thesis on technology acceptance

Technology Acceptance Model: History, Theory, and Application

• First Online: 11 September 2022

Cite this chapter

• Ömer Faruk Ursavaş 2  

Part of the book series: Springer Texts in Education ((SPTE))

622 Accesses

1 Citations

Changes in technology have been influential on human life in many periods of history. Changes in fields such as military, architecture, and communication have brought with them results such as the opening and closing of an era. In the following years, developments in technology, especially in computer and communication technologies, have led to significant changes in educational and instructional processes by enabling the use of various systems independent of time and space in addition to providing communication. It is extremely important to understand and reveal the factors that encourage or restrict teachers’ use of technologies during an effective technology adoption process. In line with this importance, the Technology Acceptance Model has been examined in an educational context to understand what factors affect teachers’ acceptance and use of technology.

The reader who completes this section will be able to (a) explain the overall structure of the model and for what purpose it was developed (b) explain the factors in the model and describe their relationship with each other (c) give an example of the use of a technology in an educational context (d) expand TAM models in an educational context, add different variables, construct hypotheses/problems, collect and analyze data, and (e) show, discuss and evaluate the results of a study using TAM.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28 (3), 557–582.

Article   Google Scholar  

Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30 (2), 361–391.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50 , 179–211.

Bates, T., & Poole, G. (2003). Effective teaching with technology in higher education: Foundations for success . Jossey-Bass.

Google Scholar  

Beugelsdijk, S., Maseland, R., & Van Hoorn, A. (2015). Are scores on H ofstede’s dimensions of national culture stable over time? A cohort analysis. Global Strategy Journal, 5 (3), 223–240.

Bradley, G., & Russell, G. (1997). Computer experience, school support and computer anxieties. Educational Psychology, 17 (3), 267–284.

Chen, L., Gillenson, L., & Sherrell, L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39 (8), 709–719. https://doi.org/10.1016/S0378-7206(01)00127-6

Chua, S. L., Chen, D.-T., & Wong, A. F. (1999). Computer anxiety and its correlates: A meta-analysis. Computers in Human Behavior, 15 (5), 609–623.

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19 , 189–211.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319–400.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35 (8), 982–1003.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22 (14), 1111–1132.

Ertmer, P. A. (1999). Addressing first-and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47 (4), 47–61.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research . Addison-Wesley.

Fullan, M. (2001). The new meaning of educational change . Routledge.

Book   Google Scholar  

Gefen, D., Straub, D., & Boudreau, M. (2000). Structural equation modeling techniques and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4 (7), 1–78.

Green, B. C. (2005). Building sport programs to optimize athlete recruitment, retention, and transition: Toward a normative theory of sport development. Journal of Sport Management, 19 (3), 233–253.

Hofstede, G. (1980). Culture and organizations. International Studies of Management and Organization, 10 (4), 15–41.

Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online readings in psychology and culture, 2 (1), 8. https://doi.org/10.9707/2307-0919.1014

Hoftede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind: intercultural cooperation and its importance for survival . McGraw-Hill.

Huang, F., Teo, T., & Zhou, M. (2017). Factors affecting Chinese English as a foreign language teachers’ technology acceptance: A qualitative study. Journal of Educational Computing Research , 1–23. https://doi.org/10.1177/0735633117746168

Huang, F., Teo, T., Sánchez-Prieto, J. C., García-Peñalvo, F. J., & Olmos-Migueláñez, S. (2019). Cultural values and technology adoption: A model comparison with university teachers from China and Spain. Computers & Education, 133 , 69–81. https://doi.org/10.1016/j.compedu.2019.01.012

Huang, F., Teo, T., & Scherer, R. (2020). Investigating the antecedents of university students’ perceived ease of using the Internet for learning. Interactive Learning Environments , 30 (6) 1–17. https://doi.org/10.1080/10494820.2019.1710540

Kaba, B., & Osei-Bryson, K.-M. (2013). Examining influence of national culture on individuals’ attitude and use of information and communication technology: Assessment of moderating effect of culture through cross countries study. International Journal of Information Management, 33 (3), 441–452.

Karahanna, E., Agarwal, R., & Angst, C. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30 (4), 781–804. doi:10.2307/25148754.

Kelman, H. C. (1958). Compliance, identification, and internalization three processes of attitude change. Journal of Conflict Resolution, 2 (1), 51–60.

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43 , 740–755. https://doi.org/10.1016/j.im.2006.05.003

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). Guilford Press.

