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Education and Information Technologies

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07.01.2021

User acceptance of augmented reality welding simulator in engineering training

verfasst von: Christos Papakostas, Christos Troussas, Akrivi Krouska, Cleo Sgouropoulou

Erschienen in: Education and Information Technologies | Ausgabe 1/2022

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Abstract

The integration of Augmented Reality (AR) in welding training is considered to increase the efficiency, security and time gain in operations, reducing consumable and infrastructures costs. Prior research has examined the integration of AR-simulation in applications, like medical operations or aviation, showing the need for greater usability of these systems. However, research on AR integration in welding training is yet limited. It can help new welders learn effectively and be prepared to work in the industry. The motivation of this study was the ongoing use of AR in manufacturing training and its novelty is the analysis of the most significant factors affecting the actual AR system use. The main objective of this research is to evaluate the use of AR technology in welding training using an innovative model, appropriately extended to consider pedagogy and technology. The contribution of this paper is the exploration and understanding of the factors associated with the AR welding training which can lead to goal fulfillment and, subsequently, impact users’ choice. This study is based on a modified Technology Acceptance Model, extending it by two external variables (perceived enjoyment and system quality) and the sample is 200 trainees. The findings show that the external variables are predictors of perceived usefulness and ease of use. The intention to use AR simulator is positively influenced directly by system quality and perceived ease of use. The findings help AR developers enhance the quality of AR-simulation training systems to enhance users’ experience and their behavioral intention to use them.

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https://www.soldamatic.com/wp-content/uploads/2016/10/Success_Stories.pdf

https://www.soldamatic.com/

https://www.iso.org/obp/ui/#iso:std:iso:9606:-1:ed-2:v1:en

Ahn, T., Ryu, S., & Han, I. (2004). The impact of the online and offline features on the user acceptance of internet shopping malls. Electronic Commerce Research and Applications, 3(4), 405–420. https://doi.org/10.1016/j.elerap.2004.05.001.CrossRef

Akçayır, M., & Akçayır, G. (2017). Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educational Research Review, 20, 1–11. https://doi.org/10.1016/j.edurev.2016.11.002.CrossRef

Alqahtani, H., Kavakli, M., & Sheikh, N. U. (2018). Analysis of the technology acceptance theoretical model in examining users’ Behavioural intention to use an augmented reality app (IMAPCampus). International Journal of Engineering and Management Research, 8(5), 37–49. https://doi.org/10.31033/ijemr.8.5.6.CrossRef

Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B. (2001). Recent advances in augmented reality. IEEE Computer Graphics and Applications, 21(6), 34–47. https://doi.org/10.1109/38.963459.CrossRef

Bagozzi, R., & Yi, Y. (1988). On the evaluation of structure equation models. Journal of the Academy of Marketing Science, 16, 74–94. https://doi.org/10.1007/BF02723327.CrossRef

Cabero-Almenara, J., Fernández-Batanero, J. M., & Barroso-Osuna, J. (2019). Adoption of augmented reality technology by university students. Heliyon, 5(5), e01597. https://doi.org/10.1016/j.heliyon.2019.e01597.CrossRef

Chin, W., & Marcoulides, G. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 8.

Chin, W. W., & Todd, P. A. (1995). On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution. MIS Quarterly, 19(2), 237–246. https://doi.org/10.2307/249690.CrossRef

Chin, W., Marcolin, B., & Newsted, P. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14, 189–217. https://doi.org/10.1287/isre.14.2.189.16018.CrossRef

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (PhD thesis) Massachusetts Institute of Technology, Sloan School of Management, 1–291. http://hdl.handle.net/1721.1/15192.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008.CrossRef

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. https://doi.org/10.1287/mnsc.35.8.982.CrossRef

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x.CrossRef

Elshafey, A., Chai, S., Aminudin, E., Gheisari, M., & Usmani, A. (2020). Technology Acceptance Model for Augmented Reality and Building Information Modeling Integration in the Construction Industry. Electronic Journal of Information Technology in Construction, 25, 161–172. https://doi.org/10.36680/j.itcon.2020.010.CrossRef

Fast, K., Gifford, T., & Yancey, R. (2004). Virtual training for welding. Third IEEE and ACM International Symposium on Mixed and Augmented Reality, Arlington, VA, USA, 2004, (pp. 298–299). https://doi.org/10.1109/ISMAR.2004.65.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312.CrossRef

Garson, G. D. (2016). Partial least squares: Regression and structural equation models. Asheboro: Statistical Associates Publishers.

