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A SEM-STELLA approach for predicting decision-makers’ adoption of cloud computing data center

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Abstract

Cloud computing is the next generation of on-demand information technology services and products that deliver various applications over the Internet. Cloud computing is often adopted as a superior alternative by data centers to replace their current system. However, cloud computing services are still accompanied by many issues which hinder their adoption in data centers. Therefore, this study proposed a Cloud Computing Data Center (CCDC) adoption model for administration activities in higher education institutions. Technology Organization Environment (TOE), Diffusion of Innovation theory (DOI), and Institutional theory were considered as theoretical bases of CCDC model. A new Structural Equation Modelling (SEM)-STELLA method was applied to examine the proposed model and simulate it like a real system to investigate the respondents' interest in adopting cloud by passing the time. A questionnaire instrument was designed, and data were collected from 204 decision-makers at Malaysian universities. The results showed that eight out of ten factors, namely relative advantage, Complexity, compatibility, top management support, policy and standardization, competitive pressure, outage, and security influenced CCDC adoption. Finally, STELLA simulated the value changing of some factors or sub factors on the level of interest in adopting CCDC. Results showed that security and policy play the highest influence on the adoption of cloud computing. This research contributes to a theoretical understanding of factors that influence CCDC adoption. Meanwhile, it provides a better understanding of changes in users' behavior during the adoption of cloud computing services.

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References

  • Abba Ari, A. A., Ngangmo, O. K., Titouna, C., Thiare, O., Kolyang, Mohamadou, A., & Gueroui, A. M. (2020). Enabling privacy and security in Cloud of Things: Architecture, applications, security & privacy challenges. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2019.11.005

  • Abidin, S. S. Z., & Husin, M. H. (2018). Improving accessibility and security on document management system: A Malaysian case study. Applied Computing and Informatics, 16(1/2), 137–154. https://doi.org/10.1016/j.aci.2018.04.002

  • Abied, O., & Ibrahim, O. (2021). Cloud service adoption model in the Libyan e-government implementation. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (pp. 1–7). IEEE.

  • Adedokun, A. (2021). The impact of Cloud computing on IT security skills and roles: A case study Doctoral dissertation. Auckland University of Technology.

    Google Scholar 

  • Ahani, A., Rahim, N. Z. A., & Nilashi, M. (2017). Forecasting social CRM adoption in SMEs: A combined SEM-neural network method. Computers in Human Behavior, 75, 560–578.

    Google Scholar 

  • Ahmad, S. Z., Abu Bakar, A. R., & Ahmad, N. (2019). Social media adoption and its impact on firm performance: the case of the UAE. International Journal of Entrepreneurial Behavior & Research, 25(1), 84–111. https://doi.org/10.1108/IJEBR-08-2017-0299

  • Al Hadwer, A., Tavana, M., Gillis, D., & Rezania, D. (2021). A systematic review of organizational factors impacting cloud-based technology adoption using Technology-organization-environment framework. Internet of Things, 15, 100407.

    Google Scholar 

  • Al Rawajbeh, M., Al Hadid, I., Aqaba, J., & Al-Zoubi, H. (2019). Adoption of cloud computing in higher education sector: An overview. Indian Journal of Science and Technology, 5(1), 23–29.

    Google Scholar 

  • Alam, K. A., Ahmed, R., Butt, F. S., Kim, S.-G., & Ko, K.-M. (2018). An uncertainty-aware integrated fuzzy AHP-WASPAS model to evaluate public cloud computing services. Procedia Computer Science, 130, 504–509.

    Google Scholar 

  • Alashhab, Z. R., Anbar, M., Singh, M. M., Leau, Y.-B., Al-Sai, Z. A., & Alhayja’a, S. A. (2021). Impact of coronavirus pandemic crisis on technologies and cloud computing applications. Journal of Electronic Science and Technology, 19(1), 100059.

    Google Scholar 

  • Aleixo, A. M., Azeiteiro, U. M., & Leal, S. (2020). Are the sustainable development goals being implemented in the Portuguese higher education formative offer?. International Journal of Sustainability in Higher Education, 21(2), 336–352. https://doi.org/10.1108/IJSHE-04-2019-0150

  • Ali, M. (2018). The barriers and enablers of the educational cloud: A doctoral student perspective. Open Journal of Business and Management, 7(1), 1–24.

    Google Scholar 

  • Ali, O., & Soar, J. (2018). Technology innovation adoption theories. In Technology Adoption and Social Issues: Concepts, Methodologies, Tools, and Applications (pp. 821–860). IGI Global. https://doi.org/10.4018/978-1-5225-5201-7.ch037

  • Ali, M. B., Wood-Harper, T., & Mohamad, M. (2018). Benefits and challenges of cloud computing adoption and usage in higher education: A systematic literature review. International Journal of Enterprise Information Systems (IJEIS), 14(4), 64–77.

    Google Scholar 

  • Ali, O., Shrestha, A., Osmanaj, V., & Muhammed, S. (2020). Cloud computing technology adoption: an evaluation of key factors in local governments. Information Technology & People, 34(2), 666–703. https://doi.org/10.1108/ITP-03-2019-0119

  • Al-Issa, Y., Ottom, M. A., & Tamrawi, A. (2019). eHealth cloud security challenges: a survey. Journal of healthcare engineering, 2019. https://doi.org/10.1155/2019/7516035

  • Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25(6), 5261–5280.

    Google Scholar 

  • Aman, A. H. M., Yadegaridehkordi, E., Attarbashi, Z. S., Hassan, R., & Park, Y.-J. (2020). A survey on trend and classification of internet of things reviews. IEEE Access, 8, 111763–111782.

