For this assignment you will read the Amron et al. peer-reviewed journal ?article on cloud computing linked in Resources. As you read

TELKOMNIKA Telecommunication, Computing, Electronics and Control

Vol. 19, No. 1, February 2021, pp. 124~133

ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018

DOI: 10.12928/TELKOMNIKA.v19i1.17883  124

Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA

Cloud computing acceptance among public sector employees

Mohd Talmizie Amron1, Roslina Ibrahim2, Nur Azaliah Abu Bakar3 1Universiti Teknologi MARA, Terengganu, Malaysia

2,3Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

Article Info ABSTRACT

Article history:

Received Jun 4, 2020

Revised Jul 20, 2020

Accepted Aug 27, 2020

Cloud computing is one of the platforms that drive organisations and users to be better prepared for a simpler computing platform and offers significant

benefits to the quality of work. The transition from conventional computing to

the virtual world helps organisations to maximise their potential. However, not

all users can accept cloud computing adoption. Failure to understand the

factors of user's acceptance will negatively impact the organisation's mission

of empowering the technology. Therefore, this study proposes to assess to

what extent the users are accepting cloud computing. This study adopts the

unified theory of acceptance and use of technology (UTAUT) and six

technological and human factors assessed for the Malaysian public sectors.

Survey data from several ministries were analysed using partial least squares-

structural equation modelling (PLS-SEM). The study found out that

performance expectancy, compatibility, security, mobility, information

technology (IT) knowledge, and social influence had a significant impact on

the user's intention to accept cloud computing. The results of this study

contribute to a clear understanding of the factors affecting the Malaysian

public sectors about cloud computing.

Keywords:

Acceptance

Behavioural intention

Cloud computing

PLS-SEM

Public sector

UTAUT

This is an open access article under the CC BY-SA license.

Corresponding Author:

Mohd Talmizie Amron

Faculty of Computer and Mathematical Sciences

Universiti Teknologi MARA, Cawangan Terengganu

21080 Kuala Terengganu, Terengganu, Malaysia

Email: [email protected]/[email protected]

1. INTRODUCTION Recent technological advancements have brought a new dimension to the patterns of computerisation.

Previously, every organisation competed in the information communication and technology (ICT)

infrastructure with a variety of tools, devices, hardware, software, and more. However, in today's rapidly

changing technology, with the transition to the industrial revolution 4.0 (IR 4.0) environment, it has opened a

new dimension to the world of computing. The emergence of cloud computing technology as a new platform

for computing has opened the eyes of technology industry players to further benefit from this innovation.

Many studies have proven that this technology provides many benefits to the industry and users such as its

ability to reduce operating costs, improve collaboration, more secure security levels, and more mobile

accessibility [1, 2]. Cloud computing allows more users and organisations to share resources that are optimised

for their users. This scenario will reduce user dependence on hardware and software installed on an individual

workstation.

In 2018, the Asia Cloud Computing Association (ACCA) report listed Southeast Asian countries in

cloud computing implementation. The report placed Malaysia at 8th, far behind Singapore at the top [3]. The

report stated that Malaysia had a high potential for developing cloud computing applications as Malaysia

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government had a clear policy and strategy to enable the delivery of cloud-based public services as well as to

drive the private sectors' adoption of cloud technologies. However, some focus needs to be improved, such as

physical cloud infrastructure and internet speed to effectively reach the aims of the Malaysian Public Sector

ICT Strategies [4].

The Government of Malaysia has introduced a cloud-based unified communication and collaboration

services as an initiative to enhance cloud computing technology in the public sectors. This service is a platform

that integrates all communication channels such as email, live telecast calls, video conferences, instant

messaging, and big transfer files application. The centralisation of communication channels for ministries and

government agencies in the cloud is an effort to optimise the use of existing resources as well as a more

comprehensive saving effort. The implementation of the service known as MyGovUC covers all ministries and

almost all federal-level agencies since 2017.

However, according to reports of impact studies conducted by the regulatory agency on the service,

the use of this service was significantly lower than the number of account holders across Malaysia. The report

found out that 75% of users only used email applications, while only 43% used teleconferencing applications

and only half used big mail transfer applications. The study concluded that this service usage rates other than

email were due to a lack of knowledge and skills among consumers, a lack of infrastructure that could support

teleconference applications, and poor awareness of services.

Consequently, this study finds out that there is a significant problem in the adoption of cloud-based

services as its implementation cannot be matched with the usage of services and applications offered.

MAMPU's report [5] showed that there is a gap that needs to be addressed, which is to determine factors that

influence these employees to accept cloud computing in their daily work. Then, a research model that could

evaluate the public sector employees’ acceptance of cloud-based services is developed, validated, and tested.

This study aims to identify the factors that can be considered, which can affect the cloud-based applications

used by the Malaysian public sectors. This study adopts the unified theory of acceptance and use of technology

(UTAUT) and six technological and human factors assessed for the Malaysian public sectors.

2. LITERATURE REVIEW 2.1. Cloud computing background

Cloud computing can be defined as “a model for enabling ubiquitous, convenient, on-demand network

access to a shared pool of configurable computing resources that can be rapidly provisioned and released with

minimal management effort or service provider interaction” [6]. Cloud computing is used to share resources

(data and applications) in a cloud platform that host space over the internet. There are three types of deployment

model; software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) [6].

Besides, there are three types of service models in cloud computing; public cloud (provides services and

infrastructure to the public and organisations in a shared manner), private cloud (provides dedicated services

and support to one organisation), and hybrid cloud (a combination of public and private cloud). According to

the report by RightScale [7], a public cloud used for the business sector is 33% compared to the hybrid cloud,

which is 28% and private cloud, which is 17%.

2.2. Information technology innovation acceptance

The acceptance of new technologies by consumers or organisations varies depending on how

technology is going them perform the task faster and better [8]. The skill and magnitude of technology are tools

to facilitate the job, but in many issues and situations, its efficacy and benefits for users are subjective.

Davis [9] defined acceptance as the user's decision on how and when to use the innovations. Thinking at the

issues which often prevent users from embracing and using technologies, many concerns need to be addressed

before users or organisations in adopting cloud computing. Multiple studies were done to assess the user's

acceptance of cloud computing at both individual levels [10, 11] and organisational levels [12, 13].

Additionally, these studies incorporated other factors which may influence the acceptance of the user in diverse

situations. Theory of reasoned action [14], Theory of planned behavior [15], technology acceptance model [9],

diffusion of innovation [16], and task-technology fit [17] are among the theories used in the acceptance of

innovation. Meanwhile, UTAUT [18] is a great research framework incorporating eight acceptance theories

based on groundbreaking studies of individual acceptance. The UTAUT is intended to explain the user's

intention to use an IS and subsequent behaviour. Thus, UTAUT is adopted in this study as an underlying

theoretical framework to explore the acceptability of users of cloud computing in the Malaysian public sector.

2.3. UTAUT

Venkatesh et al. [18], developed UTAUT, which clarified the user's intention to use IS and subsequent

usage behaviour. The strength of UTAUT is that it focuses on so many models and gives an investigator a

wider view of all current models [19]. Applied research has been comprehensive on the UTAUT model. This

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model offers a framework that does not only describes information technology (IT) and IS adoption but also

explains how these technologies and systems are used [20]. The UTAUT model contributes substantially to the

study of technology acceptance and uses because it can integrate different TAMs [18]. Table 1 shows the

authors, areas of study, and results of the study that used the UTAUT framework in their study. The results of

previous studies using the UTAUT framework showed different research findings, especially with various

suggestions on other factors relevant to the context of the study.

Table 1. The studies that used the UTAUT framework Author Area of study Results/Findings

[21] RFID usage in HEI All four factors of UTAUT are significant in the study.

[22] e-Learning adoption All four factors of UTAUT are significant in the study.

[23] On-demand services user acceptance

Using UTAUT2 and incorporated with DOI. Significant factors: personalisation, compatibility, social influence, and perceived risk.

[24] Mobile banking

adoption

Using UTAUT2. Significant factors: Performance expectancy, effort expectancy, social

influence, habit, hedonic motivation, and perceived risk. [25] Impact of social media

technology

Incorporated with other factors. Significant factors: utilitarian value, hedonic value and social

influence.

[26] Web-based services acceptance

All four factors of UTAUT are significant in the study.

[27] Online banking adoption Incorporated with other factors. Significant factors: brand trust, performance expectancy,

perceived risk, initial trust. [28] Internet banking

adoption

Incorporated with other factors. Significant factors: performance expectancy, effort

expectancy, compatibility, innovativeness, and perceived technology security.

3. RESEARCH MODEL AND HYPOTHESIS DEVELOPMENT This study proposes a research model to understand the cloud computing acceptance trend in the

Malaysian public sectors. It is comprised of three factors that originate from the UTAUT model, namely

performance expectancy, effort expectancy, and social influence. They have also incorporated six additional

factors (compatibility, security, mobility, IT knowledge, top management support and awareness) that derive

from a related study in cloud computing acceptance. Figure 1 depicts the proposed model of the study.

Figure 1. Research proposed model

3.1. Technological factors

The technological factors focus on technological aspects linked to cloud computing, features, and

factors that make this technology acceptable. These factors include performance expectancy, effort expectancy,

compatibility, security, and mobility. Each factor is explained by the basis of the selection of those factors. The

hypothesis of these factors to the acceptance of cloud computing technology is also included in this section.

a. Performance expectancy Performance expectancy refers to the degree to which cloud computing is used in daily work, thus

strengthening the perceptions of individuals about innovations. Venkatesh et al. [18] also stressed that the

performance expectancy, as one assumes, the program would help them to achieve job performance gains. This

aspect also helps people who embrace innovation to have clear advantages compared to others [29]. Before

using cloud computing, the positive impacts are among the most important things organisations evaluate. The

organisation is looking for lucrative returns, and much investment is proliferating. Therefore, it is appropriate

to argue that performance expectation has a positive impact on the acceptance of cloud computing. Hence, the

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study proposes that: H1. Performance Expectancy has a positive influence on behavioural intention to accept

cloud computing.

b. Effort expectancy Effort expectancy described the degree of ease associated with system use [18]. User experience is

important to indicate an individual's comfort while using technology. The invention should be useful and

helpful. However, innovation is hard to accept because it is difficult to learn, not user-friendly, and too

complex. It would not fully exploit new technology. Bozan et al. [30] addressed this component as one of the

key factors and found out that it had changed considerably among users in their relationship with behavioural

intentions. Thus, individuals with adequate expectations of effort have a more definite intention towards

accepting cloud computing. Hence, the study proposes that: H2. Effort Expectancy has a positive influence on

behavioural intention to accept cloud computing.

c. Compatibility According to Rogers [16], compatibility refers to the degree of perceived innovation by current values,

past experiences, and desires of future adopted people. A less vague concept of a future supporter is more

compatible. Technology can either be consistent or incompatible 1) with sociocultural values or beliefs, 2) with

innovations that had previously been implemented or 3) with user requirements for innovation. Sallehudin [31]

explained that compatibility of the technological innovation with an existing infrastructure or technology either

sped up or delayed its organisational acceptance rate. In additions, it is essential to ensure that existing

infrastructure and systems are perfectly fit for the innovations to be applied so as not to be harmful after they

are used. Hence, the study proposes that: H3. Compatibility has a positive influence on behavioural intention

to accept cloud computing.

d. Security Security aspects are critical in ensuring the protection of data and information stored in cloud

computing. Information system and technology security generally focus on protecting three main aspects of

security, namely confidentiality, integrity, and availability or known as "CIA" [32]. According to

Singh et al. [33], the cloud model based on the virtual machine environment reveals stored and shared data on

the cloud becomes vulnerable for the security breach. Therefore, it is essential for a standard security

mechanism that can be applied and implemented by all stakeholders, including service providers. Hence, the

study proposes that: H4. Security has a positive influence on behavioural intention to accept cloud computing.

e. Mobility Mobility allows applications to use cloud computing to conveniently connect over the internet, which

is one of the technology features. Taib et al. [34] stated mobility as a ubiquitous connection which allows users

to access anytime and anywhere using the services remotely. Furthermore, mobility is the main predicted factor

for the adoption of the new mobile innovation by potential users. This should be an indicator of user acceptance

as reviewed by Saxena [35], which stressed the importance of mobility factor to implement new mobile and

electronic innovation. Hence, the study proposes that: H5. Mobility has a positive influence on behavioural

intention to accept cloud computing.

3.2. Human factors

Human factors are viewpoints in assessing human response to technology. There are four factors; IT

knowledge, top management support, social influence, and awareness. Each human factor is explained by the

basis of the selection of those factors. The hypothesis of these factors to the acceptance of cloud computing

technology is also included in this section.

a. IT Knowledge Each individual must be armed with IT knowledge in order to accept new technology. The potential

of employees to utilise technology should be observed in order not to disrupt their adoption. According to

Sallehudin et al. [31], technology-savvy employees will lead to the adoption of IT technologies through

knowledge and innovation transformation. Competent staff can develop innovation and the need for new

technologies. This scenario provides the employee with added value in constantly seeking space to improve

work productivity. The internal expertise or IT knowledge of employees in the company is another key element.

The adoption of innovation is also expected to have an impact on IT or non-IT employees [36]. Hence, the

study proposes that: H6.IT knowledge has a positive influence on behavioural intention to accept cloud

computing.

b. Top management support Support from top management is important for the successful implementation of innovation in the

organisation. Top management is a higher-level group managing the policies and decisions of the organisation.

A great move in the acceptance process is critical, and it will lead to the successful implementation of any

project. According to Sallehudin et al. [31], in the decision process, the power of top management serves as an

agent of transition. Top management support in cloud computing implementation can ensure employees'

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efficiency and reaction. Thus, this factor refers to how the top management support affects the daily tasks of

the individual employee. Hence, the study proposes that: H7. Top Management Support has a positive influence

on behavioural intention to accept cloud computing.

c. Social influence According to Venkatesh et al. [18], social influence refers to the degree to which a person sees what

others think about how the new system should be used. In this study, social influence tests how people are

affected by their environments by motivating them to consider and use cloud-based applications. A study by

Farah et al. [24] showed that the user relies heavily on feedback and experience from others who use new

technology in the first place. The study also showed that the most significant factor in estimating the outcome

for a user to take mobile applications is social influence. Hence, the study proposes that: H8. Social Influence

has a positive influence on behavioural intention to accept cloud computing.

d. Awareness Awareness refers to the extent to which a person is aware of cloud-based applications services.

Implementing innovation will not work unless the innovation is used. Therefore, it is essential to provide people

with knowledge of the technologies. The use of new technology should not only be acknowledged to users but

also the opportunities to be utilised. When users realise the benefits of one technology, they are motivated and

attempt to use it. Hence, the study proposes that: H9. Awareness has a positive influence on behavioural

intention to accept cloud computing.

4. RESEARCH METHODOLOGY Quantitative data were collected using a questionnaire, and SmartPLS 3.2 software was used to

analyse the data and validate the research model. However, before that, three activities need to be considered

to ensure that the research objectives are met. These activities include instrument development, sample

preparation, and data collection. Besides, common method bias is also emphasised in this study as it involves

single-source research.

4.1. Instrument development

Based on the seven factors included in the measurement model, the instrument was developed to

collect data. Items of measurement for each factor were adopted from previous studies and had gone through

a validity and reliability process, which were face validity and expert content validity [37].

4.2. Sample

The study population comprises of Malaysian public sectors' employees who are using MyGovUC

application services. In order to determine the sample size, the G*Power software has been used to calculate

the minimum required sample size with effect size medium (0.15), the power needed as 0.8 and eleven

predicators. The minimum number of respondents needed is 114. The individual sample is a public sector staff

comprising various positions from various government agencies that use MyGovUC services in their daily

work. Respondents must be of those who have access to MyGovUC and are experienced in using MyGovUC.

A convenience sampling and snowball technique were employed as they helped make the survey material

easier to distribute to target groups. Convenience sampling is a type of sampling whereby recruiting the

respondents that are most easily accessible [38]. Snowball technique involves the recruitment of respondents

who in turn, recruit other respondents.

4.3. Data collection

The data collection was done via three approaches which are surveying using Google Form, emailing

the questionnaire to the public relations officer (PRO) of each ministry for the escalation within the ministry

as well as distributing the paper surveys at the ministry offices. A total of 200 paper surveys were distributed,

and 190 forms were returned (95% response rate). A total of 169 responses were received from Google form,

resulting in a total of 359 responses. This response rate is above Baruch's [39] recommendation of between

50% and 80% for an overall survey response. Only eight responses were rejected for not meeting the set criteria

and having incomplete answers. As a result, a total of 351 valid questionnaires with a response rate of 94%

were used for further data analysis. The respondents' profile of the study is shown in Table 2.

4.3. Common method bias

Method bias can be an issue since the data collection only involved a single source. Harman's single

factor test was carried out to determine whether bias was on the questionnaire data. Bias occurred when

Harman's single factor test resulted in the variance value greater than 40% [40]. In this research, Harman’s

single factor test showed that the first factor had a value of 37.10% variance (less than the 40% limit of the

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Cloud computing acceptance among public sector employees (Mohd Talmizie Amron)

129

total variance). In this regard, the results of this test showed that the method of sampling of this study was

independent of the common bias variant.

Table 2. Total of respondents (𝑁 = 351) Demographic Category Frequency

Gender Male 148

Female 203

Age Less than 25 55 26-35 142

36-45 111

More than 46 39 N/A 4

Academic Qualification Diploma 76

Bachelor 146

Master 74

PhD 15 Others 40

5. DATA ANALYSIS AND RESULTS This study employs a PLS-SEM data analysis approach to measure the factors that influence a user's

intention to accept cloud computing. The PLS-SEM is chosen in this study because its goals are to predict key

target variables, and the research model is an extension of an existing theory [41]. Therefore, predicting factors

that influence the intention of the consumer to consider cloud computing is appropriate for the objective of this

study. This study assesses multivariate normality using online Web Power tools. The analysis shows that the

p-value of Mardia's multivariate skewness and kurtosis coefficients are less than 0.05, which confirm

multivariate non-normality data. The result is available at https://webpower.psychstat.org/models/kurtosis/

results.php?url=1367ad76416b40c8f6d4e2750c8804f2.

