Many organizations adopt business intelligence (BI) systems to improve their operations and decision-making processes. Even though many organizations have acquired these systems, the implementation process may not be successful. This paper seeks to explore and discuss the critical success factors (CSFs) that influence the success of BI implementation in organizations. This is accomplished using a systematic literature review following Kitchenham & Charters (2007) guidelines to review the extracted and analyzed articles from the selected journals and resources. The report aims to capture BI delivery critical success factors and categorize them based on association to implementation, organization’ operation, and user adoption. The Author can define 60 CSFs associated with an organization’s process, 53 CSFs related to Implementation, and 42 CSFs associated with user adoption, indicating that most of the available research is associated with organizations’ operations. In contrast, less research is done about Implementation and user adoption. The study’s findings show a gap in the body of the literature when addressing BI implementation and delivery success based on user adoption and Implementation.
A business intelligence (BI) system is not just an archive for organizational data. It is also not just a means of accessing organizational data. A business intelligence system should be considered from a data perspective regarding decision-making. BI systems have tools and utilities to support the information needs of managers of all levels. They are not complex data queries but are general tools to access insightful information accurately and timely (Ravasan & Savoji, 2019). Besides, they are designed for users to recognize the information they need and access it using simple tools. Business intelligence systems often have a single or combination of specific technologies. Among these are data analytic technologies for processing and analyzing large volumes of data. BI systems also consist of data querying and discovery technologies. This technology helps users discover the trend of data for decision-making. Other technologies include integrated BI solutions, data mining, visualization, warehouse, and artificial intelligence. Organizations implement business intelligence systems to integrate the day-to-day data generated from different sources into a single, consistent, and reliable structure to support analysis and decision-making (Borissova et al., 2016).
Implementing IT projects in an organization is not often a simple process. Many organizations have previously invested in IT projects that end up failing. This leads to organizations making a lot of financial losses. Different critical success factors (CSFs) determine the success or failure of IT and information system projects. Good IT project management requires project managers to identify these factors and adequately monitor them during Implementation. The critical success factors of IT and IS projects include the availability of technology, management support, leadership, and end-user support (Adzmi & Hassan, 2018). When an organization has matured in technology acquisition, its IT/IS projects are more likely to succeed than an organization that has not. Appropriate leadership during the project implementation and support from top management and the end-users will successfully implement IT/IS projects.
The implementation process of BI systems adopts the same approach as the Implementation of IT/IS systems. Therefore, the project managers of BI system implementation projects must understand different critical success factors influencing IT/IS projects before the BI project commences. Implementing BI systems requires integrating one or multiple BI technologies such as data analytics, data warehousing, dashboards, data visualization, AI-integrated BI, and enterprise reporting (Borissova et al., 2016). The acquisition and implementation of these technologies are often expensive. Therefore, organizations have to map out the process before defining the required resources and the parameters that businesses will use to measure the success or failure of the different components and the failure of the project (Ali & Khan, 2019).
Business intelligence systems are not a stand-alone discipline but a broad field that encompasses different aspects of technology, organizational strategy, and user engagement. For the study to conclusively and effectively conduct a systematic literature review on the chosen topic, understanding the needed Breadth and depth of the specific subject is essential. Davidson et al. (2019) describe the Breadth and depth of a research project as “Breadth of learning refers to the full span of knowledge of a subject. Depth of learning refers to the extent to which specific topics are focused upon, amplified, and explored.”
Depth and Breadth of study
The study is structured as a meta-analysis SLR and will help adequately understand the subject and its Breadth and depth.
The Breadth of the study
The Breadth of study covers all fields of BI systems (and other associated fields such as machine learning, AI, and data analytics) to fully understand the selected subject and its success factors (Yeoh & Popovic, 2016). For successful implementation of a BI system, reviewing everything about Business intelligence, the influencing factors, and its maturity models is key (Min-Hooi & Kee-Luen, 2011). In a nutshell, understanding all aspects, the required knowledge, and expertise needed will go a long way in identifying and classifying the critical success factors for BI implementation and delivery.
