Introduction
In the ever-evolving landscapes of financial services and cybersecurity, technological advancements such as Microservices Architecture (MSA) and Artificial Intelligence (AI)/Machine Learning (ML) are pivotal in shaping industry practices. Isaiah Angweh Chig Mobit’s study, “Technological, Organizational, and Environmental Factors and the Adoption of Microservices in the Financial Services Sector,” dissects the multidimensional factors influencing the adoption of MSA within the financial realm. In contrast, James Gusman’s research, “The Deployment of Artificial Intelligence and Machine Learning Within The Field of Cybersecurity for Intelligent Decision Making: A Qualitative Study,” delves into the interplay of AIAI and ML in bolstering cybersecurity decision-making. Comparing these scholarly contributions provides a nuanced understanding of technology’s role in decision-making processes across different but equally critical domains, offering insights into how organizations navigate and integrate technological advancements within their operational frameworks.
Article 1: Technological, Organizational, and Environmental Factors and the Adoption of Microservices in the Financial Services Sector by Isaiah Angweh Chig Mobit
In Isaiah Angweh Chig Mobit’s study, the central research question focuses on assessing the influence of specific technological, organizational, and environmental factors on adopting Microservices Architecture (MSA) within the financial services sector. The study examines seven key factors: scalability, maintainability, security, reliability, agility, compatibility, and regulatory compliance, categorizing them into technological, organizational, and environmental dimensions. The primary hypothesis (Ha) posits a relationship between MSA adoption and these seven factors, while sub-hypotheses (H1-H7) delve into the individual impact of each factor on the adoption decision. This research addresses the pressing need for empirical guidance in transitioning from monolithic applications to MSA in the financial industry, offering valuable insights to assist decision-makers and enhance digital transformation efforts.
In order to comprehend the adoption of Microservice Architecture (MSA), several independent factors were investigated in the study using the Technology-Organization-Environment (TOE) paradigm. The technological aspects (scalability, maintainability, security, and dependability), organizational elements (agility, compatibility), and environmental factors (regulation needs) were used to group these variables. Scalability measures MSA’s capacity to handle increased workloads; maintainability assesses the ease of managing microservices; security focuses on protection against unauthorized access and data security; and reliability evaluates the consistency of microservices. Organizational factors included agility, which measured the ability to adapt to change through MSA, and compatibility, assessing integration with existing systems. Environmental factors considered regulatory requirements and their facilitation through MSA adoption. The study used Likert scales to collect data, with participants rating their agreement or disagreement with statements related to these variables. A pilot test involving doctoral students and ITIT professionals was conducted to ensure reliability, leading to survey refinements and confirming internal consistency. The dependent variable was the adoption decision, whether MSA was adopted or not, reflecting the combined influence of the independent variables.
The study in question targeted professionals in the financial sector who had substantial experience transitioning from monolithic applications to microservices or had worked with microservices for at least a year. The participants were seasoned ITIT professionals, including senior software developers, software architects, and higher-level executives such as CIOs and CTOs, with firsthand involvement in the transition process. Purposeful sampling was utilized to select these knowledgeable individuals, leveraging networks and professional groups interested in microservices, including the Google Microservice group, the Microservices Community, and pertinent connections on LinkedIn. To broaden the participant base and ensure diversity of expertise in Microservice Architecture (MSA), the study also employed the snowball sampling method during its pilot phase and collaborated with the survey marketing research company Sentiment to distribute the survey. The determined sample size for adequate representation and statistical reliability was 144, calculated using G*Power software with a power level of 0.85, a 5% alpha level, and a two-tailed test.
The researcher collected data through an online survey on the QuestionPro platform, initially contacting 450 professionals directly, yielding a low response rate of 9.6%. To enhance this, Sentiment, an online survey research firm, disseminated the survey to another 365 individuals, resulting in a much-improved response rate of 56%. The collected data were then downloaded from QuestionPro and analyzed using SPSS software. The instrument adapted from Yoon’s validated technology adoption survey focused on demographic information and key factors related to microservices adoption, such as scalability, maintainability, agility, security, compatibility, and reliability, using a seven-point Likert scale for responses. With Yoon’s consent, the instrument was modified especially for this research, including questions related to adoption problems for microservices and eliminating demographic elements that needed to be more relevant. The purposeful acquisition of data, a thorough analytical approach, and professional distribution made it possible to correctly analyze the study’s premise and address the research questions regarding the variables that impact the adoption of microservices architecture in organizations.
