Abstract
The association between information management and cloud computing has been supported substantially in the existing literature. However, little is known about the role of national culture in this relationship. Accordingly, this study aims to investigate the role of national culture in the relationship between information management and the adoption of cloud computing. A total of 300 Turkish and 349 Malay undergraduate students were recruited for this study. A multi-group structural equation modeling approach is used to investigate cross-cultural differences. The results showed that the relationship between perceived ease of use and usefulness was positively significant for both countries, but this relationship was stronger for Malaysia. Similarly, the relationship between perceived ease of use and behavioral intention was stronger for Malaysia. The results also indicated that perceived usefulness significantly predicts behavioral intention for Turkey, while this relationship was not significant for Malaysia. The results showed that information management impacts perceived usefulness in Turkey, while this relationship was not supported in Malaysia. In conclusion, the findings revealed that national culture plays an essential role in the relationship between information management and the adoption of cloud computing.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T.
AlAjmi, Q., Al-Sharafi, M. A., & Chellathurai, G. J. (2021). Fit-viability approach for e-learning based Cloud computing adoption in higher education institutions: a conceptual model. Recent advances in Technology Acceptance Models and Theories (pp. 331–348). Cham: Springer. https://doi.org/10.1007/978-3-030-64987-6_19.
Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136. https://doi.org/10.2307/3250961.
Al-Emran, M., & Granić, A. (2021). Is it still valid or outdated? A bibliometric analysis of the technology acceptance model and its applications from 2010 to 2020. Recent advances in Technology Acceptance Models and Theories recent advances in technology acceptance models and theories (pp. 1–12). Cham: Springer. https://doi.org/10.1007/978-3-030-64987-6_1.
Al-Emran, M., & Mezhuyev, V. (2019, October). Examining the effect of knowledge management factors on mobile learning adoption through the use of importance-performance map analysis (IPMA). In International conference on advanced intelligent systems and informatics (pp. 449–458). Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_41.
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2020). Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance. Technology in Society, 61, 101247. https://doi.org/10.1016/j.techsoc.2020.101247.
Al-Ramahi, N. M., Odeh, M., Alrabie, Z., & Qozmar, N. (2022). The TOEQCC Framework for sustainable adoption of Cloud Computing at Higher Education Institutions in the Kingdom of Jordan. Sustainability, 14(19), 12744. https://doi.org/10.3390/su141912744.
Al-Sharafi, M. A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N. A., & Arpaci, I. (2022). Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interactive Learning Environments. https://doi.org/10.1080/10494820.2022.2075014.
Alwabel, A. S. A., & Zeng, X. J. (2021). Data-driven modeling of technology acceptance: a machine learning perspective. Expert Systems with Applications, 185, 115584. https://doi.org/10.1016/j.eswa.2021.115584.
Arpaci, I. (2017). Antecedents and consequences of cloud computing adoption in education to achieve knowledge management. Computers in Human Behavior, 70(5), 382–390. https://doi.org/10.1016/j.chb.2017.01.024.
Arpaci, I. (2019). A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. Computers in Human Behavior, 90, 181–187. https://doi.org/10.1016/j.chb.2018.09.005.
Arpaci, I., Al-Emran, & Al-Sharafi, M. A. (2020). The impact of knowledge management practices on the acceptance of massive Open Online Courses (MOOCs) by engineering students: a cross-cultural comparison. Telematics and Informatics, 54, 101468. https://doi.org/10.1016/j.tele.2020.101468.
Asadi, Z., Abdekhoda, M., & Nadrian, H. (2020). Cloud computing services adoption among higher education faculties: development of a standardized questionnaire. Education and Information Technologies, 25(1), 175–191. https://doi.org/10.1007/s10639-019-09932-0.
Atobishi, T., & Podruzsik, S. (2017). Factors Affecting the Decision of Adoption Cloud Computing Technology. In MIC 2017: Managing the Global Economy; Proceedings of the Joint International Conference, Monastier di Treviso, Italy, 24–27 May 2017 (pp. 135–139). University of Primorska Press.
Baishya, K., & Samalia, H. V. (2020). Extending unified theory of acceptance and use of technology with perceived monetary value for smartphone adoption at the bottom of the pyramid. International Journal of Information Management, 51, 102036. https://doi.org/10.1016/j.ijinfomgt.2019.11.004.
Behrend, T. S., Wiebe, E. N., London, J. E., & Johnson, E. C. (2011). Cloud computing adoption and usage in community colleges. Behaviour & Information Technology, 30(2), 231–240. https://doi.org/10.1080/0144929X.2010.489118.
Bhatiasevi, V., & Naglis, M. (2016). Investigating the structural relationship for the determinants of cloud computing adoption in education. Education and Information Technologies, 21(5), 1197–1223. https://doi.org/10.1007/s10639-015-9376-6.
Birkmeyer, S., Wirtz, B. W., & Langer, P. F. (2021). Determinants of mHealth success: an empirical investigation of the user perspective. International Journal of Information Management, 59, 102351. https://doi.org/10.1016/J.IJINFOMGT.2021.102351.