Depth of study
The depth of study, on the other hand, concerns itself with conducting an in-depth analysis of the Implementation and delivery of BI systems in business and CSFs that enable the fruition implementation process (Bajaj & Rai, 2018). Systematic literature review (SLR) on implementing and delivering BI systems is critical because correctly implemented BI systems provide the business with the end product. The end product is better service delivery and adoption of excellent e-logistical solutions such as ERP systems (Antoniadis et al., 2015), improved decision making, ability to forecast the future, and ability to improve the products and services (Indriasari et al., 2019). BI systems also provide the ability to understand their customers through developing compelling customer profiles using machine learning capabilities.
Understanding the depth of the research outlined above provides the most critical component of the study: to give businesses competitiveness and thus improve productivity (Caseiro & Coelho, 2018). The depth of research aims to develop or classify the identified CSFs into more meaningful and simplified ones that will be easily understandable by the business and thus make it easy to implement and deliver the business intelligence systems. There are mediating effects that affect how the CSFs relate to the implementation process of the BI systems (Anjariny et al., 2015), and proper classification of the CSFs into meaningful categories that are easily understandable is one of the mediating. The objective
The objective of the review
The SLR helps businesses understand how to implement BI systems for better service delivery at enterprises—in terms of operations and logistics, such as supply chain management (Sarangi, 2016). The main objective of the proposed review is to fill in the gaps by defining, identifying, evaluating, and successfully classifying the CSFs of BI implementation and delivery. The SLR aims to simplify the CSFs into more meaningful and understandable forms.
For organizations and SMEs to successfully implement and deliver their BI systems, understanding the critical success factors is necessary (Mungree et al., 2013; Yeoh & Popovic, 2016). This meta-analysis SLR will help organizations understand the essential success factors for their BI implementation (Harison, 2012) by categorizing the identified success factors.
Significance/contribution of the study
The significance of the study is encompassed in the benefits of BI systems to businesses. As mentioned above, business intelligence systems have numerous potential benefits to companies (Dobrev & Hart, 2014)—better service delivery (Popovic et al., 2018) and improved decision making. Other honors include the ability to forecast the future, the ability to improve the products and services (Indriasari et al., 2019), and the ability to understand their customers through the development of compelling customer profiles using machine learning capabilities (Rouhani et al., 2016)—thereby necessitating the SLR.
Additionally, it is beneficial to policymakers who may find it helpful in future framework developments and to decision makers such as managers and supervisors (Trujillo & Mate, 2011) in Making decisions about BI implementation and change management. At the same time, scholars and other researchers may use it to conduct further investigations in the future in the field mentioned above or conduct studies to fill the gaps where the study has not been completed (Ain et al., 2019).
Background and Theoretical Framework
Enterprises need to generate, collect, and transform data into actionable knowledge for a predictable future. Many enterprises are adopting systems to exploit data, improve analytical capabilities, and make information more agile and better. Businesses must evaluate the adoption of these systems by analyzing the Implementation of critical success factors (CSFs) (Eder & Koch, 2018). Organizations must deeply assess the critical success factors theory to understand the CSFs. The critical success factors theory describes CSFs as the limited number of areas where a satisfactory outcome will ensure the successful competitive performance of an organization (Ravasan & Savoji, 2014). Critical success factors form the core of an implementation strategy. A BI system’s environment is assumed to possess fundamental limitations and requirements, opportunities, and threats to which a BI system must align its strategy to succeed (Ranjbarfard & Hatami, 2020). An organization cannot afford to develop a BI system without considering the key factors underlying its success in the industry.
Initiating a business intelligence system without determining the key elements that will affect its success is a recipe for failure. The best sources of defining the factors that affect the success of a BI project are the enterprise organization’s strategic plan, the Organization’sOrganization’s internal and external environment, and the user needs. The factors that influence the success of a BI project ought to form the basis of the BI enterprise architecture and the data design architecture. Organizations must never implement BI systems without determining the strategic business, information requirements, and other related factors (Chen et al., 2000).
During the Implementation of BI systems, the organization and implementation team need to establish the correct parameters to measure the outcome of the implementation process and the final implemented product. An enterprise should be able to tell whether the implementation process is on the right track and the critical deliverables are being met. After the Implementation, the enterprise must also know whether the BI system is meeting the defined goals and the stakeholders’ expectations. All these have to be measured. The most valuable and effective performance measures are supposed to be cross-functional and ought to be linked with the objectives, strategies, and performance criteria of the BI system (Ravasan & Savoji, 2019).
The weakness of a project strategy will lead to poor analysis of the CSFs, inefficient use of project resources, poor definition of the project scope, poor budget estimates, and increased risks (Adzmi & Hassan, 2018). This will lead to project failure.
