:::

詳目顯示

回上一頁
題名:運用計畫行為理論探討行動學習意圖之跨國比較分析
作者:林書旭
作者(外文):Shu Hsu Lin
校院名稱:長庚大學
系所名稱:企業管理研究所博士班
指導教授:張錦特
學位類別:博士
出版日期:2021
主題關鍵詞:行為意圖行動學習態度跨國高等教育Behavior intentionM-learningAttitudeCross-countryHigher education
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:3
探索影響學生的態度對於使用新的教學方式-行動學習是一件有趣的研究,尤其在COVID-19疫情下更應利用此技術。雖然對於行動學習接受度的研究過去已經有不少的文獻,但是在高等教育的領域中,關於跨國因素對行動學習接受程度影響的實證研究相對較少。來自不同國家的學生參加課程學習後,如何增加學習興趣和提高跨國大學生的吸收效果是至關重要的問題。為了解決這個問題,透過整合不同構面來探討對跨國學生學習動機的影響。本研究使用計劃行為理論概念模型作為理論基礎,以解決兩個問題:(1)從跨國角度以文化敏感度的方式檢驗跨國行動學習的問題,(2)從跨國大學生的行為意圖來識別行為控制、態度、主觀規範彼此影響的異同。本研究總共蒐集了來自台灣、越南、中國和印尼的938名參與者的數據。結果顯示,學生的態度、主觀規範和感知行為控制會影響行為意圖。同時,感知行為控制並不是中國和印尼學生的重要預測指標,而是台灣和越南學生的重要預測指標。相反的,主觀規範在台灣和越南並不重要,但在中國和印尼是重要的指標。根據我們的發現,主觀規範在中國和印尼之間這種關係似乎比較強烈。
結果顯示,越南和台灣的模型解釋能力為81.2%和82.3%。說明這些大學生呈現出他們的個別特徵,即以PBC為導向。這表明越南和台灣的大學生對接受新技術帶來的課程充滿信心,並有能力使用它們以及願意對行動學習新嘗試。同時,印尼和中國的高等教育學生的模型解釋能力分別為80.2%和77.3%,有趣的現象是這些學生在SN中也具有明顯的特徵和行為。調查結果表明,同儕對行動學習的偏好會影響學生的決定。本研究有助於理解如何處理不同文化背景下行動學習所面臨的問題,同時為行動學習提供有益的設計概念做出具體而有用的貢獻。
In light of the COVID-19 pandemic, the factors that affect students’ attitudes towards using new pedagogical models of mobile learning (ML) are an increasingly relevant and interesting topic to study. Although the determinants of ML acceptance have been tested in previous studies, there are relatively few empirical studies that evaluate the influence of transnational factors on ML acceptance specific to higher education. As students from different counties participate in classroom learning, it is crucial to increase the learning interest and effectiveness of cross-country undergraduates.
In order to solve this problem, various integrated constructs were employed to analyze the influence of ML on the learning motivation of cross-country students and to understand key factors that impact the intention of students from different countries. The framework and conceptual model of Planned Behavior Theory (TPB) are used as the theoretical basis for the research. Moreover, the two issues are addressed as follows: (1) Examine cross-country ML issues in a culturally sensitive way from a cross-country perspective, and (2) Identify the similarities and differences between Perceived Behavior Control (PBC), Attitudes (ATT), and Subjective Norms (SN) of cross-country undergraduates’ behavioral intentions (BI). Online surveys were utilized to collect data from 947 participants in Vietnam, Taiwan, Mainland China, and Indonesia. The results indicated that ATT, SN, and PBC affected BI toward ML among undergraduates in Vietnam, Taiwan, Indonesia, and Mainland China. Although PBC is an important predictor of __INT___ in Taiwanese and Vietnamese students, PBC was not found to be a significant predictor in Chinese and Indonesian students. Additionally, SN is an important indicator of __INT__ in Chinese and Indonesian students but not in Taiwanese and Vietnamese students.
