Research Article
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Influence of Technical Support on Technology Acceptance Model to Examine the Project PAIR E-Learning System in Distance Learning Modality

Year 2022, Volume: 9 Issue: 5, 467 - 485, 01.09.2022
https://doi.org/10.17275/per.22.124.9.5

Abstract

Adopting technology in this new normal education improved students' engagement and motivation to learn. This paper aimed to investigate the impact of technical support on Technology Acceptance Model to examine Project PAIR in the distance learning modality employing Partial Least Squares-Structural Equation Modeling. Applying a convenience sampling technique, the investigation involved 305 senior high school learners from a secondary school in Cagayan, Philippines. Sample sizes were calculated using the inverse square root and gamma-exponential methods. Results showed that technical support directly impacts the perceived ease of use, usefulness, and attitude toward using. The findings also revealed that the perceived ease of use of PAIR has a direct impact on its perceived usefulness and attitude toward use. In contrast, perceived usefulness directly influences the attitude toward using and behavioral intention to use. Likewise, attitude towards using directly impacts the behavioral intention and actual use, while behavioral intention directly influences actual use. This paper concluded that technical support is a reliable external variable of the technology acceptance model. Hence, the application of PAIR for remote learning is strongly recommended for the school and the public. It is also recommended that the schools must ensure that they have provided technical support to ensure the PAIR functioning runs appropriately. Further implications for institutions and future studies are also discussed in this paper.

Supporting Institution

LAL-LO NATIONAL HIGH SCHOOL

Project Number

1

Thanks

The researchers believe that this publication will demonstrate their appreciation to the institution and all individuals who contributed to the study's success through their involvement, collaboration, and support.