    Google Scholar 

  • Anderson, R. E. (2019). A History of the Coolidge High School Band: Building a Rural Program through Community Engagement and Stakeholder Support, 1935–1980. Arizona State University.

    Google Scholar 

  • Anderson, S. F. (2020). Using prior information to plan appropriately powered regression studies: A tutorial using BUCSS. Psychological Methods, 26(5), 513.

  • Aremu, A. Y., Shahzad, A., & Hassan, S. (2021). The empirical evidence of enterprise resource planning system adoption and implementation on firm’s performance among medium-sized enterprises. Global Business Review, 22(6), 1375–1404.

    Google Scholar 

  • Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., . . . Stoica, I. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.

  • 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.

    Google Scholar 

  • Asadi, S., Nilashi, M., Samad, S., Abdullah, R., Mahmoud, M., Alkinani, M. H., & Yadegaridehkordi, E. (2021a). Factors impacting consumers’ intention toward adoption of electric vehicles in Malaysia. Journal of Cleaner Production, 282, 124474.

    Google Scholar 

  • Asadi, S., Nilashi, M., Samad, S., Rupani, P. F., Kamyab, H., & Abdullah, R. (2021b). A proposed adoption model for green IT in manufacturing industries. Journal of Cleaner Production, 297, 126629.

    Google Scholar 

  • Atieh, A. T. (2021). The next generation cloud technologies: A review on distributed cloud, fog and edge computing and their opportunities and challenges. ResearchBerg Review of Science and Technology, 1(1), 1–15.

    Google Scholar 

  • Awa, H. O., Ukoha, O., & Emecheta, B. C. (2016). Using TOE theoretical framework to study the adoption of ERP solution. Cogent Business & Management, 3(1), 1196571.

    Google Scholar 

  • Awa, H. O., Ukoha, O., & Igwe, S. R. (2017). Revisiting technology-organization-environment (T-O-E) theory for enriched applicability. The Bottom Line, 30(01), 2–22. https://doi.org/10.1108/BL-12-2016-0044

  • Ayong, K. T., & Naidoo, R. (2019). Modeling the adoption of cloud computing to assess South African SMEs: An integrated perspective. In Proceedings of 4th International Conference on the Internet, Cyber Security and Information Systems 2019 (Vol. 12, pp. 43–56). Kalpa Publications in Computing.

  • Azarnik, A., & Shayan, J. (2012). Associated risks of cloud computing for SMEs. Open International Journal of Informatics (OIJI), 1(1), 37–45.

    Google Scholar 

  • Badie, N., Hussin, A. R. C., & Lashkari, A. H. (2015). Cloud computing data center adoption factors validity by fuzzy AHP. International Journal of Computational Intelligence Systems, 8(5), 854–873.

    Google Scholar 

  • Badie, N., & Yadegaridehkordi, E. (2013). The policy as repudiation factors of adopting cloud computing in university administration. Journal of Information Systems Research and Innovation (JISRI), 54–63. 

  • Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2021). A model for decision-makers’ adoption of big data in the education sector. Sustainability, 13(24), 13995.

    Google Scholar 

  • Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173.

    Google Scholar 

  • Basu, S., Bardhan, A., Gupta, K., Saha, P., Pal, M., Bose, M., . . . Sarkar, P. (2018). Cloud computing security challenges & solutions-A survey. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC),

  • Begemann, M. J., Thompson, I. A., Veling, W., Gangadin, S. S., Geraets, C. N., van‘t Hag, E., . . . Van Der Gaag, M. (2020). To continue or not to continue? Antipsychotic medication maintenance versus dose-reduction/discontinuation in first episode psychosis: HAMLETT, a pragmatic multicenter single-blind randomized controlled trial. Trials, 21(1), 1-19.

  • Bellini, E., Iraqi, Y., & Damiani, E. (2020). Blockchain-based distributed trust and reputation management systems: A survey. IEEE Access, 8, 21127–21151.

    Google Scholar 

  • Belzunegui-Eraso, A., & Erro-Garcés, A. (2020). Teleworking in the context of the Covid-19 crisis. Sustainability, 12(9), 3662.

    Google Scholar 

  • Benlian, A., & Hess, T. (2011). Opportunities and risks of software-as-a-service: Findings from a survey of IT executives. Decision Support Systems, 52(1), 232–246.

    Google Scholar 

  • Berthevas, J.-F. (2021). How protection motivation and social bond factors influence information security behavior. Systemes D’information Management, 26(2), 77–115.

    Google Scholar 

  • Bertrand, A., Maxwell, W., & Vamparys, X. (2021). Do AI-based anti-money laundering (AML) systems violate European fundamental rights? International Data Privacy Law, 11(3), 276–293. https://doi.org/10.1093/idpl/ipab010

  • Beshdeleh, M., Real Angel, A., & Sinless Bolour, L. (2018). EBET agency requested to review all pervious study based on DOI theory and TOE framework. International Journal of Innovation Technology Research, 101(20), 15–19.

  • Beshdeleh, M., Real Angel, A., & Sinless Bolour, L. (2020). Adoption of EBET Agency's Cloud Casino Software by using TOE and DOI Theory as a Solution for Gambling Website. Maxwell Beshdeleh et al. Adoption of EBET Agency's Cloud Casino Software by using TOE and DOI Theory as a Solution for Gambling Website, Journal of Innovation and Business Research, 116, 100–119.

  • Bhushan, B., Sahoo, C., Sinha, P., & Khamparia, A. (2021). Unification of Blockchain and Internet of Things (BIoT): Requirements, working model, challenges and future directions. Wireless Networks, 27(1), 55–90.

