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CHAPTER-IV
ANALYSIS OF FACTORS INFLUENCING THE TIME
MANAGEMENT AND MEASUREMENT OF PROFESSIONAL
DEVELOPMENT OUTCOMES OF I.T. PROFESSIONALS
4.1 INTRODUCTION:
The issues of job stress, time management and balancing one’s personal
and professional life is a significant dilemma in today’s society. The basis for
time management rests on effectiveness of time spending and dominance on
time is the only secret of human success. In a complete system of time
management, some techniques are used in which people can achieve the most
results with spending less time. It also gives the courage for empowerment and
thus can maximize their productivity1. In today’s society, where people are
under pressure, efficient time management and planning not only is a useful
affair, but also it has been turned into a necessary and inevitable one in
achieving goals in both areas of work and life2. Time management behaviours
have direct and indirect relationship with work interference with family and
family interference with work. The effective implementation of work-life
balance offers a win-win situation for employers and work force3.
For example, a study by Nick & Steve Jacob4 documented the
significance of time and work management on work place stress. They found
that proactive work day planning, scheduling and time management were
highly correlated with less individual stress. Good time management
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behaviours such as setting goals and priorities as well as monitoring the use of
time can facilitate productivity and minimize stress5.
Thus, if time is managed more effectively and efficiently, it reduces
stress and frustration and gives self confidence through relaxed feeling. Rather
than running around in a state of frenzy, it gives a sense of empowerment from
getting things done and having the time to enjoy life’s simple pleasures6. Time
management helps to be more productive, gives more energy to accomplish
tastes, a feel of less stressed, to pursue personal interest, get more things done
and to feel better about self.
Many studies have been carried out regarding time management in the
western countries and its effects on individuals, family as well as in work
organizations. They have shown that the percentage of working long hours is
higher in Asian and North American countries compared to European countries
and former countries enjoy the privilege of balancing between work and
personal life7.
This chapter presents an empirical analysis of various factors
influencing the time management and its impact on professional development
outcomes of IT personnel. Further, the analysis highlights the degree of
various factors that influence the techniques of time management, level of time
management techniques adopted, personal time planning, measurement of
professional development outcomes and impediments of time management.
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Thus, in order to understand the degree of influence of the various factors
on the time management, following statistical tools like, factor analysis, cluster
analysis are used along with correlation and regression analysis.
4.2 INTER-CORRELATION AMONG THE VARIABLES
To examine the inter relationship among all the selected variables,
‘Pearson’s correlation matrix’ was drawn showing inter correlation among the
techniques of time management, level of time management techniques adopted,
personal time planning, measurement of professional development outcomes of
IT personnel and impediments of time management. The statistically
significant variables are indicated by * as listed below
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
The Pearson correlation indicated in Table 4.1 shows that the relationships
of following time management perceptions of IT professionals are statistically
significant with the other variables.
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Table-4.1
PEARSON’S CORRELATION MATRIX SHOWING INTER
CORRELATION AMONG SELECTED VARIABLES
Co
rrel
atio
ns
Tec
hn
iqu
eso
fti
me
man
agem
ent
Lev
elo
fti
me
man
agem
ent
tech
niq
ues
ado
pte
d
Imp
edim
ents
of
tim
em
anag
emen
t
Per
son
alti
me
pla
nn
ing
Pro
fess
ion
al
dev
elo
pm
ent
ou
tco
mes
of
IT
pro
fess
ion
al
Techniques of
time management 1 .628** .212** -.345** .571**
Level of time
management
techniques
adopted
.628** 1 .326** -.390** .553**
Impediments of
time management .212** .326** 1 -.182** .180**
Personal time
planning -.345** -.390** -.182** 1 -.382**
Professional
development
outcomes of IT
professional
.571** .553** .180** -.382** 1
Source: Primary data
No. of cases 522, two tailed, Significance ** = 0.001 Level.
Following inferences are drawn from the above table.
1. Perceptions of IT professionals on techniques of time management
significantly correlated with ‘level of time management techniques
adopted’ (0.628), ‘professional development outcomes (0.571) and
‘Impediments of time management’ variables (0.212).
2. Techniques of time management and personal time planning
shows negative relationship (-0.345).
