An exploration of the impact of Artificial Intelligence on the digital transformation of Human Resource Management within the framework industry 4.0.

 

Rekha ¹

 

¹ Research scholar, Apeejay Stya University, Sohna-Gurugram, India  

 

1. INTRODUCTION

In the era of Industry 4.0, the HR department has an important role in connecting technology with people management. While technology is taking over many traditional HR tasks, there is still a need for flexible HR practices to address the challenges of managing employees. To stay flexible, HR can use technology to bring agility into its processes. Agility refers to the ability to move quickly and smoothly, and it's a concept already adopted by major companies like Google, Apple, Facebook, Amazon, and Microsoft. In HR, agility means being able to quickly adapt and support people, strategies, and the organisation when things change unexpectedly. For HR and Learning and Development (L&D) professionals, being agile means helping employees stay engaged and committed, while ensuring that HR practices align with the company's goals. HR agility is especially useful in situations where it’s hard to standardize HR tasks due to constant changes. For organizations to become agile, their employees need to focus on meeting customer needs and providing value to them. However Since the HR department doesn't directly deal with customer incentives, it is often criticized for being slow to respond, which can lead to employee dissatisfaction. To stay competitive and attract top talent, organizations need to encourage HR to be more responsive to changes in technology and business needs. In an agile organization, HR still handles tasks like recruitment, employee development, and performance management, but does so using agile methods.

HR agility has three main parts: quickly identifying problems that need attention, reducing the time it takes to address those issues, and using analysis and design thinking to plan and create programs that are likely to succeed. With the fast growth of technology, especially AI in HR, many HR processes are changing. As organizations continue to digitize HR operations, it's important to understand how AI impacts areas like employee productivity, safety, payroll, employee comfort, and real-time feedback. Also, it's important to see how these HR functions influence the overall business. Organizational network analysis and design can help organizations use AI to improve their overall efficiency and effectiveness. The goal of this research is to explore the connection between AI, HR digitization, and how HR digitization impacts organizational network analysis and design. The research aims to look at how AI affects areas like employee productivity, health and safety, payroll automation, employee comfort, and real-time feedback. It also focuses on the benefits and challenges of HR digitization and its impact on these areas, as well as how it affects organizational network analysis and design.

The study aims to provide insights into how AI influences HR digitization and how this, in turn, affects the overall structure and function of organizations. Additionally, the research will offer recommendations on how organizations can use AI to improve their HR practices and increase their efficiency and effectiveness.

The study focuses on two key areas of HR management: (i) how AI is applied in HR, and (ii) how agility is integrated into HR practices. It uses components from various research papers and web articles, as there has been limited research in these areas so far.

The study addresses two main research questions:

1)     How can AI impact HR management to meet the demands of Industry 4.0?

2)     How can AI bring sustainability to HR functions in Industry 4.0?

To answer these questions, the study sets the following objectives:

1)     Explore current trends in AI within HR practices.

2)     Assess how AI impacts HR practices to meet the demands of Industry 4.0.

3)     Analyze how AI can contribute to sustainability in Industry 4.0.

To achieve these objectives, the study develops a conceptual framework that highlights key areas where AI can be applied in HR. A thorough literature review is conducted to analyze existing studies on AI in HR. The proposed framework helps to prioritize the implications of AI in HR and contributes to the existing research. The findings of the study can guide organizations in overcoming challenges related to implementing AI in HR.

 

2. Literature review

Human resources functions have changed and evolved over time, becoming more dynamic. Research shows that AI has been used in sectors like healthcare to make HR more agile and effective. One key feature of AI is its ability to connect physical objects (called the Internet of Things, or IoT) to the internet. This includes items like vehicles, screens, pacemakers, and motors. The practical side of IoT involves technology that can sense, process, and communicate information. Sensors placed on objects can collect data on various factors such as location, speed, temperature, usage conditions, faults, and stress. The data collected is detailed, continuous, reliable, and can be used in real-time to inform AI systems, making the process of gathering new information very effective and useful.

