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3.5	Data Analysis Techniques
A comprehensive data analysis process is crucial to producing meaningful and relevant findings that align with the research objectives. This study employs a combination of both descriptive and inferential statistics to systematically analyse the quantitative data obtained through the online questionnaire.
Descriptive statistics provide a summary and representation of the central tendencies, dispersion, and distribution of the data (Field, 2018). In this study, descriptive statistics such as mean, median, mode, standard deviation, and variance will be used to summarise the key patterns and trends in the data. This aids in understanding the central tendencies and shape of the distribution for each variable related to the impact of digitalization on transaction banking (Hair et al., 2015).
To test the research hypotheses and identify potential relationships between variables, inferential statistics are employed. Inferential statistics allow for conclusions to be drawn beyond the sample data and support the generalisation of the findings to the broader target population (Sekaran and Bougie, 2016). This study will utilise multiple regression analysis to identify the potential relationships between digitalization and transaction banking performance indicators such as customer satisfaction, operational efficiency, and market competitiveness. Additionally, the study will conduct correlation analysis to assess the strength and direction of the relationships between the variables (Field, 2018).
To ensure the accuracy and validity of the statistical analysis, several assumptions will be tested prior to conducting the multiple regression and correlation analysis. These assumptions include the normality of the data, homoscedasticity, linearity, and multicollinearity (Pallant, 2016). Addressing and confirming these assumptions is crucial to ensuring that the results of the statistical analysis are reliable and valid.
For the analysis of the categorical variables, such as the demographic characteristics of the respondents, chi-square tests will be employed. Chi-square tests enable the researcher to determine whether there are significant associations among categorical variables (Field, 2018). This is valuable in identifying potential patterns and trends related to specific demographic groups that may influence their perceptions and experiences of digitalization in transaction banking.
The data analysis will be performed using Statistical Package for the Social Sciences (SPSS) software, which is widely recognised for its efficiency and reliability in conducting complex analyses of large datasets (Bryman and Cramer, 2019). The results of the data analysis will be presented using clear and concise tables, graphs, and charts to visually represent the findings and facilitate the interpretation of the data.
Overall, the combination of descriptive and inferential statistical techniques will enable an in-depth analysis of the data, addressing the research objectives and testing the associated hypotheses. By adopting a rigorous data analysis process involving multiple regression analysis, correlation analysis, and chi-square tests, this study will contribute valuable findings on the impact of digitalization on transaction banking in the UK, offering key insights for practitioners to optimise their digital strategies for improved customer experiences and sustained competitiveness.

3.6	Ethical Considerations
Maintaining ethical standards is paramount when conducting research that involves human participants, such as the study on the effect of digitalization on transaction banking in the UK. Therefore, this section outlines the various ethical considerations that have been taken into account throughout the research process.
Firstly, informed consent is a critical aspect of any research involving human subjects (Bryman and Bell, 2015). Every participant will be provided with an information sheet detailing the nature and purpose of the study, the voluntary nature of their participation, and the confidentiality measures in place. They will also be informed of their right to withdraw from the study at any time without any consequences (Saunders et al., 2018). Informed consent will be obtained from all participants before they complete the online questionnaire, ensuring that they comprehend and agree to the terms and conditions of participation.
Also, confidentiality and anonymity are essential ethical principles that must be upheld (Creswell, 2017). All personal information collected in the study, such as names, contact details, and personal identifiers, will be securely stored and inaccessible to anyone except the researcher. To safeguard participants' privacy, the data collected will be reported without any identifiers, ensuring that no individual can be traced back to their responses (Sekaran and Bougie, 2016).
Furthermoe, the General Data Protection Regulation (GDPR) (European Parliament and Council, 2016) outlines the requirements for data protection. These principles involve securing personal data, only collecting necessary information, and restricting access to authorised personnel. The collected data will be stored on password-protected devices and deleted once the research is complete and no longer required (Smith et al., 2016).
Lastly, it is crucial to ensure the study does not cause any harm or distress to participants (Creswell, 2017). The questionnaire will be designed to avoid any sensitive or inappropriate questions that could potentially cause discomfort. Moreover, the researcher will offer adequate debriefing to all participants, addressing any concerns and clarifying any misconceptions related to the research findings (Bryman and Bell, 2015).
Therefore, this study is committed to upholding the highest ethical standards in all stages of the research process. By ensuring informed consent, confidentiality, anonymity, data protection, and the well-being of participants, the study aims to contribute valuable and trustworthy findings on the impact of digitalization in transaction banking.

3.7	Reliability and Validity
The quality and credibility of research findings are contingent on the reliability and validity of the methods and measurements employed. In the context of this study, reliability refers to the consistency and stability of the results over time, whereas validity addresses the accuracy and relevance of the data in relation to the research questions and objectives (Bryman and Bell, 2015).
To ensure the reliability of the study, the online questionnaire will be tested for internal consistency using Cronbach's alpha coefficient (Pallant, 2016). This widely used measure indicates the extent to which the responses to the questions in the questionnaire are consistent, with a higher Cronbach's alpha value pointing to greater reliability (Field, 2018). Additionally, the study will employ test-retest reliability, whereby the questionnaire will be administered to a small sample during pilot testing, and then administered again after a specified period. Comparing the responses from both tests will provide insights into the stability and consistency of the questionnaire (Hair et al., 2015).
To ensure the validity of the study, appropriate content and construct validity tests will be conducted. Content validity involves assessing whether the questionnaire adequately covers all relevant dimensions of the research topic (Field, 2018). This will be achieved through a thorough literature review to develop the questionnaire's content, consultation with experts, and pilot testing to identify potential gaps or redundancies that require revision (Saunders et al., 2018).
Construct validity, on the other hand, pertains to the accuracy and relevance of the variables in the questionnaire in representing the constructs (Sekaran and Bougie, 2016). To strengthen construct validity, the study will draw on established theories and frameworks from the literature when developing the questionnaire's variables. Factor analysis will also be employed to explore and confirm the underlying structure and relationships between the variables (Field, 2018).
Therefore, by ensuring both the reliability and validity of the research methods and measurements, this study seeks to provide a robust and meaningful contribution to the understanding of digitalization's impact on transaction banking. The application of rigorous reliability and validity tests throughout the research process will enhance the credibility and trustworthiness of the findings, ultimately benefiting banks, policymakers, and customers in the UK and beyond.

3.8	Limitations of Research Methodology
While positivism offers various advantages over other research paradigms, it is not without its limitations. One major limitation of this approach is that it may reduce the complexity of social phenomena to measurable variables (Bryman 2016). This reductionism might lead to oversimplification or misinterpretation of complex social structures and processes. In addition, positivism relies heavily on quantitative data collection methods such as surveys or experiments which may not fully capture the nuances of human experience.
Another potential limitation associated with applying positivist methodology in this study is that while correlation can be established between variables ,it does not necessarily imply causation .Thus additional testing would be required in order to provide conclusive evidence regarding digitalization effects within transaction banking sector (Morgan & Smircich,1980). 

3.9	Appropriateness of Research Design
The research design of this study is cross-sectional, which is appropriate for examining the effects of digitalization on transaction banking. A cross-sectional design allows for the collection of information from participants at a specific point in time, providing a snapshot of the population's characteristics and attitudes (Creswell & Creswell, 2017). This research design is suitable as the focus is on gaining an understanding of digitalization in transaction banking from the perspective of commercial bank staff in the UK.
The use of an online well-structured questionnaire with a 5 Likert response scale enables the researchers to gather quantitative data measuring the employees' attitudes and experiences with digitalization in transaction banking. This method is both efficient and valuable for the study, as it allows for a standardized approach to data collection, enhancing reliability (Saunders, Lewis, & Thornhill, 2016).
The choice of commercial bank staff as the study population contributes to the study's validity, as these individuals are directly involved in the use and adoption of digital technologies within the sector. By ensuring that the sample population is representative and encompasses diverse socio-demographic characteristics, researchers can generate comprehensive and relevant findings. However, the study may be limited by its focus on UK-based institutions, thereby limiting the generalisability of the findings to other regions.