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3. Level of time management techniques adopted is also positively
correlated with ‘techniques of time management’ (.628),
‘impediments of time management’ (.326) and ‘Professional
development outcomes of IT employees’ (.553).
4. Level of time management techniques adopted and personal time
planning are negatively correlated (-0.345).
5. Impediments of time management has significant relationship with
techniques of time management, level of time management
techniques adopted and professional development outcomes of IT
professional variables.
6. Impediments of time management shows significantly negative
relationship with personal time planning.
7. Personal time planning also has negative relationship with all other
variables.
8. Professional development outcomes of sample IT personnel are
correlated significantly with techniques of time management, level
of time management techniques adopted and impediments of time
management but its relationship with personal time planning is
negatively correlated.
Inferences:
The study reveals that IT professionals in general seem to have higher
perceptions on techniques of time management and its time utilization adopted
which is reflected in professional development outcomes. These aspects are
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playing vital role for better performance at work, when IT professionals
demonstrate the competencies like understanding the importance of time
management, general planning in time management, implementation efforts,
self management, programmed line of activity, organizing, delegation, co-
operation, daily work schedule, work life balance etc. Further, additional
interest in resource planning, social networking, work ethics, professional
skills, stress control, adaptability, initiative, communication, leadership quality,
awareness, motivation, team building, goal setting, positive attitude,
supervision and flexibilty etc., will prove that performance is bound to be more
effective. The results of the study explain that time management competency
capabilities are most important for the success of IT professionals in their
respective work settings.
Impediments of time management had the inverse correlation with
personal time planning among alternative variables which means that the time
wasted by the professionals leads to lack of time for personal time planning.
A time waster is any activity that includes a low quantitative relation of profit
to time spent. Time wasters are one sensitive issue where corporations and the
organizations should provide methods to reduce time wasters to achieve the
goals.
Personal time planning also has negative relationship with all other
variables which indicates that the professionals are not giving much importance
to the personal time as they have to work long hours in a day and even
weekends in the workplace.
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As the general time availability of the respondents is eroded with early
start, late finish, lack of free time, work on holiday, inadequate leave etc., they
find lacking in work-life balance. Work place rigidity affects more on the life
of the respondents than their work. Personal commitments have rigorous
impact in matching work and life, the big collision is on work than the life.
The organisations have to focus on conquering adverse work life policies for
the betterment of the professionals.
Hence, in present corporate work enviornment, which is increasingly
global , it is imperative that IT professoinals have to pursue their professional
goals persistantly with vibrent work culture. A high levels of achievement
motivation help them to lead and getting people to work together as a team,
when they acquire effective time management skills.
Multiple stepwise Regression Analysis:
Multiple regression analysis is a statistical technique that can be used to
analyse the relationship between a single dependent (criterion) variable and
several independent (predictor) variables. The objective of multiple regression
analysis is to use the independent variables, whose values are known to predict
the single dependent value selected. This has been performed by analysing the
relationship between techniques of time management, level of time
management techniques, personal time planning and the professional
development outcomes.
Present work setting, particularly in India requires information
technology professionals to work longer hours, even at odd hours, weekends
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and other non-standard times which compels them to manage their time
efficiently. Effective time management encompasses on creating a certain
amount of time, so as to organize how the rest of the time should be spent, to
make sure that their time priorities are well organized. For this purpose, proper
time management will improve productivity, make scheduling earlier, make
employees to perform their task at their highest skill levels to prioritize and
accomplish important task and guide them to achieve their personal and career
goals.
In this part of the chapter, the relationship between the techniques, the
level of time management techniques adopted, personal time planning and the
professional development outcomes such as work life balance, stress reduction
etc., are analyzed empirically.
4.2.1 The relationship between Techniques of time management and other
Variables
Hypothesis
H0: There is no relationship between the techniques of time
management and other variables.
H1: There is significant relationship between the techniques of time
management and other variables.
In this part of the analysis, techniques of time management and other
variables are entered in stepwise regression analysis. The independent variables
are level of time management techniques adopted, impediments of time
management, personal time planning and measurement of professional
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development outcomes of sample IT professionals and the dependent variable
is techniques of time management.