Koch, C. (2004a). Nike Rebounds how (and Why) Nike recovered from its supply chain disaster. CIO , 17 (17), 56-63.

Koch, C. (2004b). When bad things happen to good projects (Vol. 18, pp. 50–59). CIO.

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40 (3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4

Liu, Q., Geertshuis, S., & Grainger, R. (2020). Understanding academics’ adoption of learning technologies: A systematic review. Computers & Education, 151 , 103857.

Ma, W. W. K., Andersson, R., & Streith, K. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning, 21 (6), 387–395.

Martocchio, J. J., & Webster, J. (1992). Effects of feedback and cognitive playfulness on performance in microcomputer software training. Personnel Psychology, 45 (3), 553–578.

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2 (3), 192–222.

Morris, M. G., & Dillon, A. (1997). How user perceptions influence software use. IEEE Software, 14 (4), 58–65.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory . McGraw-Hill, Inc..

Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied nultivariate analysis . Taylor and Francis.

Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. The Internet and Higher Education, 13 (3), 134–140.

Rogers, E. (1995). Diffusion of innovations (4th ed.). Free Press.

Rosen, L. D., & Weil, M. M. (1995). Computer availability, computer experience and technophobia among public school teachers. Computers in Human Behavior, 11 (1), 9–31.

Saylık, A., Bumin, A., Sınağ, A., & Çorapçıoğlu, D. (2017). Okul müdürlerinin paternalist (babacan) liderlik davranışları ile Hofstede’nin kültür boyutları arasındaki ilişki. Unpublished Doctoral dissertation]. Ankara University, Ankara, Turkey.

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44 , 90–103. https://doi.org/10.1016/j.im.2006.10.007

Sivo, S. A., & Cheng-Chang, P. (2005). Undergraduate engineering and psychology students’ use of a course management system: A factorial invariance study of user characteristics and attitudes. Journal of Technology Studies, 31 (2), 94–103.

Sivo, S. A., Pan, C. C. S., & Hahs-Vaughn, D. L. (2007). Combined longitudinal effects of attitude and subjective norms on student outcomes in a web-enhanced course: A structural equation modelling approach. British Journal of Educational Technology, 38 (5), 861–875.

Social, B. A. (1986). Foundations of thought and action: A social cognitive theory . Prentice-Hall.

Stockman, C. (2017). Decoding technology acceptance in education: A cultural studies contribution . Routledge.

Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42 (1), 85–92.

Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: A structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25 (3), 306–328.

Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12 (2), 137–155.

Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52 (2), 302–312. https://doi.org/10.1016/j.compedu.2008.08.006

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. The Asia-Pacific Education Researcher, 11 , 253–262. https://doi.org/10.1007/s12564-009-9066-4

Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57 (4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008

Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. In Advances in experimental social psychology (Vol. 29, pp. 271–360). Elsevier.

Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly, 23 (2), 239–260.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11 (4), 342–365.

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 extention of the technology acceptance model: Four longitudinal field studies. Management Science, 46 (2), 186–204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478.

Worthington, V. L., & Zhao, Y. (1999). Existential computer anxiety and changes in computer technology: What past research on computer anxiety has missed. Journal of Educational Computing Research, 20 (4), 299–315.

Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42 (5), 719–729.

Yoo, B., Donthu, N., & Lenartowicz, T. (2011). Measuring Hofstede’s five dimensions of cultural values at the individual level: Development and validation of CVSCALE. Journal of International Consumer Marketing, 23 (3-4), 193–210.

Yuen, A. H., & Ma, W. W. (2002). Gender differences in teacher computer acceptance. Journal of Technology and Teacher Education, 10 (3), 365–382.

Download references

Author information

Authors and affiliations.

Recep Tayyip Erdoğan University, Rize, Turkey

Ömer Faruk Ursavaş

You can also search for this author in PubMed   Google Scholar

4.1 Electronic Supplementary Materials

Chpiv-sample-data (zip 43 kb), rights and permissions.

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Ursavaş, Ö.F. (2022). Technology Acceptance Model: History, Theory, and Application. In: Conducting Technology Acceptance Research in Education . Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-031-10846-4_4

Download citation

DOI : https://doi.org/10.1007/978-3-031-10846-4_4

Published : 11 September 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-10845-7

Online ISBN : 978-3-031-10846-4

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

What affects students’ acceptance and use of technology?