References

  • Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS quarterly, 227-247.
  • Agaton, C. B., & Cueto, L. J. (2021). Learning at Home: Parents' Lived Experiences on Distance Learning during COVID-19 Pandemic in the Philippines. International Journal of Evaluation and Research in Education, 10(3), 901-911.
  • Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological bulletin, 84(5), 888.
  • Alea, L. A., Fabrea, M. F., Roldan, R. D. A., & Farooqi, A. Z. (2020). Teachers' Covid-19 awareness, distance learning education experiences and perceptions towards institutional readiness and challenges. International Journal of Learning, Teaching and Educational Research, 19(6), 127-144.
  • Alqahtani, A. Y., & Rajkhan, A. A. (2020). E-learning critical success factors during the covid-19 pandemic: A comprehensive analysis of e-learning managerial perspectives. Education sciences, 10(9), 216.
  • Al-Suqri, M. N., & Al-Kharusi, R. M. (2015). Ajzen and Fishbein's theory of reasoned action (TRA)(1980). In Information seeking behavior and technology adoption: Theories and trends (pp. 188-204). IGI Global.
  • Amir, L. R., Tanti, I., Maharani, D. A., Wimardhani, Y. S., Julia, V., Sulijaya, B., & Puspitawati, R. (2020). Student perspective of classroom and distance learning during COVID-19 pandemic in the undergraduate dental study program Universitas Indonesia. BMC medical education, 20(1), 1-8.
  • Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human relations, 45(7), 659-686.
  • 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.
  • Bassok, D., Smith, A. E., Markowitz, A. J., & Doromal, J. B. (2021). Child Care Staffing Challenges during the Pandemic: Lessons from Child Care Leaders in Virginia.
  • Caratiquit, K., & Pablo, R. (2021). Exploring the practices of secondary school teachers in preparing for classroom observation amidst the new normal of education. Journal of Social, Humanity, and Education, 1(4), 281-296.
  • Chen, H., Li, L., & Chen, Y. (2021). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36-68.
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  • Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application (No. 1). Cambridge university press.
  • Deepika, V., Soundariya, K., Karthikeyan, K., & Kalaiselvan, G. (2021). 'Learning from home': role of e-learning methodologies and tools during novel coronavirus pandemic outbreak. Postgraduate Medical Journal, 97(1151), 590-597.
  • Deng, L., & Tavares, N. J. (2013). From Moodle to Facebook: Exploring students' motivation and experiences in online communities. Computers & Education, 68, 167-176.
  • Efron, B., & Tibshirani, R. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical science, 54-75.
  • Farahat, T. (2012). Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia - Social and Behavioral Sciences, 64(9), 95-104.
  • Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2).
  • Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e-Learning during COVID-19 pandemic. Computer networks, 176, 107290.
  • Fearnley, MR, & Amora, J. T. (2020). Learning management system adoption in higher education using the extended technology acceptance model. IAFOR Journal of Education,8(2), 89–106.
  • Ferran, F. (2021). Extended technology acceptance model to examine the use of Google forms – based lesson Playlist in online distance learning. Recoletos Multidisciplinary Research Journal, 9(1), 147-161.
  • Fitzgerald, D. A., Scott, K. M., & Ryan, M. S. (2021). Blended and e-learning in pediatric education: harnessing lessons learned from the COVID-19 pandemic. European journal of pediatrics, 1-6.
  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.
  • Gismalla, M. D. A., Mohamed, M. S., Ibrahim, O. S. O., Elhassan, M. M. A., & Mohamed, M. N. (2021). Medical students' perception towards E-learning during COVID 19 pandemic in a high burden developing country. BMC Medical Education, 21(1), 1-7.
  • Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593.
  • Haenlein, M., & Kaplan, A. M. (2004). A beginner's guide to partial least squares analysis. Understanding statistics, 3(4), 283-297.
  • Ho, J. C., Wu, C. G., Lee, C. S., & Pham, T. T. T. (2020). Factors affecting the behavioral intention to adopt mobile banking: An international comparison. Technology in Society, 63, 101360.
  • Holden, H., & Rada, R. (2011). Understanding the Influence of Perceived Usability and Technology Self-Efficacy on Teachers' Technology Acceptance. Journal Of Research On Technology In Education (International Society For Technology In Education), 43(4), 343-367.
  • Hu, P., Clark, T. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: a longitudinal study. Information & Management, 41(2), 227.
  • Jang, J., Ko, Y., Shin, W. S., & Han, I. (2021). Augmented Reality and Virtual Reality for Learning: An Examination Using an Extended Technology Acceptance Model. IEEE Access, 9, 6798-6809.
  • Jena, P. K. (2020). Impact of pandemic COVID-19 on education in India. International journal of current research (IJCR), 12.
  • Kock, N. (2012). WarpPLS 3.0 User Manual. Laredo, Texas: ScriptWarp Systems.
  • Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods. Information Systems Journal, 28(1), 227-261.
  • Kristanto, A., & Mariono, A. (2017). The Development of Instructional Materials E-Learning Based on Blended Learning. International Education Studies, 10(7), 10-17.
