Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Technology, Knowledge and Learning
Published in: Technology, Knowledge and Learning 4/2022

15-04-2021 | Original research

Students’ Perception of Cell Phones Effect on their Academic Performance: A Latvian and a Middle Eastern University Cases

Authors: Bashar Zogheib, Linda Daniela

Published in: Technology, Knowledge and Learning | Issue 4/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This study is unique in its attempt to combine the Technology Acceptance Model (TAM) with external variables and the Information System Continuance Theory, in the context of mobile learning. It extends TAM with the external variables perceived mobility and perceived enjoyment. Mainly, students’ perception of cell phones effect on their academic performance is investigated. The study was conducted at two different universities—in Europe and the Middle East. Cell phone usage is analyzed from the perspective of mobile learning. The data in this study were collected from 103 students from the University of Latvia and 106 students from a Middle Eastern private American University. This study shows that there is no significant difference on how the two groups perceive the use of cell phone and their opinion of its impact on their academic performance. The obtained results are in line with the theoretical model. They show the influence of perceived enjoyment and perceived usefulness on students’ attitudes towards using cell phones. Data shows that there is a positive relationship between cell phone usage and students’ perception of its effect on their academic performance. This study reveals several interesting findings, and it is believed that this research offers the educators and administrators further insight in analysing the current effect of cell phones on students’ academic performance and the possibility of adopting mobile learning as one of the technological tools which supports technology enhanced learning.
previous article Mobile Phones in Schools: With or Without you? Comparison of Students’ Anxiety Level and Class Engagement After Regular and Mobile-Free School Days
next article An E-safety Framework for Secondary Schools in Zimbabwe

