Research Article

Rethinking Communication and Crowdsourced Technology: Mediating Role of Mobile-Learning Tie to Broadband

Sayibu Muhideen 1 , Jianxun Chu 1 * , Olayemi Hafeez Rufai 1, Riffat Shahani 1, Tunde Simeon Amosun 1
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1 University of Science and Technology of China, Anhui-Hefei, CHINA* Corresponding Author
European Journal of Interactive Multimedia and Education, 2(1), January 2021, e02106, https://doi.org/10.30935/ejimed/9703
Submitted: 18 August 2020, Published: 13 February 2021
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ABSTRACT

The proliferation of online crowdsourcing information via mobile technology intervention achieved progressive learning in recent times. The study seeks the mobility of crowds using internet-contents as crowdsourcing knowledge phenomenon in community-learning task actualization. Bandura’s Social Learning Theory (SLT) and TPB induced and investigated 361 respondents among international students using IBM Amos v. 25 for the analysis. Results found exogenous variables were positively significant, whiles broadband moderation on mobile learning behavior run-up. Mobile learning mediation magnifies the behavior actualization effectiveness. Significantly, crowdsource at the individual level colored internet-content via mobile learning technology collaborated communication problem-solving tasks. Mobility of learning makes a mountain of molehills in knowledge sourcing, communication community-centered performance.
Keywords: technology, mobile learning, crowdsourcing (SC), broadband, innovative communication

CITATION (APA)

Muhideen, S., Chu, J., Rufai, O. H., Shahani, R., & Amosun, T. S. (2021). Rethinking Communication and Crowdsourced Technology: Mediating Role of Mobile-Learning Tie to Broadband. European Journal of Interactive Multimedia and Education, 2(1), e02106. https://doi.org/10.30935/ejimed/9703

