Behavioural intention to adopt cloud computing: a quantitative analysis with a mediatory factor using bootstrapping

Indonesian Journal of Electrical Engineering and Computer Science
Vol. 32, No. 1, October 2023, pp. 458~467
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v32.i1.pp458-467  458
Journal homepage: http://ijeecs.iaescore.com
Behavioural intention to adopt cloud computing: a quantitative
analysis with a mediatory factor using bootstrapping
Aiman Athambawa1,2
, Md Gapar Md Johar1
, Ali Khatibi1
1
Post Graduate Centre, Management and Science University, Shah Alam, Malaysia
2
Department of Information Technology, Sri Lanka Institute of Advanced Technological Education, Colombo, Sri Lanka
Article Info ABSTRACT
Article history:
Received Mar 9, 2023
Revised Jul 7, 2023
Accepted Jul 16, 2023
This article discusses the research conducted to determine the factors
contributing to behavioural intentions (BI) to adopt cloud computing. Small
and medium enterprises (SMEs) in the Sri Lanka information technology (IT)
sector were surveyed using a questionnaire. The diffusion of innovation (DOI)
by Roger and the unified theory of acceptance and use of technology 2
(UTAUT2) by Venkatesh, Thong, and Xu was used as the theoretical
framework for evaluating the reference model. Two hundred fifty-six
important IT executives from Sri Lanka companies participated in the study.
Quantitative techniques of data coding and analysis were applied in this
investigation. Statistical package for social sciences (SPSS) software is used
for exploratory factor analysis, and analysis of moment structures (AMOS)
software is used for confirmatory factor analysis, including bootstrapping for
mediator analysis of BI. The results show that among seven hypothesised
factors, three factors: relative advantage (RA), compatibility (CT), and
complexity (CX), significantly influence BI to adopt cloud computing.
Keywords:
Behavioural intention
Cloud adoption
Cloud computing
Mediatory factor analysis
Quantitative analysis
This is an open access article under the CC BY-SA license.
Corresponding Author:
Aiman Athambawa
Post Graduate Centre, Management and Science University
Shah Alam, Selangor Darul Ehsan, Malaysia
Email: matheeh@yahoo.com
1. INTRODUCTION
The adoption of cloud computing (ACC) is the latest phenomenon to be more productive in each
industry. As cloud computing has already become popular across the globe and different people from different
sectors use cloud computing for different reasons [1]. Traditional on-premises computing is shifting to the
cloud, which benefits both businesses and individuals [2].
Sri Lanka is one of the fastest developing countries in Asia. Information technology (IT) is a rapidly
growing industry among the other industries in Sri Lanka. The workforce of IT doubled over five years, and it
has grown by 120% over the past five years, making it one of the highest growth areas in the economy [3].
Currently, almost all the small and medium enterprises (SMEs) in Sri Lanka use at least one cloud service,
knowingly or unknowingly using the software as a service (SaaS). During the COVID-19 pandemic, all the
traditional (office-based) work switched to working from home, and SMEs utilised it without any downtime to
continue their business. The IT companies encourage employees to work from home by tunnelling staff through
virtual private networks (VPN) or collaborative platforms. COVID-19 enables Sri Lanka SMEs employees
with a new culture of work by working in the comfort of their homes by utilising cloud-based software.
Technology acceptance is one of the major aspects that every individual or organisation is willing to adopt
cloud computing in their business. Researchers have been trying to pinpoint what characteristics lead to the

Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752 
Behavioural intention to adopt cloud computing: a quantitative analysis … (Aiman Athambawa)
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widespread acceptance of new technologies for decades. Several ideas and models have been developed to
examine how people take in and use new technology.
Behavioral intention has been identified as a crucial factor for cloud computing adoption, as evidenced
by studies [4]–[9]. The current research specifically focuses on the behavioral intentions of IT industry SMEs
in Sri Lanka regarding the ACC. This study aims to gain insights into the factors that influence SMEs’
intentions to embrace cloud computing in Sri Lanka.
The process of adopting to new technology is the subject of a number of theories and models.
The diffusion of innovations (DOI) [10], the technology-organization-environment (TOE) framework [11], the
theory of reasoned action (TRA) [12], the technology acceptance model (TAM) [13], the technology
acceptance model 2 (TAM2) [14], the technology acceptance model 3 (TAM3) [15], unified theory of
acceptance and use of technology (UTAUT) [16] and unified theory of acceptance and use of technology 2
(UTAUT2) [17] are most prominent theoretical frameworks in this field. DOI and UTAUT2 are the most
extensively used theories that seek to explain and predict cloud computing adoption. The majority of these
theories explain and forecast the adoption choice based on technological factors.
