Mobile-based assessment: Investigating the factors that influence behavioral intention to use

Nikou, Stavros A.; Economides, Anastasios A.

doi:10.1016/j.compedu.2017.02.005

Cited by 388Mobile Learning in EducationTechnology Adoption and User BehaviourUsability and User Interface Design

Mobile-based assessment: Investigating the factors that influence behavioral intention to use

Nikou, Stavros A., Economides, Anastasios A.·2017·Computers & Education

DOI 10.1016/j.compedu.2017.02.005

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.compedu.2017.02.005

Cites

109 of 109 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Behavior intention has a positive effect on adoption of an e-wallet.
“Moreover, Nikou and Economides [44], who investigated cognitive feedback and user interface to predict the relationship between behavior intention to use an e-wallet, discovered the positive effect of behavior intention on adoption of an e-wallet.”
From Cashless Transactions: A Study on Intention and Adoption of e-Wallets (2021) · cites this paper as Nikou and Economides [44]
PEOU has a positive influence on BITU (H8).
“This supports the research findings of Nikou and Economides (2017) regarding the positive influence of PEOU on BITU.”
From Measuring The Acceptance Level of User Interface Design of ERP System at PT Allure Alluminio Using Technology Acceptance Model (TAM) Method (2024) · cites this paper as Nikou and Economides (2017)

Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,588 · see citing works
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
Statistical power analysis for the behavioral sciences
Cohen, J. (1990) · Computers Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,406 · see citing works
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · see citing works
Statistical Power Analysis for the Behavioral Sciences (2nd ed.).
Lachenbruch, P. A., & Cohen, J. (1989) · Journal of the American Statistical Association · doi:10.2307/2290095 · Cited by 37,385 · see citing works
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709 · see citing works
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066 · see citing works
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921 · see citing works
The Power of Feedback
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905 · see citing works
Untitled work
(2014) · DigitalCommons - Kennesaw State University (Kennesaw State University) · Cited by 12,785 · see citing works
Untitled work
(1998) · Cited by 12,725 · see citing works
Computer Self-Efficacy: Development of a Measure and Initial Test
Compeau, D. R., & Higgins, C. A. (1995) · MIS Quarterly · doi:10.2307/249688 · Cited by 6,553 · see citing works
Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage1
Agarwal, R., & Karahanna, E. (2000) · MIS Quarterly · doi:10.2307/3250951 · Cited by 4,726 · see citing works
A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology
Agarwal, R., & Prasad, J. (1998) · Information Systems Research · doi:10.1287/isre.9.2.204 · Cited by 3,575 · see citing works
A meta-analysis of the technology acceptance model
King, W. R., & He, J. (2006) · Information & Management · doi:10.1016/j.im.2006.05.003 · Cited by 2,906 · see citing works
The Technology Acceptance Model: Past, Present, and Future
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003) · Communications of the Association for Information Systems · doi:10.17705/1cais.01250 · Cited by 2,572 · see citing works
The effects of integrating mobile devices with teaching and learning on students' learning performance: A meta-analysis and research synthesis
Sung, Y.-T., Chang, K.-E., & Liu, T.-C. (2016) · Computers & Education · doi:10.1016/j.compedu.2015.11.008 · Cited by 1,574 · see citing works
Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology
Lu, J., Yao, J. E., & Yu, C. (2005) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2005.07.003 · Cited by 1,461 · see citing works
NMC Horizon Report: 2017 Higher Education Edition
Becker, S. A., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Díaz, V., & Pomerantz, J. (2017) · Cited by 1,243 · see citing works
Investigating the determinants and age and gender differences in the acceptance of mobile learning
Wang, Y., Wu, M., & Wang, H. (2008) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2007.00809.x · Cited by 1,233 · see citing works
On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution
Chin, W. W., & Todd, P. (1995) · MIS Quarterly · doi:10.2307/249690 · Cited by 1,223 · see citing works
An investigation of mobile learning readiness in higher education based on the theory of planned behavior
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.04.015 · Cited by 1,057 · see citing works
Review of trends from mobile learning studies: A meta-analysis
Wu, W.-H., Jim Wu, Y.-C., Chen, C.-Y., Kao, H.-Y., Lin, C.-H., & Huang, S.-H. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.03.016 · Cited by 946 · see citing works
A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students
Hwang, G., & Chang, H.-F. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.12.002 · Cited by 784 · see citing works
Students’ perceptions about evaluation and assessment in higher education: a review
Struyven, K., Dochy, F., & Janssens, S. (2005) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930500099102 · Cited by 782 · see citing works
University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model
Park, S. Y., Nam, M., & Cha, S.-B. (2011) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2011.01229.x · Cited by 773 · see citing works
Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community
Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. (2009) · Computers & Education · doi:10.1016/j.compedu.2009.09.009 · Cited by 741 · see citing works
A Theory of Learning for the Mobile Age
Sharples, M., Taylor, J., & Vavoula, G. (2009) · VS Verlag für Sozialwissenschaften eBooks · doi:10.1007/978-3-531-92133-4_6 · Cited by 735 · see citing works
Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies
Ong, C., Lai, J., & Wang, Y. (2003) · Information & Management · doi:10.1016/j.im.2003.08.012 · Cited by 650 · see citing works
User Acceptance of Computer Technology
DavisFred, D., BagozziRichard, P., & WarshawPaul, R. (1989) · Management Science · doi:10.5555/2797314.2797319 · Cited by 620 · see citing works
A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types
Šumak, B., Heričko, M., & Pušnik, M. (2011) · Computers in Human Behavior · doi:10.1016/j.chb.2011.08.005 · Cited by 602 · see citing works
Criteria, Strategies and Research Issues of Context-Aware Ubiquitous Learning
Hwang, G. J., Tsai, C., & Yang, S. J. H. (2008) · Cited by 566 · see citing works
What seams do we remove in mobile-assisted seamless learning? A critical review of the literature
Wong, L. H., & Looi, C. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.06.007 · Cited by 555 · see citing works
Adams Becker, S.; Cummins, M.; Davis, A.; Freeman, A.; Hall Giesinger, C.; Ananthanarayanan, V. NMC Horizon Report: 2017 Higher Education Edition. Austin, Texas: The New Media Consortium, 2017
Jaramillo, S. G. (2017) · Cuaderno activa · Cited by 514 · see citing works
Handbook of Mobile Learning
Berge, Z. L., & Muilenburg, L. Y. (2013) · Routledge eBooks · doi:10.4324/9780203118764 · Cited by 489 · see citing works
Factors driving the adoption of m-learning: An empirical study
Yong, L., Li, H., & Carlsson, C. P. O. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.05.018 · Cited by 469 · see citing works
The acceptance and use of computer based assessment
Terzis, V., & Economides, A. A. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.11.017 · Cited by 444 · see citing works
Self-citation
Antecedents and consequences of mobile phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty
Lee, D., Moon, J., Kim, Y. J., & Yi, M. Y. (2014) · Information & Management · doi:10.1016/j.im.2014.12.001 · Cited by 427 · see citing works
A two-tier test approach to developing location-aware mobile learning systems for natural science courses
Chu, H., Hwang, G., Tsai, C., & Tseng, J. C. R. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.07.004 · Cited by 424 · see citing works
Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications
Shee, D. Y., & Wang, Y. (2006) · Computers & Education · doi:10.1016/j.compedu.2006.09.005 · Cited by 422 · see citing works
Assessment and the promotion of academic values
Boud, D. J. (1990) · Studies in Higher Education · doi:10.1080/03075079012331377621 · Cited by 412 · see citing works
Learning with mobile technologies – Students’ behavior
