Factors influencing student and in-service teachers’ satisfaction and intention to use a user-participatory cultural heritage platform

Abstract

Nowadays, ICT is widely used by teachers in the teaching of cultural heritage. Although many researchers have studied different factors which affect ICT use in education, there is little research examining the factors that influence educators’ satisfaction and intention to use a specific cultural ICT system in their teaching. This study proposes a model to identify these factors, which can explain in-service and student teachers’ intention to use and satisfaction with Culture Gate—a User-Participatory Cultural Heritage Platform in educational environments. The proposed model was based on variables of TAM, TPB and IS success models. Data were obtained from 309 in-service and student teachers, and it was tested against the research model using the PLS approach of Structural Equation Modeling. The results mainly revealed that in-service and student teachers’ intention to use and satisfaction with a User-Participatory Cultural Heritage Platform can be explained to a substantial extent by their perceptions of the quality value (i.e. educational, technical, content and information quality) of the platform. The results have significant practical and theoretical implications for educators in terms of design and usage of a participatory cultural heritage platform for educational purposes.

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Data availability

The authors declare the all data and material are available.

References

  1. Ajzen, I. (1988). Attitudes, personality, and behaviour. Milton-Keynes: Open University Press.

    Google Scholar 

  2. Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes,50(2), 179–211.

    Google Scholar 

  3. Ajzen, I. (2006). Constructing a TpB questionnaire: Conceptual and methodological considerations. Retrieved January 30, 2020, from https://www-unix.oit.umass.edu/~aizen/pdf/tpb.measurement.pdf.

  4. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. England Cliffs: Prentice Hall Inc.

    Google Scholar 

  5. Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior,102, 67–86.

    Google Scholar 

  6. Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education,80, 28–38.

    Google Scholar 

  7. Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the path to e-learning success. Computers in Human Behavior,66, 388–399.

    Google Scholar 

  8. Athanassoula-Reppa, A., & Koutouzis, M. (2002). Women in managerial positions in Greek education: Evidence of inequality. Education Policy Analysis Archives,10, 11.

    Google Scholar 

  9. Balaban, Ι., Mu, Ε., & Divjak, Β. (2013). Development of an electronic Portfolio system success model: An information systems approach. Computers & Education,60(1), 396–411.

    Google Scholar 

  10. Barclay, D. W., Higgins, C. A., & Thompson, R. (1995). The partial least squares approach to causal modeling: Personal computer adoption and use as illustration. Technology Studies,2(2), 285–309.

    Google Scholar 

  11. Barghi, R., Zakaria, Z., Hamzah, A., & Hashim, N. H. (2017). Heritage education in the primary school standard curriculum of Malaysia. Teaching and Teacher Education,61, 124–131.

    Google Scholar 

  12. Caballé, S., Daradoumis, T., Xhafa, F., & Conesa, J. (2010). Enhancing knowledge management in online collaborative learning. International Journal of Software Engineering and Knowledge Engineering,20(4), 485–497.

    Google Scholar 

  13. Chang, C.-C., Hung, S.-W., Cheng, M.-J., & Wu, C.-Y. (2015). Exploring the intention to continue using social networking sites: The case of Facebook. Technological Forecasting & Social Change,95, 48–56.

    Google Scholar 

  14. Chang, C.-T., Hajiyev, J., & Su, C.-R. (2017). Examining the students’ behavioral intention to use E-learning in Azerbaijan? The general extended technology acceptance model for E-learning approach. Computers & Education,111, 128–143.

    Google Scholar 

  15. Chen Hsieh, J. S., Huang, Y.-M., & Wu, W.-C. V. (2017). Technological acceptance of LINE in flipped EFL oral training. Computers in Human Behavior,70, 178–190.

    Google Scholar 

  16. Chen, S.-C., Yen, D. C., & Hwang, M. I. (2012). Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers in Human Behavior,28, 933–941.

    Google Scholar 

  17. Chen, X., & Choi, J. H. (2010). Designing online collaborative location-aware platform for history learning. Journal of Educational Technology Development and Exchange,3(1), 13–26.

