research-article

Proposing an Integrated Conceptual Framework for Understanding the Behavior of Using WeChat-Based EFL Learning Apps

Author:

Yang ChenAuthors Info & Claims

ICEBT '18: Proceedings of the 2018 2nd International Conference on E-Education, E-Business and E-Technology

Pages 52 - 56

https://doi.org/10.1145/3241748.3241776

Published: 05 July 2018 Publication History

ICEBT '18: Proceedings of the 2018 2nd International Conference on E-Education, E-Business and E-Technology

Proposing an Integrated Conceptual Framework for Understanding the Behavior of Using WeChat-Based EFL Learning Apps

Pages 52 - 56

Abstract
References

Abstract

People have used a variety of social and mobile applications in language learning activities. WeChat-based English learning applications are constantly earning their popularity among Chinese English learners. However, we have still lacked understanding of how the applications work in influencing users' usage behavior. In order to investigate this issue, a theoretical analysis under the frameworks of the technology acceptance model and the theory of planned behavior was conducted in the present study. Then, a conceptual framework that could be helpful for understanding the behavior of using WeChat-based English learning applications was proposed as the research findings. This theoretical analysis study will benefit future research in further exploration of mobile Apps with social features for English as foreign language learning.

References

[1]

Pindeh, N., Suki, N.M., and Suki, N.M. 2016. User acceptance on mobile Apps as an effective medium to learn Kadazandusun language. Procedia Economics and Finance, 37, 372--378.

[2]

Ma. Q. 2017. A multi-case study of university students' language-learning experience mediated by mobile technologies: a socio-cultural perspective. Computer Assisted Language Learning, 30, (Mar. 2017), 183--203.

[3]

Simonova. I. 2016. Mobile devices in technical and engineering education with focus on ESP. International Journal of Interactive Mobile Technologies, 10, 33--40.

[4]

Stockwell, G., and Hubbard, P. 2013. Some emerging principles for mobile-assisted language learning, The International Research Foundation for English Language Education, 285--291.

[5]

Toetenel, L. 2014. Social networking: A collaborative open educational resource. Computer Assisted Language Learning, 27, 149--162.

[6]

Teodorescu, A. 2015. Mobile learning and its impact on business English learning. Procedia - Social and Behavioral Sciences. 180 (May. 2015), 1535--1540.

[7]

Oz, H. 2014. Prospective English Teachers' Ownership and Usage of Mobile Devices as M-learning Tools. Procedia-Social and Behavioral Science. 141 (Aug. 2014), 1031--1041.

[8]

Chuang, Y.T. 2017. MEMIS: A Mobile-Supported English-Medium Instruction System. Telematics and Informatics. 34(May. 2017), 640--656.

[9]

Sandberg, J., Maris, M., and Geus, K.D. 2011. Mobile English learning: An evidence-based study with fifth graders. Computers & Education. 57(Aug, 2011), 1334--1347.

Digital Library

[10]

Cavus, N. 2016. Development of an Intelligent Mobile Application for Teaching English Pronunciation. Procedia Computer Science. 102, 365--369.

Digital Library

[11]

Godwin-Jones, R. 2011. Emerging technologies: Mobile apps for language learning. Language Learning & Technology. 15, 2--11.

[12]

Nooriafshar, M. 2012. Educational applications of the merging technologies available on handheld devices such as iPad and iPhone. Asian Journal of Management Sciences and Education. 1, 5--11.

[13]

Martinez, R. and Schmitt, N. 2010. Invited commentary: Vocabulary. Language Learning & Technology. 14, 26--29.

[14]

Kukulska-Hulme, A. 2009. Will mobile lewarning change language learning? ReCALL. 34, 157--165.

Digital Library

[15]

Basoglu, E.B. and Akdemir, O. A comparison of undergraduate students' English vovabulary learning: Using mobile phones and flash cards. Turkish Online Journal of Educational Technology. 72, 1--7.

[16]

Wang, H., Smyth, R., and Cheng, Z. 2017. The economic returns to proficiency in English in China. China Economic Rewiew. 43 (Apr, 2017), 91--104.