Observability, relative advantage (RA), compatibility (CT), complexity (CX), and trialability
comprise DOI theory’s five main components [10]. RA, CT, and CX are chosen as the study’s independent
variables from among these criteria. The following elements are included in the UTAUT2 theory: price value
(PV), habit, awareness, behavioural intention (BI), and use behavior. performance expectancy (PE), effort
expectancy (EE), social influence, facilitating conditions, and hedonic motivation (HM) [17]. Among these
factors, PE, EE, HM, and PV are selected as independent factors of this study. Many studies have shown that
serves as a strong mediator [4]–[8], [14]–[17] that mediate the dependent factor, ACC with all independent
factors.
2. LITERATURE REVIEW
To establish a theoretical foundation for evaluating the reference model, the study extensively
examined two prominent technological adoption models: DOI and UTAUT2. By thoroughly reviewing these
models, the study aimed to gain a comprehensive understanding of the factors influencing technology adoption
and usage, enabling a comprehensive assessment of the reference model’s efficacy and relevance. The in-depth
analysis of DOI and UTAUT2 served as essential pillars for constructing a robust theoretical framework for
evaluating the reference model.
2.1. Diffusion of innovation
Roger’s original DOI theory [10] is been used since 1962, yet it’s still widely used to explain how
new technologies emerge. The theory specifies the elements contributing to the spread of novel innovations or
practices throughout a society [10]. Roger outlined the five main components of invention: “RA, CT, CX,
trialability, and observability” [10]. Among these five factors, RA, CX, and CT are the most impactful on
adopting different innovations [18].
2.1.1. Relative advantage
Rogers et al. [10] defines RA as “the degree to which an innovation is perceived as being better than
the idea it supersedes.” According to Lee [19], the RA of adopting new information systems (IS) innovation is
seen as a key indicator. The larger an organisation’s perceived benefit from an innovation, the higher the
possibility it could adopt it. RA has a significant relationship with the intention towards ACC [20]–[24].
2.1.2. Complexity
Rogers et al. [10] stated that CX is “the degree to which an innovation is perceived as relatively
difficult to understand and use”. According to Sahin [25], CX refers to the difficulty of understanding and
using innovations. To increase adoption, new technologies must be user-friendly and simple to use, whereas
adoption is less likely if innovations are more complicated to use. CX is linked negatively to decision-making
on adoption. CX has a significant relationship with the intention toward ACC [21]–[23], [26], [27].
2.1.3. Compatibility
Rogers et al. [10] states that “the degree to which an innovation is perceived to be consistent with
current values, experiences, and needs of potential adopters is called CT”. Innovations that are in line with
human norms and principles or social framework expectations are more likely to be accepted in a society.
According to Lertwongsatien and Wongpinunwatana [28], CT refers to technological and operational
requirements which must be compatible with the adopting organisation’s principles and technological needs.
The perceived high CT of innovation with current technology in the organisation may positively impact the
adoption process [11]. CT has a significant relationship with the intention toward ACC [20]–[24], [26].

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2.2. Unified theory of acceptance and use of technology 2
The theory is proposed by Venkatesh et al. [17]; it is the second iteration of UTAUT proposed by
Venkatesh et al. [16] by incorporating several new elements that play a role in determining the level of
acceptance of the technology. UTAUT2 includes the following factors; “performance expectancy, EE, social
influence, facilitating conditions, HM, PV, habit, awareness, BI, and use behavior” [17]. From these factors,
PE, EE, HM, PV, and BI are adopted to form the conceptual model of this study.
2.2.1. Performance expectancy
The PE refers to the degree to which an individual perceives that using a system will help them
improve job performance. It is a strong BI predictor in which an individual assumes that the system can help
to make job performance enhancements [16]. PE has a significant relationship with the intention to adopt cloud
computing [4]–[8], [29].
2.2.2. Effort expectancy
The EE refers to the level of ease of use associated with the use of IT. It is a BI predictor which
measures the level of easiness related to the use of the information system [16]. EE has a significant relationship
with the intention to adopt cloud computing [4]–[8], [29].
