101–123.

    Google Scholar 

  • Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal (AMJ), 25(1), 76–81.

    Google Scholar 

  • He, T., Zhu, C., & Questier, F. (2018). Predicting digital informal learning: an empirical study among Chinese University students. Asia Pacific Education Review, 19(1), 79–90.

    Google Scholar 

  • Healey, D. (2016). Language Learning and Technology: Past, Present and Future. In The Routledge Handbook of Language Learning and Technology, 35–49. Routledge.

  • Heift, T., & Vyatkina, N. (2017). Technologies for teaching and learning L2 grammar. In The Handbook of Technology and Second Language Teaching and Learning, 26–44. Wiley Online Library.

  • Heijden van der. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695. https://doi.org/10.2307/25148660.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2–20.

    Google Scholar 

  • Hew, J.-J., Leong, L.-Y., Tan, G. W.-H., Lee, V.-H., & Ooi, K.-B. (2018). Mobile social tourism shopping: a dual-stage analysis of a multi-mediation model. Tourism Management, 66, 121–139.

    Google Scholar 

  • Hsieh, J. S. C., Huang, Y.-M., & Wu, W.-C. V. (2017). Technological acceptance of LINE in flipped EFL oral training. Computers in Human Behavior, 70, 178–190.

    Google Scholar 

  • Hu, P. J., Chau, P. Y. K., Liu Sheng, O. R., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91–112.

    Google Scholar 

  • Huang, G., & Ren, Y. (2020). Linking technological functions of fitness Mobile apps with continuance usage among Chinese users: moderating role of exercise self-efficacy. Computers in Human Behavior, 103, 151–160.

    Google Scholar 

  • Hubbard, P. (2017). Technologies for teaching and learning L2 listening. In The Handbook of Technology and Second Language Teaching and Learning, 93–106. Wiley Online Library.

  • Hwang, J., Lee, J.-S. & Kim, H. (2019). Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. International Journal of Hospitality Management. Elsevier, 81, 94–103.

  • Ifinedo, P. (2017). Examining students’ intention to continue using blogs for learning: perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior, 72, 189–199.

    Google Scholar 

  • Joo, Y. J., So, H.-J., & Kim, N. H. (2018). Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260–272.

    Google Scholar 

  • Jovanovic, J., Mirriahi, N., Gašević, D., Dawson, S., & Pardo, A. (2019). Predictive power of regularity of pre-class activities in a flipped classroom. Computers & Education, 134, 156–168.

    Google Scholar 

  • Jung, I. (2001). Building a theoretical framework of web-based instruction in the context of distance education. British Journal of Educational Technology, 32(5), 525–534.

    Google Scholar 

  • Kara, M., Kukul, V., & Cakır, R. (2020). Self-regulation in three types of online interaction: How does it predict online pre-service teachers’ perceived learning and satisfaction? Asia-Pacific Education Researcher.

  • Kim, M. S., & Kim, S. (2018). Factors influencing willingness to provide personal information for personalized recommendations. Computers in Human Behavior, 88, 143–152.

    Google Scholar 

  • Kim, M., Kim, J., Choi, J., & Trivedi, M. (2017). Mobile shopping through applications: understanding application possession and Mobile purchase. Journal of Interactive Marketing, 39, 55–68.

    Google Scholar 

  • Kim, M. K., Wang, Y., & Ketenci, T. (2020). Who are online learning leaders? Piloting a leader identification method (LIM). Computers in Human Behavior, 105, 106205.

    Google Scholar 

  • Kızıl, A.Ş. (2019). Computer-assisted language learning and Design for Learning: Potential synergies. In Optimizing Instructional Design Methods in Higher Education, 129–46. IGI Global.

  • Kock, N. (2017). WarpPLS User Manual: Version 6.0. ScriptWarp Systems: Laredo, TX, USA.

  • Kuo, Y.-C., & Belland, B. R. (2016). An exploratory study of adult learners’ perceptions of online learning: minority students in continuing education. Educational Technology Research and Development, 64(4), 661–680.

    Google Scholar 

  • Lang, C. (2018). Perceived risks and enjoyment of access-based consumption: identifying barriers and motivations to fashion renting. Fashion and Textiles, 5(1), 23.

    Google Scholar 

  • Lee, J. S. (2019). Informal digital learning of English and second language vocabulary outcomes: can quantity conquer quality? British Journal of Educational Technology, 50(2), 767–778.

    Google Scholar 

  • Lee, J. S., & Dressman, M. (2018). When IDLE hands make an English workshop: informal digital learning of English and language proficiency. TESOL Quarterly, 52(2), 435–445.

    Google Scholar 

  • Lee, C., Yeung, A. S., & Cheung, K. W. (2019a). Learner perceptions versus technology usage: a study of adolescent English learners in Hong Kong secondary schools. Computers & Education, 133, 13–26.

    Google Scholar 

  • Lee, J., Kim, J., & Choi, J. Y. (2019b). The adoption of virtual reality devices: the technology acceptance model integrating enjoyment, social interaction, and strength of the social ties. Telematics and Informatics, 39, 37–48.