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Literature
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS quarterly, 35, 227–247CrossRef
Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Taylor and Francis.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice Hall.
Alalwan, N., Al-Rahmi, W. M., Alfarraj, O., Alzahrani, A., Yahaya, N., & Al-Rahmi, A. M. (2019). Integrated three theories to develop a model of factors affecting students’ academic performance in higher education. IEEE Access, 7, 98725–98742CrossRef
Ally, M., & Prieto-Blázquez, J. (2014). What is the future of mobile learning in education? mobile learning applications in higher education [special section]. Revista de Universidad y Sociedad del Conocimiento (RUSC)., 11(1), 142–151CrossRef
Aguayo, C., Eames, C., & Cochrane, T. A. (2020). Framework for mixed reality free-choice. (p. 28). Self-Determined Learning.
Al-Rahmi, W. M., Alias, N., Othman, M. S., Marin, V. I., & Tur, G. (2018). A model of factors affecting learning performance through the use of social media in Malaysian higher education. Computers and Education, 121, 59–72CrossRef
Anckar, B., & D’incau, D. (2002). Value creation in mobile commerce: Findings from a consumer survey{JITTA}. Journal of Information Technology Theory and Application, 41(8), 356
Bachmair, B., Cook, J., & Kress, G. R. (2010). Mobile learning: structures, agency, practices. Springer.
Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technology studies, 2(2), 285–309
Barreh, Kadar A., & Abas Z. W. (2013). A Framework for Mobile Learning for Enhancing Learning in Higher Education. Malaysian Online Journal of Educational Technology, 3(3). Retrieved from www.​mojet.​net
Batra, R., & Ray, M. L. (1986). Affective responses mediating acceptance of advertising. Journal of Consumer Research, 13(2), 234–249CrossRef
Bedué, P. (2020). Just fun and games? Utilitarian and hedonic chatbot perceptions and their role for continuance intentions. In W. Abramowicz & G. Klein (Eds.), Business information systems BIS 2020. lecture notes in business information processing vol 389.Springer.
Bhattacherjee, A. (2001). Understanding information systems continuance. An Expectation Confirmation Model, MIS Quarterly, 25(3), 351–370
Bollen, K., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological bulletin, 110(2), 305CrossRef
Burden, K., & Kearney, M. (2016). Conceptualising authentic mobile learning. In D. Churchill, J. Lu, K. F. T. Chiu, & B. Fox (Eds.), Mobile learning design: theories and application. (pp. 27–42). Springer.
Cao, Y., Ajjan, H., & Hong, P. (2013). Using social media applications for educational outcomes in college teaching: A structural equation analysis. British Journal of Educational Technology, 44(4), 581–593CrossRef
Cassel, C., Hackl, P., & Westlund, A. H. (1999). Robustness of partial least-squares method for estimating latent variable quality structures. Journal of applied statistics, 26(4), 435–446CrossRef
Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of retailing, 77(4), 511–535CrossRef
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295–336
Coursaris, C., Hassanein, K., & Head, M. (2003). M-commerce in Canada: An interaction framework for wireless privacy. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l’Administration, 20(1), 54–73CrossRef
Daniela, L. (2020) The Concept of Smart Pedagogy for Learning in the Digital World, in Epistemological Approaches to Digital Learning in Educational Contexts, ed. L.Daniela, Taylor&Francis, (pp. 1–16) ISBN 9780367333799
Davis, F.D. (1986), “A technology acceptance model for empirically testing new end-user information systems: theory and results”, doctoral dissertation, MIT Sloan School of Management, Cambridge, MA.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339CrossRef
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003CrossRef
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), 1111–1132CrossRef
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research., 3(1), 60–95CrossRef
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems., 19(4), 9–30CrossRef
Derveni, K., & Dagdilelis, V. (2020). Perceptions of primary education teachers on mobile learning: An approach in Greek and Latvian teachers. International Journal of Smart Education and Urban Society (IJSEUS), 11(3), 52–68CrossRef
Fernández, R. C., McCoy, S., Yáñez, S. L., & Yáñez, M. D. (2014). Technology acceptance model: Worried about the cultural influence? In F. F. H. Nah (Ed.), HCI in business HCIB lecture notes in computer science.Cham: Springer.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18, 39–50CrossRef
Fu, J. R., Farn, C. K., & Chao, W. P. (2006). Acceptance of electronic tax filing: A study of taxpayer intentions. Information and Management, 43, 109–126CrossRef
Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones and social media. The Internet and Higher Education, 19, 18–26CrossRef
Herrington, A., Herrington, J., & Mantei, J. (2009). Design principles for mobile learning. In J. Herrington, A. Herrington, J. Mantei, I. Olney, & B. Ferry (Eds.), New Technologies, New Pedagogies: Mobile Learning in Higher Education (pp. 129–138). Wollongong: University of Wollongong. Retrieved from http://​ro.​uow.​edu.​au/​
Hill, T. R., & Roldan, M. (2005). Toward third generation threaded discussions for mobile learning: Opportunities and challenges for ubiquitous collaborative environments. Information Systems Frontiers, 7(1), 55–70CrossRef