Students’ acceptance and use of technologies introduced in their academic environments is an important factor in determining the success of these technologies. This research project used the Unified Theory of Acceptance and Use of Technology (UTAUT), a prominent technology acceptance and use model, as a theoretical basis to conduct empirical research testing the factors that influence students’ acceptance and use of technology in their academic environment.

Although several studies applied UTAUT in various organizational and cultural contexts, very few implement the full model and examine all of its constructs. By focusing on students in higher-education and technology that is new to them, and by using the full UTAUT model with no changes or elimination of constructs, this study places itself in the area where there are no exhaustive studies. Also, this research addresses the question of whether UTAUT is applicable in an academic environment for educational technologies introduced to the students.

The study found a mixed support for UTAUT. Performance expectancy, effort expectancy, facilitating conditions and attitude towards using technology were significant determinants of technology acceptance. All moderating variables had a significant moderating influence except for experience, which was surprisingly not significant. The UTAUT model is found to be applicable to some extent in the educational setting, but might need a few modifications to fit the context.

The study provides recommendations to help prepare the appropriate environment and training before a new technology is introduced for students. It also provides recommendation for further research examining students’ acceptance and use of technology.

Academic Program

• Information Systems

Usage metrics

• Information Systems Management

• Bibliography
• More Referencing guides Blog Automated transliteration Relevant bibliographies by topics
• Automated transliteration
• Relevant bibliographies by topics
• Referencing guides

Dissertations / Theses on the topic 'Technology Acceptance Model (TAM)'

Create a spot-on reference in apa, mla, chicago, harvard, and other styles.

Consult the top 50 dissertations / theses for your research on the topic 'Technology Acceptance Model (TAM).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.

Zambelli, Nicole, and n/a. "Technology acceptance in modern organisations : the role of past behaviour and perceived behavioural control." University of Canberra. Human & Biomedical Sciences, 2001. http://erl.canberra.edu.au./public/adt-AUC20061112.114544.

Langa, GZ, and DP Conradie. "Perceptions and attitudes with regard to teleworking among public sector officials in Pretoria: applying the Technology Acceptance Model (TAM)." Unisa Press, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001859.

Compton, Shane M., and n/a. "Information technology implementation and acceptance: a case study of change management." University of Canberra. Applied Science, 2002. http://erl.canberra.edu.au./public/adt-AUC20050331.145848.

Faltin, Viktor, and Erdyni Garmaev. "Digitaliseringen av svensk bankverksamhet : Om prisets påverkan av kundernas val." Thesis, Högskolan i Gävle, Företagsekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-23421.

Zeneli, Egzona. "Hur påverkas du av dina kollegor? : En kvalitativ studie om socialt inflytande vid accepterande av ny teknologi." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17449.

MacMillan, Richard Scott. "Examining the Adoption of Donor Management Systems in Not-for-Profit Organizations with an Extended Technology Acceptance Model." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/223.

Meli, Peggy. "PERSPECTIVES OF HEALTH INFORMATION MANAGEMENT FACULTY USE OF AN E-LEARNING LABORATORY AND TECHNOLOGY ACCEPTANCE." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4173.

Eile, Paulina. "Faktorer för en förbättrad digitaliserad blankett : En studie baserad på the Technology Acceptance Model (TAM) och användbarhetstester." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-76985.

Siegel, Daniel. "ACCEPTING TECHNOLOGY AND OVERCOMING RESISTANCE TO CHANGE USING THE MOTIVATION AND ACCEPTANCE MODEL." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2448.

Park, Kunsoon. "User Acceptance of the Intranet in Restaurant Franchise Systems: An Empirical Study." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30234.

DeGennaro, Alfred Joseph. "Application of Multiple Intelligence Theory to an e-Learning Technology Acceptance Model." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1273053153.

Pettersson, Dennis, and Pär Arvidsson. "Usage of Business Intelligence : Testing the Technology Acceptance Model on a BI System." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-176651.

KU, CHENG-HSIN. "Extending the Technology Acceptance Model Using Perceived User Resources in Higher Education Web-Based Online Learning Courses." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3084.

Norten, Adam. "Nurses' Acceptance of RFID Technology in a Mandatory-Use Environment." NSUWorks, 2011. http://nsuworks.nova.edu/gscis_etd/263.

Kim, Jiyeon Forsythe Sandra Monk. "Sensory enabling technology acceptance model (SE-TAM) the usage of sesnory enabling technologies for online apparel shopping /." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Summer/Dissertations/KIM_JIYEON_27.pdf.