    Google Scholar 

  • Bisong, A., & Rahman, M. (2011). An overview of the security concerns in enterprise cloud computing. arXiv preprint arXiv:1101.5613.

  • Borgman, H. P., Bahli, B., Heier, H., & Schewski, F. (2013). Cloudrise: exploring cloud computing adoption and governance with the TOE framework. 2013 46th Hawaii international conference on system sciences,

  • Brailsford, S. (2014). Theoretical comparison of discrete-event simulation and system dynamics. In S. Brailsford, L. Churilov, & B. Dangerfiled (Eds.), Discrete-Event Simulation and System Dynamics for Management Decision Making (pp. 105-124). Wiley.

  • Bramante, J., Frank, R., & Dolan, J. (2010). IBM 2000 to 2010: Continuously transforming the corporation while delivering performance. Strategy & Leadership (2010).

  • Brous, P., Janssen, M., & Herder, P. (2020). The dual effects of the Internet of Things (IoT): A systematic review of the benefits and risks of IoT adoption by organizations. International Journal of Information Management, 51, 101952.

    Google Scholar 

  • Brutschin, E., Cherp, A., & Jewell, J. (2021). Failing the formative phase: The global diffusion of nuclear power is limited by national markets. Energy Research & Social Science, 80, 102221.

    Google Scholar 

  • Buyya, R., Broberg, J., & Goscinski, A. M. (2010). Cloud computing: Principles and paradigms. Wiley.

    Google Scholar 

  • Cai, C., &, Chen C. (2021). Optimization of human resource file information decision support system based on cloud computing. Complexity, 2021, 12. https://doi.org/10.1155/2021/8919625

    Article  Google Scholar 

  • Caiado, R. G. G., Scavarda, L. F., Gavião, L. O., Ivson, P., de MattosNascimento, D. L., & Garza-Reyes, J. A. (2021). A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management. International Journal of Production Economics, 231, 107883.

    Google Scholar 

  • Canali, C., Chiaraviglio, L., Lancellotti, R., & Shojafar, M. (2018). Joint minimization of the energy costs from computing, data transmission, and migrations in cloud data centers. IEEE Transactions on Green Communications and Networking, 2(2), 580–595.

    Google Scholar 

  • Chang, H.-H., & Chou, H.-W. (2011). Drivers and effects of enterprise resource planning post-implementation learning. Behaviour & Information Technology, 30(2), 251–259.

    Google Scholar 

  • Chembessi, C., Beaurain, C., & Cloutier, G. (2022). Analyzing Technical and Organizational Changes in Circular Economy (CE) Implementation with a TOE Framework: Insights from a CE Project of Kamouraska (Quebec). Circular Economy and Sustainability. https://doi.org/10.1007/s43615-021-00140-y

    Article  Google Scholar 

  • Chen, C.-J., & Hung, S.-W. (2010). To give or to receive? Factors influencing members’ knowledge sharing and community promotion in professional virtual communities. Information & Management, 47(4), 226–236.

    Google Scholar 

  • Chen, H., Li, L., & Chen, Y. (2021). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36–68.

    MathSciNet  Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

    Google Scholar 

  • Chin, W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 120(12), 2161–2209. https://doi.org/10.1108/IMDS-10-2019-0529

  • Choi, J.-J., Robb, C. A., Mifli, M., & Zainuddin, Z. (2021). University students’ perception to online class delivery methods during the COVID-19 pandemic: A focus on hospitality education in Korea and Malaysia. Journal of Hospitality, Leisure, Sport & Tourism Education, 29, 100336.

    Google Scholar 

  • Chung, M. K., Louis, G. M. B., Kannan, K., & Patel, C. J. (2019). Exposome-wide association study of semen quality: Systematic discovery of endocrine disrupting chemical biomarkers in fertility require large sample sizes. Environment International, 125, 505–514.

    Google Scholar 

  • Chutipong, K., & Hitoshi, M. (2012). Cloud computing adoption and determining factors in different industries: A case study of Thailand. In 19th Biennial Conference of the International Telecommunications Society (ITS): "Moving forward with future technologies: opening a platform for all", Bangkok, Thailand, 18th-21th November 2012. International Telecommunications Society (ITS) Calgary.

    Google Scholar 

  • Cobb, C., Sudar, S., Reiter, N., Anderson, R., Roesner, F., & Kohno, T. (2018). Computer security for data collection technologies. Development Engineering, 3, 1–11.

    Google Scholar 

  • Darban, M., & Polites, G. L. (2020). Why is it hard to fight herding? The roles of user and technology attributes. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 51(4), 93–122.

    Google Scholar 

  • Deem, R. (2020). New managerialism in higher education. The International Encyclopedia of Higher Education Systems and Institutions. https://doi.org/10.1007/978-94-017-8905-9

  • Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277.

    Google Scholar 

  • DiMaggio, P., & Powell, W. W. (1983). The iron cage revisited: Collective rationality and institutional isomorphism in organizational fields. American Sociological Review, 48(2), 147–160.

    Google Scholar 

  • Dimitrova, D. (2020). Rethinking the body in South Asian traditions. Routledge.

    Google Scholar 

  • Do Chung, B., Jeon, H., & Seo, K. K. (2014). A framework of cloud service quality evaluation system-focusing on security quality evaluation. International Journal of Software Engineering and Its Applications, 8(4), 41–46.

    Google Scholar 

  • Domini, G., Grazzi, M., Moschella, D., & Treibich, T. (2021). Threats and opportunities in the digital era: Automation spikes and employment dynamics. Research Policy, 50(7), 104137.