2.2.3. Hedonic motivation
The enjoyment or pleasure extracted from technology is known as HM. It is significant when assessing
technological acceptance and predicts BI to use technology [17]. HM has a significant relationship with the
intention to adopt cloud computing [4]–[6], [8].
2.2.4. Price value
The PV is described as the emotional relationship of users between the advantages provided by
applications and the monetary costs associated with their use. The cost and price system can greatly impact the
use of technology by users. The PV is favourable since it has been demonstrated that the technology’s
advantages are more than the expense. The PV positively affects BI to use and is determined as a predictor of
BI [17]. PV has a significant relationship with the intention to the ACC [4], [6]–[8], [21], [26], [30].
2.3. Behavioural intention
BI is “a person’s subjective probability that he will perform some behaviour” [12]. Schiffman and
Kanuk et al. [31] stated that “adoption or usage intention is the psychological decision-making process where
a user has a need, he or she would be induced to satisfy the need by searching relative information according
to his and her own experience and external environment, then evaluating and considering all information, and
deciding to use a product after comparison and judgment of another alternative”. BI has a significant
relationship with the ACC [4]–[8].
The independent and dependent variables have been explored in the previous studies as the factors in
technology adoption theories to enhance knowledge of how emerging technologies have been implemented.
There are different variations in the viewpoint of DOI and UTAUT2 adoption theories described. All possible
factors affecting the BI to adopt new technology have been described as integrated into a single comprehensive
model. Certain variables in each study are not equivalent in all studies. Some variables are important in the
study and have been chosen, while others are not appropriate.
3. METHOD
This study focuses on the BI of small and medium-sized IT companies in Sri Lanka to adopt cloud
computing. The conceptual framework illustrated in Figure 1 by evaluating with two technology adoption
theories: Rogers et al. [10] DOI and Venkatesh et al. [17] UTAUT2. For this study, a review of the literature
and tests done with SMEs in Sri Lanka’s IT industry showed that seven independent variables can be used to
make a conceptual model for cloud adoption. These variables include RA, CT, CX, PE, EE, HM, and PV.
In addition, a BI variable that acts as a mediator and ACC that acts as a dependent variable was found.
In addition, based on the literature, seven hypothetical relationships are constructed, as depicted in
Table 1. In order to verify the validity of the hypothesis, responses from 256 different IT companies in Sri
Lanka were gathered and analysed in the survey. The most senior officials from each organisation provided
their input for the data collection. In this study, quantitative research approaches were employed to collect data
in the form of a pre-structured questionnaire. The information gathered is then entered into version 27 of the
SPSS software, where it is coded and analysed. The dependability of the information gathered is then tested

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with cron batches alpha. AMOS software version 28 is employed to generate results to obtain the model fit of
the conceptual model.
Figure 1. Conceptual framework
Table 1. Research hypothesis
Hypothesis Relationship
H1 Behavioural intention mediates relative advantage towards the
adoption of cloud computing
H2 Behavioural intention mediates compatibility toward the adoption
of cloud computing
H3 Behavioural intention mediates complexity towards the adoption
of cloud computing
H4 Behavioural intention mediates performance expectancy towards
the adoption of cloud computing
H5 Behavioural intention mediates effort expectancy towards the
H6 Behavioural intention mediates hedonic motivation toward the
H7 Behavioural intention mediates price value towards the adoption
of cloud computing
4. RESULTS AND DISCUSSION
4.1. Descriptive analysis
The descriptive analysis carried out and the descriptive summary obtained on each factor including
mean and standard deviation with correlation matrix. We confirm that all variables’ average and standard
deviation are within acceptable ranges. According to the correlation estimations, all latent variables were
positively connected to one another.
The exploratory factor analysis carried out on all the factors as the results shown in Table 2,
the Kaiser-Meyer-Olkin (KMO) value for all the factors above 0.80 (>=0.60: [32], [33]), which is considered
as excellent. A single factor that explained on each factor is well above the threshold value (>60%: [32]) of the
total variation was extracted. The smallest factor loading on all the factors are more than 0.7 (>0.5: [32]), and
the smallest communality value in all the factors are more than 0.5 (>0.3: [33]) is also considered excellent.
