Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
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Assessing the Effectiveness of Smartphone Usage to Interact with Learning Materials in Independent Learning Outside of Classrooms among Undergraduate Students

  • Received : 2020.09.12
  • Accepted : 2021.01.22
  • Published : 2021.03.31
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Abstract

Clearly, the smartphone is increasingly playing a greater role in everyday life, thus providing opportunities to evaluate how well the use of the smartphone meets the requirements of undergraduate students in independent learning outside of a classroom setting. This study used the task-technology fit (TTF) model to explore the effectiveness of smartphone usage to interact with learning materials in independent learning outside of classrooms, the need for smartphone support, and the fit of devices to tasks as well as performance. First, the study used interviews, observation, and survey data to identify what are the most important constructs of smartphones that stimulate students to interact with learning materials in independent learning outside of classrooms. Based on the findings from the exploratory study and Task Technology Fit theory, we postulated the Navigation design, Ergonomic design, Content support, and Capacity as the essential dimension of the smartphone construct. Then, we proposed a research model and empirically tested hypotheses with the structural model analysis. The results reveal a significant positive impact of task and technology on TTF for smartphone usage to interact with learning materials in independent learning outside of classrooms; it also confirmed the TTF and performance have a direct effect on actual use.

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References

  1. Ajzen, I. (1991). The theory of planned behaviour. Organizational behaviour and human. Decision Processes, 50(2), 179-211. 
  2. Alasmari, T., and Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: An extended framework and a mixed-method study. Education and Information Technologies, 24, 2127-2144.  https://doi.org/10.1007/s10639-019-09865-8
  3. Alsayed, S., Bano, N., and Alnajjar, H. (2019). Evaluating practice of smartphone use among university students in undergraduate nursing education. Health Professions Education, 6(1), 238-246.  https://doi.org/10.1016/j.hpe.2019.06.004
  4. Ambra, J. D., Wilson, C. S., and Akter, S. (2013). Application of the task-technology fit model to structure and evaluate the adoption of E-books by Academics. Journal of the American Society for Information Science and Technology Archive, 64(1), 48-64.  https://doi.org/10.1002/asi.22757
  5. Anshari, M., Almunawar, M. N., Shahrill, M., Wicaksono, D. K., and Huda, M. (2017). Smartphones usage in the classrooms: Learning aid or interference? Educ. Inf. Technol., 22, 3063-3079.  https://doi.org/10.1007/s10639-017-9572-7
  6. Bagozzi, R. P., and Yi, Y. (1988). On the evaluation of structural equation model. Journal of Academy of Marketing Science, 16(1), 74-94.  https://doi.org/10.1007/BF02723327
  7. Botella, F., Moreno, J. P., and Penalver, A. (2015). Comparing the efficiency of performing complex tasks with a tablet and a smartphone. DYNA, 82(193), 202-211.  https://doi.org/10.15446/dyna.v82n193.53492
  8. Boudreau, M. C., Gefent, D., and Straub, D. W. (2001). Validation in information system research: A state-of-the art assessment. MIS Quarterly, 25(1), 1-16.  https://doi.org/10.2307/3250956
  9. Brooks, D. C., and Pomerantz, J. (2017). ECAR study of undergraduate students and information technology. Research Report, Louisville, CO: ECAR. 
  10. Budiu, R. (2016). Mobile user experience: Limitations and strengths. Retrieved from https://www.nngroup.com/articles/mobile-ux. 
  11. Budiu, R., and Nielsen, J. (2016). Tablet website and application UX: Design guidelines for improving the usability of websites viewed on tablets and tablet-specific apps. Retrieved from http://www.nngroup.com/reports/tablets/. 
  12. Campbell, D. T., and Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81-105.  https://doi.org/10.1037/h0046016
  13. Chang, Y. F., Chen, C. S., and Zhou, H. (2009). Smartphone for mobile commerce. Computer Standards and Interfaces, 3, 740-747.  https://doi.org/10.1016/j.csi.2008.09.016
  14. Daw, J. (2001). Comparing data gathered using five point vs eleven point scales. Bridging Marketing Theory and Practice, 1, 1-8. 
