Abstract
This study aims to understand the perception of university teachers on MOOCs and explore the critical drives that impact teachers to work with MOOCs based on an incorporated model of theory of planned behaviour (TPB) and Playbour (PL). Besides, this study also adopts Hofstede’s Cultural Dimensions Theory to include the culture as a moderator to explore how university teachers with different cultural backgrounds perceive MOOCs. The results show that Attitude (ATT), Subjective norms (SN) and Perceived behavioural control (PBC) are crucial determinants that impact teachers’ behavioural intention (BI) towards MOOCs. Besides, PL is found as a strong mediator to explain the great importance of ATT of university teachers to adopt MOOCs. Regarding the moderating effect, the significant difference in Spain and China are detected to explain teachers’ BI towards MOOCs. Additionally, the validity and model fit of the incorporated model are proved, which further enriches the field of TPB to explain teachers’ behaviour towards MOOCs.
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Data availability statements
The datasets generated during and/or analysed during the current study are not publicly available due to privateness but are available from the corresponding author on reasonable request.
References
Ahmed, N., Li, C., Khan, A., Qalati, S. A., Naz, S., & Rana, F. (2021). Purchase intention toward organic food among young consumers using theory of planned behavior: Role of environmental concerns and environmental awareness. Journal of Environmental Planning and Management, 64(5), 796–822.
Ahmmadi, P., Rahimian, M., & Movahed, R. G. (2021). Theory of planned behavior to predict consumer behavior in using products irrigated with purified wastewater in Iran consumer. Journal of Cleaner Production, 296, 126359.
Ajzen, I. (1988). Attitudes, personality and behavior. Open University Press.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
Alegre, J., & Chiva, R. (2013). Linking entrepreneurial orientation and firm performance: The role of organizational learning capability and innovation performance. Journal of Small Business Management, 51(4), 491–507.
Al-Nuaimi, M. N., Bouazza, A., & Abu-Hilal, M. M. (2020). Parameters of ICT-associated deviant behaviour among Omani undergraduates: A socio-psychological perspective. Global Knowledge, Memory and Communication, 70(3), 225–253.
Askeroth, J. H., & Richardson, J. C. (2019). Instructor perceptions of quality learning in MOOCs they teach. Online Learning, 23(4), 135–159.
Barbrook, R., & Cameron, A. (1996). The californian ideology. Science as culture, 6(1), 44–72.
Beugelsdijk, S., & Welzel, C. (2018). Dimensions and dynamics of national culture: Synthesizing Hofstede with Inglehart. Journal of Cross-Cultural Psychology, 49(10), 1469–1505.
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.
Beugelsdijk, S., Kostova, T., & Roth, K. (2017). An overview of Hofstede-inspired country-level culture research in international business since 2006. Journal of International Business Studies, 48(1), 30–47.
Bhaskar, P., Joshi, A., Dayalan, P., & Vinay, M. (2022). Investigating the barriers and motivators to MOOCs adoption: A qualitative analysis of teacher’s perspective. International Journal of Knowledge and Learning, 15(2), 120–147.
Bissessar, C. (2018). An application of Hofstede’s cultural dimension among female educational leaders. Education Sciences, 8(2), 77.
Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6:149.
Cai, S., Long, X., Li, L., Liang, H., Wang, Q., & Ding, X. (2019). Determinants of intention and behavior of low carbon commuting through bicycle-sharing in China. Journal of Cleaner Production, 212, 602–609.
Carranza, R., Díaz, E., Martín-Consuegra, D., & Fernández-Ferrín, P. (2020). PLS–SEM in business promotion strategies. A multigroup analysis of mobile coupon users using MICOM. Industrial Management & Data Systems, 120(12), 2349–2374.
Chan, M. M., Plata, R. B., Medina, J. A., Alario-Hoyos, C., Rizzardini, R. H., & de la Roca, M. (2018). Analysis of behavioral intention to use cloud-based tools in a MOOC: A technology acceptance model approach. J UCS, 24(8), 1072–1089.
