Chapter 3: Research Methodology The objective of this chapter is to explain-Get Answer Now

Chapter 3: Research Methodology The objective of this chapter is to explain-Get Answer Now
Chapter 3: Research Methodology 
The objective of this chapter is to explain the methodology that will be used to study these research questions:
a) How can organisations involve employees when designing cybersecurity policies?
b) How does employee participation in designing an organisation’s security policies affect security-related outcomes?
A systematic literature review will be used for this study to summarize the existing research relevant to these questions. The justification and explanation for this choice are provided below.
Philosophical Paradigms
This research follows a qualitative research design categorized under the interpretative philosophical paradigm. Like positivism, interpretivism emerged from the field of anthropology (Gemma, 2018).  Interpretivism is also regarded as anti-positivism as it opposes positivist beliefs and values. Interpretivism holds that knowledge and truth tend to be subjective and historically and culturally situated, based on individuals’ experiences and comprehension (Gemma, 2018).  The paradigm asserts that it is not possible to separate researchers from their beliefs and values fully, and so they will automatically inform how they gather, analyze as well as interpret data.
Ethical Considerations 
Ethical consideration is key to conducting research. One of the ethical considerations for this study is ensuring that authors are accurately acknowledged and that there is no instance of plagiarism. Given that the study will be analyzing previously published research articles related to the research topic, the researcher will be careful to ensure that all authors quoted in the research are acknowledged and cited. Moreover, the researcher shall not “copy and paste” text from sources without citing them. Plagiarism amounts to pandemic dishonesty and is prohibited; therefore, this study will ensure high-level academic honesty is adhered to and remain ethical.
Two other aspects of ethical research are having informed consent from the research participants and providing them with confidentiality and anonymity (Fleming, 2018). However, since this study will use secondary data, no research participants will be involved and these two aspects are not relevant for this study.
A final aspect of research ethics is conflicts of interest.  Existing relationships or activities that the researcher is engaged in before the research starts could lead to conflicts of interest, and they should be reported within the ethical approval application process. For example, researchers could be carrying out a study on topics or programs in which they have a personal interest, and this could affect the eligibility and quality of their research. The research committee will offer guidance and directions on managing conflicts of interest (Fleming, 2018). In this study, the researcher did not have any knowledge of any conflicts of interest. 
Secondary sources of data
Primary data is data gathered by researchers with a specific aim. Primary data is first-hand data. Researchers who collect primary data are familiar with the data gathering process as well as the research design. In contrast, secondary data refers to data gathered by people other than the researcher. Researchers who use secondary data frame their research questions such that they can be answered by analyzing data that the researcher did not collect herself (UCL, 2022). It should be noted that a data set can be primary data for one researcher and also a secondary dataset for another researcher (UCL, 2022).
There are several categories of secondary data. Raw data comprises data from newspapers, websites, and organizational databases. Compiled data is from industry reports, journals, books, and government publications. According to Sindin (2017),  there is a third form of secondary data which lies between compiled and raw data, which includes the data gathered using survey methods such as census data. Examples of data in this classification include employee attitude, market trends, spending trends, and varied surveys (Sindin, 2017).
Advantages of secondary data
One of the key advantages of secondary data is its economic advantage. Since another person has already gathered the data, a researcher does not need to put a huge amount of resources into collecting secondary data (Boslaugh, 2022).  Even though some secondary data may be purchased, the researcher does not spend as much as he or she would with primary data. Primary data requires the researcher to cater for transportation costs, data compilation costs, and the salaries of those that help in data compilation, such as data entry clerks and interviewers. However, with secondary data, a researcher can simply browse and gather stored data and then analyze it (Boslaugh, 2022).  
Besides being cheaper, secondary data also saves time. Secondary data is data that has been collected, stored, and even cleaned, ready to be used. Researchers who use secondary data do not spend time collecting the data through interviews or observation; instead, they use data that is ready to use, which saves a lot of time compared to using primary data (Boslaugh, 2022).  
Another key advantage of secondary data is that there is a high amount of data available online and in university libraries. Few people would have the time and resources to gather data from participants in nearly all parts of a country, eliminating repeating data gathering each year.  Governments and companies have gathered a wide variety of secondary data readily available online or through libraries.  Because there is a wide variety of secondary data, researchers can complete high-quality projects and research using secondary data. The variety also gives the researcher the freedom to select and compare data before using it to answer the research questions or test hypotheses.
Another merit of using or employing secondary data is the fact that the data gathering procedure is usually done professionally and by experts, which would be difficult with small research projects. For example, numerous national health surveys employ advanced sample designs and weighting systems that permit researchers to calculate population-related estimates of behaviours and conditions (Boslaugh, 2022). Even though smaller projects and research could adopt similar methods, the issue is that they may use a convenience sample, therefore, making the generalizability of the findings questionable. Researchers may also lack skills and experience in gathering primary data, making secondary data more suitable for small projects whose researchers do not have adequate skills and experience in data collection procedures.
