ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTELLIGENCE AND TALENT MANAGEMENT PROCESSES

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International Journal of Management (IJM)
Volume 11, Issue 11, November 2020, pp. 3873-3887, Article ID: IJM_11_11_384
Available online at https://iaeme.com/Home/issue/IJM?Volume=11&Issue=11
ISSN Print: 0976-6502 and ISSN Online: 0976-6510
DOI: https://doi.org/10.17605/OSF.IO/7GE2S
© IAEME Publication Scopus Indexed
ATTRITION IN THE IT INDUSTRY DURING
COVID-19 PANDEMIC: LINKING EMOTIONAL
INTELLIGENCE AND TALENT MANAGEMENT
PROCESSES
S. Vidhya
Research Scholar, PG & Research Department of Management, Marudupandiyar College,
(Affiliated to Bharathidasan University, Tiruchirappalli), Thanjavur, Tamil Nadu, India
Dr. P. Kothai
PG & Research Department of Management, Marudupandiyar College,
(Affiliated to Bharathidasan University, Tiruchirappalli), Thanjavur, Tamil Nadu, India
ABSTRACT
Globally, Millions of dollars were spent by the organizations for employing skilled
Information Technology (IT) professionals. It is costly to replace unskilled employees
with IT professionals possessing technical skills and competencies that aid in
interconnecting the business processes. The organization’s employment tactics were
forced to alter by globalization along with technological innovations as they
consistently diminish to remain lean, outsource to concentrate on core competencies
along with restructuring/reallocate personnel to gather efficiency. As other jobs,
organizations or professions have become reasonably more appropriate in a shifting
employment landscape, the above alterations trigger both involuntary as well as
voluntary turnover. The employee view on jobs is also afflicted by the COVID-19
pandemic along with the employee-driven labour market. So, having effective strategies
is necessary to tackle the withdrawal rate of employees. By associating Emotional
Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in
attrition rate was analyzed in this study. Only 303 respondents were collected out of
350 participants to whom questionnaires were distributed. From the employees of IT
organizations located in Bangalore (India), the data were congregated. A simple
random sampling methodology was employed to congregate data as of the respondents.
Generating the hypothesis along with testing is eventuated. The effect of EI and TM
along with regression analysis between TM and EI was analyzed. The outcomes
indicated that employee and Organizational Performance (OP) were elevated by
effective EI along with TM.
Key words: Emotional intelligence, Human resource management, competencies,
Talent management, attrition rate, Performance.

Attrition in the IT Industry During Covid-19 Pandemic: Linking Emotional Intelligence and
Talent Management Processes
Cite this Article: S. Vidhya and P. Kothai, Attrition in the IT Industry During Covid-
19 Pandemic: Linking Emotional Intelligence and Talent Management Processes,
International Journal of Management (IJM), 11(11), 2020, pp. 3873-3887.
https://iaeme.com/Home/issue/IJM?Volume=11&Issue=11
1. INTRODUCTION
Employee retention and resignation are more crucial as like two sides of the same coin. An
adverse effect was generated by the effectiveness of employees’ resignations on the
organization [1]. Owing to COVID-19, a great disruption was witnessed recently in the working
environment. The apparent change in employee attitude along with the exponential elevation
of mass resignation cases was initiated that left businesses struggling to acquire and retaining
talent. And, another major reason for this increase in resignation rate is the change in workplace
culture. Job insecurity and lack of reorganization, failure to recognize employees, and high
levels of innovation are the other factors driving the resignation rate. Huge attrition was
witnessed by India in the last 20 years. 48% of U.S. workers from all job types were not
engaged or actively disengaged in the month of May 2020 leading to 3.6 million resignations.
And, higher levels of resignation were faced by the organization that was not ready to offer
better employee benefits, flexible work schedules. The IT industry employees containing a
higher percentage of engineering along with technical employees are mainly affected. The
increase in attrition rate was combated by the EI along with TM process. The skills and abilities
enabling awareness of the emotional states of oneself and others along with the capacity to
regulate or utilized emotions to influence the role performance positively by utilizing emotions
of his/her own or others are referred to as an EI [2, 3]. Four connected emotional skills
encompassing EI [4,and 5] are
 Perception
 Absorption
 Recognition and management of self
 Others emotion
Anger, fear, jealousy, anxiety, and other negative emotions were experienced by the
employees along with others who were low on EI when confronted with issues. Same negative
emotions were less experienced by the people high on EI. But, these potentially disruptive
feelings were successfully handled by their ability to regulate their emotions. The emotions are
regulated by people high on EI to excel at work as well as in life. They also execute empathic
prosocial behaviors [6, 7, and 8]. The people skilled in signifying personal together with social
capabilities, like resilience, empathy, together with self-awareness in their dealings with
management, colleagues, along with customers are employed to generate beneficial outcomes,
which was appreciated by many organizations [9, 10]. A critical success factor increasingly
identified within organizations is the TM in the 21st century decade. It becomes vital
managerial preoccupation in this greatly dynamic along with the frequently vague market
environment. New innovative approaches to the development along with the deployment of
human resources were requisite in the contemporary business environment that was largely
driven by TM [11, 12]. By linking EI along with TM processes within the IT industry, the
effects of increase in attrition rate are analyzed in the presented research model. The increasing
resignation rate and EI and TM in the IT industry were analyzed by conducting a well-structured
questionnaire survey. Figure 1 shows the graphical representation of EI along with TM.