Chen, B., Fan, Y., Zhang, G., Liu, M., & Wang, Q. (2020). Teachers’ networked professional learning with MOOCs. PLoS ONE, 15(7), e0235170.
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.
Chung, P. K., Zhang, C. Q., Liu, J. D., Chan, D. K. C., Si, G., & Hagger, M. S. (2018). The process by which perceived autonomy support predicts motivation, intention, and behavior for seasonal influenza prevention in Hong Kong older adults. BMC Public Health, 18(1), 65.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
Cronbach, L. J., & Thorndike, R. L. (1971). Educational measurement. Test validation, Washington, D.C.: American Council on Education. 443–507.
Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.
Ferrer-Conill, R. (2018). Playbour and the gamification of work: Empowerment, exploitation and fun as labour dynamics. In Technologies of labour and the politics of contradiction Palgrave Macmillan, Cham pp. 193–210.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior. Wiley.
Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equations models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Fuchs, M. (2015). Total gamification. In M. Fuchs (Ed.), Diversity of play (pp. 7–20). Meson Press.
Geisser, S. (1975). A predictive approach to the random effect model. Biometrika, 61(1), 101–107.
Goel, P., Raj, S., Garg, A., Singh, S., & Gupta, S. (2022). Peeping in the minds of MOOCs instructors: using fuzzy approach to understand the motivational factors. Online Information Review, Vol. ahead-of-print No. ahead-of-print.
Goggin, J. (2011). Playbour, farming and leisure. Ephemera: Theory & Politics in Organization, 11(4), 357-368.
Gómez-Ramirez, I., Valencia-Arias, A., & Duque, L. (2019). Approach to M-learning acceptance among university students: An integrated model of TPB and TAM. International Review of Research in Open and Distributed Learning, 20(3), 142–164.
Grant, A., & Dacin, P. A. (2019). Understanding co-creation through a playbour lens. In R. Bagchi, L. Block, & L. Lee (Eds.), NA - advances in consumer research (Vol. 47, pp. 297–303). Association for Consumer Research.
Guritno, D. C., Kurniawan, M. L. A., Mangkunegara, I., & Samudro, B. R. (2020). Is there any relation between Hofstede’s cultural dimensions and corruption in developing countries?. Journal of Financial Crime, 28(1), 204–13.
Habibi, A., Razak, R. A., Yusop, F. D., Muhaimin, M., Asrial, A., Mukminin, A., & Jamila, A. (2022). Exploring the factors affecting pre-service science teachers’ actual use of technology during teaching practice. South African Journal of Education, 42(1), 1–11.
Han, H., Hsu, L. T. J., & Sheu, C. (2010). Application of the theory of planned behavior to green hotel choice: Testing the effect of environmental friendly activities. Tourism Management, 31(3), 325–334.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Emerald Group Publishing Limited.
Hernández, R., Gütl, C., Amado-Salvatierra, H.R. (2014). Cloud Learning Activities Orchestration for MOOC Environments. In: Uden, L., Sinclair, J., Tao, YH., Liberona, D. (eds) Learning Technology for Education in Cloud. MOOC and Big Data. LTEC 2014. Communications in Computer and Information Science, Springer, Cham. 446, 25–36.
Hofstede, G. (1980). Culture’s consequences: International differences in work-related values. Sage Publications.
Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Sage publications.
Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1), 2307–919.
Hossain, R., Hasan Mahmud, S.M., Hossin, M.A., Bhuiyan, T., Hua, Y.X. (2019). Effects of cognitive ability, trust and time-saving: Predicting further amelioration and successive usage of E-ticketing with TAM, TPB and cognitive frameworks. In: Fong, S., Akashe, S., Mahalle, P. (eds) Information and communication technology for competitive strategies. Lecture Notes in Networks and Systems, vol 40. Springer, Singapore.