Liu, Shijing. "Technology Acceptance Model for Determining the Effects of Age, Usability, and Content on Mobile Application Usage." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1339700970.

Perez, Sebastian, Jorge Cabrera, Jose Rodriguez, and Carlos Raymundo. "E-Government Adoption Model Extended with Public Value in Peru." Institute of Electrical and Electronics Engineers Inc, 2019. http://hdl.handle.net/10757/656351.

Harmon, Eric. "Strategies Used by Cloud Security Managers to Implement Secure Access Methods." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/4728.

Smith, Jo. "THE EFFECT OF SOCIAL PRESENCE ON TEACHER TECHNOLOGY ACCEPTANCE, CONTINUANCE INTENTION, AND PERFORMANCE IN AN ONLINE TEACHER PROF." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3665.

Pérez, Chacón Sebastián Ramón, Vilchez Jose Luis Rodriguez, Berrios Jorge Antonio Cabrera, Ibañez Carlos Arturo Raymundo, and David Santos Mauricio. "Increasing e-government adoption by emphasizing environmental sustainability: an extended case study in Peru." Emerald Group Holdings Ltd, 2021. http://hdl.handle.net/10757/655952.

Ologun, Rita Oluwanifesimi. "Use and acceptance of education technologies by academics in a school of nursing in the Western Cape, South Africa." University of the Western Cape, 2016. http://hdl.handle.net/11394/6091.

Chiou, Yu-Fang. "Perceived Usefulness, Perceive Ease of Use, Computer Attitude, and Using Experience of Web 2.0 Applications as Predictors of Intent to Use Web 2.0 by Pre-service Teachers for Teaching." Ohio University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1303314743.

Günther, Franziska. "Zur studentischen Nutzung von E-Learning- und Social Web-Anwendungen im universitären Kontext – eine explorative Analyse." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-235060.

Sachidanandam, Sivanarulselvan. "Why Physicians Do Or Do not Use Computerized Physician Order Entry Systems: Applying the Technology Acceptance Model." Connect to this title online, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=bgsu1151258487.

Wollny, Lisa, Simon Ahrenstedt, and Jiahao Huang. "A study on factors influencing the acceptance of mobile payment applications in Sweden." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Företagsekonomi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-26738.

Al, Shuraimi Abdulaziz. "A study on the effects of cultural context differences on extended technology acceptance model (TAM) : case of Saudi Arabia and France." Paris 6, 2011. http://www.theses.fr/2011PA066001.

Andersson, Alex. "Användaracceptans av professionella möten i en Virtual Realityapplikation." Thesis, Mittuniversitetet, Institutionen för data- och systemvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-43212.

Zaki, Maria, and Sepideh Asadollah-Broujerdi. "Factors Affecting Technology Adoption: A study of the Smart Cart." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20392.

Lee, Seungwon Shawn. "Exploratory study of the impact of Information and Communication Technology (ICT)-based features in conference center selection/recommendation by meeting planners." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/37858.

Mörch, Henric. "Påverkansfaktorer i digitaliseringsprocessen : En kvalitativ studie om användares syn på implementering av nya informationssystem." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-433830.

Sandema-Sombe, Christina Ndiwa. "Relationship Between Perceived Usefulness, Ease of Use, and Acceptance of Business Intelligence Systems." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7736.

Obasi, Jonah E. "Police Officers' Perceptions of Body-Worn Camera Technology." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/4544.

Fesenmaier, Margaret Anna. "Permeating the wall: Transmitting knowledge remittances as a strategy for health information campaigns in The Republic of Moldova." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23307.

Altmann, Valerie, and Maike Gries. "Factors influencing the usage intention of mHealth apps : An Empirical Study on the example of Sweden." Thesis, Karlstads universitet, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-56706.

Zhang, Stefan, and Belinda Quainoo. "Acceptans av mobil förstärkt verklighet (MAR) inom möbelbranschen." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20860.

Alboqami, Hassan Abdullah. "Investigating the factors affecting customers' trust and acceptance of online banking : the case of Saudi Arabia." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17127.

Kern, Alexander Günter. "Blockchain technology : a technology acceptance model (TAM) analysis." Master's thesis, 2018. http://hdl.handle.net/10400.14/25478.

LEE, CHIA-YU, and 李佳育. "Technology Acceptance Model (TAM), Electronic Luggage Tags." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8mahr6.

Huang, Yao-Wei, and 黃耀緯. "Re-Assessment of Technology Acceptance Model (TAM) on Needs." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/12054230448103708884.