  • Kumar, A., & Ayedee, D. (2021). Technology adoption: A solution for SMEs to overcome problems during COVID-19. Forthcoming, Academy of Marketing Studies Journal, 25(1).
  • Lassoued, Z., Alhendawi, M., & Bashitialshaaer, R. (2020). An exploratory study of the obstacles for achieving quality in distance learning during the COVID-19 pandemic. Education Sciences, 10(9), 232.
  • Lee, Y., Hsieh, Y., & Chen, Y. (2013). An investigation of employees' use of e-learning systems: applying the technology acceptance model. Behavior & Information Technology, 32(2), 173-189.
  • Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.
  • Lynch, M. (2020). E-Learning during a global pandemic. Asian Journal of Distance Education, 15(1), 189-195.
  • Mallya, J., & Lakshminarayanan, S. (2017). Factors influencing usage of internet for academic purposes using technology acceptance model. DESIDOC Journal of Library & Information Technology, 37(2), 119.
  • McGill, T.J., Klobas, J.E., & Renzi, S. (2011). LMS use and instructor performance: The role of task technology fit. International Journal on E-Learning, 10(1), 43–62.
  • Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). e-Learning, online learning, and distance learning environments: Are they the same?. The internet and higher education, 14(2), 129-135.
  • Mustafa, A. S., Alkawsi, G. A., Ofosu-Ampong, K., Vanduhe, V. Z., Garcia, M. B., & Baashar, Y. (2022). Gamification of E-Learning in African Universities: Identifying Adoption Factors Through Task-Technology Fit and Technology Acceptance Model. In Next-Generation Applications and Implementations of Gamification Systems (pp. 73-96). IGI Global.
  • Ngai, E. T., Poon, J. L., & Chan, Y. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250-267.
  • Pajo, K. & Wallace, C. (2001). Barriers to the Uptake of Web-based Technology by University Teachers. The Journal of Distance Education, 16(1), 70-84.
  • Panda, S., & Mishra, S. (2007). E-Learning in a Mega Open University: Faculty attitude, barriers and motivators. Educational Media International, 44(4), 323-338. doi: 10.1080/09523980701680854
  • Park, I., Kim, D., Moon, J., Kim, S., Kang, Y., & Bae, S. (2022). Searching for New Technology Acceptance Model under Social Context: Analyzing the Determinants of Acceptance of Intelligent Information Technology in Digital Transformation and Implications for the Requisites of Digital Sustainability. Sustainability, 14(1), 579.
  • Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162.
  • Peñarroja, V., Sánchez, J., Gamero, N., Orengo, V., & Zornoza, A. M. (2019). The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice. Behaviour & Information Technology, 38(8), 845-857.
  • Radha, R., Mahalakshmi, K., Kumar, V. S., & Saravanakumar, A. R. (2020). E-Learning during lockdown of Covid-19 pandemic: A global perspective. International journal of control and automation, 13(4), 1088-1099.
  • Ringle, C., Da Silva, D., & Bido, D. (2015). Structural equation modeling with the SmartPLS. Bido, D., da Silva, D., & Ringle, C.(2014). Structural Equation Modeling with the Smartpls. Brazilian Journal Of Marketing, 13(2).
  • Ringle, C., Wende, S., & Will, A. (2005). SmartPLS 2.0 (Beta). Hamburg, (www.smartpls.de).
  • Rotimi, O., Orah, N., Shaaban, A., Daramola, A. O., & Abdulkareem, F. B. (2017). Remote teaching of histopathology using scanned slides via Skype between the United Kingdom and Nigeria. Archives of pathology & laboratory medicine, 141(2), 298-300.
  • Ruggeri, K., Farrington, C., & Brayne, C. (2013). A global model for effective use and evaluation of e-learning in health. Telemedicine and e-Health, 19(4), 312-321.
  • Shanahan, M. C. (2008). Transforming information search and evaluation practices of undergraduate students. International Journal of Medical Informatics, 77(8), 518-526.
  • Teeroovengadum, V., Heeraman, N., & Jugurnath, B. (2017). Examining the antecedents of ICT adoption in education using an Extended Technology Acceptance Model (TAM). International Journal of Education and Development Using Information and Communication Technology, 13(3), 4–23.
  • Tenenhaus, M., Vinzi, V.E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.\
  • Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended Technology Acceptance Model. Asia Pacific Education Review, 11(2), 253-262
  • Teo, T. (2012). Examining the intention to use technology among pre-service teachers: an integration of the Technology Acceptance Model and Theory of Planned Behavior. Interactive Learning Environments, 20(1), 3-18. doi: 10.1080/10494821003714632
  • Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475.
  • Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre‐service teachers' computer attitudes: applying and extending the technology acceptance model. Journal of computer assisted learning, 24(2), 128-143.
  • Unal, E., & Uzun, A. M. (2021). Understanding university students' behavioral intention to use Edmodo through the lens of an extended technology acceptance model. British Journal of Educational Technology, 52(2), 619-637.
  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
  • Wang, W., & Wang, C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774.
  • Wetzels, M., Odekerken-Schroder, G., & van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177-196.
  • Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
Year 2022, Volume: 9 Issue: 5, 467 - 485, 01.09.2022
https://doi.org/10.17275/per.22.124.9.5