Hou, M., Lin, Y., Shen, Y., & Zhou, H. (2022). Explaining pre-service teachers’ intentions to use technology-enabled learning: An extended model of the theory of planned behavior. Frontiers in Psychology, 13:900806.
Howcroft, D., & Bergvall-Kåreborn, B. (2019). A typology of crowdwork platforms. Work, Employment and Society, 33(1), 21–38.
Huang, S. S., & Crotts, J. (2019). Relationships between Hofstede’s cultural dimensions and tourist satisfaction: A cross-country cross-sample examination. Tourism Management, 72, 232–241.
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.
Huizinga, Johan (1938/1955), Homo Ludens; A Study of the PlayElement in Culture. Beacon Press.
Ifinedo, P. (2011). Internet/e-business technologies acceptance in Canada’s SMEs: An exploratory investigation. Internet Research, 21(3), 255–281.
Jansen, R. S., van Leeuwen, A., Janssen, J., Conijn, R., & Kester, L. (2020). Supporting learners’ self-regulated learning in Massive Open Online Courses. Computers & Education, 146, 103771.
Khan, I. U., Hameed, Z., Yu, Y., Islam, T., Sheikh, Z., & Khan, S. U. (2018). Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory. Telematics and Informatics, 35(4), 964–978.
Khlif, H. (2016). Hofstede’s cultural dimensions in accounting research: a review. Meditari Accountancy Research, 24 (4), pp. 545–573.
Kianpour, K., Jusoh, A., Mardani, A., Streimikiene, D., Cavallaro, F., Md Nor, K., & Zavadskas, E. K. (2017). Factors influencing consumers’ intention to return the end of life electronic products through reverse supply chain management for reuse, repair and recycling. Sustainability, 9, 1–23.
Kirkman, B. L., Lowe, K. B., & Gibson, C. B. (2006). A quarter century of culture’s consequences: A review of empirical research incorporating Hofstede’s cultural values framework. Journal of International Business Studies, 37(3), 285–320.
Kuo, T. M., Tsai, C. C., & Wang, J. C. (2021). Linking web-based learning self-efficacy and learning engagement in MOOCs: The role of online academic hardiness. The Internet and Higher Education, 51, 100819.
Kong, X., Liu, N. J., & Zhang, M. H. (2021). Analysis of online teaching data before and after the COVID-19 epidemic. Journal of Tsinghua University (Science and Technology)., 02, 104–116. https://doi.org/10.16511/j.cnki.qhdxxb.2020.21.017
Kovanović, V., Joksimović, S., Poquet, O., Hennis, T., de Vries, P., Hatala, M., Shane Dawson, George Siemens, & Gašević, D. (2019). Examining communities of inquiry in Massive Open Online Courses: The role of study strategies. The Internet and Higher Education, 40, 20–43.
Koukopoulos, Z., Koutromanos, G., Koukopoulos, D., & Gialamas, V. (2020). Factors influencing student and in-service teachers’ satisfaction and intention to use a user-participatory cultural heritage platform. Journal of Computers in Education, 7(3), 333–371.
Kumar, A. (2019). Exploring young adults’e-waste recycling behaviour using an extended theory of planned behaviour model: A cross-cultural study. Resources, Conservation and Recycling, 141, 378–389.
Lai, H. M., Hsiao, Y. L., & Hsieh, P. J. (2018). The role of motivation, ability, and opportunity in university teachers’ continuance use intention for flipped teaching. Computers & Education, 124, 37–50.
Lamm, A. J., & Lamm, K. W. (2019). Using non-probability sampling methods in agricultural and extension education research. Journal of International Agricultural and Extension Education, 26(1), 52–59.
Leonard, L. N., Cronan, T. P., & Kreie, J. (2004). What influences IT ethical behavior intentions—planned behavior, reasoned action, perceived importance, or individual characteristics? Information & Management, 42(1), 143–158.