  15. Doyle, S. (2001). Understanding information and communication technology for AS level. Nelson Thorner ltd.: United Kingdom. 
  16. Duffy, B., and Smith, K. (2003). Comparing data from online and face-to-face surveys. International Journal of Market Research, 47(6), 615-639.  https://doi.org/10.1177/147078530504700602
  17. Field, A. (2005). Discovering statistics using SPSS. California, CA: SAGE Publication. 
  18. Fornell, C., and Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440-452.  https://doi.org/10.1177/002224378201900406
  19. Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable and measurement error. Journal of Marketing Research, 8(1), 39-50.  https://doi.org/10.1177/002224378101800104
  20. Fuller, R. M., and Dennis, A. R. (2009). Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks. Journal Information Systems Research Archive, 20(1), 2-17.  https://doi.org/10.1287/isre.1070.0167
  21. Gartner Inc. (2019). Gartner forecasts flat worldwide device shipments until. Retrieved from http://www.gartner.com/newsroom/id/3560517. 
  22. Gebregiorgis, S. A., and Altmann, J. (2015). IT service platforms: Their value creation model and the impact of their level of openness on their adoption. Comput. Sci., 68, 73-187. 
  23. Gefen, D., Straub, D. W., and Boudreau, M. C. (2000). Structural equation modelling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1-70. 
  24. Gierdowski, D. C. (2019). ECAR study of undergraduate students and information technology. Research Report, Louisville, CO: ECAR. 
  25. Goodhue, D. L. (1995). Understanding user evaluations of information system. Management Science, 41(2), 827-1844.  https://doi.org/10.1287/mnsc.41.12.1827
  26. Goodhue, D. L., and Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.  https://doi.org/10.2307/249689
  27. Goodhue, D. L., Klein, B. D., and March, S. T. (2000). User evaluations of information system as surrogates for objective performance. Information and Management, 38(2), 87-101.  https://doi.org/10.1016/S0378-7206(00)00057-4
  28. Green, M. (2019). Smartphones, distraction narratives, and flexible pedagogies: Students mobile technology practices in networked writing classroom. Computers and Composition, 52, 91-106.  https://doi.org/10.1016/j.compcom.2019.01.009
  29. Haile, H., and Altmann, J. (2018). Evaluating investments in portability and interoperability between software service platforms. Future Generation Computer Systems, 78, 224-241.  https://doi.org/10.1016/j.future.2017.04.040
  30. Hair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C. (1998). Multivariate data analysis. Englewood Cliffs, NJ Prentice Hall. 
  31. Hair Jr, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C. (1995). Multivariate data analysis (3rd ed.). New York: Macmillan.
  32. Hair, J. F., Black, W. C., Babi, B. J., and Anderson, R. E. (2010). Multivariate data analysis. Englewood Cliffs, NJ Prentice Hall. 
  33. Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. Eur. Bus. Rev., 31, 2-24.  https://doi.org/10.1108/EBR-11-2018-0203
  34. Hamidi, H., and Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053-1070.  https://doi.org/10.1016/j.tele.2017.09.016
  35. Hartley, K., Bendixen, L. D., Olafson, L., Gianoutsos, D., and Shreve, E. (2020). Development of the smartphone and learning inventory: Measuring self-regulated use. Education and Information Technologies, 25, 4381-4395. doi:10.1007/s10639-020-10179-3. 
  36. Hau, K. T., Wen, Z., and Chen, Z. (2004). Structural equation model and its applications. Beijing Educational Science Publishing House. 
  37. Heo, J., Ham, D. H., Park, S., Song, C., and Yoon, W. C. (2009). A framework for evaluating the usability of mobile phones based on multi-level, hierarchical model of usability factors. Interacting with Computers, 21(4), 263-275.  https://doi.org/10.1016/j.intcom.2009.05.006
  38. Huff, K. C. (2015). The comparison of mobile devices to computers for web-based assessments. Computers in Human Behavior, 49, 208-212.  https://doi.org/10.1016/j.chb.2015.03.008
  39. Interaction Design Foundation (2020). What you need to know about smartphones vs. tablet use of the mobile internet. Retrieved from https://www.interaction-design.org. 