    Google Scholar 

  18. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education,63, 160–175.

    Google Scholar 

  19. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  20. Chow, M., Herold, D. K., Choo, T.-M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education,59, 1136–1144.

    Google Scholar 

  21. Chu, T.-H., & Chen, Y.-Y. (2016). With Good We Become Good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education,92–93, 37–52.

    Google Scholar 

  22. Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  23. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika,16(3), 297–334.

    Google Scholar 

  24. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly,13(3), 319–340.

    Google Scholar 

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

    Google Scholar 

  26. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research,3(1), 60–95.

    Google Scholar 

  27. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems,19(4), 9–30.

    Google Scholar 

  28. Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association,82(397), 171–185.

    Google Scholar 

  29. Estriegana, R., Medina-Merodio, J. A., & Barchino, R. (2019). Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model. Computers & Education,135, 1–14.

    Google Scholar 

  30. EuroGender (2018). Women in decision-making in the field of education in Greece (May 2018). E-bulletin. General Secretariat for Gender Equality—Greek Ministry of Interior. Retrieved January 30, 2020, from https://eurogender.eige.europa.eu/posts/women-decision-making-field-education-greece-may-2018.

  31. Fisher, R. A. (1935). The design of experiments. New York: Hafner.

    Google Scholar 

  32. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research,18(1), 39–50.

    Google Scholar 

  33. Galani, A., & Karatza, A. (2019). European identity and european heritage: A critical approach of Greek, British and Spanish geography textbooks. American Journal of Educational Research,7(12), 966–975.

    Google Scholar 

  34. Gefen, D., Rigdon, E., & Straub, D. (2011). EDITOR’S COMMENTS: An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly,35(2), iii–xiv.

    Google Scholar 

  35. Geisser, S. (1974). A predictive approach to the random effects model. Biometrika,61(1), 101–107.

    Google Scholar 

  36. Goodarzparvari, P., & Camejo, F. C. B. (2018). Preservation of cultural heritage via education of children, utilizing visual communication: Persepolis as a case study. Creative Education,9, 141–151.

    Google Scholar 

  37. Hadjerrouit, S. (2005). Constructivism as guiding philosophy for software engineering education. ACM SIGSE Bulletin,37(4), 45–49.

    Google Scholar 

  38. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  39. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice,19(2), 139–151.

    Google Scholar 

  40. Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications,39, 10959–10966.

    Google Scholar 

  41. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., et al. (2014). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods,17(2), 182–209.

    Google Scholar 

  42. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science,43(1), 115–135.

    Google Scholar 

  43. Ho, L.-H., Hung, C.-L., & Chen, H.-C. (2013). Using theoretical models to examine the acceptance behavior of mobile phone messaging to enhance parent–teacher interactions. Computers & Education,65, 105–114.

    Google Scholar 

  44. Ho, T. K.-L., & Lin, H.-S. (2015). A web-based painting tool for enhancing student attitudes toward learning art creation. Computers & Education,89, 32–41.

    Google Scholar 

  45. Hoburg, J. F., & Davis, J. L. (1983). A student-oriented finite element program for electrostatic potential problems. IEEE Transactions on Education,26(4), 138–142.

    Google Scholar 

  46. Hong, J.-C., Hwang, M.-Y., Hsu, H.-F., Wong, W.-T., & Chen, M.-Y. (2011). Applying the technology acceptance model in a study of the factors affecting usage of the Taiwan digital archives system. Computers & Education,57(3), 2086–2094.

    Google Scholar 

  47. 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.

    Google Scholar 

  48. Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal computing acceptance factors on small firms: A structural equation model. MIS Quarterly,21(3), 279–305.

    Google Scholar 

  49. Jeng, Y.-L., Wu, T.-T., Huang, Y.-M., Tan, Q., & Yang, S. J. H. (2010). The add-on impact of mobile applications in learning strategies: A review study. Educational Technology & Society,13(3), 3–11.