Lim, H. R., & An, S. (2021). Intention to purchase wellbeing food among Korean consumers: An application of the Theory of Planned Behavior. Food Quality and Preference, 88, 104101.
Liu, Q., Xu, N., Jiang, H., Wang, S., Wang, W., & Wang, J. (2020). Psychological driving mechanism of safety citizenship behaviors of construction workers: Application of the theory of planned behavior and norm activation model. Journal of Construction Engineering and Management, 146(4), 04020027.
Lizin, S., Van Dael, M., & Van Passel, S. (2017). Battery pack recycling: Behaviour change interventions derived from an integrative theory of planned behaviour study. Resources, Conservation and Recycling, 122, 66–82.
Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008-2012. International Review of Research in Open and Distributed Learning, 14(3), 202–227.
Lopes, J. R. N., de Araújo Kalid, R., Rodríguez, J. L. M., & Ávila Filho, S. (2019). A new model for assessing industrial worker behavior regarding energy saving considering the theory of planned behavior, norm activation model and human reliability. Resources, Conservation and Recycling, 145, 268–278.
Lund, A. (2015). A contribution to a critique of the concept playbour. In Reconsidering value and labour in the digital age. Palgrave Macmillan, London pp. 63–79.
Lung-Guang, N. (2019). Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model. Computers & Education, 134, 50–62.
Malik, S., Taqi, M., Martins, J. M., Mata, M. N., Pereira, J. M., & Abreu, A. (2021). Exploring the relationship between communication and success of construction projects: The mediating role of conflict. Sustainability, 13(8), 4513.
Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review, 98(2), 224–253.
Matikainen, J. T. (2015). Motivations for content generation in social media. Participations: Journal of Audience and Reception Studies, 12(1), 41–58.
McBride, M., Carter, L., & Phillips, B. (2020). Integrating the theory of planned behavior and behavioral attitudes to explore texting among young drivers in the US. International Journal of Information Management, 50, 365–374.
Meet, R. K., Kala, D., & Al-Adwan, A. S. (2022). Exploring factors affecting the adoption of MOOC in Generation Z using extended UTAUT2 model. Education and Information Technologies, 27, 1–23.
Menon, A., Bharadwaj, S. G., & Howell, R. (1996). The quality and effectiveness of marketing strategy: Effects of functional and dysfunctional conflict in intraorganizational relationships. Journal of the Academy of Marketing Science, 24(4), 299.
Min, L., & Bin, G. (2022). Online teaching research in universities based on blockchain. Education and Information Technologies, 27, 1–24.
Moreno-Marcos, P. M., Alario-Hoyos, C., Muñoz-Merino, P. J., & Kloos, C. D. (2018). Prediction in MOOCs: A review and future research directions. IEEE Transactions on Learning Technologies, 12(3), 384–401.
Muthen, B., & Christoffersson, A. (1981). Simultaneous factor analysis of dichotomous variables in several groups. Psychometrika, 46(4), 407–419.
Najafi, H., Rolheiser, C., Harrison, L., & Håklev, S. (2015). University of Toronto instructors’ experiences with developing MOOCs. International Review of Research in Open and Distributed Learning, 16(3), 233–255.
Ng, S. I., & Lim, X. J. (2019). Are Hofstede’s and Schwartz’s values frameworks equally predictive across contexts? Revista Brasileira de Gestão de Negócios, 21(1), 33–47.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. McGraw-Hill.
Pan, Y., Huang, Y., Kim, H., & Zheng, X. (2021). Factors influencing students’ intention to adopt online interactive behaviors: merging the theory of planned behavior with cognitive and motivational factors. The Asia-Pacific Education Researcher, 1–10.
Rialp-Criado, A., & Rialp-Criado, J. (2018). Examining the impact of managerial involvement with social media on exporting firm performance. International Business Review, 27(2), 355–366.