  40. Isaac, O., Aldholay, A., Abdullah, Z., and Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Computers and Education, 136, 113-129.  https://doi.org/10.1016/j.compedu.2019.02.012
  41. Johns, R. (2010). Likert items and scale. Retrieved from https://www.surveynet.ac.uk/sqb/datacollection/likertfactsheet.pdf. 
  42. Kaiser, H. F. (1960). The application of electronic computer to factor analysis. Educational and Psychological Measurement, 20, 141-151.  https://doi.org/10.1177/001316446002000116
  43. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.  https://doi.org/10.1007/BF02291575
  44. Kaur, G., Kaur, L., and Kaur, R. (2019). Elements and digitization of computer. Education Publishing, India. 
  45. Ketola, P., and Roykkee, M. (2001). Three facets of usability in mobile handsets, In Proceeding of CHI 2001 Workshop Mobile Communication: Understanding User Adoption and Design, Seattle, WA: ACM. 
  46. Kiljander, H. (2004). Evolution and usability of mobile phone interaction styles. Unpublished Ph.D. Dissertation, Helsinki University of Technology. 
  47. Kim, J. H., Aulck, L., Bartha, M. C., Harper, C. A., and Johnson, P. W. (2014). Differences in typing forces, muscle activity, comfort, and typing performances among virtual, notebook, and desktop keyboards. Applied Ergonomics, 45(6), 1406-1413.  https://doi.org/10.1016/j.apergo.2014.04.001
  48. Kossey, J., Berger, A., and Brown, V. (2015). Connecting to educational resources online with QR codes. Retrieved from https://www.fdla.com/wp-content/uploads/2015/04/connecting-to-educational-resources-online-with-qr-codes.pdf. 
  49. Kulas, J. T., Stachowski, A. A., and Haynes, B. A. (2008). Middle response functioning Likert-responses to personality items. Journal of Business and Psychology, 22(3), 51-259. 
  50. Lieberman, M. (2019). Students are using mobile even if you aren't. Inside Higher Ed, Retrieved from https://www.insidehighered.com/. 
  51. Lu, H. P., and Yang, Y. W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323-332.  https://doi.org/10.1016/j.chb.2013.10.020
  52. Luo, Y., and Li, M. (2015). The application and research of information technology of cloud computing in colleges and universities. In International Conference on Materials Engineering and Information Technology Applications (MEITA 2015), 1011-1013. 
  53. Marsh, H. W., Wen, Z., and Hau, K. T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9, 275-300.  https://doi.org/10.1037/1082-989X.9.3.275
  54. Meyer, B. (2016). Mobile devices and spatial enactments of learning: ipads in lower secondary schools. In The 12th International Conference on Mobile Learning 2016, Vila Moura, Algarve, Portugal, 9-11. 
  55. Molina, N. A., Mahan, R. P., and IIIingworth, A. J. (2014). Establishing the measurement equivalence of online selection assessments delivered on mobile versus nonmobile devices. International Journal of Selection and Assessment, 22(2), 124-138.  https://doi.org/10.1111/ijsa.12063