Table 4.2
THE RELATIONSHIP BETWEEN TECHNIQUES OF TIME
MANAGEMENT AND OTHER VARIABLES
MODEL SUMMARY
Model R R Square Adjusted R SquareStd. Error of the
Estimate
1 .628(a) .394 .393 6.56386
2 .683(b) .466 .464 6.16927
a). Predictors: (Constant), Level of Time Management Techniques Adopted
b). Predictors: (Constant),Impediments of Time Management Techniques
Adopted, Personal time planning and Professional Development Outcomes of
IT Personnel
c) Dependent variable : Techniques of Time management
The above table shows the model summary for the R, R2, adjusted R
2 and
standard error of the estimate. The R2 value indicates the percent of variance in
the criterion (dependent) variable that is accounted for by the linear
combination of predictor (independent) variables. Model 1 has an R2
= 0.394,
model 2 has an R2 0.466. This indicates the variance accounted for by the linear
combination of level of time management techniques adopted, personal time
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planning, impediments of time management, personal time planning and
measurement of professional development outcomes of IT personnel.
Table-4.2.1
THE RELATIONSHIP BETWEEN TECHNIQUES OF TIME
MANAGEMENT AND OTHER VARIABLES
ANALYSIS OF VARIANCE
ModelSum of
Squares Df
Mean
Square F Sig.
1 Regression 14554.577 1 14554.577 337.817 .000(a)
Residual 22360.732 520 43.084
Total 36915.309 521
2 Regression 17200.303 2 8600.151 225.964 .000(b)
Residual 19715.006 519 38.060
Total 36915.309 521
a). Predictors: (constant), level of time management techniques adopted
b). Predictors: (constant), Impediments of time management techniques
adopted, personal time planning and professional development outcomes of IT
personnal
c). Dependent variable: techniques of time management
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Table-4.2.2
THE RELATIONSHIP BETWEEN TECHNIQUES OF TIME
MANAGEMENT AND OTHER VARIABLES
COEFFICIENTS (a)
Model
Unst
and
ariz
ed
Coef
fici
ents
Sta
ndar
diz
ed
Coef
fici
ents
T Sig.
B Std. Error Beta
1 (Constant) 34.052 2.536 13.430 .000
LEVEL OF TIME
MANAGEMENT
TECHNIQUES
ADOPTED
.618 .034 .628 18.380 .000
2 (Constant) 24.683 2.635 9.368 .000
LEVEL OF TIME
MANAGEMENT
TECHNIQUES
ADOPTED
.442 .038 .449 11.626 .000
PROFESSIONAL
DEVELOPMENT
OUTCOMES
OF IT
PROFESSIONAL
.290 .035 .322 8.338 .000
a Dependent Variable: TECHNIQUES OF TIME MANAGEMENT
Source: Primary data
The table shows that the two models are significant. The ‘F-values’ for
two models are statistically significant. Hence, the null hypothesis is rejected in
the above analysis and it can be concluded that, there is significant relationship
between different techniques of time management and level of time
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management techniques adopted and professional development outcomes of IT
employees. A study done by R.Hassanzabeh and A.G.Ebadi9 on “Measure the
share of Effective factors and time management” confirms the above results at
F=292/0.82 with df=5 by concluding that there is a relationship between
effective factors in time management and the extent of time management
practices among managers.
INFLUENCE OF LEVEL OF TIME MANAGEMENT TECHNIQUES
ADOPTED ON PROFESSIONAL DEVELOPMENT OUTCOMES:-
4.2.2. The relationship between level of time management techniques
adopted and other Variables:-
Hypothesis
H0: There is no relationship between the level of time management
techniques adopted and other variables.
H1: There is significant relationship between the level of time
management techniques adopted and other variables.
