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Exploring Cloud Computing Adoption in Higher Educational Environment: An Extension of the TPB Model with Trust, Peer Influences, Perceived Usefulness and Ease of Use


Affiliations
1 Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia
 

Cloud computing is regarded as the next generation of computing. It is progressively being used as a launching pad for digital innovation and organizational agility. Cloud computing is frequently used by private and public organizations due to its flexibility, collaboration, cost-effectiveness, and scalability. These characteristics make cloud computing indispensable for individuals and businesses such as higher education institutes. Several prior studies covered the technological facets of cloud-based contexts, including cloud security, scalability, and virtualization. However, it is contend that the main barrier to cloud computing isn't technical but cognitive or behavioural, and in particular attitudinal. Thus, this research aims to study higher education’ students’ attitudes and their intention to adopt cloud computing, with a specific concentration on the effect of trust, peer influences, perceived usefulness and ease of use in order to investigate the factors influencing the adoption of cloud computing in higher educational environment in Saudi Arabia. This study presents an extended Decomposed Theory of Planned Behaviour (DTPB) to include trust, peer influences, perceived usefulness and ease of use as a cognition, representing a person’s perception of social influence to perform or not perform a behaviour under consideration. The proposed model was able to explain 62% of the variance in behavioural intention and 65% of students' attitudes towards the adoption of cloud computing in higher educational environment. The study's findings show that the proposed model explained a significant amount of variation in cloud computing adoption. It suggests that the model expansion by incorporating trust, peer influences, perceived usefulness and ease of use factors were valuable explorations. Further, the findings demonstrate that university students' attitudes toward using cloud computing are significantly influenced by perceived ease of use, trust in cloud computing service provider and perceived usefulness, which have the ability to explain their attitude by 22.15%, 21.9% and 20.9% respectively. The result also shows that "subjective norm" alone explains 33.95% of students' "behavioural intentions" towards using cloud computing, followed by their "attitude," which explains around 14.24% of "behavioural intentions," and then university students’ "self-efficacy," with 13.71%.

Keywords

Cloud Computing, DTPB, Trust, Technology Acceptance, Peer influences, Perceived usefulness, Perceived ease of use.
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  • W. Liu, "Research on cloud computing security problem and strategy," in 2012 2nd international conference on consumer electronics, communications and networks (CECNet), 2012.

  • C. C. Chen and M. Nakayama, "Key factors increasing the trust and intention to adopt standard cloud-based applications," International Journal of Information Systems and Change Management, vol. 8, no. 2, pp. 144-159, 2016.

  • ResearchAndMarkets, "Global cloud computing in higher education market, by institute type, by application, by ownership, by deployment, by region, estimation & forecast, 2017 - 2027," ResearchAndMarkets, September 2021. [Online]. Available: https://www.researchandmarkets.com/reports/5456925/global-cloud-computing-in-higher-education . [Accessed 3 April 2022].

  • C. Rodríguez Monroy, G. C. Almarcha Arias and Y. Núñez Guerrero, "The new cloud computing paradigm: The way to IT seen as a utility," Latin American and Caribbean Journal of Engineering Education, vol. 6, no. 2, pp. 24-31, 2012.

  • Y. A. Qasem, S. Asadi, R. Abdullah, Y. Yah, R. Atan, M. A. Al-Sharafi and A. A. Yassin, "A multianalytical approach to predict the determinants of cloud computing adoption in higher education institutions," Applied Sciences, vol. 10, no. 14, p. 4905, 2020.

  • B. Alexander, Social networking in higher education. The Tower and the Cloud, EDUCAUSE, 2008, pp. 197-201..

  • R. N. Katz, The gathering cloud: Is this the end of the middle. The tower and the cloud: Higher education in the age of cloud computing, EDUCAUSE, 2008, pp. 2-42..

  • Z. Asadi, M. Abdekhoda and H. Nadrian, "Cloud computing services adoption among higher education faculties: development of a standardized questionnaire," Education and Information Technologies, vol. 25, no. 1, pp. 175-191, 2020.

  • N. Sultan, "Cloud computing for education: A new dawn?.," International Journal of Information Management, vol. 30, no. 2, pp. 109-116, 2010.

  • S. Ashtari and A. Eydgahi, "Student perceptions of cloud applications effectiveness in higher education," Journal of computational science, vol. 23, pp. 173-180, 2017.

  • I. Arpaci, "Antecedents and consequences of cloud computing adoption in education to achieve knowledge management," Computers in Human Behavior, vol. 70, pp. 382-390, 2017.