    Google Scholar 

  • Dong, X., Yu, J., Luo, Y., Chen, Y., Xue, G., & Li, M. (2014). Achieving an effective, scalable and privacy-preserving data sharing service in cloud computing. Computers & Security, 42, 151–164.

    Google Scholar 

  • Ekufu, T. K. (2012). Predicting cloud computing technology adoption by organizations: An empirical integration of technology acceptance model and theory of planned behavior. Capella University.

    Google Scholar 

  • Farahnak, L. R., Ehrhart, M. G., Torres, E. M., & Aarons, G. A. (2020). The influence of transformational leadership and leader attitudes on subordinate attitudes and implementation success. Journal of Leadership & Organizational Studies, 27(1), 98–111.

    Google Scholar 

  • Farahzadi, A., Shams, P., Rezazadeh, J., & Farahbakhsh, R. (2018). Middleware technologies for cloud of things: A survey. Digital Communications and Networks, 4(3), 176–188.

    Google Scholar 

  • Feuerlicht, G., & Margaris, N. (2012). Cloud adoption: A comparative study. In WSEAS International Conference on Cloud Computing. WSEAS Press.

    Google Scholar 

  • Fisher, R. A. (1992). Statistical Methods for Research Workers. In Kotz, S., & Johnson, N. L. (eds), Breakthroughs in Statistics. Springer Series in Statistics. Springer. https://doi.org/10.1007/978-1-4612-4380-9_6

  • Fornell, C., & Bookstein, F. L. (1982). A comparative analysis of two structural equation models: LISREL and PLS applied to market data. In C. Fornell (Ed.), A second generation of multivariate analysis (pp. 289–324). Praeger.

  • Fritsch, M., Sorgner, A., Wyrwich, M., & Zazdravnykh, E. (2019). Historical shocks and persistence of economic activity: Evidence on self-employment from a unique natural experiment. Regional Studies, 53(6), 790–802.

    Google Scholar 

  • Galiveeti, S., Tawalbeh, L. a., Tawalbeh, M., & El-Latif, A. A. A. (2021). Cybersecurity analysis: Investigating the data integrity and privacy in AWS and azure cloud platforms. In Artificial Intelligence and Blockchain for Future Cybersecurity Applications (pp. 329–360). Springer.

  • Garay, L., Font, X., & Corrons, A. (2019). Sustainability-oriented innovation in tourism: An analysis based on the decomposed theory of planned behavior. Journal of Travel Research, 58(4), 622–636.

    Google Scholar 

  • Gaubert, C. (2018). Firm sorting and agglomeration. American Economic Review, 108(11), 3117–3153.

    Google Scholar 

  • Gaur, B., Shukla, V. K., & Verma, A. (2019). Strengthening people analytics through wearable IOT device for real-time data collection. 2019 international conference on automation, computational and technology management (ICACTM),

  • Gettman, D. (2019). Raising awareness of artificial intelligence for transportation systems management and operations (No. FHWA-HOP-19-052). United States. Federal Highway Administration. Office of Operations.

  • Goldberg, S. B., Riordan, K. M., Sun, S., & Davidson, R. J. (2022). The empirical status of mindfulness-based interventions: A systematic review of 44 meta-analyses of randomized controlled trials. Perspectives on Psychological Science, 17(1), 108–130.

    Google Scholar 

  • Götz, O., Liehr-Gobbers, K., & Krafft, M. (2010). Evaluation of structural equation models using the partial least squares (PLS) approach. In Handbook of partial least squares (pp. 691–711). Springer.

  • Greenwood, R., Raynard, M., Kodeih, F., Micelotta, E. R., & Lounsbury, M. (2011). Institutional complexity and organizational responses. The Academy of Management Annals, 5(1), 317–371.

    Google Scholar 

  • Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861–874.

    Google Scholar 

  • Gupta, N., Sharma, N., & Sood, S. (2022). Empirical Analysis on Parameters for Adoption of Cloud-Based e-learning in Indian Higher Education System: A User’s Perspective. In Information and Communication Technology for Competitive Strategies (ICTCS 2020) (pp. 977–991). Springer.

  • 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.

    Google Scholar 

  • Hair, J., Hult, G. T., Ringle, C., & Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (1st ed.). SAGE Publication.

    MATH  Google Scholar 

  • Hanafiah, M. H. (2020). Formative vs. reflective measurement model: Guidelines for structural equation modeling research. International Journal of Analysis and Applications, 18(5), 876–889.

    Google Scholar 

  • Hansch, G. (2020). Automating security risk and requirements management for cyber-physical systems (Doctoral dissertation). Georg-August-Universität Göttingen.

    Google Scholar 

  • Hanus, B., Windsor, J. C., & Wu, Y. (2018). Definition and multidimensionality of security awareness: close encounters of the second order. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 49(SI), 103–133.

    Google Scholar 

  • Hassani, H., & Silva, E. S. (2018). Big Data: A big opportunity for the petroleum and petrochemical industry. OPEC Energy Review, 42(1), 74–89.

    Google Scholar 

  • Helali, L., & Omri, M. N. (2021). A survey of data center consolidation in cloud computing systems. Computer Science Review, 39, 100366.

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(1), 277–319.

    Google Scholar 

  • Hong, W., & Zhu, K. (2006). Migrating to internet-based e-commerce: Factors affecting e-commerce adoption and migration at the firm level. Information & Management, 43(2), 204–221.

    Google Scholar 

  • Hoover, S. J. (2003). IT professionals’ response to adoption and implementation of innovations in the workplace: Incorporating accessibility features into information technology for end users with disabilities. University of Minnesota.