[17]

CNNIC. 2018. China Internet network information center report of 2018. Data retrieved from the website of http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201803/t20180305_70249.htm

[18]

Matemba, E.D. and Li, G. 2018. Consumers' willingness to adopte and use WeCht wallet: An empirical study in South Africa. Technology in Society. in press.

[19]

Liu, J. et al. 2018. Effects of using WeChat-assisted peroperative care instructions for parents of pediatric patients undergoing day surgery for herniorrhaphy. Patient Education and Counseling. in press.

Digital Library

[20]

Chen, L., Chin, F.G., Sun, Y., and Amran, R. 2017. Integrating guanxi into technology acceptance: An empirical investigation of WeChat. Telematics and Informatics. 34 (Nov. 2017), 1125--1142.

Digital Library

[21]

Davis, F.D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319--339.

Digital Library

[22]

Verman, S., Bhattacharyya, S.S., and Kumar, S. 2018. An extension of the technology acceptance model in the big data analytics system implementation environment. Information Processing & Management. in press.

[23]

Hussein, Z. 2017. Leading to intention: the role of attitude in relation to technology acceptance model in E-Learning. Procedia Computer Science. 105, 159--164.

Digital Library

[24]

Lee, Y., Kozar, K.A., and Larsen, K.R.T. 2003. The technology acceptance model: past, present, and future. Communications of the Association for Information Systems. 12, 752--780.

[25]

Ajzen, I. 2011. The theory of planned behavior. In Theory of planned behavior Handbook Theories of Social Psychology, Van Lange, P. A., Kruglanski, A.W., and Higgins, T.O., Ed. Sage Publications. London.

[26]

Cheung, M. and To, W.M. 2016. Service co-creation in social media: an extension of the theory of planned behavior. Computers in Human Behavior. 65 (Dec, 2016), 260--266.

Digital Library

[27]

Cheon, J., Lee, S., Crooks, S.M., and Song, J. 2012. An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education. 59 (Nov, 2012), 1054--1064.

Digital Library

[28]

Venkatesh, V. and Davis, F.D. 2000. A theoretical extension of the technology acceptance model: four longitudinal field studies. Management science. 42, 186--204.

Digital Library

[29]

Bargshady, G., Pourmahdi, K., Khodakarami, P., Khodadadi, T., and Alipanah, F. 2015. The effective factors on user acceptance in mobile business intelligence. Jurnal Teknologi (Sciences & Engineering). 72, 49--54.

[30]

Hansen, J.M, Saridakis, G., and Benson, V. 2018. Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers' use of social media for transactions. Computers in Human Behavior. 80 (Mar. 2018), 197--206.

Digital Library

[31]

Boyle, S.C., Earle, A. M., Labrie, J.W., and Smith, D.J. 2017. Pnf 2.0? Initial evidence that gamification can increase the efficacy of brief, web-based personalized normative feedback alcohol interventions. Addictive Behaviors. 67, 8--17.

[32]

Hamari, J. and Koivisto, J. 2015. Working out for likes': an empirical study on social influence in exercise gamification. Computers in Human Behavior. 50, 333--347.

Digital Library

[33]

Zhao, L., Lu, Y., Zhang, L., and Chau, P.Y. 2012. Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decision Support Systems. 52, 645--656.

Digital Library

[34]

Szymanski, D.M. and Hise, R.T. 2000. E-satisfaction: an initial examination. Journal of Retailing. 76, 309--322.

[35]

Johnson, S.D., Aragon, S.R., and Shaik, N. 2000. Comparative analysis of learner satisfaction and learning outcomes in online and face-to-face learning environments. Journal of Interactive Learning Research. 11, 29--49.

Digital Library

[36]

Heflin, H., Shewmaker, J., and Nguyen, J. 2017. Impact of mobile technology on student attitudes, engagement, and learning. Computers & Education. 107, 91--99.

Digital Library

[37]

Al-Emran, M., Elsherif, H.M., and Shaalan, K. 2016. Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior. 56, 93--102.

Digital Library

[38]

Echegaray, F. and Hansstein, F.V. 2016. Assessing the intention-behavior gap in electronic waste recycling: the case of Brazil. Journal of Cleaner Production.142, 180--190.