For this Meta-analysis systematic literature review (SLR), Developing a Conceptual practical framework is necessary. The conceptual framework will help understand how each study variable relates to the other (Kivunja, 2018). The main variables include the critical success factors of business intelligence (Independent) and their effect on implementation and service delivery. The moderating variables include other factors that have a role in the implementation and service delivery success.
E1 = Three-tier elimination process of removing duplicates, Title and abstract search, and full-text reading.
M1= Matrix query search that delivers 887 CSFs.
S1=streamlined CSFs through categorization of related CSFs.
C1= Categorization of Streamlined CSFs
I/D1= Successful implementation and delivery of the Business intelligence system.
The above conceptual framework indicates how the different variables (CSFs) and Implementation of BI systems relate. It also shows the role of the moderating variables. The first step was to screen the three identified librarians to determine the most relevant papers relevant to the topic. The Author used a matrix search query (M1) to interrogate the documents, and 887 CSFs were derived. The next step (E1) was to perform a 3 tier elimination process that involved removing duplicates, Title and abstract search, and full-text reading leading to 93 papers that were most relevant to the review.
Further classification of related CSFs was conducted through streamlining (S1), and 97 streamlined CSFs were derived. To make them even more meaningful, the streamlined CSFs were categorized (C1) into three major categories; OrganizationOrganization, Implementation, and user adoption. The three categories aided in the successful implantation of the BI system and its delivery
The approach used in this systematic literature review to investigate the critical success factors in implementing BI projects is the one proposed by Kitchenham & Charters (2007). Articles relevant to the Implementation of BI projects published between 2011 and 2021 are employed in this study, given that technology has taken great milestones within the last ten years, and significant focus has been put on BI systems and related research. Therefore, papers published from 2011 and beyond have a lot of literature on the subject. The study will use the articles collected from ACM, Web of Science, and ABI.
Having identified the specific variables needed for the study, a general search query will be the next logical step. Generic search queries are essential in getting the available picture of the study (Breadth) as they have a broad reach and provide the research with the meat to help narrow it down to more specific study aspects. An example of a generic search query entails using two plausible keywords to search the three databases to get a general feel of the topic. The Keywords used in the study include success factors, business intelligence, business intelligence success, business intelligence implementation, business intelligence adoption, business intelligence assessment, and business intelligence measurement, among others.
The Author developed a matrix of search queries based on the defined/ predetermined keyword to properly search all the databases and find all the relevant information regarding the topic. The matrix of search queries was used to develop a coding intersection between the two lists of items selected (Titles of the paper and abstracts of papers) to ensure the Author collected all the relevant results related to the research topic. Example of matrix element used include; Title: “business intelligence”] AND [Title: “implementation”] orTitle: “business intelligence”] AND [Abstract: “implementation”] or Abstract: “business intelligence”] AND [Title: “implementation”], Title: “business intelligence success factors”] AND [Abstract: “measurement”] or Abstract: “business intelligence success factors”] AND [Abstract: “measurement”] or Title: “business intelligence success factors”] AND [Title: “measurement”], [Abstract: “business intelligence”] AND [Abstract]: “success factors” or [Title: “business intelligence”] AND [Abstract: “success factors” or Title: “business intelligence”] AND [Title: “success factors”]
The matrix search query Abstract: “business intelligence”] AND [Abstract: “success factors,” generated 19 articles that were relevant to the research topic, the matrix element Title: “business intelligence”] AND [Title: “implementation, generated three articles that were relevant to the research topic, matrix element Title: “business intelligence”] AND [Abstract: “success factors” generated three articles that were relevant to the research topic and matrix element Title: “business intelligence success factors”] AND [Abstract: “measurement”] generated 1 article that was relevant to the research topic and matrix element. The matrix of search queries satisfied the Author through the results they generated that were relevant to the research topic.
This study generally focuses on understanding the different critical success factors in implementing BI projects. The inclusion criteria on selected papers are defined by accepting articles that discuss critical success factors of BI implementation and delivery. The exclusion criteria exclude any study that does not discuss the critical success factors of Bi implementation and delivery.
Article Screening Process:
Why the study?
