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City University eJournal of Academic Research (CUeJAR)e-ISSN : 2682-910XCUeJAR Homepage: https://www.city.edu.my/CUeJAR

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Received: 25th October 2019 | Revised: 11th November 2019 | Accepted: 22nd November 2019

The influence of technology, organizational and environmental factors on cloud computing adoption by Malaysian IT small and medium enterprises

Vickneshwaran Jayaramana, Anuar Shah, Bali Mahomedb, Jayaraman Munusamyc a Centre for Postgraduate Studies, Asia Metropolitan University, b Faculty of Economics and Management,

Universiti Putra Malaysia, c City Graduate School, City University Malaysia

Abstract Introduction: Malaysia’s adoption rate of cloud computing (CC) remains low, despite the Government’s initiatives to create a digital economy and accelerate technology adoption. In this context, this research investigated the adoption of CC by Malaysian IT-based small and medium enterprises (SMEs) in the Klang Valley, Malaysia. Based on the literature, the Technology-Organization-Environment (TOE) framework was considered appropriate for this study with the incorporation of the variables of the organizational context (OC), environmental context (EC), cloud computing risks (CCR) for the SMEs’ adoption of cloud computing (A). Methodology: A quantitative approach, with a web survey, was used to analyse the factors that influence the adoption of CC by SMEs. The targeted population comprised Malaysian IT-SMEs in the Klang Valley. 300 questionnaires were distributed, and the response rate was 57% translating to 170 usable and complete questionnaires. Partial Least Square – Structured Equation Modelling (PLS-SEM) was used to analyse the data. Findings and discussion: The findings from the statistical analysis revealed that both the organizational and environmental contexts significantly influence the SMEs’ CC adoption. While there was a similar result for CC risks, it however indicated a negative influence towards CC adoption. Conclusion and recommendations: The research findings provide for a greater understanding of the important factors for the adoption of CC by the IT SMEs. The findings extend the body of knowledge, particularly to SMEs owners and managers, on the factors that influence the adoption of CC technology and the recommendations offered can lead to increasing the adoption rate by Malaysian IT SMEs. Keywords: Cloud computing, TOE, environmental context, organizational context, cloud computing risks.

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City University eJournal of Academic Research (CUeJAR), 1(2) 2019; 125-139

1. Introduction Several developed and developing countries recognize the significance of small and media enterprises (SMEs) for creating jobs, reducing poverty reduction and GDP growth (Schroder & Schmitt-Rodermund, 2006). In Malaysia, SMEs are major contributors in terms of GDP, exports and employment (Borneo Post Online, 2015). In 2015, there were 645,136,000 SMEs. They accounted for 97.3% of all business enterprises, contributed 32.7% of Malaysia’s GDP and 57.4% of total employment (ACCA, 2015). E-commerce, mobile and digital technologies are generating new electronic markets (SME Corporation, 2012), which requires Malaysian SMEs to embrace Industrial Revolution 4.0 and new technologies for remaining competitive. Although they have to emphasize on innovation (The Edge, 2017), they have limited innovative capacity due to resource constrains, talent management and access to technology (Beh, 2013). This is reflected by 73% of SMEs not using Information and Communications Technology (ICT) for the business practices. Of the remaining 17% who did so, only 12% had their own websites (ACCA, 2015). CC is a platform for the development of computational solutions for multiple fields of knowledge (Carlos, Elisabete, Paulo & Pedro, 2017), as it offers cost-saving mechanisms and increased efficiency to organizations which opt do adopt cloud deployment as their IT solutions. However, Malaysian SMEs were only ranked as eleventh in the 2015 SME Cloud Computing Market Attractiveness Index among the 14 countries covered by a survey conducted by the Asia Cloud Computing Association (ACCA, 2015). The Market Attractiveness Index used 5 parameters namely addressable market, early adoption, demand drivers, affordability and support. In view of this low adoption rate, the aim of the study was to identify the challenges that Malaysian SMEs face for adopting CC and to identify the factors that drive the adoption of CC. This paper has 5 Sections, and Section 2 reviews the pertinent literature with the aim of identifying the key variables and the development of 3 hypotheses. Section 3 presents the methodology while Section 4 presents the results. The final Section presents the conclusion and recommendations. 2. Literature review This Section commences with a review of the literature on the Malaysian Government’s support programs for SMEs in particular the SME Master Plan and the Digital Malaysia (DM) before reviewing the literature on CC. It then justifies the use of the Technology, Organization and Environmental (TOE) framework, developed by Tornatzky & Fleischer (1990), as the framework for this research.