 

2.1.  Role of AI in HR Practices

In recent years, AI has become increasingly important in Human Resource Management (HRM), especially with the rise of Industry 4.0. This has created a need for more automation, digitization, and flexibility in HR practices. AI can greatly improve HR by making processes more efficient, accurate, and data-driven. One area where AI can have a big impact is in recruitment and hiring. AI algorithms can scan resumes and job applications to quickly find candidates that match the job requirements, saving time and effort compared to manual screening. It can also analyze data to predict which candidates are most likely to succeed, improving the quality of hiring decisions. AI can also help with employee engagement and retention. By analyzing employee data, AI can spot patterns that may show low engagement or high turnover. This allows HR to take action, such as offering training programs or improving company culture. AI can also improve learning and development by identifying knowledge gaps in employees and recommending training programs to fill those gaps. Personalized learning platforms powered by AI can cater to individual employee needs, improving learning outcomes. Another way AI benefits HR is in performance management. By analyzing employee performance data, AI can highlight areas where improvements can be made. This information can be used to create personalized plans for employees to boost performance and productivity. AI also plays an important role in workplace safety. By analyzing data from sensors and devices, AI can spot safety risks and recommend actions to prevent accidents and injuries, ensuring compliance with safety rules.

In conclusion, AI has the potential to transform HR practices by improving efficiency and decision-making in areas like recruitment, talent management, learning, performance, and safety. However, it’s important to address concerns like bias and job loss to ensure AI benefits HR without causing ethical issues. The success of AI in HR will depend on how well organizations balance automation with the human aspects of HR, such as empathy and judgment.

 

2.2.  Conceptual Framework

2.2.1.   Improving Health and Safety in the Workplace with AI

AI can be used to improve health and safety at work by identifying and preventing hazards. AI systems can analyze data from sensors, cameras, and other devices to detect potential dangers in the workplace. This can help create a safer environment for employees. AI can also be used to monitor employees' health data and spot patterns that may suggest health problems. This information can be used to prevent health issues and provide personalized recommendations for better health. AI-powered chatbots can offer employees immediate assistance by answering questions about safety guidelines and providing support in emergency situations. Additionally, AI can help improve workplace ergonomics. AI systems can monitor employees' movements and detect potential risks for musculoskeletal problems. This data can be used to adjust workstations and reduce the chances of workplace injuries.

 

2.2.2.   Enhancing Employee Comfort with AI

AI can help make the workplace more comfortable for employees in several ways. First, AI-powered systems can use data from sensors, like temperature sensors, to adjust the workplace environment for optimal comfort. For example, AI can change the temperature and humidity based on how many employees are in the office. Second, AI can personalize the work experience for employees by making recommendations for comfort. For instance, AI can suggest adjustments to desks or chairs based on an employee's body type and preferences. Third, AI can help identify stressors in the workplace and suggest ways to reduce them. By monitoring employee engagement and communication patterns, AI can spot areas of stress and help HR take steps to improve comfort and reduce stress levels.

 

2.2.3.   Measuring Employee Productivity

AI can help HR departments by automating routine tasks, freeing up time for HR professionals to focus on more important work. This can boost productivity within HR teams. AI can also measure employee productivity in real-time by tracking data such as time spent on tasks and completion rates. This data can help managers give employees feedback on their performance and highlight areas where they can improve. Additionally, AI can provide more objective measurements of productivity compared to traditional methods, like performance reviews, which can be biased. AI uses data to make more accurate and fair decisions about employee performance.

 

2.2.4.   Automating Payroll Processing

AI can simplify payroll by automatically calculating salaries, taxes, and handling time-off requests. This saves HR time and reduces the risk of mistakes. AI also helps improve the accuracy of payroll by spotting errors, like duplicate payments or incorrect tax calculations. This helps ensure that payroll is processed correctly. Finally, AI can make sure that payroll complies with legal requirements, such as minimum wage laws and overtime regulations, reducing the risk of legal issues for the organization.

 

2.2.5.   Providing Real-Time Feedback

AI can provide real-time feedback to employees in several ways. First, AI can track performance and give immediate feedback on progress, pointing out areas that need improvement. Second, AI provides more objective feedback than traditional methods, which can sometimes be biased. AI uses data and analytics to ensure feedback is accurate and fair. Lastly, AI can give feedback quickly, often in real-time, so employees can act on it right away to improve their performance. This helps employees make immediate improvements and stay on track.