Briz-Ponce, L., Pereira, A., Carvalho, L., Méndez, J. A. J., & García‐Peñalvo, F. J. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.027 · Cited by 386 · see citing works
Factors influencing students’ acceptance of m-learning: An investigation in higher education
Abu-Al-Aish, A., & Love, S. (2013) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v14i5.1631 · Cited by 377 · see citing works
Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education
Çelik, V., & Yeşilyurt, E. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.06.008 · Cited by 356 · see citing works
Elucidating user behavior of mobile learning
Huang, J., Lin, Y., & Chuang, S. (2007) · The Electronic Library · doi:10.1108/02640470710829569 · Cited by 331 · see citing works
The role of feedback and self-efficacy on web-based learning: The social cognitive perspective
Wang, S., & Wu, P.-Y. (2008) · Computers & Education · doi:10.1016/j.compedu.2008.03.004 · Cited by 327 · see citing works
Applications, impacts and trends of mobile technology-enhanced learning: a review of 2008-2012 publications in selected SSCI journals
Hwang, G.-J., & Wu, P. (2014) · International Journal of Mobile Learning and Organisation · doi:10.1504/ijmlo.2014.062346 · Cited by 316 · see citing works
Review of computer‐based assessment for learning in elementary and secondary education
Shute, V. J., & Rahimi, S. (2017) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12172 · Cited by 314 · see citing works
Understanding and predicting students' intention to use mobile cloud storage services
Arpacı, İ. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.12.067 · Cited by 281 · see citing works
A meta-analysis of the effects of audience response systems (clicker-based technologies) on cognition and affect
Hunsu, N. J., Adesope, O., & Bayly, D. J. (2016) · Computers & Education · doi:10.1016/j.compedu.2015.11.013 · Cited by 272 · see citing works
Policy Guidelines for Mobile Learning
Vosloo, M. W. A. S. (2013) · Cited by 263 · see citing works
A meta-analysis of the UTAUT model: Eleven years later
Khechine, H., Lakhal, S., & Ndjambou, P. (2016) · Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration · doi:10.1002/cjas.1381 · Cited by 246 · see citing works
Effects of feedback in a computer-based assessment for learning
der Kleij, F. M. V., Eggen, T. J. H. M., Timmers, C. F., & Veldkamp, B. P. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.07.020 · Cited by 245 · see citing works
Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China
Yong, L., & Li, H. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.11.014 · Cited by 231 · see citing works
Learning in a u-Museum: Developing a context-aware ubiquitous learning environment
Chen, C., & Huang, T.-C. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.04.003 · Cited by 226 · see citing works
Understanding trust and perceived usefulness in the consumer acceptance of an e-service: a longitudinal investigation
Mou, J., Shin, D., & Cohen, J. F. (2016) · Behaviour and Information Technology · doi:10.1080/0144929x.2016.1203024 · Cited by 217 · see citing works
The psychology of meaningful verbal learning : an introduction to school learning.
Ausubel, D. P. (1963) · Medical Entomology and Zoology · Cited by 214 · see citing works
Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers
Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.07.002 · Cited by 205 · see citing works
The design and implementation of a meaningful learning-based evaluation method for ubiquitous learning
Huang, Y., Chiu, P., Liu, T., & Chen, T.-S. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.05.023 · Cited by 197 · see citing works
Understanding the factors driving m‐learning adoption: a literature review
Yong, L., Han, S., & Li, H. (2010) · Campus-Wide Information Systems · doi:10.1108/10650741011073761 · Cited by 192 · see citing works
Teacher perception for m-learning: scale development and teachers' perceptions
Uzunboylu, H., & Özdamlı, F. (2011) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2011.00415.x · Cited by 184 · see citing works
Influence of polling technologies on student engagement: An analysis of student motivation, academic performance, and brainwave data
Sun, J. C. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.10.010 · Cited by 179 · see citing works
Students’ assessment preferences, perceptions of assessment and their relationships to study results
de Watering, G. V., Gijbels, D., Dochy, F. J. R. C., & van der Rijt, J. (2008) · Higher Education · doi:10.1007/s10734-008-9116-6 · Cited by 169 · see citing works
Comparing the role of ICT literacy and anxiety in the adoption of mobile learning
Callum, K. M., Jeffrey, L. M., & Kinshuk, K. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.05.024 · Cited by 165 · see citing works
Understanding the effects of professors' pedagogical development with Clicker Assessment and Feedback technologies and the impact on students' engagement and learning in higher education
Han, J. H., & Finkelstein, A. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.02.002 · Cited by 155 · see citing works
The use of a mobile learning management system at an online university and its effect on learning satisfaction and achievement
Shin, W., & Kang, M. (2015) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v16i3.1984 · Cited by 152 · see citing works
A framework for sustainable mobile learning in schools
Ng, W., & Nicholas, H. (2012) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2012.01359.x · Cited by 152 · see citing works
The implementation and evaluation of a mobile self- and peer-assessment system
Chen, C. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.01.008 · Cited by 151 · see citing works
Untitled work
(2013) · Cited by 136 · see citing works
Mobile learning: research, practice and challenges
Sharples, M. (2013) · Open Research Online (The Open University) · Cited by 133 · see citing works
The design and evaluation of a computerized adaptive test on mobile devices
Triantafillou, E., Georgiadou, E., & Economides, A. A. (2007) · Computers & Education · doi:10.1016/j.compedu.2006.12.005 · Cited by 130 · see citing works
Self-citation
How technology shapes assessment design: Findings from a study of university teachers
Bennett, S., Dawson, P., Bearman, M., Molloy, E., & Boud, D. (2017) · British Journal of Educational Technology · doi:10.1111/bjet.12439 · Cited by 130 · see citing works
Integrating user interface and personal innovativeness into the TAM for mobile learning in Cyber University
Joo, Y. J., Lee, H. W., & Ham, Y.-K. (2014) · Journal of Computing in Higher Education · doi:10.1007/s12528-014-9081-2 · Cited by 124 · see citing works
The influence of students' ICT skills and their adoption of mobile learning
Callum, K. M., & Jeffrey, L. M. (2013) · Australasian Journal of Educational Technology · doi:10.14742/ajet.298 · Cited by 114 · see citing works
The effect of emotional feedback on behavioral intention to use computer based assessment
Terzis, V., Moridis, C. N., & Economides, A. A. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.03.003 · Cited by 106 · see citing works
Self-citation
Use of clickers vs. mobile devices for classroom polling