In this part of the analysis, level of time management techniques and
other variables are used for stepwise regression analysis. The independent
variables entered into the analysis are techniques of time management,
professional development outcomes of IT personnel, Impediments of time
management and Personal time planning. The dependent variable is level of
time management techniques adopted
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Table-4.3
THE RELATIONSHIP BETWEEN LEVEL OF TIME MANAGEMENT
TECHNIQUES ADOPTED AND OTHER VARIABLES
MODEL SUMMARY
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .628(a) .394 .393 6.67411
2 .672(b) .452 .450 6.35540
3 .696(c) .484 .481 6.17028
4 .705(d) .497 .493 6.10171
a). Predictors: (Constant), Techniques of time management
b). predictors: (constant), Techniques of time management, professional
development outcomes of IT personnel
c). predictors: (constant),Techniques of time management, Professional
development outcomes of IT personnel, Impediments of time management
d). predictors: (constant), Techniques of time management, Professional
development outcomes of IT personnel, Impediments of time management,
Personal time planning
e)Dependent variable : Level of time management
The above table shows the model summary for the R, R2, adjusted R
2
and standard error of the estimate. The R2 value indicates the percent of
variance in the criterion (dependent) variable that is accounted for by the linear
combination of predictor (independent) variables. Model 1 has an R2
= 0.394,
model 2 has an R2 0.452, model 3 has an R
2 0.484 and model4 has an R
2 0.497.
This indicates the variance explained by linear combination of techniques of
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time management, professional development outcomes, impediments of time
management and personal time planning.
Table-4.3.1
THE RELATIONSHIP BETWEEN LEVEL OF TIME MANAGEMENT
TECHNIQUES ADOPTED AND OTHER VARIABLES
ANALYSIS OF VARIANCE
ModelSum of
Squares Df
Mean
Square F Sig.
1 Regression 15047.607 1 15047.607 337.817 .000(a)
Residual 23118.192 520 44.544
Total 38165.799 521
2 Regression 17243.213 2 8621.606 213.453 .000(b)
Residual 20922.586 519 40.391
Total 38165.799 521
3 Regression 18482.373 3 6160.791 161.818 .000(c)
Residual 19683.426 518 38.072
Total 38165.799 521
4 Regression 18954.644 4 4738.661 127.278 .000(d)
Residual 19211.155 517 37.231
Total 38165.799 521
Source: Primary data
a). Predictors: (Constant), Techniques of time management
b). Predictors: (constant), Techniques of time management, professional
development outcomes of IT personal.
c). Predictors: (constant),Techniques of time management, professional
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development outcomes of IT professionals, impediments of time management
d). Predictors: (constant), Techniques of time management, professional
development outcomes of IT professionals, impediments of time management,
personal time planning
e) Dependent Variable: Level of time management techniques adopted
Table-4.3.2
THE RELATIONSHIP BETWEEN LEVEL OF TIME MANAGEMENT
TECHNIQUES ADOPTED AND OTHER VARIABLES
COEFFICIENTS (a)
Model
Un
stan
dar
diz
ed
Coef
fici
ents
Sta
ndar
diz
ed
Coef
fici
ents
T Sig.
BStd.
ErrorBeta
1
(Constant) 23.676 2.807 8.436 .000
TECHNIQUES OF TIME
MANAGEMENT .638 .035 .628 18.380 .000
2 (Constant)16.499 2.844 5.801 .000
TECHNIQUES OF TIME
MANAGEMENT .469 .040 .461 11.626 .000
PROFESSIONAL
DEVELOPMENT
OUTCOMES OF IT
PROFESSIONAL
.268 .036 .292 7.373 .000
3 (Constant)8.822 3.072 2.872 .004
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TECHNIQUES OF TIME
MANAGEMENT .438 .039 .431 11.094 .000
PROFESSIONAL
DEVELOPMENT
OUTCOMES OF IT
PROFESSIONAL
.253 .035 .276 7.152 .000
IMPEDIMENTS OF
TIME MANAGEMENT .169 .030 .185 5.705 .000
4 (Constant)20.188 4.406 4.582 .000
TECHNIQUES OF TIME
MANAGEMENT .417 .040 .410 10.552 .000
PROFESSIONAL
DEVELOPMENT
OUTCOMES OF IT
PROFESSIONAL
.222 .036 .243 6.181 .000
IMPEDIMENTS OF
TIME MANAGEMENT .158 .029 .173 5.369 .000
PERSONAL TIME
PLANNING -.100 .028 -.123 -3.562 .000
a Dependent Variable: LEVEL OF TIME MANAGEMENT
TECHNIQUES ADOPTED
Source: Primary data
The results indicate that the four models are significant. The ‘F-value’
for four models is statistically significant. Hence, the null hypothesis is rejected
in the case of relationship between the ‘Level of time management techniques
adopted’ and ‘techniques of time management, professional development
outcomes of it professional, impediments of time management and personal
time planning.’ In a separate study, Schummacker, et.al7 concluded that
students who scored low in time management significantly scored lower in
academic achievement. Prevatt et al8
concluded students who do not use time
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management strategies, have significantly lower Grade Point Averages when
compared to those students who use time management strategies with
significantly higher GPAs.