The results from Vietnamese and Taiwanese students explain the proposed model by 81.2% and 82.3% INT, respectively. It is critical to show that these undergraduates present distinguishing PBC-orientated characteristics. This indicates that undergraduates in Vietnam and Taiwan show confidence in their acceptance and willingness to adopt courses brought forth by new technologies such as ML.
Regarding SN in Indonesian and Chinese students, the proposed model can be explained by 80.2% and 77.3% INT, respectively. The results of the survey show that peers’ preference for ML affects students’ decisions.
This research aids in the understanding of how to deal with the problems faced by ML under different cultural backgrounds, and at the same time, makes specific and useful contributions to the provision of ML and conceptual learning design.
[1] M. Kearney, K. Burden, T. Rai. Investigating teachers' adoption of signature mobile pedagogies, Computers & Education 80 (2015) 48-57.
[2] M.H. Fagan. Factors influencing student acceptance of mobile learning in higher education, Computers in the schools 36(2) (2019) 105-121.
[3] S.A. Nikou, A.A. Economides. Mobile-based assessment: Investigating the factors that influence behavioral intention to use, Computers & Education 109 (2017) 56-73.
[4] S.-S. Liaw, H.-M. Huang. A Case of study of investigating users’ acceptance toward mobile learning, in: F.L. Gaol (Ed.) Recent progress in Data engineering and internet technology, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 299-305.
[5] P. Wouters, C. van Nimwegen, H. van Oostendorp, E.D. van der Spek. A meta-analysis of the cognitive and motivational effects of serious games, Journal of Educational Psychology 105(2) (2013) 249-265.
[6] J.D. Slotta, M.C. Linn. WISE Science: Web-based inquiry in the classroom. Technology, Education--Connections, ERIC2009.
[7] B. Sezer. Faculty of medicine students' attitudes towards electronic learning and their opinion for an example of distance learning application, Computers in Human Behavior 55 (2016) 932-939.
[8] J. Osakwe, N. Dlodlo, N. Jere. Where learners' and teachers' perceptions on mobile learning meet: A case of Namibian secondary schools in the Khomas region, Technology in Society 49 (2017) 16-30.
[9] M. Sarraba, H. Al-Shihib, Z. Al-Khanjaric, H. Bourdoucend. Development of mobile learning application based on consideration of human factors in Oman, Technology in Society 55 (2018) 183-198.
[10] H.A. Zahrani, K. Laxman. A critical meta-analysis of mobile learning research in higher education, Journal of Technology Studies 42(1) (2016) 2-17.
[11] M. Merhi, K. Hone, A. Tarhini. A cross-country study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust, Technology in Society 59 (2019) 101151.
[12] F. D. Davis (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
[13] I. Ajzen. The theory of planned behavior, Organizational behavior human decision processes 50(2) (1991) 179-211.
[14] V. Venkatesh, M.G. Morris, G.B. Davis, F.D. Davis. User acceptance of information technology: Toward a unified view, MIS quarterly (2003) 425-478.
[15] C.-T. Chang, J. Hajiyev, C.-R. Su. Examining the students’ behavioral intention to use e-learning in Azerbaijan? The General Extended Technology Acceptance Model for E-learning approach, Computers & Education 111 (2017) 128-143.
[16] J. Zhang. A cultural look at information and communication technologies in Eastern education, Educational Technology Research and Development 55(3) (2007) 301-314.
[17] D. Churchill, B. Fox, M. King. Framework for designing mobile learning environments, Theories and Application, Springer Singapore, 2016, pp. 3-25.
[18] M.B. Ada. Interrelationship between pedagogy, theories, objectives, and features: Mobile learning design, marketing initiatives for sustainable educational development, IGI Global, 2018, pp. 119-145.
[19] O. Alharbi, H. Alotebi, A. Masmali, N. Alreshidi. Instructor acceptance of mobile learning in Saudi Arabia: A case study of Hail university, International Journal of Business 12(5) (2017) 27-35.