    Google Scholar 

  • Houser, J. (2007). How many are enough? Statistical power analysis and sample size estimation in clinical research. Journal of Clinical Research Best Practices, 3(3), 1–5.

    Google Scholar 

  • Hsu, P.-F., Ray, S., & Li-Hsieh, Y.-Y. (2014). Examining cloud computing adoption intention, pricing mechanism, and deployment model. International Journal of Information Management, 34(4), 474–488.

    Google Scholar 

  • Hu, S., Hsu, C., & Zhou, Z. (2021). The impact of SETA event attributes on employees’ security-related Intentions: An event system theory perspective. Computers & Security, 109, 102404.

    Google Scholar 

  • Hung, Y.-H. (2019). Investigating how the cloud computing transforms the development of industries. IEEE Access, 7, 181505–181517.

    Google Scholar 

  • Hurwitz, J. S., & Kirsch, D. (2020). Cloud computing for dummies. Wiley.

    Google Scholar 

  • Ibrahim, H., Aburukba, R. O., & El-Fakih, K. (2018). An integer linear programming model and adaptive genetic algorithm approach to minimize energy consumption of cloud computing data centers. Computers & Electrical Engineering, 67, 551–565.

    Google Scholar 

  • Ilyas, S., Hu, Z., & Wiwattanakornwong, K. (2020). Unleashing the role of top management and government support in green supply chain management and sustainable development goals. Environmental Science and Pollution Research, 27(8), 8210–8223.

    Google Scholar 

  • Iranmanesh, M., Zailani, S., Hyun, S. S., Ali, M. H., & Kim, K. (2019). Impact of lean manufacturing practices on firms’ sustainable performance: Lean culture as a moderator. Sustainability, 11(4), 1112.

    Google Scholar 

  • Ireland, R. D. (2012). Management research and managerial practice: A complex and controversial relationship. Academy of Management Learning & Education, 11(2), 263–271.

    Google Scholar 

  • Jaeger, P. T., Lin, J., & Grimes, J. M. (2008). Cloud computing and information policy: Computing in a policy cloud? Journal of Information Technology & Politics, 5(3), 269–283.

    Google Scholar 

  • Jansen, W., & Grance, T. (2011). Guidelines on security and privacy in public cloud computing. NIST Special Publication 800. http://csrc.nist.gov/publications/nistpubs/800-144/SP800-144.pdf

  • Jones, P., Maas, G., Kraus, S., & Lloyd Reason, L. (2021). An exploration of the role and contribution of entrepreneurship centres in UK higher education institutions. Journal of Small Business and Enterprise Development, 28(2), 205–228. https://doi.org/10.1108/JSBED-08-2018-0244

  • Judd, C. M., Yzerbyt, V. Y., & Muller, D. (2014). Mediation and moderation. Handbook of Research Methods in Social and Personality Psychology, 2, 653–676.

    Google Scholar 

  • Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47(4), 742–770. https://doi.org/10.1108/K-02-2017-0045

  • Khajeh-Hosseini, A., Greenwood, D., Smith, J. W., & Sommerville, I. (2012). The cloud adoption toolkit: supporting cloud adoption decisions in the enterprise. Software: Practice and Experience, 42(4), 447–465.

    Google Scholar 

  • Khan, H. U., & Alhusseini, A. (2015). Optimized web design in the Saudi culture. In 2015 Science and Information Conference (SAI) (pp. 906–915). IEEE. https://doi.org/10.1109/SAI.2015.7237250

  • Khayer, A., Talukder, M. S., Bao, Y., & Hossain, M. N. (2020). Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach. Technology in Society, 60, 101225.

    Google Scholar 

  • Kiely, P., Busby, A., Nikiphorou, E., Sullivan, K., Walsh, D., Creamer, P., . . . Young, A. (2019). Is incident rheumatoid arthritis interstitial lung disease associated with methotrexate treatment? Results from a multivariate analysis in the ERAS and ERAN inception cohorts.BMJ open, 9(5), e028466.

  • Kim, W., Kim, S. D., Lee, E., & Lee, S. (2009). Adoption issues for cloud computing. Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia,

  • Kopp, B., Lange, F., & Steinke, A. (2021). The reliability of the Wisconsin card sorting test in clinical practice. Assessment, 28(1), 248–263.

    Google Scholar 

  • Le, P. B., & Lei, H. (2019). Determinants of innovation capability: the roles of transformational leadership, knowledge sharing and perceived organizational support. Journal of Knowledge Management, 23(3), 527–547. https://doi.org/10.1108/JKM-09-2018-0568

  • Leavitt, N. (2009). Is cloud computing really ready for prime time. Growth, 27(5), 15–20.

    Google Scholar 

  • Lee, O. K., Wang, M., Lim, K. H., & Peng, Z. (2009). Knowledge management systems diffusion in Chinese enterprises: A multistage approach using the technology-organization environment framework. Journal of Global Information Management, 17(1), 70–84.

    Google Scholar 

  • Liang, Y., Qi, G., Wei, K., & Chen, J. (2017). Exploring the determinant and influence mechanism of e-Government cloud adoption in government agencies in China. Government Information Quarterly, 34(3), 481–495.

    Google Scholar 

  • Lin, C.-S. (2006). Organizational, technological, and environmental determinants of electronic commerce adoption in small and medium enterprises in Taiwan. Lynn University.

    Google Scholar 

  • Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems, 111(7), 1006–1023. https://doi.org/10.1108/02635571111161262

  • Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Professional Communication, 57(2), 123–146.

    Google Scholar 

  • Luciano, M. M., DeChurch, L. A., & Mathieu, J. E. (2018). Multiteam systems: A structural framework and meso-theory of system functioning. Journal of Management, 44(3), 1065–1096.