It is inferred from the above analysis that the respondents surveyed are
using the time management techniques purposefully which leads to achieve
their life goals, both personal and work. They are very well aware of the factors
impeding their management of time, but they give very less importance to their
family and personal life goals.
INFLUENCE OF IMPEDIMENTS OF TIME MANAGEMENT ON
LEVEL OF TIME MANAGEMENT TECHNIQUES ADOPTED:
4.2.3. The relationship between impediments of Time Management and
other variables
Hypothesis
H0: There is no relationship between the impediments of time
management and other variables.
H1: There is significant relationship between the impediments of time
management and other variables.
In this part of the analysis, Techniques of time management and other
variables are entered in stepwise regression analysis. The independent variables
entered into the analysis are namely, level of time management techniques
adopted, personal time planning, techniques of time management, Personal
time planning and measurement of professional development outcomes of IT
personnel and the dependent variable is impediments of time management.
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Table-4.4
THE RELATIONSHIP BETWEEN IMPEDIMENTS OF TIME
MANAGEMENT AND OTHER VARIABLES
MODEL SUMMARY
Model R R Square Adjusted R SquareStd. Error of
the Estimate
1 .326(a) .106 .105 8.86226
a) Predictors: (Constant), Level of Time Management Techniques Adopted
The above table shows the model summary for the R, R2, adjusted R
2
and standard error of the estimate. The R2 value indicates the percent of
variance in the criterion (dependent) variable that is accounted for by the linear
combination of predictor (independent) variables. Model 1 has an R2
= 0.106.
This indicates the variance explained by the linear combination of level of time
management techniques adopted.
Table-4.4.1
THE RELATIONSHIP BETWEEN IMPEDIMENTS OF TIME
MANAGEMENT AND OTHER VARIABLES
ANALYSIS OF VARIANCE
ModelSum of Squares
DfMeanSquare
F Sig.
1.
Regression 4852.193 1 4852.193 61.780 .000(a)
Residual 40762.095 520 78.540
Total 45614.288 521
Source: Primary data
a). Predictors: (Constant), Level of time management techniques adopted
b). Dependent Variable: Impediments of time management
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Table-4.4.2
THE RELATIONSHIP BETWEEN IMPEDIMENTS OF TIME
MANAGEMENT AND OTHER VARIABLES
COEFFICIENTS (a)
Unstandardized
Coefficients
Standardized
CoefficientsT Sig.
Model BStd.
ErrorBeta
1 (Constant) 39.973 3.423 11.676 .000
LEVEL OF TIME
MANAGEMENT
TECHNIQUES ADOPTED
.357 .045 .326 7.860 .000
a ) Dependent Variable: IMPEDIMENTS OF TIME MANAGEMENT
Source: Primary data
The above table explained that, the three models are significant. The ‘F-
value’ for the above model is statistically significant. Hence, the null
hypothesis is rejected and there is significant relationship between the level of
time management techniques adopted and the impediments of time
management.
It is inferred from the above analysis the respondents are capable of
avoiding and reducing the impediments of time management and they use time
logs and productive time schedules to meet deadlines which helped them to
increase their level of time management techniques adopted.
INFLUENCE OF PERSONAL TIME PLANNING ON LEVEL OF TIME
MANAGEMENT TECHNIQUES AND PROFESSIONAL OUTCOMES:-
4.2.4 The relationship between personal time planning and other Variables
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Hypothesis
H0: There is no relationship between the personal time planning and other
variables.
H1: There is significant relationship between the personal time planning and
other variables.