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The Malaysian Government support programs Malaysia’s goal is to become a developed high-income country by 2020 and this largely depends on the development of SMEs (Omar, Arokiasamy & Ismail, 2009). The Eleventh Malaysia Plan (11MP) 2016-2020 emphasizes the economic significance of SMEs and their pivotal roles in national economic development (Economic Planning Unit, 2016). Accordingly, Malaysia SME benefit from a range of Government assistance programs to enable them to address the new demands of the highly competitive international market place (Chong, 2012). Mobile and digital technologies are transforming businesses by creating opportunities to drive growth (Laudon & Traver, 2011). E-commerce has generated new electronic markets and strengthened the relationship between the firms and the customers, although they are separated geographically. A survey conducted by the SME Corporation found that only 28% of 965 survey respondents were engaged in e-commerce (SME Corporation Malaysia, 2012). In view of this low response rate, the Government’s support policies, as stated in the SME Master Plan, emphasize on information and communication technology for e-commerce adoption (SME Corporation Malaysia, 2012). The SME Master Plan has six high impact programs for creating highly competitive SMEs. One of them is the technology commercialization platform which is designed to promote innovative ideas from the proof of concepts to the commercialization stage. Another policy initiative is DM, announced in 2016, which emphasizes on maximizing the use of digital technologies. DM is also aimed at creating a new class of digital entrepreneurs and the creation of an eco-system that promotes the pervasive use of ICT in all aspects of the economy (MCMC, 2016). Subsequently, the MCMC was reorganized and established as the Malaysia Digital Economy Corporation (MDEC), to direct and to oversee the Multimedia Super Corridor (MSC) and the national ICT development initiatives. The initiatives include cloud computing, with a program providing 6-month subscription fee rebates or up to RM1,500 of total subscription fee from any cloud Software-as-a-Service (SaaS) solutions from any MSC Malaysia Status companies (ACCA, 2015). Another significant initiative is the Malaysian-ecommerce Master Plan which encourages the adoption if e-commerce by strengthening the regulatory framework for creating a critical mass of internet users and introducing electronic payments. These initiatives have increased internet penetration and the online purchases of goods and services (MCMC, 2016). Cloud computing CC which dates to the 1960s is viewed as a promising trend in the IT industry. The United States National Institute of Standards and Technology (NIST) defined CC as a paradigm which enables omnipresent, easy, on-demand network access to a shared pool of configurable computing resources, such as networks, databases, storage, applications and services, which can be quickly



and released with minimal management or service provider intervention (Mell & Grance, 2011). CC has provided a shift in the trend in which computing is seen as a service instead as a product (Khajeh-Hosseini, Greenwood, Smith & Sommerville, 2011). CC gives users the autonomy to process, manage and store data efficiently at high speeds at reasonable prices. Customers of cloud computing are not required to install any application or software and it allows them to access their resources from any part of the world with the convenience of an internet connection. Users are no longer tied to their desktop computers and it is easier for them to collaborate from different locations of the globe (Miller, 2009). The Technology-Organization-Environment (TOE) framework The TOE framework developed by Tornatzky & Fleischer (1990) has three constructs namely OC, EC and CC risks that aim to identify their influence towards the adoption and implementation of a technological innovation. The framework suggests that adoption is influenced by technology development (Kauffman & Walden, 2001), organizational conditions, business and organizational reconfiguration (Chatterjee, Grewal & Sambamurthy, 2002), and industry environment (Kowtha & Choon, 2001). The TOE framework has been widely used in studies on the adoption and the assimilation of different types of IT innovation (Oliveira & Martins, 2011). It was therefore considered appropriate to adapt the TOE framework to serve as the research framework to meet the purposes of this research. The literature on each of these constructs are examined. Figure 1: The research framework