Abstract

Project Number

1

References

  • Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS quarterly, 227-247.
  • Agaton, C. B., & Cueto, L. J. (2021). Learning at Home: Parents' Lived Experiences on Distance Learning during COVID-19 Pandemic in the Philippines. International Journal of Evaluation and Research in Education, 10(3), 901-911.
  • Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological bulletin, 84(5), 888.
  • Alea, L. A., Fabrea, M. F., Roldan, R. D. A., & Farooqi, A. Z. (2020). Teachers' Covid-19 awareness, distance learning education experiences and perceptions towards institutional readiness and challenges. International Journal of Learning, Teaching and Educational Research, 19(6), 127-144.
  • Alqahtani, A. Y., & Rajkhan, A. A. (2020). E-learning critical success factors during the covid-19 pandemic: A comprehensive analysis of e-learning managerial perspectives. Education sciences, 10(9), 216.
  • Al-Suqri, M. N., & Al-Kharusi, R. M. (2015). Ajzen and Fishbein's theory of reasoned action (TRA)(1980). In Information seeking behavior and technology adoption: Theories and trends (pp. 188-204). IGI Global.
  • Amir, L. R., Tanti, I., Maharani, D. A., Wimardhani, Y. S., Julia, V., Sulijaya, B., & Puspitawati, R. (2020). Student perspective of classroom and distance learning during COVID-19 pandemic in the undergraduate dental study program Universitas Indonesia. BMC medical education, 20(1), 1-8.
  • Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human relations, 45(7), 659-686.
  • 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.
  • Bassok, D., Smith, A. E., Markowitz, A. J., & Doromal, J. B. (2021). Child Care Staffing Challenges during the Pandemic: Lessons from Child Care Leaders in Virginia.
  • Caratiquit, K., & Pablo, R. (2021). Exploring the practices of secondary school teachers in preparing for classroom observation amidst the new normal of education. Journal of Social, Humanity, and Education, 1(4), 281-296.
  • Chen, H., Li, L., & Chen, Y. (2021). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36-68.
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  • Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application (No. 1). Cambridge university press.
  • Deepika, V., Soundariya, K., Karthikeyan, K., & Kalaiselvan, G. (2021). 'Learning from home': role of e-learning methodologies and tools during novel coronavirus pandemic outbreak. Postgraduate Medical Journal, 97(1151), 590-597.
  • Deng, L., & Tavares, N. J. (2013). From Moodle to Facebook: Exploring students' motivation and experiences in online communities. Computers & Education, 68, 167-176.
  • Efron, B., & Tibshirani, R. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical science, 54-75.
  • Farahat, T. (2012). Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia - Social and Behavioral Sciences, 64(9), 95-104.
  • Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2).
  • Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e-Learning during COVID-19 pandemic. Computer networks, 176, 107290.
  • Fearnley, MR, & Amora, J. T. (2020). Learning management system adoption in higher education using the extended technology acceptance model. IAFOR Journal of Education,8(2), 89–106.
  • Ferran, F. (2021). Extended technology acceptance model to examine the use of Google forms – based lesson Playlist in online distance learning. Recoletos Multidisciplinary Research Journal, 9(1), 147-161.
  • Fitzgerald, D. A., Scott, K. M., & Ryan, M. S. (2021). Blended and e-learning in pediatric education: harnessing lessons learned from the COVID-19 pandemic. European journal of pediatrics, 1-6.
  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.
  • Gismalla, M. D. A., Mohamed, M. S., Ibrahim, O. S. O., Elhassan, M. M. A., & Mohamed, M. N. (2021). Medical students' perception towards E-learning during COVID 19 pandemic in a high burden developing country. BMC Medical Education, 21(1), 1-7.
  • Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593.
  • Haenlein, M., & Kaplan, A. M. (2004). A beginner's guide to partial least squares analysis. Understanding statistics, 3(4), 283-297.
  • Ho, J. C., Wu, C. G., Lee, C. S., & Pham, T. T. T. (2020). Factors affecting the behavioral intention to adopt mobile banking: An international comparison. Technology in Society, 63, 101360.
  • Holden, H., & Rada, R. (2011). Understanding the Influence of Perceived Usability and Technology Self-Efficacy on Teachers' Technology Acceptance. Journal Of Research On Technology In Education (International Society For Technology In Education), 43(4), 343-367.
  • Hu, P., Clark, T. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: a longitudinal study. Information & Management, 41(2), 227.
  • Jang, J., Ko, Y., Shin, W. S., & Han, I. (2021). Augmented Reality and Virtual Reality for Learning: An Examination Using an Extended Technology Acceptance Model. IEEE Access, 9, 6798-6809.
  • Jena, P. K. (2020). Impact of pandemic COVID-19 on education in India. International journal of current research (IJCR), 12.
  • Kock, N. (2012). WarpPLS 3.0 User Manual. Laredo, Texas: ScriptWarp Systems.
  • Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods. Information Systems Journal, 28(1), 227-261.
  • Kristanto, A., & Mariono, A. (2017). The Development of Instructional Materials E-Learning Based on Blended Learning. International Education Studies, 10(7), 10-17.
  • Kumar, A., & Ayedee, D. (2021). Technology adoption: A solution for SMEs to overcome problems during COVID-19. Forthcoming, Academy of Marketing Studies Journal, 25(1).
  • Lassoued, Z., Alhendawi, M., & Bashitialshaaer, R. (2020). An exploratory study of the obstacles for achieving quality in distance learning during the COVID-19 pandemic. Education Sciences, 10(9), 232.