    Google Scholar 

  • Leong, L. W., Ibrahim, O., Dalvi-Esfahani, M., Shahbazi, H., & Nilashi, M. (2018). The moderating effect of experience on the intention to adopt Mobile social network sites for pedagogical purposes: an extension of the technology acceptance model. Education and Information Technologies, 23(6), 2477–2498.

    Google Scholar 

  • Li, L. (2016). CALL Tools for Lexico-Grammatical Acquisition. In The Routledge Handbook of Language Learning and Technology, 487–503. Routledge.

  • Li, Z., Dursun, A., & Hegelheimer, V. (2017). Technology and L2 writing. In The Handbook of Technology and Second Language Teaching and Learning, 77–92. Wiley Online Library.

  • Liaw, M.-L., & English, K. (2017). Technologies for teaching and learning L2 Reading. In The Handbook of Technology and Second Language Teaching and Learning, 62–76. Wiley Online Library.

  • Lin, H.-F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of information management. Elsevier, 31(3), 252–260.

  • Lin, Z., & Filieri, R. (2015). Airline passengers’ continuance intention towards online check-in services: the role of personal innovativeness and subjective knowledge. Transportation Research Part E: Logistics and Transportation Review, 81, 158–168.

    Google Scholar 

  • Lomicka, L., & Lord, G. (2016). Social Networking and Language Learning. In The Routledge Handbook of Language Learning and Technology, 255–68. Routledge.

  • Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: a structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114–120.

    Google Scholar 

  • Ma, Q. (2017). Technologies for teaching and learning L2 vocabulary. In The Handbook of Technology and Second Language Teaching and Learning, 47–61. Wiley Online Library.

  • Manis, K. T., & Choi, D. (2019). The virtual reality hardware acceptance model (VR-HAM): extending and individuating the technology acceptance model (TAM) for virtual reality hardware. Journal of Business Research, 100, 503–513.

    Google Scholar 

  • Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context, Information & management. Elsevier, 38(4), 217–230.

  • Moore, M. (1989). Editorial: three types of interaction. American Journal of Distance Education, 3(2), 1–7. https://doi.org/10.1080/08923648909526659.

  • Nguyen, T.D., Nguyen, T.M., Pham, Q.-T., & Misra, S. (2014). Acceptance and Use of E-Learning Based on Cloud Computing: The Role of Consumer Innovativeness. In International Conference on Computational Science and Its Applications, 159–74. Springer.

  • Nikou, S. (2019). Factors driving the adoption of smart home technology: an empirical assessment. Telematics and Informatics, 45, 101283.

    Google Scholar 

  • Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance. Computers in Human Behavior, 68, 83–95.

    Google Scholar 

  • Otto, S.E.K. (2017). From past to present: A hundred years of technology for L2 learning. In The Handbook of Technology and Second Language Teaching and Learning, 10–25. Wiley Online Library.

  • Park, S.-T., Im, H., & Noh, K.-S. (2016). A study on factors affecting the adoption of LTE Mobile communication service: the case of South Korea. Wireless Personal Communications, 86(1), 217–237.

    Google Scholar 

  • Patil, P. P., Rana, N. P., & Dwivedi, Y. K. (2018). Digital payments adoption research: a meta-analysis for generalising the effects of attitude, cost, innovativeness, mobility and price value on behavioural intention’, in International Working Conference on Transfer and Diffusion of IT. Springer, pp. 194–206.

  • Peeters, W. (2018). Applying the networking power of web 2.0 to the foreign language classroom: a taxonomy of the online peer interaction process. Computer Assisted Language Learning, 31(8), 905–931.

    Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Google Scholar 

  • Purarjomandlangrudi, A., & Chen, D. (2020). Exploring the Influence of Learners’ Personal Traits and Perceived Course Characteristics on Online Interaction and Engagement. Educational Technology Research and Development, 1–23.

  • Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J., & Martín-Velicia, F. (2019). Analysing the acceptation of online games in Mobile devices: an application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85–93.

    Google Scholar 

  • Revythi, A., & Tselios, N. (2019). Extension of technology acceptance model by using system usability scale to assess behavioral intention to use E-learning. Education and Information Technologies, 24(4), 2341–2355.

    Google Scholar 

  • Ringle, C.M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. http://www.smartpls.com.

  • Rogers, E.M. (1995). Diffusion of Innovations: Modifications of a Model for Telecommunications. In Die Diffusion von Innovationen in Der Telekommunikation, 25–38. Springer.

  • Rogers, E. M. (2002). Diffusion of preventive innovations, Addictive behaviors. Elsevier, 27(6), 989–993.

  • Rogers, P. L. (2009). Encyclopedia of distance learning. IGI Global.

  • Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.

  • Ros, S., Hernández, R., Caminero, A., Robles, A., Barbero, I., Maciá, A., & Holgado, F. P. (2015). On the use of extended TAM to assess students’ acceptance and intent to use third-generation learning management systems. British Journal of Educational Technology, 46(6), 1250–1271.