    Google Scholar 

  50. Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education,122, 260–272.

    Google Scholar 

  51. Kim, K., Trimi, S., Park, H., & Rhee, S. (2012). The impact of CMS quality on the outcomes of e-learning systems in higher education: An empirical study. Decision Sciences Journal of Innovative Education,10(4), 575–587.

    Google Scholar 

  52. Korkmaz, S., Goksuluk, D., & Zararsiz, G. (2014). MVN: An R package for assessing multivariate normality. The R Journal,6(2), 151–162.

    Google Scholar 

  53. Koukopoulos, Z., & Koukopoulos, D. (2017). Integrating educational theories into a feasible digital environment. Applied Computing and Informatics,15(1), 19–26.

    Google Scholar 

  54. Koukopoulos, Z., Koukopoulos, D., & Jung, J. J. (2017). A trustworthy multimedia participatory platform for cultural heritage management in smart city environments. Multimedia Tools and Application,76(24), 25943–25981.

    Google Scholar 

  55. Koutromanos, G., Styliaras, G., & Christodoulou, S. (2015). Student and in-service teachers’ acceptance of modern hypermedia in their teaching: The case of Hypersea. Education and Information Technologies,20(3), 559–578.

    Google Scholar 

  56. Kwok, D., & Yang, S. (2017). Evaluating the intention to use ICT collaborative tools in a social constructivist environment. International Journal of Educational Technology in Higher Education,14, 32. https://doi.org/10.1186/s41239-017-0070-1.

    Article  Google Scholar 

  57. Laverde, A. C., Cifuentes, Y. S., & Rodriguez, H. Y. R. (2007). Toward an instructional design model based on learning objects. Educational Technology Research and Development,55(6), 671–681.

    Google Scholar 

  58. Lay, J.-G., Chi, Y.-L., Hsieh, Y.-S., & Chen, Y.-W. (2013). What influences geography teachers’ usage of geographic information systems? A structural equation analysis. Computers & Education,62, 191–195.

    Google Scholar 

  59. Lin, T., & Chen, C. (2012). Validating the satisfaction and continuance intention of e-learning systems: Combining TAM and IS success models. International Journal of Distance Education Technologies (IJDET),10(1), 44–54.

    Google Scholar 

  60. Madirov, E., & Absalyamova, S. (2015). The influence of information technologies on the availability of cultural heritage. Procedia—Social and Behavioral Sciences,188, 255–258.

    Google Scholar 

  61. Măduţa, C. (2014). Education and National Identity. The local cultural heritage and its effects upon present local educational policies in Arad County from Romania. Procedia—Social and Behavioral Sciences,116, 2847–2851.

    Google Scholar 

  62. Mahalanobis, P. C. (1936). On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India,2(1), 49–55.

    Google Scholar 

  63. Malegiannaki, I., & Daradoumis, T. (2017). Analyzing the educational design, use and effect of spatial games for cultural heritage: A literature review. Computers & Education,10, 1–10.

    Google Scholar 

  64. Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika,57(3), 519–530.

    Google Scholar 

  65. Mayer, R. E., & Moreno, R. (2002). Aids to computer-based multimedia learning. Learning and Instruction,12(1), 107–119.

    Google Scholar 

  66. Melero, J., Hernández-Leo, D., & Manatunga, K. (2015). Group-based mobile learning: Do group size and sharing mobile devices matter? Computers in Human Behavior,44, 377–385.

    Google Scholar 

  67. Merhi, Μ. (2015). Factors influencing higher education students to adopt podcast: An empirical study. Computers & Education,83, 32–43.

    Google Scholar 

  68. Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers & Education,45, 359–374.

    Google Scholar 

  69. Monahan, T., McArdle, G., & Bertolotto, M. (2008). Virtual reality for collaborative e-learning. Computers & Education,50(4), 1339–1353.

    Google Scholar 

  70. Nikou, S. A., & Economides, A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education,109, 56–73.

    Google Scholar 

  71. Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.