S. Vidhya and P. Kothai
Figure 1 A key to successful organization by emotional intelligence and talent management
The presented research’s structure is signified as follows, in section 2 the existing research
is given, in section 3 the proposed research methodology is described, in section 4 the
methodology’s result values are explained, and the work is concluded in section 5 along with
future enhancement.
2. RELATED WORKS
Muhammad Asrar-ul-Haq et al. [13] examined the EI’s impact on teachers' Job Performance
(JP) in Pakistan’s education sector. From universities’ 166 teachers, the sample was gathered
here. The EI relationship between the teacher’s JP was examined . The reliability along with
the validity of variables was tested by utilizing a measurement scheme of Partial Least Square
in Structural Equation Modelling (PLS-SEM). The outcomes exhibited that significant
importance was made by EI on the teacher’s JP. Also, the outcomes revealed that a positive
along with noteworthy relationship was made by self-confidence, emotional self-awareness,
achievement, and conflict management with the teacher’s JP. But, variation in outcomes was
found in other sectors as this approach only concentrated on the educational sector of Pakistan.
Ji Wen et al. [14] intended to analyze the EI and Emotional Labour (EL) on Job Satisfaction
(JSF) in a Moderated Mediation Model (MMM). The EI strategies are propounded by MMM
as a mediator between the JSF along with EI and organization support. 279 Chinese hotel
employees from seven hotels in South China were utilized for data collection. The outcomes
suggest that EI partially mediated the deep acting on JSF while surface acting did not mediate.
The mediation of EI, JSF, and deep acting was effectively moderated by organizational support.
The averaging item technique was utilized to combine the items in the same factor; however,
some variance along with information was removed by this technique.
Chao Miao et al. [15] investigated the influences of leader emotions on subordinating task
performance along with Organizational Citizenship Behavior (OCB). A multiple regression
analysis was utilized to assess the EI incremental validity. After regulating the cognitive ability
and Big Five, the outcomes exhibits that leader's EI incremental validity was demonstrated by
meta-analysis along with predicting the subordinate's task performance and OCB. With higher
power distance, better uncertainty avoidance, restraint cultures, and a long-term orientation, the
relationship between the leader's EI and subordinates' OCB was stronger. However, low sample
size was contained in a few moderator subgroups in the meta-analysis without ruling out the
reverse causality's possibility.