    Google Scholar 

  • Rouibah, K., Lowry, P. B., & Hwang, Y. (2016). The effects of perceived enjoyment and perceived risks on trust formation and intentions to use online payment systems: new perspectives from an Arab country. Electronic Commerce Research and Applications, 19, 33–43.

    Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67.

    Google Scholar 

  • Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting the E-learning acceptance: a case study from UAE. Education and Information Technologies, 24(1), 509–530.

    Google Scholar 

  • Tao, D., Fu, P., Wang, Y., Zhang, T., & Qu, X. (2019). Key characteristics in Designing Massive Open Online Courses (MOOCs) for user acceptance: an application of the extended technology acceptance model. Interactive Learning Environments, 1–14.

  • Tarhini, A., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students’ adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business.

  • Tseng, C.-T. H. (2017). Teaching “Cross-Cultural Communication” through content based instruction: curriculum design and learning outcome from EFL learners’ perspectives. English Language Teaching, 10(4), 22–34.

    Google Scholar 

  • 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.

    Google Scholar 

  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.

    Google Scholar 

  • 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, 157–178.

  • Vlachopoulos, D., & Makri, A. (2019). Online communication and interaction in distance higher education: a framework study of good practice. International Review of Education, 65(4), 605–632.

    Google Scholar 

  • Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232.

    Google Scholar 

  • Xiao, J. (2017). Learner-content interaction in distance education: the weakest link in interaction research. Distance Education, 38(1), 123–135.

    Google Scholar 

  • Yang, M., Shao, Z., Liu, Q., & Liu, C. (2017). Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Educational Technology Research and Development, 65(5), 1195–1214.

    Google Scholar 

  • Zainab, B., Bhatti, M. A., & Alshagawi, M. (2017). Factors affecting E-training adoption: an examination of perceived cost, computer self-efficacy and the technology acceptance model. Behaviour & Information Technology, 36(12), 1261–1273.

    Google Scholar 

  • Zhou, T. (2018). Examining users’ switch from online banking to Mobile banking. International Journal of Networking and Virtual Organisations, 18(1), 51–66.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, Selangor, Malaysia

    Shima Balouchi & Arshad Abdul Samad

Authors
  1. Shima Balouchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arshad Abdul Samad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shima Balouchi.

Ethics declarations

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Survey instrument

Survey instrument

1.1 Perceived usefulness (Davis 1989; Lee et al. 2019a, b)

  1. 1.

    Online technologies improve my English learning.

  2. 2.

    Online technologies help me to learn English more efficiently.

  3. 3.

    Online technologies make my English learning easier.

  4. 4.

    Online technologies help me keep active and motivated as I can have English learning sources available everywhere and anytime.

  5. 5.

    Online technologies increase my English learning productivity.

  6. 6.

    On the whole, I find online technologies useful for my English learning.

1.2 Perceived Ease of use (Davis 1989; Tarhini et al. 2017)

  1. 1.

    Online technologies are easy to use

  2. 2.

    Online technologies are easy to learn.

  3. 3.

    Online technologies are easy to access.

  4. 4.

    Online technologies are easy to understand.

  5. 5.

    Online technologies are convenient.

1.3 Perceived interaction (Ros et al. 2015)

  1. 1.

    Online technologies let me interact easily with other English native speakers or L2 learners.

  2. 2.

    Online technologies are capable of improving interest and communication to my learning environment.

  3. 3.

    I usually improve my learning simultaneously with other English native speakers or English learners by online technologies.

  4. 4.

    Using online technologies enables English learners to share information or ask questions about English with people that are not directly involved with the university.

  5. 5.

    On the whole, I believe online technologies provides good opportunities for interaction with other English learners.

1.4 Perceived enjoyment (Venkatesh 2000; Lee et al. (2019a, b))

  1. 1.

    I find using online technologies enjoyable.

  2. 2.

    The actual process of using the Online technologies is pleasant.

  3. 3.

    I have fun using online technologies.

  4. 4.

    I find using online technologies entertaining.

  5. 5.

    I find using online technologies interesting.

1.5 Perceived innovativeness (Agarwal and Prasad 1998)

  1. 1.

    If I heard about a new online technology for learning English, I would look for ways to experiment with it.

  2. 2.

    Among my peers, I am usually the first to try out new online technologies.

  3. 3.

    I like to experiment with new online technologies.

1.6 Behavioural intention (Lin 2011; Venkatesh and Bala 2008)

  1. 1.

    Assuming I had access to online technologies, I do not hesitate to use them for my informal English learning.

  2. 2.

    Assuming I had access to online technologies, I intend to use them for my informal English learning.

  3. 3.

    Given that I had access to online technologies, I predict that I would use them for my informal English learning.

  4. 4.

    I plan to use online technologies for my informal English learning in future.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balouchi, S., Samad, A.A. No more excuses, learn English for free: Factors affecting L2 learners intention to use online technology for informal English learning. Educ Inf Technol 26, 1111–1132 (2021). https://doi.org/10.1007/s10639-020-10307-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-020-10307-z

Keywords

Search

Navigation