5.1. Measurement model

As recommended by Ramayah et al. [42], the analysis should be carried out in three main assessment

criteria, namely the internal consistency reliability, convergent validity, and discriminant validity. Table 3

shows the validity of the measurement model by measuring the loadings, composite reliability (CR), average

variance explained (AVE), and variance inflation factors (VIF). Based on [43], values in Table 3 are passed

the threshold value for all criteria. Thus, the measurement model is accepted. The list of measurement

items can be found at https://bit.ly/2O3Mb3e. The study reports the discriminant validity using the

heterotrait-monotrait (HTMT) ratio. If the HTMT value is greater than 0.85 [44], it indicates a severe issue in

discriminant validity. Table 4 shows that discriminant validity has been established as all values are less

than 0.85.

5.2. Structural model

The collinearity test is performed before the evaluation of the structural model. As indicated in

Table 2, the results of collinearity test (VIF) are lower than the threshold value of 5.0 [43]. The structural model

was assessed using the standard beta value, t-values, predictive relevance (𝑄2), and the effect sizes (𝑓 2). Table 5 shows the assessment results of the structural model for all hypotheses.

It is shown that PER, COM, SEC, MOB, ITK, and SOC have a significant relationship with the

intention to accept cloud computing with PER (𝛽 = 0.235, 𝑡 = 3.324, 𝑝 < 0.05), COM (𝛽 = 0.123, 𝑡 = 1.731, 𝑝 < 0.05), SEC (𝛽 = −0.136, 𝑡 = 2.943, 𝑝 < 0.05), MOB (𝛽 = 0.429, 𝑡 = 8.051, 𝑝 < 0.05), ITK (𝛽 = 0.086, 𝑡 = 1.938, 𝑝 < 0.05), and SOC (𝛽 = 0.810, 𝑡 = 2.667, 𝑝 < 0.05. Thus, H1, H3, H4, H5, H6, and H8 are supported, while H2, H7, and H9 are rejected.

5.3. Evaluating the effect sizes

The value for the determination coefficient (R2 = 0.632) is the sum of variance in the dependent

variable structure described in the research model by all the independent variables. This study suggests that the

independent variables (PER, COM, SEC, MOB, ITK, and SOC) normally explain 63.2% of variances in

intention. As per Hair et al. [43], 𝑄2 refers to the measure of how the model and its parameter estimates and reconstructs well-observed values. The model has predictive relevance where the value of 𝑄2 > 0. Since the value of 𝑄2 is 0.567, the cross-validated redundancy measures indicate that the structural model for this study has predictive relevance. The details results were presented in Table 6.

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Table 3. Measurement model result Construct # Loading (>0.5) CR (>0.7) AVE (>0.5) VIF (<0.5)

Performance Expectancy (PER) 1 0.860 0.791 0.938 3.008 2 0.913

3 0.906

4 0.877 Effort Expectancy (EFF) 1 0.880 0.776 0.945 3.534

2 0.894

3 0.864 4 0.899

5 0.867

Compatibility (COM) 1 0.851 0.795 0.939 3.105 2 0.877

3 0.922

4 0.914 Security (SEC) 1 0.844 0.713 0.952 1.855

2 0.862

3 0.871 4 0.875

5 0.818

6 0.872 7 0.791

8 0.818

Mobility (MOB) 1 0.901 0.817 0.957 1.889 2 0.899

3 0.915

4 0.895 5 0.910

IT Knowledge (ITK) 1 0.878 0.788 0.949 1.661

2 0.894 3 0.893

4 0.859

5 0.913

Top Management Support (TOP) 1 0.858 0.718 0.939 2.598

2 0.883

3 0.780 4 0.888

5 0.803

6 0.867 Social Influence (SOC) 1 0.841 0.746 0.922 3.047

2 0.888

3 0.871 4 0.854

Awareness (AWA) 1 0.835 0.663 0.887 2.312

2 0.716 3 0.862

4 0.836

Behavioural Intention (BEH) 1 0.922 0.881 0.957 1.000 2 0.949

3 0.944 Actual Use (USE) 1 0.938 0.755 0.901 –

2 0.929

3 0.722

Table 4. Discriminant validity–HTMT analy USE AWA COM EFF ITK MOB PER BEH SEC SOC TOP

USE

AWA 0.681

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