Stowell, J. R. (2014) · Computers & Education · doi:10.1016/j.compedu.2014.12.008 · Cited by 103 · see citing works
Using Mobile Learning: Determinates Impacting Behavioral Intention
Lowenthal, J. N. (2010) · American Journal of Distance Education · doi:10.1080/08923647.2010.519947 · Cited by 101 · see citing works
Seamless Connection between Learning and Assessment- Applying Progressive Learning Tasks in Mobile Ecology Inquiry
Hung, P., Hwang, G., Lin, Y., Tsung‐Hsun, W., & Su, I.-H. (2013) · Educational Technology & Society · Cited by 100 · see citing works
The use of mobile technology for work‐based assessment: the student experience
Coulby, C., Hennessey, S., Davies, N., & Fuller, R. (2009) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2009.01022.x · Cited by 100 · see citing works
Reliability and validity of Web-based portfolio peer assessment: A case study for a senior high school's students taking computer course
Chang, C.-C., Tseng, K.-H., Chou, P.-N., & Chen, Y.-H. (2011) · Comput. Educ. · doi:10.1016/j.compedu.2011.01.014 · Cited by 97 · see citing works
Implementing computer-based assessment – A web-based mock examination changes attitudes
Deutsch, T., Herrmann, K., Frese, T., & Sandholzer, H. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.11.013 · Cited by 91 · see citing works
Evaluation of mobile assessment in a learning management system
Bogdanović, Z. M., Barać, D., Jovanić, B. R., Popović, S., & Radenković, B. (2013) · British Journal of Educational Technology · doi:10.1111/bjet.12015 · Cited by 89 · see citing works
Formative Assessment Design for PDA Integrated Ecology Observation
Hung, P., Lin, Y., & Hwang, G. (2010) · Cited by 86 · see citing works
Upper Secondary Bruneian Science Students’ Perceptions of Assessment
Dhindsa, H. S., Omar, K., & Waldrip, B. G. (2007) · International Journal of Science Education · doi:10.1080/09500690600991149 · Cited by 71 · see citing works
Using mobile and web‐based computerized tests to evaluate university students
Romero, C., Ventura, S., & Bra, P. M. D. (2009) · Computer Applications in Engineering Education · doi:10.1002/cae.20242 · Cited by 69 · see citing works
Mobile usability and user experience
Kukulska‐Hulme, A. (2007) · Routledge eBooks · doi:10.4324/9780203003428-12 · Cited by 63 · see citing works
The Use of Mobile Devices in High‐stakes Remotely Delivered Assessments and Testing
Arthur, W., Doverspike, D., Muñoz, G. J., Taylor, J. E., & Carr, A. E. (2014) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12062 · Cited by 61 · see citing works
QuesTInSitu: From tests to routes for assessment in situ activities
Santos, P., Pérez‐Sanagustín, M., Hernández‐Leo, D., & Blat, J. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.06.020 · Cited by 57 · see citing works
The Sage handbook of e-learning research
(2008) · Choice Reviews Online · doi:10.5860/choice.45-2982 · Cited by 57 · see citing works
The effects of computer self-efficacy, training satisfaction and test anxiety on attitude and performance in computerized adaptive testing
Lu, H. S., Hu, Y.-P., Gao, J.-J., & Kinshuk, K. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.04.012 · Cited by 55 · see citing works
Research Trends in the Use of Mobile Learning in Mathematics
Crompton, H., & Burke, D. (2015) · International Journal of Mobile and Blended Learning · doi:10.4018/ijmbl.2015100101 · Cited by 48 · see citing works
The influence of learning characteristics on evaluation of audience response technology
MacGeorge, E. L., Homan, S. R., Dunning, J. B., Elmore, D., Bodie, G. D., Evans, E., Khichadia, S., & Lichti, S. M. (2008) · Journal of Computing in Higher Education · doi:10.1007/bf03033425 · Cited by 46 · see citing works
Consequences of Test Anxiety on Adaptive Versus Fixed Item Testing
Ortner, T. M., & Caspers, J. (2011) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000062 · Cited by 43 · see citing works
How factors of personal attitudes and learning environments affect gender difference toward mobile learning acceptance
Liaw, S.-S., & Huang, H. (2015) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v16i4.2355 · Cited by 30 · see citing works
The comparison of mobile devices to computers for web-based assessments
Huff, K. C. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.03.008 · Cited by 29 · see citing works
An exploration into improving examinees' acceptance of participation in an online exam
Liu, I.-F., Chen, R., & Lu, H. (2015) · Educational Technology & Society · Cited by 28 · see citing works
Conative Feedback in Computer-Based Assessment
Economides, A. A. (2009) · Computers in the Schools · doi:10.1080/07380560903095188 · Cited by 26 · see citing works
Self-citation
Mobile User Experience
Zeiler, V. (2016) · Springer eBooks · doi:10.1007/978-3-658-15816-3_22 · Cited by 23 · see citing works
Implementation of a mobile peer assessment system with augmented reality in a fundamental design course
Chao, K. H., Lan, C. H., Kinshuk, K., Chang, K.-E., & Sung, Y. (2014) · Knowledge Management & E-Learning An International Journal · doi:10.34105/j.kmel.2014.06.009 · Cited by 18 · see citing works
An Outdoor Mobile-Based Assessment Activity: Measuring Students’ Motivation and Acceptance
Nikou, S. A., & Economides, A. A. (2016) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v10i4.5541 · Cited by 18 · see citing works
Self-citation
Mobile User Experience
(2014) · Elsevier eBooks · doi:10.1016/c2012-0-03599-5 · Cited by 16 · see citing works
Mobile Assessment: State of the Art
Dyson, L. E., Andrews, T., Smyth, R., & Wallace, R. (2013) · Routledge eBooks · doi:10.4324/9780203118764-39 · Cited by 13 · see citing works
User Acceptance of a Proposed Self-Evaluation and Continuous Assessment System
Robles-Gómez, A., Ros, S., Hernández, R., Tobarra, L., Caminero, A. C., & Agudo, J. M. (2015) · Educational Technology & Society · Cited by 10 · see citing works
The Acceptance Of E-Assessment Considering Security Perspective: Work In Progress
Thamadharan, K., & Maarop, N. (2015) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1099873 · Cited by 7 · see citing works
Mobile Testing for Authentic Assessment in the Field
Miyasawa, Y., & Ueno, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39112-5_73 · Cited by 6 · see citing works
Mobile-Based Peer Assessment APP and Elementary Students' Perception: Project Works of Computer Curriculum as an Example
Lai, A., & Chen, C. (2013) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2013.156 · Cited by 5 · see citing works
Performance, Assessment and Communication in one App: Mobile Tablet Assessment is Here to Stay
Campbell, A., & Mrs, S. J. M. (2014) · Australasian Journal of Paramedicine · Cited by 3 · see citing works
The Impact of an Outdoor Mobile-Supported Learning Activity on Students’ Motivation, Perceived Performance, and Satisfaction
Nikou, S. A., & Economides, A. A. (2015) · Routledge eBooks · doi:10.4324/9781315745831-5 · Cited by 3 · see citing works
Self-citation
The role of Personal Innovativeness and Previous Experience in explaining and predicting Mobile-based Assessment adoption
Nikou, S. A., & Economides, A. A. (2015) · E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education · Cited by 2 · see citing works
Self-citation