  • R. Estriegana, J. A. Medina-Merodio and R. Barchino, "Student acceptance of virtual laboratory and practical work: An extension of the technology acceptance model," Computers & Education, vol. 135, pp. 1-14, 2019.

  • J. A. González-Martínez, M. L. Bote-Lorenzo, E. Gómez-Sánchez and R. Cano-Parra, "Cloud computing and education: A state-of-the-art survey," Computers & Education, vol. 80, pp. 132-151, 2015.

  • G. Militaru, A. A. Purcărea, O. D. Negoiţă and A. Niculescu, "Examining cloud computing adoption intention in higher education: exploratory study," in Exploring Services Science: 7th International Conference, IESS 2016, Bucharest, Romania, 2016.

  • A. Sharma and U. K. Singh, "Modelling of Smart Risk Assessment Approach for Cloud Computing Environment using AI & supervised machine learning algorithms," Global Transitions Proceedings, pp. 1-358, 3 June 2022.

  • S. A. Sheik and A. P. Muniyandi, "Secure authentication schemes in cloud computing with glimpse of artificial neural networks: A review," Cyber Security and Applications, vol. 1, p. 100002, 2023.

  • S. Marston, Z. Li, S. Bandyopadhyay, J. Zhang and A. Ghalsasi, "Cloud computing—The business perspective," Decision support systems, vol. 51, no. 1, pp. 176-189, 2011.

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

  • O. Ali, A. Shrestha, M. Ghasemaghaei and G. Beydoun, "Assessment of complexity in cloud computing adoption: A case study of local governments in Australia," Information Systems Frontiers, vol. 24, no. 2, pp. 595-617, 2022.

  • P. Mell and T. Grance, The NIST definition of cloud computing. U.S. Department of Commerce, MD, USA: National Institute of Standards and Technology: Gaithersburg, 2011.

  • M. A. Mohamed Hashim, I. Tlemsani and R. Matthews, "Higher education strategy in digital transformation.," Education and Information Technologies, pp. 1-25, 2021.

  • G. Thorsteinsson, T. Page and A. Niculescu, "Using virtual reality for developing design communication," Studies in Informatics and Control, vol. 19, no. 1, pp. 93-106, 2010.

  • F. P. Tulinayo, P. Ssentume and R. Najjuma, "Digital technologies in resource constrained higher institutions of learning: a study on students’ acceptance and usability," International Journal of Educational Technology in Higher Education, vol. 15, no. 1, pp. 1-19, 2018.

  • A. El Mhouti, M. Erradi and A. Nasseh, "Using cloud computing services in e-learning process: Benefits and challenges," Education and Information Technologies, vol. 23, pp. 893-909, 2018.

  • R. Thavi, R. Jhaveri, V. Narwane, B. Gardas and N. Jafari Navimipour, "Role of cloud computing technology in the education sector," Journal of Engineering, Design and Technology, 2021.

  • P. Y. Thomas, "Cloud computing: A potential paradigm for practising the scholarship of teaching and learning," The Electronic Library, vol. 29, no. 2, pp. 214-224, 2011.

  • K. Bagley, "Checkpoints, cloud and collaboration (C3): A learning framework to improve learning outcomes for international students in computer science," Journal of Computing Sciences in Colleges, vol. 33, no. 6, p. 22–28, 2018.

  • B. G. Batista, C. G. Ferreira, D. M. Segura , D. M. Leite Filho and M. M. Peixoto, "A QoS-driven approach for cloud computing addressing attributes of performance and security," Future Generation Computer Systems, vol. 68, p. 260–274, 2016.

  • M. Sharma, R. Gupta and P. Acharya, "Analysing the adoption of cloud computing service: A systematic literature review," Global Knowledge, Memory and Communication, vol. 70, no. 1/2, pp. 114-153, 2020.

  • A. Chiniah, A. E. Mungur and K. Naidoo Permal, "Evaluation of cloud computing adoption using a hybrid TAM/TOE model," in Information Systems Design and Intelligent Applications, Singapore, 2019.

  • A. N. Tashkandi and I. M. Al-Jabri, "Cloud computing adoption by higher education institutions in Saudi Arabia: an exploratory study," Cluster Computing, vol. 18, pp. 1527-1537, 2015.

  • Y. A. Qasem, R. Abdullah, Y. Yah, R. Atan, M. A. Al-Sharafi and M. Al-Emran, "Towards the development of a comprehensive theoretical model for examining the cloud computing adoption at the organizational level," Recent Advances in Intelligent Systems and Smart Applications, pp. 63- 74, 2021.

  • S. Taylor and P. A. Todd, "Understanding information technology usage: A test of competing models," Information systems research, vol. 6, no. 2, pp. 144-176, 1995.