The factors must be valid and reliable to be considered internally consistent and measured using an
acceptable scale. Validity is measured by the KMO value; if the value is more than or equal to 0.60 considered
acceptable, more than 0.70 is considered good, and over 0.80 is considered excellent [32], [33]. Cronbach’s
alpha measures the reliability; if the cronbach’s alpha is near 1 considered higher reliability of internal
consistency. Although the alpha less than 0.60 is considered poor, more than 0.70 is considered acceptable,
and over 0.80 is considered good [34]. The validity and reliability summary of the all factors are valid and
reliable.

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Table 2. Summary of exploratory factor analysis with validity and reliability
4.2. Structural equation modelling
SEM is an approach that was used to investigate the correlations between the variables in the research
model. SEM is a multivariate technique “combining aspects of factor analysis and multiple regression that
enables the researcher to simultaneously examine a series of interrelated dependence relationships among the
measured variables and latent constructs (variates) as well as between several latent constructs” [30].
A two-phase procedure was used to analyse SEM data in this research. The first phase was utilising
CFA to verify the items that measure the variable and evaluate the measurement model’s fitness. The second
phase was to create a structural model by establishing dependency connections between the study model’s
hypothesised variables. In this second phase of SEM, the hypotheses were tested. AMOS software was used
for the SEM analysis.
4.3. Measurement model fit with confirmatory factor analysis
CFA is a multivariate method that can be used to test the validity of a measurement theory. Using
CFA, it can be verified that the indicator values accurately match those of the latent variable. The main
objective of the CFA is to check if the data fits a certain model structure. The factors and relationships between
the variables in CFA are first identified based on a theory [32]. The model’s parameters are estimated using
the maximum-likelihood technique based on variance-covariance matrices [32]. In order to evaluate the
model’s fit, there are a number of fit indices that can be used. According to Awang [35], there are mainly three
model fit categories in CFA: absolute fit, incremental fit and parsimonious fit and they are indicated
respectively; RMSEA=0.049 (<0.08: [36]), CFI=0.945 (CFI >0.90: [37]) and normed chi-square=1.601 (<5.0:
[38]) are acceptable. As per the measurement model, all the items with a factor loading of more than 0.5 are
considered acceptable [32]. It has been confirmed that the data fit well with our model by meeting the
recommended threshold values for fit indices. The measurement model for the study was constructed with all
the items, as shown in Figure 2.
4.4. Structural model evaluation
According to Awang [35], the structural model is “the model that demonstrates the inter-relationships
among constructs in the study. The constructs are assembled into the structural model based on the hypothesis
stated in the theoretical framework”. A structural model is considered a measurement model with constraints.
In the measurement model, structural paths replace the correlations between the constructs. These paths show
how important the relationships between the constructs are. Also, only hypothesised direct links are drawn
between exogenous variables, with the exception of correlational links (covariances). These are the main
differences between measurement models and structural models [32]. Figure 3 shows the structural model with
model fit indices. The model fit indices absolute fit, incremental fit and parsimonious fit indicated respectively;
RMSEA=0.049 (<0.08: [36]), CFI=0.945 (CFI>0.90: [37]) and normed chi-square=1.601 (<5.0: [38]) are
acceptable. It has been confirmed that the data fit well with our model by meeting the recommended threshold
values for fit indices. However, it should be taken into consideration that the incremental fit measured via CFI
value is fairly lower than the required level in this situation. In contrast, the absolute and parsimonious fit
values are well in the accepted level.
Nonetheless, this model should be accepted and authorised because goodness-of-fit indices only
provide statistical significance [32], [39]–[41]. Besides this statistical significance, there are also
methodological and theoretical justifications in the social science studies those ought to be remembered before
declining a model. Thus, it ought to not just depend on statistical significance. Regarding this issue, Byrne [41]
detailed out the consideration and justification: “Global fit indices alone cannot possibly envelop all that needs
Factors KMO TVE
Smalle
r factor
loading
Smaller
commu-
nality
Cronbach’s
alpha
No.
of
items
Reliability
Relative advantage (RA) 0.883 71.757% 0.827 0.684 0.900 5 Higher reliability
Compatibility (CT) 0.864 71.985% 0.788 0.621 0.901 5 Higher reliability
Complexity (CX) 0.898 75.858% 0.851 0.724 0.920 5 Higher reliability
Performance expectancy (PE) 0.909 82.564% 0.897 0.805 0.947 5 Higher reliability
Effort expectancy (EE) 0.892 74.567% 0.824 0.679 0.914 5 Higher reliability
Hedonic motivation (HM) 0.873 77.385% 0.838 0.703 0.927 5 Higher reliability
Price value (PV) 0.841 63.284% 0.745 0.555 0.852 5 Good reliability
Behavioural intention (BI) 0.899 75.177% 0.843 0.710 0.917 4 Higher reliability
Adoption of cloud computing
(ACC)
0.940 78.395% 0.843 0.710 0.954 7 Higher reliability
Overall 0.960 70.943% 0.973 46 Higher reliability

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to be known about a model to judge the adequacy of its fit to the sample data. Exclusive reliance on goodness-
of-fit indices is unacceptable. Indeed, fit indices provide no guarantee whatsoever that a model is useful.
In fact, a model can fit well and yet still be incorrectly specified. Fit indices yield information bearing only on
the model’s lack of fit. More importantly, they can in no way reflect the extent to which the model is plausible;
this judgment rests squarely on the shoulders of the researcher. Thus, assessment of model adequacy must be
based on multiple criteria that take into account: theoretical, statistical, and practical considerations”. All in all,
it is noted that the structural model demonstrated the best-fit indices as shown in Figure 3.
Figure 2. Measurement model
4.5. Mediating relationships
According to Flurry et al. [32], Sekaran and Roger [34], the mediating variable emerges between the
moment that the independent variables begin to influence the dependent variable and the time that their effect
is felt on the dependent variable. The mediating effect is the “effect of a third variable/construct intervening
between two other related constructs” [30]. The study consists of mediating factor BI that mediates between
seven independent factors: RA, CT, CX, PE, EE, HM, and PV towards the dependent factor ACC.
Flurry et al. [32] described that the mediation is represented by the indirect effect formed from two
causal paths explained as full mediation and partial mediation. The “full mediation is a sequence relationship
proposing that an independent variable causes an intermediate variable, which in turn causes an outcome
variable (X→M→Y). Partial mediation is an effect when a direct relationship between a predictor and an
outcome is reduced but remains significant when a mediator is also entered as an additional predictor” [30].
Testing the mediation effect mainly two methods: sobel test and bootstrapping. Asadi et al. [30] stated
that the Sobel test is “a parametric statistical test for the significance of the indirect effect in mediation.
Criticised for low power, researchers have suggested using bootstrapping as an alternative test of significance”.
Further [42], explained that using AMOS with bootstrapping is one of the best methods and can be achieved
with a few steps. The structural model is performed with 2,000 bootstrapping samples. The results of

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standardised direct effects and standardised indirect effects are measured with a bootstrap estimate and two-
tailed significance values depicted in Table 3.
The bootstrapping test on mediation effect found that there is a partial mediating relationships of factor
BI is available between the factor RA and ACC, factor CT and ACC; the full mediating relationship of BI is
available between CX and ACC. Further, it is found that there is no mediation relationship of BI is available
between the factors PE, EE, HM, and PV with ACC.
Figure 3. Structural equation model
Table 3. Mediating relationships
Relationship Direct effect Indirect effect Result
RA→BI→ACC 0.304* 0.131* Partial mediation
CM→BI→ACC 0.214* 0.344* Partial mediation
CX→BI→ACC -0.19(NS) 0.078* Full mediation
PE→BI→ACC 0.270* 0.100(NS) No mediation
EE→BI→ACC 0.003 (NS) -0.016(NS) No mediation
HM→BI→ACC -0.100 (NS) 0.058(NS) No mediation
PV→BI→ACC 0.032 (NS) 0.008(NS) No mediation
*=p<0.05; NS-not significant
4.6. Hypothesis testing
This section addresses the hypotheses put forward in Table 1 in light of the SEM results. The final
model of the study, depicted in Figure 3 as the structural equation model, was used to test these hypotheses.
Since the study only focus on the BI on the adoption of cloud computing, the mediation relationship only
required to be analyses as shown in the Table 3. The regression weight results of the final structural model are
shown in Table 4, which is used to measure the p-value (<0.05: [32], [34]) of each relationship; the relationship

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between the factors is RA and ACC, CM and ACC, and CX and ACC are significant. The relationship between
the factors PE and ACC, EE and ACC, HM and ACC, and PV and ACC are not significant. The standardised
regression weights result of the final structural model are also shown in Table 4 measures the estimated value
or strength of each relationship. As per the analysis and results, it is concluded that the hypothesis H1-H3 were
supported: H1 and H2 supported with partial mediation and H3 is supported with full mediation. Further
hypothesis H4-H7 were not supported.