Hossain, S. F. A., Nurunnabi, M., Hussain, K., Saha, S. K., & Wang, S. (2019). Effects of variety-seeking intention by mobile phone usage on university students’ academic performance. Cogent Education, 6(1), 1574692CrossRef
Hu, P. J., Chau, P. Y. K., Liu Sheng, O. R., & Yan Tam, K. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91–112CrossRef
Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning. The electronic library, 25(5), 585–598CrossRef
Igbaria, M., Meredith, G., & Smith, D. C. (1995). Career orientations of information systems employees in South Africa. The Journal of Strategic Information Systems, 4(4), 319–340CrossRef
Jabbour, K. K. (2014). An analysis of the effect of mobile learning on lebanese higher education. Informatics in Education, 13, 1–15CrossRef
Junco, R., & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers and Education, 59(2), 505–514CrossRef
Keller, J. M. (2010). Motivational design for learning and performance: The ARCS model approach. Springer.
Kiliç, E. (2014). Determining the factors of affecting the Moodle use by using TAM. The story of a univer-sity after a destructive earthquake. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 29(1), 169–179
King, W. R., & He, W. R. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740–755CrossRef
Lee, D.-C., Lin, S.-H., Ma, H.-L., & Wu, D.-B. (2017). Use of a modified UTAUT model to investigate the perspectives of internet access device users. International Journal of Human-Computer Interaction, 33(7), 549–564CrossRef
Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation. Information and Management, 42, 1095–1104CrossRef
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Inf. Manag., 40, 191–204CrossRef
Lepp, A., Barkley, J. E., & Karpinski, A. C. (2014). The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Computers in human behavior, 31, 343–350CrossRef
Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010a). Extending the TAM model to explore the factors that affect Intention to Use an online learning community. Computers and Education, 54(2), 600–610CrossRef
Liu, Y., Han, S., & Li, H. (2010b). Understanding the factors driving m-learning adoption: A literature review. Campus-Wide Information Systems, 27, 210–226CrossRef
López Cruz, C. S. & Gutierrez Cortes, F. I. (2012). Mobility in Learning: Interdisciplinary Experiences. In Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2012 (pp. 1845–1849). Chesapeake, VA: AACE.
Lowenthal, J. N. (2010). Using mobile learning: determinates impacting behavioral intention. American Journal of Distance Education, 24(4), 195–206CrossRef
MacGeorge, E. L., Homan, S. R., Dunning, J. B., Elmore, D., Bodie, G. D., Evans, E., & Geddes, B. (2008). Student evaluation of audience response technology in large lecture classes. Educational Technology Research and Development, 56(2), 125–145CrossRef
Mallat, N., Rossi, M., Tuunainen, V. K., & Oorni, A. (2006, January). The impact of use situation and mobility on the acceptance of mobile ticketing services. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06) (Vol. 2, pp. 42b-42b). IEEE.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14, 81–95CrossRef
Mun, Y. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of human-computer studies, 59(4), 431–449CrossRef
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide-web context. Information and Management, 38(4), 217–230CrossRef
Norazah, M. S., Ramayah, T., & Norbayah, M. S. (2008). Internet shopping acceptance: Examining the influence of intrinsic versus extrinsic motivations. Direct Marketing: An International Journal, 2(2), 97–110CrossRef
Nordlöf, C., Hallström, J., & Höst, G. E. (2019). Self-efficacy or context dependency?: Exploring teachers’ perceptions of and attitudes towards technology education. International Journal of Technology and Design Education, 29, 123–141CrossRef
Nunnally, J. C., & Bernstein, I. H. (1994). The assessment of reliability. Psychometric theory, 3, 248–292
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, 17(4), 460–469CrossRef
Park, S. Y. (2009). An analysis of the Technology Acceptance Model in understanding university students’ behavioral intention to use e-Learning. Educational Technology and Society, 12(3), 150–162
Park, E., & Kim, K. J. (2013). User acceptance of long-term evolution (LTE) services. Program: An application of extended technology acceptance model”. Program, 47(2), 188–205CrossRef
Park, S. (2015). The effects of social cue principles on cognitive load, situational interest, motivation, and achievement in pedagogical agent multimedia learning. Educational Technology and Society, 18(4), 211–229
Qashou, A. (2021). Influencing factors in M-learning adoption in higher education. Education and Information Technologies, 26, 1755–1785CrossRef
Qin, L., Kim, Y., & Tan, X. (2018). Understanding the intention of using mobile social networking apps across cultures. International Journal of Human-Computer Interaction, 34(12), 1183–1193CrossRef
Rogers, E. M. (1962). Diffusion of innovations (1. (st). Free Press.
Rogers, E. M. (1983). Diffusion of innovations (4. (th). The Free Press.
Saroia, A. I., & Gao, S. (2019). Investigating university students’ intention to use mobile learning management systems in Sweden. Innovations in Education and Teaching International, 56(5), 569–580CrossRef