The BI system and Implementation success depend on different critical success factors (CSFs). The classification of the CSFs stems from two vital needs in Business intelligence systems; Ensuring successful Implementation and delivery of BI and understanding how the different CSFs influence or impact the implementation process and delivery of the systems (Hawking & Sellito, 2018). To correctly classify the CSFs, the study conducted a systematic literature review on the elements.
The SLR generated 897 CSFs, then streamlined into 97 CSFs for more straightforward analysis. The CSFs are further analyzed, and three significant categories emerged, which provided a more meaningful understanding of the critical success factors. CSFs are more meaningful to businesses when put into meaningful frames (Hamidinava et al., 2021), such as the three categories—Organizational operation, Implementation, and user adoption—we adopted. The principal interest of businesses adopting or changing their BI is simplified information on how CSFs will impact their significant departments: OrganizationOrganization, operations (Implementation), and customers (Côrte-Real et al., 2014), and not raw data which has not been analyzed.
Collectively analysis from the perspectives of organization-related, implementation-related, and user adoption-related views has not been extensively done, necessitating the need for this SLR. Indeed, different authors have attempted to simplify the process of BI implementation by discussing various factors that influence BI implementation. Ali & Khan (2019) examined the importance of organization capability for successfully implementing the business intelligence systems and procedures, while Ali & Miah (2018) focused on how businesses can identify the organizational factors for the successful implementation of BI systems.
Organizations are essential in the implementation process. Organization readiness and management strategies are crucial in ensuring that the BI systems are successfully implemented and delivered (Anjariny & Zeki, 2014). Ensuring that there is management goodwill is, therefore, critical in successfully implementing the BI (Chen et al., 2016). Organization Ensures all the critical aspects are considered and taken care of through the implementation process.
Change management is also a function of the OrganizationOrganization, and it is crucial both to the OrganizationOrganization and to the Implementation of the new BI. Capacity is one of the BI critical success factors influenced by organizational change (Chaudhry & Dhingra, 2021).
Organizations have to change the capacity of the existing BI systems to accommodate the new organizational change. CSFs that are associated with organizational factors are Data Access, Management and Quality, Resources Expertise and Availability, Management Support, Sponsorship, Champion, Strategy, Vision and Goal, Project Management, Schedule and, Delivery, Information Quality, Accuracy, and Relevance, Impact and Effectiveness, Usability, Training, Technology and tool Selection, User Participation and Engagement, Satisfaction, Organization Culture, Change Management, Infrastructure, Competitive and Environmental, Pressure, Governance and Control, Personnel Attitude, Behavior and Acceptance, Collaboration, Communication, Innovation, and Support.
Other organizational CSFs include Knowledge Management, Accessibility, Agility, Business, Processes, Methodology, Competitive, Social, and Environmental Pressure, Decision Making Enablement, Vendor Selection and Engagement, Defined Roles And Responsibilities, Organization and Users Readiness For BI, Organization Size, Risk Management, Business Linking Solutioning, Expectancy, Advantages, Cost Saving, Existence of BI, Organization Structure, Trust, Environmental Uncertainty, Market Impact, Dynamics and Trends, Motivation, Organization Resistance, Technical and Business Challenges, Technology Management, Environmental Sustainability, Environmental Velocity, Observation, Organization Capabilities, Organization Maturity, Organization Relationship, Organization Silos, Organization Success, Organizational Capacity, Organizational Compatibility, Project Nature, Standards And Regulations, and Vendor Selection and Engagement. Proper consideration of the above-highlighted factors will ensure the successful Implementation and delivery of the BI.
System implementation is considered the break or makes a stage of delivering a successful BI system (Dooley et al., 2018), as systematic errors in this stage are more costly compared to other categories. System implementation involves the acquisition of necessary technical infrastructure and expertise, adopting an implementation framework, and deciding on the usability and budget, among others (Harison, 2012). Understanding the CSFs in this category is essential in ensuring a successful, easy-to-use BI system is delivered.
The CSFs for this category entail; Data Access, Management and Quality, Resources Expertise and Availability, Project Management, Schedule and Delivery, Information Quality, Accuracy and Relevance, Usability, System Capabilities, System Quality, Technology and tool Selection, System Integration, System and Technology Requirements, Change Management, Infrastructure, Governance and Control, Collaboration, Budget and Cost, System Reliability, Innovation, Security, Support, Accessibility, Agility, Architecture & Structure, Methodology, Performance Quality, System Compatibility, Vendor Selection and Engagement, Defined Roles And, Responsibilities and system Flexibility.