Several important findings were drawn from the study on using Microservices Architecture (MSA) in the financial services industry. First, the choice of MSA was heavily impacted by aspects like maintainability, agility, security, dependability, and scalability. The two factors that contributed most were maintainability and agility, respectively. Maintainability negatively correlated with adoption, but agility had a favorable effect. Security, scalability, and dependability were other important factors. It is interesting that the acceptance of MSAs was unaffected significantly by elements like compatibility and regulatory concerns. These results shed important light on the objectives and factors that financial institutions consider while implementing MSA. It sheds attention on the particular components of MSA that are most pertinent to the financial industry and emphasizes the significance of issues relating to maintainability, agility, and security in influencing adoption choices. Overall, these findings advance our knowledge of the intricate processes behind the financial sector’s adoption of new technologies and provide direction for businesses hoping to use MSA successfully.
Article 2: The Deployment of Artificial Intelligence and Machine Learning Within The Field of Cybersecurity for Intelligent Decision Making: A Qualitative Study By James Gusman
The study topic, “How does Artificial Intelligence and Machine Learning technologies influence cybersecurity staff reliance on decision making?” was the focus of researcher James Gusman’s investigation. This inquiry examines how AI and ML technologies affect cybersecurity professionals’ decision-making processes. The paper explores this link by investigating how the use of AI and ML in cybersecurity impacts the need for human decision-making in the context of risk mitigation and information security. The major goal is to comprehend how technology and human labor interact in cybersecurity, illuminating how AI and ML are changing decisions in this crucial area.
The main goal of James Gusman’s qualitative research study was to investigate how AI and ML technologies may be used in the cybersecurity industry to enable intelligent decision-making. The main factors and ideas that are being studied are the use of AI and ML technologies, how they are incorporated into cybersecurity procedures, and how they affect cybersecurity professionals’ decision-making processes. The survey also examined how much cybersecurity personnel rely on these tools to handle security threats and safeguard company information and systems. The research used a qualitative general inquiry approach to examine these variables and ideas. Ten cybersecurity subject matter experts were interviewed, and their insights into their experiences and viewpoints about the application and significance of AI and ML in cybersecurity were obtained.
Ten participants were chosen by purposive selection, a non-probability sampling technique, to make up the sample for James Gusman’s qualitative research study. Purposive sampling was conducted to guarantee that the selected participants had extensive expertise and were recognized as authorities in cybersecurity, artificial intelligence (AI), and machine learning (ML). Those who could offer insightful commentary on the application of AI and ML technologies in cybersecurity were chosen thanks to this methodology. Participants have to be in the field of cybersecurity professionally for at least ten years in order to be considered for inclusion. Using snowball sampling inside the purposive sample framework, Gusman was able to find possible participants, including individuals with management and military backgrounds, using his professional networks and work relationships. In this case, snowball sampling was especially helpful since it made finding individuals with comparable backgrounds and experiences easier, even when more conventional random sample techniques made it difficult to contact them. Every participant received an email inviting them to participate in the study and providing information about it. It was anticipated that they would also suggest additional people with appropriate backgrounds for the research.
James Gusman, the study’s researcher, used a systematic approach to gather data from ten participants with at least five years of experience, training, or post-graduate education in cybersecurity. Both thorough notes collected throughout the semi-structured interview procedure and audio-recorded interviews were used as data-gathering techniques. Before starting the interviews, participants’ completed consent forms were obtained, and they were made sure they had no reservations. The audio recording was disclosed to the participants, who were allowed to stop at any time. During the interviews, the participants’ demographics were discussed with the research questions and any necessary follow-up questions. After that, the information was securely saved, transcribed, and verified for correctness by participants. It was also highlighted that NVivo software is used for data analysis, emphasizing the importance of organizing and analyzing data effectively. Overall, the researcher’s methods of collecting data seem suitable given the qualitative character of the study. The combination of audio-recorded interviews and detailed notes allowed for a comprehensive exploration of participants’ insights and experiences regarding the deployment of AI and ML in cybersecurity decision-making. The meticulous approach to data collection and storage, along with the use of software for analysis, enhances the reliability and validity of the collected data, facilitating an accurate examination of the original research questions and hypotheses.
The results of James Gusman’s study provide valuable insights into the impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on cybersecurity staff and decision-making processes. Contrary to the initial presumption that these technologies might replace human roles in cybersecurity, participants generally believed that AI and ML systems would not make them obsolete but would instead be used with human expertise. Three main themes emerged from the data analysis: First, employees in the IT field are expected to remain the dominant decision-makers due to technology limitations. Second, decision-making by AI and ML systems was predicted to become more prevalent in the workplace. Third, participants acknowledged a steep learning curve and the need for training in deploying these technologies. Two subthemes emphasized that employees would have to adjust significantly and that there was a strong desire to learn more about AI and ML systems. These results challenge the fear of technology replacing jobs and highlight the potential for technology and human expertise to coexist effectively. They underscore the importance of ongoing training and adaptation in the evolving landscape of cybersecurity and technology, which is a significant consideration for organizations and professionals in this field.
Comparison of the two articles
In comparing the two distinct research studies by Isaiah Angweh Chig Mobit and James Gusman, several aspects must be highlighted concerning their methodological approaches, findings, and contributions to the body of knowledge in their respective fields. Mobit’s study employed a non-experimental, survey-based quantitative research design. This approach is appropriate for examining the relationships between predefined variables without manipulating the subjects or their environment. It seeks to understand patterns and make predictions about phenomena, typically using statistical analysis. Non-experimental designs like Mobit’s are useful when experimental manipulation is infeasible or unethical (Christensen et al., 2015). In contrast, Gusman’s research used a qualitative approach, employing semi-structured interviews to collect rich, narrative data. Unlike Mobit’s quantitative method, Gusman’s qualitative design does not aim to test hypotheses with statistical methods but to develop a deep understanding of human experiences and perspectives. This design aligns with Busetto et al. (2020) description of qualitative inquiry as being concerned with the complexity and depth of understanding.
The variables in Mobit’s study were well-defined, lending themselves to measurement on a Likert scale. The hypotheses posited specific relationships between these variables and the adoption of Microservices Architecture (MSA), which were then tested using statistical methods. Gusman’s study, however, did not formulate hypotheses but focused on exploratory research questions. The concepts under investigation, such as the influence of AI and ML on decision-making, were explored through participant narratives without requiring the rigid operationalization characteristic of quantitative research.
While Mobit and Gusman utilized purposive sampling, their approaches reflect their methodological divergence. Mobit aimed for a statistically representative sample suitable for quantitative analysis, prioritizing breadth and generalizability across the financial services sector. In contrast, Gusman’s qualitative study embraced a smaller, more focused group of cybersecurity experts, seeking rich, detailed insights into the influence of AI and ML on decision-making. This difference underscores the distinct priorities of quantitative versus qualitative research—the former on broad applicability the latter on in-depth understanding (Creswell & Creswell, 2017).
Mobit’s study concluded with specific findings regarding the factors influencing MSA adoption in the financial services sector, such as the significant role of maintainability and agility. These results have direct practical implications, as they can inform specific recommendations for organizations considering MSA adoption. Gusman’s findings revealed themes about the role of AI and ML in cybersecurity decision-making, emphasizing the complementarity of human and machine intelligence. The implications here are more conceptual, encouraging further reflection and investigation into how these technologies are integrated into the human workforce.
Mobit’s non-experimental design allowed correlating correlations across a broad financial sector population, testing theoretical constructs with a wide lens. However, this approach might not capture the nuanced experiences and motivations that qualitative research, like Gusman’s study, provides through its deep, narrative-driven exploration of individual perspectives. While Gusman’s method yields in-depth insights, its findings are not as easily extrapolated to a larger population due to the inherent specificity and smaller scale of qualitative inquiry. Both approaches offer valuable information, but their applicability varies depending on the research aim and context.
Conclusion
In conclusion, Mobit’s and Gusman’s studies provide key insights into technology adoption within the financial and cybersecurity sectors. Mobit quantitatively shows how agility and scalability drive Microservices Architecture adoption in finance. In contrast, Gusman qualitatively reveals that AI and ML technologies enhance rather than replace human decision-making in cybersecurity. Though methodologically distinct, these findings collectively highlight a trend toward leveraging technology in tandem with human expertise. Both studies contribute to a nuanced understanding that in the modern corporate landscape, technological and human capital are not mutually exclusive but are integral to one another’s evolution and success.
References
Busetto, L., Wick, W., & Gumbinger, C. (2020). How to use and assess qualitative research methods. Neurological Research and Practice, 2(1), 1–10. B.M.C. https://doi.org/10.1186/s42466-020-00059-z
Christensen, L. B., Johnson, R. B., & Turner, L. A. (2015). Research Methods, Design, and Analysis, Global Edition. (12th edition)
Creswell. JWJW and Creswell, J.D. (2017) Research Design Qualitative, Quantitative, and Mixed Methods Approaches. 4th Edition, Sage, Newbury Park. – References – Scientific Research Publishing. (2017). Scirp.org. https://www.scirp.org/(S(351jmbntvnsjt1aadkozje))/reference/references papers. aspx?referenceid=2969274
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