Source: Adapted from Tornatzky and Fleischer (1990) Organizational context The OC is viewed as the characteristics of the organization and its internal resources (Oliveira, Thomas & Espadanal, 2014; Baker, 2012). Organization characteristics include organization size, status, industry and scope. Internal organizational resources include knowledge capability,



senior management support and organization readiness (Gangwar, Date & Raoot, 2014). Organization size is a prime factor which influences technical innovation and adoption (Oliveira et al., 2014; Aboelmaged, 2014; Pan & Jang, 2008; Zhu, Kraemer, Xu & Dedrick, 2004). The findings of prior research revealed that larger organizations are more likely to adopt new technology (Pan & Jang, 2008; Zhu et al., 2006). However, small organizations do not usually adopt the newest technologies (Oliveira et al., 2014; Lippert & Govindarajulu, 2006) although they tend to be more flexible and versatile when coordinating the required changes (Abdollahzadehgan, Che Hussin, Gohary & Amini, 2013). Furthermore, the largest companies are more active in respect of innovation activities (Messerschmidt & Hinz, 2013). Within the sphere of information systems, several empirical studies have consistently found that organization size is positively related to innovation use (Wang, Wang & Yang, 2010; Dholakia & Kshetri, 2004; Pan & Jang, 2008). Senior management, the decision makers of the organization, influence the adoption of innovation (Lai, Lin & Tseng, 2014) in terms of goal specificity, resource management and commitment (Swink, 2000). Senior management support contributes to catalyze innovation adoption by creating a fertile environment and providing resources (Premkumar & Roberts, 1999). They play the leadership role in digitization (El Sawy, Kræmmergaard, Amsinck & Vinther, 2016). Abdollahzadehgan et al. (2013) defined senior management support as the degree of support provided by the higher management in adopting the new technology for business. A critical factor is whether the employees understand the technology adequately to fully accept its adoption. Senyo, Effah and Addae (2016) found that senior management support significantly influences cloud computing adoption. Similar findings were also recorded by Alshamaila, Papagiannidis and Li (2013), Lian, Yen and Wang (2014) and, Gangwar, Date and Ramaswamy (2015). Therefore, the decisions of the senior managements are important for the adoption of new technology. The findings of the reviewed prior research indicate that the organizational context has an influence over technology adoption. Accordingly, this permitted the development of the first hypothesis stated as:

H1: Organizational context (OC) has an influence over the SMEs’ cloud computing adoption (A).

Environmental context EC refers to the external factors that influence the adoption of technology includes government regulations and initiatives, service providers and competitors (Gangwar et al., 2014). The environmental context includes the structure of the industry, the presence or absence of technology service providers and the regulatory environment. Competitive pressure refers to the extent to which rivals exert pressure on a particular organization (Oliveira & Martins, 2010). Zhu et al. (2003, p. 190) defined competitive pressure as “the degree that the company is affected by competitors in the market”. In the context of cloud computing, it has been argued that competitive pressure is very influential in the adoption of technology in general (Ramdani,



Chevers & Williams, 2013) although one study found that there is no relation between the competitive pressure and the adoption of cloud computing technology (Chao, Peng, Dutta & Choudhary, 2014). Other researchers such as Yoo and Kim (2018), Lian et al. (2014), Low, Chen and Wu (2011), Gangwar et al. (2015), Gutierrez, Boukrami and Lumsden. (2015), Senyo, Effah and Addae (2016), and Oliveira et al. (2014) have affirmed that competitive pressure has a positive effect on cloud computing adoption. Government support refers to the regulations, public policies and initiatives for encouraging enterprises to adopt CC. Government support may be beneficial or detrimental towards the adoption of a new technology (Baker, 2011). Stringent government policies may hinder an organization’s IT adoption processes (Cheng, 2017). Cloud computing is an example of an internet-based technology which is subject to Government policies (Safari, Safari & Hasanzadeh, 2015). Oliveira et al. (2014) and Zhu et al. (2006) have stated in their research that Government regulation can play an important role in the adoption of technology innovation. Regulation can encourage or discourage cloud computing adoption (Oliveira et al., 2014; Baker, 2012). Government regulations have more influence on the adoption of E-business in developing countries as compared with developed countries (Lai et al., 2014). As the literature indicates that environmental factors influence the adoption of cloud computing, the second hypothesis can be stated as:

H2: Environmental context (EC) has a significant influence towards the adoption of cloud computing amongst SMEs (A).