Flagged first, then by citations

2 of the 111 references couldn’t be matched to a known paper.

Sorted by citations

Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model
Chao, C.-M. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01652 · Cited by 1,041 · see citing works
Technology Acceptance Model in M-learning context: A systematic review
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018) · Comput. Educ. · doi:10.1016/j.compedu.2018.06.008 · Cited by 624 · see citing works
Measuring Students’ Use of Zoom Application in Language Course Based on the Technology Acceptance Model (TAM)
Alfadda, H. A., & Mahdi, H. S. (2021) · Journal of Psycholinguistic Research · doi:10.1007/s10936-020-09752-1 · Cited by 438 · see citing works
Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA
Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A. A. A., Ghobakhloo, M., Annamalai, N., & Naghmeh‐Abbaspour, B. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2226495 · Cited by 481 · see citing works
Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00420-7 · Cited by 413 · see citing works
Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05410 · Cited by 443 · see citing works
A meta-analysis of antecedents and consequences of trust in mobile commerce
Sarkar, S., Chauhan, S., & Khare, A. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.008 · Cited by 375 · see citing works
Technology Acceptance, Technological Self-Efficacy, and Attitude Toward Technology-Based Self-Directed Learning: Learning Motivation as a Mediator
Pan, X. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.564294 · Cited by 329 · see citing works
“Previous studies have discussed learners’ acceptance of different types of technology, such as mobile technology (Pindeh et al., 2016; Nikou and Economides, 2017), computer-based communication technology (Park et al., 2014), social media (Hsieh et al., 2017), and MOOCS courses (Joo et al., 2018).”
Background
Cashless Transactions: A Study on Intention and Adoption of e-Wallets
Yang, M., Mamun, A. A., Mohiuddin, M., Nawi, N. C., & Zainol, N. R. (2021) · Sustainability · doi:10.3390/su13020831 · Cited by 326 · see citing works
“Moreover, Nikou and Economides [44], who investigated cognitive feedback and user interface to predict the relationship between behavior intention to use an e-wallet, discovered the positive effect of behavior intention on adoption of an e-wallet.”
Background
Influence of Pedagogical Beliefs and Perceived Trust on Teachers’ Acceptance of Educational Artificial Intelligence Tools
Choi, S., Jang, Y., & Kim, H. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2049145 · Cited by 294 · see citing works
The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model
Lew, S., Tan, G. W., Loh, X.-M., Hew, J.-J., & Ooi, K. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101430 · Cited by 305 · see citing works
Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms
Alshurideh, M. T., Kurdi, B. H. A., Salloum, S. A., Arpacı, İ., & Al‐Emran, M. (2020) · Interactive Learning Environments · doi:10.1080/10494820.2020.1826982 · Cited by 280 · see citing works
A Technology Acceptance Model Survey of the Metaverse Prospects
Aburbeian, A. M., Owda, A. Y., & Owda, M. (2022) · Applied Informatics · doi:10.3390/ai3020018 · Cited by 216 · see citing works
Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries
Al-Azawei, A., & Alowayr, A. S. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101325 · Cited by 204 · see citing works
Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran
Chavoshi, A., & Hamidi, H. (2018) · Telematics and Informatics · doi:10.1016/j.tele.2018.09.007 · Cited by 203 · see citing works
University students’ use of mobile technology in self-directed language learning: Using the integrative model of behavior prediction
Lai, Y., Saab, N., & Admiraal, W. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104413 · Cited by 218 · see citing works
Influencing factors in M-learning adoption in higher education
Qashou, A. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10323-z · Cited by 181 · see citing works
Application of Learning Management System (LMS) during the COVID-19 Pandemic: A Sustainable Acceptance Model of the Expansion Technology Approach
Alturki, U., & Aldraiweesh, A. (2021) · Sustainability · doi:10.3390/su131910991 · Cited by 185 · see citing works
Determinants of College Students’ Actual Use of AI-Based Systems: An Extension of the Technology Acceptance Model
Li, K. (2023) · Sustainability · doi:10.3390/su15065221 · Cited by 157 · see citing works
The Predicting Role of EFL Students’ Achievement Emotions and Technological Self-efficacy in Their Technology Acceptance
Wang, Y., Wang, Y., Pan, Z., & Ortega-Martín, J. L. (2023) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-023-00750-0 · Cited by 170 · see citing works
Determinants of mobile learning acceptance for STEM education in rural areas
Mutambara, D., & Bayaga, A. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104010 · Cited by 143 · see citing works
Applying the technology acceptance model to understand maths teachers’ perceptions towards an augmented reality tutoring system
İbi̇li̇, E., Resnyansky, D., & Billinghurst, M. (2019) · Education and Information Technologies · doi:10.1007/s10639-019-09925-z · Cited by 134 · see citing works
Predicting behavioral intention to use e-learning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh
Humida, T., Al Mamun, M. H., & Keikhosrokiani, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10707-9 · Cited by 133 · see citing works
Unpacking the Inherent Design Principles of Mobile Microlearning
Jahnke, I., Lee, Y.-M., Pham, M. T. N., He, H., & Austin, L. (2019) · Technology Knowledge and Learning · doi:10.1007/s10758-019-09413-w · Cited by 131 · see citing works
Mobile-based assessment: A literature review of publications in major referred journals from 2009 to 2018
Nikou, S. A., & Economides, A. A. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.06.006 · Cited by 127 · see citing works
Self-citation
Student continuance of learning management system use: A longitudinal exploration
Cheng, M., & Yuen, A. H. K. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.02.004 · Cited by 121 · see citing works
Video conferencing in the e-learning context: explaining learning outcome with the technology acceptance model
Bailey, D., Almusharraf, N., & Almusharraf, A. M. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10949-1 · Cited by 141 · see citing works
Mobile learning acceptance and use in higher education during social distancing circumstances: an expansion and customization of UTAUT2
Sitar‐Tăut, D., & Mican, D. (2021) · Online Information Review · doi:10.1108/oir-01-2021-0017 · Cited by 125 · see citing works
Automated writing evaluation (AWE) feedback: a systematic investigation of college students’ acceptance
Zhai, N., & Ma, X. (2021) · Computer Assisted Language Learning · doi:10.1080/09588221.2021.1897019 · Cited by 113 · see citing works
Mobile learning acceptance in social distancing during the COVID ‐19 outbreak: The mediation effect of hedonic motivation
Sitar‐Tăut, D. (2021) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.261 · Cited by 119 · see citing works
Factors Affecting the Use of Smart Mobile Examination Platforms by Universities’ Postgraduate Students during the COVID-19 Pandemic: An Empirical Study
Alshurideh, M. T., Kurdi, B. H. A., AlHamad, A., Salloum, S. A., Alkurdi, S. H., Dehghan, A. H., Abuhashesh, M., & Masa’deh, R. M. T. (2021) · Informatics · doi:10.3390/informatics8020032 · Cited by 111 · see citing works
Factors that influence behavioral intention to use mobile‐based assessment: A STEM teachers’ perspective
Nikou, S. A., & Economides, A. A. (2018) · British Journal of Educational Technology · doi:10.1111/bjet.12609 · Cited by 108 · see citing works
Self-citation
Exploring Behavioral Intention to Use a Mobile Health Education Website: An Extension of the UTAUT 2 Model
Yu, C.-W. F., Chao, C., Chang, C.-F., Chen, R.-J., Chen, P.-C., & Liu, Y. (2021) · SAGE Open · doi:10.1177/21582440211055721 · Cited by 117 · see citing works
