Description:The present invention relates to the Strategic Evaluation of Electronic Human Resource Management system (E HRM) in IT and ITES Sector .
[02] BACKGROUND OF THE INVENTION
The present study gives the details of the data tabulation, analysis and its result which are based on the research objectives and research design. The study data was collected from 684 respondents and 30 CHRO’s from various IT and ITES organizations. The various statistical tools like descriptive statistics were used in compiling the results and in the testing of defined hypothesis.
Rapid advances in the Information and Communication Technologies (ICTs) have electronically transformed the Human Resource (HR) sector of many organizations. The management of resources involving the strategic management of employees and related issues falls under Human Resource Management (HRM). This ensures the efficient management of people employed at different levels in the organization in order to increase the employee engagement, organizational productivity, employee performance, get a strong foothold in the market and improve the business outcomes. In various industries, many administrative and management aspects of work have been automated with the help of IT. The IT and ITES sector hires millions of employees all across the globe with different skill set, efficiencies and knowledge. Therefore, the management of employees using traditional or manual HR practice will exert greater pressure on HR in the management of organizational performance.
The implementation of E-HRM has simplified and combined many of the HR practices for an efficient management of the people practices and empowerment of employees and employers. However, the implementation of E-HRM in any organization involves tackling of various issues/challenges associated with E-HRM practices. The usage of E-HRM practices, technical challenges, trusting technology and change management etc. are few of the key challenges while implementing HR practices in an organization. In this study, evidence is provided based on employee as well as employer perception on different organizational factors, comfort in using E-HRM practice, the pros and cons of adoption of E-HRM in IT/ITES sector. In the present study, the influence of E-HRM practice has been studies on the non-financial aspect of the organizational performance.
[03] SUMMARY OF THE PRESENT INVENTION
In the present study, both employees and employer’s perception have been collected to understand the realities and to provide an insight into measures of E-HRM practice and its implementation in the IT and ITES industry. Some of the measures of E-HRM practices such as payroll practice has been efficiently adopted by organizations irrespective of their strength. It was found that both employers and employees were aware of the benefits of E-HRM practice. Global orientation, remote access, reduced time to accomplish a task, enhanced skill, knowledge and competencies, increased productivity were some of the positive outcomes of the implementation of E-HRM practice. However, there were enough challenges; tackling of data theft, technical failure, regular updates of technology and increased security measures were necessitated.
[04] BRIEF DESCRIPTION OF THE DRAWINGS
The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Figure 1: Size of the organization
Figure 2: Level of Employment of the Respondents
Figure 3: Respondents Using E-HRM Practices in an Organization
Figure 4: Respondents’ Experience Levels
Figure 5: Respondents’ Educational Level
Figure 6: Organization profile
Figure 7: Size of the Organization
Figure 8: Level of Employment of the Respondents
Figure 9: Percentage of Respondents Using E-HRM Tools
[05] DETAILED DESCRIPTION OF THE INVENTION
EMPLOYEE DATA
Demographic Profile
The data pertaining to the demographic profile of the employees is analyzed in this section.
Gender of the Respondents
Data about gender of respondents is presented below.
Table 1. Gender of the Respondents
Frequency Percent
Male 393 57.5
Female 291 42.5
Total 684 100.0