    Google Scholar 

  • MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1), 99–128.

    Google Scholar 

  • Mani, N., Singh, A., & Nimmagadda, S. L. (2020). An IoT guided healthcare monitoring system for managing real-time notifications by fog computing services. Procedia Computer Science, 167, 850–859.

    Google Scholar 

  • Marston, S., Li, Z., & Bandyopadhyay, S. (2011). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176–189. In.

    Google Scholar 

  • Masood, T., & Egger, J. (2019). Augmented reality in support of Industry 4.0—Implementation challenges and success factors. Robotics and Computer-Integrated Manufacturing, 58, 181–195.

    Google Scholar 

  • Merhi, M., Hone, K., & Tarhini, A. (2019). A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust. Technology in Society, 59, 101151.

    Google Scholar 

  • Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169.

    Google Scholar 

  • Misra, D. P., Zimba, O., & Gasparyan, A. Y. (2021). Statistical data presentation: A primer for rheumatology researchers. Rheumatology International, 41(1), 43–55.

    Google Scholar 

  • Mitra, T., Kapoor, R., & Gupta, N. (2022). Studying key antecedents of disruptive technology adoption in the digital supply chain: an Indian perspective. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-07-2021-1052

  • Mohammadzadeh, A., Masdari, M., & Gharehchopogh, F. S. (2021). Energy and cost-aware workflow scheduling in cloud computing data centers using a multi-objective optimization algorithm. Journal of Network and Systems Management, 29(3), 1–34.

    Google Scholar 

  • Mohammed, A., & Ferraris, A. (2021). Factors influencing user participation in social media: Evidence from twitter usage during COVID-19 pandemic in Saudi Arabia. Technology in Society, 66, 101651.

    Google Scholar 

  • Mora, N., Grossi, F., Russo, D., Barsocchi, P., Hu, R., Brunschwiler, T., . . . Nunziata, S. (2019). Iot-based home monitoring: supporting practitioners’ assessment by behavioral analysis. Sensors, 19(14), 3238.

  • Muda, I., Omar, N. H., Said, J., & Kholis, A. (2019). The Constraints and Barriers for Loan Distribution by Financing Institutions to Cooperant Members. Journal of Southwest Jiaotong University, 54(3).

  • Nartey, I. (2021). Effects of teamwork on employees‟ performance; the case of Compassion International Ghana (Doctoral dissertation, UCC).

    Google Scholar 

  • Nilashi, M., Ibrahim, O., & Ahani, A. (2016). Accuracy improvement for predicting Parkinson’s disease progression [Article]. Scientific Reports, 6, 34181. https://doi.org/10.1038/srep34181

    Article  Google Scholar 

  • Njenga, K., Garg, L., Bhardwaj, A. K., Prakash, V., & Bawa, S. (2019). The cloud computing adoption in higher learning institutions in Kenya: Hindering factors and recommendations for the way forward. Telematics and Informatics, 38, 225–246.

    Google Scholar 

  • Nouri, S. M. R., Li, H., Venugopal, S., Guo, W., He, M., & Tian, W. (2019). Autonomic decentralized elasticity based on a reinforcement learning controller for cloud applications. Future Generation Computer Systems, 94, 765–780.

    Google Scholar 

  • Nuryyev, G., Wang, Y.-P., Achyldurdyyeva, J., Jaw, B.-S., Yeh, Y.-S., Lin, H.-T., & Wu, L.-F. (2020). Blockchain technology adoption behavior and sustainability of the business in tourism and hospitality SMEs: An empirical study. Sustainability, 12(3), 1256.

    Google Scholar 

  • Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14(1), 110–121.

    Google Scholar 

  • Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497–510.

    Google Scholar 

  • Ooi, K.-B., Lee, V.-H., Tan, G.W.-H., Hew, T.-S., & Hew, J.-J. (2018). Cloud computing in manufacturing: The next industrial revolution in Malaysia? Expert Systems with Applications, 93, 376–394.

    Google Scholar 

  • Ortega-Gutiérrez, J., Cepeda-Carrión, I., & Alves, H. (2022). The role of absorptive capacity and organizational unlearning in the link between social media and service dominant orientation. Journal of Knowledge Management, 26(4), 920–942. https://doi.org/10.1108/JKM-06-2020-0487

  • Othman, B. A., Harun, A., De Almeida, N. M., & Sadq, Z. M. (2021). The effects on customer satisfaction and customer loyalty by integrating marketing communication and after sale service into the traditional marketing mix model of Umrah travel services in Malaysia. Journal of Islamic Marketing, 12(2), 363–388. https://doi.org/10.1108/JIMA-09-2019-0198

  • 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.

    Google Scholar 

  • Puklavec, B., Oliveira, T., & Popovič, A. (2018). Understanding the determinants of business intelligence system adoption stages: An empirical study of SMEs. Industrial Management & Data Systems, 118(1), 236–261. https://doi.org/10.1108/IMDS-05-2017-0170

  • Püschel, J., Afonso Mazzon, J., & Hernandez, J. M. C. (2010). Mobile banking: proposition of an integrated adoption intention framework. International Journal of Bank Marketing, 28(5), 389–409. https://doi.org/10.1108/02652321011064908

  • 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.

    Google Scholar 

  • Qasem, Y. A., Abdullah, R., Jusoh, Y. Y., Atan, R., & Asadi, S. (2021). Analyzing continuance of cloud computing in higher education institutions: Should We Stay, or Should We Go? Sustainability, 13(9), 4664.

    Google Scholar 

  • Qi, W., Sun, M., & Hosseini, S. R. A. (2022). Facilitating big-data management in modern business and organizations using cloud computing: a comprehensive study. Journal of Management & Organization, 1–27.

