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Research Article
Open Access Peer-reviewed

Cloud Computing Acceptance in Small and Medium Enterprises (SMEs) in Uganda

Kalinaki Hussein , Mukuuma Kassim, Mwase Ali
Saudian Review of Financial Technology and Management Studies. 2022, 2(1), 1-10. DOI: 10.12691/srftms-2-1-1
Received January 20, 2022; Revised February 23, 2022; Accepted March 01, 2022

Abstract

Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [1,2]. This research focuses on small and medium enterprises (SMEs) in Uganda and how they accept cloud computing. SMEs are widely defined in terms of their characteristics such as the size of capital investment, the turnover, the number of employees, the location, the management style, and the market share. The study adopted a cross-sectional survey methodology. The study findings contribute significantly towards understanding the relationship between perceived service quality, perceived credibility and fostering the acceptance of social networking technologies in SMEs in Uganda. Finally, the study recommends further studies focus on investigating the mediating role of Gender, Age, Experience, and Voluntarism as determinants or moderating factors of Performance Expectancy, Effort Expectancy, Facilitating Conditions, and Behavioral Intention.

1. Introduction

In this age of information and globalization, massive computing power is desired to generate business insights and competitive advantage 3. Traditionally, companies invest in computing resources that let people access software files and computer power on their desktops 4. 1 It has been observed over the years that traditional computing can be costly and less effective, due to the rapidly increasing data processing requests. However, traditional computing presents various frustrations that block effective and efficient business operations. Amongst these are traditional applications are designed and built to be operated on premise, hosted and managed end-to-end by the internal IT organization 5, poor data backup, managing and maintaining your own hardware, and minimal disaster recovery options. Thus, traditional computing approaches might not be enough to address the challenges that modern cloud workloads present for business enterprises.

In 2006, technology industry giants like Amazon.com, and Google launched cloud computing as an alternative for in-house data centres for enterprises 3. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services 6.

In developed countries, cloud computing acceptance has increased tremendously among SMEs. For instance, in the UK acceptance rate is at 99 percent with enterprises investing more in Hybrid clouds 4. In developing countries like Tanzania and Rwanda, data center projects are already operational and some in the process of being set up. In Benin and Burundi, strategies are emerging for the introduction of cloud computing, in a bid to support cloud computing acceptance 7. In 2011, MTN Ltd, in partnership with Seven Seas Technologies, EMC and Cisco, made an entry into cloud computing service with a cumulative Sh3.5 billion capitals.

Economies are comprised of several companies, majority of which are small and Medium Sized Enterprises (SMEs). These SMEs play a very significant role in the economy. SMEs significantly contribute to a country’s Gross Domestic Product (GDP) and its labour market. It is therefore important to develop new systems or strategies that can help these SMEs to become more efficient and productive this is because of their enormous contributions to a country’s economy 8. It is argued that the appropriate use of Information and Communication Technologies (ICT) is one of the strategies that can help SMEs become more efficient. However, traditional ICTs have been faced with numerous challenges as customers strive to gain access to computing resources (servers, storage, applications services and networks etc). This is because customers are not responsible for designing or operating the actual technology. Nonetheless, Cloud computing comes with great potential that benefit the customers or end users 9.

The pay-as-you-go payment model of cloud computing allows companies to pay for only the services that they have used. Companies can therefore use and access the most sophisticated computing services, without being required to invest significant amounts of money in advance. There is no need to employ highly technical personnel to maintain the IT infrastructure. What businesses do now is that they rent a space on a server located anywhere in the world so that they can access their software applications. Companies do not need to pay upfront for buying, installing or licensing the system 9. Furthermore, companies are not responsible for maintaining and upgrading hardware and software applications. A lot of large businesses have moved to the clouds and it is expected that small and medium-sized businesses will follow suit 10.

Accordingly, Cloud Computing services and deploying models, offers the opportunity for small businesses to subscribe to pay-per-use top class solutions at an affordable price and fulfill their operational needs to access infrastructure, platform and software over the Internet, without having to host or maintain the services themselves 11.Typically, some of the Cloud applications most used by SMEs are website hosting, file-server, e-mail system and related features (e.g. address book, calendar) 9.

1 examined five major factors in adopting new IT solutions by SMEs in two states of Malaysia. These factors are perceived cost, perceived benefits, ICT knowledge and skill, government support and external pressures. Despite the several advantages associated with cloud computing, its acceptance rate by SMEs in Uganda is not growing as fast as expected 12. Acceptance and usage of any beneficial technology not only have a positive influence to the SMEs in Uganda but to the economy as a whole.

According to 10, issues affecting cloud computing acceptance for SMEs are different from those for larger organizations. Outage of the service provider affects the service availability which depends critically on the reliability of internet infrastructure. Security of data is often perceived as an important weakness. From a technical and practical perspective however, data in the cloud is often more secure than in-house hosted data, especially for SMEs who often lack staff with security expertise.

Uganda’s Vision 2020 seeks to transform the country into a middle income country relying ICT as one of the avenues 13. Despite government efforts of supporting SMEs through ICT by enhancing internet infrastructure, acceptance of cloud computing is still very low among SMEs 12. These low rates of cloud computing acceptance have resulted into high infrastructure acquisition costs, the higher licensing fees for software, failure to compete favorably and increased loss of records. Through its service models of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), cloud computing presents a solution to the limiting ICT factors by SMEs.

Therefore, this study was aimed at establishing the relationship between performance expectance, effort expectance, security, and facilitating conditions and Behavioral intention to accept Cloud Computing by SMEs in Uganda.