In this part of the analysis, personal time planning and other variables
are entered for stepwise regression analysis. The independent variables entered
in to the analysis are techniques of time management, level of time
management techniques adopted, impediments of time management and
measurement of professional development outcomes. The dependent variable is
personal time planning.
Table-4.5
THE RELATIONSHIP BETWEEN PERSONAL TIME
PLANNING AND OTHER VARIABLES
MODEL SUMMARY
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .389(a) .152 .150 9.71428
2 .439(b) .193 .190 9.48523
a). predictors: (constant), level of time management techniques adopted
b). predictors: (constant), level of time management techniques adopted,
professional development outcomes of IT professionals
The above table reveals the model summary for the R, R2, adjusted R
2
and standard error of the estimate. The R2 value indicates the percent of
variance in the criterion (dependent) variable that is accounted for by the linear
combination of predictor (independent) variables. Model 1 has an R2
= 0.152
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and model 2 has an R2 0.193. This indicates the variance explained by the
linear combination of personal time planning, level of time management
techniques adopted and professional development outcomes of IT personnel.
Table-4.5.1
THE RELATIONSHIP BETWEEN PERSONAL TIME PLANNING
AND OTHER VARIABLES
VARIANCE ANALYSIS
Model Sum of Squares Df
Mean
Square
F Sig.
1
Regression 8746.224 1 8746.224 92.683 .000(a)
Residual 48976.617 520 94.367
Total 57722.841 521
2
Regression 11118.586 2 5559.293 61.791 .000(b)
Residual 46604.255 519 89.970
Total 57722.841 521
Source: Primary data
a). predictors: (constant), level of time management techniques adopted
b). predictors: (constant), level of time management techniques adopted,
professional development outcomes of it professional
c). dependent variable: personal time planning
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Table-4.5.2
THE RELATIONSHIP BETWEEN PERSONAL TIME
PLANNING AND OTHER VARIABLES
COEFFICIENTS (a)
Unst
andar
diz
ed
Coef
fici
ents
Sta
ndar
diz
ed
Coef
fici
ents
t Sig.
Model BStd.
ErrorBeta
1
(Constant) 101.623 3.753 27.081 .000
LEVEL OF TIME
MANAGEMENT
TECHNIQUES
ADOPTED
-.479 .050 -.389 -9.627 .000
2
(Constant) 110.495 4.051 27.276 .000
LEVEL OF TIME
MANAGEMENT
TECHNIQUES
ADOPTED
-.312 .058 -.254 -5.344 .000
PROFESSIONAL
DEVELOPMENT
OUTCOMES OF IT
PROFESSIONAL
-.275 .053 -.244 -5.135 .000
a) Dependent Variable: PERSONAL TIME PLANNING
Source: Primary data
The above table shows that, the two models are significant. The ‘F-
value’ for two models is also statistically significant. Hence, the null
hypothesis is rejected in the above case and it can be concluded that, there is
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significant relationship between the level of time management techniques
adopted, professional development outcomes of IT professionals and personal
time planning.
It is inferred from the above analysis that IT professionals selected for
the study are conscious with the work life balance and constantly update their
professional skills by way of planning their work time and personal time to
meet deadlines. They review their long term goals both in personal life and
work life constantly, which resulted in developing their profession and career.
The above result is confirmed in the study of “time management skills –
impact on self efficacy and academic performance” by Karim, et.al9, that the
time management skill is effective in increasing self-efficacy and academic
performance. In the same way, Classens et.al10
suggested that the applications
of time management behaviours are positively associated with understanding
time control, work job interference, job performance, job satisfaction, health
and time allocated for high priority tasks.
4.2.5 The relationship between professional development outcomes of IT
personnel with techniques of Time management, level of time management
techniques adopted and personal time planning
Hypothesis
H0: There is no relationship between the professional development outcomes
of IT personnel and techniques of time management, level of time management
techniques adopted and personal time planning
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H1: There is significant relationship between the professional development
outcomes of IT personnel and techniques of time management, level of time
management techniques adopted and personal time planning
In this part of the analysis, professional development outcomes of IT
and other variables are entered stepwise regression analysis. The independent
variables entered into the analysis are level of time management techniques
adopted, personal time planning, impediments of time management and
Techniques of time management. The dependent variable is professional
development outcomes of IT personnel.