Buket Karatop et al. [16] intended to analyze the employee’s competency level along with
acquiring a yield as of the employee's optimal level. A fuzzy logic approach was utilized to
handle uncertainty along with vagueness in the TM assessment. Job-ability match and level of
perception were distinct for each employee. In crisp logic like black and white, the TM purposes
were usually evaluated. The outcomes suggest that an effective pairing of evaluation methods
and objective assessment along with more suitable candidates occurs with the right positions
that diminished employee turnover.
Abhishek Shukla and Rajeev Srivastava [17] explored the relationship between EI, Job
Stressors (JS), and socio-demographic variables. The EI moderating effect was examined on
the relationship between JS and social demographic variables. 564 retail employees in New
Delhi were used for data collection. The data was collected by descriptive statistics, hierarchical
multiple regression, and Pearson correlations along with testing the outcomes. The outcomes
indicated that the noteworthy influencing factor for JS than gender, age, education, marital
status, annual income, along with work experience was the trait EI. The employees with high
EI are elevated along with diminishing external stressors. However, the sample collected was
grounded in North India along with restricted by its cross-sectional design was the limitation
occurred.
Ye Hoon Lee and Packianathan Chelladurai [18] examined the relationships between EI,
JSF, coach burnout, and Turnover Intentions (TI) amongst high school athletic coaches. From
322 high school coaches in the United States, data was gathered. Confirmatory factor analysis
along with structural equation modeling was utilized to check the presented hypothesis. The
outcomes exhibited that a noteworthy impact on ‘3’ forms of EL was made by EI. A negative
association was shown by deep acting and genuine expression with coach burnt along with a
positive association with JSF. Furthermore, a positive association was exhibited by surface
acting with coach burnout, and a negative association was shown with JSF. The TI was
positively allied with coach burnout along with negatively linked with JSF. However, a low
response rate could limit the generalizability.
3. RESEARCH METHODOLOGY
The effects of increase in attrition rate were evaluated by conducting this study. The EI impacts
and the TM process were examined along with an understanding of the link between the EI and
TM within the IT industry. Here, a simple random sampling grounded on a well-structured
questionnaire and quantitative research was adopted by this methodology. The IT
organization’s employees located in Bangalore (India) were the source from which the data was
collected. As Bangalore is the residence of many national as well as international IT companies,
it is chosen as the study area. It is also named the 'silicon valley of India'. The chosen
respondents were provided with a total of 350 questionnaires. By utilizing the 5-point Likert
scale, the questionnaire is prepared. Only 303 respondents out of 350 respondents completed
the survey. The structured questions were not properly replied by the remaining 47 respondents.
Owing to incomplete along with biased responses, the remaining was discarded. Table 1
presented the questionnaire distribution and collection counts in the table format.
Table 1 Analysis of questionnaire distribution and the respondent collection count
Questionnaire distribution
count
Accepted response count Rejected response count
350 303 47
The team leaders at these firms were the prime source for the collected data. After that,
some informal questions concerning their work, satisfaction, along with intention to continue

or turnover was enquired by approaching the employees through online Questioner form.
Complete anonymity combined with confidentiality was assured to them. Also, the details of
their colleagues who have quit their jobs in the last year or either working as a freelancer or an
entrepreneur are gathered. From papers, journals, books and reviews, and websites, secondary
data was collected. Then, analyzing the relationship between EI and TM variables occurs
Understanding the link between the EI and TM within the IT industry along with the impact
of EI on the TM process, and the impact of increase in attrition rate on employees' EI by
measuring the changes in competence levels and effective Human Resource Planning (HRP)
and overcoming the higher attrition rate is the main objective pondered in this research
methodology. Figure 2 exhibits the resignation of an employee’s graphical representation.
Figure 2 Analysis of self-reliance of employee resignation
Since the respondents diverged as of every age group, gender, education, work experience,
marital status, etc., the data was gathered as of the respondents by the simple random sampling
technique. The demographic variable encompassing respondents' age, education, gender, and
work experience are tapped by intricately designing the questionnaire. To elevate the
employee's performance along with the organization of an IT industry, information about the
factors is gathered. Table 2 shows selected respondents of demographic characteristics analysis.
Table 2 Demographic characteristics of the respondents
(a)
Age Count Percentage
21-30 114 37.62
31-40 126 41.58
41-50 43 14.19
Above 50 20 6.60
(b)
Gender Count Percentage
Male 179 59.07
Female 124 40.92