 

2.2.6.   Impact on Digitization of HR

AI-powered systems can automate many HR tasks, such as recruitment, onboarding, performance management, and employee engagement. For example, AI can automate the process of screening and shortlisting job applications, saving time and effort compared to manual processing. AI can also make onboarding easier by providing personalized training programs for new employees. Additionally, AI can track employee performance in real-time, giving HR teams valuable data to improve performance management and keep employees engaged. AI's impact on HR goes beyond improving efficiency. It can also help HR make better decisions by providing data-driven insights. For instance, AI can identify skills gaps within the workforce, allowing HR to create targeted training programs to upskill employees.

 

2.2.7.   Organizational Network Analysis (ONA)

AI can assist in automating the collection and analysis of data for Organizational Network Analysis (ONA). AI can analyze email communication patterns to identify key influencers in the organization or use social media data to discover informal networks. It can also analyze employee survey data to understand factors that affect employee engagement and collaboration. AI's role in ONA goes beyond just gathering data; it can also identify and fix issues within the organization’s networks. For example, AI can pinpoint communication problems or bottlenecks and help HR create targeted solutions to improve collaboration and communication.

 

 2.2.8. Organizational DesignAI can help automate the analysis of data related to organizational design. For example, it can look at job performance, skills, and experience to match the best candidates to specific roles. AI can also analyze employees' preferences and interests to find opportunities for talent development. AI's impact on organizational design goes beyond just analyzing data. It can also help create more flexible and adaptable structures. For instance, AI can recognize changes in customer demands or market trends and suggest adjustments to job roles and structures to respond to these changes. Additionally, AI can help design more inclusive and diverse organizations by identifying biases in job descriptions and recruitment processes. To better understand how AI can make HR more agile, the study proposes a conceptual framework (as shown in Fig. 1).

 

3. Methodology

3.1.  Research Design

The study used a descriptive research design with a cross-sectional approach. This design is suitable for studying the impact of AI on HR digitalization in Industry 4.0 because it allows the researcher to collect data from a large group of people at one specific point in time.

 

 

 

 

 

 

3.2.  Population and Sampling

The study focused on human resource professionals working in IT, IT-enabled services (ITES), manufacturing, and service sectors in Chennai and Bengaluru. These cities were chosen because they cover a wide range of industries. Private sector banks were included in the service sector. A multi-stage sampling method was used: first, the geographical locations were selected, then firms in each sector were ranked, and finally, HR professionals were chosen from the selected firms. A total of 360 questionnaires were sent out via Google Forms, and 271 were eligible for analysis, giving a response rate of 75%. This sample size of 271 is justified, as previous studies recommend a sample size of at least 200 for structural equation modelling (SEM) analysis.

 

3.3.  Scale Development and Validation

To measure the key concepts in the research, new scales were created by adapting existing relevant literature. These scales were tested for validity and reliability to ensure they accurately measured what they were supposed to. Validity checks how well the scale measures what it's intended to measure, while reliability ensures that the measurements are consistent over time. The validity and reliability of the scales were tested using confirmatory factor analysis (CFA). The CFA results showed that the scales were valid and reliable. All constructs had composite reliability (CR) values above 0.7, showing high consistency, and average variance extracted (AVE) values above 0.5, showing good validity. This means the scales were suitable for measuring the concepts in the study.

 

3.4.  Data Collection

Data was collected using a structured questionnaire with three parts. The first part collected demographic information, the second part focused on AI applications in HRM, and the third part measured Human Resource Agility. Both parts two and three used a five-point Likert scale to gather responses.

 

3.5.  Data Analysis

The data collected for the study were analyzed using SPSS for basic statistical analysis, and the proposed model was tested using AMOS. The scales used in the study were checked for validity and reliability, and the results showed they were suitable for further analysis.

 

 

 

3.6.  Assessing the Assumptions of SEM

To ensure the data met the assumptions for Structural Equation Modeling (SEM), the researcher checked for multivariate normality by looking at the skewness and kurtosis values for each variable. All values were within the acceptable range (between -2 and +2). They also used maximum likelihood estimation, which assumes the data follows a normal distribution.