Table 4. Hypothesis testing using p-value and standardised estimates
Hypothesis Relationship P-value Estimate Decision
H1 RA→BI→ACC 0.003 0.131 Supported with partial mediation
H2 CM→BI→ACC 0.000 0.344 Supported with partial mediation
H3 CX→BI→ACC 0.010 0.078 Supported with full mediation
H4 PE→BI→ACC 0.122 0.100 Not supported
H5 EE→BI→ACC 0.744 -0.016 Not supported
H6 HM→BI→ACC 0.135 0.058 Not supported
H7 PV→BI→ACC 0.783 0.008 Not supported
5. CONCLUSION
The adoption of cloud computing is increasing in Sri Lanka due to its significant benefits. The research
focused on the BI to adopt cloud computing in SMEs in Sri Lanka, especially in IT industry. As highlighted,
SMEs in Sri Lanka are hesitant to use cloud computing for various reasons. The study discovered that three of
the seven independent factors that were tested contributed to the intention to adopt cloud computing: RA,
complexity, and CT. The independent factors performance expectation, EE, HM, and PV had no effect on the
intention to use cloud computing.
REFERENCES
[1] A. Mazumdar and H. Alharahsheh, “Insights of trends and developments in cloud computing,” South Asian Research Journal of
Engineering and Technology, vol. 01, no. 03, pp. 98–107, Nov. 2019, doi: 10.36346/sarjet.2019.v01i03.003.
[2] A. Sether, “Cloud computing benefits,” SSRN Electronic Journal, 2016, doi: 10.2139/ssrn.2781593.
[3] International Trade Administration, “Sri Lanka-Country Commercial Guide,” U.S. Department of Commerce, 2021.
https://www.export.gov/apex/article2?id=Sri-Lanka-Information-and-Communication-Technologies (accessed Mar. 02, 2023).
[4] F. H. Alhamazani, “Exploratory factor analysis toward adoption of cloud computing by faculty members in Saudi Arabian
Universities for Teaching And Learning,” International Journal of Computer Science and Network Security, vol. 20, no. 2, pp. 8–
15, 2020, [Online]. Available: http://paper.ijcsns.org/07_book/202002/20200202.pdf.
[5] A. M. A. Ireda, M. A. Ben-Mubarak, and A. Buhari, “Factors That influence an academic institution’s intention to accept cloudIOT:
a proposed framework,” 2019, doi: 10.2991/icoiese-18.2019.25.
[6] F. Nikolopoulos and S. Likothanassis, “Using UTAUT2 for cloud computing technology acceptance modeling,” in ACM
International Conference Proceeding Series, Mar. 2017, pp. 1–6, doi: 10.1145/3018896.3025153.
[7] D. Vrsajkovic, “Evaluating determinants of cloud computing acceptance in croatian SME organizations,” Rochester Institute of
Technology-Croatia, 2016. http://scholarworks.rit.edu/theses (accessed Mar. 02, 2023).
[8] R. A. Amponsah, J. K. Panford, J. B. Hayfron-Acquah “Factors affecting cloud computing adoption in a developing Country-
Ghana : using extended unified theory of acce acce ptance and use of technology (UTAUT2) model,” International Research
Journal of Engineering and Technology (IRJET), vol. 3, no. 11, pp. 59–76, 2016.
[9] A. Athambawa, M. G. M. Johar, and A. Khathibi, “Secure cloud adoption model: novel hybrid reference model,” Indonesian Journal
of Electrical Engineering and Computer Science, vol. 27, no. 2, pp. 936–943, Aug. 2022, doi: 10.11591/ijeecs.v27.i2.pp936-943.
[10] E. M. Rogers, A. Singhal, and M. M. Quinlan, “Diffusion of innovations,” An integrated approach to communication theory and
research. Routledge, pp. 432-448, 2014
[11] R. Drazin, “The processes of technological innovation,” in The Journal of Technology Transfer, vol. 16, no. 1, Lexington, 1991,
pp. 45–46.
[12] M. Fishbein and I. Ajzen, Belief, attitude, intention and behaviour: an introduction to theory and research, no. August. Reading
MA: Addison-Wesley, 1975.
[13] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly:
Management Information Systems, vol. 13, no. 3, pp. 319–339, Sep. 1989, doi: 10.2307/249008.