(c)
Education Count Percentage
Post graduate 140 46.20
Under graduate 163 53.79
(d)
Work experience Count Percentage
Less than 2 years 55 18.15
3-5 years 142 46.86
Above 5 years 106 34.98
Regarding age, gender, education, and work experience, the respondent’s details were
shown in the above table.
Age: In table 2 (a), the respondent’s age details are given. The respondents' majority are
presented under 31-40 age groups (i.e., 41.58 %) followed by 21-30 age groups (37.62 %), 41-
50 age groups(14.19 %), and a low percentage of 6.60 % were obtained by above 50 age groups.
Gender: The respondent’s gender is given in table 2 (b). Male and female are the gender
classification. The male respondent’s percentage is 59.07 and the female respondent’s
percentage is 40.92.
Education: The education detail of participants’ respondents was shown in table 2 (c). The
post-graduate and undergraduate education qualification is given. Here, 53.79 % of the
respondents were qualified under graduated along with the 112 respondents were qualified as
post-graduate (i.e. 43.57%).
Work experience: The respondent employees' working experience is shown in Table 2 (d).
The respondents below 2 years of experience are 55, which is 18.15 %. 142 respondents have
3-5 years experience which constitutes 46.86 % and the respondents above 5 years experience
are 106, which is 34.98%. The respondents' working experience is majorly about 3 to 5 years.
Figure 3 exhibits the graphical representation,
(a)

(b)
(c)
(d)
Figure 3 Graphical representation of (a) ages (b) Gender (c) Education (d) Work experience

3.1 Emotional Intelligence and Job Stressors of IT Employees
Table 3 Analysis of the measure for EI and job stressors
Measure Mean SD Α
EI measure
Use of emotion 12.54 2.68 .912
Self emotion appraisal 12.25 2.83 .927
Regulation of emotion 13.18 2.45 .917
Other emotion appraisals 12.50 2.61 .926
Job stressors
Role expectation conflict 13.63 4.67 .919
Anxiety stress 13.41 3.23 .854
Time stress 10.60 3.41 .872
Work life balance 10.73 3.20 .869
Co-worker support 11.99 1.97 .882
By computing the mean, Standard Deviation (SD) along with reliability coefficient for the
EI measures as well as JS, Table 3 provides the measure of EI and JS. To investigate the
relationships between diverse variables, all variable’s means, SDs, and reliabilities were
determined. Emotion, self-emotion appraisal, emotion control, and other emotion appraisals are
the measures taken for EI. Role expectation conflict, anxiety stress, time stress, work-life
balance, and coworkers' support are the measures taken for the JS. A high mean score of 13.18
is attained by the regulation of emotion in the EI measure followed by the use of emotion
(12.54), other emotion appraisals (12.50), and self-emotion appraisal (12.25), which had the
lowest mean score when analogized to other EI measures. A high mean value of 13.63 was
attained by the Role expectation conflict, which outperformed the other JS measures, while
anxiety stress came in second with a mean value of 13.41, followed by coworkers' support
(11.99), work-life balance (10.73), and time stress (10.60).
3.2. Chi-Square Analysis of Factors of The Challenges Faced by The Employee
Table 4 Chi-square analysis for the employment status
Predictor variables Resigned employee (%) Current employee (%) p
Increased workload
demands
20 23 .32
Lack of personnel
protective equipment
17 15 .35
Managing personal needs
and family demands
22 29 .21
Lack of guidance to take
care of residents
6 7 .86
Understaffing 33 31 .45
Perceived stress level 34 30 .41
Lack of childcare 5 11 .02*

