Determining Effective Factors in Cloud Computing Acceptance Using Rogers’ Diffusion of Innovation Model and Davis’ Technology Adoption Model (A Case of Financial Institution)

KardanMoghaddam, H.; Rajaei, A.; Jafari, F.

doi:10.22061/jecei.2021.8057.470

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	Determining Effective Factors in Cloud Computing Acceptance Using Rogers’ Diffusion of Innovation Model and Davis’ Technology Adoption Model (A Case of Financial Institution)
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 15، دوره 10، شماره 1، فروردین 2022، صفحه 175-194 اصل مقاله (1.36 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2021.8057.470
نویسندگان
H. KardanMoghaddam^* ¹؛ A. Rajaei²؛ F. Jafari¹
¹Department of Computer Engineering, Birjand University of Technology, Birjand, Iran
²Faculty of Computer Engineering, University of Velayat, Iranshahr, Iran
تاریخ دریافت: 19 اردیبهشت 1400، تاریخ بازنگری: 24 شهریور 1400، تاریخ پذیرش: 02 مهر 1400
چکیده
Background and Objectives: Determining effective factors in cloud computing adoption on employees of Noor credit institution in South Khorasan province, Iran, is the purpose of the present study. A practical oriented method is applied regarding the research objective and a descriptive-survey method is used for collecting field data. Employees of Noor credit institution of South Khorasan province (50 people) are selected as the research sample. Methods: Accurate questionnaires are analyzed. Two researcher-made questionnaires are conducted as research tools. These questionnaires consist of effective factors in cloud computing adoption (12 factors including 47 items) and cloud computing adoption intention (3 items). The reliability of the research is evaluated using Cronbach's alpha coefficient which is obtained α=0.54% for the questionnaire of effective factors in cloud computing adoption and has various values for the questionnaire of the cloud computing adoption intention. Also, the descriptive statistics approach is used to define variables, and confirmatory factor analysis and path analysis are applied in the inferential section. Structural equation modeling using Smart-PLS software is used to determine the effective factors in cloud computing adoption Results: All 12 determined factors in cloud computing adoption are considered as dependent variables and cloud computing adoption intention is considered as an independent variable. 12 effective factors in cloud computing adoption on Noor credit institution of South Khorasan in Iran are determined and examined. Conclusion: It can be concluded that all factors (except the support of the top manager) have a positive and significant effect on cloud computing adoption intention, nonetheless, the value of statistic t for path analysis of the support of the top manager on cloud computing adoption intention is less than 1.96. This shows that the support of the top manager does not have a significant effect on cloud computing adoption intention.
کلیدواژه‌ها
Cloud Computing Adoption؛ The Support of Manager؛ Information Technology؛ Technology Readiness

مراجع
[1]T. Oliveira, M. Thomas, M. Espadanal, “Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors,” Inf. Manage., 51(5), 497-510, 2014. [2] C. Christauskas, R. Misevičienė,” Cloud-computing based accounting for small to medium sized business," Econ. Eng. Decis., 23(1), 14-21, 2012. [3] V. Ghobadpour, N. Naghshineh, A. Sabetpour, “From cloud computing to cloud library: proposing cloud model to configure future libraries,” Iran. J. Inf. Process. Manage., 28(4): 859-877, 2014. [4] V.I. Chang, “A proposed framework for cloud computing adoption,” in Proc. Sustainable Business: Concepts, Methodologies, Tools, and Applications : 978-1003, 2020. [5] W. Yaokumah, R.A. Amponsah, Examining the contributing factors for cloud computing adoption in a developing country, In Cloud Security: Concepts, Methodologies, Tools, and Applications: 1663-1685, IGI Global, 2019. [6] H.M. Sabi, F.M.E. Uzoka, K. Langmia, F.N. Njeh, C.K. Tsuma, “A cross-country model of contextual factors impacting cloud computing adoption at universities in sub-Saharan Africa,” Inf. Sys. Front., 20(6), 1381-1404, 2018. [7] S. Jones, Z. Irani, U. Sivarajah, P.E. Love, “Risks and rewards of cloud computing in the UK public sector: A reflection on three Organisational case studies,” Inf. Sys. Front., 21(2): 359-382, 2017. [8] A. Alhammadi, C. Stanier, A. Eardley, “The