[14] V. Venkatesh and F. D. Davis, “Theoretical extension of the technology acceptance model: Four longitudinal field studies,”
Management Science, vol. 46, no. 2, pp. 186–204, 2000, doi: 10.1287/mnsc.46.2.186.11926.
[15] V. Venkatesh and H. Bala, “Technology acceptance model 3 and a research agenda on interventions,” Decision Sciences, vol. 39,
no. 2, pp. 273–315, 2008, doi: 10.1111/j.1540-5915.2008.00192.x.
[16] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: toward a unified view,”
MIS Quarterly, vol. 27, no. 3, pp. 425–478, 2003, doi: https://doi.org/10.2307/30036540.
[17] V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer acceptance and use of information technology: extending the unified theory
of acceptance and use of technology,” MIS Quarterly: Management Information Systems, vol. 36, no. 1, pp. 157–178, 2012, doi:
10.2307/41410412.
[18] L. G. Tornatzky and K. J. Klein, “Innovation characteristics and innovation adoption-implementation: a meta-analysis of findings.,”
IEEE Transactions on Engineering Management, vol. EM-29, no. 1, pp. 28–45, 1982, doi: 10.1109/TEM.1982.6447463.
[19] J. Lee, “Discriminant analysis of technology adoption behavior: a case of Internet technologies in small businesses,” Journal of
Computer Information Systems, vol. 44, no. 4, pp. 57–66, 2004.

 ISSN: 2502-4752
466
[20] K. K. Hiran and A. Henten, “An integrated TOE–DoI framework for cloud computing adoption in the higher education sector: case
study of Sub-Saharan Africa, Ethiopia,” International Journal of System Assurance Engineering and Management, vol. 11, no. 2,
pp. 441–449, Apr. 2020, doi: 10.1007/s13198-019-00872-z.
[21] U. M. Z. Usman, M. N. Ahmad, and N. H. Zakaria, “The determinants of adoption of cloud-based ERP of Nigerian’s SMES
manufacturing sector using toe framework and doi theory,” International Journal of Enterprise Information Systems, vol. 15, no. 3,
pp. 27–43, Jul. 2019, doi: 10.4018/IJEIS.2019070102.
[22] N. Al Mudawi, N. Beloff, and M. White, “Cloud computing in government organizations-towards a new comprehensive model,” in
Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable
Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019,
Aug. 2019, pp. 1473–1479, doi: 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00266.
[23] A. Alkhalil, R. Sahandi, and D. John, “An exploration of the determinants for decision to migrate existing resources to cloud
computing using an integrated TOE-DOI model,” Journal of Cloud Computing, vol. 6, no. 1, p. 2, Dec. 2017, doi: 10.1186/s13677-
016-0072-x.
[24] N. Kuruwitaarachchi, M. S. A. Yajid, A. Khatibi, and S. M. F. Azam, “Information technology factors influence the adoption to
ecommerce in small and medium scale organizations in Sri Lanka: a research agenda,” International Journal of e-Education, e-
Business, e-Management and e-Learning, vol. 10, no. 1, pp. 95–103, 2020, doi: 10.17706/ijeeee.2020.10.1.95-103.
[25] I. Sahin, “Detailed review of Roger’s diffusion of innovations theory and educational technology,” The Turkish Online Journal of
Educational Technology, vol. 5, no. 2, pp. 14–23, 2006, [Online]. Available: https://files.eric.ed.gov/fulltext/ED501453.pdf.
[26] O. Ali, J. Soar, and A. Shrestha, “Perceived potential for value creation from cloud computing: a study of the Australian regional
government sector,” Behaviour and Information Technology, vol. 37, no. 12, pp. 1157–1176, Dec. 2018, doi:
10.1080/0144929X.2018.1488991.
[27] P. R. Palos-Sanchez, F. J. Arenas-Marquez, and M. Aguayo-Camacho, “Cloud computing (SaaS) adoption as a strategic technology:
results of an empirical study,” Mobile Information Systems, vol. 2017, pp. 1–20, 2017, doi: 10.1155/2017/2536040.
[28] C. Lertwongsatien and N. Wongpinunwatana, “E-commerce adoption in thailand: an empirical study of small and medium
enterprises (smes),” Journal of Global Information Technology Management, vol. 6, no. 3, pp. 67–83, Jul. 2003, doi:
10.1080/1097198X.2003.10856356.