REFERENCES

  1. Abu-Al-Aish, A. (2014). Toward mobile learning deployment in higher education (Doctoral Thesis). Brunel University, London. Retrieved from https://bura.brunel.ac.uk/bitstream/2438/7998/1/FulltextThesis.pdf
  2. Ajzen, I., Netemeyer, R., & Ryn, M. Van. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/j.drugalcdep.2011.10.011
  3. Alenezi, A. M., & Salem, M. A. (2017). Implementation of Smartphones, Tablets and their Applications in the Educational Process Management at Northern Border University. International Journal of Educational Sciences, 18(1-3), 56-64. https://doi.org/10.1080/09751122.2017.1335051
  4. Allen, B. J., Chandrasekaran, D., & Basuroy, S. (2018). Design Crowdsourcing: The Impact on New Product Performance of Sourcing Design Solutions from the “Crowd. American Marketing Association Journal of Marketing, 82(2), 106-123. https://doi.org/10.1509/jm.15.0481
  5. Alqahtani, M., & Mohammad, H. (2015). Mobile Applications’ Impact on Student Performance and Satisfaction. Tojdel The Online Journal of Distance Education and E-Learning., 14(4), 102-112.
  6. Awang, Z., Afthanorhan, A., Mamat, M., & Aimran, N. (2017). Modeling Structural Model for Higher Order Constructs (HOC) Using Marketing Model. World Applied Sciences Journal, 35(8), 1434-1444. https://doi.org/10.5829/idosi.wasj.2017.1434.1444
  7. Awang, Z., Afthanorhan, A., Mohamad, M., & Asri, M. A. M. (2016). An evaluation of measurement model for medical tourism research: the confirmatory factor analysis approach. International Journal of Tourism Policy, 6(1), 29. https://doi.org/10.1504/ijtp.2015.075141
  8. Ayvazo, S. (2015). Applied behavior analysis services in public schools in Israel: examining functional behavior assessment processes. European Journal of Behavior Analysis, 16(2), 338-350. https://doi.org/10.1080/15021149.2015.1108540
  9. Baek, Y., & Touati, A. (2017). Exploring how individual traits influence enjoyment in a mobile learning game. Computers in Human Behavior, 69, 347-357. https://doi.org/10.1016/j.chb.2016.12.053
  10. Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50(2), 248-287. https://doi.org/10.1016/0749-5978(91)90022-L
  11. Barber, W., King, S., & Buchanan, S. (2015). Problem based learning and authentic assessment in digital pedagogy: Embracing the role of collaborative communities. Electronic Journal of E-Learning, 13(2), 59-67.
  12. Beglar, D., & Nemoto, T. (2014). Developing Likert-scale questionnaires. JALT2013 Conference Proceedings (pp. 1-8). Tokyo: JALT. Retrieved from https://jalt-publications.org/sites/default/files/pdf-article/jalt2013_001.pdf
  13. Bervell, B., & Umar, I. N. (2018). Blended learning or face-to-face? Does Tutor anxiety prevent the adoption of Learning Management Systems for distance education in Ghana? Open Learning: The Journal of Open, Distance and e-Learning, 35(2), 159-177. https://doi.org/10.1080/02680513.2018.1548964
  14. Bhattacherjee, A., Perols, J., & Sanford, C. (2008). Information technology continuance: A theoretic extension and empirical test information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems, 49(1), 17-26. https://doi.org/10.1080/08874417.2008.11645302
  15. Çakar Mengü, S., & Mengü, M. (2017). The importance of international distance learning for the development of intercultural communication. In Proceedings of 8h INTE ITICAM IDEC (vol. 3, pp. 726-745).
  16. Campbell, M., Detres, M., & Lucio, R. (2019). Can a digital whiteboard foster student engagement? Social Work Education, 38(6), 735-752. https://doi.org/10.1080/02615479.2018.1556631
  17. Cortez, C. P. (2020). Blended, Distance, Electronic and Virtual-Learning for the New Normal of Mathematics Education: A Senior High School Student’s Perception. European Journal of Interactive Multimedia and Education, 1(1), e02001. https://doi.org/10.30935/ejimed/8276
  18. Crittenden, W. F., Biel, I. K., & Lovely, W. A. (2018). Embracing Digitalization: Student Learning and New Technologies. Journal of Marketing Education, 41(1), 5-14. https://doi.org/10.1177/0273475318820895
  19. Cumiskey, K. M., & Ling, R. (2015). The Social Psychology of Mobile Communication. In S. S. Sundar (Ed.), The Handbook of the Psychology of Communication Technology. https://doi.org/10.1002/9781118426456.ch10
  20. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982-1003. https://doi.org/10.1287/mnsc.35.8.982
  21. Doargajudhur, M. S., & Dell, P. (2018). The Effect of Bring Your Own Device (BYOD) Adoption on Work Performance and Motivation. Journal of Computer Information Systems, 60(6), 518-529. https://doi.org/10.1080/08874417.2018.1543001
  22. Eze, K., N. O. Sadiku, M., & M. Musa, S. (2018). 5G Wireless Technology: A Primer. International Journal of Scientific Engineering and Technology, 7(7), 62-64. https://doi.org/10.5958/2277-1581.2018.00015.3