As presented in Table 1, more than 50 percent of the employees were male (57.5%) and the remaining 42.5% were female, suggesting relatively higher proportion of male employees in IT and ITES sectors. Age of Respondents
The respondents’ age profile is presented below.
Table 2: Age of Respondents
Frequency Percent
20 – 25 years 76 11.1
26 – 30 years 181 26.5
31 – 35 years 132 19.3
36 – 40 years 170 24.9
>40 years 125 18.3
Total 684 100.0
In this study, the majority of the respondents 26.5% belonged to the age group of 26- 30 years, followed by 24.9% in the age group of 36-40 years. The respondents in the age group of 31-35 years and >40 years corresponded to 19.3% and 18.3%, respectively. Only 11.1% of the respondents were in the age group of 20-25 years. The age trend shows that more than 70% of the employees who were employed in IT and ITES sector were in their prime years i.e., between 26-40 years and represents a mixed- age population of employees (Table 2).
Respondents’ Experience Levels
Experience Levels are presented below.
Table 3: Respondents’ Experience Levels
Frequency Percent
<1 year 19 2.8
1 – 5 years 266 38.9
6 – 10 years 276 40.4
>10 years 123 18.0
Total 684 100.0
The data collected on the respondents’ experience is presented in Table 3. Respondents (276) with 6-10 years of experience corresponded to 40.4%, followed by 266 respondents (38.9%) with 1-5 years of experience in the IT and ITES sector. Approximately one-fifth of the resident (n=123, 18.0%) had more than 10 years of experience. The respondents with <1 year of experience corresponded to only 2.8%. The data indicate that in the present study, the majority of respondents (78%) had one to ten years of experience in the industry.
Respondents’ Educational Levels
Educational levels of the respondents are presented below.
Table 4: Respondents’ Educational Level
Frequency Percent
PhD 28 4.1
Undergraduate 214 31.3
Postgraduate 278 40.6
Technical Course/Diploma 164 24.0
Total 684 100.0
The educational qualifications of the respondents are represented in Table 4. It was found that 40.6% of respondents had a postgraduate degree followed by 31.3% with undergraduate degrees. Respondents who took a technical course or diploma corresponded to 24%. Only 4% of the respondents had a PhD degree. Thus, it can be inferred that IT/ITES sector is dominated by postgraduate employees.
Organization’s Profile
The organization profile of the respondents is presented.
Table 5: Organization profile
Frequency Percent
IT 577 84.4
ITES 107 15.6
Total 684 100.0
The majority (84.4%) of the respondents were employed in IT Industry. The remaining 15.6% were employed in ITES (Table 5).
Size of the Organization
Organization size has been provided in Fig. 1
The size of the organization is tabulated in Fig 1. It was found that the majority of the IT/ITES organization had more than 500 employees. The organization with employee size of 501-1000 employees accounted for 44.6% and organization having >1000 employees corresponded to 31.7%. Organizations with smaller number of employees such as <100 employees and 100-500 employees corresponded to 15.6% and 8.0%, respectively.
Level of Employment of the Respondents
The various levels of employment have been presented in Fig 2.
The level of employment is represented in Fig 2. More than 70% of the respondents were employed in the middle-level (77.0%), followed by 20.8% in the senior level and 2.2% in the entry level. It can be inferred that in IT/ITES sector, there is more requirement of employees to do a middle level job.
Respondents Using E-HRM Practices in an Organization
Presentation of E-HRM Usage.
Respondents Using E-HRM Practices in an Organization is shown in Fig. 3.
The percentage of respondents using E-HRM practices such as payroll, hiring, learning and development, etc. is exemplified in Fig 3. The payroll practice (100.0%) was completely achieved by E-HRM. The recruitments or hiring (81.9%) and employee self-service (80.6%) was also tracked by E-HRM practice. Other practices like performance management (64.0%), learning and development (60.1%), rewards and recognition (59.5%) were also processed through E-HRM, suggesting that organizations have successfully implemented some of the E-HRM practices.
EMPLOYER DATA
Demographic Profile
The data pertaining to the employer’s demographic profile was also analyzed and is presented below.
Gender of Respondents
Data about gender of respondents is presented below.
Table 6: Gender of Respondents
Frequency Percent
Male 23 76.7
Female 7 23.3
Total 30 100.0