Cloud computing risks The risks of cloud computing adoption are fundamental aspects that determine CC adoption. Security of cloud computing is a big concern in organizations (Pearson & Yee, 2013). The security issues relating to CC arise from the abstraction of the infrastructure, which results in the lack of visibility and capability to integrate many familiar security controls, especially at the network layer (Cloud Security Alliance, 2009, p. 25). While some authorities view security risks as the highest risk element in the adoption of cloud computing (Carroll, van der Merwe & Kotze, 2011; Chao et al., 2014), others contend that there is no relationship between security and cloud adoption (Rahimli, 2013). Based on the literature, the third hypothesis can be stated as:

H3: Cloud computing risks (CCR) has an influence towards the SMEs’ cloud computing adoption (A).



3. Methodology A quantitative approach, with a web survey, was used to analyse the factors that influence the adoption of CC by SMEs. The targeted population comprised Malaysian IT-SMEs in the Klang Valley. Using systematic random sampling, 300 participates were targeted and the questionnaires were distributed to them. 170 useable and completed questionnaires were received indicating a response rate of 57%. The respondents were from diverse backgrounds in terms of age, gender, education levels and their work positions in their enterprises. Partial Least Square – Structured Equation Modelling (PLS-SEM) was used to analyse the data. 4. Findings and discussion Hypotheses testing A preliminary CC adoption model was developed to conduct the hypotheses tests. The results of the statistical tests of the model labelled 1a are shown in Figure 2 and Table 1. The outcomes show that the t statistic for the influence of OC over A is lower than 1.96, indicating an insignificant direct effect of OC and A. Based on this finding, the model was revised by omitting the direct effect arrow from OC to A and the revised model 1b is presented as Figure 3 and Table 2. Figure 2: Model 1a – Preliminary cloud computing adoption model

Source: Developed from the data analysis



Table 1: Results for Model 1a (direct effect) Path Beta S.E. t value CCR->A -0.195 0.069 2.847 EC->A 0.163 0.061 2.652 OC->Ans 0.046 0.073 0.635 ns: not significant at 0.05

Source: Developed from the data analysis Figure 3: Model 1b – Revised cloud computing adoption model

Source: Developed from the data analysis Table 2: Results for Model 1b (direct effect) Path Beta S.E. t value CCR->A -0.200 0.070 2.850 EC->A 0.169 0.064 2.638

Source: Developed from the data analysis



After the direct effect arrow between OC and A was removed from the model, the other two independent variables indicated a slightly higher significance level. The R2 values between the two models were then evaluated. The R square value of a dependent variable refers to the variance explained by its predictors or factors. As shown in Table 3, there was only a 0.001 decrease in the R square value for A, after the removal of the direct arrow from OC to A. This suggests that although Model 1b is a simpler model, it is able to predict as much as Model 1a. Additionally, by employing Model 1b, the 65% of changes in the cloud computing adoption (A) were explained by the predictors, which include OC, EC and CCR. Table 3: R square value for Model 1a and 1b Dimension Model 1a Model 1b A 0.651 0.650

Source: Developed from the data analysis The results of the hypotheses tests are shown in Table 4. As explained by Kenny (2018), the total effect is the summation of direct effects and indirect effects, where indirect effect is the mediation effect. If a mediator does not exist, that means that there is no mediation effect (indirect effect), thus total effect will be equivalent to direct effect. Table 4: Summary of results of Model 1b (Hypotheses Testing)

Direct Effect Total Effect Path Beta S.E. t value Beta S.E. t value CCR -> A -0.200 0.070 2.850 -0.375 0.064 5.859 EC -> A 0.169 0.064 2.638 0.338 0.057 5.920 OC -> A ns ns ns 0.186 0.042 4.405

ns: not significant at 0.05 Source: Developed from the data analysis The total effect was observed to determine the impact of the independent variables over the dependent variable. All t values under the total effect, CCR (t = 5.859), EC (t = 5.920) and OC (t = 4.405), were found to be above the value of 1.96 which indicated a significant influence towards cloud computing adoption (A). When the beta values are observed, OC and EC were found to have positive significant influence over A. While this indicates that the organizational and environmental factors accelerate the adoption of cloud computing, CCR however had a negative influence towards A. This indicates that cloud computing risks decelerate the adoption of CC.