Guest, W., Wild, F., Vovk, A., Lefrere, P., Klemke, R., Fominykh, M., & Kuula, T. (2018). A technology acceptance model for augmented reality and wearable technologies. Journal of Universal Computer Science, 24(2), 192–219.

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6.CrossRef

Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (1st edn., pp. 1–328). SAGE Publications, Inc.

Haugstvedt, A. C., & Krogstie, J. (2012). Mobile augmented reality for cultural heritage: A technology acceptance study. ISMAR 2012 - 11th IEEE International Symposium on Mixed and Augmented Reality 2012. Science and Technology Papers, pp. 247–255. https://doi.org/10.1109/ISMAR.2012.6402563.

Henseler, J., Ringle, C., & Sinkovics, R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014.CrossRef

Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204 http://www.jstor.org/stable/3094025.CrossRef

Karakus, M., Ersozlu, A., & Clark, A. (2019). Augmented reality research in education: A bibliometric study. Eurasia Journal of Mathematics, Science and Technology Education, 15(10), 1–12. https://doi.org/10.29333/ejmste/103904.

Kim, M. J. (2013). A framework for context immersion in mobile augmented reality. Automation in Construction, 33, 79–85. https://doi.org/10.1016/j.autcon.2012.10.020.CrossRef

Kim, H. C., & Hyun, M. Y. (2016). Predicting the use of smartphone-based augmented reality (AR): Does telepresence really help? Computers in Human Behavior, 59, 28–38. https://doi.org/10.1016/j.chb.2016.01.001.CrossRef

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003.CrossRef

Kompaniets, A., Chemerys, H., & Krasheninnik, I. (2019). Using 3D modelling in design training simulator with augmented reality. Proceedings of the 2nd Student Workshop on Computer Science & Software Engineering (CS&SE@SW2019), Kryvyi Rih, Ukraine, 2546, 213–223.

Lavrentieva, O. O., Arkhypov, I. O., Kuchma, O. I., & Uchitel, A. D. (2020). Use of simulators together with virtual and augmented reality in the system of welders’ vocational training: Past, present, and future. CEUR Workshop Proceedings, 2547, 201–216.

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

Lin, H. F., & Chen, C. H. (2017). Combining the technology acceptance model and uses and gratifications theory to examine the usage behavior of an augmented reality tour-sharing application. Symmetry, 9(7), 113. https://doi.org/10.3390/sym9070113.CrossRef

Lo, J. H., & Lai, Y. F. (2019). The study of using augmented reality technique in Children’s natural ecology learning by technology acceptance model. Proceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI, 2019, 1045–1046. https://doi.org/10.1109/IIAI-AAI.2019.00219.CrossRef

Masrom, M. (2007). Technology acceptance model and E-learning. In 12th International Conference on Education, Sultan Hassanal Bolkiah Institute of Education Universiti Brunei Darussalam (Vol. 21, pp. 1–10).

Okimoto, M. L. L. R., Okimoto, P. C., & Goldbach, C. E. (2015). User experience in augmented reality applied to the welding education. Procedia Manuf, 3, 6223–6227. https://doi.org/10.1016/j.promfg.2015.07.739.CrossRef

Olsson, T., Lagerstam, E., Kärkkäinen, T., & Väänänen-Vainio-Mattila, K. (2013). Expected user experience of mobile augmented reality services: A user study in the context of shopping centres. Personal and Ubiquitous Computing, 17(2), 287–304. https://doi.org/10.1007/s00779-011-0494-x.CrossRef

Pellas, N., & Kazanidis, I. (2019). Developing and assessing augmented reality applications for mathematics with trainee instructional media designers: An exploratory study on user experience. Journal of Universal Computer Science, 25(5), 489–514.

Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31, 623–656. https://doi.org/10.2307/25148814.CrossRef

Rai, A., Lang, S., & Welker, R. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13, 50–69. https://doi.org/10.1287/isre.13.1.50.96.CrossRef

Rauschnabel, P. A., & Ro, Y. K. (2016). Augmented reality smart glasses: An investigation of technology acceptance drivers. International Journal of Technology Marketing, 11(2), 123. https://doi.org/10.1504/ijtmkt.2016.075690.CrossRef

Rese, A., Schreiber, S., & Baier, D. (2014). Technology acceptance modeling of augmented reality at the point of sale: Can surveys be replaced by an analysis of online reviews? Journal of Retailing and Consumer Services, 21(5), 869–876. https://doi.org/10.1016/j.jretconser.2014.02.011.CrossRef

Rese, A., Baier, D., Geyer-Schulz, A., & Schreiber, S. (2017). How augmented reality apps are accepted by consumers: A comparative analysis using scales and opinions. Technological Forecasting and Social Change, 124, 306–319. https://doi.org/10.1016/j.techfore.2016.10.010.CrossRef

Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020.CrossRef

Saeed, K., & Abdinnour, S. (2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & Management, 45, 376–386. https://doi.org/10.1016/j.im.2008.06.002.CrossRef

Scaravetti, D., & Doroszewski, D. (2019). Augmented reality experiment in higher education, for complex system appropriation in mechanical design. Procedia CIRP, 84, 197–202. https://doi.org/10.1016/j.procir.2019.04.284.CrossRef

Silva, R., Ringle, C., Silva, D., & Bido, D. (2014). Structural Equation Modeling with the Smartpls. Revista Brasileira de Marketing, 13, 56–73.CrossRef

Söbke, H., Baalsrud Hauge, J., Stefan, I. A., & Stefan, A. (2019). Using a location-based AR game in environmental engineering. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11863 LNCS, 466–469. https://doi.org/10.1007/978-3-030-34644-7_47.CrossRef

Soldamatic AR welding simulator. (2017). Retrieved August 08, 2020, from https://www.soldamatic.com/soldamatic-2018-new-generation-ar-welding-training-solutions/ (Permission for image reproduction received).

Spreer, P., & Kallweit, K. (2014). Augmented reality in retail: Assessing the acceptance and potential for multimedia product presentation at the PoS. SOP Transactions on Marketing Research, 1(1), 23–31. https://doi.org/10.15764/mr.2014.01002.CrossRef

Torres-Jimenez, E., Rus-Casas, C., Dorado, R., & Jimenez-Torres, M. (2018). Experiences using QR codes for improving the teaching-learning process in industrial engineering subjects. Revista Iberoamericana de Tecnologias Del Aprendizaje, 13(2), 56–62. https://doi.org/10.1109/RITA.2018.2831738.CrossRef

Van Der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly: Management Information Systems, 28(4), 695–704. https://doi.org/10.2307/25148660.CrossRef

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 342–365. https://doi.org/10.1287/isre.11.4.342.11872.CrossRef

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences - DECISION SCI, 39, 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x.CrossRef

Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926.CrossRef

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478. https://doi.org/10.2307/30036540.CrossRef

Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 157–178. https://doi.org/10.2307/41410412.CrossRef

Wang, Y., Anne, A., & Ropp, T. (2016). Applying the technology acceptance model to understand aviation students’ perceptions toward augmented reality maintenance training instruction. International Journal of Aviation, Aeronautics, and Aerospace, 3(4), 1–14. https://doi.org/10.15394/ijaaa.2016.1144.

White, S., Prachyabrued, M., Baghi, D., Aglawe, A., Reiners, D., Borst, C., & Chambers, T. (2009). Virtual Welder Trainer. IEEE Virtual Reality Conference, 2009, 303–303. https://doi.org/10.1109/VR.2009.4811066.CrossRef

White, G., Cabrera, C., Palade, A., & Clarke, S. (2019). Augmented reality in IoT. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11434 LNCS, 149–160. https://doi.org/10.1007/978-3-030-17642-6_13.CrossRef

Wild, F., Klemke, R., Lefrere, P., Fominykh, M., & Kuula, T. (2017). Technology acceptance of augmented reality and wearable technologies. In Communications in Computer and Information Science (Vol. 725, pp. 129–141). Springer. https://doi.org/10.1007/978-3-319-60633-0_11.

Wojciechowski, R., & Cellary, W. (2013). Evaluation of learners’ attitude toward learning in ARIES augmented reality environments. Computers and Education, 68, 570–585. https://doi.org/10.1016/j.compedu.2013.02.014.CrossRef

Wong, K. (2013). Partial least square structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24, 1–32.

Yilmaz, R. M. (2018). Augmented reality trends in education between 2016 and 2017 years. State of the Art Virtual Reality and Augmented Reality Knowhow. https://doi.org/10.5772/intechopen.74943.

Yusoff, R. C. M., Ibrahim, R., Zaman, H. B., & Ahmad, A. (2011). Evaluation of user acceptance of mixed reality technology. Australasian Journal of Educational Technology, 27(8), 1369–1387. https://doi.org/10.14742/ajet.899.CrossRef

Titel: User acceptance of augmented reality welding simulator in engineering training
verfasst von: Christos Papakostas
Christos Troussas
Akrivi Krouska
Cleo Sgouropoulou
Publikationsdatum: 07.01.2021
Verlag: Springer US
Erschienen in: Education and Information Technologies / Ausgabe 1/2022
Print ISSN: 1360-2357
Elektronische ISSN: 1573-7608
DOI: https://doi.org/10.1007/s10639-020-10418-7

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