  56. Nunnally, J. C. (1978). Psychometric theory. McGraw-Hill, New York. 
  57. O'Connor, S., and Andrews, T. (2018). Smartphones and mobile applications (apps) in clinical nursing education: A student perspective. Nurse Education Today, 69, 172-178.  https://doi.org/10.1016/j.nedt.2018.07.013
  58. Oliveira, T., Fariaa, M., Thomas, M. A., and Popovic, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34, 689-703.  https://doi.org/10.1016/j.ijinfomgt.2014.06.004
  59. Park, C. W., Kim, D., Cho, S., and Han, H. J. (2019). Adoption of multimedia technology for learning and gender difference. Computers in Human Behavior, 92, 288-296.  https://doi.org/10.1016/j.chb.2018.11.029
  60. Radhakrishna, R., Ewing, J., and Chikthimmah, N. (2012). TPS (Think, Pair and Share) as an active learning strategy. NACTA Journal. Retrieved from https://www.nactateachers.org 
  61. Rezaei, R., Chiew, T. K., Lee, S. P., and Aliee, Z. S. (2014). Interoperability evaluations model: A systematic review. Computer in Industry, 65(10), 1-23.  https://doi.org/10.1016/j.compind.2013.09.001
  62. Roebuck, K. (2013). Tablet computer: High-impact emerging technology-What you need to know: Definition, adoption, impact, benefits, maturity, venders. San Bernardino: CA. 
  63. Sanchez, C. A., and Branaghan, R. J. (2011). Turning to learn: Screen orientation and reasoning with small devices. Computer in Human Behavior, 27, 793-797.  https://doi.org/10.1016/j.chb.2010.11.004
  64. Sanchez, C. A., and Goolsbee, J. Z. (2010). Character size and reading to remember from small displays. Computers and Education, 55(3), 1056-1062.  https://doi.org/10.1016/j.compedu.2010.05.001
  65. Shitkova, M., Holler, J., Heide, T., Clever, N., and Becker, J. (2015). Towards Usability guidelines for mobile websites and applications. In The 12th International Conference on Wirtschaftsinformatik, Osnabruck, Germany, 1603-1617. 
  66. Sun, S., Xiong, C., and Chang, V. (2018). Acceptance of information and communication technologies in education: An investigation into university's students intentions to use mobile educational apps. International Journal of Enterprise Information Systems, 15(1), 24-44. 
  67. Sung, Y. T., Chang, K. E., and Liu, T. C. (2016). The effects of integrating mobile devices with teaching and learning on students' learning performance: A meta-analysis and research synthesis. Computers and Education, 94, 252-275.  https://doi.org/10.1016/j.compedu.2015.11.008
  68. Scott, K. S., Sorokti, K. H., and Merrell, D. M. (2016). Learning 'beyond the classroom' within an enterprise social network system. The Internet and Higher Education, 29, 75-90.  https://doi.org/10.1016/j.iheduc.2015.12.005
  69. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 245-478. 
  70. Wainwright, C. (2017). How to make a QR code in 7 easy steps. Retrieved from http://www.blog.hubspot.com/blog/tabid/6307/bid/29449/how-to-create-a-qr-code-in-4-quick-steps.aspx. 
  71. Willemse, J. J., Jooste, K., and Bozalek, V. (2019). Experiences of undergraduate nursing students on an authentic mobile learning enactment at a higher education institution in South Africa. Nurse Education Today, 74, 69-75.  https://doi.org/10.1016/j.nedt.2018.11.021
  72. Wagoner, A., and Myrick, A. (2020). Best keyboards for android 2020. Retrieved from https://www.androidcentral.com/best-keyboard-android. 
  73. Worcester, R. M., and Burn, T. R. (1975). A statistical examination of the relative precision of verbal scales. Journal of the Market Research Society, 17(3), 181-197. 
  74. Wu, B., and Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.  https://doi.org/10.1016/j.chb.2016.10.028
  75. Yen, D. C., Wu, C. S., Cheng, F. F., and Huang, Y. W. (2010). Determinants of user's intention to adopt Wireless technology: An empirical study by integrating TTF with TAM. Computer in Human Behavior, 26, 906-915.  https://doi.org/10.1016/j.chb.2010.02.005
  76. Tao, Z., Yang, X. Y., Lai, I. K. W., and Yin, K. C. (2018). A research on the effect of smartphone use, student engagement and self-directed learning on individual impact: China empirical study. In The 3rd International Conference Symposium on Educational Technology, Osaka, Japan, 1-6. 
  77. Zhou, T., Lu, Y., and Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013