Other CSFs include System Responsiveness, Measurement and Testing, System Scalability, System Implementation, Pilot System Enablement, System Complexity, Technology, Maturity, Consistency, Continuous Improvement, Deployability, System Centralization, System, Customization, System Dependency, Technical and Business Challenges, Technology Management, Tool, and Technology Availability, Functionality, Project Nature, Purge And Archive Planning, Standards And Regulations, Sustainability, System Capacity, System Simplicity, System Success and Vendor Selection and engagement.
User participation and involvement in the implementation process are crucial to the successful delivery of the BI project (Yusof et al., 2013). Jamaludin and Mansor (2011) argue that user participation plays a significant role in the success of BI projects. This is because the end-users are the ones who will be interacting and using the system and are likely to resist change, thereby also necessitating the adoption of relevant management models such as Kotter’s theory of evolution in the implementation process.
Ensuring that the BI solution meets their needs is essential to increase their satisfaction and acceptance. Users should be actively involved and trained throughout the implementation process, and businesses should communicate the change in time (Propeti & Giuliani, 2017). Most importantly, increased users’ satisfaction will increase their intention and motivation to use the BI solution, thereby increasing their performance. This will lead to positive net benefits that will directly impact the performance of an organization (Serumaga-Zake, 2017).
The CSFs extracted from the literature concerning user adoption and satisfaction include Resources Expertise and Availability, Project Management, Schedule and Delivery, Information Quality, Accuracy, and Relevance; Impact and Effectiveness, Training, Technology and tool Selection, User Participation and Engagement, Organization Culture, Adoption, Governance and Control, Personnel Attitude, Behavior and Acceptance, Collaboration, Communication, Budget, and Cost,
Other CSFs include Innovation, Support, Knowledge Management, Accessibility and Agility, Business Processes, Competitive, Social and Environmental Pressure, Decision Making Enablement, Domain Analytics Knowledge, Defined Roles, And Responsibilities, Organization and Users Readiness For BI, Risk Management, Business Linking Solutioning, Expectancy, Advantages, Existence of BI, Pilot System Enablement, Trust, User Motivation and Empowerment, Market Impact, Dynamics and Trends, Motivation, Technology Management, User Quality, Users Traits, Observation, Standards, And Regulations User Preference.
After analyzing the critical success factors available in the literature and generating the common names of these vital factors, the Author identified and analyzed 60 CSFs associated with the Organization’sOrganization’s operation, 53 CSFs related to Implementation, and 42 CSFs associated with user adoption. It is therefore indicative that most of the available research is associated Organization’sOrganization’s operations, but the margin between all the categories is minimal.
Figure 4: CSFs by category
Table 1 contains the breakdown of the CSFs by category.
Table 1: Breakdown of CFS by Category
|Organization||System Implementation||User Adoption|
|1||Data Access, Management, and Quality||Data Access, Management, and Quality||Resources Expertise and Availability|
|2||Resources Expertise and Availability||Resources Expertise and Availability||Project Management, Schedule, and Delivery|
|3||Management Support, Sponsorship, And Champion||Project Management, Schedule, and Delivery||Information Quality, Accuracy, and Relevance|
|4||Strategy, Vision, and Goal||Information Quality, Accuracy, and Relevance||Impact and Effectiveness|
|5||Project Management, Schedule, and Delivery||Usability||Training|
|6||Information Quality, Accuracy, and Relevance||System Capabilities||Technology and tool Selection|
|7||Impact and Effectiveness||System Quality||User Participation and Engagement|
|8||Usability||Technology and tool Selection||Satisfaction|
|9||Training||System Integration||Organization Culture|
|10||Technology and tool Selection||System and Technology Requirements||Adoption|
|11||User Participation and Engagement||Change Management||Governance and Control|
|12||Satisfaction||Infrastructure||Personnel Attitude, Behavior, and Acceptance|
|13||Organization Culture||Governance and Control||Collaboration|
|15||Infrastructure||Budget and Cost||Budget and Cost|
|16||Competitive and Environmental Pressure||System Reliability||Innovation|
|17||Governance and Control||Innovation||Support|
|18||Personnel Attitude, Behavior, and Acceptance||Security||Knowledge Management|
|22||Support||Architecture & Strucutre||Competitive, Social, and Environmental Pressure|
|23||Knowledge Management||Methodology||Decision-Making Enablement|
|24||Accessibility||Performance Quality||Domain Analytics Knowledge|
|25||Agility||System Compatibility||Defined Roles And Responsibilities|
|26||Business Processes||Vendor Selection and Engagement||Organization and Users’ Readiness For BI|
