This chapter describes the research design used in exploring the relationship between the risk management structures and the decision-making processes in the projects within the private sector. The study is particularly focused on construction, IT, and finance industries, which are known to exist in high uncertainty and complexity levels. The objective of the study is to critically look at the importance of risk management in making decisions in various stages of the project life cycle including initiation, planning, and execution. The proposed methodology is based on the Research Onion framework (Saunders et al., 2023) that offers a systematic and holistic way of organising the research design. This chapter critically looks into the reason as to why the methodology of the research was selected, the sources of data and the method used to analyse data.
Research Philosophy
The study adheres to an interpretivist research philosophy, which is most suitable in comprehending the subject meanings that individuals or organisations assign to risk management frameworks. Contrary to positivism, which involves measurable information and objective examination, interpretivism examines intricacies in human perception, feelings, and values, especially in decision-making activities. The philosophical position is opportune to analyse the ways that project managers and organisational heads view and implement risk management strategies, especially in demanding sectors where uncertainty prevails, as in construction, IT and finance.
Interpretivism understands that social, organisational and contextual factors influence decision-making and may not be easily measured and quantified. By doing so, one can gain a better insight into the perception of risk management as not just a technical means but a strategic contribution to organisational determination at the project lifecycle. The study may also analyse the integration of risk management models, such as Enterprise Risk Management (ERM) and Risk-Adjusted Return on Capital (RAROC), into decision-making through the prism of subjective experiences of decision-makers (Saunders et al., 2023). This philosophy, in the end, makes it possible to consider the wider social and organisational consequences of risk management and make sure it is examined as both a technical and a strategic element of the decision-making process.
Research Approach
The study employs an inductive methodology that aims at investigating the application of current risk management frameworks, namely Enterprise Risk Management (ERM) and Risk-Adjusted Return on Capital (RAROC), in the business context concerning construction, information technology, and finance. Instead of receiving pre-determined hypotheses, this research seeks to comprehend the application of such frameworks in real-life projects decision-making (Smith, 2020).
In an inductive method, the researcher starts with certain observations and data that relate to the implementation of ERM and RAROC at different stages of the project lifecycle, such as initiation, planning, and implementation. In such a manner, it becomes possible to identify new patterns, themes, and observations regarding the efficiency of these frameworks in addressing risks within these sectors (Tufano, 2018). In the framework of the inductive perspective, the research problem is as follows: key variables affecting successful ERM and RAROC implementation in a complex and uncertain environment are to be identified. These observations form the basis of the study, which produces new theories or insights to enrich the knowledge of the understanding of applying these models in practice. The inductive approach of the research allows exploring sector-specific applications, including in the construction, IT, and finance industries, which are commonly highly uncertain and complex (Johnson & Davis, 2019).
The inductive method is adapted to this study, as it will enable an open-ended, flexible exploration of how these frameworks are practically applied. This will assist in revealing the subtleties of their implementation, propose improvements, and give understanding of where the future progress in risk management models must be directed (Roberts and Harris, 2021)..
Research Design
To support this study, the research design is applied using the Research Onion framework proposed by Saunders et al. (2023). It allows a systematic treatment of methodology by addressing different layers such as philosophical assumptions, research approach, pattern of data collection and time horizon.
Philosophical Approach:An interpretivist approach is an appropriate method because the research topic aims to determine the ways project managers and organisational leaders perceive and use risk management in making decisions. The growth of interpretivism as opposed to positivism is founded on the objective measurement and statistical description of variables. Regarding the aspect of risk management, an interpretivist approach gives the opportunity to consider the concept of risk management not only as a technical resource, but as a strategic component of the decision-making flow (Saunders et al., 2023).
Theory Development: The research employs aninductive approach, which starts with the already developed theories and concepts of risk management and decision making. The inductive methodology is suited since the study aims to address the hypothesis of testing the application of current risk management models, including Enterprise Risk Management (ERM) and Risk-Adjusted Return on Capital (RAROC), in risk management decision-making at the project lifecycle. With the deductive approach, it is possible to use the available theories in real-world situations in the building, information technology and finance industries (Tufano, 2018).