    Google Scholar 

  72. Osuna, C. A., & Dimitriadis, Y. A. (1999). A framework for the development of educational-collaborative applications based on social constructivism. In Proceedings of the String Processing and Information Retrieval Symposium and International Workshop on Groupware (pp. 254–261). Cancun, Mexico: IEEE Press.

  73. Ott, M., & Pozzi, F. (2011). Towards a new era for Cultural Heritage Education: Discussing the role of ICT. Computers in Human Behavior,27(4), 1365–1371.

    Google Scholar 

  74. Ott, M., Dagnino, F. M., & Pozzi, F. (2015). Intangible Cultural Heritage: Towards collaborative planning of educational interventions. Computers in Human Behavior,51(B), 1314–1319.

    Google Scholar 

  75. Papanastasiou, E. (2016). Gender and Leadership in Greek Primary Education. PhD thesis. London Metropolitan University. Retrieved January 30, 2020, from https://pdfs.semanticscholar.org/f603/cbf7ea31d645e8e0eb908c875f0074c7620a.pdf.

  76. Petter, S., & McLean, E. R. (2009). A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level. Information & Management,46, 159–166.

    Google Scholar 

  77. Preston, C., Cox, M., & Cox, K. (2000). Teachers as innovators. London: Miranda Net.

    Google Scholar 

  78. Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education. https://doi.org/10.1016/j.compedu.2019.103732.

    Article  Google Scholar 

  79. Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement,21(2), 173–184.

    Google Scholar 

  80. Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing,26(4), 332–344.

    Google Scholar 

  81. Revelle, W. (2017). psych: Procedures for Personality and Psychological Research. Computer software manual (R package version 1.7.8). Retrieved April 30, 2020, from https://CRAN.R-project.org/package=psych.

  82. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Computer software. Retrieved April 30, 2020, from https://www.smartpls.com.

  83. Rizvic, S., Boskovic, D., Okanovic, V., Sljivo, S., & Zukic, M. (2019). Interactive digital storytelling: Bringing cultural heritage in a classroom. Journal of Computers in Education,6(1), 143–166.

    Google Scholar 

  84. Romero, C., Ventura, S., Zafra, A., & de Bra, P. (2009). Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems. Computers & Education,53(3), 828–840.

    Google Scholar 

  85. Ronen, M., Kohen-Vacs, D., & Raz-Fogel, N. (2006). Adopt & adapt: Structuring, sharing and reusing asynchronous collaborative pedagogy. In Proceedings of the 7th International Conference on Learning Sciences (pp. 599–605). Bloomington, USA: International Society of the Learning Sciences.

  86. Sanchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior,72, 644–654.

    Google Scholar 

  87. Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior,55(A), 519–528.

    Google Scholar 

  88. Sawang, S., Sun, Y., & Salim, S. A. (2014). It’s not only what I think but what they think! The moderating effect of social norms. Computers & Education,76, 182–189.

    Google Scholar 

  89. Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education,128, 13–35.

    Google Scholar 

  90. Shee, D. Y., & Wang, Y.-S. (2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education,50(3), 894–905.

    Google Scholar 

  91. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society,36(2), 111–147.

    Google Scholar 

  92. Su, A. Y. S., Yang, S. J. H., Hwang, W.-Y., & Zhang, J. (2010). A web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments. Computers & Education,55(2), 752–766.

    Google Scholar 

  93. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research,6, 144–176.

    Google Scholar 

  94. Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education,52(1), 302–312.

    Google Scholar 

  95. Teo, T. (2011). Factors influencing teachers' intention to use technology: Model development and test. Computers & Education,57(4), 2432–2440.

    Google Scholar 

  96. Teo, T., & Lee, C. B. (2010). Explaining the intention to use technology among student teachers: An application of the Theory of Planned Behavior (TPB). Campus-Wide Information Systems,27(2), 60–67.