Can COVID-19 pandemic influence experience response in mobile learning?
Yuan, Y.-P., Tan, G. W., Ooi, K., & Lim, W. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2021.101676 · Cited by 107 · see citing works
Factors Influencing Student Acceptance of Mobile Learning in Higher Education
Fagan, M. H. (2019) · Computers in the Schools · doi:10.1080/07380569.2019.1603051 · Cited by 101 · see citing works
Exploring chatbot trust: Antecedents and behavioural outcomes
Alagarsamy, S., & Mehrolia, S. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16074 · Cited by 126 · see citing works
Students’ adoption towards behavioral intention of digital learning platform
Songkram, N., Chootongchai, S., Osuwan, H., Chuppunnarat, Y., & Songkram, N. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11637-4 · Cited by 93 · see citing works
Examining teachers’ behavioural intention for online teaching after COVID-19 pandemic: A large-scale survey
Khong, T. D. H., Çelik, İ., Le, T. T. T., Lai, V. T. T., Nguyen, A., & Bui, H. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11417-6 · Cited by 91 · see citing works
Examining EFL learners’ individual antecedents on the adoption of automated writing evaluation in China
Li, R., Meng, Z., Tian, M., Zhang, Z., Ni, C., & Wei, X. L. (2019) · Computer Assisted Language Learning · doi:10.1080/09588221.2018.1540433 · Cited by 87 · see citing works
M-learning in the COVID-19 era: physical vs digital class
Matzavela, V., & Alepis, E. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10572-6 · Cited by 93 · see citing works
Exploring Iranian english as a foreign language teachers’ acceptance of ChatGPT in english language teaching: Extending the technology acceptance model
Dehghani, H., & Mashhadi, A. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12660-9 · Cited by 97 · see citing works
Adoption of mobile library apps as learning tools in higher education: a tale between Hong Kong and Japan
Yip, K. H. T., Lo, P., Ho, K. K. W., & Chiu, D. K. W. (2020) · Online Information Review · doi:10.1108/oir-07-2020-0287 · Cited by 79 · see citing works
An explanatory study of factors influencing engagement in AI education at the K-12 Level: an extension of the classic TAM model
Li, W., Zhang, X., Li, J., Yang, X., Li, D., & Liu, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-64363-3 · Cited by 78 · see citing works
Mobile application user behavior in the developing countries: A survey in Iran
Hajiheydari, N., & Ashkani, M. (2018) · Information Systems · doi:10.1016/j.is.2018.05.004 · Cited by 73 · see citing works
A Study on the Influencing Factors of Continued Intention to Use MOOCs: UTAUT Model and CCC Moderating Effect
Li, Y., & Zhao, M. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.528259 · Cited by 71 · see citing works
THE EXTENDED UTAUT MODEL AND LEARNING MANAGEMENT SYSTEM DURING COVID-19: EVIDENCE FROM PLS-SEM AND CONDITIONAL PROCESS MODELING
Ahmed, R. R., Štreimikienė, D., & Štreimikis, J. (2021) · Journal of Business Economics and Management · doi:10.3846/jbem.2021.15664 · Cited by 71 · see citing works
ChatGPT as a tool for self-learning English among EFL learners: A multi-methods study
Tram, N. H. M., Nguyen, T. T., & Tran, C. D. (2024) · System · doi:10.1016/j.system.2024.103528 · Cited by 105 · see citing works
Effects of Using Artificial Intelligence Teaching System for Environmental Education on Environmental Knowledge and Attitude
Huang, S.-P. (2018) · EURASIA Journal of Mathematics, Science and Technology Education · doi:10.29333/ejmste/91248 · Cited by 67 · see citing works
Continuing to teach in a time of crisis: The Chinese rural educational system’s response and student satisfaction and social and cognitive presence
Wang, J., Yang, Y., Li, H., & van Aalst, J. (2021) · British Journal of Educational Technology · doi:10.1111/bjet.13129 · Cited by 66 · see citing works
Essential factors for the application of education information system using mobile learning: A case study of students of the university of technology
Hamidi, H., & Jahanshaheefard, M. (2018) · Telematics and Informatics · doi:10.1016/j.tele.2018.10.002 · Cited by 66 · see citing works
Design quality in building behavioral intention through affective and cognitive involvement for e-learning on smartphones
Faisal, C. M. N., Fernández-Lanvin, D., Suárez, J. D. A., & Rodríguez, M. G. (2020) · Internet Research · doi:10.1108/intr-05-2019-0217 · Cited by 68 · see citing works
Behavioral intention towards mobile learning in Taiwan, China, Indonesia, and Vietnam
Lin, S. H., Lee, H.-C., Chang, C., & Fu, C. J. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101387 · Cited by 65 · see citing works
Students’ continuance intention to use MOOCs: empirical evidence from India
Rekha, I. S., Shetty, J., & Basri, S. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11308-w · Cited by 64 · see citing works
Theories Integrated With Technology Acceptance Model (TAM) in Online Learning Acceptance and Continuance Intention: A Systematic Review
Mustafa, A. S., & Garcia, M. B. (2021) · Conference on Online Teaching for Mobile Education (OT4ME) · doi:10.1109/ot4me53559.2021.9638934 · Cited by 63 · see citing works
A UTAUT Evaluation of WhatsApp as a Tool for Lecture Delivery During the COVID-19 Lockdown at a Zimbabwean University
Maphosa, V., Dube, B., & Jita, T. (2020) · International Journal of Higher Education · doi:10.5430/ijhe.v9n5p84 · Cited by 62 · see citing works
Exploring factors influencing the acceptance of visual programming environment among boys and girls in primary schools
Cheng, G. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.11.043 · Cited by 64 · see citing works
Adoption of E-learning systems: An integration of ISSM and constructivism theories in higher education
Sayaf, A. M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13014 · Cited by 77 · see citing works
The mobile augmented reality acceptance model for teachers and future teachers
Koutromanos, G., Mikropoulos, A. T., Mavridis, D., & Christogiannis, C. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12116-6 · Cited by 59 · see citing works
Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool
Cortez, P., Ong, A. K. S., Diaz, J. F. T., German, J. D., & Jagdeep, S. J. S. S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25896 · Cited by 59 · see citing works
PREDICTING FACTORS AFFECTING INTENTION TO USE WEB 2.0 IN LEARNING: EVIDENCE FROM SCIENCE EDUCATION
Muhaimin, M., Habibi, A., Mukminin, A., Pratama, R., Asrial, A., & Harja, H. (2019) · Journal of Baltic Science Education · doi:10.33225/jbse/19.18.595 · Cited by 63 · see citing works
Students’ Actual Use of E-Learning in Higher Education During the COVID-19 Pandemic
Gurban, M. A., & Almogren, A. S. (2022) · SAGE Open · doi:10.1177/21582440221091250 · Cited by 55 · see citing works
M-learning adoption of management students’: A case of India
Shukla, S. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10271-8 · Cited by 56 · see citing works
Determinants of ChatGPT adoption among students in higher education: the moderating effect of trust
Foroughi, B., Iranmanesh, M., Ghobakhloo, M., Senali, M. G., Annamalai, N., Naghmeh‐Abbaspour, B., & Rejeb, A. (2024) · The Electronic Library · doi:10.1108/el-12-2023-0293 · Cited by 53 · see citing works
Predicting mobile learning acceptance: An integrated model and empirical study based on higher education students' perceptions
Alowayr, A. S., & Al-Azawei, A. (2021) · Australasian Journal of Educational Technology · doi:10.14742/ajet.6154 · Cited by 55 · see citing works
Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review
Gupta, Y., Khan, F. M., & Agarwal, S. (2021) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v15i12.22503 · Cited by 65 · see citing works
Acceptance of a flipped smart application for learning: a study among Thai university students
Khlaisang, J., Teo, T. P., & Huang, F. (2019) · Interactive Learning Environments · doi:10.1080/10494820.2019.1612447 · Cited by 59 · see citing works
Student engagement with mobile‐based assessment systems: A survival analysis