  • Lee, Y., Hsieh, Y., & Chen, Y. (2013). An investigation of employees' use of e-learning systems: applying the technology acceptance model. Behavior & Information Technology, 32(2), 173-189.
  • Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.
  • Lynch, M. (2020). E-Learning during a global pandemic. Asian Journal of Distance Education, 15(1), 189-195.
  • Mallya, J., & Lakshminarayanan, S. (2017). Factors influencing usage of internet for academic purposes using technology acceptance model. DESIDOC Journal of Library & Information Technology, 37(2), 119.
  • McGill, T.J., Klobas, J.E., & Renzi, S. (2011). LMS use and instructor performance: The role of task technology fit. International Journal on E-Learning, 10(1), 43–62.
  • Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). e-Learning, online learning, and distance learning environments: Are they the same?. The internet and higher education, 14(2), 129-135.
  • Mustafa, A. S., Alkawsi, G. A., Ofosu-Ampong, K., Vanduhe, V. Z., Garcia, M. B., & Baashar, Y. (2022). Gamification of E-Learning in African Universities: Identifying Adoption Factors Through Task-Technology Fit and Technology Acceptance Model. In Next-Generation Applications and Implementations of Gamification Systems (pp. 73-96). IGI Global.
  • Ngai, E. T., Poon, J. L., & Chan, Y. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250-267.
  • Pajo, K. & Wallace, C. (2001). Barriers to the Uptake of Web-based Technology by University Teachers. The Journal of Distance Education, 16(1), 70-84.
  • Panda, S., & Mishra, S. (2007). E-Learning in a Mega Open University: Faculty attitude, barriers and motivators. Educational Media International, 44(4), 323-338. doi: 10.1080/09523980701680854
  • Park, I., Kim, D., Moon, J., Kim, S., Kang, Y., & Bae, S. (2022). Searching for New Technology Acceptance Model under Social Context: Analyzing the Determinants of Acceptance of Intelligent Information Technology in Digital Transformation and Implications for the Requisites of Digital Sustainability. Sustainability, 14(1), 579.
  • Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162.
  • Peñarroja, V., Sánchez, J., Gamero, N., Orengo, V., & Zornoza, A. M. (2019). The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice. Behaviour & Information Technology, 38(8), 845-857.
  • Radha, R., Mahalakshmi, K., Kumar, V. S., & Saravanakumar, A. R. (2020). E-Learning during lockdown of Covid-19 pandemic: A global perspective. International journal of control and automation, 13(4), 1088-1099.
  • Ringle, C., Da Silva, D., & Bido, D. (2015). Structural equation modeling with the SmartPLS. Bido, D., da Silva, D., & Ringle, C.(2014). Structural Equation Modeling with the Smartpls. Brazilian Journal Of Marketing, 13(2).
  • Ringle, C., Wende, S., & Will, A. (2005). SmartPLS 2.0 (Beta). Hamburg, (www.smartpls.de).
  • Rotimi, O., Orah, N., Shaaban, A., Daramola, A. O., & Abdulkareem, F. B. (2017). Remote teaching of histopathology using scanned slides via Skype between the United Kingdom and Nigeria. Archives of pathology & laboratory medicine, 141(2), 298-300.
  • Ruggeri, K., Farrington, C., & Brayne, C. (2013). A global model for effective use and evaluation of e-learning in health. Telemedicine and e-Health, 19(4), 312-321.
  • Shanahan, M. C. (2008). Transforming information search and evaluation practices of undergraduate students. International Journal of Medical Informatics, 77(8), 518-526.
  • Teeroovengadum, V., Heeraman, N., & Jugurnath, B. (2017). Examining the antecedents of ICT adoption in education using an Extended Technology Acceptance Model (TAM). International Journal of Education and Development Using Information and Communication Technology, 13(3), 4–23.
  • Tenenhaus, M., Vinzi, V.E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.\
  • Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended Technology Acceptance Model. Asia Pacific Education Review, 11(2), 253-262
  • Teo, T. (2012). Examining the intention to use technology among pre-service teachers: an integration of the Technology Acceptance Model and Theory of Planned Behavior. Interactive Learning Environments, 20(1), 3-18. doi: 10.1080/10494821003714632
  • Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475.
  • Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre‐service teachers' computer attitudes: applying and extending the technology acceptance model. Journal of computer assisted learning, 24(2), 128-143.
  • Unal, E., & Uzun, A. M. (2021). Understanding university students' behavioral intention to use Edmodo through the lens of an extended technology acceptance model. British Journal of Educational Technology, 52(2), 619-637.
  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
  • Wang, W., & Wang, C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774.
  • Wetzels, M., Odekerken-Schroder, G., & van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177-196.
  • Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
There are 69 citations in total.

Details

Primary Language English
Subjects Other Fields of Education
Journal Section Research Articles
Authors

Lovely Jean Caratıquıt 0000-0002-4411-6473

Kevın Caratıquıt 0000-0003-0883-0300

Project Number 1
Publication Date September 1, 2022
Acceptance Date July 5, 2022
Published in Issue Year 2022 Volume: 9 Issue: 5

Cite

APA Caratıquıt, L. J., & Caratıquıt, K. (2022). Influence of Technical Support on Technology Acceptance Model to Examine the Project PAIR E-Learning System in Distance Learning Modality. Participatory Educational Research, 9(5), 467-485. https://doi.org/10.17275/per.22.124.9.5