Mean Mean p
Number of a non-work-
specific challenge
.93 .98 .38
Number of work-specific
challenges
1.11 1.11 .59
Perceived level of
preparedness
3.97 4.23 .00*
*p<.05
The chi-square analysis’ frequencies and p-values for difficulties specifically related to the
work are shown in Table 4 by employment status. For both the resigned and existing employees,
the analysis was computed. The two most often cited stressful work issues are understaffing
and elevated workload demands. Additionally, employees' difficult work-specific challenges
included managing personal requirements along with family demands. There was just one
statistically significant outcome from the Chi-square analysis of the stressful work-related
issues grounded on resignation status. The employees, who left their jobs by mentioning
childcare lack as a problem, were very less. The number of obstacles ranged from 0 to 5; Also,
the average number of stressful work-related together with non-work stress-related challenges
was 1. Grounded on the resignation status, no statistically noteworthy differences were
identified by the independent t-tests in the number of stressful non-work-specific difficulties,
stressful work-related issues, or the overall stress level. However, the independent-samples t-
test's findings exposed that a higher mean score on the preparation rating together with the two
communication quality indicators were attained by the current employees when compared to
the resigned employees.
3.3. Factor loadings and significant figures of questions for Attrition rate Analysis
Table 5 Confirmatory factor analysis
Statement Emotional
intelligence
Coefficients
(factor loadings)
Explained
variance
Significant
(t – value)
S1.
Overcoming emotions
and anger while
disagreement with
other colleagues
0.83 0.65 14.62
S2.
Respecting the ideas
of other colleagues
even if it’s not true
0.78 0.56 13.47
S3.
Describing my
emotions and feelings
to colleagues
0.78 0.56 13.23
S4.
Understanding the
feelings of other
colleagues
0.74 0.55 13.13
Statement Job satisfaction
Coefficients
(factor loadings)
Explained
variance
Significant efforts
b(t – value)
S5.
Satisfaction with the
salaries along with
benefits
0.84 0.65 14.05
S6.
Feeling valuable by
performing duties
0.88 0.78 17.33
S7. JSS 0.76 0.62 14.15

Statement TI
Coefficients
(factor loadings)
Explained
variance
Significant
(t – value)
S8.
Searching for a new
job, next year
0.94 0.87 18.30
S9.
Possibility of job
desertion in case of
finding a new job
0.89 0.80 17.63
S10.
I think about my job
desertion
0.95 0.89 18.93
The confirmatory factor for EI, JSF, and turnover intentions are analyzed in Table 5. When
it comes to EI, the highest coefficient factor loading (0.83) was scored by statement S1 followed
by expressing my emotions and feelings to colleagues (0.78), respecting other co-workers' ideas
even if they are untrue (0.78), and understanding other co-workers' feelings (0.74). Identical
coefficient factors were obtained S2 and S3 here. When analogized to the other statement of
satisfaction with salaries and benefits and Job Security Satisfaction (JSS), high coefficient
factors (0.88) were attained by the feeling valuable by performing duties in JSF. Here, the JSS
was 0.76, and the coefficient factor for salary satisfaction was 0.84. Searching for a new job
next year, the possibility of job desertion in case of finding a new job, I think about my job
desertion are the statements analyzed in the turnover intention. Here, a large t-value of 18.93
and a high coefficient factor of 0.95 were attained by the statement “I think about my job”. And
the second-highest coefficient (0.94) was attained by the searching for a new job next year along
with a significant t-value of 18.30 followed by the statement desertion in case of finding a new
job attained 0.89 and the significant t-value is 17.63.
3.4. Effective Human Resource Planning and Reduce Employee’s Attrition Rate
Table 6 Analyze the factors to reduce employee’s attrition rate
Factors Factor loading
Organizational culture .838
Better Remuneration and Career Prospects .879
Perceptions of training & resources .786
Shift Timings & Week-offs .549
The factor loading with various factors is analyzed in the table above. Better remuneration
along with career opportunities have the largest factor loading, as per the result's analysis, which
indicates that salaries are the most crucial factors for any firm. When analogized with
organizational culture, perceptions of training and resources, shift timings, and weekdays off,
the highest factor loading (.879) was attained for better remuneration and career prospects. The
organization's further development might benefit from executing periodic appraisal programs.
And, the second-highest ranking was attained by the organizational culture with a factor loading
of .838. Importance for the employees affiliated with the organization was always provided by
it. That indicates the company will grow readily along with controlling the attrition if the culture
or working environment is good. A factor loading of.786 was achieved by the factor of
perceptions of resources and training. The organization should schedule periodic training for
the staff's betterment. If an employee has strong knowledge, they always perform better, and
they can be able to earn a favorable appraisal through this kind of activity. Week-offs and shift
times are additional significant aspects that can be easily managed. The week off and shift
timings loading factor is .549.