The gender distribution of employers is presented in Table 6, the majority (n=23; 76.7%) were male. Less than one-fourth (n=7; 23.3%) were females, suggesting more number of male employers in IT and ITES sectors.
Respondents’ Age Group
The age group of respondents has been presented.
Table 7: Respondents’ Age Group
Frequency Percent
26–30 years 3 10.0
31-35 years 6 20.0
36-40 years 4 13.3
>40 years 17 56.7
Total 30 100.0
In this study, the majority of respondents (n=17; 56.7%) belonged to the age group of >40 years, followed by 20% in the age group of 31-35 years (n=6) and 13.3% in the age group of 36-40 years (n=4). One tenth of the employers belonged to the age group of 20-30 years (n=3; 10.0%). The data suggests that majority of the employers are from a higher age group (Table 7).
Respondents’ Experience Levels
Levels of Experience of Respondents are presented in Fig. 4.
Figure 4 represent the employer’s experience. Nearly three-fourth (n=22; 73.3%) of the employer’s had more than 10 years of experience. The remaining had either 1-5 years (n=4; 13.3%) or 6-10 years (n=4; 13.35) of experience. It can be inferred that several senior level employers were part of the study.
Respondents’ Educational Level
Educational Levels of Respondents presented in Fig. 5.
The educational qualification of the employers is exemplified in Figure 5. The majority of employer’s (n=26; 86.7%) have a postgraduate degree. The employer’s with undergraduate degree (n=2) corresponded to 6.7% and similarly, very few had a higher qualification, the PhD degree (n=2; 6.7%). It can be inferred that most of the employers are a postgraduate which could be a direct requirement of the job.
Organization’s Profile
Organization profile of the respondents is being presented in Fig 6.
The majority (60.0%) of the respondents who participated in the study were employed in IT industry. The remaining 40.0% were employed in ITES (Figure 6).
Size of the Organization
Representation of the Size of the organization is shown in Fig. 7.
The size of the organization is represented in Figure 7. The majority of employer’s either belonged to large-sized organization (n=13; 43.3%) with >1000 employees or small-sized organization (n=12; 40.0%) with 100-500 employees. Employer’s from organizations with smaller number of employees such as <100 employees and medium-sized organization with 100-500 employees corresponded to 6.7% and 10.0%, respectively.
Level of Employment of the Respondents
Employment Levels of respondents have been presented in Fig. 8.
The majority of respondents (n=24; 80.0%) in this study were from the senior level’s in the industry, i.e., at VP and above. This was followed by respondents at mid-manager level (n=4; 13.35) and manager level (n=2; 6.7%) (Figure 8).
Percentage of Respondents Using E-HRM Tools
E-HRM tools used by the respondents have been presented in Fig. 9.
There is a 100% usage of e-payroll practice followed by 96.7% use of e-hiring by the respondents. Further, 86.7% of the respondents used e-performance management as well as e-employee self-service. Other practices like e-learning and development (70.0%) and e-rewards and recognition (36.7%) were the usage of E-HRM tools (Figure 9).
Impact of E-HRM Practices on the Employee’s Perception about Work
Linear regression analysis was applied to study the impact of E-HRM practices on the employee’s perception of work. The results are summarized in Table 8, Table 9 and Table 10. The variation of 69.6% in the employees’ perception of work can be explained by E-HRM practices. A significant and positive correlation between E-HRM practices and employee’s perception of work was established (F(6, 677)=258.258, p<0.01). Among all the E-HRM practices, e-hiring, e-rewards and recognition, e- payroll process and e-performance management significantly predicts the employee’s perception about work (Table 10). The positive Beta coefficient values explains the positive, whereas the negative beta coefficient value explains inverse relationship between the variables. The B value of e-hiring was 0.599, e-rewards and recognition were 0.214, and e-performance and management was 0.099, which was significant at 0.01 level, suggesting that increased e-hiring, e-rewards and recognition, and e- performance management will result in improved employee’s perception about work. However, the regression coefficient of e-payroll was -0.208, suggesting that there is a negative and significant impact of the e-payroll process on employee’s perception about work. Untimely or incorrect processing of payroll can upset the employee’s which will affect their productivity and retention in the company. However, other parameters like appreciating their effort, provision for training modules to improve their skills and online services will increase their positive perception about the work and organization. Therefore, null hypothesis is rejected and alternate hypothesis is retained.
Hypothesis 1: E-HRM practices have an impact on employees’ perception of work.
Table 8: Descriptive Statistics for Measures of E-HRM and Employee’s Perception about Work
Mean Standard Deviation
Employee perception 4.187 0.730
e-Hiring 4.167 0.679
e-Rewards and Recognition 4.097 0.700
e-Payroll Process 4.159 0.691
e-Performance Management 4.114 0.710
e-Employee Self-Service 4.141 0.701
e-Learning and Development 4.085 0.743
Table 9: Model Summary for Impact of E-HRM Practices on Employee’s Perception about Work