2. Literature Review

2.1. Cloud Computing

14 argues that the year 2007 saw the emergency of Cloud computing. According to 15 however, the concept was initially propelled by industry giants like Microsoft, Google, Amazon, etc. Cloud computing has been defined by several authors. It is important to note that there is no standard definition used. This research however adopted the definition by National Institute of Standards and Technology (NIST) which states that Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction 2.

In Uganda, Cloud computing comes with various benefits i.e. reduces infrastructure costs and levels the ground for SMEs, 16. Secondly, unlike traditional computing that necessitates installation and configuration of software and constant updates, software on the cloud would be easier to install, maintain and update 17. This benefit covers the users who have less IT training 18. Thirdly, cloud services provide the user with the flexibility of scaling up the use if the demand increases. This approach requires a low upfront investment which is ideal for SMEs. Fourthly, software is available and free in Software as a Service (SaaS). This implies that software piracy is likely to reduce 9. Fifth, in developing economies with poor broadband, cloud computing can overcome these barriers 19. Finally, with the issue of the cloud security, cloud computing allows having a business model in which third parties can provide cost-effective security for SMEs 20. 1 Nonetheless, these observations underscore how institutional and economic problems remain central to the diffusion of cloud computing among SMEs in the developing world.

2.2. Characteristics of Cloud Computing

Broad network access: In order to access the cloud services, Internet and other networks are paramount using standard protocols. Therefore, access must be through Internet. Customers can only access cloud services through the browser 21.

Rapid Elasticity: Elasticity in the sense that depending on the client demands, resource allocation can scale up or down. To the consumer, the available computing capabilities for provisioning often seem to be unlimited and can be purchased in any quantity at any time 22.

Measured service: Customers’ use can be monitored, measured, controlled, reported and charged fairly since the billing system is automatically metered. This implies that the pay-as-you-consume arrangement used just like air time, electricity or municipality water IT services are charged per usage metrics, this is done with transparency 10, 14.

On demand self-services, as and when consumers require, they can unilaterally provision Computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service’s provider 22.

Resource pooling; physical and virtual resources are dynamically assigned and reassigned as and when user demand changes 22. The pooling and assigning of resources saves costs to the consumers’ side. Example of resources include; computation capabilities, storage and memory 21.

2.3. Cloud Computing Service Models

The distinction between cloud computing and other technologies is viewed through the three layers that users can utilize the technology. These are; Software as a service (SaaS) which serves the end user, Platform as a service (PaaS) which serves the application developers and Infrastructure as a service (IaaS) which serves the network architects 23.

Software as a Service (SaaS) is a service model the applications are accessible from several client devices through a thin client interface, like a web browser just like the way we access web-based mails. The consumer here uses the cloud provider’s application running on the cloud infrastructure. Here the customer does not manage neither control the cloud infrastructure such as network, operating system, servers, or storage except some configuration settings. SaaS provides ready to use software online. Applications that run on SaaS include CRM, project management systems, social media platforms and mail 18.

The Platform as a Service (PaaS) layer is the middle layer, which offers a pre-built application platform to the client and platform oriented services. It is not the client’s concern to build underlying infrastructure for their application. Examples of PaaS providers include Google AppEngine, force.com and Amazon web services. This model offers some control to the deployed applications but not to the Cloud infrastructure 18.

Infrastructure as a Service (IaaS) is the lowest layer that provides basic infrastructure components and support services to the client. Components provided may include networks. Virtual machines, firewalls, storage and load balancers. Single clients are offered dedicated resources and no sharing with unknown third parties. The advantage is that the customer has control over the deployed applications, storage and selected network components 18.

Depending on the relationship between the provider and the consumer, a cloud can be classified as private cloud, public cloud or a hybrid model 22. Some scholars have differently referred to cloud computing as “service boundary” and “cloud mode” 24. Accordingly, deployment models are classified basing on these four characteristics; who owns the cloud infrastructure? Who takes charge of the cloud infrastructure management? Where is the cloud infrastructure located? And lastly who has access to cloud services?

2.4. Cloud Computing Deployment Models

Public cloud, a public cloud is the one which is available to the general public in a utility billing mode. In a public cloud, the provider is the owner of the infrastructure and therefore manages the infrastructure 25. The most popular public clouds include Microsoft Azure, Amazon Web Services, and Google Apennine 21.

Private cloud is an internal an internal data center of a business or other organization for utilization of cloud technologies where the cloud infrastructure is managed solely for one organization, maintained in-house and solely accessible to internal users within that organization 26. Organizations use software that enables cloud functionality, such as VMWare, vCloud Director, or Open Stack

Community cloud is a resource or service provided for and shared by a limited range of clients/users or several organizations and supports a specific community that has shared concerns. It may be managed by the organizational IT resources or a third party and may exist on premise or off premise 18.

Hybrid cloud is a combination of two or more types of clouds; private, community, or public 24. Hybrid clouds combine benefits from each cloud deployment type. For example, an organization may bridge its internally operated private cloud with other public clouds together by standardized or proprietary technology in order to satisfy business needs 21.

2.5. Characteristics of SMEs

Although there’s no internationally accepted definition of what an SME is, the major differentiator is their small size and the number of employees in a particular enterprise 27.

The category of SMEs is made up of enterprises which employ fewer than 250 persons and which have an annual turnover not exceeding 50 million euro, and/or an annual balance sheet total not exceeding 43 million euros 28. However, some set the limit at 200 employees, while the United States considers SMEs to include firms with fewer than 500 employees.