Cormican, K., & O’Sullivan, D. (2003). A collaborative knowledge management tool for product innovation management. International Journal of Technology Management, 26(1), 53–67.
Creswell, J. W. (2005). Educational research: planning conducting and evaluating quantitative and qualitative approaches to research (2nd ed.). Upper Saddle River, NJ: Merrill/Pearson Education.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. https://doi.org/10.2307/249008.
Doukas, C., Pliakas, T., & Maglogiannis, I. (2010, August). Mobile healthcare information management utilizing Cloud Computing and Android OS. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (pp. 1037–1040). IEEE. https://doi.org/10.1109/IEMBS.2010.5628061.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y.
Granić, A. (2022). Educational Technology Adoption: a systematic review. Education and Information Technologies, 27(7), 9725–9744. https://doi.org/10.1007/S10639-022-10951-7/TABLES/2.
Hair, J. F., Tatham, R. L., Anderson, R. E., & Black, W. (2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
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. https://doi.org/10.1007/s11747-014-0403-8.
Hofstede (2021). Retrieved November 29, 2021, from https://www.hofstede-insights.com/country-comparison/malaysia,turkey/
Hussein Alghushami, A., Zakaria, N. H., & Mat Aji, Z. (2020). Factors influencing cloud computing adoption in higher education institutions of least developed countries: evidence from Republic of Yemen. Applied Sciences, 10(22), 8098. https://doi.org/10.3390/app10228098.
Jahangiri, P., Saberi, M. K., & Vakilimofrad, H. (2021). Development and psychometric evaluation of the cloud computing acceptance questionnaire for academic libraries. The Journal of Academic Librarianship, 47(5), 102395. https://doi.org/10.1016/j.acalib.2021.102395.
Karimi-Alaghehband, F., & Rivard, S. (2019). Information technology outsourcing and architecture dynamic capabilities as enablers of organizational agility. Journal of Information Technology, 34(2), 129–159. https://doi.org/10.1177/02683962188162.
Lee, C. P., Lee, G. G., & Lin, H. F. (2007). The role of organizational capabilities in successful e-business implementation. Business Process Management Journal, 13(5), 677–693. https://doi.org/10.1108/14637150710823156.
Lee, J. N. (2001). The impact of knowledge sharing, organizational capability and partnership quality on IS outsourcing success. Information & Management, 38(5), 323–335. https://doi.org/10.1016/S0378-7206(00)00074-4.
Lew, S., Tan, G. W. H., Loh, X. M., Hew, J. J., & Ooi, K. B. (2020). The disruptive mobile wallet in the hospitality industry: an extended mobile technology acceptance model. Technology in Society, 63, 101430. https://doi.org/10.1016/j.techsoc.2020.101430.
Man, S. S., Alabdulkarim, S., Chan, A. H. S., & Zhang, T. (2021). The acceptance of personal protective equipment among Hong Kong construction workers: an integration of technology acceptance model and theory of planned behavior with risk perception and safety climate. Journal of Safety Research, 79, 329–340. https://doi.org/10.1016/j.jsr.2021.09.014.
Mullins, J. K., & Cronan, T. P. (2021). Enterprise systems knowledge, beliefs, and attitude: a model of informed technology acceptance. International Journal of Information Management, 59, 102348. https://doi.org/10.1016/j.ijinfomgt.2021.102348.
Pandey, J., Gupta, M., Behl, A., Pereira, V., Budhwar, P., Varma, A., & Kukreja, P. (2021). Technology-enabled knowledge management for community healthcare workers: the effects of knowledge sharing and knowledge hiding. Journal of Business Research, 135, 787–799. https://doi.org/10.1016/j.jbusres.2021.07.001.
Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: problems and prospects. Journal of Management, 12(4), 531–544. https://doi.org/10.1177/01492063860120040.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879.
Qasem, Y. A., Asadi, S., Abdullah, R., Yah, Y., Atan, R., Al-Sharafi, M. A., & Yassin, A. A. (2020). A multi-analytical approach to predict the determinants of cloud computing adoption in higher education institutions. Applied Sciences, 10(14), 4905. https://doi.org/10.3390/app10144905.
Raj, A. (2022). Retrieved December 11, 2022, from https://techwireasia.com/2022/05/theres-a-new-government-hybrid-cloud-service-in-malaysia/.
Rajak, M., & Shaw, K. (2021). An extension of technology acceptance model for mHealth user adoption. Technology in Society, 67, 101800. https://doi.org/10.1016/j.techsoc.2021.101800.
Rejali, S., Aghabayk, K., Mohammadi, A., & Shiwakoti, N. (2021). Assessing a priori acceptance of shared dockless e-scooters in Iran. Transportation Research Part D: Transport and Environment, 100, 103042. https://doi.org/10.1016/j.trd.2021.103042.