Selim, H. M. (2003). An empirical investigation of student acceptance of course websites. Computers and Education, 40(4), 343–360CrossRef
Seppälä, P., & Alamäki, H. (2003). Mobile learning in teacher training. Journal of computer assisted learning, 19(3), 330–335CrossRef
Sheppard, M., & Vibert, C. (2019). Re-examining the relationship between ease of use and usefulness for the net generation. Education and Information Technologies, 24, 3205–3218CrossRef
Staddon, R. V. (2020). Bringing technology to the mature classroom: age differences in use and attitudes. International Journal of Educational Technology in Higher Education, 17, 11CrossRef
Statista. (2017). Number of smartphone users in the U.S. 2010–2022. Statista. Retrieved from: https://​www.​ statista.com/statistics/201182/forecast-of- smartphone-users-in-the-us
Subramanian, G. H. (1994). A replication of perceived usefulness and perceived ease of use measurement. Decision Sciences, 25(5–6), 863–874CrossRef
Suki, N. M., & Ramayah, T. (2010). User acceptance of the e-government services in Malaysia: structural equation modelling approach. Interdisciplinary Journal of Information, Knowledge, and Management, 5(1), 395–413CrossRef
Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review., 10(3), 251–296CrossRef
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers and Education, 52(2), 302–312CrossRef
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57, 2432–2440CrossRef
Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the technology acceptance model (TAM) and theory of planned behavior (TPB). Interactive Learning Environments, 20(1), 3–18CrossRef
Teo, T. S. H., & Lim, V. K. G. (1997). Usage patterns and perceptions of the internet: The gender gap. Equal Opportunities International, 16(6/7), 1–8CrossRef
Ting, R. Y. L. (2005, July). Mobile learning: Current trend and future challenges. In Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05) (pp. 603–607). IEEE.
Van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: Contributions from technology and trust perspectives. European Journal of Information Systems, 12(1), 41–48CrossRef
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), 342–365CrossRef
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315CrossRef
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204CrossRef
Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297–316CrossRef
Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of Internet banking: An empirical study. International Journal of Service Industry Management, 4(5), 501–519CrossRef
Wexler, J. (2001). Why computer users accept new systems. MIT Sloan Management Review, 42(3), 17
Wu, X., & Gao, Y. (2011). Applying the extended technology acceptance model to the use of clickers in student learning: Some evidence from macroeconomics classes. American Journal of Business Education, 4(7), 43–50
Wu, X., Tam, C. M., & Fang, S. (2020). Users’ behavioral intention toward M-learning in tourism english education: A case study of macao. In L. K. Lee, F. L. Wang, S. K. S. Cheung, O. Au, & K. C. Li (Eds.), Technology in education innovations for online teaching and learning ICTE 2020.Singapore: Springer.
Xu, Z., Turel, O., & Yuan, Y. (2012). Online game addiction among adolescents: Motivation and prevention factors. European Journal of Information Systems, 21(3), 321–340CrossRef
Yu, J., Ha, I., Choi, M., & Rho, J. (2005). Extending the TAM for a t-commerce. Information and Management, 42(7), 965–976CrossRef
Yu, C., Yu, C.-C., & Yu, T.-F. (2012). The exploring of the perceived quality, confirmation and satisfaction on E-learning continuance intention in the context of E-learning environment. International Journal of Digital Content Technology and Its Applications, 6, 76–85CrossRef
Zhai, X., & Shi, L. (2020). Understanding how the perceived usefulness of mobile technology impacts physics learning achievement: A pedagogical perspective. Journal of Science Education and Technology, 29, 743–757CrossRef
Zogheib, B., Rabaa’i, A., Zogheib, S., & Elsaheli, A. (2015). University student perceptions of technology use in mathematics learning. Journal of Information Technology Education: Research, 14, 417–438CrossRef
Metadata
Title
Students’ Perception of Cell Phones Effect on their Academic Performance: A Latvian and a Middle Eastern University Cases
Authors
Bashar Zogheib
Linda Daniela
Publication date
15-04-2021
Publisher
Springer Netherlands
Published in
Technology, Knowledge and Learning / Issue 4/2022
Print ISSN: 2211-1662
Electronic ISSN: 2211-1670
DOI
https://doi.org/10.1007/s10758-021-09515-4

Other articles of this Issue 4/2022

Technology, Knowledge and Learning 4/2022 Go to the issue

Original research

The Acceptance of Learning Management Systems and Video Conferencing Technologies: Lessons Learned from COVID-19

Original research

Mobile Phones in Schools: With or Without you? Comparison of Students’ Anxiety Level and Class Engagement After Regular and Mobile-Free School Days

Original research

Differential Analysis of the Years of Experience of Higher Education Teachers, their Digital Competence and use of Digital Resources: Comparative Research Methods

Original research

Affordances of Audience Response Systems: Effects of Instant and Regular Feedback

Original research

132 Words: A Critical Examination of Digital Technology, Education, and Citizenship

Original research

The Internet and K-12 Education: Capturing Digital Metrics During the COVID-19 Era

Premium Partners

    Image Credits