SMEs by nature are enterprises that require relatively small capital investment to set off. An SME can be described as a simple business structure, which allows the company to be flexible and quickly make necessary changes without requirements such as seeking approval from board members or stockholders. They are normally sole proprietorships or partnerships 4, 29.

30 States that by Ugandan standards, a Small Enterprise (SE) refers to an enterprise employing a maximum of 50 people with annual sales turnover of maximum 360 million Ugandan Shillings and total assets of maximum 360 million Ugandan Shillings. Medium Enterprise on the other side is defined as an enterprise employing more than 50 people with annual sales/revenue turnover of more than 360 million Ugandan Shillings and total assets of more than 360 million Ugandan Shillings.

According to 31, SMEs normally focus on a few products. They also lack management skills and technological advancement which results into low productivity levels and poor quality. Resources available to SMEs are also poor compared to large enterprises. External environment changes are likely to have a more significant impact on the SMEs compared to their larger counterparts 4, 29.

In this study, a small scale enterprise refers to an enterprise or a firm employing less than 5 but with a maximum of 50 employees. A medium enterprise has a value of assets, excluding land, building and working capital of less than Ug.Shs 50 million (US$ 30,000). A Medium sized enterprise is considered a firm, which employs between 50 - 100 workers and the annual income turnover of between Ugshs.10 -50 million (US$6,000 - 30,000) 32.

2.6. Theoretical Analysis

Various theories have been proposed in the last couple of decades to examine factors underlying the acceptance or intention to use a new technology. Notable among such theories are Venkatesh 33 Unified Theory of Acceptance and Use of Technology Model (UTAUT).


2.6.1. Unified Theory of Acceptance and Use of Technology Model (UTUAT) Model

This model of individual acceptance of Technology was formulated through a combination of a number of theories from different scholars such as the Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Motivational Model (MM), Theory of Planned Behavior (TPB), Model Combining the Technology Acceptance Model and Theory of Planned Behavior (C-TAM-TPB), Model of PC Utilization (MPCU), Innovation Diffusion Theory (IDT), and Social Cognitive Theory (SCT) 33, 34, 35.

The model suggests that when users are presented with a new technology, a number of factors influence their decision about how and when they will use it. These factors are Performance Expectancy defined as degree to which the individuals believe that the use of the technologies will results in performance gains. This may also be regarded as the perceived usefulness of the technologies 36. Effort expectancy refers to the ease of use of the technologies. The extent to which users find the system or software easy to use. Social factors defined as extent to which the individuals believe that important others believe that they should use the technologies. This influences whether this individual will perform a given behavior in question, for example, accepting Cloud computing 37. These factors are considered to be the primary determinants for accepting a new technology and are influenced by other variables such as Facilitating Conditions 38.


2.6.2. Critical Analysis of UTAUT Model

According to 39, the UTAUT Model is inflexible, in that the cultural context in western countries where the model was first applied differ greatly from those of in developing and less developed countries like uganda. Therefore the model cannot be applied in the African context without modification. 40 indicates that the three mediating factors in Vankatesh’s original UTAUT model,that is experience,Volutariness, gender and age cannot be applied as they are,in different cultural context. It is posited that the higher the social-economic status of user the faster the acceptance of a technology. In Africa however most users have a low economic status and such factors may not be relevant to analysing their uptake of a technology. Social factors have been removed in this study because according to 33 these do not predict behavioural intention to accept a technology 33.

2.7. Research Model and Hypotheses

This study adopted the UTAUT model of 33 that was, modified to suit the context of developing countries like Uganda where the study was carried out. This came up after a critical review of literature.


2.7.1. Description of the Research Model

This section explains the theoretical (research) model that has been adapted from Unified Theory of Acceptance and Use of Technology Model (UTAUT) 33, the expectancy theory 41 and the theory of Planned Behavior 42.

Performance Expectance and Behavioral Intention to accept Cloud computing

The Customer expectancy theory assumes that behavior results from conscious choices among alternatives whose purpose is to maximize pleasure and minimize pain. It also states that Behavioral Intention to adopt a system is based on individuals’ factors such as personality, skills, knowledge, experience and abilities 41.

The behavior to use and adopt cloud computing can be influenced by the value the customer expects to gain after using a system. These are characterized by fair reward where the customer evaluates the significant benefits such as social benefits. These benefits are considered as the most significant rewards which are a malformation of society’s opinion, economic and functional rewards 43. The researchers assume that the customer can only be satisfied after all the above have been achieved and this will therefore increase their loyalty to the system.

Accordingly, 44 adds that the Performance Expectancy assumes that consumer behavioral intention to use a technological innovation, for example cloud computing are strongly determined by people’s expectations after acceptance. This intention to use will be motivated by their conscious expectations of what will happen if they do, and are more productive when they believe their expectations will be realized.

Security and Behavioral Intention to Accept Cloud computing

45 asserts that security has an influence on consumer Behavioral intention to adopt cloud computing. It influences the decision whether an individual will use Cloud Computing or not. Reliability also has a direct influence on consumer Behavioral intention to adopt cloud computing. How reliable the technology is will determine the adoption of the technology by users. It’s without doubt that cloud computing faces as considerable security threats as they are existing in systems and networks. The main aspect describing the achievement of any new computing technology is the height of security it provides whether the data located in the cloud is protected at that level that it can avoid any sort of security issue. Security therefore has direct influence on user’s behavioral intention to adopt and use Cloud computing 1.