[20] J.C. Sánchez-Prieto, S. Olmos-Migueláñez, F.J. García-Peñalvo. Mobile learning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model, Computers in Human Behavior 72 (2017) 644-654.
[21] A.S. Al-Adwan, A. Al-Adwan, H. Berger. Solving the mystery of mobile learning adoption in higher education, International Journal of Mobile Communications 16(1) (2018) 24-49.
[22] Z. Putnik. Mobile learning, student concerns and attitudes, Mobile learning design, Springer2016, pp. 139-153.
[23] M. Al-Emran, H.M. Elsherif, K. Shaalan. Investigating attitudes towards the use of mobile learning in higher education, Computers in Human Behavior 56 (2016) 93-102.
[24] A. Abu-Al-Aish, S. Love. Factors influencing students’ acceptance of m-learning: An investigation in higher education, The international review of research in open distributed learning 14(5) (2013).
[25] L. Briz-Ponce, A. Pereira, L. Carvalho, J.A. Juanes-Méndez, F.J. García-Peñalvo. Learning with mobile technologies – Students’ behavior, Computers in Human Behavior 72 (2017) 612-620.
[26] M. Fishbein, I. Ajzen. Belief, attitude, intention and behavior: an introduction to theory and research (1975).
[27] H. Han, L.-T. Hsu, C. Sheu. Application of the Theory of Planned Behavior to green hotel choice: Testing the effect of environmental friendly activities, Tourism Management 31(3) (2010) 325-334.
[28] S. Taylor, P. Todd. Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions, International Journal of Research in Marketing 12(2) (1995) 137-155.
[29] M. Fishbein, I. Ajzen. Belief, attitude, intention, and behavior: An introduction to theory and research, (1977).
[30] F. Liñán, Y.W. Chen. Development and cross–cultural application of a specific instrument to measure entrepreneurial intentions, Entrepreneurship Theory and Practice 33(3) (2009) 593-617.
[31] A. Bandura. Self-efficacy mechanism in human agency, American Psychologist 37(2) (1982) 122-147.
[32] F.D. Davis. Perceived usefulne, perceived ease of use, and user acceptance of information technology, MIS quarterly (1989) 319-340.
[33] M. Dalvi-Esfahani, H. Shahbazi, M. Nilashi. Moderating effects of demographics on green information system adoption, International Journal of Innovation Technology Management 16(01) (2019) 1-24.
[34] L. Barnard-Brak, H. Burley, S.M. Crooks. Explaining youth mentoring behavior using a theory of planned behavior perspective, International Journal of Adolescence and Youth 15(4) (2010) 365-379.
[35] S.S. Al-Gahtani. Empirical investigation of e-learning acceptance and assimilation: A structural equation model, Applied Computing and Informatics 12(1) (2016) 27-50.
[36] I. Ajzen. Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior 1, Journal of applied social psychology 32(4) (2002) 665-683.
[37] J. Cheon, S. Lee, S.M. Crooks, J. Song. An investigation of mobile learning readiness in higher education based on the theory of planned behavior, Computers & Education 59(3) (2012) 1054-1064.
[38] S. Kim, H. Kim, S. Han. A development of learning widget on m-learning and e-learning environments, Behaviour & Information Technology 32(2) (2013) 190-202.
[39] F. Abdullah, R. Ward, E. Ahmed. Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios, Computers in Human Behavior 63 (2016) 75-90.
[40] H. Mohammadi. Social and individual antecedents of m-learning adoption in Iran, Computers in Human Behavior 49 (2015) 191-207.
[41] T.-I. Han. Determinants of organic cotton apparel purchase: A comparison of young consumers in the USA and South Korea, Sustainability 10(6) (2018) 2025-2037.
[42] D.R. Compeau, C.A. Higgins. Computer self-efficacy: Development of a measure and initial test, MIS quarterly (1995) 189-211.
[43] S.-S. Liaw, H.-M. Huang, G.-D. Chen. An activity-theoretical approach to investigate learners’ factors toward e-learning systems, Computers in Human Behavior 23(4) (2007) 1906-1920.