[39]

Haia, A., Moulaa, M.E., and Seppäläa, U. 2017. Results of intention-behaviour gap for solar energy in regular residential buildings in Finland. International Journal of Sustainable Built Environment. 5, 457--464.

[40]

Huda, H.M.J. and Zainuddin, H. 2015. Applying UTAUT to evaluate the acceptance of mobile learning in higher education in Iraq. International Journal of Science and Research. 4, 952--954.

[41]

Pai, F. and Yeh, T. 2014. The effects of information sharing and interactivity on the intention to use social networking websites. Quality and Quantity. 48, 2191--2207.

[42]

Trisha, T.C.L. and Li, L. 2014. Perceived characteristics, perceived pupularity, and playfulness: youth adoption of mobile instant messaging in China. China Media Research. 10, 60--71.

[43]

Chau, P.Y.K. 1996. An empirical investigation on factors affecting the acceptance of CASE by systems developers. Information Management. 30, 269--280.

Digital Library

[44]

Hu, P.J., Chau, P.Y.K., Sheng, O.R.L., and Tam, K.Y. 1999. Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information System. 16, 91--112.

Digital Library

[45]

Straub, D., Keil, M., and Brenner, W. 1997. Testing the technology acceptance model across cultures: a three country study. Information Management. 33, 1--11.

Digital Library

[46]

Gefen, D., and Straub, D.W. 1997. Gender difference in the perception and use of E-mail: an extension to the technology acceptance model. Mis Quarterly. 21, 389--400.

Digital Library

[47]

Venkatesh, V. 2000. Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research. 11, 342--365.

Digital Library

[48]

Venkatesh, V. and 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, 115--139.

Digital Library

[49]

Chow, M., Herold, D.K., Choo, T.M., and Chan, K. 2012. Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computer & Education. 59, 1136--1144.

Digital Library

Cited By

Carolan J(2022)English proficiency as a performance of digital social capital: understanding how Chinese study abroad students use WeChat for the symbolic purpose of English language learningThe Journal of Chinese Sociology10.1186/s40711-022-00177-y9:1Online publication date: 25-Nov-2022
https://doi.org/10.1186/s40711-022-00177-y

Index Terms

Proposing an Integrated Conceptual Framework for Understanding the Behavior of Using WeChat-Based EFL Learning Apps
1. Applied computing
  1. Education
    1. Computer-assisted instruction

Recommendations

The Influence of Quizizz-online Gamification on Vocabulary Learning Effectiveness and Engagement among College EFL Learners in China
ICETT '23: Proceedings of the 9th International Conference on Education and Training Technologies

Game-based learning is considered to be an effective way to enhance students' learning participation and has been applied to various learning contexts since its birth. Nowadays, it is getting more popular due to the increasingly advanced application ...
The effect of using a game-based translation learning app on enhancing college EFL learners’ motivation and learning experience
Abstract
This study investigated the motivation of Taiwanese undergraduate EFL students in learning English-to-Chinese translation and the intention to use a digital game-based learning app called CHEN-slate. The app, consisting of a learning zone, ...
Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior

This paper extends Ajzen's (1991) theory of planned behavior (TPB) to explain and predict the process of e-commerce adoption by consumers. The process is captured through two online consumer behaviors: (1) getting information and (2) purchasing a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICEBT '18: Proceedings of the 2018 2nd International Conference on E-Education, E-Business and E-Technology

July 2018

202 pages

ISBN:9781450364812

DOI:10.1145/3241748

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Beijing University of Technology

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICEBT 2018

ICEBT 2018: 2018 The 2nd International Conference on E-Education, E-Business and E-Technology

July 5 - 7, 2018

Beijing, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
122
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Carolan J(2022)English proficiency as a performance of digital social capital: understanding how Chinese study abroad students use WeChat for the symbolic purpose of English language learningThe Journal of Chinese Sociology10.1186/s40711-022-00177-y9:1Online publication date: 25-Nov-2022
https://doi.org/10.1186/s40711-022-00177-y

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten