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Explore the Factors Affecting Fog Computing Adoption and the Associated Challenges and Benefits within Saudi Arabian Public Organizations

Ch3: The Research Framework
3.1 Introduction: Technology Adoption Theories
Different theories are relied upon during the adoption of particular technology. Consulting these theories helps to identify the best practice to undertake when adopting technology. Using different theories helps to ascertain the best course of action in the practice of technology adoption (Chhonker, Verma and Kar, 2017).
When adopting a technology, necessary data must be evaluated on the actual impact of this technology after it has been accepted. The role of theories is to help properly define the essential information that should be collected in the technology acceptance model. This is to avoid collecting information that is not or merely related to the technology at hand. Having theories also help to ascertain the proper framework of technology acceptance. This helps avoid following a framework of technology acceptance that is less effective. Theories also help to know whether or not a model will be sufficient to prove specific technology fit for acceptance.
Several models and theories related to technology acceptance are often used to study and analyse IT adoption, such as the Theory of Reasoned Action (TRA), the Technology Organisation Environment (TOE) framework, the Technology of Acceptance Model (TAM), the Diffusion of Innovation Theory (DOI), and the Unified Theory of Acceptance and Use of Technology (UTAUT). Several previous studies have proposed Fog adoption models. These studies have identified several factors that significantly influence the decision makers to adopt Fog Computing.
3.1.1 Theory of Reasoned Action (TRA)
This is the third theory that can be used in sourcing new technology. Any time before a new technology is adopted, it is crucial to understand how human behaviour is likely to be related to their attitude. This theory helps understand the attitude of those who will be using the technology to be adopted. Having understood their attitude, it is easy to have a forecast of how users are likely to behave as they interact with the new technology (Nickerson, 2022). If the potential users intend to make the most of the new technology, then they will support the new technology once it has been accepted for adoption. If potential users intend to try something out that has more modern advanced features than what they are currently using, they become more willing to spend on acquiring new technology. In Figure 3.1, the elements of the Theory of Reasoned Action are shown.
Figure 3.1: Theory of Reasoned Action (TRA) (Ajzen and Fishbein, 1975)
3.1.2 Technology-Organization-Environment (TOE) Framework
In 1990, Tornatzky and Fleischer’s The Processes of Technological Innovation described The technology–organisation–environment (TOE) framework. The TOE framework explains the adoption of technology in organisations and how its elements influence how technological innovations are adopted and implemented (Tornatzky and Fleischer, 1990). The TOE framework explains how adoption decisions are influenced by three variables within an organisation’s context. Contexts are categorised as technological, organisational, and environmental. Each of these factors influences technological innovation. The first dimension is the technical aspects faced by the organisation when adopting technology, such as IT infrastructure, security, quality, and hardware. A second factor refers to internal and external forces that affect an organisation, such as market competition and regulations, while the third element refers to the nature of the organisation, its size, and its structure (al Hadwer et al., 2021). Figure 3.2 illustrates the framework for Technology-Organization-Environment.
Figure 3.2: Technology-Organisation-Environment Framework (Tornatzky and Fleischer, 1990)
3.1.3 Technology Acceptance Model (TAM)
This is a model used by users of technology and lays down how technology is accepted and even used by the users (Kamal, Shafiq and Kakria, 2020). Using the technology acceptance model when considering sourcing specific new technology depends on four attributes. One of these attributes is the ease of use of the new technology. Technology users are only open and willing to use technology that is simpler to use than what existed previously. If the technology being considered for acceptance is not easy like what exists, then it beats the purpose of adopting such technology in the first place. Such technology would create a more challenging environment to work from.
The second attribute of TAM is perceived usefulness. The new technology considered for adoption must show its worth to users. Users will only support technology being considered for acceptance it. It is more useful for the existing technology. If the existing suggested technology for adoption has fewer features, it would be deemed less interactive with users’ needs.
Attitude is also a salient determinant of the effectiveness of the technology acceptance model. How people perceive the new technology largely determines if they will support it or if they are still stuck in the old technology. Actual behaviour is the fourth attribute that dictates the technology acceptance model. Users’ behaviours must align with the technology’s applicability (Kamal, Shafiq and Kakria, 2020). If users’ behaviour is not in inclination toward the functionality of the new technology, then the new technology is destined to result in chaos and disputes over job roles. As shown in Figure 3.3, the Technology Acceptance Model (TAM) consists of several components.
Figure 3.3: Technology Acceptance Model (TAM) (Davis, Bagozzi and Warshaw, 1989)
3.1.4 Diffusion of Innovation Theory (DOI)
This is the second TAM theory that is consulted during the adoption of new technology. This theory postulates that when a new technology is adopted, the adopter must look closely at how it will spread among intended users. Spreading, in this case, is about the new technology, its applicability, and its suitability to users’ needs. Users of technology look into how to spread information amongst themselves about the new technology. Here, they assess the power and freedom of sharing knowledge and information in the face of new technology. If technology inhibits sharing of knowledge, users tend to be reluctant to accept and support the adoption of new technology (Iqbal and Zahidie, 2022).
This theory is used to assess how the information about technology adoption would be spread. It sheds light on the reasons cited for adopting new technology to reduce the likelihood of rejection of the adopted technology (Syahadiyanti and Subriadi, 2018). If there are not enough means of spreading information about adopting new technology, users will be hesitant to adopt it. Users look for the reason that makes the new technology not just useful for users but also for society at large.
There are five stages of diffusion of innovation theory. The first stage of these five is the creation of knowledge. This involves exposing users to the new technology just for mere understanding of more options of technology available. The second stage of the diffusion of innovation theory is the persuasion theory. This is where potential technology users get interested in new technology and start to actively look for information regarding the functionality of the technology in question (Lee, 2021).
The third stage is the decision phase. Here, a potential user of a new technology looks and both pros and cons of using new technology. The potential users evaluate if the pros outdo the existing technology. If pros are more than cons, potential users might decide to adopt the technology, but if disadvantages are the majority, a user might reject a new technology altogether. The user finally incorporates new technology within the designated platforms in the fourth implementation stage. The fifth stage of information follows this. This is where users look at whether the newly adopted technology meets its purpose and how important it is to the users and society at large (Lee, 2021). According to DOI, adoption of innovation is classified as follows: (a) Relative advantage (b) Compatibility (c) Complexity (d) Trialability (e) Observability (Rogers, 1995). The diffusion of innovation theory is illustrated in figure 3.4 below.
Figure 3.4: Diffusion of Innovation Theory (Rogers, 1995)
3.1.5 Unified theory of acceptance and use of technology (UTAUT)
This is the first model of technology acceptance that is consulted during technology adoption. According to this theory, users of new technology accept to adopt technology based on their expectancy of how the technology will perform. If the users’ unified view is that the accepted technology will make their work easier, they will accept it. If users perceive the new technology to be adopted as a cause of hardships in their work, they are likely to boycott the adoption of new technology.
This theory also postulates that users of new technology accept to adopt it if the facilitating conditions are present. These environmental facilitating conditions enable the new technology to meet its purpose in utilization. If the enabling factor of the new technology is not present, users feel pressure amounting to them. This is because they fear being attributed to the cause of new technology failing to perform simply because the enabling environment is not sufficiently available.
Acceptance of the new technology also depends on the expected social influence of the technology in question (Dwivedi et al., 2020). If the technology being considered for adoption will lead to a positive influence on society, users will support it. They attribute new technology to social benefit and support it for full implementation and success of new technology. As shown in Figure 3.5, the UTUAT model consists of several components.
Figure 3.5: Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003)
3.2 Reasons for Choosing the TOE Model for Fog Computing Adoption
The TOE Framework can be used to study both the potential and actual impacts of innovation on organizations. It has a great deal of flexibility and has been used in a variety of types of research. This model mainly affects IT implementation (Oliveira, Rosario Oliveira Martins and Fraga Martins, 2011). The literature review has shown, however, that more direct influences should be included alongside those already included in the Framework. The TOE framework has been used to examine IT adoption to determine the critical factors influencing Fog Computing adoption. Furthermore, the TOE Framework has been used in several studies to try and understand IT adoption across the three contexts (see Table 3.1).
This new technology, Fog Computing, should be adopted for several reasons. One, it would help to ease human functionality. It would also support decision-based intervention and the use of the technology by all users. Users will likely not be open to accepting a technology they have not justified in its usefulness. Therefore, some users tend to be reluctant to use technology that they know could be beneficial. They tend to be reluctant because they are yet to establish a balance of all factors that dictate the suitability of technology to be adopted.
The new technology needs to be reviewed just before it is adopted. This is to ensure it is the exact solution to the gap that wants to be filled. This is to avoid adopting technology that will come and cause more disaster within an organization. It is also through a review that gaps that could result in inconsistencies could be filled. This equally applies to identifying new opportunities through which the new technology could be made even more effective and practical in a real-life setting.
IT Adoption Analysed Variables Methods Data, and context Author(s)
EDI Technological context : perceived direct benefits; perceived indirect benefits.

Organizational context : perceived financial cost; perceived technical competence.

Environmental context : perceived industry pressure; perceived government pressure. Factor analysis (FA), and Logistic regression Letter with questionnaires was sent; 575 small firms

Hong Kong (Kuan and
Chau
2001)
Open systems Characteristics of the “Open Systems
Technology” Innovation : perceived
Benefits; perceived barriers; perceived Importance of compliance to standards, interoperability, and Interconnectivity.

Organizational technology : complexity of IT infrastructure; satisfaction with existing systems; formalization of system development and management.

External environment : market uncertainly T-test, FA, logistic regression Face-to-face interview, 89 firms

Hong Kong (Chau and
Tam 1997)
Web site Technological context : technology readiness; technology integration; security applications.

Organizational context : perceived benefits of electronic correspondence; IT training programmes; access to the IT system of the firm; internet and email norms.

Environmental context : web site competitive pressure

Controls  Services sector. Multiple correspondence analysis (MCA), and probit model 3155 small and
637 large firms

Portuguese (Oliveira and Martins
2008)
Web site

E-commerce Technological context : technology readiness; technology integration; security applications.

Organizational context : perceived
benefits of electronic correspondence; IT training programmes; access to the IT system of the firm; internet and email norms.

Environmental context : web site competitive pressure; e-commerce competitive pressure.

Controls  Services sector. MCA, and probit model 2626 firms

Portuguese (Oliveira and Martins
2009)
Internet

Web site

E-commerce Technological context : technology readiness; technology integration; security applications.

Organizational context : perceived benefits of electronic correspondence; IT training programmes; access to the IT system of the firm; internet and email norms.

Environmental context : internet competitive pressure; web site competitive pressure; e-commerce competitive pressure.

Controls  Services sector. MCA, and logit model 3155 small firms

Portuguese (Martins and Oliveira
2009)
e-commerce
development level (0-14)
Technological : support from technology; human capital; potential support from technology.

Organizational : management level for information; firm size.

Environmental : user satisfaction; ecommerce security.

Controls : firm property. FA and OLS e-mail survey, online survey and telephone interview during 2006; 156 firms.

Shaanxi, China (Liu 2008)
ERP Technological context : IT infrastructure; technology readiness.

Organizational context : size; perceived barriers.

Environmental context : production and operations improvement; enhancement of products and services; competitive pressure; regulatory policy. FA, and Logistic regression Face-to-face interview, 99 firms

Taiwan (Pan and
Jang 2008)
Deployment of B2B ecommerce: B2B firms versus nonB2B firms Technological inhibitors : unresolved technical issues; lack of IT expertise and infrastructure; lack of interoperability.

Organizational inhibitors : difficulties in organizational change; problems in project management; lack of top management support; lack of ecommerce strategy; difficulties in costbenefit assessment.

Environmental inhibitors : unresolved legal issues; fear and uncertainty. FA, t-tests and discrimination analysis 249 firms

North America and Canada (Teo et al.
2006)
E-business Technology competence : IT infrastructure; e-business know-how.

Organizational context : firm scope,
firm size.

Environmental context : consumer readiness; competitive pressure; lack of trading partner readiness.

Controls (industry and country effect) Confirmatory factor analysis (CFA), second order factor
modelling, logistic regression, and cluster analysis
(CA) Telephone interview during 2000; 3552 firms

European
(Germany, UK,
Denmark,
Ireland, France,
Spain, Italy, and
Finland) (Zhu et al.
2003)
E-Business usage Technological context technology competence.

Organizational context : size; international scope; financial commitment.

Environmental context : competitive pressure; regulatory support.

e-Business functionalities : front-end functionality; back-end integration. CFA, secondorder factor
modelling, and
SEM Telephone interview during 2002, 624 firms across 10
countries

Developed
(Denmark,
France,
Germany,
Japan,
Singapore, U.S.) and developing
(Brazil, China,
Mexico and Taiwan) countries (Zhu and
Kraemer
2005)
E-Business
initiation

E-Business
adoption

E-Business routinization Technological context technology readiness; technology integration.

Organizational context : firm size; global scopes; trading globalization; managerial obstacles.

Environmental context : competition intensity; regulatory environment. CFA, and structural equation
modelling (SEM)
Telephone interview during 2002, 1857 firms across 10
countries

Developed
(Denmark,
France,
Germany,
Japan,
Singapore, U.S.) and developing
(Brazil, China,
Mexico and Taiwan) countries (Zhu et al.
2006b)
E-business Technological context : technology readiness; technology integration; security applications.

Organizational context : perceived benefits of electronic correspondence; IT training programmes; access to the IT system of the firm; internet and email norms.

Environmental context : web site competitive pressure

Controls  Services sector. T-test, FA, and
CA Telephone interview during 2006, 6964 firms across 27
countries

UE27 countries (Oliveira and Martins
2010a)
Internal integration of
e-business

External diffusion of use of ebusiness Technological context : IS infrastructure; IS expertise.

Organizational context : organizational compatibility; expected benefits of e-business.

Environmental context : competitive pressure; trading partner readiness. CFA, and SEM e-mail survey during 2006; 163 large firms

Taiwan (Lin and
Lin 2008)
Table 3.1: Studies that have applied Tornatzky and Fleischer’s (1990) TOE Framework (Oliveira, Rosario Oliveira Martins and Fraga Martins, 2011)

3.3 Developing a Conceptual Framework and Hypotheses for the Adoption of Fog Computing
3.3.1 Technological Context
3.3.1.1 Quality of Service (QS)
H1: A high level of service quality is positively associated with the intention of public organizations to adopt fog computing.
3.3.1.2 Security (SE)
H2: A higher level of security positively influences public organizations’ intention to adopt fog computing.
3.3.1.3 Privacy (PR)
H3: A higher level of privacy influences public organizations’ intention to adopt fog computing.
3.3.1.4 Compatibility (CM)
H4: A high degree of compatibility impacts the intention to adopt fog computing in public organizations in a positive way.
3.3.1.5 Complexity (CO)
H5: A decrease in complexity will positively influence a public organization’s intention to adopt fog computing.
3.3.1.6 Awareness (AW)
H6: Awareness of fog computing is associated with an increased interest in adopting it by public organizations.
3.3.2 The Organisational Context
3.3.2.1 Senior Management Support (SM)
H7: The support of senior management influences the intention to adopt fog computing for public organizations.
3.3.2.2 Technology Readiness (TR)
H8: The readiness of technology influences the decision to adopt fog computing in public organizations.
3.3.3 Environmental Context
3.3.3.1 Competitive Pressures (CP)
H9: Competitive pressures positively influence the decision to adopt fog computing in public organizations.

3.3.3.2 Compliance with Regulations (CR)
H10: A less restrictive regulatory environment will help public organizations adopt fog computing.
3.3.4 Financial Context
3.3.4.1 Cost (CT)
H11: Lower costs will positively influence the adoption of Fog Computing for public organizations.
3.3.4. Maintenance (MA)
H12: Lower maintenance will positively influence the adoption of Fog Computing for public organizations.
3.4 The Conceptual Framework for Fog Computing Adoption in Saudi Arabian Public Organisations (FCASAPO)

Figure X: The Conceptual Framework for Fog Computing Adoption in Saudi Arabian Public Organisations (FCASAPO)
3.5 Summary

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