Disadvantages of secondary data
One of the core demerits of using secondary data in answering research questions is that it could fail to answer the particular research questions or may fail to have specific content that the researcher could be specifically looking for. In addition, the available secondary data could not have been gathered from the geographical place in which the researcher is interested or studying. The research question for this study is to explain how companies could make their employees participate in cybersecurity policy development. The researcher could obtain secondary data related to the topic but fail to have the exact content to answer the research question, therefore, posing a huge disadvantage to the researcher. Because the researcher did not participate in gathering the secondary data, the researcher also lacks control of what is found in the secondary data set (UCL, 2022).  As a result, it could restrict the researchers’ breadth of research question analyses or could also make the researcher change the research question to match the available secondary data set. The issue with this is that variables of the secondary data could be described differently compared to what the researcher has selected.
In addition, another disadvantage of utilizing secondary data for a research project is that the researcher is not well conversed with the data gathering process and does not know if the collection procedure was done well or poorly (UCL, 2022).  The data collection procedure is indeed crucial as it determines the failure and success of a research project.  Researchers using secondary data sources are only interested in the content and findings but fail to pay attention to the techniques and processes of data gathering, and if data collection was done poorly, there is a high chance that the research question has been answered poorly. Moreover, the researcher’s unfamiliarity with the data gathering procedures means that issues in data collection, such as the reasons for a low response rate or if there was any misunderstanding among the respondents, could impact the final results of the study (UCL, 2022). Secondary data sets usually do not include documentation of the challenges that were experienced during the data-gathering phase, so the researchers will not be aware of them. This study uses secondary data and will keep these limitations in mind. 
Data Collection
According to Templier & Pare (2015), there are four forms of literature review: narrative, cumulative, aggregation, and developmental. This research project will adopt a narrative form of review. The narrative is crucial because it provides a summary of the existing or past published literature or research work on the topic under investigation (Templier & Pare, 2015). Narrative concentrate on theories as well as concepts, search results and research methods, and majorly put together and synthesize the existing literature and offer readers with a detailed report on the present knowledge state of the research topic.
On a given topic of investigation (Templier & Pare, 2015). The topic under investigation requires a description and summary of the present literature and research to provide detailed knowledge about it. As a result, this study shall adopt the narrative review technique. Given that the study shall rely on secondary data, it is possible to find many articles regarding the topic. However, the articles shall be screened for eligibility and quality to ensure that only relevant studies will form part of the review.
According to Liberati et al. (2009), secondary data article selection shall go through four key phases to ensure that the contents are satisfactorily reviewed and screened before having the final 25 pieces of literature on the topic. From a possible search of 2466, the selection process shall ensure that duplicates are eliminated, screening for quality, eligibility, and inclusivity are done thoroughly to eliminate those that do not pass the criteria and have a final 25 quality and relevant articles that can be used to answer the research problem.
Identification Phase
The first step or phase is the identification phase, where the articles on the topic shall be searched through online websites and databases. I searched and used articles talking about how effectively organizations can ensure their workers take part in cybersecurity design and implementation. The key theme searched include training, awareness, coordination, employee empowerment, employee motivation, and employee collaboration. I searched for primary data articles on the same topic. The keywords searched include employee participation, cybersecurity designs, and implementations.
Grey literature
Apart from searching academic literature for this study, the research also searched and reviewed grey literature. According to Adams et al. (2017), grey literature is literature or secondary source of data that is not categorized as academic material or literature and does not need to be peer-reviewed. The grey literature reviewed for this study includes industry reports as well as statistics reports.
The second phase of reviewing articles for this study is the screening phase, where articles that do not pass the quality needed will be eliminated.  I checked the articles that were collected for duplication and eliminated those which are duplicates.
The third phase includes the eligibility phase to review the entire articles for eligibility and delete those not eligible for this study. For instance, I checked the relevance, lack of business, the year in which the articles were published, and searched if the articles included content that could help answer the research topic. Once I analyzed the complete text of the articles, including grey literature and academic literature, I excluded those which were not published in 2010 and above. This means that I excluded those articles that were published earlier than 2010 and were biased. Research bias can be explicated as deviation from the research truth, especially in data gathering, analysis, interpretation, as well as a publication which may lead to false or incorrect conclusions (Šimundić, 2012). Bias could either be unintentional or intentional. In this article selection process, articles shall be screened for publication, gathering, analysis, as well as interpretation of biases and exclude such articles. The purpose of this would be to help come up with a more accurate conclusion about the research topic and avoid any form of biasness. The research shall be including articles that are published in 2010 and above because the research requires to be answered by the current solutions and not outdated one. The field of cybersecurity is evolving and changing from time to time. As technology advances, the threats have also evolved and so finding the latest solution would be the most appropriate way for businesses and managers. As such, articles published in 2010 and above would be the most preferred for this research.
Finally, the fourth phase will include inclusivity, where the articles that meet qualitative research design shall be included in the study. I included articles that are qualitative and also those which are quantitative. The academic research literature used was checked if they adopted quantitative or qualitative, and those which were eligible and relevant to the research questions were included. However, those academic articles that did not adopt either qualitative or quantitative methods (that is, those that were purely conceptual) were eliminated or excluded.
Grey literature articles identified by searching through online websites
Grey literature articles identified by searching through online websites
The academic articles identified after searching online websites
The academic articles identified after searching online websitesIdentification
The number of articles eliminated after duplicates were checked
The number of articles eliminated after duplicates were checkedScreening
Number of articles excluded after screening
Number of articles excluded after screening
Number of articles screened
Number of articles screened
The number of articles excluded for not meeting eligibility requirements
The number of articles excluded for not meeting eligibility requirements
Number of complete articles screened for eligibility
Number of complete articles screened for eligibilityEligibility
The number of grey and academic articles included in qualitative analysis
The number of grey and academic articles included in qualitative analysisInclude
The number of articles used or included in quantitative analysis
The number of articles used or included in quantitative analysis
Source: Liberati et al. (2009)
The study will use secondary data sources on the topic. The secondary data and existing published literature shall be screened for quality and eligibility using four phases. The phases include an identification phase, a screening phase, an eligibility phase, and an inclusivity phase. The article selected for the study is those that were published in the year 2010 and above.
Data Analysis
The study involves analyzing secondary data sources on the research topic, which is how effective organizations can make their workers participate in cybersecurity implementation programs.  Academic databases were searched to collect articles on this topic and analysed using thematic analysis. Thematic analysis can be described as the procedure of establishing themes and trends in a qualitative data set (Maguire & Delahunt, 2017). Braun & Clarke (2006) stated that thematic analysis is valuable because it includes key skills that are important for carrying out other forms of analysis. One advantage of adopting thematic analysis is that, instead of a methodology, it is a technique that is not fixed to a specific theoretical or epistemological perspective, unlike other forms of qualitative methodologies. This advantage makes thematic analysis a flexible technique, especially in researching topics with a high level of diversity (Maguire & Delahunt, 2017).
There are several ways to handle thematic analysis. This indicates that there is a lack of clarity about the nature of thematic analysis and its distinctiveness from other qualitative techniques such as content analysis (Maguire & Delahunt, 2017). In this study, the thematic analysis approach to be followed will be similar to Braun Clarke’s (2006) six-phase model. The framework is among the most widely used in the world of social sciences research, mainly due to its ability to provide a usable and clear model for conducting thematic analysis (Maguire & Delahunt, 2017).
The main aim of thematic analysis is to identify themes and patterns or trends in secondary data that are interesting or crucial to the research topic. The researcher, therefore, utilizes the themes to tackle the research issue or to answer the research question. Thematic analysis is more than just summarizing data from secondary sources, as it entails interpreting data as well as ensuring that sense is created from the data. A common issue with thematic analysis is when the researchers utilize the key interview questions to be the themes (Maguire & Delahunt, 2017). Normally, it mirrors the fact that data may have been organized and summarized instead of being analyzed.
Braun & Clarke (2006) differentiate between two forms of thematic analysis: latent and semantic analysis. As far as semantic themes are concerned, the researcher is interested in the surface or rather explicit data meanings and does not search for something beyond what a respondent has stated or what has been written in the secondary data (Maguire & Delahunt, 2017). For semantic themes, the researcher is interested in establishing the themes based on semantic levels. In comparison, the analysis of latent themes is different as the researcher is interested in meaning beyond what has been said or written down in the secondary data, and the focus is on explaining as well as interpreting the themes in the second dataset (Maguire & Delahunt, 2017). For latent theme analysis, the researcher begins with establishing the underlying ideas and conceptualizations and makes assumptions that are theorized as informing or simply shaping the semantic content of the second dataset. Therefore, it will use themes to analyze the data. 
Explain which type of analysis you are using- latent or semantic.
Boslaugh, S. (2022).  Secondary Data Sources for Public Health: A Practical Guide.
Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(1), 77-101.
Fleming, J. (2018). Methodologies, methods and ethical considerations for conducting research in work-integrated learning.
Liberati A, Altman, D., Tetzalaff, J., Murlow, C., Loannidis, J., Devereaux, P., Kleijnen, J., & Moher, D. (2009). The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies that Evaluate Health Care Interventions: Explanation and Elaboration. PLOS Medicine .
Lorelli S. Nowell, J. M. (2017). Thematic Analysis: Striving to Meetthe Trustworthiness Criteria. International Journal of Qualitative Methods, 16(2): 1-13.
Maguire, M. & Delahunt, B. (2017). Doing a Thematic Analysis: A Practical, Step-by-Step Guide for Learning and Teaching Scholars.*AISHE-J  3(2).
Ryan, Gemma (2018). Introduction to positivism, interpretivism and critical theory. Nurse Researcher, 25(4) pp. 41–49
Sindin, P. (2017). Secondary Data. The SAGE Encyclopedia of Communication Research Methods.
Simundic, A. (2012). Bias in research. Biochemia Medica, 23(1):12–5.
Templier, M., & Paré, G. (2015). A Framework for Guiding and Evaluating Literature Reviews. Communications of the Association for Information Systems, 37(1), 6.
UCL. (2022). Secondary data analysis.

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