  • M. Hernandez and J. Mazzon, "Adoption of internet banking: proposition and implementation of an integrated methodology approach," The International Journal of Bank Marketing, vol. 25, no. 2, pp. 72-88, 2007.

  • Y. C. Ku, R. Chen and H. Zhang, "Why do users continue using social networking sites? An exploratory study of members in the United States and Taiwan," Information & Management, vol. 50, no. 7, pp. 571-581, 2013.

  • Z. Belkhamza and S. Wafa, "The Effect of Perceived Risk on the Intention to Use E-commerce: The Case of Algeria," Journal of Internet Banking and Commerce, vol. 14, no. 1, pp. 1-10, 2009.

  • J. Lansing and A. Sunyaev, "Trust in cloud computing: Conceptual typology and trust-building antecedents," ACM sigmis database: The database for advances in Information Systems, vol. 47, no. 2, pp. 58-96, 2016.

  • J. Opara-Martins, R. Sahandi and F. Tian, "Critical review of vendor lock-in and its impact on adoption of cloud computing," in International Conference on Information Society (i-Society 2014), IEEE, 2014.

  • E. Udoh, "Technology acceptance model applied to the adoption of grid and cloud technology," International Journal of Grid and High Performance Computing (IJGHPC), vol. 4, no. 1, pp. 1-20, 2012.

  • F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS Quarterly, vol. 13, no. 3, pp. 319-340, 1989.

  • M. Igbaria, S. J. Schiffman and T. J. Wieckowski, "The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology," Behaviour & information technology, vol. 13, no. 6, pp. 349-361, 1994.

  • F. Abdullah and R. Ward, "Developing a General Extended Technology Acceptance Model for ELearning (GETAMEL) by analysing commonly used external factors," Computers in human behavior, vol. 56, pp. 238-256, 2016.

  • A. Granić and N. Marangunić, "Technology acceptance model in educational context: A systematic literature review," British Journal of Educational Technology, vol. 50, no. 5, pp. 2572-2593, 2019.

  • F. D. Davis, R. P. Bagozzi and P. R. Warshaw, "User acceptance of computer technology: A comparison of two theoretical models," Management Science, vol. 35, no. 8, pp. 982-1003, 1989.

  • I. Ajzen and M. Fishbein, Understanding Attitudes and Predicting Social Behaviour, Englewood Cliffs, NJ: Prentice-Hall, 1980.

  • I. Ajzen, "The theory of planned behaviour," Organizational Behaviour and Human Decision Processes, vol. 50, no. 2, pp. 179-211, 1991.

  • M. Deutsch and H. Gerard, "Deutsch, M., & Gerard, H. (1995). A study of normative and informational social influences upon individual judgment," Journal of Abnormal and Social Psychology, vol. 51, pp. 624-36, 1995.

  • Y. C. Lee, "An empirical investigation into factors influencing the adoption of an e-learning system," Online Information Review, vol. 30, no. 5, pp. 517-541, 2006.

  • E. J. Kim, J. J. Kim and S. H. Han, "Understanding student acceptance of online learning systems in higher education: Application of social psychology theories with consideration of user innovativeness," Sustainability, vol. 13, no. 2, p. 896, 2021.

  • W. L. Shiau and P. Y. Chau, "Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach," Information & Management, vol. 53, no. 3, pp. 355-365, 2016.

  • D. R. Compeau and C. A. Higgins, "Computer self-efficacy: development of a measure and initial test," MIS Quarterly, vol. 19, no. 2, pp. 189-211, 1995.

  • A. Bandura, "Self-efficacy: toward a unifying theory of behavioural change," Psychol Rev, vol. 84, no. 2, pp. 191-215, 1997.

  • D. R. Compeau and S. Huff, "Social cognitive theory and individual reactions to computing technology: a longitudinal study," MIS Quarterly, vol. 23, no. 2, pp. 145-158, 1999.

  • T. Hill, N. D. Smith and M. F. Mann, "Role of efficacy expectations in predicting the decision to use advanced technologies: the case of computers," Journal of Applied Psychology, vol. 72, no. 2, p. 307– 313, 1997.

  • M. Hsu and C. Chiu, "Internet self-efficacy and electronic service acceptance," Decision Support Systems, vol. 38, no. 3, pp. 369-381, 2004.

  • Igbaria and M. J. Iivari, "The effects of self-efficacy on computer usage," Omega, vol. 23, no. 6, p. 587–605, 1995.

  • C. Huang, "Gender differences in academic self-efficacy: A meta-analysis," European journal of psychology of education, vol. 28, pp. 1-35, 2013.