R
R
Square
Adjusted R Square Std.
Error of the Estimate Change Statistics
R
Square Change F
Change
df1
df2 Sig. F Change
.834a 0.696 0.693 0.404 0.696 258.258 6 677 0.000

Table 10: Coefficients for Impact of E-HRM Practices on Employee’s
Perception about Work
Unstandardized Coefficients Standardized Coefficients

t

Sig.
B Std. Error Beta
(Constant) 0.416 0.098 4.269 0.000
e-Hiring 0.645 0.098 0.599 6.547 0.000
e-Rewards and Recognition 0.222 0.059 0.213 3.760 0.000
e-Payroll Process -0.220 0.090 -0.208 -2.442 0.015
e-Performance Management 0.102 0.049 0.099 2.098 0.036
e-Employee Self-Service 0.077 0.067 0.074 1.141 0.254
e-Learning nd Development 0.087 0.046 0.088 1.865 0.063
Impact of E-HRM Practices on the Workplace Interactions
The regression analysis was applied to analyze the impact of E-HRM practices on the workplace interactions (Table 12). As per the model summary, R2 value of 0.596 indicates that 59.6% of variance in workplace interactions will be explained by E-HRM measures. The measures of E-HRM significantly predicted the interactions at workplace, F(6, 677)=166.714, p<0.01. Based on Pearson correlation coefficient value (r=0.772), a strong and positive correlation between the studied variables at a significance level of <0.01 was predicted. The multiple regression showed that e-hiring, e-payroll process and e-performance management have a significant impact on workplace interactions (Table 13). The positive B value of 0.278 on e-hiring, 0.147 on e-performance management and 0.200 on e-payroll process significantly increases the workplace interactions. Other factors did not impact the workplace interactions. Therefore, null hypothesis is rejected and alternate hypothesis is accepted.
Hypothesis 2: E-HRM practices have an impact on the workplace interactions
Table 11: Descriptive Statistics for Measures of E-HRM and Workplace
Interactions

Mean Std.
Deviation
Workplace interaction 4.1842 0.734
e-Hiring 4.1670 0.678
e-Rewards and Recognition (e-RR) 4.0972 0.700
e-Payroll Process 4.1591 0.690
e-Performance Management 4.1140 0.710
e-Employee Self-Service 4.1412 0.701
e-Learning and Development 4.0848 0.743
Table 12: Model Summary for Impact of E-HRM Practices on Workplace
Interactions

R

R
Square

Adjusted R Square
Std. Error of the Estimate Change Statistics
R
Square Change
F
Change

df1

df2
Sig. F Change
.772a 0.596 0.593 0.469 0.596 166.714 6 677 0.000

Table 13: Coefficients for Impact of E-HRM Practices on Workplace
Interactions
Unstandardized Coefficients Standardized Coefficients

t

Sig.
B Std. Error Beta
(Constant) 0.659 0.113 5.824 0.000
e-Hiring 0.301 0.114 0.278 2.636 0.009
e-Rewards and Recognition 0.035 0.068 0.034 0.518 0.605
e-Payroll Process 0.213 0.105 0.200 2.036 0.042
e-Performance Management 0.152 0.056 0.147 2.696 0.007
e-Employee Self-Service 0.070 0.078 0.067 0.902 0.367
e-Learning and Development 0.080 0.054 0.080 1.477 0.140