    Google Scholar 

  97. Teo, T., & Tan, L. (2012). The theory of planned behavior (TPB) and pre-service teachers' technology acceptance: A validation study using structural equation modeling. Journal of Technology and Teacher Education,20(1), 89–104.

    Google Scholar 

  98. Un Jan, A., & Contreras, V. (2016). Success model for knowledge management systems used by doctoral researchers. Computers in Human Behavior,59, 258–264.

    Google Scholar 

  99. Uotila, K., Huvila, I., & Paalassalo, J.-P. (2010). Learning, access and moBility in cultural heritage education: Developments, lessons and findings from the project. In Proceedings of the 38th Conference on Computer Applications and Quantitative Methods in Archaeology (pp. 423–426). Granada, Spain.

  100. Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application,11(2), 5–40.

    Google Scholar 

  101. Valtonen, T., Kukkonen, J., Kontkanen, S., Sormunen, K., & Dillon, P. (2015). The impact of authentic learning experiences with ICT on pre-service teachers’ intentions to use ICT for teaching and learning. Computers and Education,81, 49–58.

    Google Scholar 

  102. Vavoula, G., Sharples, M., Rudman, P., Meek, J., & Lonsdale, P. (2009). Myartspace: Design and evaluation of support for learning with multimedia phones between classrooms and museums. Computers & Education,53(2), 286–299.

    Google Scholar 

  103. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences,39(2), 273–315.

    Google Scholar 

  104. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science,46(2), 186–204.

    Google Scholar 

  105. 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.

    Google Scholar 

  106. Virvou, M., & Alepis, E. (2005). Mobile educational features in authoring tools for personalised tutoring. Computers & Education,44(1), 53–68.

    Google Scholar 

  107. Wang, H. C., & Chiu, Y. F. (2011). Assessing e-learning 2.0 system success. Computers & Education,57, 1790–1800.

    Google Scholar 

  108. Wang, Q. (2009). Design and evaluation of a collaborative learning environment. Computers & Education,53(4), 1138–1146.

    Google Scholar 

  109. Wang, Y. S. (2008). Assessing e-commerce systems success: a respecification and validation of the DeLone and McLean model of IS success. Information Systems Journal,18, 529–557.

    Google Scholar 

  110. Wang, Y. S., Tseng, T. H., Wang, W. T., Shih, Y. W., & Chan, P. Y. (2019). Developing and validating a mobile catering app success model. International Journal of Hospitality Management,77, 19–30.

    Google Scholar 

  111. Wheeler, S., Yeomans, P., & Wheeler, D. (2008). The good, the bad and the wiki: Evaluating student-generated content for collaborative learning. British Journal of Educational Technology,39(6), 987–995.

    Google Scholar 

  112. Wong, K.-T., Osman, R.-B. T., Goh, P. S. C., & Rahmat, M. K. (2013). Understanding student teachers’ behavioural intention to use technology: Technology acceptance model (TAM) validation and testing. International Journal of Instruction,6(1), 89–104.

    Google Scholar 

  113. Xu, Z., Yin, Z., & Saddik, A. E. (2003). A Web services oriented framework for dynamic e-learning systems. In Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (pp. 943–946). Montreal, Canada: IEEE Press.

  114. Yang, Y., & Xiangling, W. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers & Education,133, 116–126.

    Google Scholar 

  115. Zaied, A. N. H. (2012). An integrated success model for evaluating information system in public sectors. Journal of Emerging Trends in Computing and Information Sciences,3(1), 2079–8407.

    Google Scholar 

  116. Zhou, M. (2016). Chinese university students' acceptance of MOOCs: A self-determination perspective. Computers & Education,92–93, 194–203.

    Google Scholar 

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Acknowledgements

This research was fully conducted by Z. Koukopoulos, G. Koutromanos, D. Koukopoulos and V. Gialamas.

Funding

This research received no external funding.