determinants of cloud computing adoption in Saudi Arabia,” Comput. Scie. Inf. Technol., 5: 55-67, 2015. [9] F.C. Tung, S.C. Chang, C.M. Chou, “An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry,” Int. J. Med. Inf., 77(5): 324-335, 2008. [10] I. Arpaci, “Antecedents and consequences of cloud computing adoption in education to achieve knowledge management,” Comput. Hum. Behav., 70: 382-390, 2017. [11] Y. Alshamaila, S. Papagiannidis, F. Li, “Cloud computing adoption by SMEs in the north east of England: A multi-perspective framework,” J. Enterp. Inf. Manage., 26(3): 250-275, 2013. [12] A. Lin, N.C. Chen, “Cloud computing as an innovation: Perception, attitude, and adoption,” Int. J. Inf. Manage., 32(6): 533-540, 2012. [13] E.O. Yeboah-Boateng, K.A. Essandoh, “Cloud computing: the level of awareness amongst small & medium-sized enterprises (SMEs) in developing economies”, J. Emerging Trends in Comput. Inf. Sci., 4(11): 832-839, 2013. [14] H. Gangwar, H. Date, R. Ramaswamy, “Understanding determinants of cloud computing adoption using an integrated TAM-TOE model,” J. Enterp. Inf. Manage., 28(1): 107-130, 2015. [15] M.H. Raza, A.F. Adenola, A. Nafarieh, W. Robertson, “The slow adoption of cloud computing and IT workforce,” Procedia Comput. Sci., 52: 1114-1119, 2015. [16] P.Priyadarshinee, “Cloud computing adoption: A scale development approach,” In Modern Principles, Practices, and Algorithms for Cloud Security: 107-128, 2020. [17] www.noorbank.ir [18] C. Changchit, C. Chuchuen, “Cloud computing: an examination of factors impacting users’ adoption,” J. Comput. Inf. Sys., 58(1): 1-9, 2018. [19] M. Fishbein, I. Ajzen, “Belief, attitude, intention and behavior: An introduction to theory and research,” 1975. [20] I. Ajzen, “The theory of planned behavior,” Organ. Behav. Hum. Decis. Process., 50(2): 179-211, 1991. [21] L. Tornatzky, M. Fleischer, “The processes of technological innovation,” 1990. [22] E.M. Rogers, Diffusion of innovations: Simon and Schuster, 2010. [23] V. Venkatesh, M.G. Morris, G.B. Davis, F.D. Davis, “User acceptance of information technology: Toward a uniﬁed view,” MIS quarterly, 425-478, 2003. [24] B. Bernstein, P.J. Singh, “Innovation generation process: Applying the adopter categorization model and concept of “chasm” to better understand social and behavioral issues,” Eur. J. Innovation Manage., 11(3): 366-388, 2008. [25] E.M. Rogers, Everett, Diffusion of Innovations (4 ed.). New York: The Free press, 1995. [26] N. Ebadolahi, M. Cheshmeh Sohrabi, F. Nooshinfard, “Analysis of technological factors influencing on adoption based on Rogers' diffusion of innovation theory: Case study Namaye Nashryat software,” 7(26): 79-92, 2014. [27] E. Rogers, F.F. Shoemaker, “Communication of innovations: A cross-cultural approach, 2nd ed.”, 1971. [28] H.F. Lin, “Predicting consumer intentions to shop online: An empirical test of competing theories,” Electron. Commerce. Res. Appl., 6: 433–442, 2007. [29] F.D. Davis, R.P. Bagozzi, P.R. Warshaw, “User acceptance of computer technology: a comparison of two theoretical models,” Manage. Sci., 35(8): 982-1003, 1989. [30] W.W. Chin, P.A .Todd, “On the use , usefulness , and ease of use of structural equation modeling in mis research,” MIS Quarterly, 19(2): 237 -246, 1995. [31] M. Wright, D. Charlett, “New product diffusion models in marketing: An assessment of two approaches,” Market. Bull., 6: 32-41, Article 4: 32-41, 1995. [32] T.S. Behrend, E.N. Wiebe, J.E. London, E.C. Johnson, “ Cloud computing adoption and usage in community colleges,” Behav. Inf. Technol., 30(2), 231-240, 2011. [33] N. Opitz, T.F. Langkau, N.H. Schmidt, L.M. Kolbe, “Technology acceptance of cloud computing: empirical evidence from German IT departments,” in Proc. 2012 45th Hawaii International Conference on System Science (HICSS): 1593-1602, 2012. [34] S.T. Alharbi, “Users’ acceptance of cloud computing in Saudi Arabia: an extension of technology acceptance model,” Int. J. Cloud Appl. Comput., (IJCAC), 2(2): 1-11, 2012. [35] M. Akbari, M.C. Sohrabi, E.A. Zanjani, “Analysis of search engines and meta search engines' position by university of isfahan users based on rogers' diffusion of innovation theory,” Iran. J. Inf. Process. Manage., 27(4): 961-984, 2012. [36] L. Luo, “Reference librarians’ Adoption of cloud computing technologies: An exploratory study,” Internet Ref. Serv. Q., 17(3-4): 147-166, 2012. [37] G.R. Heidari, M.B. Alizadeh Aghdam, A. Hamdipour, “Factors