Table 5: Hypotheses testing results

Hypotheses Results H1 Organizational context (OC) has an influence over the SMEs’ cloud

computing adoption (A). Supported

H2 Environmental context (EC) has a significant influence towards the adoption of cloud computing amongst SMEs (A).

Supported

H3 Cloud computing risks (CCR) has an influence towards the SMEs’ cloud computing adoption (A).

Supported

Source: Developed from the data analysis Discussion The findings revealed that the environmental context is the most significant factor which influences CC adoption among the IT-based SMEs in the Klang Valley. The results support the findings of earlier studies on competitive pressure (Gangwar et al., 2014; Oliveira & Martins, 2010; Ramdani et al., 2013; Yoo & Kim, 2018; Lian et al., 2014; Low et al., 2011; Gangwar et al., 2015) and Government support (Gutierrez et al., 2015; Senyo et al., 2016; Oliveira et al., 2014; Cheng, 2017; Oliveira et al., 2014; Baker, 2012; Lai et al., 2014). The similarities in the findings indicate that the environmental context influences the adoption of CC by IT SMEs in the Klang Valley. CC risks were found to be the second most significant factor which influences CC adoption, although it has a negative influence. This indicates that when SMEs observe and consider the risks of transitioning and adopting CC, it hinders or decelerates their adoption. When risks are present, the value and benefits of CC are not taken into account by the SMEs. This finding supports the findings of earlier studies (Pearson & Yee, 2013; Cloud Security Alliance, 2009; Carroll et al., 2011; Chao et al., 2014). However, it contradicts the findings of a study conducted by Rahimli (2013) who contended that there is no relationship between cloud computing risks and CC adoption. Finally, although the organizational context is a factor that influences the adoption of CC, it has the lowest significance level. This indicates that organizational factors such as organization size, top management support and organization readiness are not the main deciding factors for the adoption of CC. While they play a role in influencing CC adoption, the environmental and risks factors play more significant roles for determining the adoption of CC. The findings therefore suggest that SMEs observe the industry landscape and also the external factors, including weighing the risk factors, then the internal organizational factors prior to deciding CC adoption. The influence of organizational context for CC adoption supports the findings of prior research supports the findings from the past.



5. Conclusion and recommendations This research investigated the factors which influence the adoption of CC amongst small and medium enterprises from the IT industry in the Klang Valley, Malaysia. The findings extend the body of knowledge on the factors which accelerate or hinder the adoption of CC. The research findings indicate that organizational and environmental factors significantly influence the adoption of CC. However, CC risks pose a threat and therefore hinders the adoption of CC. Based on the findings, 5 recommendations are offered for consideration by the Government, the SMEs, and the cloud service providers (CSPs): � The first relates to the risks associated with CC services. Since the issues of security and

privacy of the data on the cloud is well within the control and responsibility of the SMEs, it is necessary for them to fully understand and comply with their security responsibilities. They should also be aware that the CSPs have a shared-responsibility for the security aspect of the cloud with the CSPs being accountable for some aspects of security and sharing the other aspects with the consumer.

� The CSPs on their part should continue to provide the necessary security protocols and abide to international standards to discharge their professional responsibilities.

� Major CSPs have made Singapore their focal point to deliver cloud services within the

ASEAN region. They should consider establishing a footprint in Malaysia to deliver cloud services as one of the major concerns with SMEs is that when they subscribe to cloud services, they would be required to host their data in the data centres of the CSP, which technically resides out of the country. This ties to the data sovereignty aspect.

� The Malaysian Government should consider and implement policies, similar to those of

the Singapore Government, to attract investors and CSPs to Malaysia to build local data centers to boost the cloud adoption rate not only by SMEs but also by all enterprises as well as the Government agencies.



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