  23. Falode, O. C., Chukwuemeka, E. J., Bello, A., & Baderinwa, T. (2020). Relationship between Flexibility of Learning, Support Services and Students’ Attitude towards Distance Learning Programme in Nigeria. European Journal of Interactive Multimedia and Education, 1(1), e02003. https://doi.org/10.30935/ejimed/8320
  24. Furr, R. M., & Bacharach, V. R. (2014). Validity: Estimating and evaluating convergent and discriminat validity evidence. In Psychometrics: An introduction (pp. 61-77.
  25. Gregori, E. B., Zhang, J., Galván-Fernández, C., & Fernández-Navarro, F. de A. (2018). Learner support in MOOCs: Identifying variables linked to completion. Computers and Education, 122, 153-168. https://doi.org/10.1016/j.compedu.2018.03.014
  26. Gün, A., Demir, Y., & Pak, B. (2019). Urban design empowerment through ICT-based platforms in Europe. International Journal of Urban Sciences, 1-27. https://doi.org/10.1080/12265934.2019.1604250
  27. Hair, Joe F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
  28. Hair, Joseph F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1-2), 1-12. https://doi.org/10.1016/j.lrp.2013.01.001
  29. Halder, I., Halder, S., & Guha, A. (2015). Educational Use of Mobile Phones by Undergraduate Students: An Indian Perspective. Online Journal of Communication and Media Technologies, 5(4), 64-76. https://doi.org/10.29333/ojcmt/2526
  30. Hayes, A. F. (2017). Using SPSS: A Little Syntax Guide. Retrieved from http://afhayes.com/using-spss-a-little-syntax-guide.html
  31. Ho, S. M., Ocasio-Velázquez, M., & Booth, C. (2017). Trust or consequences? Causal effects of perceived risk and subjective norms on cloud technology adoption. Computers and Security, 70, 581-595. https://doi.org/10.1016/j.cose.2017.08.004
  32. 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), 1574692. https://doi.org/10.1080/2331186X.2019.1574692
  33. Hwang, G.-J., & Fu, Q.-K. (2019). Trends in the research design and application of mobile language learning: a review of 2007-2016 publications in selected SSCI journals. Interactive Learning Environments, 27(4), 567-581. https://doi.org/10.1080/10494820.2018.1486861
  34. Ibrahim, W. (2018). Cloud computing implementation in libraries: A synergy for library services optimization. International Journal of Library and Information Science, 10(2), 17-27. https://doi.org/10.5897/IJLIS2016.0748
  35. Rodríguez, A. I., Riazaa, B. G., & Sánchez Gómez, M. C. (2017). Collaborative learning and mobile devices: An educational experience in Primary Education. Computers in Human Behavior, 72, 664-677. https://doi.org/10.1016/j.chb.2016.07.019
  36. iSALT Team. (2014). Theory of Planned Behavior. iSALT Resources: Theories, Concepts, and Measures. Paper 1. Retrieved from https://cornerstone.lib.mnsu.edu/cgi/viewcontent.cgi?article=1000&context=isalt_resources
  37. Karimi, S. (2016). Do learners ‘ characteristics matter ? An exploration of mobile-learning adoption in self-directed learning. Computers in Human Behavior, 63, 769-776. https://doi.org/10.1016/j.chb.2016.06.014
  38. Keenan, M., Presti, G., & Dillenburger, K. (2019). Technology and behaviour analysis in higher education. European Journal of Behavior Analysis, 21(1), 26-54. https://doi.org/10.1080/15021149.2019.1651569
  39. Lewis, T. (2017). Fit Statistics commonly reported for CFA and SEM. Cornell Statistics Department, 8, 0-1.
  40. Lin, C.-C., Lin, V., Liu, G.-Z., Kou, X., Kulikova, A., & Lin, W. (2019). Mobile-assisted reading development: a review from the Activity Theory perspective. Computer Assisted Language Learning, 33(8), 833-864. https://doi.org/10.1080/09588221.2019.1594919
  41. Lin, H., Wang, Y., Li, C., Shih, Y., & Lin, S. (2016). The Measurement and Dimensionality of Mobile Learning Systems Success: Development and Validation. Journal of Educational Computing Research, 55(4), 449-470. https://doi.org/10.1177/0735633116671324
  42. Mayer, R. E. (2020). Where is the learning in mobile technologies for learning? Contemporary Educational Psychology, 60(December 2019), 101824. https://doi.org/10.1016/j.cedpsych.2019.101824
  43. Morschheuser, B., Hamari, J., Koivisto, J., & Maedche, A. (2017). Gamified crowdsourcing: Conceptualization, literature review, and future agenda. International Journal of Human Computer Studies, 106, 26-43. https://doi.org/10.1016/j.ijhcs.2017.04.005
  44. Pagani, C. (2014). Diversity and social cohesion. Intercultural Education, 25(4), 300-311. https://doi.org/10.1080/14675986.2014.926158
  45. Paulin, D., & Haythornthwaite, C. (2016). Crowdsourcing the curriculum: Redefining e-learning practices through peer-generated approaches. Information Society, 32(2), 130-142. https://doi.org/10.1080/01972243.2016.1130501
