Positivism, which emphasizes the acquisition of information by scientific methods and empirical observation, was selected as the research philosophy for this study (Zyphur & Pierides, 2020). The objective analysis of events to find general laws and patterns guiding the natural and social realms is emphasized by positivism. Because of its focus on methodical data gathering and analysis to produce empirical insights, it is consistent with the use of quantitative research procedures (Dehalwar & Sharma, 2023). According to positivism, reality may be measured and observed closely in order to understand its existence, independent of human perception. This study employs a positivist research philosophy in an effort to uphold neutrality, demonstrate causation, and produce trustworthy information regarding the impact of AI technology on HR decision-making procedures.

The research approach employed in this study is deductive reasoning, which involves developing theories or hypotheses based on existing knowledge or theoretical frameworks and then testing them through empirical observation and data analysis (Wang et al., 2020). Deductive reasoning in this study begins with accepted theories and notions about AI technology and HR decision-making, which provide the framework for developing research questions, survey instruments, and hypotheses. Comment by Dale Mancini: Is quantitative method

Various reasons account for the choice of the deductive method for this research. First of all, it results in enlargement of already presented theoretical frameworks and empirical data, which contributes to the carefully conducted research and a theoretically sound examination of the research issue (Casula et al., 2021). The deductive approach in addition provides a systematic route to hypothesis testing by proceeding from established theories, allowing to form specific and unambiguous hypothesis on the links between the variables. As well, through the deductive approach to make sure that the research findings are linked to accepted theories and concepts in a logical manner, the validity and rigor of the study are strengthened, which results in better knowledge for HR management. As regards the research design, the cross-sectional survey approach was selected as the best method of data gathering. A survey allows for obtaining data from a wide and varied group of HR executives and employees, thereby gathering a broad view about the impact of AI technology on different types of organizations. Furthermore, a survey design provides the possibility to determine the values of variables at a particular time point, hence this method is convenient and effective for objectively measuring current AI usage in HR.

For this study, a quantitative research approach based on positivist ideology and a logical reasoning framework was selected. Although qualitative approaches provide deep insights into people’s subjective experiences and views, they might not have the statistical power or generalizability needed to make more general conclusions regarding how AI technology affects HR decision-making. Large-scale data collection and the identification of statistically significant associations are judged to be better served by a quantitative approach, given the study’s emphasis on analyzing the general trends and patterns in HR practices impacted by AI technology. Additionally, by comparing diverse groups or situations, quantitative approaches enable researchers to evaluate the relative efficacy of AI-enabled HR technologies in relation to traditional methods across a range of organizational scenarios. The study therefore attempts to offer solid empirical evidence to support evidence-based decision-making in HR management by utilizing a quantitative research design. This method offers empirical insights into the phenomena and permits a thorough examination of the ways in which AI technology impacts HR decision-making processes (Mohajan, 2020).

In order to gain a clear and impartial picture of the events being studied, quantitative techniques provide a strong foundation for analyzing correlations, patterns, and trends within data sets (Habes et al., 2021). By identifying connections, causal relationships, and statistical significance, statistical analysis applied to quantitative data enhances the validity and reliability of study findings. Furthermore, quantitative research adds to the corpus of knowledge in the subject of human resource management by making it easier to extrapolate results to bigger populations. In addition, the study’s research questions were written in a way that made them amenable to quantitative analysis. Numerical data is needed for study of questions including how well AI technologies work in HR decision-making, how AI-enabled tools affect the accuracy and efficiency of decision-making, and how satisfied employees are with AI-driven procedures. The study used quantitative approaches in an effort to provide unbiased, quantifiable responses to these research questions, enabling a thorough comprehension of the phenomenon being studied.

Other designs and methods may have been taken into consideration, but a quantitative strategy was selected for this study since it was in line with the research aims and could supply numerical data for analysis. Focus groups and interviews are examples of qualitative techniques that may offer in-depth insights into people’s subjective experiences and perceptions of AI technology in HR decision-making. Comparing these techniques to quantitative methods, however, they might be less statistically powerful and less generalizable. Furthermore, by triangulating data from many sources, mixed-methods designs that use both quantitative and qualitative approaches may provide a thorough grasp of the research topic. However, it was determined that the quantitative approach would best enable this study to effectively accomplish its particular research goals.

Research Questions

The study’s suggested research questions seek to explore the usefulness, significance, difficulties, and moral issues surrounding the integration of AI technology into HR decision-making procedures. Every inquiry is intended to tackle a distinct facet of AI integration into HR management, facilitating an exhaustive exploration of its consequences.

The efficacy of AI technologies incorporated into diverse HR decision-making processes, including hiring, performance review, and talent management, is the subject of the first study question. This inquiry aims to assess the degree to which artificial intelligence (AI)-powered solutions can streamline HR procedures, improve talent management tactics, quicken hiring procedures, and raise the precision of performance reviews. Comment by Dale Mancini: List your 3 research questions as stand alone statements.

The influence of AI-enabled HR tools on decision-making efficiency, accuracy, and bias reduction in comparison to conventional techniques is the subject of the second study question. The purpose of this analysis is to ascertain whether AI-driven solutions are more effective than traditional approaches at reducing bias, improving decision accuracy, and streamlining processes.

The third study question investigates the difficulties and moral dilemmas posed by the use of AI in HR decision-making. By highlighting potential roadblocks including algorithmic prejudice, data privacy issues, and ethical ramifications, this inquiry aims to provide light on the moral and practical conundrums businesses may encounter when introducing AI-driven decision-making processes.

The final research question is about how satisfied workers are with the fairness and openness of AI-driven HR decision-making procedures. The study attempts to determine whether AI technology is viewed as an upgrade over traditional techniques and whether it increases trust and confidence in HR operations by measuring employees’ opinions of fairness and transparency.

Hypothesis

H1: There is no significant impact of the integration of AI technologies into HR decision-making processes, including recruitment, performance evaluation, and talent management. Comment by Dale Mancini: Change to null hypothesis.

H2: There is no significant difference in decision-making accuracy, efficiency, and bias reduction between AI-enabled HR tools and traditional methods.

H3: There is no significant association between the adoption of AI technologies in HR decision-making and the challenges and ethical considerations faced by organizations.

The study’s hypotheses are designed to test the null hypothesis, which states that there is no meaningful relationship, impact, or difference between the use of AI technologies in HR decision-making and a variety of outcomes, including employee satisfaction, process effectiveness, bias reduction, and ethical considerations. These theories offer an empirical testing framework that enables the assessment of the research questions and the verification of the study’s conclusions.

The source population for this study consists of employees and HR professionals working within Accenture, a global professional services company. Accenture is selected as the case company due to its significant presence in the technology and consulting sectors, making it relevant for investigating the integration of AI technologies in HR decision-making processes. The characteristics of the population include individuals employed across various departments and roles within Accenture, ranging from entry-level employees to senior executives. These individuals possess diverse backgrounds, experiences, and expertise in their respective fields, contributing to the organization’s dynamic workforce. Additionally, HR professionals within Accenture are responsible for managing HR functions and implementing HR policies and procedures, making them key stakeholders in the adoption of AI technologies in HR decision-making.

The qualifications for participation in the study include being an employee or HR professional currently employed by Accenture and having firsthand experience or knowledge of the organization’s HR processes and practices. Participants should also be willing to provide insights and opinions on the integration of AI technologies in HR decision-making. The study aims to include a representative sample of the population, comprising employees and HR professionals from different departments, levels of seniority, and geographical locations within Accenture. The sample size will be determined based on the principles of statistical sampling, aiming for adequate representation to ensure the generalizability of findings.

Data collection will be conducted through surveys. The timing and location of data collection will be coordinated with Accenture’s HR department to ensure minimal disruption to employees’ work schedules and operations. Additionally, data collection may take place at multiple Accenture office locations to capture diverse perspectives from employees across various regions. Comment by Dale Mancini: What survey

The sampling frame for this study will consist of employees and HR professionals currently employed by Accenture across different departments and geographic locations. Based on practical considerations about the viability of data collection within the limitations of time and resources available for the study, the sample size of 50 respondents was chosen. Furthermore, obtaining a varied representation of viewpoints and insights from Accenture workers and HR experts only requires a sample size of 50 respondents. Purposive sampling will be used as the sample technique, and participants will be chosen based on how well-suited they are to the study’s goals as well as how often they have personally used AI in HR decision-making at Accenture. This methodology guarantees the inclusion of persons in the sample who possess significant insights into the subject matter being studied. In order to begin the selection process, prospective participants will either be contacted directly by key informants within the firm or through the HR department. Comment by Dale Mancini: Why 50?

Moreover, stratification will be used in the sample process to guarantee that representatives from various Accenture departments, seniority levels, and geographical regions are included. By using this method, the sample’s diversity of viewpoints is improved, facilitating a deeper examination of the study topics. Overall, the sampling strategy seeks to ensure practicality and efficiency in data collection procedures while optimizing the relevance and quality of the data gathered.

The data collection process for this study will involve the administration of structured surveys using Google Forms as the primary tool. Because of its advanced functionality for data collecting and maintenance, accessibility, and user-friendly interface, Google Forms will be used (Causton et al., 2023). In order to guarantee that the data gathered answers the particular research questions stated in the study, the structured survey questions will be meticulously created to correspond with the research hypotheses and objectives
. You need to include a section that states no data will be collected without approval from the Saint Leo IRB. Comment by Dale Mancini: We need to talk about this.

The researcher will first develop a Google Form with semi-structured survey questions in order to start the data collection procedure. The purpose of these questions is to collect quantitative information about respondents’ viewpoints, attitudes, and experiences with regard to the use of AI in HR decision-making processes (Zou, 2020). In order to enable systematic data collecting and analysis, the survey questions will be created in a standard style, guaranteeing consistency and dependability in the responses received. Comment by Dale Mancini: I suggest you find a survey already in use.

Following the creation of the Google Form, the researcher will send the link to the survey to the intended audience, which consists of HR specialists and workers at Accenture, the case firm. The goal of the study, the voluntary nature of participation, and participant rights, such as response confidentiality and anonymity, will all be explained to participants (Zyphur & Pierides, 2020). Before allowing any participant to finish the survey, their informed consent will be sought. Comment by Dale Mancini: How will you get there email addresses?

Because the survey will be conducted online, participants can access it and answer the questions whenever it’s convenient for them. Participants will have access to the survey from April through May of 2024. To maximize response rates and promote participation, reminders might be given. The researcher will stay in touch with the participants to answer any questions or issues they may have about the survey during the data collection period. Moreover, the researcher will use Google Forms’ data collecting and administration capabilities to track survey replies in real-time, enabling prompt analysis and modification as needed.

To ensure confidentiality and data security, the survey data will be safely stored on password-protected electronic devices and encrypted cloud storage systems (Zyphur & Pierides, 2020). Following the conclusion of the data collection period, the researcher will download the survey responses and begin data analysis.

The data analysis for this study will primarily involve descriptive analysis using Excel and SPSS (Statistical Package for the Social Sciences). Cooksey & Cooksey (2020) define descriptive analysis as the process of summarizing and comprehending data through the use of statistical metrics including mean, median, mode, standard deviation, and frequency distributions. With the use of these statistical techniques, the effects of AI technology on HR decision-making procedures can be thoroughly investigated. Because Excel and SPSS can calculate summary statistics and create graphical representations of data, they are ideal tools for undertaking descriptive analysis. Comprehensive data analysis and interpretation are made possible by the extensive variety of statistical operations and procedures provided by SPSS in particular (Habes et al., 2021). Excel, on the other hand, is appropriate for more uncomplicated descriptive analytical work because to its user-friendly interface and simple visualization features (Chandra & Dwivedi, 2022).

The data analysis process will begin with importing the survey responses collected through Google Forms into both Excel and SPSS. Basic descriptive statistics like mean, median, mode, and standard deviation will be computed after the data is imported to provide an overview of the data’s major patterns and variability. In order to comprehend the distribution of answers across various variables, frequency distributions will also be developed. Additionally, SPSS can be used to run inferential statistical tests to look at differences and correlations between variables. To find out if there are any notable variations in participant groups’ opinions or views regarding AI technology in HR decision-making processes, t-tests or analysis of variance (ANOVA) will be utilized.

Additionally, graphical representations such as histograms, bar charts, and scatter plots will be created using both Excel and SPSS to visually depict the patterns and trends present in the data. These graphics will help in the understanding of the data and offer more information about the connections between the variables. Through the use of descriptive analysis in Excel and SPSS, this study will be able to fully comprehend how AI technology affects HR decision-making procedures. It will be easier to explore research issues and hypotheses when statistical computations and graphical representations are combined, in accordance with the selected technique and study design.

Summary

This chapter provided a comprehensive overview of the study’s methodology, including the research philosophy, approach, technique, and design selected to examine how AI technology affects HR decision-making procedures. The method for gathering data using Google Forms-administered structured survey questions was explained, as well as the justification for doing data analysis in Excel and SPSS. The data analysis and findings will be presented in Chapter 4, offering insights into the usefulness of AI technologies in HR decision-making, their influence on the accuracy and efficiency of decisions, difficulties and moral issues, and employee satisfaction with AI-driven procedures.

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