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Contributions

Conceptualization, ZK, GK, DK and VG; methodology, ZK, GK, DK and VG; formal analysis, ZK, GK, DK and VG; investigation, ZK, GK, DK and VG; resources, ZK, GK, DK and VG; data curation, ZK, GK, DK and VG; writing-original draft preparation, ZK, GK, DK and VG; writing-review and editing, ZK, GK, DK and VG; visualization, ZK, GK, DK and VG; supervision GK.

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Correspondence to Dimitrios Koukopoulos.

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Appendix: Construct measures

Appendix: Construct measures

Educational quality

  • EdQ_1. Culture Gate provides collaborative learning.

  • EdQ_2. Culture Gate provides required facilities such as chat and forum.

  • EdQ_3. Culture Gate provides possibility of communicating with other students.

  • EdQ_4. Culture Gate provides possibility of learning evaluation.

  • EdQ_5. Culture Gate is appropriate with my learning style.

Technical system quality

  • TchQ_1. Culture Gate is aesthetically satisfying.

  • TchQ_2. Culture Gate optimizes response time.

  • TchQ_3. Culture Gate is user friendly.

  • TchQ_4. Culture Gate provides interactive features between users and system.

  • TchQ_5. Culture Gate possesses structured design.

  • TchQ_6. Culture Gate has flexible features.

  • TchQ_7. Culture Gate has attractive features.

  • TchQ_8. Culture Gate is reliable.

  • TchQ_9. Culture Gate is secure.

Content and Information quality

  • CntQ_1. Culture Gate provides information that is relevant to my needs.

  • CntQ_2. Culture Gate provides comprehensive information.

  • CntQ_3. Culture Gate provides information that is exactly what I want.

  • CntQ_4. Culture Gate provides me with organized content and information.

  • CntQ_5. Culture Gate provides up to date content and information.

  • CntQ_6. Culture Gate provides required content and information.

Perceived ease of use

  • EoU_1. Culture Gate is easy to use.

  • EoU_2. Culture Gate is easy to learn.

  • EoU_3. Culture Gate is easy to access.

  • EoU_4. Culture Gate is easy to understand.

  • EoU_5. Culture Gate is convenient.

Perceived usefulness

  • Usfl_1. Culture Gate helps to save time.

  • Usfl_2. Culture Gate helps to save cost.

  • Usfl_3. Culture Gate helps me to be self-reliable.

  • Usfl_4. Culture Gate helps to improve my knowledge.

  • Usfl_5. Culture Gate helps to improve my performance.

  • Usfl_6. Culture Gate is effective.

  • Usfl_7. Culture Gate is efficient.

Subjective norm

  • SbjN_1. My colleagues believe that I should use Culture Gate in class.

  • SbjN_2. My students/friends believe that I should use Culture Gate in class.

  • SbjN_3. People cooperating with me at school/at university believe that I should use Culture Gate in class.

Perceived behavioural control

  • BhC_1. The required conditions for the use of Culture Gate are available to me.

  • BhC_2. I have the necessary knowledge to use Culture Gate.

  • BhC_3. I think that my colleagues or the administration team of Culture Gate can help me in case I face a problem with its use.

Satisfaction

  • Stsf_1. Culture Gate is enjoyable.

  • Stsf_2. I am pleased enough with the participatory platform Culture Gate.

  • Stsf_3. Culture Gate satisfies my educational needs.

  • Stsf_4. I am satisfied with Culture Gate performance.

  • Stsf_5. Culture Gate is pleasant to me.

  • Stsf_6. Culture Gate gives me self-confidence.

Intention

  • Int_1. I tend to use Culture Gate in class.

  • Int_2. I believe that the use of Culture Gate is available.

  • Int_3. I am likely to use Culture Gate in class in the near future.

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Koukopoulos, Z., Koutromanos, G., Koukopoulos, D. et al. Factors influencing student and in-service teachers’ satisfaction and intention to use a user-participatory cultural heritage platform. J. Comput. Educ. 7, 333–371 (2020). https://doi.org/10.1007/s40692-020-00159-4

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Keywords

  • ICT in cultural heritage education
  • Participatory platforms
  • Technology acceptance
  • Teachers