4. RESULT AND DISCUSSION
Using descriptive analyses of the mean, SD, and correlation, this part analyses and discusses
the gathered respondents' data. The correlation coefficient between EI and psychological
distress was examined here. Additionally, a regression approach was used to construct and test
the hypothesis. And, to determine the competence level of an organization, Cronbach's alpha
was examined for the variables of organizational performance, EI, and TM.
4.1. Emotional Intelligence and Psychological Distress
Table 7: Analyzes of the correlation coefficient
1 2 3 4 5
Social skills 1 - - - -0.245***
Empathy 0.592***
1 0.642***
- -0.118*
Creative use of
emotions
0.591***
- 1 - -0.223***
Self-management of
emotions
0.482***
0.425***
0.486***
1 -0.358***
Psychological distress -
- - - 1
Mean 20.72 3.64 3.81 3.65 3.82
Standard deviation 6.53 0.71 0.65 0.62 0.63
***P < 0.001; **P < 0.01; *P < 0.05
The correlation coefficient for EI and psychological distress was analyzed in the above
table. The coefficient correlation of social skills, empathy, creative use of emotions, self-
management of emotions, and psychological distress was analyzed. The mean and SD for EI
and psychological distress were computed. A negative correlation was exhibited by social skills
with psychological distress -0.245***
. A positive relationship between social skills (0.592***
)
and creative use of emotions (0.642***
) was made by empathy and a negative correlation was
exhibited with psychological distress (-0.118*
). A positive relationship with social skills
(0.591***
) along with a negative relationship with psychological distress (-0.223***
) was made
by creative utilization of emotions. Social skills (0.482***
), empathy (0.425***
), creative use of
emotions (0.486***
) were positively correlated with self-management along with negatively
correlated with psychological distress (-0.358***). A high mean value of 20.72 was attained by
the social skills followed by psychological distress (3.82), creative use of emotions (3.81), self-
management of emotions (3.65), and empathy (3.64). 6.53 is the high SD attained by the social
skills along with attaining a low SD of 0.62 for self-management of emotions.
4.2. Testing of Hypothesis
The hypotheses are generated and tested to analyze the OP for effective HRP and overcoming
the higher attrition rate. The hypotheses are,
Hypothesis 1: There is a positive relationship between TM and EI.
Hypothesis 2: There is a positive relationship between TM and OP.
Hypothesis 3: There is a positive relationship between EI and OP.
Hypothesis 4: The relationship between TM and OP were mediated by the EI.

Table 8 Analyzing Cronbach’s alpha for the variables for the impact of emotional intelligence on the
talent management process
Variables Mean SD Cronbach’s alpha
Strategies of TM 4.11 0.705 0.947
TD 4.05 0.736 0.885
TA 3.99 0.673 0.841
Talent development and
succession planning
3.82 0.797 0.876
TR 3.76 0.775 0.913
EI 3.94 0.832 0.897
Social awareness 3.66 0.875 0.796
Self-awareness 3.71 0.725 0.731
Self-management 3.69 0.683 0.899
RM 4.13 0.706 0.803
OP 4.57 0.828 0.915
Financial performance 4.13 0.825 0.899
Technical performance 3.91 0.762 0.943
Operational performance 3.86 0.693 0.782
A positive along with significant relationship was expressed by the relationship between
TM with EI, TM with OP, and EI with OP. The Cronbach's alpha was utilized to analyze the
talent of TM, EI, and OP strategies. The Cronbach's alpha values range from 0-1.0. The
acceptable coefficient value should range from 0.60 to 0.70 and at least 0.70 or higher. Talent
Development (TD), Talent Attraction (TA), TD and succession planning, and Talent Retention
(TR) are the strategies in TM. A high mean value of 4.05 is attained by the TD followed by TA
(3.99), TD and succession planning (3.82), and TR (3.76). Social awareness, self-awareness,
self-management, and Relationship Management (RM) are the strategies in EI. A high mean
value (4.13) was attained by the RM than the other strategies. The second highest mean value
was attained by the self-management strategies which are 3.71 followed by self-management
(3.69) and social awareness (3.71). Financial performance, technical performance, and
operational performance are the strategies in OP. The highest mean value of 4.13 along with
the Cronbach's value of 0.899 was attained by the financial performance followed by the
technical performance having a mean value of 3.91 and the Cronbach's value of 0.943. The
operational performance's mean value is 3.86 and the Cronbach's alpha is 0.782.
4.3. Regression analysis for understanding link between the talent management
and emotional intelligence in organizational performance
Table 9 Analysis of the link between the effect of TM and EI
Variables OP (Step 1) EI
(Step 2)
OP (Step 3) OP (Step 4)
Constant 4.286***
3.597***
4.075***
4.634**
TM 0.625***
0.499***
- 0.583***
EI - - 0.508***
0.313***
R 0.625 0.499 0.508 0.615
R2
0.384 0.249 0.255 0.376
Adj. R2
0.382 0.243 0.251 0.367
F-value 110.185***
92.471***
97.736***
90.225***
*** p≤ 0.01