[44] L. Briz-Ponce, F.J. García-Peñalvo. An Empirical assessment of a technology acceptance model for apps in medical education, Journal of Medical Systems 39(11) (2015) 176.
[45] Y.-M. Shiue. Investigating the sources of teachers' instructional technology use through the decomposed theory of planned behavior, Journal of Educational Computing Research 36(4) (2007) 425-453.
[46] C. Kim, M. Mirusmonov, I. Lee. An empirical examination of factors influencing the intention to use mobile payment, Computers in Human Behavior 26(3) (2010) 310-322.
[47] C. Fornell, D.F. Larcker. Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research 18(1) (1981) 39-50.
[48] M.R. Martínez-Torres, S.L. Toral Marín, F.B. García, S.G. Vázquez, M.A. Oliva, T. Torres. A technological acceptance of e-learning tools used in practical and laboratory teaching, according to the European higher education area, Behaviour & Information Technology 27(6) (2008) 495-505.
[49] R.G. Lomax. A beginner's guide to structural equation modeling (3rd ed.), New York: Routledge 2010.
[50] R.H. Perry, B. Charlotte, M. Isabella, C. Bob. SPSS explained, Routledge: London, UK, 2004.
[51] C. Courtois, H. Montrieux, F. De Grove, A. Raes, L. De Marez, T. Schellens. Student acceptance of tablet devices in secondary education: A three-wave longitudinal cross-lagged case study, Computers in Human Behavior 35 (2014) 278-286.
[52] P. Kanthawongs, P. Kanthawongs. Individual and social factors affectingstudent’s usage intention in using learning management system, Procedia - Social and Behavioral Sciences 88 (2013) 89-95.
[53] V. Vamvaka, C. Stoforos, T. Palaskas, C. Botsaris. Attitude toward entrepreneurship, perceived behavioral control, and entrepreneurial intention: dimensionality, structural relationships, and gender differences, Journal of Innovation and Entrepreneurship 9(1) (2020) 5.
[54] L. Zhang, J. Zhu, Q. Liu. A meta-analysis of mobile commerce adoption and the moderating effect of culture, Computers in Human Behavior 28(5) (2012) 1902-1911.
[55] Y.-K. Lee, J.-H. Park, N. Chung, A. Blakeney. A unified perspective on the factors influencing usage intention toward mobile financial services, Journal of Business Research 65(11) (2012) 1590-1599.
[56] K.K. Kapoor, Y.K. Dwivedi, M.D. Williams. Innovation adoption attributes: a review and synthesis of research findings, European Journal of Innovation Management 17(3) (2014) 327-348.
[57] Y.-L. Chiu, C.-C. Tsai. The roles of social factor and internet self-efficacy in nurses' web-based continuing learning, Nurse Education Today 34(3) (2014) 446-450.
[58] N. Pellas. The influence of computer self-efficacy, metacognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of Second Life, Computers in Human Behavior 35 (2014) 157-170.
[59] D. Manasijević, D. Živković, S. Arsić, I. Milošević. Exploring students’ purposes of usage and educational usage of Facebook, Computers in Human Behavior 60 (2016) 441-450.
[60] M. Shorfuzzaman, M. Alhussein. Modeling learners’ readiness to adopt mobile learning: A perspective from a GCC higher education institution, Mobile information systems 2016 (2016) 1-10.
[61] M. Al-Emrana, V. Mezhuyevb, A. Kamaludina. Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance, Technology in Society 61 (2020) 101247.
[62] H.-R. Chen, H.-F. Tseng. Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan, Evaluation and program planning 35(3) (2012) 398-406.
[63] R.A. Sánchez, A.D. Hueros, M.G. Ordaz. E‐learning and the university of Huelva: a study of WebCT and the technological acceptance model, Campus-Wide Information Systems 30(2) (2013) 135-160
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
:::
QR Code
QRCODE