Impact of E-HRM Practices on the HR Process Effectiveness
According to the model summary of the impact of E-HRM practice on the HR process effectiveness, R2 explained the variance of 0.741. It can be presumed that 74.1% of the variance in the effectiveness of the HR process can be explained by E-HRM practices (Table 14). Using multiple regression analysis impact of E-HRM practice like e- hiring, e-rewards and recognition, e-payroll process, e-Performance Management, e- employee self-service and e-learning and development on effectiveness of HR process was measured (Table 15). The measures of E-HRM statistically and significantly predicted the effectiveness of HR process (F(6, 677)=322.124, p<0.01). Based on the Pearson correlation, a positive and strong correlation between the studied variables (r=0.861) and a highly significant relationship was observed. Among the different measures of E-HRM, e-hiring, e-payroll process and e-performance management significantly predicted the HR process effectiveness. The positive B value of 0.458 on e-hiring, 0.250 on e-payroll process, 0.168 on e-performance management increases the effectiveness of HR process. Further, e-rewards and recognition, e-employee self- service and e-learning and development had no impact on HR process effectiveness. Therefore, we reject null hypothesis and accept alternate hypothesis:
Hypothesis 3: E-HRM practices have an impact on the HR process effectiveness
Table 14: Descriptive Statistics for Measures of E-HRM and the HR Process
Effectiveness
Mean Std. Deviation
Process Effectiveness 4.178 0.680
e-Hiring 4.167 0.679
e-Rewards and Recognition (e-RR) 4.097 0.700
e-Payroll Process 4.159 0.691
e-Performance Management 4.114 0.710
e-Employee Self-Service 4.141 0.701
e-Learning and Development 4.085 0.743

Table 15: Model Summary for Impact of E-HRM Practices on the HR Process
Effectiveness

R

R
Square

Adjusted R Square
Std. Error of the Estimate Change Statistics
R
Square Change
F
Change

df1

df2
Sig. F Change
.861a 0.741 0.738 0.348 0.741 322.124 6 677 0.000

Table 16: Coefficients for Impact of E-HRM Practices on the HR Process
Effectiveness
Unstandardized Coefficients Standardized Coefficients

t

Sig.
B Std. Error Beta
(Constant) 0.551 0.084 6.557 0.000
e-Hiring 0.459 0.085 0.458 5.410 0.000
e-Rewards and Recognition -0.052 0.051 -0.053 -1.018 0.309
e-Payroll Process 0.246 0.078 0.250 3.174 0.002
Unstandardized Coefficients Standardized Coefficients

t

Sig.
B Std. Error Beta
e-Performance Management 0.161 0.042 0.168 3.852 0.000
e-Employee Self-Service 0.052 0.058 0.054 0.903 0.367
e-Learning and Development 0.006 0.040 0.007 0.160 0.873

Key Performance Indicators that Assess the Effectiveness of E-HRM Practices
The rank analysis for organizational performance revealed significant importance of E- HRM effectiveness in profitability, talent retention, continuous innovation and competitive position (Chi-square=33.914, p<0.05) (Table 17).
Table 17: Friedman Rank Analysis for Organizational Performance