Rojo, J., Everett, B., Ramjan, L. M., Hunt, L., & Salamonson, Y. (2020). Hofstede’s cultural dimensions as the explanatory framework for performance issues during clinical placement: A mixed methods study. Nurse Education Today, 94, 104581.
Ru, X., Qin, H., & Wang, S. (2019). Young people’s behaviour intentions towards reducing PM2. 5 in China: Extending the theory of planned behaviour. Resources, Conservation and Recycling, 141, 99–108.
Salas-Rueda, R. A., Castañeda-Martínez, R., Eslava-Cervantes, A. L., & Alvarado-Zamorano, C. (2022). Teachers’ perception about MOOCs and ICT during the COVID-19 pandemic. Contemporary Educational Technology, 14(1), ep343.
Scherer, R., Tondeur, J., Siddiq, F., & Baran, E. (2018). The importance of attitudes toward technology for pre-service teachers’ technological, pedagogical, and content knowledge: Comparing structural equation modeling approaches. Computers in Human Behavior, 80, 67–80.
Scholz, T. (Ed.). (2012). Digital labor: The Internet as playground and factory. Routledge.
Shao, F., Frederick, D. J., Haggard, D. L., Haggard, K. S., & Pace, G. R. (2020). Industrial actions and Hofstede’s cultural dimensions. Business Management Dynamics, 9(7), 1.
Si, H., Shi, J. G., Tang, D., Wu, G., & Lan, J. (2020). Understanding intention and behavior toward sustainable usage of bike sharing by extending the theory of planned behavior. Resources, Conservation and Recycling, 152, 104513.
Sinkovics, R. R., Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review. 33(3), 405–431.
Srite, M. (2006). Culture as an explanation of technology acceptance differences: An empirical investigation of Chinese and US users. Australasian Journal of Information Systems, 14(1), 5–26.
Steenkamp, J. E. M. (2001). The role of national culture in international marketing research. International Marketing Review, 18(1), 30–44.
Tama, R. A. Z., Ying, L., Yu, M., Hoque, M. M., Adnan, K. M., & Sarker, S. A. (2021). Assessing farmers’ intention towards conservation agriculture by using the Extended Theory of Planned Behavior. Journal of Environmental Management, 280, 111654.
Tang, L., & Horta, H. (2021). Women academics in Chinese universities: A historical perspective. Higher Education, 82(5), 865–895.
Taras, V., Steel, P., & Kirkman, B. L. (2012). Improving national cultural indices using a longitudinal meta-analysis of Hofstede’s dimensions. Journal of World Business, 47(3), 329–341.
Tarhini, A., Hone, K., & Liu, X. (2015a). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46(4), 739–755.
Tarhini, A., Scott, M., Sharma, S., & Abbasi, M. S. (2015b). Differences in intention to use educational RSS feeds between Lebanese and British students: A multi-group analysis based on the technology acceptance model. Electronic Journal of E-Learning, 13(1), 14–29.
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440.
Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: a Chinese perspective. Interactive Learning Environments, 27(4), 530–546.
Teo, T., & Dai, H. M. (2019). The role of time in the acceptance of MOOCs among Chinese university students. Interactive Learning Environments, 30(4), 651–664.
Thompson, P. (1989). The nature of work. Palgrave Macmillan.
Törhönen, M., Hassan, L., Sjöblom, M., & Hamari, J. (2019). Play, playbour or labour? The relationships between perception of occupational activity and outcomes among streamers and YouTubers. Proceedings of the 52nd Hawaii International Conference on System Sciences, 2558–2567.
Trung Pham, Q., Minh Dang, N., & Trung Nguyen, D. (2020). Factors affecting on the digital piracy behavior: An empirical study in Vietnam. Journal of Theoretical and Applied Electronic Commerce Research, 15(2), 122–135.
Tseng, T. H., Lin, S., Wang, Y. S., & Liu, H. X. (2022). Investigating teachers’ adoption of MOOCs: The perspective of UTAUT2. Interactive Learning Environments, 30(4), 635–650.