Table-4.6
THE RELATIONSHIP BETWEEN PROFESSIONAL DEVELOPMENT
OUTCOMES OF IT PROFESSIONAL AND OTHER VARIABLES
a)
predictors: (constant), techniques of time management
b ) predictors: (constant), techniques of time management, level of time
management techniques adopted
c ) predictors: (constant), techniques of time management, level of time
MODEL SUMMARY
Model R R Square Adjusted R SquareStd. Error of
the Estimate
1 .571(a) .327 .325 7.68364
2 .625(b) .391 .388 7.31672
3 .641(c) .411 .408 7.19768
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management techniques adopted, personal time planning
The above table shows the three models summary for the R, R2, adjusted
R2 and standard error of the estimate. The R
2 value indicates the percent of
variance in the criterion (dependent) variable that is accounted for by the linear
combination of predictor (independent) variables. Model 1 has an R2
= 0.325,
model2 has an R2
=0.391 and Model3 has an R2
=0.411. This indicates the
variance explained by the linear combination of techniques of time
management, level of time management techniques adopted and personal time
planning.
Table- 4.6.1
THE RELATIONSHIP BETWEEN PROFESSIONAL DEVELOPMENT
OUTCOMES OF IT PERSONNEL AND OTHER VARIABLES
ANALYSIS OF VARIANCE
Model Sum of Squares Df Mean Square F Sig.
1 Regression 14860.520 1 14860.520 251.710 .000(a)
Residual 30640.874 520 59.038
Total 45501.394 521
2 Regression 17770.578 2 8885.289 165.973 .000(b)
Residual 27730.816 519 53.534
Total 45501.394 521
3 Regression 18717.383 3 6239.128 120.431 .000(c)
Residual 26784.010 518 51.807
Total 45501.394 521
Source: Primary data
a) predictors: (constant), techniques of time management
b) predictors: (constant), techniques of time management, level of time
management techniques adopted
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c) predictors: (constant), techniques of time management, level of time
management techniques adopted, personal time planning
d) dependent variable: professional development outcomes
Table-4.6.2
THE RELATIONSHIP BETWEEN PROFESSIONAL DEVELOPMENT
OUTCOMES OF IT PROFESSIONAL AND OTHER VARIABLES
COEFFICIENTS (a)
Model
d i z e d C o d C
T Sig.B Std. Error Beta
1 (Constant) 26.812 3.231 8.298 .000
TECHNIQUES OF
TIME MANAGEMENT.634 .040 .571 15.865 .000
2 (Constant) 18.412 3.281 5.612 .000
TECHNIQUES OF
TIME MANAGEMENT.408 .049 .367 8.338 .000
LEVEL OF TIME
MANAGEMENT
TECHNIQUES
ADOPTED
.355 .048 .325 7.373 .000
3 (Constant) 33.677 4.813 6.997 .000
TECHNIQUES OF
TIME MANAGEMENT.379 .049 .341 7.791 .000
LEVEL OF TIME
MANAGEMENT
TECHNIQUES
ADOPTED
.306 .049 .280 6.273 .000
PERSONAL TIME
PLANNING -.140 .033 -.158 -4.275 .000
a Dependent Variable: professional development outcomes
Source: Primary data
The above table reveals that, the ‘F-Value’ of the three models of above
analysis is statistically significant. Hence, the null hypothesis is rejected, and
there is significant relationship between the professional development
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outcomes and techniques of time management, level of time management
techniques adopted, and personal time planning.
The study of “Influence of work motivation, leadership effectiveness
and time management on employees’ performance in some selected industries
in Ibadan”, Oyo et.al. 11
concludes that time management was least contributor
to employees’ performance, when properly not planned.
The study of “Relationship between the success and Time Managment
of executives” 12
confirms our results by showing a significant relationship
between the overall job performance and time management scores. Further,
literature also supports the relationship between time management and job
performance as evidenced by the earlier studies of Islam12
for rural
development officers and business executives. These studies found significant
relationship between management of time or allocation of time to managerial
tasks and job performance, concluding that management of time is key to
managerial performance. Further, in another study13
it was found that there is a
strong relationship of 0.78 between job performance and time management
tactics and strategies.