  • Ra, C. K., Cho, J., Stone, M. D., De La Cerda, J., Goldenson, N. I., Moroney, E., . . . Leventhal, A. M. (2018). Association of digital media use with subsequent symptoms of attention-deficit/hyperactivity disorder among adolescents. Jama, 320(3), 255-263.

  • Rahi, S., Ghani, M., Alnaser, F., & Ngah, A. (2018). Investigating the role of unified theory of acceptance and use of technology (UTAUT) in internet banking adoption context. Management Science Letters, 8(3), 173–186.

    Google Scholar 

  • Rao, A. S., Ramana, A. V., & Ramasubbareddy, S. (2022). Implementation of data mining to enhance the performance of cloud computing environment. International Journal of Cloud Computing, 11(1), 27–42.

    Google Scholar 

  • Ravichandran, T. (2018). Exploring the relationships between IT competence, innovation capacity and organizational agility. The Journal of Strategic Information Systems, 27(1), 22–42.

    MathSciNet  Google Scholar 

  • Richmond, B. J., & Goldberg, M. E. (1985). On computer science, visual science, and the physiological utility of models. Behavioral and Brain Sciences, 8(2), 300–301.

    Google Scholar 

  • Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). Free Press; Collier Macmillan.

  • Romero-Hernandez, A., Gonzalez-Riojo, M., Sagredo-Olivenza, I., & Manero, B. (2021). Comparison of a tablet versus computer-based classical theatre game among 8–13 year children. IEEE Access, 9, 44283–44291.

    Google Scholar 

  • Ross, V. W. (2010). Factors influencing the adoption of cloud computing by decision making managers. Capella University.

    Google Scholar 

  • Sabbatinelli, J., Giuliani, A., Matacchione, G., Latini, S., Laprovitera, N., Pomponio, G., . . . Moretti, M. (2021). Decreased serum levels of the inflammaging marker miR-146a are associated with clinical non-response to tocilizumab in COVID-19 patients. Mechanisms of Ageing and Development, 193, 111413.

  • Salah, O. H., Yusof, Z. M., & Mohamed, H. (2021). The determinant factors for the adoption of CRM in the Palestinian SMEs: The moderating effect of firm size. PLoS ONE, 16(3), e0243355.

    Google Scholar 

  • Sallehudin, H., Aman, A. H. M., Razak, R. C., Ismail, M., Bakar, N. A. A., Fadzil, A. F. M., & Baker, R. (2020). Performance and key factors of cloud computing implementation in the public sector. International Journal of Business and Society, 21(1), 134–152.

    Google Scholar 

  • Shannon, R. E. (1975). Simulation: A survey with research suggestions. AIIE Transactions, 7(3), 289–301.

    MathSciNet  Google Scholar 

  • Shi, S., He, D., Li, L., Kumar, N., Khan, M. K., & Choo, K.-K.R. (2020). Applications of blockchain in ensuring the security and privacy of electronic health record systems: A survey. Computers & Security, 97, 101966.

    Google Scholar 

  • Sovacool, B. K., Monyei, C. G., & Upham, P. (2022). Making the internet globally sustainable: Technical and policy options for improved energy management, governance and community acceptance of Nordic datacenters. Renewable and Sustainable Energy Reviews, 154, 111793.

    Google Scholar 

  • Srinivasan, S. (2015). Risk management in the cloud and cloud outages. In Cloud Technology: Concepts, Methodologies, Tools, and Applications (pp. 1721–1731). IGI Global. https://doi.org/10.4018/978-1-4666-6539-2.ch079

  • Sturgeon, T. J. (2021). Upgrading strategies for the digital economy. Global Strategy Journal, 11(1), 34–57.

    Google Scholar 

  • Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of Network and Computer Applications, 34(1), 1–11.

    Google Scholar 

  • Sulaiman, M. S., Abood, M. M., Sinnakaudan, S. K., Shukor, M. R., You, G. Q., & Chung, X. Z. (2021). Assessing and solving multicollinearity in sediment transport prediction models using principal component analysis. ISH Journal of Hydraulic Engineering, 27(sup1), 343–353.

    Google Scholar 

  • Sultan, N. (2014). Making use of cloud computing for healthcare provision: Opportunities and challenges. International Journal of Information Management, 34(2), 177–184.

    Google Scholar 

  • Sultan, N. (2010). Cloud computing for education: A new dawn? International Journal of Information Management, 30(2), 109–116.

  • Sun, S., Cegielski, C. G., Jia, L., & Hall, D. J. (2018). Understanding the factors affecting the organizational adoption of big data. Journal of Computer Information Systems, 58(3), 193–203.

    Google Scholar 

  • Tabrizchi, H., & Kuchaki Rafsanjani, M. (2020). A survey on security challenges in cloud computing: Issues, threats, and solutions. The Journal of Supercomputing, 76(12), 9493–9532.

    Google Scholar 

  • Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960–967.

    Google Scholar 

  • Talukder, M. S., Sorwar, G., Bao, Y., Ahmed, J. U., & Palash, M. A. S. (2020). Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technological Forecasting and Social Change, 150, 119793.

    Google Scholar 

  • Tamjidyamcholo, A., Baba, M. S. B., Shuib, N. L. M., & Rohani, V. A. (2014). Evaluation model for knowledge sharing in information security professional virtual community. Computers & Security, 43, 19–34.

    Google Scholar 

  • Tamjidyamcholo, A., Gholipour, R., & Kazemi, M. A. (2020). Examining the perceived consequences and usage of MOOCs on learning effectiveness. Iranian Journal of Management Studies, 13(3), 495–525.