Jhang, Yuan-Jhe, and 張遠哲. "Technology Acceptance Model,TAM for Brain Computer Interfaces Game." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/25302020867165743413.

Lo, Jia-Lin, and 羅嘉琳. "A Study on Mobile Learning Device- Technology Acceptance Model (TAM)." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/47354170014747730531.

Pei-Chun, Lin, and 林姵君. "Users’ Acceptance of VoiceTube for English Learning: An Extension of the Technology Acceptance Model (TAM)." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fsvj8v.

Almojaibel, Abdullah. "Understanding intention to use telerehabilitation : applicability of the Technology Acceptance Model (TAM)." Diss., 2017. http://hdl.handle.net/1805/14970.

Chen, Meng-Kung, and 陳孟功. "A Study on Campus Wireless Local Area Network (WLAN) － Technology Acceptance Model (TAM)." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/73233723930044808939.

YANG, SU-PI, and 楊素碧. "Utilizing Technology Acceptance Model (TAM) to Explore the Reward Points Operating Model Implemented by Family Mart." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/38148689748870528086.

Linh, Le Thi, and 黎詩玲. "An Enterprise Resource Planning (ERP) system adoption study base on Technology Acceptance Model (TAM)." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/02548028603022817087.

Chen, Po-Chih, and 陳柏志. "The study of significant factors of promoting electronic trade from Technology Acceptance Model (TAM)." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/60894423529377279852.

LOPEZ, RENZO DEL GIUDICE, and 洛子謙 Renzo Del Giudice Lopez. "Analysis of the Purchase Intention of Bitcoin by Applying the Technology Acceptance Model (TAM)." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4nsd2f.

Ming-HuiKuo and 郭明輝. "Applying Technology Acceptance Model (TAM) to Investigating Consumers’ Willingness to Use Third-Party Payment." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/hrr5bd.

Rau, Yi-Fan, and 饒怡凡. "Using the Technology Acceptance Model (TAM) to Examine the Adoption of LTE Smartphone in Taiwan." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/qd4xkb.

HOW TECHNOLOGY IMPACTS STUDENT ACHIEVEMENT IN THE CLASSROOM

The integration of technology in classrooms has become increasingly prevalent, presenting both opportunities and challenges for educators. This study examines the impact of technology on student performance and behavior, particularly in seventh and eighth-grade classrooms. The COVID-19 pandemic accelerated the shift to online learning, raising concerns about learning loss and disparities in access to technology. Using a needs-based assessment survey, this research investigates teachers' perceptions of technology's effects on student engagement, academic achievement, and retention of curriculum content. The study explores the positive and negative implications of technology use, as well as non-technological strategies employed by teachers to support student learning. Findings reveal that while technology offers benefits such as student-centered education and immediate feedback, it also poses challenges such as distractions and decreased engagement. The study underscores the importance of understanding how technology impacts student learning and behavior and provides insights for developing effective intervention strategies. By considering the perspectives of educators, this research contributes to the ongoing dialogue on technology integration in education and informs evidence-based practices for promoting student success in technology-rich classrooms.

Degree Type

• Master of Science
• Educational Studies

Campus location

Advisor/supervisor/committee chair, advisor/supervisor/committee co-chair, additional committee member 2, usage metrics.

• Secondary education

On a crucial earnings call, Musk reminds the world Tesla is a tech company. ‘Even if I’m kidnapped by aliens tomorrow, Tesla will solve autonomy’

As the debate over whether Tesla should be valued as an automaker or a software company rages, CEO Elon Musk laid out his view in no uncertain terms. 

“If you value Tesla as just an auto company, fundamentally, it’s just the wrong framework,” Musk said during Tesla’s first-quarter earnings call on Tuesday. 

Musk’s comments came during a critical moment for Tesla. Heading into the earnings announcement, Tesla faced mounting pressure from investors over its future. Investors were especially concerned that Tesla might scrap plans for a new, more affordably priced car altogether, given that Musk had repeatedly telegraphed his intentions to turn much of Tesla’s resources toward robo-taxis and self-driving car technology. Investors have balked at the idea that a car company with declining sales would delay the release of its new model in favor of developing a technology that does not exist yet. 

Instead, Tesla split the difference. It moved up the production schedule of its new models from late 2025 to early 2025, with the possibility they may even arrive by the end of this year, according to Musk. At the same time the CEO made it crystal clear that the investment thesis for the company should be entirely focused on its tech endeavors. 

