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1 The Use of Ai in Employer Branding, the Recruitment Process and Retention of Potential Employees in the Canadian IT Sector (Chapter 3)

2 Table of Contents METHODOLOGY ...................................................................................................................................... 3 3.1 Introduction ........................................................................................................................................... 3 3.2 Research Design .................................................................................................................................... 3 3.3 Target Population ................................................................................................................................... 4 3.4 Sampling ................................................................................................................................................ 4 3.4.1 Interview Sampling ............................................................................................................................. 5 3.5 Research Approach ................................................................................................................................ 6 3.6 Analysis Methods .................................................................................................................................. 7 3.7 Data Collection ...................................................................................................................................... 9 3.8 Ethical Considerations ......................................................................................................................... 12 3.9 Justification of the Study ..................................................................................................................... 13 Appendices ................................................................................................................................................ 19

3 The Use of Ai in Employer Branding, the Recruitment Process and Retention of Potential Employees in the Canadian IT Sector METHODOLOGY 3.1 Introduction Chapter 3 entails the research approach used to accomplish the study's goals. It examines the target audience, data collection techniques, and data analysis approaches. 3.2 Research Design According to Atmowardoyo (2018), descriptive survey research aims to generate statistical data regarding the subject of the study. For this study, a descriptive research design is chosen to determine the application of AI in employer branding, the Recruitment Process and Retention of Potential Employees in the Canadian IT Sector. In preliminary analysis, illustrative designs have helped collect, summarize, display, and classify data. In this research, the employment of AI in the Canadian IT industry has been approached from the ethical, epistemological, and metaphysical approach. AI poses significant ethical concerns regarding its moral and societal concerns. For instance, fears regarding AI's effects on social justice, security, and privacy may surface. Ethical theories such as consequentialism, deontology, and virtue ethics were applied to investigate these issues and direct ethical decision-making in the creation and use of AI (Gal et al., 2022). The application of AI presents issues regarding how machines obtain and represent knowledge from an epistemological standpoint. It raised issues relating to the nature of knowledge, the validity of knowledge produced by AI, and the function of human agency in creating and applying AI.

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4 These issues were investigated using philosophical stances from constructivism, rationalism, and empiricism. The application of AI raises philosophical issues regarding the nature of reality and the interaction between people and machines. These issues and the nature of AI and its connection to human experience were investigated using philosophical viewpoints like dualism, materialism, and functionalism. Understanding the ethical, epistemological, and metaphysical ramifications of AI deployment in the Canadian IT sector was made possible thanks to philosophical perspectives. The research gained a more nuanced understanding of the potential contribution that AI may make to society by interacting with diverse viewpoints. It is working to ensure that its application is consistent with our values and objectives. 3.3 Target Population The study's target population was about 2,800 employers, recruiters, cooper producers, and HR managers from various Canadian-based businesses. However, the research could only be done there because the headquarters was the only office with all levels of staff, from the Managing Trustee and senior management to the subordinates. 3.4 Sampling The study used a stratified random selection methodology, in which each employee was chosen based on their department, randomly allocated one number, and then randomly chosen. The sample provided generalized conclusions about the study's population, and the sample frame's tentacles were dispersed among the numerous departments. The sample size, 10% of the 2,800 total population, produced a sample of 280 respondents.

5 3.4.1 Interview Sampling This process involved the gathering of data for the reason of theoretical development. Additionally, it involved the analyst working together to collect, code, and analyze the data. He then decides which facts to gather next and where to find them to build the emergent hypothesis. Additionally, it was in charge of gathering formal or substantial data. This idea draws attention to a crucial aspect of theoretical sampling: it was a continuous process rather than a discrete stage (Bryman et al., 2019). Although a study sample's design can take many forms, the most popular ones considered for thwas research were random sampling and selected samples. -Quantitative research was frequently related to random sampling. -A selected sample was advised when using interviews as a data collection method for qualitative research. The selection of the interview subjects was the initial step in the data gathering for the interviews. Johnson et al. (2020) assert that a sample must be chosen as soon as it becomes unfeasible to interview every member of the population who was relevant to the research issue. We relied on "selective sampling" in this study because we are unsure of the size of the population. All those with expertise in HR, decision-making, and AI are considered the population of the study. We first contacted them (primarily through contacts or using LinkedIn) and scheduled virtual online interviews based on their careers in the Human Resources (HR) department and Artificial Intelligence. Only the roughly 50 specialists we contacted responded to the inquiries and agreed to an interview. In order to deal with numerous discoveries, we needed to choose professionals from different nations, companies, and professional backgrounds based in Canada.

6 3.5 Research Approach The study's aims and research questions served as the framework for the research methodology used to examine AI in the Canadian IT industry. A mixed-methods strategy was employed to create a thorough understanding of the application of AI in the Canadian IT sector. This technique incorporated quantitative and qualitative methods. A sample of Canadian IT organizations was surveyed as part of the quantitative research technique (Rahman, 2020). It was accomplished using surveys or questionnaires created to collect data on the level of AI usage in various areas of the IT sector, such as hiring, retaining, and operating. The qualitative study methodology includes gathering information from a smaller sample of IT businesses, employees, and other industry participants. In-depth interviews, focus groups, and case studies were used. Qualitative data allowed for a more profound knowledge of people's attitudes, perceptions, and experiences with AI in the IT sector. The obtained information was analyzed using thematic analysis, content analysis, and discourse analysis to find new themes and patterns in the data. A critical research approach was used to analyze further the social, ethical, and policy consequences of AI deployment in the Canadian IT sector. To identify potential dangers and concerns related to the use of AI in the IT sector, stakeholders in the industry, including policymakers, regulators, and civil society organizations, must be involved in the conversation. A critical research strategy and a mixed-methods research approach gave researchers a thorough grasp of the application of AI in the Canadian IT sector. The system offered insights into the possibilities, difficulties, and consequences of using AI in the sector, influencing industry policies and procedures.

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7 3.6 Analysis Methods Both qualitative and quantitative research methods are used in this thesis. A mixed methods study combines the two approaches. The term "mixed methods research" is a handy abbreviation for research that combines quantitative and qualitative methodologies in a single project. For example, the research combines semi-structured interviews with ethnography or structured interviews with structured observation. In addition, there is research that combines surveys and experiments. Research incorporating methodologies from both research strategies are called mixed methods research. According to Taguchi (2018), mixed-methods research has the following benefits: Because the researcher is not constrained to one research methodology, it is possible to handle a broader range of research questions. It has several advantages, including the following: 1. It emphasizes that words, photos, and narratives can add meaning to numbers, while numbers were used to add precision to words, photos, and narratives. 2. It can present a more robust conclusion. 3. It can offer enhanced validity through triangulation (cross-validation). 4. It can add insight and understanding. The difficulties with mixed methods research include the following: a) they were challenging for a single researcher, mainly when the two functions are best used simultaneously, in which case the study may require a research team; b) they were more time-consuming and expensive when concurrency is involved; and c) in order to integrate several ways intelligently, justify the use of multiple methods, utilize them professionally, and other things, they demand that the researchers understand various methods. They are also not conflict-free. The ethical concerns relevant to quantitative and qualitative technique approaches are likewise relevant to

8 mixed methods research, claim Toraman and Clark (2019). According to Clark and Veale (2018), quantitative research entails gathering data whose meaning is expressed by numerical values; the data is frequently more exact and thorough. In order to evaluate and analyze the gathered data, techniques like statistics, charts, and graphs may be used. A deductive approach to the link between theory and research is part of a quantitative research strategy, emphasizing quantification in data gathering and interpretation. The natural scientific model's procedures and standards have been included in the emphasis on testing theories. Particularly in positivism, social reality is seen as an impersonal, objective reality (ibid.). The foundation of qualitative research is the expression of meanings through language (Fuster Guillen, 2019). Qualitative data is frequently employed in interpretive methodologies and were considered fleeting. Interviews were used to acquire qualitative data, which enables researchers to create a theory and draw a conclusion based on the evidence they have gathered. In qualitative research, the language is displayed through handwritten notes, interview data, papers, and visual images. The emphasis of primarily qualitative research is on an inductive approach to the interaction between theory and research, which rejects positivist natural scientific models, practices, and norms in favor of producing theories (ibid). It is an "attractive annoyance" because, although qualitative researchers adore the richness of qualitative data, it were challenging to identify analytic pathways through it. As a result, the researcher must try to avoid becoming overwhelmed by the richness of the data to the extent that they cannot assess the data's broader significance (Mills, 2018). Qualitative data analysis does not typically involve this kind of restriction of analytic methods, and many writers contend it is not desirable.

9 3.7 Data Collection The qualitative research method was used to gather data through a questionnaire delivered to the respondents via a Google Form link (Busetto et al., 2020). Data should be collected for the quantitative component early in the study process. They confirmed that choices taken at the beginning of the research process would affect the sorts of analyses (ibid.). Interviews "A process for gathering primary data in which a sample of interviewees were asked questions to ascertain what they think, do, or feel" were the definition of an interview. The results of the interviews give us access to specialized industry insights based on the interviewee's experience. Interviews are the most often used data collection approach in qualitative research since they are frequently viewed as better than other data collection techniques (Hockey & Forsey, 2020). It will be easier to understand the subject of this thesis if qualitative data has been collected with enough time to allow for detailed analysis and transcription. All of the interviews were electronically conducted over the phone or Zoom app. Information not gathered in natural situations were a common criticism of interviews (DeJonckheere & Vaughn, 2019). Interviews are "artificial" situations that only provide information about how people answer interview questions, not about how they would actually act or think (ibid.). There were no sure way to dispel the common criticisms of interviews. However, we can avoid bias by being aware of the variety of problems and trying not to bias the questions or think about the results in advance. Survey

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10 The second step in gathering information was creating a questionnaire with a range of options to point in a specific direction and collect information from a sample of people. A questionnaire made it possible to collect data from vast numbers of people quickly and economically. Also, it was a successful approach to compile demographic information that outlines the make-up of the sample. According to Berinsky (2018), surveys do not measure anything; they estimate the actual population. The questionnaire provided 32 options for fixed alternatives for the closed questions and one option for an open-ended question. The importance of this kind of questions to the research were for instance, in a self-completion questionnaire, the respondent or the interviewer, using a scheduled interview schedule, marked the appropriate responses with a checkmark or a circle. According to Krosnick (2018), a questionnaire may include some of the questions listed below:  Open-ended inquiries invited countless solutions.  Checklists present a list of things, and participants marked those that pertain to the given circumstance.  Two-way questions only allowed for possible answers (yes and no).  With multiple-choice questions, the respondent choose the response that best fits the question.  Participants were asked to rank a set of things using ranking scales.

11 All criteria were applied, except for multiple-choice questions and ranking scales, by the design of this thesis survey, which had the five possibilities mentioned above. When allowed to interact, an open-ended question increases the respondent's connection to the topic. The pre- codes are set aside with the fixed-choice responses, allowing the correct code to be created almost mechanically from the chosen response. A questionnaire was typically made to gather several types of data, including facts and descriptions about the respondents (Matthews and Flynn et al. (2018). For instance, a recent occurrence, knowledge, beliefs, attitudes, and background data about the responder may be connected to the study's primary regions and essential points. The most crucial step in this type of research was creating the questionnaire since, after it was created, the researcher will have chosen the questions and answers and will not be able to return and get more data. We did the best to construct the questions so that the responses only revealed evidence or assumptions at the end of the questionnaire to keep them unbiased and avoid giving a biased answer. It was best to ask only a few background questions because doing so increases the likelihood that the respondent will feel overwhelmed and not complete the survey entirely or honestly. By making it easier to show the relationship between variables and compare respondents or different types of respondents, closed questions increase the comparability of results (Bryman et al., 2019). The fixed-choice responses on a closed questionnaire may be problematic since different respondents may perceive them differently (ibid.). According to Bryman et al. (2019), when asking a question, there was always a chance that some terms will be interpreted differently by respondents. To reduce these risks, we decided to use straightforward and primary language because doing otherwise might jeopardize the document's credibility. There was always a possibility that people will offer fascinating comments that are unrelated to the prepared ones

12 (Bryman et al., 2019). Online surveys are effective at collecting ten times more accurate data than any other conventional method using analytical logic and branching technology. 3.8 Ethical Considerations Ethical issues must be taken into account when when conducting any kind of resaerch, including the current Canadian IT sector. Among the ethical issues that were involved in this research: 1. Informed Consent: It's critical to gain participants' informed consent before doing research on human subjects. This entails informing them about the study and its goals in a manner that is both clear and understandable and offering them the option to willingly participate or withdraw at any moment. 2. Privacy and Data Protection: Any personal information gathered for the purpose of the study should be protected and treated ethically in line with applicable privacy laws and regulations. The confidentiality of the data should be protected with the appropriate safeguards. 3. Transparency and Explainability: It's crucial to make sure that the procedures employed and the outcomes obtained are transparent and comprehensible when employing AI systems in research. As a result, the researchers ought to be able to describe how the AI system functions and the reasoning behind its judgements. 4. Preventing Bias and Discrimination: When using AI in research, researchers should take precautions to prevent biases and discriminatory outcomes from occurring. This could entail making sure the data set is diverse and utilising techniques to avoid or lessen bias.

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13 5. Accountability and Human Oversight: When using AI systems in research, scientists should make sure that there is both accountability and human oversight. This means that there should be systems in place to watch over, assess, and take corrective action when necessary based on the outcomes produced by the AI system. In order to ensure that the research is carried out in a fair, transparent, and responsible manner, researchers undertaking studies involving AI in the Canadian IT industry should take ethical considerations into account. These factors can aid in safeguarding the rights and privacy of human participants and guaranteeing the validity and dependability of the results. 3.9 Justification of the Study Artificial intelligence (AI) is now widely used in the Canadian IT industry and has grown significantly in recent years. AI is employed in many different contexts, including hiring, choosing, and keeping candidates for employment. The need for this study arises from the need for more research on the application of AI in various fields, particularly in the Canadian IT industry. This study investigates how AI is used in the Canadian IT industry for employer branding, hiring, and staff retention. The study intends to shed light on the possible advantages and difficulties of employing AI in the Canadian IT sector by studying its use in these fields. This study can assist in informing policy and decision-making in the IT industry, especially about using AI in HR procedures. Employers interested in utilizing AI in the Canadian IT sector to enhance their HR procedures might also benefit from this study's findings. It can assist them in comprehending how to use AI to draw in top personnel, keep them on board, and build a great employer brand while ensuring that moral considerations are taken into account. Additionally, particularly in the

14 Canadian context, this study might add to the larger academic literature on the application of AI in HR practices. It can aid in bridging the gap in current research and serve as a starting point for subsequent studies. In conclusion, this study is warranted because it fills a gap in the body of knowledge about the application of AI to employer branding, hiring, and staff retention in the Canadian IT sector. In addition to adding to the body of knowledge on this topic, the study's findings can assist companies in using AI to enhance their HR practices. They can guide policy and decision-making in the IT industry.

15 References Allmark, P., & Machaczek, K. (2018). Realism and Pragmatism in a mixed methods study. Journal of advanced nursing , 74 (6), 1301-1309. https://onlinelibrary.wiley.com/doi/abs/10.1111/jan.13523 Atmowardoyo, H. (2018). Research methods in TEFL studies: Descriptive research, case study, error analysis, and R & D. Journal of Language Teaching and Research , 9 (1), 197-204. http://academypublication.com/issues2/jltr/vol09/01/25.pdf Berinsky, A. J. (2018). Telling the truth about believing the lies? Evidence for the limited prevalence of expressive survey responding. The Journal of Politics , 80 (1), 211-224. https://www.journals.uchicago.edu/doi/abs/10.1086/694258 Busetto, L., Wick, W., & Gumbinger, C. (2020). How to use and assess qualitative research methods. Neurological Research and practice , 2 , 1-10. https://link.springer.com/article/10.1186/s42466-020-00059-z Clark, K. R., & Vealé, B. L. (2018). Strategies to enhance data collection and analysis in qualitative research. Radiologic technology , 89 (5), 482CT-485CT. http://www.radiologictechnology.org/content/89/5/482CT.short DeJonckheere, M., & Vaughn, L. M. (2019). Semistructured interviewing in primary care research: a balance of relationship and rigour. Family medicine and community health , 7 (2). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6910737/ Flynn, B., Pagell, M., & Fugate, B. (2018). Survey research design in supply chain management: the need for evolution in Theexpectations. Journal of Supply Chain Management , 54 (1), 1-15. https://onlinelibrary.wiley.com/doi/abs/10.1111/jscm.12161

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16 Fuster Guillen, D. E. (2019). Qualitative Research: Hermeneutical Phenomenological Method. Journal of Educational Psychology-Propositos y Representaciones , 7 (1), 217- 229. https://eric.ed.gov/?id=EJ1212514 Gal, U., Hansen, S., & Lee, A. S. (2022). Research Perspectives: Toward Theoretical Rigor in Ethical Analysis: The Case of Algorithmic Decision-Making Systems. Journal of the Association for Information Systems , 23 (6), 1634-1661. https://aisel.aisnet.org/jais/vol23/iss6/1/ Hockey, J., & Forsey, M. (2020). Ethnography is not participant observation: Reflections on the interview as participatory qualitative research. In The Interview (pp. 69-87). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781003087014-4/ethnography- participant-observation-reflections-interview-participatory-qualitative-research-jenny- hockey-martin-forsey Houser, K. A. (2019). Can AI solve the diversity problem in the tech industry: Mitigating noise and bias in employment decision-making. Stan. Tech. L. Rev. , 22 , 290. https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/stantlr22§ion=9 Jackson, M. C. (2022). Rebooting the systems approach by applying the thinking of Bogdanov and the Pragmatists. Systems Research and Behavioral Science . https://onlinelibrary.wiley.com/doi/abs/10.1002/sres.2908 Johnson, J. L., Adkins, D., & Chauvin, S. (2020). A review of the quality indicators of rigor in qualitative research. American journal of pharmaceutical education , 84 (1). https://www.ajpe.org/content/84/1/7120.abstract

17 Kansteiner, K., & König, S. (2020, January). The role (s) of qualitative content analysis in mixed methods research designs. In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research (Vol. 21, No. 1, p. 3412). DEU. https://www.qualitative- research.net/index.php/fqs/article/download/3412/4513?inline=1 Krosnick, J. A. (2018). Questionnaire design. The Palgrave handbook of survey research , 439- 455. https://link.springer.com/chapter/10.1007/978-3-319-54395-6_53 Mills, K. A. (2018). What are the threats and potentials of big data for qualitative research?. Qualitative Research , 18 (6), 591-603. https://journals.sagepub.com/doi/pdf/10.1177/1468794117743465 Mohajan, H. K. (2018). Qualitative research methodology in social sciences and related subjects. Journal of economic development, environment and people , 7 (1), 23-48. https://www.ceeol.com/search/article-detail?id=640546 Rahman, M. S. (2020). The advantages and disadvantages of using qualitative and quantitative approaches and methods in language “testing and assessment” research: A literature review. https://pearl.plymouth.ac.uk/handle/10026.1/16598 Taguchi, N. (2018). Description and explanation of pragmatic development: Quantitative, qualitative, and mixed methods research. System , 75 , 23-32. https://www.sciencedirect.com/science/article/pii/S0346251X1830109X Toraman, S., & Clark, V. L. P. (2019). Reflections about intersecting mixed methods research with social network analysis. In Mixed Methods Social Network Analysis (pp. 175-188). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9780429056826-

18 16/reflections-intersecting-mixed-methods-research-social-network-analysis-sinem- toraman-vicki-plano-clark Varpio, L., Paradis, E., Uijtdehaage, S., & Young, M. (2020). The distinctions between theory, theoretical framework, and conceptual framework. Academic Medicine , 95 (7), 989-994. https://www.ingentaconnect.com/content/wk/acm/2019/00000095/00000007/art00021

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19 Appendices Appendix A The appendix contains the questionnaire questions to which the 280 respondents among them employers, recruiters, and cooperate producers used to answers. 1. What gender do you identify as? Male/Female/Non-Binary 2. What is your age range? Less than 18 18-25 26-40 40 + 3. Have you applied for a job? Yes/No 4. Are you aware of employers using artificial intelligence in the hiring process? Yes/No 5. Do you believe that human interaction in the hiring process is needed? Yes/No 6. Have you ever faced any inequalities in the hiring process? Yes/No 7. If yes please explain what kind of inequalities (open-ended question). ……………………………………………….. 8. Have you ever needed to respond to any digital test sent out by the recruiter? Yes/No

20 9. Are you aware that there are biases in the hiring process with the use of AI? Yes/No/Maybe Appendix B The appendix contains the semi-structured interview questions to which the respondents needed to answer. a) How useful, in your opinion, is the use of AI in building a solid employer brand in the Canadian IT sector? .................................................................................................................................... ................................................................................. b) Has the use of AI-assisted recruitment processes had an effect on candidate retention? ............................................................................................................................... ...................................................................................... c) Do you think that incorporating AI into hiring procedures can encourage more inclusive and diverse hiring practises? ............................................................................................................................... ...................................................................................... d) How crucial do you believe it is for Canadian IT businesses to incorporate AI into their retention and recruitment plans? .................................................................................................................................... ................................................................................. e) Have you observed any differences in the calibre of applicants found using AI-assisted hiring

21 procedures? ........................................................................................................................... .......................................................................................... f) How does the use of AI to hiring and retention procedures differ from conventional hiring practises in your opinion? ................................................................................................................................. .................................................................................... g) How do you envision AI's role in Canadian IT employer branding, recruiting, and retention in the future? .................................................................................................................................... .................................................................................

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