  46. Peng, T., Liang, H., & Zhu, J. J. H. (2019). Introducing computational social science for Asia- Pacific communication research. Asian Journal of Communication, 29(3), 205-216. https://doi.org/10.1080/01292986.2019.1602911
  47. Petrovčič, A., Slavec, A., & Dolničar, V. (2018). The Ten Shades of Silver: Segmentation of Older Adults in the Mobile Phone Market. International Journal of Human-Computer Interaction, 34(9), 845-860. https://doi.org/10.1080/10447318.2017.1399328
  48. Riley, S., Brooks, J., Goodman, S., Cahill, S., Branney, P., Treharne, G. J., & Sullivan, C. (2019). Celebrations amongst challenges: Considering the past, present and future of the qualitative methods in psychology section of the British Psychology Society. Qualitative Research in Psychology, 16(3), 464-482. https://doi.org/10.1080/14780887.2019.1605275
  49. Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519-528. https://doi.org/10.1016/j.chb.2015.07.002
  50. Schultze, U., and D. E. L. (2002). Studying knowledge management in information sys- tems research: Discourses and theoretical assumptions. MIS Quarterly, 26(1), 213-242.
  51. Schunk, D. H., & DiBenedetto, M. K. (2020). Motivation and social cognitive theory. Contemporary Educational Psychology, 60(December 2019), 101832. https://doi.org/10.1016/j.cedpsych.2019.101832
  52. Seidel, V. P., Langner, B., & Sims, J. (2017). Dominant communities and dominant designs: Community-based innovation in the context of the technology life cycle. Strategic Organization, 15(2), 220-241. https://doi.org/10.1177/1476127016653726
  53. Sharma, P. K., & Misra, R. K. (2017). Core Self Evaluations Scale: An Empirical Attestation among Software Professionals. Procedia Computer Science, 122, 79-85. https://doi.org/10.1016/j.procs.2017.11.344
  54. Sharples, M., Taylor, J., & Vavoula, G. (2005). Towards a theory of mobile learning. Proceedings of MLearn 2005, (May 2014).
  55. Sheng, M., & Hartono, R. (2015). An exploratory study of knowledge creation and sharing in online community: a social capital perspective. Total Quality Management & Business Excellence, 26(1-2), 93-107. https://doi.org/10.1080/14783363.2013.776769
  56. Smirnov, N., Easterday, M. W., & Gerber, E. M. (2018). Infrastructuring Distributed Studio Networks: A Case Study and Design Principles. Journal of the Learning Sciences, 27(4), 580-631. https://doi.org/10.1080/10508406.2017.1409119
  57. Statista. (2019). Internet usage worldwide. Statista.
  58. Su, W., Sui, D., & Zhang, X. (2018). Satellite image analysis using crowdsourcing data for collaborative mapping: current and opportunities. International Journal of Digital Earth, 13(6), 645-660. https://doi.org/10.1080/17538947.2018.1556352
  59. Swanlund, D., & Schuurman, N. (2016). Mechanism Matters: Data Production for Geosurveillance. Annals of the American Association of Geographers, 106(5), 1063-1078. https://doi.org/10.1080/24694452.2016.1188680
  60. Toyama, K. (2018). From needs to aspirations in information technology for development. Information Technology for Development, 24(1), 15-36. https://doi.org/10.1080/02681102.2017.1310713
  61. Van der Linden, W. J., Klein Entink, R. H., & Fox, J.-P. (2010). IRT Parameter Estimation with Response Times as Collateral Information. Applied Psychological Measurements, 34(5), 327-347. https://doi.org/10.1177/0146621609349800
  62. Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). Technology acceptance model - research. MIS Quarterly, 27(3), 425-478.
  63. Wang, Q., Ding, G., & Yu, S. (2019). Crowdsourcing mode-based learning activity flow approach to promote subject ontology generation and evolution in learning. Interactive Learning Environments, 27(7), 965-983. https://doi.org/10.1080/10494820.2018.1509875
  64. Yan, B., Jian, L., Ren, R., Fulk, J., Sidnam-Mauch, E., & Monge, P. (2020). The Paradox of Interaction: Communication Network Centralization, Shared Task Experience, and the Wisdom of Crowds in Online Crowdsourcing Communities. Communication Research, May 2020. https://doi.org/10.1177/0093650220915033
  65. Yan, G. (2019). Simulation analysis of key technology optimization of 5G mobile communication network based on Internet of Things technology. International Journal OfDistributed Sensor, 15(6). https://doi.org/10.1177/1550147719851454
  66. Yang, H. L., & Lin, S. L. (2019). The reasons why elderly mobile users adopt ubiquitous mobile social service. Computers in Human Behavior, 93, 62-75. https://doi.org/10.1016/j.chb.2018.12.005
  67. Yang, J. C., Lin, Y. L., & Liu, Y.-C. (2017). Effects of locus of control on behavioral intention and learning performance of energy knowledge in game-based learning. Environmental Education Research, 23(6), 886-899. https://doi.org/10.1080/13504622.2016.1214865