affecting the adoption of Electronic Information Resources (EIR) by Iranian knowledge and Information science faculty members, based on Roger's Diffusion of Innovation Theory,” Libr. Inform. Sci., 16(3): 123, 2013. [38] H.P Fung, “Using porter five forces and technology acceptance model to predict cloud computing adoption among IT outsourcing service providers,” Internet Technol. Appl. Res. ITAR, 1(2): 18-24, 2013. [38] L. Sun, H. Dong, F.K. Hussain, O.K. Hussain, E. Chang, “Cloud service selection: State-of-the-art and future research directions,” J. Network Comput. Appl., 45: 134-150, 2014. [40] M. Yuvaraj, “Examining librarians’ behavioural intention to use cloud computing applications in Indian central universities,” Ann. Libr. Inform. Stud. (ALIS), 60(4): 260-268, 2014. [41] R. Rai, G. Sahoo, S. Mehfuz, “Exploring the factors influencing the cloud computing adoption: a systematic study on cloud migration,” Springerplus, 4(1): 197, 2015. [42] G.Mangai, P. Ganesan, D. Kirana, Kumar “A perspective study of cloud computing in library services,” in Proc. International Conference on Library Space and Content Management for Networked Society. Bangalore: DVK, 2014. [43] N.M. Yaghoubi, H.R. Jafari, J. Shukuhy, “Identification and ranking of the risk factors of cloud computing in State-Owned organizations,” Inf. Sci. Technol., 30(3): 759-784, 2015. [44] B. Scholtz, J. Govender, J.M. Gomez, “Technical and environmental factors affecting cloud computing adoption in the south african public sector,” in Proc. International Conference on Information Resources, 2016. [45] C. Valmohammadi, M. Mazaheri, “Clarification of factors affecting the decision to use cloud computing among IRIB employees based on a Technology Acceptance Model,” IT Manage. Stud., 5(19): 105-124, 2017. [46] M.E. Villapol, H. Jansen, A. Morales, J. Flores, H. odrigues, M. Huerta, “Recording, storing and querying judo match data using an Android tool and Cloud computing,” Proc. Inst. Mech. Eng., Part P: J. Sports Eng. Technol., 231(4): 243-252, 2017. [47] J.H. Dahooie, N. Mohammadi, A.Vanaki, M. Jamali, “Developing proper systems for successful cloud computing implementation using fuzzy ARAS method (Case Study: University of Tehran Faculty of New Science and Technology),” J. Inf. Technol. Manage., 9(33): 759-786, 2017. [48] C. Changchit, C. Chuchuen, “Cloud computing: an examination of factors impacting users’ adoption,” J. Comput. Inf. Sys., 58(1): 1-9, 2018. [49] M.B.Ali, T. Wood-Harper, R. Ramlogan, “A framework strategy to overcome trust issues on cloud computing adoption in higher education”, In Proc. Modern Principles, Practices, and Algorithms for Cloud Security: 162-183, 2020. [50] E. Alidoust Ghahfarokhi, A. Safarpour, A. Amani Samani, “ Identifying effective factors in cloud computing adoption among staff of physical education faculties in Tehran”, J. Hum. Resource Manage. Sport, 7(2): 245-263, 2020. [51] A. Albelaihi, N. Khan, “Top Benefits and hindrances to cloud computing adoption in saudi arabia: A brief study,” J. Inf. Technol. Manage., 12(2): 107-122, 2020. [52] D.H. Tesema,“Cloud computing adoption challenge in case of commercial bank of Ethiopia,” Int. J. Devel. Res., 10(01): 33562-33565, 2020. [53] O.Ali, O., A. Shrestha, M. Ghasemaghaei, et al., “Assessment of complexity in cloud computing adoption: a case study of local governments in Australia,” Inf. Syst. Front. , 2021. [54] E. Rogers, Diffusion of innovation. 5th ed. NY. USA: Free Press, 2003 [55] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, 13(3): 319-340, 1989. [56] A. Tarhini, A. Al-Badi, M. Almajali, S.H. Alrabayaah, “Factors Influ. Employees¡¯ Intention to Use Cloud Computing”, J. Manage. Strategy, 8(2): 47-62, 2017. [57] S.Paquette, P.T. Jaeger, S.C. Wilson, “Identifying the security risks associated with governmental use of cloud computing,” Gov. Inf. Quarterly, 27(3): 245-253, 2010. [58] C. Hinde, J.P. Van Belle, “Cloud computing in South African SMMEs: Risks and rewards for playing at altitude,” Int. J. Comput. Sci. Electr. Eng., 1(1): 1-10, 2012. [59] S.K. Panda, I. Gupta, P.K. Jana, “Task scheduling algorithms for multi-cloud systems: allocation-aware approach,” Inf. Sys. Front., 21(2): 241-259, 2019.
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Determining Effective Factors in Cloud Computing Acceptance Using Rogers’ Diffusion of Innovation Model and Davis’ Technology Adoption Model (A Case of Financial Institution)