Bacca-Acosta, J., & Avila, C. (2020) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12475 · Cited by 56 · see citing works
Feelings of Being for Mobile User Experience Design
Dirin, A., Nieminen, M., & Laine, T. H. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2108964 · Cited by 49 · see citing works
Pre-service teachers’ acceptance of mobile-technology-supported learning activities
İslamoğlu, H., Yurdakul, I. K., & Ursavaş, Ö. F. (2021) · Educational Technology Research and Development · doi:10.1007/s11423-021-09973-8 · Cited by 49 · see citing works
Data-driven modeling of technology acceptance: A machine learning perspective
Alwabel, A. S. A., & Zeng, X. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115584 · Cited by 54 · see citing works
Computer-assisted language proficiency assessment tools and strategies
Bahari, A. (2020) · Open Learning The Journal of Open Distance and e-Learning · doi:10.1080/02680513.2020.1726738 · Cited by 53 · see citing works
Exploring the Key Factors Influencing College Students’ Willingness to Use AI Coding Assistant Tools: An Expanded Technology Acceptance Model
Pan, Z., Xie, Z., Liu, T., & Xia, T. (2024) · Systems · doi:10.3390/systems12050176 · Cited by 48 · see citing works
Designing learning experiences for outdoor hybrid learning spaces
Mettis, K., & Väljataga, T. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.13034 · Cited by 47 · see citing works
CROWDFUNDING AND ISLAMIC SECURITIES: THE ROLE OF FINANCIAL LITERACY
Majid, R., & Nugraha, R. A. (2022) · Journal of Islamic Monetary Economics and Finance · doi:10.21098/jimf.v8i1.1420 · Cited by 44 · see citing works
Exploring the Effects of Automated Written Corrective Feedback on EFL Students’ Writing Quality: A Mixed-Methods Study
Fan, N. (2023) · SAGE Open · doi:10.1177/21582440231181296 · Cited by 44 · see citing works
High school students’ acceptance and use of mobile technology in learning mathematics
AÇIKGÜL, K., & Şad, S. N. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10466-7 · Cited by 44 · see citing works
Students Perceptions, System Characteristics and Online Learning During the COVID-19 Epidemic School Disruption
Quadir, B., & Zhou, M. (2021) · International Journal of Distance Education Technologies · doi:10.4018/ijdet.20210401.oa1 · Cited by 43 · see citing works
Adoption, use and enhancement of virtual learning during COVID-19
Zhou, M., Dzingirai, C., Hove, K., Chitata, T., & Mugandani, R. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10985-x · Cited by 50 · see citing works
Promoting Sustainable Financial Services Through the Adoption of eWallet Among Malaysian Working Adults
Nawi, N. C., Mamun, A. A., Hayat, N., & Seduram, L. (2022) · SAGE Open · doi:10.1177/21582440211071107 · Cited by 63 · see citing works
A new trend of learning and teaching: Behavioral intention towards mobile learning
Hameed, F., Qayyum, A., & Khan, F. A. (2022) · Journal of Computers in Education · doi:10.1007/s40692-022-00252-w · Cited by 41 · see citing works
From Virtual Trees to Real Forests: The Impact of Gamification Affordances on Green Consumption Behaviors in Ant Forest
Chen, S., Li, B., Zhou, Q., & Liu, H. (2023) · Environmental Communication · doi:10.1080/17524032.2023.2213850 · Cited by 41 · see citing works
Understanding Continuance Usage of Mobile Learning Applications: The Moderating Role of Habit
Wang, Y.-T., & Lin, K. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.736051 · Cited by 50 · see citing works
Understanding the Predictors of Rural Customers’ Continuance Intention toward Mobile Banking Services Applications during the COVID-19 Pandemic
Amin, M. A., Arefin, M. S., Alam, M. S., & Rasul, T. (2022) · Journal of Global Marketing · doi:10.1080/08911762.2021.2018750 · Cited by 44 · see citing works
Student Engagement with Technology-Enhanced Resources in Mathematics in Higher Education: A Review
Shé, C. N., Fhloinn, E. N., & Bhaird, C. M. A. (2023) · Mathematics · doi:10.3390/math11030787 · Cited by 59 · see citing works
Consumer usage behavior of internet-based services (IBS) in Pakistan during COVID-19 crisis from the perspective of technology acceptance model
Ishfaq, N., & Huang, M. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-15868-1 · Cited by 37 · see citing works
Using Structural Equation Modeling to Assess Online Learning Systems’ Educational Sustainability for University Students
Alismaiel, O. A. (2021) · Sustainability · doi:10.3390/su132413565 · Cited by 40 · see citing works
Exploring Sustainability Environment Educational Design and Learning Effect Evaluation through Migration Theory: An Example of Environment Educational Serious Games
Su, C.-H. (2018) · Sustainability · doi:10.3390/su10103363 · Cited by 37 · see citing works
Understanding elementary mathematics teachers’ intention to use a digital game through the technology acceptance model
Yeo, S., Rutherford, T. A., & Campbell, T. G. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11073-w · Cited by 41 · see citing works
The Determinants for Food Safety Push Notifications on Continuance Intention in an E-Appointment System for Public Health Medical Services: The Perspectives of UTAUT and Information System Quality
Lee, Y.-P., Tsai, H.-Y., & Ruangkanjanases, A. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17218287 · Cited by 36 · see citing works
Challenges and Possibilities of ICT-Mediated Assessment in Virtual Teaching and Learning Processes
Torres-Madroñero, E. M., Torres-Madronero, M. C., & Botero, L. D. R. (2020) · Future Internet · doi:10.3390/fi12120232 · Cited by 35 · see citing works
Web-based videoconferencing for teaching online: Continuance intention to use in the post-COVID-19 period
Nikou, S. A. (2021) · Interaction design & architecture(s)/ID&A Interaction design & architecture(s) · doi:10.55612/s-5002-047-006 · Cited by 35 · see citing works
Self-citation
The Role of Technology in English Language Learning in Online Classes at Tertiary Level
Shahid, C., Gurmani, M. T., Rehman, S. U., & Saif, L. (2023) · journal of social sciences review · doi:10.54183/jssr.v3i2.215 · Cited by 40 · see citing works
Teachers’ perception of the use of mobile technologies with smart applications to enhance students’ thinking skills: a study among primary school teachers in Thailand
Khlaisang, J., Songkram, N., Huang, F., & Teo, T. P. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2021.1993933 · Cited by 36 · see citing works
Analyzing students’ e-learning usage and post-usage outcomes in higher education
Ullah, M. S., Hoque, M. R., Aziz, M. A., & Islam, M. (2023) · Computers and Education Open · doi:10.1016/j.caeo.2023.100146 · Cited by 33 · see citing works
Proposing a conceptual model for the adoption of artificial intelligence by teachers in STEM education
Ateş, H., & GÜNDÜZALP, C. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2457350 · Cited by 37 · see citing works
Influential Characteristics of Enterprise Information System User Interfaces
Türetken, O., Ondracek, J., & IJsselsteijn, W. A. (2017) · Journal of Computer Information Systems · doi:10.1080/08874417.2017.1339367 · Cited by 33 · see citing works
A bibliometric analysis of mobile learning in the education sector
Khan, F. M., & Gupta, Y. (2021) · Interactive Technology and Smart Education · doi:10.1108/itse-03-2021-0048 · Cited by 40 · see citing works
Students' adoption of e-learning: evidence from a Moroccan business school in the COVID-19 era
Alami, Y., & Idrissi, I. E. (2022) · Arab Gulf Journal of Scientific Research · doi:10.1108/agjsr-05-2022-0052 · Cited by 33 · see citing works
Using mobile-based micro-learning to enhance students; retention of IT concepts and skills
Kadhem, H. (2017) · IEEE International Conference on Knowledge Engineering and Applications (ICKEA) · doi:10.1109/ickea.2017.8169915 · Cited by 32 · see citing works
Investigation of the relationships among educational application (APP) quality, computer anxiety and student engagement
Lee, J.-C., & Xiong, L. (2021) · Online Information Review · doi:10.1108/oir-08-2020-0348 · Cited by 33 · see citing works