|27||Methodology||Defined Roles And Responsibilities||Risk Management|
|28||Competitive, Social, and Environmental Pressure||System Flexibility||Business Linking Solutioning|
|29||Decision-Making Enablement||System Responsiveness||Expectancy|
|30||Vendor Selection and Engagement||Measurement and Testing||Advantages|
|31||Defined Roles And Responsibilities||System Scalability||Existence of BI|
|32||Organization and Users’ Readiness For BI||System Implementation||Pilot System Enablement|
|33||Organization Size||Pilot System Enablement||Trust|
|34||Risk Management||System Complexity||User Motivation and Empowerment|
|35||Business Linking Solutioning||Technology Maturity||Market Impact, Dynamics, and Trends|
|37||Advantages||Continuous Improvement||Technology Management|
|38||Cost Saving||Deployability||User Quality|
|39||Existence of BI||System Centralization||Users Traits|
|40||Organization Structure||System Customization||Observation|
|41||Trust||System Dependency||Standards And Regulations|
|42||Environmental Uncertainty||Technical and Business Challenges||User Preference|
|43||Market Impact, Dynamics, and Trends||Technology Management|
|44||Motivation||Tool and Technology Availability|
|46||Technical and Business Challenges||Project Nature|
|47||Technology Management||Purge And Archive Planning|
|48||Environmental Sustainability||Standards And Regulations|
|51||Organization Capabilities||System Simplicity|
|52||Organization Maturity||System Success|
|53||Organization Relationship||Vendor Selection and engagement|
|59||Standards And Regulations|
|60||Vendor Selection and Engagement|
Figure 5: Breakdown of CSFs by category
The implication of the study
The above analysis shows that all the categories are crucial in implementing the BI system and delivery. Businesses should focus on all categories when implementing and delivering their business intelligence systems, as each category has proven to be equally important in the study (Organization -60, implementation -53, and user adoption 53). The three categories provided a focused approach to the implementation process by allowing businesses to understand what CSFs and how they will affect the Implementation from the three fronts mentioned above.
Villamarin & Diaz (2017) notes that lack of clarity and complexity are some of the main challenges of implementing, delivering, and adopting BI systems. Also, the complex nature associated with implementing the BI system often leads to increased implementation costs (Dooley et al., 2018), highlighting the crucial importance of the three categories developed by this SLR (Muriithi & Kotze, 2013). The systematic literature review has also proven that CSFs of BI implementation and delivery are essential across all three categories. Adopting them will simplify the BI implementation and delivery processes, cutting the implementation cost and improving business efficiency.
Conclusion and Limitation
Data engineering for business intelligence is not the same as traditional application development. It has a broader scope, more visibility, a larger user population, and is more prone to failure. Before starting a business intelligence data project, an organization should assess the critical success factors of the BI solution from many different perspectives. Aspects of analysis include organizational, Implementation, and user adoption points of view, as each category has proven to be equally important in the study (Organization -60, implementation -53, and user adoption 53).
Previously, studies were carried out on BI implementation critical success factors. Many of these factors focused on the performance of the OrganizationOrganization after implementing the BI system. No studies have been carried out on categorizing the critical success factors of BI implementation from the perspective of OrganizationOrganization, Implementation, and user adoption.
This research paper has analyzed the available critical success factors in the literature and categorized them based on their association with the OrganizationOrganization, Implementation, and user adoption. It is noticeable that the essential success factors are OrganizationOrganization or Implementation related, with fewer factors concentrated on user adoption and satisfaction. The Author hopes the study will help researchers and organizations streamline the critical success factors of BI implementation and delivery. Scholars should also analyze them from other perspectives and hope it will help cover the gap by doing more research on user adoption and satisfaction and adding more to the BI user adoption critical success factors.
The Author encountered some limitations during the research due to the libraries used. One limitation is the grouping of the resources in some of the libraries used. The ACM digital library and direct science libraries did not have an appropriate order of classifying the materials in the library; this made it hard to trace some of the published articles. Other limitations are associated with using only peer-reviewed articles.
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