Organizational performance Mean Rank Rank
Profitability 11.013 1
Talent Retention 10.380 2
Continuous Innovation 10.307 3
Competitive Position 10.300 4
Chi- Square= 33.914, p<0.05
Table 18: Descriptive for Influence of Organizational Performance on Effectiveness of E-HRM Practice
Mean Std. Deviation
Effectiveness 4.154 0.180
Talent Retention 4.053 0.297
Competitive Position 4.053 0.336
Continuous Innovation 4.047 0.388
Profitability 4.133 0.411
Cycle Time of recruitment has reduced by 2.567 0.504
Percentage Reduction in HR Operational expenses 2.567 0.504
Rate of increase in process efficiency 2.400 0.498
As per the model summary, R2 explained the variance of 0.544 suggesting that 54.4% of effectiveness of E-HRM practice could be explained by organizational performance, F(7, 22)=3.749, p<0.01 (Table 19). A strong and positive correlation between the two variables is explained by Pearson correlation coefficient value (r=0.738). Based on coefficient value, the positive B value of 0.302 indicates that every one-unit increase in profitability increases the organizational performance by 0.302 times (Table 20). Other factors had no impact on organizational performance. In light of the findings, null hypothesis was rejected and alternate hypothesis was accepted.
Hypothesis 4: Key performance indicators like talent retention, competitive position, continuous innovation, profitability, cycle time of recruitment, operational expenses and process efficiency have an impact on effectiveness of E-HRM practice
Table 19: Model Summary for Influence of Organizational Performance on
Effectiveness of E-HRM Practice

R R
Squar e Adjusted R
Square Std. Error of the Estimate Change Statistics
R Square Change F
Change
df1
df2 Sig. F Change
0.738 0.544 0.399 0.140 0.544 3.749 7 22 0.008

Table 20: Coefficients for Organizational Performance on Effectiveness of E-
HRM Practice

Unstandardized Coefficients Standardize d Coefficients

t

Sig.

B Std. Error
Beta
(Constant) 2.537 0.503 5.047 0.000
Talent Retention -0.020 0.111 -0.034 -0.183 0.856
Competitive Position 0.127 0.094 0.237 1.345 0.192
Continuous Innovation -0.020 0.090 -0.042 -0.218 0.830
Profitability 0.302 0.078 0.689 3.872 0.001
Cycle Time of recruitment 0.062 0.057 0.174 1.099 0.284
Percentage Reduction in HR Operational expenses 0.011 0.065 0.032 0.178 0.860
Rate of increase in process efficiency -0.072 0.059 -0.199 -1.219 0.236

Impact of Personnel Effectiveness on Organizational Performance
According to the model summary, the impact of employee’s personnel effectiveness on organizational performance was explained by R2 value=0.839 (Table 21) which indicates that 83.9% of the variance in the organizational performance will be explained by the personnel effectiveness. The personnel effectiveness significantly predicted the organizational performance, (F(5, 24)=25.009, p<0.05) (Table 21). Further, the Pearson coefficient value of (r=0.916) predicts a strong and positive relationship between the two variables. Among all the measures of personnel effectiveness, workplace perception, job satisfaction, productivity, and interaction and effectiveness successfully predicted the organizational performance. The highest positive beta coefficient (B) value of 0.462 and 0.426 suggests that every unit increase in productivity and job satisfaction will increase the organization performance by 0.462 and 0.426 times, respectively. Similarly, the other two factors, workplace perception, and interaction and effectiveness had a B value of 0.258 and 0.208, respectively, which indicates that every 1-unit increase in these variables will increase organizational performance by 0.258 and 0.208 times, respectively (Table 22). Therefore, null hypothesis was rejected and alternate hypothesis was accepted.
Hypothesis 5: Personnel effectiveness (employee) derived from E-HRM practices have an impact on organizational performance
Table 20: Descriptive for Personnel Effectiveness on Organizational
Performance

Mean Std.
Deviation
N
Organizational Performance 4.072 0.265 30
Workplace perception 4.009 0.439 30
Motivation 3.953 0.443 30
Job Satisfaction 3.982 0.380 30
Productivity 4.056 0.359 30
Interaction and Effectiveness 3.838 0.505 30
Table 21: Model Summary for Personnel Effectiveness on Organizational
Performance