Sabi, H. M., Uzoka, F. M. E., Langmia, K., Njeh, F. N., & Tsuma, C. K. (2018). A cross-country model of contextual factors impacting cloud computing adoption at universities in sub-saharan Africa. Information Systems Frontiers, 20(6), 1381–1404. https://doi.org/10.1007/s10796-017-9739-1.
Sharma, R., Singh, G., & Sharma, S. (2020). Modelling internet banking adoption in Fiji: a developing country perspective. International Journal of Information Management, 53, 102116. https://doi.org/10.1016/j.ijinfomgt.2020.102116.
Sharma, S. K., Al-Badi, A. H., Govindaluri, S. M., & Al-Kharusi, M. H. (2016). Predicting motivators of cloud computing adoption: a developing country perspective. Computers in Human Behavior, 62, 61–69. https://doi.org/10.1016/j.chb.2016.03.073.
Shiau, W. L., & Chau, P. Y. (2016). Understanding behavioral intention to use a cloud computing classroom: a multiple model comparison approach. Information & Management, 53(3), 355–365. https://doi.org/10.1016/j.im.2015.10.004.
Statista (2022). Retrieved December 11, 2022, from https://www.statista.com/outlook/tmo/public-cloud/turkey#revenue.
Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2021). Consumer Acceptance and Use of Information Technology: a Meta-Analytic evaluation of UTAUT2. Information Systems Frontiers, 23(4), 987–1005. https://doi.org/10.1007/S10796-020-10007-6/FIGURES/4.
Tashkandi, A. N., & Al-Jabri, I. M. (2015). Cloud computing adoption by higher education institutions in Saudi Arabia: an exploratory study. Cluster Computing, 18(4), 1527–1537. https://doi.org/10.1007/s10586-015-0490-4.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management science, 46(2), 186–204. https://doi.org/10.1287/MNSC.46.2.186.11926.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 425–478. https://doi.org/10.2307/30036540.
Yuvaraj, M. (2016). Determining factors for the adoption of cloud computing in developing countries: a case study of indian academic libraries. The Bottom Line, 4, 259–272. https://doi.org/10.1108/BL-02-2016-0009.
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Appendix. Constructs and items
Appendix. Constructs and items
Constructs | Items |
---|---|
Information access | IR1. “I use cloud computing to retrieve learning materials and information anytime and anywhere.” |
IR2. “I access course materials and information using cloud computing.” | |
IR3. “I use cloud computing to access learning materials and information resources.” | |
IR4. “Using cloud computing enables me to ubiquitous access to documents and files.” | |
Information storage | IS1. “Using cloud computing enables me to the retrievable storage of electronic information and documents.” |
IS2. “I store course materials and documents using cloud computing.” | |
IS3. “Using cloud computing enables me to store learning materials and information with ubiquitous access.” | |
Information sharing | ISh1. “Using cloud computing enables me to exchange information and documents with classmates.” |
ISh2. “I share course materials and information with classmates using cloud computing.” | |
ISh3. “I share learning materials and documents with classmates using cloud computing.” | |
Information application | IA1: “I apply knowledge and experience gained by using cloud computing to complete learning tasks.” |
IA2. “I use knowledge obtained by using cloud computing in decision making processes.” | |
IA3. “I employ knowledge and intelligence gained from using cloud computing in problem-solving activities.” | |
Perceived ease of use | PEU1: “Learning to use cloud computing would be easy for me.” |
PEU2: “My interaction with cloud computing would be clear and understandable.” | |
PEU3: “It would be easy for me to become skillful at using cloud computing for information management.” | |
PEU4: “I found cloud computing easy to use.” | |
PEU5: “It would be easy for me to manage information using cloud computing.” | |
Perceived usefulness | PU1: “Using cloud computing would improve my academic performance.” |
PU2: “Using cloud computing would increase the efficiency of my studies and work.” | |
PU3: “Using cloud computing would make it easier to manage information.” | |
PU4: “Using cloud computing in information management would increase my productivity.” | |
PU5: “Using cloud computing would enable me to accomplish tasks more quickly.” | |
Behavioral intention | BI1. “I intend to use cloud computing for educational purposes in the future.” |
BI2. “I plan to use cloud computing for personal information management in the future.” | |
BI3. “I predict that I would frequently use cloud computing in the future.” | |
BI4. “I predict that I would frequently use cloud computing for educational purposes.” | |
BI5. “I predict that I would frequently use cloud computing for personal information management.” | |
Actual Behavior | AB1. “I use cloud computing for educational purposes.” |
AB2. “I use cloud computing to create a retrievable archive of personal information.” | |
AB3. “I use cloud computing to exchange information.” | |
AB4. “I use cloud computing for personal information management.” | |
AB5. “I use cloud computing to exchange learning materials.” |
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Arpaci, I., Masrek, M.N., Al-Sharafi, M.A. et al. Evaluating the actual use of cloud computing in higher education through information management factors: a cross-cultural comparison. Educ Inf Technol 28, 12089–12109 (2023). https://doi.org/10.1007/s10639-023-11594-y
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DOI: https://doi.org/10.1007/s10639-023-11594-y