    Google Scholar 

  • Tanveer, J., Haider, A., Ali, R., & Kim, A. (2022). Machine learning for physical layer in 5G and beyond wireless networks: A survey. Electronics, 11(1), 121.

    Google Scholar 

  • Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation. Lexington Books.

    Google Scholar 

  • Tweel, A. (2012). Examining the relationship between technological, organizational, and environmental factors and cloud computing adoption. Northcentral University.

    Google Scholar 

  • Utterback, J. M. (1971). The process of technological innovation within the firm. Academy of Management Journal, 14(1), 75–88.

    Google Scholar 

  • Vaquero, L. M., Rodero-Merino, L., & Morán, D. (2011). Locking the sky: A survey on IaaS cloud security. Computing, 91(1), 93–118.

    MATH  Google Scholar 

  • Wang, W.-T., & Lin, Y.-L. (2021). The relationships among students’ personal innovativeness, compatibility, and learning performance. Educational Technology & Society, 24(2), 14–27.

    Google Scholar 

  • Wang, C., Xu, H., & Li, G. (2018). The corporate philanthropy and legitimacy strategy of tourism firms: A community perspective. Journal of Sustainable Tourism, 26(7), 1124–1141.

    Google Scholar 

  • Watson, D. (2021). An Empirical Study of Cloud Computing Technology Acceptance in the Developing Economy of Jamaica. Capella University.

    Google Scholar 

  • Wilson, J. M., Weiss, A., & Shook, N. J. (2020). Mindfulness, self-compassion, and savoring: Factors that explain the relation between perceived social support and well-being. Personality and Individual Differences, 152, 109568.

    Google Scholar 

  • Wimmer, M. A., Pereira, G. V., Ronzhyn, A., & Spitzer, V. (2020). Transforming government by leveraging disruptive technologies: Identification of research and training needs. JeDEM-eJournal of eDemocracy and Open Government, 12(1), 87–113.

    Google Scholar 

  • Won, J. Y., & Park, M. J. (2020). Smart factory adoption in small and medium-sized enterprises: Empirical evidence of manufacturing industry in Korea. Technological Forecasting and Social Change, 157, 120117.

    Google Scholar 

  • Wu, B., Fang, H., Jacoby, G., Li, G., & Wu, Z. (2021). Environmental regulations and innovation for sustainability? Moderating effect of political connections. Emerging Markets Review, 50, 100835. https://doi.org/10.1016/j.ememar.2021.100835

  • Xu, L., Peng, X., & Prybutok, V. (2019). Formative measurements in operations management research: Using partial least squares. Quality Management Journal, 26(1), 18–31.

    Google Scholar 

  • Yadegaridehkordi, E., Nizam Bin Md Nasir, H., FazmidarBintiMohd Noor, N., Shuib, L., & Badie, N. (2018b). Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches. Applied Soft Computing, 66, 77–89. https://doi.org/10.1016/j.asoc.2017.12.051

    Article  Google Scholar 

  • Yadegaridehkordi, E., Nilashi, M., Shuib, L., Nasir, M. H. N. B. M., Asadi, S., Samad, S., & Awang, N. F. (2020). The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications, 40, 100921.

    Google Scholar 

  • Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018). Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change, 137, 199–210. https://doi.org/10.1016/j.techfore.2018.07.043

  • Yarter, L. C. (2012). Private cloud delivery model for supplying centralized analytics services. IBM Journal of Research and Development, 56(6), 10–11.

    Google Scholar 

  • Yeboah-Boateng, E. O., & Essandoh, K. A. (2014). Factors influencing the adoption of cloud computing by small and medium enterprises in developing economies. International Journal of Emerging Science and Engineering, 2(4), 13–20.

    Google Scholar 

  • Youssef, A. E., & Mostafa, A. M. (2019). Critical decision-making on cloud computing adoption in organizations based on augmented force field analysis. IEEE Access, 7, 167229–167239.

    Google Scholar 

  • Yuen, K. F., Cai, L., Qi, G., & Wang, X. (2021). Factors influencing autonomous vehicle adoption: An application of the technology acceptance model and innovation diffusion theory. Technology Analysis & Strategic Management, 33(5), 505–519.

    Google Scholar 

  • Yusoff, A. S. M., Peng, F. S., Abd Razak, F. Z., & Mustafa, W. A. (2020). Discriminant validity assessment of religious teacher acceptance: The use of HTMT criterion. Journal of Physics: Conference Series, 1529(4), 042045. IOP Publishing.

  • Zaim, H., Muhammed, S., & Tarim, M. (2019). Relationship between knowledge management processes and performance: Critical role of knowledge utilization in organizations. Knowledge Management Research & Practice, 17(1), 24–38.

    Google Scholar 

  • Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.

    Google Scholar 

  • Zheng, L. J., Xiong, C., Chen, X., Li, C. S. (2021). Product innovation in entrepreneurial firms: How business model design influences disruptive and adoptive innovation. Technological Forecasting and Social Change, 170, 120894.

    Google Scholar 

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  1. Azman Hashim International Business School, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Nasrin Badie & Ab Razak Che Hussin

  2. Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia

    Elaheh Yadegaridehkordi & Dalbir Singh

  3. School of Information Technology, York University, Toronto, Canada

    Arash Habibi Lashkari

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Badie, N., Hussin, A.R.C., Yadegaridehkordi, E. et al. A SEM-STELLA approach for predicting decision-makers’ adoption of cloud computing data center. Educ Inf Technol 28, 8219–8271 (2023). https://doi.org/10.1007/s10639-022-11484-9

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