Several regression analyses were utilized to analyze and examine the variables and
coefficients. An analysis of a simple regression step was utilized to predict the dependent
variable, supporting the independent variables. Additionally, the mediator should be recognized
by the independent variable in the second stage. In the third phase, the dependent variable
should be recognized by the mediator. The independent variable along with the mediator is
supported by a multiple regression analysis that was utilized to predict the dependent variables
in the final phase. As seen in step 1, there was a significant correlation between the independent
variable TM and the dependent variable OP. The second hypothesis is therefore supported by
these observations. The null hypothesis was thus rejected. In the second step, the outcome
suggested a significant correlation between the independent variable of TM and mediator EI
(β=0.49, p< 0.001) that assisted the first hypothesis. The third step's outcome indicated a
significant correlation between the EI’s mediator and dependent variable OP (β=0.508,
p<0.001) that aids the 3rd
hypothesis. The OP’s dependent variable was predicted by the
multiple regression analysis finally along with regressing the independent variable of TM and
the mediator of EI together. In the first regression step, the outcomes indicated the direct effect
of TM on OP (β=0.625, p<0.001) that was diminished in the 4th
regression step and was still
substantial (β=0.583, p<0.001). The succeeding table exhibits the outcome’s analysis for the
hypothesis. 0.493***
was the path’s effect between TM and EI, 0.625***
was the path’s effect
between TM and OP, 0.508***
was the path’s effect between EI and OP along with 0.256***
was the path’s effect between TM, EI, and OP.
Table 10 Analyses the result of the hypothesis
Hypothesis Path Effect Result
H1 TM→EI 0.493***
Accepted
H2 TM→OP 0.625***
Accepted
H3 EI→OP 0.508***
Accepted
H4 TM→EI→OP 0.256***
Accepted
*** p≤0.001
5. CONCLUSION
Companies must have qualified managers and employees in today's competitive business
environment if they hope to survive. Companies also make an effort to retain these employees.
Important metrics that frequently cause an employee to resign are the organization's workforce
planning and strategy. In addition to having a significant impact on future employee retention
rates among current staff, JSF, employee engagement, and the company's capacity to recruit
outstanding individuals, the employees leave their existing roles. New heights were continually
reached by employee resignation rates without showing a sign of abating. This study links EI
to TM procedures within the IT sector to examine the employees resignation rate effects. The
questionnaire distribution and collection is the concept on which the research investigation
relies. 350 participants were given the questionnaire, and 303 of their responses were pondered
in the study. All of the parameters were required by the descriptive statistics that encompassed
mean, SD, and correlation. Simple statistical tools like percentage analysis together with Chi-
square analysis were employed to examine the data interpretation. The findings of this study
demonstrate that employee performance and OP were elevated by the TM and EI. By pondering
more populations for examining the attrition rate, the effect of EI on the TM process, along with
elevating the item count, this study can be extended in the future. This study recommends
conflict management as a crucial component of efficient human resource management and aids
management to forge stronger working relationships and offers some recommendations for
integrating EI into TM for effective HRP and overcoming the higher attrition rate.

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