“If somebody doesn’t believe Tesla is going to solve autonomy, they should not be an investor in the company,” Musk said. 

When asked if Tesla could develop self-driving cars without him, Musk was confident the work was close to completion. “Even if I’m kidnapped by aliens tomorrow, Tesla will solve autonomy—maybe slower, but for vehicles at least,” he said.

A bad quarter for Tesla

However, the down-the-middle strategy Tesla opted for belied what was a particularly high-stakes earnings call given how poorly the company performed. Investors and analysts had already been primed to expect a historically bad quarter from Tesla—which it was. 

Earlier this month, Tesla released figures that showed its vehicle deliveries were down 8.5% in the first quarter, its first year-over-year decline in four years. As earnings reports showed, those poor numbers trickled down to the rest of Tesla’s business . Revenue slid 9%, the largest decline since 2012, for a total of $21 billion in the first quarter, according to an earnings release. Total vehicle sales were down 13% compared with the year before. Net income didn’t fare much better, dropping 55% , as the company brought in $1.1 billion in the quarter. 

Perhaps the one silver lining for investors was that Tesla announced it would speed up the production of its upcoming cars. Investors had been eagerly awaiting further news from Tesla leadership about when new models would hit the market after a report the company was scrapping them entirely in favor of its robo-taxi efforts. The new model, rumored to be an affordable car priced under $30,000, is still in the works. 

When pressed by an analyst on the call regarding details about the lower-cost Tesla, Musk declined to go into specifics. “We’ve said all we will on that front,” he replied. 

The lack of specifics was good enough for Wall Street, though. Tesla stock was up more than 13% and climbing in post–market trading Tuesday evening. 

Tesla is ‘solving autonomy’ for driverless cars

Alongside the new car models, Musk also gave guidance about the self-driving technology Tesla is developing. In describing the project, Musk painted a picture of flipping a switch to turn millions of Teslas around the world into self-driving cars. 

“Really the way to think of Tesla is almost entirely in terms of solving autonomy and being able to turn on that autonomy for a gigantic fleet,” Musk said. “It might be the biggest asset-value appreciation in history when that happens, when you can do unsupervised, full self-driving.”

Currently, Tesla does not have a completely self-driving car. Its latest autonomous-vehicle software, which is called Full Self-Driving, still requires human supervision. To juice demand, Tesla cut prices of the add-on software from $12,000 a year to $8,000 earlier this week. 

On the call, Tesla executives sought to reassure investors that the bad quarter was just a lull until the company could perfect its self-driving technology. Musk reiterated that it was “currently between two major growth waves.” The first wave referenced the initial proliferation of EVs that Tesla helped usher in when it succeeded in selling its cars to people other than environmentally conscious consumers. The second wave, according to Tesla, will come once self-driving cars become the norm, with it dominating the market. 

Because of that, Tesla anticipates a difficult remainder of the year with middling sales growth. “In 2024, our vehicle-volume growth rate may be notably lower than the growth rate achieved in 2023, as our teams work on the launch of the next-generation vehicle and other products,” Tesla wrote in a shareholder presentation. On the call, Musk said he did expect sales in 2024 would be higher than last year. 

A first glimpse of Tesla’s robo-taxi app and prototype will be unveiled on Aug. 8, according to a post from Musk on X . Musk made a similar claim in 2019 , saying Tesla robo-taxis would be ready in 2020. Four years later, investors are still waiting.

Latest in Tech

• 0 minutes ago

TikTok says it will sue U.S. over sell-or-ban law on First Amendment grounds. Does it have a chance?

Mark Zuckerberg warns of stock volatility as Meta bets billions more on AI investment ‘before we make much revenue’

Rubrik valued at $5.6 billion after massively oversubscribed IPO prices above range

IBM shares fall after flatlining sales in its second-largest business unit

Tesla driver said he was using Autopilot and looking at his phone before a crash that killed a motorcyclist

Meta’s stock sags under the weight of aggressive AI spending

Most popular.

Spotify CEO Daniel Ek surprised by how much laying off 1,500 employees negatively affected the streaming giant’s operations

The outlook for home prices has changed drastically in just the past month as Fed rate cuts look more and more distant

A 60-year-old worker in Texas says she’s dependent on apps that let her get paid early: ‘They get you hooked on having that money’

Three top executives at Tesla have resigned in two weeks, with the latest departure at the end of its earnings call

Your reusable water bottle may be a breeding ground for strep and fecal bacteria. Here’s how to keep it clean