[29] C. Yong, J. Tham, S. M. F. Azam, and A. Khatibi, “the Acceptance of Mobile Payment and Sustainable Usage Intention in Malaysia:
an Analysis Based on Unified Theory of Acceptance and Use of Technology (Utaut),” European Journal of Economic and Financial
Research, vol. 5, no. 3, Dec. 2021, doi: 10.46827/ejefr.v5i3.1191.
[30] S. Asadi, M. Nilashi, A. R. C. Husin, and E. Yadegaridehkordi, “Customers perspectives on adoption of cloud computing in banking
sector,” Information Technology and Management, vol. 18, no. 4, pp. 305–330, Dec. 2017, doi: 10.1007/s10799-016-0270-8.
[31] L. W. G. Schiffman and L. L. Kanuk, Consumer Behavior Consumer behavior, vol. 27, no. 23. Chicago, New York: Dryden Press,
2015.
[32] B. Flury, F. Murtagh, and A. Heck, Multivariate data analysis., Eighth edi., vol. 50, no. 181. Cengage: Andover, Hampshire, 1988.
[33] J. Pallant, SPSS survival manual: A step by Step guide to data analysis using SPSS for windows Vversion 15, 3rd ed. Open University
Press., 2007.
[34] U. Sekaran and B. Roger, Research methods for business, Seventh ed., vol. 2, no. 1. Chichester, 2016.
[35] Z. Awang, “A handbook on structural equation modeling using AMOS,” Kelantan: University Technology MARA Press, 2012.
[36] M. W. Browne and R. Cudeck, “Alternative ways of assessing model fit,” Sociological Methods & Research, vol. 21, no. 2, pp.
230–258, Nov. 1992, doi: 10.1177/0049124192021002005.
[37] P. M. Bentler, “Comparative fit indexes in structural models,” Psychological Bulletin, vol. 107, p. 238, 1990.
[38] H. W. Marsh and D. Hocevar, “Application of confirmatory factor analysis to the study of self-concept. first- and higher order factor
models and their invariance across groups,” Psychological Bulletin, vol. 97, no. 3, pp. 562–582, May 1985, doi: 10.1037/0033-
2909.97.3.562.
[39] P. M. Bentler and C. P. Chou, “Practical issues in structural modeling,” Sociological Methods & Research, vol. 16, no. 1, pp. 78–
117, 1987, doi: 10.1177/0049124187016001004.
[40] J. C. Anderson and D. W. Gerbing, “Structural equation modeling in practice: a review and recommended two-step approach,”
Psychological Bulletin, vol. 103, no. 3, pp. 411–423, 1988.
[41] B. M. Byrne, Structural equation modeling with amos: Basic concepts, applications, and programming, third edition, 3rd ed. New
York: Routledge, 2016.
[42] J. Gaskin, “‘Mediation concepts and bootstrapping in AMOS’, gaskination’s statistics.,” 2016. http://youtube.com/Gaskination
(accessed Mar. 02, 2023).
BIOGRAPHIES OF AUTHORS
Aiman Athambawa received his B.Sc. (Hons) degree in information technology
from Sheffield Hallam University, UK in 2007. His M.Sc. degree in computer forensic and
system security from University of Greenwich, UK in 2011 and is currently reading for his
doctor of philosophy at Management and Science University, Malaysia. His research interest
includes cloud computing, cyber security, computer network and the internet of things. He can
be contacted at email: matheeh@yahoo.com.

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Prof. Dr. Md Gapar Md Johar is senior vice president research, innovation,
technology and System of Management and Science University, Malaysia. He is a professor in
software engineering. Ph.D. in computer science, M.Sc. in data engineering, and B.Sc. (Hons)
in computer science and is a certified e-commerce consultant are among his qualifications.
Working and teaching in several organisations for more than 40 years has given him a broad
range of expertise. His research interests include data mining, learning content management
system, blended assessment system, knowledge management system, RFID, e-commerce,
character recognition, image processing, and healthcare management system. He can be
contacted at email: mdgapar@msu.edu.my.
Prof. Ali Khatibi has spent 39 years in academia and industry, holding various
prominent academic and administrative roles at The Management and Science University and
contributing to research, teaching, and administration. a marketing professor and SVP He has
been awarded as senior research fellow, has received the gold and silver medals for “Invention
and Innovation research,” has over 300 publications, has served as editor-in-chief, and has
written many books. He can be contacted at email: alik@msu.edu.my.

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