  • T. Muthuprasad, S. Aiswarya, K. S. Aditya and G. K. Jha, "Students’ perception and preference for online education in India during COVID-19 pandemic," Social sciences & humanities open, vol. 3, no. 1, p. 100101, 2021.

  • E. Bell, A. Bryman and B. Harley, Business research methods, Oxford university press, 2022.

  • G. A. Marcoulides and C. Saunders, "Editor's comments: PLS: a silver bullet?," MIS quarterly, pp. iii-ix, 2006.

  • K. K. K. Wong, "Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS," Marketing Bulletin, vol. 24, no. 1, pp. 1-32, 2013.

  • W. Al-Ghaith, "Understanding social network usage: impact of co-presence, intimacy, and immediacy," International Journal of Advanced Computer Science and Applications, vol. 6, no. 8, pp. 99-111, 2015.

  • G. C. Moore and I. Benbasat, "Development of an instrument to measure the perceptions of adopting an information technology innovation," Information systems research, vol. 2, no. 3, pp. 192-222, 1991.

  • W. Al-Ghaith, "Applying and extending the Technology Acceptance Model to understand Social Networking Sites (SNS) Usage: Toward proposing a comprehensive model," in Proceedings of the The 3rd Multidisciplinary International Social Networks Conference on Social Informatics 2016, Data Science 2016 (pp. 1-8), ACM, 2016.

  • W. Al-Ghaith, "Emerging Applications on Smart Phones: The Role of Privacy Concerns and its Antecedents on Smart Phones Usage," International Journal of Computer Science & Information Technology (IJCSIT), vol. 13, 2021.

  • J. Ejdys, "Building technology trust in ICT application at a university," International Journal of Emerging Markets, 2018.

  • G. Premkumar and K. Ramamurthy, "The role of Interorganizational and organizational factors of the decision mode for adoption of interorganizational systems," Decision Science, vol. 26, no. 3, pp. 303-336, 1995.

  • A. L. Comrey and H. B. Lee, A first course in factor analysis, N.J.:L.: Erlbaum Associates, 1992.

  • Z. Ali, B. Gongbing and A. Mehreen, "Understanding and predicting academic performance through cloud computing adoption: A perspective of technology acceptance model," Journal of Computers in Education, vol. 5, pp. 297-327, 2018.


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  • Exploring Cloud Computing Adoption in Higher Educational Environment: An Extension of the TPB Model with Trust, Peer Influences, Perceived Usefulness and Ease of Use

Abstract Views: 155  |  PDF Views: 57

Authors

Waleed Al-Ghaith
Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia

Abstract


Cloud computing is regarded as the next generation of computing. It is progressively being used as a launching pad for digital innovation and organizational agility. Cloud computing is frequently used by private and public organizations due to its flexibility, collaboration, cost-effectiveness, and scalability. These characteristics make cloud computing indispensable for individuals and businesses such as higher education institutes. Several prior studies covered the technological facets of cloud-based contexts, including cloud security, scalability, and virtualization. However, it is contend that the main barrier to cloud computing isn't technical but cognitive or behavioural, and in particular attitudinal. Thus, this research aims to study higher education’ students’ attitudes and their intention to adopt cloud computing, with a specific concentration on the effect of trust, peer influences, perceived usefulness and ease of use in order to investigate the factors influencing the adoption of cloud computing in higher educational environment in Saudi Arabia. This study presents an extended Decomposed Theory of Planned Behaviour (DTPB) to include trust, peer influences, perceived usefulness and ease of use as a cognition, representing a person’s perception of social influence to perform or not perform a behaviour under consideration. The proposed model was able to explain 62% of the variance in behavioural intention and 65% of students' attitudes towards the adoption of cloud computing in higher educational environment. The study's findings show that the proposed model explained a significant amount of variation in cloud computing adoption. It suggests that the model expansion by incorporating trust, peer influences, perceived usefulness and ease of use factors were valuable explorations. Further, the findings demonstrate that university students' attitudes toward using cloud computing are significantly influenced by perceived ease of use, trust in cloud computing service provider and perceived usefulness, which have the ability to explain their attitude by 22.15%, 21.9% and 20.9% respectively. The result also shows that "subjective norm" alone explains 33.95% of students' "behavioural intentions" towards using cloud computing, followed by their "attitude," which explains around 14.24% of "behavioural intentions," and then university students’ "self-efficacy," with 13.71%.

Keywords


Cloud Computing, DTPB, Trust, Technology Acceptance, Peer influences, Perceived usefulness, Perceived ease of use.

References