Behavioral intention is defined as the strength of one’s intention to perform a specific behavior 42. A customers’ intention to perform a behavior is a combination of the attitude towards performing the behavior and his/her subjective norm. It should be noted that Behavioral intention (BI) is the direct antecedent of behavior. Behavioral intention, in turn, is determined by an individual's attitude towards performing the behavior and the individual's perception of how relevant others think of the behavior 46. The rate of acceptance of cloud computing highly depends on how individual users perceive innovation attributes such as simplicity, security, cost, flexibility and accessibility 47.

Facilitating conditions and Behavioral Intention to Accept Cloud computing

Facilitating conditions is defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system 48. This variable is about whether SME owners have setup the necessary resources required to use the cloud computing. For example, hardware, Internet, electricity and software during revolution.

Effort expectance and Behavioral Intention to Accept Cloud computing

Effort expectancy according to 44, are presupposed to have strong connection to behavioral intention to use a service, as this is believed to influence people to choose to carry out a behavior especially since they find the technology easy to use. In the research model therefore, a strong predictive potential is presupposed between Performance Expectancy and Behavioral Intention to Use Cloud Computing 49; Effort Expectancy, Security and Reliability are also conceptualized to have strong linkages with Behavioral Intention to Use Cloud Computing, 45. The Behavioral Intention to use Cloud Computing will thereafter lead to actual Customer adoption of the Cloud service 49.

Effort Expectancy is the degree of ease associated with the use of the technology, that influence individuals’ attitudes to adopt and use the system. The Perceived ease of use as referred to in the Technology Acceptance Model is the degree to which an individual believes that using the system will help him or her to attain gains in job performance. The behavior to use and adopt cloud computing can be influenced by the ease of use a customer gains while using a system. Therefore, Effort Expectancy is likely to have direct influence the user’s behavioral intention to adopt and use Cloud computing.

This study was therefore, guided by the following hypotheses:

H1: There is a significant positive relationship between Performance Expectance and Behavioral Intention to accept Cloud computing

H2: There is a significant positive relationship between Effort expectance and Behavioral Intention to Accept Cloud computing

H3: There is a significant positive relationship between Security and Behavioral Intention to Accept Cloud computing

H4: There is a significant positive relationship between Facilitating conditions and Behavioral Intention to Accept Cloud computing

H5: There is a significant positive relationship between Behavioral Intention and Cloud Computing Acceptance

3. Methodology

3.1. Research Design

The study adopted a cross-sectional survey design. 50 observed that descriptive research includes surveys and fact finding studies of different kinds. It further describes the data in order to draw conclusions about the population characteristics or phenomenon being studied. It adopted a quantitative approach which focused on describing and drawing inferences on cloud computing, challenges faced in full acceptance of cloud computing and strategies for improving the acceptance of cloud computing by SMEs in Uganda. Morgan’s table for determining sample size for known population was used 51. 51, state that for a population of over 1,000,000, a sample size of 384 is adequate with the confidence level of 95% and margin of error = 5.0%. Statistics from (KCCA, 2014), show that there are about 800,000 SMEs within Uganda, 50% of which are in Kampala a sample of 384 IT managers and owners of these SMEs were selected as respondents using a Simple random sampling technique.

This research employed a quantitative research approach through utilization of a survey instrument (questionnaire). 52 argues that the questionnaire is increasingly recognized as an invaluable means of data collection due to the benefits it brings with it. These include among others; lower respondent error 29, higher response speed 53, and removal of interviewer bias 4, 27.

A structured Questionnaire was used because they enable the respondents to read and understand the questions before responding. Respondents were asked to indicate their levels of acceptance based on a 5 point Likert scale ‘strongly disagree’ ‘1’ to ‘strongly agree ‘5’.

3.2. Validity and Reliability of Instruments

The questionnaire and interview guides were pre-tested for validity using Content Validity Index. 3 experts were requested to indicate the relevance of questions on the study variables. The reliability of the instruments was tested using Cronbach’s alpha coefficient. A cut-off point of 0.7 was taken for the CVI. This is in line with 54 who recommends that the research instrument used to collect data should be valid, able to yield similar results at all time, and should measure what the researcher actually intends to measure. Similarly, a cut-off of 0.7 was considered for the Cronbach’s Alpha, in accordance to 34.

3.3. Data Processing and Analysis

Data from the filled questionnaires were compiled, sorted, classified and then entered into the computer for analysis. This involved the use of Statistical Package for Social Scientists (SPSS) Version 17 for processing quantitative data. Data analysis involved the use of descriptive statistics involving, mean and standard deviation, frequencies, percentages and also factor analysis mainly to summarize the data.

4. Results

4.1. Demographic Characteristics

The demographic characteristics of respondents that were assessed are; gender, education level, age (Years), timeframe for companies to move to cloud and the extent to which the respondent is involved in purchasing decisions. These are presented in the subsections below;


4.1.1. Gender of the Respondents

Regarding the gender of the respondents, the result from Table 1 above, indicate that majority of the study respondents were male (54.1%) and then 45.9% female. The gender however, does not affect the results of this study.


4.1.2. Age of the Respondents

Results from Table 2 above reveal that the majority of the respondents were below 25 years with a percentage of 57.5, and 36.2% belong to the age bracket 25-35 and 6% of the respondents are between 35-45 years of age. Generally, the respondents were mature enough to understand and answer the research questions.


4.1.3. Respondent’s Level of Education

Table 3 above presents the respondents education levels. Results show that, the majority of respondents were holders of bachelors’ degree with 63.2%, followed by master’s holders with 14.7% giving an indication that the majority of respondents that use cloud computing services are those that have gone to universities and institutions of higher learning. Those holding Diploma are 13.9% and PGD and certificate account for 5.3%, 3%. Generally, there is an indication that the respondents are learned enough to give feedback of high quality in as far as this study is concerned.

4.2. Relationships between the Study Variables

In order to achieve the objectives of the study, the Pearson(r) correlation coefficient was employed to execute this and to test the direction and strength of relationships between the study variables.


4.2.1. Relationship between Performance Expectance and Behavioral Intention to Accept Cloud Computing

The findings in Table 4 revealed that there was a postive and significant relationship between Performance Expectance and Behavioral Intention to Accept Cloud Computing (r = .334, p < 0.01). This is an indication that a positive change in Performance Expectance is concomitant with a positive change in Behavioral Intention to Accept Cloud Computing.


4.2.2. Relationship between Effort Expectance and Behavioral Intention to Accept Cloud Computing

The findings in Table 4 above indicated that there was a postive and significant relationship between Effort Expectance and Behavioral Intention to Accept Cloud Computing (r = .671, p < 0.01). Thus, this work demonstrated that a positive change in Performance Expectance is concomitant with a positive change in Behavioral Intention to Accept Cloud Computing.


4.2.3. Relationship between Security and Behavioural Intention to Accept Cloud Computing

Another study objective sought to examine whether there was a postive and significant relationship between Security and Behavioral Intention to Accept Cloud Computing. The results (r = .380, p < 0.01) indicate that a positive change in Security relates with a positive change in Behavioral Intention to Accept Cloud Computing.


4.2.4. Relationship between Facilitating Conditions and Behavioral Intention to Accept Cloud Computing

Findings in Table 4 above revealed that there was a postive and significant relationship between Facilitating Conditions and Behavioral Intention to Accept Cloud Computing (r = .452, p < 0.01). This therefore, indicates that a positive change in Facilitating Conditions is concomitant with a positive change in Behavioral Intention to Accept Cloud Computing.


4.2.5. Relationship between Behavioural Intention and Cloud Computing Acceptance

Findings of this study revealed that there was a postive and significant relationship between Behavioral Intention and Cloud Computing Acceptance (r = .278, p < 0.01). Thus, this is an indication that a positive change in Behavioral Intention is concomitant with a positive change in Cloud Computing Acceptance.

5. Discussion

The study focused on the predictive potential and the strength of the relationships between performance expectancy, effort expectancy, facilitating conditions, security and Behavioral intention to accept Cloud Computing in SMEs in Uganda. These are discussed below:

5.1. Performance Expectancy and Behavioural Intention

Performance expectancy was found to have a significant direct effect on the acceptance of Cloud Computing in SMEs in Uganda. According to the original UTAUT model, Performance expectancy is hypothesized to affect intention to accept a particular technology and it relates to what users perceive as the job performance benefits of using such a technology. This research found out that managers in SMEs in Uganda believe that Cloud Computing acceptance would be more useful in their job performance if successfully accepted. This might be because these managers want to accept Cloud Computing for they feel Cloud Computing acceptance would give them competitive edge over their rivals engaged in similar business. These findings are consistent with earlier studies of 55, 56, 57, 58. This stream of literature provided evidence of the significant effect of performance expectancy on intention to accept a technology. The performance expectancy -intention relationship is strongly based on the idea that, people form intention toward behaviors they believe will increase their system use, over and above whatever positive or negative feeling may be evoked toward the behavior.

These revelations further confirm studies by 50 which support that stressing perceived usefulness leads to intention to use improvements. The managerial implication of these findings seem clear, the changes of intentions of customers can be enhanced through the adoption of particular systems that customers are willing to use for the transactions. Another study by 59 found that Perceived Usefulness is an important factor in determining the adaptation of innovations. As observed by 60, a person’s willingness to transact with a particular system is already considered as Perceived Usefulness. It shows that user’s intention to adopt a technology is determined by perceptions of usefulness of the technology 53.

Based on these results, improvement in Cloud Computing acceptance will require that management of SMEs make use of Cloud Computing to accomplish tasks more quickly and advice users on the usefulness of Cloud Computing acceptance in SME so as to improve on their job performance, increase productivity and enhance effectiveness in these SMEs in Uganda.

5.2. Effort Expectancy and Behavioural Intention

Findings of this study show that effort expectancy has a positive significant effect on the acceptance of Cloud Computing. This means that cloud subscribers in Uganda agree that their acceptance of cloud computing as a substitute of traditional ICTs depends on how they perceive ease of use of such technologies by their users and themselves.

This is consistent with the findings of 57, 61, 62 who all found that for successful acceptance of a technology, the accepter must perceive the technology as free of effort to learn and use.

Cloud Computing Acceptance is perceived as easy to use and if implemented, most SMEs are most likely to reap from its benefits 63. Thus, it is necessary that IT managers of these companies help their staffs increase their perception positively through providing necessary facilities to access the cloud.

Extensive research over the past decade provides evidence of the significant effect of effort expectancy on behavioral intention, either directly or indirectly 35. Moreover, 63 found that effort expectancy had a significant positive effect on the intention to accept Cloud Computing.

Based on these results, improvement in Cloud Computing Acceptance will require that management creates and nurtures a condusive environment characterized by training users about how easy it is to operate Cloud Computing, make Internet faster and freely accessible so as for the users clearly understand and thus accept the technology.

5.3. Security and Behavioural Intention

Findings of this study show that there is a significant positive relationship between security and acceptance of cloud computing. This means that managers in SMEs in Uganda agree that security is essential in the acceptance of Cloud Computing because using Cloud Computing will be a dependable and trusted service that offers what is expected out of it. Furthermore, these findings are in line with 64 which found that a technology that helps in solving user problems improves the quality of work of the user which makes the technology dependable and easily accepted for use.

These revelations further confirm studies by 65 which support the view that increased security leads to intention to accept a technology as the user will perceive the service quality from such technologies as high. The managerial implication of these findings seem clear, the consistency of performance and dependability of Cloud Computing will enable a company to give the service in the right way the first time and keep to its promises. It shows that user’s intention to adopt a technology and perceive its quality as good is highly determined by the security of the technology 66.

Therefore, basing on these findings, improvement in Cloud Computing acceptance would require management to trust the use of Cloud Computing as a dependable platform which provides services right at the first time so as to gain trust and confidence in Cloud Computing, to maintain error free transactions and the protection of data privacy of cloud services.

5.4. Facilitating Conditions and Behavioural Intention

Facilitating conditions had a positive and significant effect on the acceptance of cloud computing. Managers in SMEs in Uganda agree that facilitating conditions are indispensable in the acceptance of Cloud computing because. This might be due to the fact that for Cloud Computing to work, it requires that one has access to reliable Internet. This is in line with previous studies by 67 which indicate that in case facilitating conditions are there for users, acceptance of such technology becomes inevitable.

5.5. Behavioural Intention and Cloud Computing Acceptance Implications

The study findings revealed that decision makers from Ugandan SMEs are willing to accept cloud computing technology, as long as they perceive that using cloud computing will benefit their organizations by raising productivity and efficiency. Furthermore, the research statistically established that the other important determinant of cloud computing acceptance was how other people in SMEs and top management perceive usefulness of cloud computing technology.

The study findings revealed that cloud computing providers need to pay special attention on delivering competitive pricing as well as to try and educate the market of the potential benefits that cloud computing technology can offer.

These findings are consistent with previous studies by 68, 69 which indicate that organizations must evaluate factors that influence their decisions before deciding to adopt cloud computing. Therefore, Ugandan SMEs can incorporate these findings to support and accelerate acceptance of cloud computing technologies within their own organization.

6. Conclusion

Conclusively, this study looked at the relationship between perceived service quality indicated by perceived ease of use, perceived usefulness, perceived reliability and perceived credibility and adoption of social networking technologies in SMEs in Uganda. In particular, the study examined relationships between the independent variables of perceived ease of use, perceived usefulness, perceived reliability, and perceived credibility. It was established that all the relationships were significantly positive.

The results of this study demonstrated that some TAM and SERVQUAL constructs had a direct effect on the managers’ intention to adopt SNTs. For that reason, there is potential for practical application in the development and management of SNTs in SMEs in Uganda.

From this study, it emerged that both on- and off-line support is useful for SNT acceptance in SMEs in Uganda. It is further concluded that perception of SNTs usefulness is crucial in its acceptance in SMEs in Uganda.

Finally, the Ugandan government could benefit from these research findings, as it will enable them to align their ICT strategy to accommodate SMEs transition to cloud.

7. Study Limitations and Recommendations

This study was conducted to establish the theoretical relationship between the independent variables (factors that determine the acceptance of Cloud Computing and the dependent variable (Cloud Computing Acceptance). It should be noted that the study was limited to only four independent variables, which explain only 52% of the rate of acceptance of Cloud Computing in SMEs in Uganda. The study does not provide data about the 48% factors that determine the variance in Cloud Computing acceptance.

Additionally, Behavioral Intention was found to be the strongest predictor of Cloud Computing Acceptance. Therefore, for Cloud Computing service providers to encourage faster rates of acceptance on their services among users, it is necessary to embark on promotional campaigns that specifically target consumer behavior, as this would encourage individual adoption and actual usage of Cloud Computing services among customers.

In addition, each of determinants explored in this study, should be individually addressed, paying special attention on educating SMEs and raising awareness of potential benefits of cloud computing technology.

This study further recommends that future research can focus on investigating the mediating role of Gender, Age, Experience, and Voluntarism as determinants or moderating factors of Performance Expectancy, Effort Expectancy, Facilitating Conditions, and Behavioral Intention, as also suggested by 33.

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Published with license by Science and Education Publishing, Copyright © 2022 Kalinaki Hussein, Mukuuma Kassim and Mwase Ali

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Cite this article:

Normal Style
Kalinaki Hussein, Mukuuma Kassim, Mwase Ali. Cloud Computing Acceptance in Small and Medium Enterprises (SMEs) in Uganda. Saudian Review of Financial Technology and Management Studies. Vol. 2, No. 1, 2022, pp 1-10. http://pubs.sciepub.com/srftms/2/1/1
MLA Style
Hussein, Kalinaki, Mukuuma Kassim, and Mwase Ali. "Cloud Computing Acceptance in Small and Medium Enterprises (SMEs) in Uganda." Saudian Review of Financial Technology and Management Studies 2.1 (2022): 1-10.
APA Style
Hussein, K. , Kassim, M. , & Ali, M. (2022). Cloud Computing Acceptance in Small and Medium Enterprises (SMEs) in Uganda. Saudian Review of Financial Technology and Management Studies, 2(1), 1-10.
Chicago Style
Hussein, Kalinaki, Mukuuma Kassim, and Mwase Ali. "Cloud Computing Acceptance in Small and Medium Enterprises (SMEs) in Uganda." Saudian Review of Financial Technology and Management Studies 2, no. 1 (2022): 1-10.
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[1]  Devesh, K., Harsh, V. S., & Piyush, V. Factors Influencing Cloud Computing Adoption by Small and Medium-Sized Enterprises (SMEs) In India. Pacific Asia Journal of the Association for Information Systems, Vol. 9 (No. 3), pp.25-48. Sep.2017.
In article      View Article
 
[2]  National Institute of Standards and Technology, NIST, “The NIST Definition of Cloud Computing,” http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf, 2011. Accessed on December, 28, 2015.
In article      
 
[3]  Yang, Haibo, and Mary Tate. “Where are we at with cloud computing?: a descriptive literature review.” 2009.
In article      
 
[4]  Alshamaila, Y., & Papagiannidis, S. Cloud computing adoption by SMEs in the north east of England. Journal of Enterprise Information. 2013.
In article      View Article
 
[5]  www.capgemini.com/cloudnative, Microservices in cloud-based infrastructure, Gunnar Menzel, June 2016.
In article      
 
[6]  Patidar, S., Rane, D., & Jain, P. A Survey Paper on Cloud Computing. 2012 Second International Conference on Advanced Computing & Communication Technologies. 2012.
In article      View Article
 
[7]  MAAREF, S. Cloud computing in Africa. Tunis: RegulatoRy & maRket enviRonment. 2012.
In article      
 
[8]  Saeed, H. Role of Small & Medium Enterprises in Economic Development. Linkedin. 2017.
In article      
 
[9]  Kwaku, N. G., OffeiOtu, M., & Agyeiwaa, F. Role of Information and Communication Technology (ICT) in the Survival of Small and Medium Scale Enterprises (SME’s) in Ghana: Evidence from selected Small and Medium Scale Enterprises in New Juaben Municipality, Koforidua. International Journal of Managing Public Sector Information and Communication Technologies, 1-14. 2016.
In article      
 
[10]  Javaid, M. A. Implementation of Cloud Computing for SMEs. World Journal of Computer Application and Technology, 66-72. 2014.
In article      View Article
 
[11]  Neves, Fátima Trindade, Fernando Cruz Marta, Ana Maria R. Correia, and Miguel de Castro Neto. “The adoption of cloud computing by SMEs: identifying and coping with external factors.” 2011.
In article      
 
[12]  Namisango, F., Byomire, G., & Miiro, M. THE USE OF CLOUD COMPUTING AND STORAGE VIRTUALIZATION FOR INFORMATION STORAGE IN UGANDAN-BASED SMALL BUSINESSES. Research Gate. 2014.
In article      
 
[13]  National Information Technology Authority (NITA). Uganda E-Government Readiness Assessment Report. ERNST & YOUNG. 2012.
In article      
 
[14]  Sultan, N.. Reaching for the “cloud”: How SMEs canmanage. International Journal of Information Management, 31(3), 272-278. 2011.
In article      View Article
 
[15]  Yang, Haibo, and Mary Tate. “A Descriptive Literature Review and Classification of Cloud Computing Research.” Communications of the Association for Information Systems 31. 2012.
In article      View Article
 
[16]  Dwininta, W., & Irwansyah. Benefits And Challenges Of Cloud Computing Technology Adoption In Small And Medium Enterprises (SMEs). Advances in Economics, Business and Management Research (AEBMR), vol 4. 2017.
In article      
 
[17]  Andra, I. N., Anamaria, C. R., Gheorghe, Z., Ivona , S., & Florian, R. Cloud Computing Usage in SMEs. An Empirical Study Based on SMEs Employees Perceptions. Sustainability. 2020.
In article      
 
[18]  Haslinda , H., Mohd Herry , M., Norhaiza, K., & Iskandar , A.. Factors influencing cloud computing adoption in small and medium enterprises. Journal of ICT, pp: 21-41. 2017, June.
In article      
 
[19]  Sabwa, Belcha A. “Cloud computing adoption by small and medium enterprises (smes) in Nairobi County.” PhD diss., University of Nairobi, 2013.
In article      
 
[20]  Sultan, Nabil Ahmed. “Reaching for the “cloud”: How SMEs can manage.” International journal of information management 31, no. 3: 272-278. 2011
In article      View Article
 
[21]  Aaqib, R., & Amit, C. Cloud Computing Characteristics and Services: A Brief Review. International Journal of Computer Sciences and Engineering, Vol.7 (Issue. 2). 2019.
In article      View Article
 
[22]  Buyya, R., Voorsluys, W. & Broberg, J.,. Introduction to Cloud Computing, in Cloud Computing: Principles and Paradigms. 1st ed. Hoboken, NJ, USA. John Wiley & Sons, Inc. 2011.
In article      View Article
 
[23]  Goscinski, A., M. Brock,. Toward dynamicand attribute based publication, discovery andselection for cloud computing. Future generationcomputer systems, 26(7): 947-970. 2010.
In article      View Article
 
[24]  Rimal, B. and Choi, E.,, A Conceptual Approach for Taxonomical Spectrum of Cloud Computing, Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications, pp. 1-6. 2009.
In article      View Article
 
[25]  Zhang, Q., Cheng, L. and Boutaba, R.,. Cloud computing: state-of-the-art and research challenges, Journal of Internet Services and Applications, Vol. 1, No. 1, pp.7-18. 2010.
In article      View Article
 
[26]  Mujinga, Mathias, and Baldreck Chipangura. b. “Cloud computing concerns in developing economies.” 2011.
In article      
 
[27]  Ahunjonov, U., Hu, S., Bandula, J., & Mu, R. Characteristics of Small and Medium Enterprise Innovativeness: Cases of Uzbekistan and China. The Internation journal of Management Science and Business Administration. 2015.
In article      View Article
 
[28]  European Commission, “Annual Report on European SMEs 2013/2014: A Partial and Fragile Recovery”, p. 10. http://ec.europa.eu/growth/smes/business-friendlyenvironment/performance-review/files/supportingdocuments/2014/annual-report-smes-2014_en.pdf, 2014. Accessed on December, 28, 2015.
In article      
 
[29]  Weible, R. and Wallace, J. Cyber research: the impact of the Internet on data collection, Market Research, 10 (3), 19-31. 1998.
In article      
 
[30]  Kasse, John Paul, F. Nakawoya, W. Balunywa, and A. K. Nansubuga. “Framework towards cloud computing adoption by SMEs in Uganda.” Asian Journal of Computer Science and Information Technology 5, no. 7 .2015.
In article      View Article
 
[31]  Alauddin MD, Chowdhury MM. Small and medium enterprise in Bangladesh-Prospects and challenges. Global Journal of Management and Business Research. 2015.
In article      
 
[32]  Louis Kasekende and Henry Opondo. Financing Small and Medium-Scale Enterprises (SMEs):. Bank of Uganda Working Paper. 2003.
In article      
 
[33]  Venkatesh, Viswanath, Michael G. Morris, Gordon B. Davis, and Fred D. Davis. “User acceptance of information technology: Toward a unified view.” MIS quarterly.425-478. 2003.
In article      View Article
 
[34]  Cronbach, Lee J. “Coefficient alpha and the internal structure of tests.” psychometrika 16, no. 3: 297-334. 1951.
In article      View Article
 
[35]  Li, Long. “A critical review of technology acceptance literature.” Referred Research Paper 4.2010.
In article      
 
[36]  Kim, Changsu, Mirsobit Mirusmonov, and In Lee. “An empirical examination of factors influencing the intention to use mobile payment.” Computers in human behavior 26, no. 3: 310-322. 2010.
In article      View Article
 
[37]  Park, Sang Cheol, and Sung Yul Ryoo. “An empirical investigation of end-users’ switching toward cloud computing: A two factor theory perspective.” Computers in Human Behavior 29, no. 1: 160-170. 2013.
In article      View Article
 
[38]  Wang, Cheng, Jennifer Harris, and Paul Patterson. “The roles of habit, self-efficacy, and satisfaction in driving continued use of self-service technologies: a longitudinal study.” Journal of Service Research 16, no. 3: 400-414. 2013.
In article      View Article
 
[39]  Mir, Shabir Ahmad, and T. Padma. “Integrated Technology Acceptance Model for the Evaluation of Agricultural Decision Support Systems.” Journal of Global Information Technology Management 23, no. 2 : 138-164. 2020.
In article      View Article
 
[40]  Liebenberg, Janet, Trudie Benade, and Suria Ellis. “Acceptance of ICT: Applicability of the unified theory of acceptance and use of technology (UTAUT) to South African students.” The African Journal of Information Systems 10, no. 3: 2018.
In article      
 
[41]  Kim, Sung-Bum, Kyung-A. Sun, and Dae-Young Kim. “The influence of consumer value-based factors on attitude-behavioral intention in social commerce: The differences between high-and low-technology experience groups.” Journal of Travel & Tourism Marketing 30, no. 1-2 .2013.
In article      View Article
 
[42]  Aziz, Shahab, Zahra Afaq, and Uzma Bashir. “Behavioral intention to adopt Islamic Banking in Pakistan: A study based on Theory of Planned Behavior.” Journal of Islamic Business and Management 8, no. 2.2018.
In article      View Article
 
[43]  Merhi, Mohamed, Kate Hone, and Ali Tarhini. “A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust.” Technology in Society 59: 2019.
In article      View Article
 
[44]  Ismail, Lukwago, Musa B. Moya, and Kato Ismael. “Structured Equation Model for Determinants of Adoption and Use of Mobile Money Transfer Services inUganda.” Global Journal of Computers & Technology Vol 5, no. 1 2016.
In article      
 
[45]  Rahman, T., Noh, M., Kim, Y.S. and Lee, C.K., 2021. Effect of word of mouth on m-payment service adoption: a developing country case study. Information Development, 2021.
In article      View Article
 
[46]  Song, HakJun, Geun-Jun You, Yvette Reisinger, Choong-Ki Lee, and Seung-Kon Lee. “Behavioral intention of visitors to an Oriental medicine festival: An extended model of goal directed behavior.” Tourism Management 42 .2014.
In article      View Article
 
[47]  Chavoshi, Amir, and Hodjat Hamidi. “Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran.” Telematics and Informatics 38: 133-165. 2019.
In article      View Article
 
[48]  Chang, Andreas. “UTAUT and UTAUT 2: A review and agenda for future research.” The Winners 13, no. 2: 10-114. 2012.
In article      View Article
 
[49]  Shin, D.H.,. User centric cloud service model in public sectors: Policy implications of cloud services. Government Information Quarterly, 30(2), pp.194-203. 2013.
In article      View Article
 
[50]  Gore, Dana, and Anita Kothari. “Social determinants of health in Canada: Are healthy living initiatives there yet? A policy analysis.” International journal for equity in health 11, no. 1: 1-14. 2012.
In article      View Article  PubMed
 
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