Research Strategy: Research strategy is based on a systematic literature review (SLR), and it is appropriate when the secondary data are peer-reviewed journal articles, books, and industry reports, which are available in such a format. The SLR approach also gains more and more popularity in management research because it combines the results of two or more studies to present a comprehensive picture of a phenomenon (Fenton et al., 2016). This is a strategy that is especially useful in alignment with the existing knowledge base and research gaps. Since this research focuses on researching the existing risk management models, the SLR method will enable in-depth search of the literature available on the topic (Setyarini et al., 2024).
Research options: The research committee will use a mono-method approach, as it will only use qualitative data through secondary sources. The rationale behind this selection is associated with the objective of the research to detect the theoretical basis of risk management frameworks and how these can be incorporated into the decision-makingprocess (Saura and Bužinskienė, 2025). Scholarly articles and industry reports are the best sources of secondary data to be used for this purpose because we will get information about the accepted practices as well as theoretical ideas without the necessity of gathering primary data.
Research Choice/Technique
This research choice is the mono-method type which implies the use of qualitative data in the form of secondary data. The main justification of using the approach is to investigate the theoretical basis of the risk management frameworks and their use in the decision-making process, without primary data collection being necessary. The secondary data, such as scholarly articles, industry reports, and books, play a significant role in knowing what practices, theories, and models have been established in the field. These sources can tell a lot about the practical use of risk management frameworks, such as Enterprise Risk Management (ERM) and Risk-Adjusted Return on Capital (RAROC) in construction, information-technology, and finance in practice.
Using secondary data means that the study has an extensive source of information which has been peer-reviewed and proven. It will be possible to understand in detail the theoretical aspects of risk management systems and how these influence the decision-making processes in organisations. In addition, secondary data helps to make the research process more time-efficient, allowing the researcher to concentrate on synthesising the available information instead of gathering novel and primary data (Saura and Bužinskienė, 2025). The method is particularly effective when it comes to studying already existing ideas in risk management and determining how they apply in various industries.
Data Collection Process
This study will collect data according to the PRISMA (Preferred Reporting Items to Systematic Review and Meta-Analyses) recommendations, which are used to make data collection transparent, rigorous and reproducible review of literature. The methodology is supposed to only capture pertinent and good-quality studies that will bring significant meaning to the relationship between risk management structures and decision-making in the projects of the private sector, in the context of the construction sector, IT, and finance sectors.
The search strategy will be based on searching academic databases like Google Scholar, and Scopus, which can be categorised as extensive, peer-reviewed resources. The words that are used in the search are follows: risk management in construction, IT risk management, and decision-making frameworks. The search of the literature will be restricted to articles dated between 2018 and 2025 to make sure that the study uses the latest theoretical and practical advances in terms of risk management and decision-making (Rad, 2018).
Inclusion criteria will be based on the peer-reviewed journal articles, academic books, or industry reports. These are credible and reliable sources (Tufano, 2018). Research should pay particular attention to how private-sector projects are carried out in the construction, IT, and finance industries and investigate how risk management models can be incorporated into decision-making at crucial steps such as initiation, planning, and execution (Mike and Kaplan,2015; Khan, and Rasheed, 2019’; Meyer and Reniers, 2025). All the studies will be included to ensure consistency, as only those written in English will be used.
The exclusion criteria will determine the studies that are not in the public sector, not peer-reviewed, or published earlier than 2018. Articles that are not related directly to risk management or decision-making (those that mention the technical components of risk management but do not relate it with decision-making) will be eliminated as well (Frigo & Anderson, 2018).
Systematic data extraction will be carried out after the inclusion and exclusion criteria are applied. Some of the key data points will be the information about the studies (the author, the year, the title), the risk management frameworks mentioned (ERM, RAROC, and ISO 31000), the decision-making processes in the different project stages, and specific details related to the sector concerning the difficulties in the implementation of the risk management (Tan and Zhang, 2023). Moreover, the main solutions of every research will be synthesised to demonstrate how risk management systems impact the decision-making steps and presuppose the project’s success (Young and McDonald, 2021). The data that will be extracted will be tabularized into a data extraction table, so as to further synthesise and make the process complete.
Such a systematic manner will make sure that the data gathering process is complete, clear, and according to the research questions, which will form a good basis for analysing the risk management in decision-making within various projects of the private industry.
