TAM as a Model to Understand the Intention of Using a Mobile-based Cancer Early Detection Learning Application

Authors

  • Hery Harjono Muljo Bina Nusantara University
  • Bens Pardamean Bina Nusantara University
  • Anzaludin Samsinga Perbangsa Bina Nusantara University
  • Yulius Lie Bina Nusantara University
  • Kartika Purwandari Bina Nusantara University
  • Bharuno Mahesworo Bina Nusantara University
  • Alam Ahmad Hidayat Bina Nusantara University
  • Tjeng Wawan Cenggoro Bina Nusantara University

DOI:

https://doi.org/10.3991/ijoe.v16i02.12609

Keywords:

mobile learning, TAM, cancer early detection

Abstract


Technology Acceptance Model (TAM) framework was utilized in this study. Its purpose was to determine the correlation between independent variables consisting of Perceived Ease of Use (PEU), Perceived Usefulness (PU), Attitude toward Using (AU) with dependent variable Behavioral Intention to Use (BIU). Data collection techniques were carried out by distributing questionnaires through group discussion forums. Respondents consisted of medical workers and health cadres both in Jakarta and Yogyakarta. Data were analyzed using correlation test and t-test. The results of the correlation test state that the correlation between PEU and AU is 0.30, which shows a weak correlation. Meanwhile, the correlation of PU and AU is 0.56, PEU and BIU is 0.41, and PU and BIU is 0.47, which are considered as moderate correlations. Finally, a strong correlation exists between AU and BIU. T-test results show that the effect of PU on AU is statistically significant with CI = 95%. Likewise, the effects of PEU on AU, AU towards BIU, PU towards BIU, and PEU towards BIU are significant (p < 0.05).

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Published

2020-02-12

How to Cite

Muljo, H. H., Pardamean, B., Perbangsa, A. S., Lie, Y., Purwandari, K., Mahesworo, B., … Cenggoro, T. W. (2020). TAM as a Model to Understand the Intention of Using a Mobile-based Cancer Early Detection Learning Application. International Journal of Online and Biomedical Engineering (iJOE), 16(02), pp. 80–93. https://doi.org/10.3991/ijoe.v16i02.12609

Issue

Vol. 16 No. 02 (2020)

Section

Papers

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