Van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and Communication, 1(1), 2–14.
Verma, V. K., & Chandra, B. (2018). An application of theory of planned behavior to predict young Indian consumers’ green hotel visit intention. Journal of Cleaner Production, 172, 1152–1162.
Villasenor Alva, J. A., & Estrada, E. G. (2009). A generalization of Shapiro–Wilk’s test for multivariate normality. Communications in Statistics—Theory and Methods, 38(11), 1870–1883.
Virani, S. R., Saini, J. R., & Sharma, S. (2020). Adoption of massive open online courses (MOOCs) for blended learning: The Indian educators’ perspective. Interactive Learning Environments, 1–17.
Vlachou, V., Tselios, D., & Aspridis, G. (2020). Studying ICT teachers’ experiences and perceptions of MOOCs. International Journal of Technology Enhanced Learning, 12(3), 275–289.
Vollero, A., Siano, A., Palazzo, M., & Amabile, S. (2020). Hoftsede’s cultural dimensions and corporate social responsibility in online communication: Are they independent constructs? Corporate Social Responsibility and Environmental Management, 27(1), 53–64.
Wang, Y., Dong, C., & Zhang, X. (2020). Improving MOOC learning performance in China: An analysis of factors from the TAM and TPB. Computer Applications in Engineering Education, 28(6), 1421–1433.
Wong, B.T.-m. (2016). Factors leading to effective teaching of MOOCs. Asian Association of Open Universities Journal, 11(1), 105–118.
Wong, T. K. M., Man, S. S., & Chan, A. H. S. (2021). Exploring the acceptance of PPE by construction workers: An extension of the technology acceptance model with safety management practices and safety consciousness. Safety Science, 139, 105239.
Wu, B. (2019). Research on the influencing factors of college teachers’ teaching input behavior——based on the perspective of planned behavior theory. Journal of Yangzhou University (Higher Education Research Edition), 23(02), 46–51.
Xing, W., Tang, H., & Pei, B. (2019). Beyond positive and negative emotions: Looking into the role of achievement emotions in discussion forums of MOOCs. The Internet and Higher Education, 43, 100690.
Yang, H. H., & Su, C. H. (2017). Learner behaviour in a MOOC practice-oriented course: In empirical study integrating TAM and TPB. International Review of Research in Open and Distributed Learning, 18(5), 35–63.
Yıldırım, B. (2020). MOOCs in STEM education: Teacher preparation and views. Technology, Knowledge and Learning, 27, 1–26.
Yusop, F. D., Habibi, A., & Razak, R. A. (2021). Factors affecting Indonesian preservice teachers’ use of ICT during teaching practices through theory of planned behavior. SAGE Open, 11(2), 21582440211027572.
Zaremohzzabieh, Z., Roslan, S., Mohamad, Z., Ismail, I. A., Ab Jalil, H., & Ahrari, S. (2022). Influencing factors in MOOCs adoption in higher education: a meta-analytic path analysis. Sustainability, 14(14), 8268.
Zhang, L., Yang, X., Fan, Y., & Zhang, J. (2021). Utilizing the theory of planned behavior to predict willingness to pay for urban heat island effect mitigation. Building and Environment, 204, 108136.
Zhao, L., Ao, Y., Wang, Y., & Wang, T. (2022). Impact of home-based learning experience during COVID-19 on future intentions to study online: A Chinese University Perspective. Frontiers in Psychology, 13: 862965.
Zhou, M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computers & Education, 92, 194–203.
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Wang, K., Van Hemmen, S.F. & Criado, J.R. “Play” or “Labour”, the perception of university teachers towards MOOCs: Moderating role of culture. Educ Inf Technol 28, 7737–7762 (2023). https://doi.org/10.1007/s10639-022-11502-w
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DOI: https://doi.org/10.1007/s10639-022-11502-w