Data Analysis
After data extraction, following the PRISMA guidelines, the next step involves data analysis. The methodology employed in this research is a systematic literature review (SLR), which entails the process of synthesising the available research and drawing conclusions from the data obtained. The research will be analysed following the PRISMA model, which provides a way to ensure that the literature search, selection, and synthesis are performed transparently and rigorously.
Search, Inclusion, and Exclusion Criteria for the SLR
| Criteria | Details | Examples |
| Search (Boolean) Criteria | The search criteria will use Boolean operators (AND, OR, NOT) to refine and focus the search process. | – “Enterprise Risk Management” AND “decision-making” AND “construction”
– “Risk-Adjusted Return on Capital” OR “RAROC” AND “IT” – “Risk management frameworks” NOT “healthcare” |
| Inclusion Criteria | Studies meeting the following criteria will be included in the review: | – Published in peer-reviewed journals between 2010 and 2023
– Focus on the application of ERM and RAROC in project decision-making – Industries: construction, IT, finance – English-language studies – Primary, secondary data, or conceptual analysis |
| Exclusion Criteria | The following criteria will exclude studies from the review: | – Published before 2010 –
Research outside the scope of ERM and RAROC – Articles on sectors like healthcare or education – Non-peer-reviewed, grey literature, or publications lacking sufficient methodological rigour – Studies in languages other than English |
Source: Self-Created
Data Synthesis: After identifying the relevant studies, the data will be synthesised through a descriptive synthesis approach. This implies that the results of the studies incorporated are summarised in accordance with the shared themes that are linked to risk management models and decision-making within different spheres, including construction, IT, and finance. The aim is to synthesise findings and cognise the trends that appear in the literature (Barton et al., 2017).
Comparative Analysis: The literature will be segregated based on the essential frameworks and concepts like Enterprise Risk Management (ERM), Risk-Adjusted Return on Capital (RAROC), and implementation into decision-making. Comparative analysis will be conducted to identify how these frameworks have been used in various stages of the project (initiation, planning, and execution) in different industries.
Gaps and Contradictions: The analysis will also point out gaps and contradictions in the extant literature, which will play a vital role in points of need to risk management models’ further refinement or development. This activity makes sure that not only is the synthesis comprehensive, but it can also provide new insights into the area of risk management (Sanchez and Garcia, 2020; Zhang et al., 2023).
The analysis will be guided by the PRISMA approach to make sure that all stages are conducted systematically and transparently, including search, selection and synthesis, which is categorised by a high level of evidence synthesis and reduces the number of biases.
Research Limitations
Though the research relies on secondary data that is found in reliable and peer-reviewed sources, the study is subject to a number of limitations. Among the limitations is the fact that secondary data might not capture all the current developments in the field. Since the study is based on articles and reports published till 2025, we might never get the chance to capture new trends or new methods published after 2025 in the analysis. This can therefore restrict the applicability of the discovery in the face of rapidly changing industries such as construction, IT and finance. Besides this, the study is limited to these three industries, and this could not be generalised further to other industries with varying risk management procedures and decision-making systems. The other weakness is due to possible publication bias, which may present some imbalance in the findings. Peer-reviewed journals tend to publish positive or novel findings, whereas negative or inconclusive results tend to be underreported, hence influencing the breadth and objectivity of existing literature (Bhimani et al., 2019). When interpreting the findings and conclusions of the study, these factors should be taken into account.
Ethical Considerations
This study includes ethical considerations as a key aspect, especially given that the study will use secondary data sources. Although secondary data is generally deemed ethical in intent, transparency and correct citation are essential in upholding integrity and preventing plagiarism. Any source, such as academic journals, books, and industry reports, will be referenced as required using the relevant referencing rules. This not only gives the original authors their due credit but also makes their study findings based on verified and trustworthy information. Objectivity in locating the relevant studies will be ensured by strictly applying the inclusion and exclusion criteria, where any source failing to meet the necessary criteria of quality and relevance will be eliminated. This will manage to not only enhance the research validity, but will also make the research carried out systematically and without prejudice. To minimise bias, the study will only make use of high-quality and peer-reviewed sources and avoid non-credible and opinion-based reporting. Compliance with these ethical principles will help gather credible results and maintain the highest academic standards of the study (Tufano, 2018; Hillson and Simon, 2021).