Keywords

Accounting Organization, Banking Operations, Management Reporting, Accounting Policy, Information Base.

Introduction

Artificial Intelligence (AI) is an emerging technology that is being adopted across various industries to boost productivity and efficiency by replicating human intelligence in different contexts. Human Resource Management (HRM) is responsible for tasks such as attracting, hiring, orienting, training, and retaining talented individuals. Every division within an organization must contribute to achieving the company's goals, objectives, vision, and mission.

Artificial Intelligence (AI) is a nascent trend in the human resource (HR) profession that has emerged as a recent phenomenon. However, it has significantly impacted people management, being perceived as crucial for the recruitment, training, development, and retention of employees in the workplace. Furthermore, the current trend of incorporating Machine Learning and AI technologies into HR activities has the capacity to result in substantial enhancements in terms of augmenting employee engagement and workplace productivity.

The Fourth Industrial Revolution (4IR) signifies a heightened utilization of emerging technologies, including artificial intelligence (AI), big data, machine learning, mobile technology, the Internet of Things, geo-tagging, virtual reality, speech recognition, and biometrics. AI encompasses a wide range of technologies that enable computers to carry out tasks that typically necessitate human cognition, such as adaptive decision-making (Tambe et al. 2019). The examination of the utilization of automation technologies in HRM indicates a lack of comprehensive understanding of the impact of AI-enabled HRM activities on employees, their work results, and the overall organizational outcomes.

HRM primarily focuses on compensation management, payrolls, and performance appraisals to encourage optimal employee performance. The integration of AI, which relies on chatbot agnostic solutions and algorithms, can significantly alleviate the workload of HR managers in areas like recruitment, selection, training, and development. This integration not only streamlines administrative processes by reducing paperwork but also enhances procedures, capturing employees' interest in their work.

AI in HRM automates tasks like processing leave requests, making employee information accessible online, and establishing automated scheduling processes. The rising use of AI in HRM is driven by its impressive results, with expectations that it will become standard practice in all businesses. AI enables HR managers to minimize administrative tasks, make more informed hiring decisions, and generate valuable HR data on the cloud through human-machine learning collaboration. Moreover, it is necessary to demonstrate how these AI applications that focus on human resources enhance favourable results while mitigating adverse effects. Therefore, we contend that the impact of the social-technological environment, including adaptable organizational framework, effective training, addressing anxiety and managing change, and enhancing employees' skills, can enhance the attainment of positive results. Additionally, we contend that it is crucial to take into account individual employee factors, such as personality and emotional intelligence, as they have the potential to impact business results.

The study emphasizes the transformational value achieved through the intelligent integration of people, processes, and technology, with AI analytics providing deeper insights into HR data usage. The research aims to explore the current and future impact of AI on human resource management, recognizing the crucial role of AI in enhancing time management and strategic value addition among employees. The last two decades have witnessed a surge in AI development, particularly due to the widespread adoption of machine learning, making this research essential in understanding AI's effects on the business world.

Literature Review

According to Scott W. O'Connor's 2020 research paper, "Artificial Intelligence in Human Resource Management," AI has already made a significant positive impact on the HRM sector, and this influence is expected to continue. HR professionals need to be adequately prepared for potential challenges, and future HR experts would benefit from staying updated on industry developments and establishing a solid foundation of HR knowledge. In this article, "To Study the Impact of Artificial Intelligence on Human Resource Management," the integration of HR procedures with AI-based applications is highlighted as significantly enhancing organizational efficiency. Despite AI's presence in various HR facets such as recruitment, training, onboarding, and performance analysis, many businesses have yet to fully incorporate AI into their HR practices. Jennifer Johansson and Senja Herranen's paper, "The Application of Artificial Intelligence in Human Resource Management" (2019), notes that only a few companies have fully implemented AI in the recruitment process due to its relatively new status. The paper mentions companies' overall resistance to new technologies as a major obstacle, while acknowledging the benefits of accelerated quality and the elimination of routine tasks. argues in his paper, "Use of Artificial Intelligence in Human Resource Management," that AI-based applications improve worker productivity by assessing, forecasting, and diagnosing resource needs with increasing accuracy. However, obstacles such as a lack of established applications, integration capabilities, or a talent mismatch are identified. In Barbara van Pay's 2018 article, "How AI Is Reinventing Human Resources," the author explains how businesses are actively seeking AI solutions but express caution about relinquishing control to machines. AI in the workplace, using tools like hike vue and mya, expedites the hiring process by sorting through resumes and application materials to identify the best-fit candidates for each position piece, "How Artificial Intelligence and Machine Learning Will Change Human Resources in the Future," predicts that chatbots powered by machine learning will handle recruitment activities, making it easier for HR professionals to use AI for screening applicants and notifying them of their status. Research from India included in Deloitte’s 5th annual global human capital trends study indicates that 53% of businesses are prepared to implement digital technologies, with 22% having already done so.