R

R
Square

Adjusted R
Square
Std. Error of the Estimate Change Statistics
R
Square Change
F
Change

df1

df2
Sig. F Change
0.916a 0.839 0.805 0.11695 0.839 25.009 5 24 0.000
Table 22: Coefficients for Personnel Effectiveness on Organizational
Performance
Unstandardized Coefficients Standardized Coefficients
t
Sig.
B Std. Error Beta
(Constant) 0.273 0.388 0.703 0.489
Workplace perception
0.154
0.060
0.254
2.555
0.017
Motivation 0.050 0.056 0.083 0.888 0.383
Job Satisfaction 0.297 0.067 0.426 4.430 0.000
Productivity 0.341 0.073 0.462 4.659 0.000
Interaction and Effectiveness
0.109
0.046
0.208
2.383
0.025
Impact of Process Effectiveness (Employer) Derived from E-HRM Practices on Organizational Performance (MANOVA)
To test the correlation between the dependent variables and the impact of process effectiveness on the organizational performance, the statistical analysis of Multivariate Analysis of Variance (MANOVA) was applied. From Wilk’s Lamba test, a significant multivariate F value of 4.265 is observed (p<0.05) (Table 23), indicating that process effectiveness including the incorporation of feedback, proper utilization of funds, increased revenues, cost-effectiveness of the process can cause a change in or influence the organizational performance in terms of talent retention, competitive position, continuous innovation and profitability. The Wilk’s lambda is a measure of variability in a dependent variable which cannot be explained by the independent variable. In the present study, the Wilk’s lambda value of 0.594, signifies that 59.4% of the variation in organizational performance cannot be explained by process effectiveness. Further, the result of a test between the variables is presented in Table 24. A significant difference in talent retention (F=11.320, p=0.002), continuous innovation (F=5.151, p=0.031) and profitability (F=9.404, p=0.005) was found (Table 24). The process effectiveness failed to have any significant impact on the competitive position (F=4.138, p>0.05). Therefore, it can be inferred that 40.6% of the variance in organizational performance concerning talent retention, continuous innovation and profitability is influenced by process effectiveness. Thus, null hypothesis was rejected and alternate hypothesis was accepted.
Hypothesis 6: Process effectiveness (employer) derived from E-HRM practices have an impact on organizational performance
Table 23: Impact of Process Effectiveness on Organizational Performance

Mean
Std.
Deviation Wilks' Lambda

F

df

Sig. Partial Eta Squared
Talent Retention 4.053 0.297 0.594 4.265 4, 25 0.009 0.406
Competitive Position 4.053 0.336
Continuous Innovation 4.047 0.388

Mean
Std.
Deviation Wilks' Lambda

F

df

Sig. Partial Eta Squared
Profitability 4.133 0.411

Table 24: Tests of Between-Subjects Effects

Source Type III Sum of Squares

df
Mean Square

F

Sig. Partial Eta Squared
Process Effectiveness Talent Retention 0.735 1 0.735 11.320 0.002 0.288
Competitive Position 0.422 1 0.422 4.138 0.052 0.129
Continuous Innovation 0.680 1 0.680 5.151 0.031 0.155
Profitability 1.234 1 1.234 9.404 0.005 0.251
R Squared = .288 (Adjusted R Squared = .262)
R Squared = .129 (Adjusted R Squared = .098)
R Squared = .155 (Adjusted R Squared = .125)
R Squared = .251 (Adjusted R Squared = .225)
, Claims:1. The current study has been conducted to examine the effectiveness of E-HRM practices from a multi-dimensional perspective on people and HR process and hence the research study was both exploratory and descriptive in nature.
2. Individuals employed in the senior, middle and entry level positions along with the CHRO’s (chief human resource officers) in the IT and ITES based companies in Bengaluru city constituted the sample frame for the study.
3. Questionnaire was adopted as the research instrument for collecting primary data for the study.
4. Global orientation, remote access, reduced time to accomplish a task, enhanced skill, knowledge and competencies, increased productivity were some of the positive outcomes of the implementation of E-HRM practice.