For missing data, the list wise deletion method was applied, which removes cases with missing information. The final sample size of 271 exceeded the recommended minimum for SEM analysis.

The model was specified based on prior research and theory, and confirmatory factor analysis (CFA) was performed to check how well the model fits the data.

 

Validity and Reliability

Table 1 presents key measures for validity and reliability:

·        Cronbach Alpha (α): This measures how reliable the constructs (variables) are. A value above 0.70 is considered good, and in this study, most constructs had values above 0.8, indicating high reliability.

·        Composite Reliability (CR): This measure also checks reliability, considering potential errors. A value above 0.7 is acceptable, and this study met that threshold.

·        Average Variance Extracted (AVE): This measures how much variance the items under each construct explain. The minimum expected value for AVE is 0.5, and all constructs in the study met this requirement, confirming convergent validity.

·        Discriminant Validity: This is tested by comparing AVE with two other values: Maximum Shared Value (MSV) and Average Shared Value (ASV). For the study, AVE was higher than both MSV and ASV, confirming discriminant validity.

Additionally, the Kaiser-Meyer-Olkin (KMO) measure was 0.872, which is above the minimum recommended value of 0.6, indicating that factor analysis was appropriate. Bartlett’s Test of Sphericity also showed significant correlations among variables, with a p-value less than 0.001, further supporting the data's suitability for analysis. The test result showed a value of 874.98 with a significance level less than 0.0001. This means the hypothesis that the correlation matrix is an identity matrix (i.e., the variables are completely unrelated) is rejected. In other words, the variables are not independent of each other. Since the significance value is less than 0.05, it indicates that performing factor analysis on this data is appropriate and useful.

Table 1

Table 1 Reliability and Validity of the Constructs
Constructs	A	CR	AVE	MSV	ASV	HIS	EEC	EPM	APP	RTF	DHR	ONA	OD
Health and safety Improvement	0.953	0.929	0.652	0.629	0.504	0.807
Enhancing employee comfort	0.938	0.934	0.587	0.54	0.500	0.700	0.766
Employee productivity measurement	0.928	0.933	0.581	0.51	0.462	0.731	0.681	0.763
Automating payroll processing	0.978	0.966	0.778	0.537	0.332	0.607	0.571	0.532	0.882
Real time feedback	0.901	0.895	0.523	0.509	0.380	0.684	0.592	0.533	0.555	0.723
Digitization of HR	0.965	0.923	0.503	0.323	0.245	0.490	0.517	0.483	0.418	0.410	0.709
Organizational network analysis	0.900	0.898	0.570	0.491	0.462	0.682	0.669	0.734	0.578	0.566	0.481	0.754
Organizational design	0.945	0.915	0.730	0.645	0.544	0.790	0.766	0.716	0.618	0.672	0.548	0.737	0.854

 

                                                                                      Table 2

Demographic Variables	Category	No of Respondents	Percentage of the Respondents
Gender	Male Female	131 140	48.3 51.7
	21-30 31-40 41-50 >50	89 121 42 29	32.8 44.6 15.5 10.7
Education	Master’s degree Bachelor’s degree	95 176	35.0 65.0
Industry Type	Manufacturing IT and ITES Service Sector	80 129 62	29.5 47.5 23.0

 

4.  Results, Analysis, and Interpretation

This section discusses the demographic profile of the respondents and presents the results and interpretations of the study.

 

4.1.  Profile of the Respondents

Table 2 shows that 51.7% of the respondents were female, while 48.3% were male, meaning more responses came from females. In terms of age, 44.6% of respondents were between 31 and 40 years old, and 32.8% were between 21 and 30 years old. The majority of responses were from people in the 31-40 age group. Regarding education, 65% of respondents had a bachelor's degree, while the rest had a master's degree. Most respondents were pursuing a bachelor's degree.

As for the industries, 47.5% of respondents worked in IT and ITES sectors, 29.5% in manufacturing, and 23% in the service sector.

Structural Equation Modeling (SEM)

The study used Structural Equation Modeling (SEM) with AMOS 20 software to test the proposed conceptual model. SEM helped analyze the relationships between AI application dimensions (independent variables) and HR agility dimensions (dependent variable).

Figure 2 shows three key relationships:

1)     AI application to HR digitization

2)     AI application and Organizational Network Analysis

3)     AI application and Organizational Design

 

 

Table 3

Table 3 Fit indices of the conceptual model
	CMIN/DF	RMSEA	CFI	IFI	GFI	AGFI	RMR	P
Model	1.563	0.043	0.997	0.997	0.993	0.868	0.006	O.154
Recommended standard	<3.0	<0.08	>0.90	>0.90	>0.90	>0.90	<0.08	>0.05

 

Table 4

Table 4 Results of the Conceptual Model
Hypothesis	Path	Standard Co-efficient	P-value	R2
H1a	Employee Productivity measurement- Digitization of HR	.422	……	0.508
H2a	Health and Safety Improvement – Digitization of HR	.109	.090
H3a	Automated Payroll Process – Digitization of HR	.261	……
H4a	Enhancing Employee Comfort - Digitization of HR	.238	……
H5a	Real time Feedback - Digitization of HR	-.084	.247
H4b	Real-time Feedback – Organisational Network Analysis	-.278	…….	0.772
H5b	Enhancing Employee Comfort- Organisational Network Analysis	.386	…….
H6b	Health and Safety Improvement - Organisational Network Analysis	.660	……
H1b	Employee Productivity Measurement-Organisational Network Analysis	.180	……
H2b	Automated Payroll – Organisational Network Analysis	.194	……
H6c	Enhancing Employee Comfort- Organisational Design	-.514	……	0.440
H5c	Health and Safety improvement- Organisational Design	.339	……
H4c	Employee Productivity Measurement- Organisational Design	-.222	.004
H7	Automated Payroll Process- Organisational Design	-.129	.064
H8	Real-time Feedback- Organisational Design	.630	…..

 

The red lines in the diagram indicate relationships that were found to be statistically insignificant. Table 4 presents the beta values and P-values for these relationships. All five dimensions of AI in HR were found to have a highly significant impact on Organizational Network Analysis, which is the dependent variable.

The analysis of the study's results shows the following findings:

·        Beta values and relationships: The study looked at five dimensions of AI in HR and their impact on different areas like HR digitization and organizational design. The beta values (which indicate the strength of the relationship between variables) for these dimensions are as follows:

1)     AI dimensions with significant influence on HR digitization: Three out of the five AI dimensions had a significant effect, with beta values of 0.422, 0.261, and 0.238.

2)     AI dimensions with significant influence on organizational design: Four dimensions of AI in HR impacted organizational design, with beta values of -0.514, 0.339, -0.222, and 0.630. However, the "automated payroll system" dimension was not found to significantly affect organizational design.

·        Goodness of fit: Table 3 shows the values of various statistical tests to check how well the proposed model fits the data. Most of the indices, such as normed chi-square, RMSEA, CFI, and GFI, fall within the acceptable range, indicating that the model fits the data well. The only index that slightly fell short was AGFI, which was just below the recommended threshold.

·        Impact on Organizational Network Analysis: The five AI dimensions together explain 77% of the variance in Organizational Network Analysis (ONA). Among these, Health and Safety Improvement had the most significant impact, with a coefficient value of 0.660. On the other hand, real-time feedback negatively impacted ONA.

·        Impact on HR Digitization: Three dimensions of AI explain 51% of the variance in HR digitization. The Employee Productivity Measurement dimension contributed the most, with a coefficient value of 0.422.

·        Impact on Organizational Design: Four dimensions of AI explained 44% of the variance in Organizational Design. Real-time feedback had a positive impact with a high coefficient value of 0.630, while Enhancing Employee Comfort negatively affected organizational design with a coefficient value of -0.514.

These findings show that while AI has a positive impact in many areas of HR, some dimensions, like real-time feedback and employee comfort, have mixed effects depending on the context.

 

5. Discussion

Employee health and well-being are important concerns for managers because healthy workers contribute to higher productivity and better results for the company. HR teams can use connected devices, like wearable to track employees' health. These devices can gather data such as food intake, walking distance, and vital signs. By analyzing this data, HR teams can spot potential health issues early and take steps to prevent them. AI can also be used to improve workplace safety. For example, AI sensors can monitor pressure in gas pipelines to detect potential leaks. This helps ensure employees' safety while avoiding health-related issues. The study found that improving employee health and safety is a key factor in making HR processes more agile through Organizational Network Analysis (ONA) and organizational design. Another AI technology tracks eye movement to understand workers' focus. This helps HR personnel identify distractions, like background noise or long work hours, that might affect an employee's concentration and productivity. If a worker becomes sleepy in the afternoon, HR can help by offering life skill training to balance work and health, ensuring better performance. AI can also improve the feedback process within HR. It's often difficult for HR to understand employees' true feelings, but AI can help gather real-time feedback. For example, AI-powered cameras can take pictures of employees after meetings to analyze their emotions. If an employee seems unhappy, the system alerts HR to address the issue, helping HR create a more dynamic and responsive organization. Furthermore, AI can detect signs of mental health issues, such as sadness or anxiety, by analyzing images of employees throughout the day. If AI identifies that an employee is showing signs of distress, it can alert HR to provide support. These uses of AI help HR teams respond quickly and effectively, improving employee well-being and overall agility in HR functions. HR teams can organize counseling sessions for employees to help them feel more comfortable at work. However, this might have a negative effect on the overall organization structure, as the study's results suggest. This finding differs from previous studies that indicated a more positive impact. AI sensors can be used to track attendance, but they may not be suitable for monitoring exact working hours in all roles. For example, office jobs often require employees to be at their desks to be productive, so sensors can be useful in these cases. However, for field-based jobs, where employees are often on the move and don't need to be at a desk, AI sensors may not be appropriate for tracking work hours. This finding aligns with earlier research on the topic.

 

6. Conclusion, limitations and scope for future study

Using AI in HR brings many benefits for both the HR department and employees. However, it also comes with some challenges, particularly in terms of cyber security risks and legal issues. Collecting more employee data increases privacy concerns, and the more devices involved, the greater the risk of cyber attacks. Before implementing AI in HR, companies must ensure that employee data is protected and that they have strong security measures in place. This study looked at how AI can be applied in various areas of HR. These areas may not be part of traditional HR activities but are important for improving human aspects through AI. The findings show how AI impacts HR's agility, especially in the digitization of HR and organizational network analysis (ONA), which are key technological aspects of HR. For these to work effectively, organizations need strong designs that support AI integration. This research connects two important elements in today's Industry 4.0 era: technology and HR. However, AI use in HR is still not widespread, especially in India, as it is a new field. Many businesses are only using AI to a limited extent in HR, so it’s difficult to gather comprehensive data. Although AI has been studied a lot, understanding its true impact is challenging because not enough organizations are fully adopting AI in their HR practices. To improve the study, more interviews could be conducted and compared. The use of AI in recruitment is still evolving, and more research is needed to explore its full potential. While this study used empirical data from several organizations, future studies could focus on specific organizations or include companies planning to use AI in the future. There are also concerns that need to be addressed. For instance, AI algorithms could introduce bias if they are trained on biased data, which could affect HR decisions negatively. Another concern is job displacement, as automation through AI could lead to some HR professionals losing their jobs. Additionally, future studies could investigate how AI-based HR decisions affect company performance and employee turnover in more concrete terms. Since there is some mistrust of AI, understanding employees' perspectives and experiences with AI in HR would also provide valuable insights.

 

List of Abbreviations

1. Statistical Package for Social Sciences (SPSS)

2. Information Technology (IT)

3. Composite reliability (CR)

4. Average Shared Value (ASV)

5. Human Resource (HR)

6. Analysis of Moment Structures (AMOS)

7. Human Resource Management Practices (HRMP).

8. Confirmatory factor analysis (CFA)

9. Average variance extracted (AVE)

10. Maximum Shared Value (MSV)

11. Kaiser-Meyer-Olkin (KMO) 

12. Structural Equation Modelling (SEM)

13. Internet of Things (IoT)

14. Artificial Intelligence (AI)

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

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