Significance of Artificial Intelligence (AI) in Human Resource Management (HRM) Practices

The human resources department has the most intricate requirements for data administration and analysis among other departments. Artificial intelligence has the capability to alleviate employees' workload, streamline operations, analyses data, and do other tasks. AI engineering establishes connections between particular machines and computers and designated gadgets. The AI framework has several applications such as applicant screening, staff participation, re-engagement, and career development. Hence, the implementation of software utilizing artificial intelligence will allow the human resources department to optimize performance. Allocate human resources people to fulfil more specialized functions and tasks that hold genuine significance for the organisation. Artificial intelligence aids in reducing the logistical workload of HR professionals and enables them to make data-driven decisions instead of relying on subjective judgement. Artificial intelligence is employed for the purposes of recruiting, minimizing bias, and maintaining high-performing individuals inside an organisation. Furthermore, with the increasing dependability and affordability of technology, it is anticipated that individuals would employ artificial intelligence. Exploiting breakthrough technology promptly, without investing excessive time and resources in costly risk research, has a distinct economic advantage. Artificial intelligence technology has the potential to enhance the data collection and processing capabilities of any department, enabling it to generate initial forecasts based on evolving circumstances.

The AI programme enables the human resources department to assist employees in several ways, such as facilitating certification preparation, providing comprehensive education, and fostering the acquisition of new skills. AI programmes enable individuals to work at their own pace, but an exceptionally remarkable programme can enhance rapid progress by offering rewards and incentives tailored to each worker's profile. Individuals that exhibit a deficiency in intellectual capacity or creative thinking may be indicated by their reliance on external resources or personal trainers.

A comprehensive examination of the existing HRM literature indicates that strategic HRM discussions are increasingly incorporating AI-enabled HRM applications across nearly all sub-functional HRM domains. This includes talent management, training and development, performance management, and employee turnover. Inherent in the implementation of AI applications in human resource management are algorithms that enhance the problem-solving and decision-making processes of HR professionals. Notwithstanding the considerable prevalence of diverse AI methodologies (e.g., algorithms) being implemented in specific HRM functions, the comprehension of a cohesive and procedural structure governing the integration of AI in HRM remains limited Alam Khan & Nazeer, (2019).

The existing literature on artificial intelligence (AI) in human resource management (HRM) highlights its significant positive impact on the sector, with applications ranging from recruitment to training and development. However, there remains a noticeable research gap in the comprehensive understanding of a cohesive and procedural structure governing the integration of AI in HRM. While studies acknowledge the potential benefits of AI, such as workload alleviation, data analysis, and improved decision-making, there is a lack of in-depth exploration into the strategic implementation and organizational impact of AI across various HRM domains. Additionally, the literature suggests that despite the anticipation of widespread AI adoption, many businesses, especially in the recruitment process, have yet to fully embrace AI due to factors like resistance to new technologies and a lack of established applications. Therefore, further research is needed to bridge this gap by delving into the practical challenges, organizational readiness, and the holistic impact of AI on HRM practices, ensuring a more nuanced understanding for practitioners and policymakers alike Figure 1.

Research Objectives

1. Exploring the Role of Artificial Intelligence in Human Resource Management.

2. Investigating the Influence of Artificial Intelligence on Human Resource Management.

Research Hypothesis

H₁: AI technology significantly influences the efficiency of HRM.

H₂: AI in recruitment significantly influence the efficiency of HRM.

H₃: AI in training and development significantly influence the efficiency of HRM.

H₄: AI in performance appraisal significantly influence the efficiency of HRM.

Research Methodology

The research employed a quantitative approach, gathering data through a survey method. Following an extensive review of existing literature, a questionnaire was devised, incorporating constructs derived from prior studies. Each statement in the questionnaire was subjected to a rating on a 5-point Likert scale, ranging from 1 for strong disagreement to 5 for strong agreement. The questionnaire focused on assessing perspectives on Efficient Human Resource Management (HRM), the utilization of AI technology, and the integration of AI in HRM processes such as recruitment, training, and performance. Notably, the items for Efficient HRM, AI technology, and AI in HRM processes were adapted from the works.

Sample and Data Collection

The study is underpinned by an extensive review of existing literature and theory. Following the identification of the research issue and formulation of research questions, the subsequent step involves selecting an appropriate research strategy. Subsequently, data collection is executed using the chosen strategies. The researcher has undertaken data analysis based on predetermined hypotheses, leveraging the information collected.

Primary data was obtained through a survey conducted among 200 randomly selected respondents from the Information Technology (IT) industry in the Delhi NCR region. The survey instrument utilized was a meticulously crafted questionnaire. Additionally, secondary sources, including previously published research, studies, and the writings and blogs of esteemed authors, have contributed to enriching the dataset and providing valuable context to the research. This comprehensive methodology ensures a multifaceted exploration of the research topic, incorporating both primary and secondary data to enhance the depth and reliability of the findings.

Data Analysis

Both descriptive and inferential statistics were employed in the study's analysis and presentation of findings. IBM SPSS version 24 was used to analyze the data, and descriptive statistics including the sample's mean, standard deviation, percentage, and frequency were calculated. In order to extrapolate from the sample to the population, we used inferential statistics like correlation and multiple regressions. After obtaining 215 total replies, a data screening was undertaken, during which questionnaires with missing data were discarded and 200 responses were chosen for final study Table 1.

Table 1 Demographic Profiles of Women Respondents (N=200)
Measures	Items	Frequency	Percentage
Gender	Male	121	60.5
	Female	79	39.5
Marital Status	Married	45	22.5
	Unmarried	155	77.5
Education	PG	81	40.5
	Secondary board/ Equivalent degree	23	11.5
	UG	96	48
Age of the respondents	<28yrs	48	24
	29 to 38 yrs	91	45.5
	38 to 48 yrs	42	21
	Above 48 yrs	19	9.5

Results

Demographic Characteristics

The demographic profile of the respondents in the study reveals a varied representation across gender, marital status, education level, and age groups. In terms of gender distribution, the majority of respondents were male, constituting 60.5%, while females accounted for 39.5%. When examining marital status, a substantial proportion of respondents, 77.5%, identified as unmarried, contrasting with the 22.5% who reported being married.

Educational backgrounds of the participants were diverse, with the highest percentage (48%) holding an undergraduate degree, followed by 40.5% having postgraduate qualifications, and 11.5% possessing a secondary board or equivalent degree. This educational diversity reflects a broad range of academic backgrounds among the respondents. The age distribution of the participants provides insight into the generational composition of the sample. A quarter of the respondents were below 28 years old, while the majority, 45.5%, fell within the age range of 29 to 38 years. A notable proportion, 21%, was aged between 38 and 48 years, and 9.5% were above 48 years old. This distribution indicates a relatively even representation across different age brackets, offering a comprehensive perspective on the views and experiences of individuals from various stages of their professional lives. Overall, the demographic data showcases the diversity within the respondent pool, contributing to the robustness and generalizability of the study's findings.

Descriptive Statistics and Scale Reliability

Responses were relatively concentrated in the middle of the scale, as indicated by the mean and standard deviation measures of central tendency. EHRM (M = 4.57, SD = 0.66) received the most favorable opinion followed by PAAI (M = 4.52, SD = 0.68), TDAI (M = 4.40, SD = 0.70), RAI (M = 4.43, SD = 0.78) and AI (M = 4.24, SD = 0.69).

Cronbach's alpha was chosen as the preferred measure of reliability to assess the consistency of the constructs utilized in the data analysis process. According to Nunnally and Bernstein's (1994) findings, criteria that meet or exceed a value of 0.7 are considered to be the threshold for reliability. The alpha values reported in Table 2 indicate that the data is reliable, as all values fall within the specified criteria, ranging from 0.850 to 0.912.

Table 2 Cronbach’s Alpha, Mean, STD. Deviation and Correlation of the Variables
Items	AIT	RAI	TDAI	PAAI	EHRM
Reliability (Alpha value)	0.850	0.875	0.912	0.888	0.903
Mean	4.2413	4.4250	4.3987	4.5163	4.5650
Standard deviation	.69608	.78508	.69911	.68457	.66263
AI technology (AIT)	1
Recruitment through AI (RAI)	0.429**	1
Training & development through AI (TDAI)	0.558**	0.455**	1
Performance appraisal through AI (PAAI)	0.578**	0.409**	0.580**	1
Efficient Human resource management (EHRM)	0.593**	0.551**	0.631**	0.599**	1

The values of all variables' correlation coefficients were also listed in table 2. Correlational analysis establishes a link between two factors. As the p-values for the coefficients of correlation are all less than 0.05, it can be concluded that there is a positive and statistically significant relationship between the two variables. When it comes to effective HRM and TDAI, R = 0.631 is the highest possible value.

Influence of AI Technology and its Adaptation in Human Resource Process on HRM Effectiveness

Multiple regression analysis was utilized to determine how four different factors affected the study's dependent variable Almarashda et al. (2021). The role of AI and ML in the HR process: As the dependent variable, Effective HRM was compared to the independent variables of Recruitment, Training & development, and Performance appraisal using AI. Before running the regression test, we made sure the multicollinearity assumption held up. The outcomes were addressed in more detail below Table 3.

Table 3 Multi-Collinearity Tests
Independent Variables		Tolerance	VIF	Durbin-Watson
AI technology (AIT)	0.573		1.746	2.123
Recruitment through AI (RAI)	0.737		1.357
Training & development through AI (TDAI)	0.559		1.788
Performance appraisal through AI (PAAI)	0.561		1.781

By assessing the variance inflation factor (VIF) values and tolerance value, we ascertained the presence of multicollinearity in the dataset. Multicollinearity was deemed absent when the VIF value was less than four and the tolerance value exceeded 0.02, indicating an insignificant correlation between any two independent variables (predictors). Examination of Table 4 revealed that both the VIF and tolerance values were below the established threshold, suggesting the absence of multicollinearity issues in the data. Furthermore, the Durbin-Watson statistic (DW = 2.123) fell within the range of 1.5 and 2.5 critical values, indicating the lack of autocorrelation in the residuals.

Table 4 Anova
ANOVA
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	49.305	4	12.326	63.133	.000
	Residual	38.072	195	.195
	Total	87.377	199

The F-test value =63.133 with a significance level of p < 0.05 (p=0.000) in the ANOVA table 4 indicates that the regression model is statistically significant in predicting Efficient HRM (dependent variable). The mean square for the regression (12.326) was considerably higher than that for the residuals (0.195). Overall, the ANOVA results support the conclusion that the regression model is statistically significant in explaining the variability in the dependent variable, reinforcing the validity of the model's predictive capabilities Chakraborty et al. (2020).

Table 5 displays the coefficients of a multiple regression model that aims to elucidate the impact of AI technology and its integration into HR processes on the effectiveness of Human Resource Management (HRM) Heric, (2018). Unstandardized coefficients, which represent the coefficients of a regression equation without any standardization or transformation, are employed to explain the association between the dependent variable and independent variables. The B coefficient signifies the relationship between the dependent and independent variables, where a unitary change in the independent variable corresponds to a corresponding change in the B value of the dependent variable. The sign of the B value indicates a positive increase or negative decrease in the dependent variable Johansson & Herranen, (2019).

Table 5 Coefficients of Multiple Regression Analysis
Model	Unstandardized Coefficients		Standardized Coefficients	T	Sig. (p)	Results
	B	Std. Error	Beta
(Constant)	0.699	.246		2.840	.005
AI technology	0.196	0.059	0.206	3.295	.001	H1 supported
Recruitment through AI	0.209	0.046	0.248	4.498	.000	H2 supported
Training & development through AI	0.263	0.060	0.277	4.388	.000	H3 supported
Performance appraisal through AI	0.211	0.061	0.218	3.456	.001	H4 supported

Standardized regression weights (β) provide insights into the strength of the impact of predictor variables on dependent variables, with a higher β value indicating a more substantial impact. Hypothesis selection criteria are based on paths with a p-value less than 0.05 and a T value exceeding 1.96.

The results presented in Table 5 demonstrate that the impact of AI technology on efficient HRM is positive and significant (β = 0.206, p = 0.001). Since the p-value is below 0.05 and the T value (3.456) exceeds 1.96, Hypothesis H1 is supported. The utilization of AI in recruitment significantly enhances HRM, with a β value of 0.248 and a p-value less than 0.05, confirming Hypothesis H2. Similarly, organizations employing AI in training and development significantly influence the efficiency of HRM, as evidenced by a path coefficient value of 0.277 and a p-value of 0.000, supporting Hypothesis H3 Lorenzo Milani (2017).

Furthermore, performance appraisal through AI has a significant and positive impact on the efficiency of HRM (β = 0.218, p = 0.001). The p-value below 0.05 and the T value (3.456) above 1.96 validate Hypothesis H4. The overall significance value of p < 0.05 affirms that AI technology and its application in HR processes exert a considerable influence on efficient HRM, thereby supporting all the hypotheses. Based on standardized regression weights, it is evident that the impact of AI involvement in training and development has the highest influence on achieving efficient HRM, followed by recruitment, and performance appraisal through AI, with AI technology also contributing significantly Maurya et al. (2023).

Table 6 shows that there is a modest level of association, represented by a R value of 0.751. The coefficient of determination (R2) for the independent variables was 0.564, indicating that they accounted for 56.4% of the variance in the dependent variable, HRM effectiveness. The standard error of the estimate, measuring the average deviation of actual values from the predicted values, is 0.44186. This value represents the accuracy of the model in predicting the dependent variable based on the independent variables. A lower standard error indicates a more precise fit of the model to the observed data Megan Marie Butler (2018).

Table 6 Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	0.751	0.564	0.555	.44186

In summary, the model exhibits a substantial level of explanatory capability, with a high R-square and a strong correlation coefficient, suggesting that the included independent variables contribute significantly to explaining the observed variability in the dependent variable. The adjusted R-square accounts for the number of predictors and refines the interpretation, while the standard error of the estimate indicates a reasonable level of accuracy in predicting the dependent variable.

Discussion and Implications

Using AI in human resources (HR) functions has been shown to have a positive impact on the HR (efficient HR). These AI-based HR tools may be able to analyse, forecast, and diagnose; yet, they lack the emotional and cognitive talents of humans. Nevertheless, they are a significant tool for any organisation.

As a result of AI's demonstrable impact on reducing jobs across numerous industries worldwide, this is the genuine problem that is overwhelming the global workforce. The truth, however, is that it is not the advent of increasingly sophisticated technological advances per se that threatens the survival of humankind, but rather the degree to which humans are able to adopt and profit from these innovations. Organizational performance is more likely to improve if HR practices are integrated with AI-based applications. These AI-based HR tools can analyses, anticipate, and diagnose, making them a valuable resource for any organisation despite their lack of human-like emotional and cognitive capacities. The findings also showed that using AI in HRM might help businesses in a variety of ways, including in terms of operations, management, strategy, organisation, data, and compliance. Better AI implementation in HRM is possible thanks to this study's insights on the practical application and impact of AI-based software in the field. Delhi NCR-based businesses would be able to make more informed choices about AI investments Pinto et al. (2021); Mishra et al. (2023).

The study delving into the impact of Artificial Intelligence (AI) on Human Resource Management (HRM) carries significant implications for both academia and practitioners in the field. The findings shed light on the transformative potential of AI in HRM practices, offering valuable insights that can inform strategic decisions and initiatives within organizations. One noteworthy implication of the research is the confirmation of the positive and significant impact of AI technology on efficient HRM. This insight emphasizes the importance of integrating AI tools and processes into HR functions to enhance overall operational effectiveness. Organizations that recognize and harness the potential of AI in HRM can gain a competitive edge in talent management, recruitment, training, and performance appraisal.

Furthermore, the study's identification of the distinct contributions of AI in recruitment, training and development, and performance appraisal provides targeted guidance for HR professionals. Understanding the specific areas where AI can have a significant impact allows organizations to tailor their implementation strategies, ensuring a more focused and effective incorporation of AI into HRM practices Surve, (2020); Tambe et al. (2018).

Conclusion

The research underscores the pivotal role that AI plays in shaping the future of HRM. The confirmed positive effects on efficiency, coupled with insights into the specific HR functions where AI adds the most value, provide a roadmap for organizations aiming to leverage technology for enhanced human resource practices. As organizations navigate the evolving landscape of HRM in the digital era, the study's implications contribute valuable knowledge for informed decision-making and successful adaptation to the transformative influence of Artificial Intelligence.

Limitation and Future Studies

Our research shows that most companies are now using AI-based recruitment tools successfully. However, in the not-too-distant future, AI will permeate all aspects of human resources. However, most companies have yet to fully incorporate AI into their HR practices, perhaps due to the high costs involved with doing so, the extent of which will be an interesting topic for research in the years to come. Because AI improves people's lives and ushers in a brighter future if used correctly, its adoption should be seen as a cause for optimism Vasantham, (2021).

References

Alam Khan, P., & Nazeer, D. I. (2019). Assessing farm management practices in Indian farming: A Review. Journal of Management (JOM), 6(2), 130-137.

Google Scholar

Almarashda, H. A. H. A., Baba, I. B., Ramli, A. A., Memon, A. H., & Rahman, I. A. (2021). Human Resource Management and Technology Development in Artificial Intelligence Adoption in the UAE Energy Sector. Journal of Applied Engineering Sciences, 11(2).

Indexed at, Google Scholar, Cross Ref

Chakraborty, S., Giri, A., Aich, A., & Biswas, S. (2020). Evaluating influence of artificial intelligence on human resource management using PLS-SEM (Partial least squares-structural equation modeling). International Journal of Scientific and Technology Research, 9(3), 5876-5880.

Google Scholar

Heric, M. (2018). HR new digital mandate-digital technologies have become essential for HR to engage top talent and add value to the business. Retrieved May, 12, 2020.

Google Scholar

Johansson, J., & Herranen, S. (2019). The application of artificial intelligence (AI) in human resource management: Current state of AI and its impact on the traditional recruitment process.

Google Scholar

Lorenzo Milani (2017) Exploring the impact of AI on HRM, Published in London school of economics, January 25th, 2017.

Maurya, P. K., Bansal, R., Ansari, Y., & Mishra, A. K. (2023). Behavioural Determinants of Health Insurance Buying Intention of Missing Middle Consumers: A Study During Covid-19 Pandemic. European Economic Letters (EEL), 13(5), 1082-1098.

Google Scholar

Megan Marie Butler (2018) Navigating the hype and impact of AI in HR, December 10th, 2018.

Google Scholar

Mishra, A. K., Bansal, R., & Maurya, P. K. (2023). Investing for a better tomorrow: Values-driven antecedents of investment in socially responsible equity funds by Indian retail investors. Journal of Cleaner Production, 420, 138441.

Indexed at, Google Scholar, Cross Ref

Pinto, M. M. P., & Darshani, R. K. N. D. (2021). Use of Artificial Intelligence in Human Resource Management.

Google Scholar

Prachant Srivastava (2018) Impact of AI on strategic HR Decision Making, April 6th, 2018.

Surve, A. A. (2020). Impact Of Artifical Intelligence In Human Resource Management (Doctoral dissertation, Doctoral Dissertation]. University of Mumbai).

Google Scholar

Tambe, P., Cappeli, P., & Yakubovish, V. (2018). AI in HRM challenges and a path forward AI.

Google Scholar

Vasantham, S. T. (2021). The Role of Artificial Intelligence in Human Resource Management. Engineering and Scientific International Journal, 8(2).

Google Scholar

Received: 11-Jan-2024, Manuscript No. AMSJ-23-14347; Editor assigned: 12-Jan-2024, PreQC No. AMSJ-24-14347(PQ); Reviewed: 29-Jan-2024, QC No. AMSJ-24-14347; Revised: 15-Apr-2024, Manuscript No. AMSJ-24-14347(R); Published: 10-May-2024