Students’ acceptance of mobile-based assessment
Saputri, N. A. K., Abdullah, A. G., & Hakim, D. L. (2021) · Momentum Physics Education Journal · doi:10.21067/mpej.v5i1.4575 · Cited by 5 · see citing works
The effects of Perceived Mobility and Satisfaction on the adoption of Mobile-based Assessment
Nikou, S. A., & Economides, A. A. (2015) · International Conference on Interactive Mobile Communication, Technologies and Learning · doi:10.1109/imctl.2015.7359579 · Cited by 17 · see citing works
Self-citation
Mobile-Based Assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance
Nikou, S. A., & Economides, A. A. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.11.020 · Cited by 416 · see citing works
Self-citation
A vision of the brain
Bruyn, G. W. (1993) · Clinical Neurology and Neurosurgery · doi:10.1016/0303-8467(93)90117-y
Factors that influence behavioral intention to use mobile‐based assessment: A STEM teachers’ perspective
Nikou, S. A., & Economides, A. A. (2018) · British Journal of Educational Technology · doi:10.1111/bjet.12609 · Cited by 108 · see citing works
Self-citation

Authors: Nikou, S. A., & Economides, A. A.
Affiliations: University of Macedonia (GR)
Corresponding author: Nikou, Stavros A.
ORCID: Nikou, Stavros A.
Economides, Anastasios A.
Venue: Computers & Education
Publisher: Elsevier BV
Published: 2017-06
Volume: 109
Pages: 56-73
ISSN: 0360-1315 (ISSN-L)
DOI: 10.1016/j.compedu.2017.02.005
OpenAlex: W2590508971
Cited by: 388
References: 111
Citations by year: 2026: 20 · 2025: 38 · 2024: 48 · 2023: 59 · 2022: 62 · 2021: 66 · 2020: 43 · 2019: 29 · 2018: 16 · 2017: 7
FWCI: 48.37
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Computers & Education (publishedVersion)
Topics: Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords: mobile-based assessment, behavioral intention, technology acceptance model, perceived ease of use, perceived usefulness, facilitating conditions, social influence, personal innovativeness, mobile self-efficacy, perceived trust, high school students, mobile learning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Mobile-based assessment: Investigating the factors that influence behavioral intention to use

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks