Evaluating Influence of Artificial Intelligence On Human Resource Management Using Pls Sem Partial Least Squares Structural Equation Modeling

 

 

INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020

ISSN 2277-8616

Evaluating Influence of Artificial Intelligence on Human Resource Management Using PLSPLS-SEM (Partial Least SquaresSquares-Structural Equation Modeling)  Modeling)  Smita Chakraborty, Arunangshu Giri, Abanti Aich, Swatee Biswas Abstract: In this competitive world, every kind of business requires Human Resource Management (HRM). It is an asset

for improving the organizational performance. An organization becomes successful when it can meet the needs and demands of a consumer and to do so, organizations will have to adopt innovative HR practices. Soon, HRM will be moving away from its traditional administrative functions like recruitment, selection, appraisal to more advanced processes like Automation, Augmented Intelligence, Robotics and Artificial Intelligence (AI). These processes will completely reshape and redefine the work of HRM in various organizations. At present AI is the buzz word as it is completely transforming HRM, providing millions of jobs, producing easy method of hiring, providing innovative applications and advanced solutions to various problems. This paper helps to study the influence of artificial intelligence (AI) on Human Resource Management (HRM) using PLS-SEM in various sectors of West Bengal. Keywords: Artificial Intelligence, Human Resource Management, Organizational Performance, PLS-SEM I. INTRODUCTION The term ‘Artificial Intelligence’ (AI) was first  coined by John McCarthy in the year 1956 in his first academic conference. It is also known as Machine Intelligence. It is an inter-disciplinary science that mimics human intelligence behavior. It can make computers perform

Improving the efficiency of HRM through the application of AI technology has become a recent trend for future development. Some of the general examples of AI application is Uber, Ola, Google Voice, Facebook, Alexa, Siri (I-Phone), Fingerprint in mobiles, Face Recognition and Biometric. Another example of AI

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those tasks which humans are answer expert atdoubts (Rich, quickly, 1983) . It helps toinretrieve database, extracts information and provide the best output. AI applications are expanding every day and many AI tools (Artificial Neutral Network, Intelligent Decision Systems, Fuzzy Sets) are used in various fields. AI helps machines to act like a human brain and can give outputs efficiently. It also uses certain algorithm and based on that it performs its actions. With the development of AI technology, a new generation of labor which is equivalent to human intelligence has become a key factor for bringing transformation and change in the system 3 (Ertel, 2018) . Application of AI in organizations eases the work of HRM. The HR team uses AI for smooth recruitment, hiring, making decisions, predicting  performances and task automation. It helps to take decisions at a faster rate, helps to speed up tedious and daily repetitive work. It provides powerful analytical support and database. Managers need not do the mechanical work anymore and can utilize their time in a 12 more valuable task (Partridge & Hussain, 1992) . HR team can smoothly coordinate with other employees and  build strategies for delivering innovative work (Holland, 5 1992) . The use of AI technology can also help HRM to reduce unnecessary cost and bring greater economic  benefits. Smita Chakraborty, Assistant Professor, Professor, Hospital Management Management Department, Haldia Institute of Health Sciences, West Bengal.  Dr. Arunangshu Giri, Associate Professor, School of Management & Social Science, Haldia Institute of Technology, MAKAUT, West Bengal.  Abanti Aich, Assistant Professor, Department of Science and Management, Haldia Institute of Health Sciences, Haldia, West Bengal, India.  Swatee Biswas, Officer in Charge, Administration Department, Haldia Institute of Management, MAKAUT, West Bengal  

application is Naukri.com. It issolutions. a newly emerged startup which provides AI based HR It aims to help every recruiting team to find the right applicant globally. It helps to asses the candidates by proper screening method and decides which candidate will fit for which  job based on requirement. Humans can do this same job  but the decision taken can be partial and time taking. Whereas AI application will help to eliminate wastage of energy, time, human biases. It will help to provide complete transparency, simplify the tasks and predict which employee can perform better. It has the power to transform HRM in organizations. This paper studies to evaluate the influence of AI on HRM in various organizations. II. REVIEW OF LITERATURE HRM team deals with people in the organization. It deals with recruitment, selection, development, training, compensation management, payroll and performance appraisal. It helps to encourage employees to do their  best in the organization and helps to make their work  productive. It also encourages employees to achieve company’s goals and objectives. Artificial Intelligence  plays an important role in HRM as it reduces the work of HR managers. It uses certain algorithms, based on which 10 the HR processes are done (Netessine & Valery, 2012) . When AI technology is linked with HRM, the process will be enhanced and will improve the performance of 11 the employees (Pickup, 2018) . It helps to reduce paper work and manual work. It also makes the employees tasks easier and productive. Thus, this literature review mainly focuses on certain factors which study the impact of AI on HRM in the organizations. Recruitment is a HR  process of shortlisting and selecting suitable candidates for jobs within an organization. If AI technology is applied while recruitment, it will help HRM to streamline 5876

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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020

ISSN 2277-8616

the monotonous and high-volume task smoothly and

automatically.

It will easily screen resumes from a large pool of applicants and find the right candidate at the right time and reject the ones which are unsuitable (Malvarez, et al., 8 2014) . Intelligent Screening Software powered by AI will select those candidates who have maximum experience, skills and performance. It will also analyze the turnover rates and accordingly select the best candidate for the job. When audio visual interviews are

should find ways to retain them in the system (Lucas, et 7 al., 2013) . However, in a large organization with a diverse workforce it is difficult to initiate retentionstrategies on time. This is when AI application is used to overcome the situation. An AI powered analytics engine can automatically scan massive data of employee communication, employee performance records, time, attendance, participation rates and helps to understand

taken using AI software, the candidate’s choice of word, speech, body language, personality traits are assessed 4 (Granados & Gupta, 2013) . This helps the HR team to easily decide the job role of that candidate. AI also eases the work of HRM by constantly updating employees about information, suggestions and feedbacks. Thus, AI helps HRM to smoothly and easily carry out the recruitment process, thereby improving the employee  performance which in turn improves the organizational  performance. HR design their training programs without any predefined parameters and are unable to train employees perfectly. HR managers complain that whatever trainees learn during the training program at least half is forgotten. But now, the application of AI in the training and development process has become very effective. AI uses certain algorithms which monitors and

which employee is looking for promotion (Kauffman, et 6 al., 2010) . It can help HRM to analyze employee interactions and engagements and give HR managers deeper insights into organizational dynamics. AI helps HRM to understand the risk and take retention decisions at the correct time. They do not have to wait for annual reviews. Thus, with the help of AI application, HRM can retain talented employees and improve their work  performance which in turn will improve the performance of organization also.

studies skills(Clemons, and attitude of 2.employees workingthe at behavior, various levels 2008) Different  people have different learning style so by using AI application, training programs can be made easier and convenient for employees. After the training, the trainees  provide feedback so that improvisations can be done. The AI helps both the employee and the HRM team to know about the gaps of the training program. It helps to understand the skills, performance and knowledge required by employees to achieve the organizational goals. These helps the HRM team to improve their  performance which in turn would increase the  performance of the organization also (Malthouse, et al., 9 2010) . Most organizations prefer to do performance management of employees backed by numbers and data. In this case, HR managers rely only on factual information for taking any decision. They need to collaborate with multiple teams and departments for collecting information which may lead to missing out of information of employee’s valuable contribution contributio n towards the organization. It further lead to inaccuracy in the 1  process (Bharadwaj, et al., 2013) . Whereas if AI technology is applied in this process, it can help HRM to collect information smoothly from multiple sources, enable HR manager to extract correct information at the right time, eliminate psychological biases related to  performance reviews. It also allows performance assessments to take place frequently rather than annually. Thus, it makes the work of HRM easier and helps to increase the employee’s performance which will automatically improve the performance of the organization. High attrition in an organization can increase unnecessary cost and lower the work  productivity. It also leads to loss of skilled employees having essential knowledge of work. HR managers are responsible for identifying dis-engaged employees and

AI’ positively influences the ‘Efficient Human Resource Management’. H3: ‘Planned Training and Development Process through AI’ positively influences the ‘Efficient Human Resource Management’.   Management’. H4:  ‘Tactical Performance Appraisal through AI’  positively influences the ‘Efficient Human Resource Management’. H5:  ‘Efficient Human Resource Management’ positively influences the ‘Effective Organizational Development’.  

III. HYPOTHESES DEVELOPMENTAND RESEARCH MODEL  H1:  ‘Strategic HR Planning through AI’ positively influences the ‘Efficient Human Resource Management’. H2:  ‘Smooth Recruitment &  &  Selection Process through

Figure 1: Hypothesized Research Model Establishment

IV. RESEARCH METHODOLOGY A survey study was conducted with HR personnel covering 4 industries in West Bengal including Healthcare, Food, Manufacturing and IT using a structured questionnaire. All variables under each factor were taken from the previous literature and were suggested by the experts from HR field. These variables

were judged using a 5-point Likert scale ranging from 5 (Strongly Agree) to1 (Strongly Disagree). 146 responses were collected through convenience sampling 5877

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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020

th

technique. Data collection period was 20  October, 2019 th to 20  December, 2019 with response rate of 73%. PLSSEM was used in this study to test the relationships among factors in the proposed hypothesized model (Figure 1). For prediction-oriented complex model with many factors and variables PLS-SEM is useful. Also large sample size is not required for executing PLS-SEM. In the first step we checked reliability and validity of the factors and variables through measurement model and then we tested hypotheses for establishing the research model through path analysis and structural model.

V. ANALYSIS AND RESULTS At first reliability and validity of the factors were checked before hypothesis testing. In this study, we used PLC-SEM for establishing the hypothesized model using SPSS 23.0 and AMOS 23.0 software. Also we checked model fitness through structural model. Here overall Cronbach alpha for all variables was 0.82 (>0.70), which indicated the tolerable range of reliability. On the other hand  construct validity was executed through Exploratory Factor Analysis (EFA) through Rotated Component Matrix (RCM). By the help of Principal

Component Analysis (PCA), 6 different factors were created with a cluster of individual ‘factor   loading’ more than 0.5 (Table 1).

ISSN 2277-8616

Table 1: Factor Analysis - Rotated Component Matrix

F acto actorr s w with ith I de dentif ntifii ed Vari able abless 1

2

3

4

5

6

q1

.950

.041

.017

.000

.033 . 033

-.089

q2

.939

.000

.061

.052

-.085

-.081

q10 q9

.036

.936

-.028

-.059

-.005

.020

.004

.914

-.127

-.023

-.089

-.131

q4

-.015

-.012

.921

-.012

.034

.048

q3

.098

-.146

.869

.162

-.037

-.079

q6

-.064

-.075

-.007

.915

.049

.004

q5

.123

-.006

.153

.878

.126

-.041

q8

.012

.043

.092

.131

.891

.040

q7

-.063

-.140

-.096

.039

.857

-.147

q12

-.002

-.066

-.030

-.065

.017

.874

q11

-.166

-.031

.008

.033

-.117

.828

Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization. 1. Strategic HR Planning; 2. Efficient HRM; 3. Smooth Recruitment & Selection; 4. Planned Training and Development; 5. Tactical Performance Appraisal; 6. Effective Organizational Development

Factors Related to Artificial Intelligence Affecting Human Hu man Resource Management Using Partial Least Squares Structural Equation Modeling (PLS-SEM)

Table 2: Measurement Model Outputs Variables/ Items

Standardized Regression Estimate

SHRP: Strategic Human Resource Planning SRS: Smooth Recruitment & Selection

q1

.773

q2

.776

q3

.788

q4

.764

PTD: Planned Training and Development TPA: Tactical Performance Appraisal

q5

.764

q6

.801

q7

.826

q8

.781

EHRM: Efficient HRM

q9

.873

q10

.877

q11

.790

q12

.787

Constructs/ Factors

EOD: Effective Organizational Development

Construct Reliability (CR)

Average variance extracted (AVE)

Maximum Shared Variance (MSV)

Average Shared Variance (ASV)

0.750

0.600

0.021

0.011

0.752

0.602

0.051

0.017

0.760

0.613

0.051

0.022

0.785

0.646

0.045

0.014

0.867

0.766

0.027

0.009

0.767

0.622

0.027

0.014

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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020

Table 4: Fit indices of Structural Model  

Table 3: Squared Correlations between Factors for Measurement Model 1

2

1. EHRM

.875

2. SHRP

.031

.775

3. SRS

-.099

.145

3

4

5

6

.776

4. PTD 5. TPA

-.047 -.058

.099 -.061

.226 .001

.783 .212

.804

6. EOD

-.163

-.132

-.068

.004

-.142

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.789

1. EHRM: Efficient HRM; 2. SHRP: Strategic HR Planning; 3. SRS: Smooth Recruitment & Selection; 4. PTD: Planned Training and Development; 5. TPA: Tactical Performance Appraisal; 6. EOD: Effective Organizational Developme Development nt *Diagonal elements are Average variance extracted (AVE).

Fit Index with Acceptable Range  χ 2/df 2/df ( 0.90)

0.055 0.989

 NFI (>0.90)

0.984

1.585

0.932

CFI (>0.90) 0.997 In this study, all fit indices (Table 4) of Structural model (Figure 2) proved that the model was fit. Figure 2: Path Diagram of Structural Model  

Standardized Regression Estimates with values of more than 0.7 indicate the significant and effective relationships between the factor and variables under it. Internal consistency among variables was described the Construct Reliabilities which should be more than 0.7. Also the conditions depicted below satisfied convergent and discriminant validity in Measurement model. AVE> 0.5 MSV < AVE CR > AVE ASV < AVE In this study all conditions were under the satisfactory range (Table 2). Also, AVE values which were larger than corresponding squared inter-construct correlation (SIC) supported discriminant validity (Table 3). After that we checked fitness indexes of structural model and tested hypotheses. Table 5: Path analysis of Structural Model Measurement Path  Hypothesis  Efficient Human Strategic Human Resource ←  Resource H1 Management Planning

Efficient Human Resource

Tactical ←  Performance

H4

Management Appraisal Efficient Human Smooth Resource ←  Recruitment H2 Management Selection Process Efficient Human Planned Training Resource ←  & Development H3 Management Process Effective Efficient Human Organizational ←  Resource H5 Development Management Significant Regression co-efficient (* for P