In this study, respondents being IT professionals, it was found that, four
core variables i.e., are techniques of time management, level of time
management techniques adopted, personal time planning and measurement of
professional development outcomes of IT personnel are directly related to one
variable or another related variables. Impediments of time management are
closely related to level of time management techniques adopted as per the
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175
present study. On the basis of statistical results revealed, a model chart is
presented below.
Fig. 4.1
REGRESSION ANALYSIS MODEL RESULTS
The above model classified main variables and interim variables. The
main variables are techniques of time management, level of time management
techniques adopted, impediments of time management and measurement of
professional development outcomes of IT personnel and interim variable is
personal time planning.
It is inferred from the above chart that the impediments of time
management and personal time planning influences the level of time
management techniques adopted and techniques of time management, which in
turn influence professional development outcomes of selected IT professionals.
Techniques of
time
management
Impediments of
time
management
Measurement of
Professional
development
outcomes
Personal time
planning
Level of time
management
techniques
adopted
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176
4.3 FACTOR ANALYSIS:-
Factor analysis is multivariate statistical method used in the analysis of
tables or matrices of correlation co-efficient. It is a method which will assist to
investigate and reach a meaningful interpretation of the way in which the
variables are related. It works mainly in two ways viz.
a) Reducing the original set of variables to a smaller number of
variables called factors
b) Reveals structural properties that may exist within the set of
relationships
Factor analyses are performed by examining the pattern of correlations or
co-variances between the observed measures. Measures that are highly
correlated either positively or negatively are likely to be influenced by the same
factors.
For this study, perceptions of IT professionals on five variables viz.
techniques of time management, level of time management techniques adopted,
personal time planning, measurement of professional development outcomes
and impediments of time management were taken into consideration.
Correlation techniques were used to understand their relationship and also to
find out the common factors.
PERCEPTIONS OF I.T. PROFESSIONALS ON TIME MANAGEMENT
COMPETENCY:
The relationship with various factors: In this study, to understand the
perception of IT professionals on time management competency, sample
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177
responses on five variables are analyzed using ‘Extraction Method’. To
eliminate the unique variances, the results are further iterated and the
communalities of the various variables after such iteration are given below.
Table-4.7
COMMUNALITIES IN IT PROFESSIONALS
EXCELLENT PERFORMANCE
Variables Initial Extraction
Techniques of time management 1.000 .662
Level of time management
techniques adopted 1.000 .711
Impediments of time
management 1.000 .197
Personal time planning 1.000 .392
Professional development
outcomes of IT professionals 1.000 .625
From the above analysis, it can be observed that impediments of time
management and personal time planning variables chosen have lesser impact
on the perceptions of the IT professionals.
On summing the communalities, the results show a value of 2.587 out of
standardised variance of 5.000. Thus the variance now is reduced to 2.587
which equals to 51.745% i.e. 2.587/5.000 x 100. That means, about 52% of the
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178
variance is common and 48% is unique. The following table gives a picture of
the common variance into two factors, both before and after varimax rotation.
Table-4.8
TOTAL VARIANCE EXPLAINED BY EXTRACTED FACTORS P
arti
cula
r
Init
ial
Eig
en
Val
ues
Ex
trac
tio
n
Su
ms
of
Sq
uar
ed
Lo
adin
gs
Ro
tati
on
Sum
s
of
Sq
uar
ed
Lo
adin
gs
Co
mp
on
ent
To
tal
%o
fv
aria
nce
Cu
mu
lati
ve
%
To
tal
%o
fv
aria
nce
Cu
mu
lati
ve
%
To
tal
%o
fv
aria
nce
Cu
mu
lati
ve
%
Factor I 2.587 51.745 51.745 2.587 51.745 51.745 2.587 51.745 51.745
Factor II
.891 17.826 69.571
Factor
III
.718 14.370 83.941
Factor
IV
.445 8.904 92.845
Factor
V
.358 7.155 100.00
The Final rotated loadings are as follows:
Table-4.9
TABLE SHOWING THE COMPONENT MATRIX
Variables
Component
Techniques of time management .814
Level of time management techniques adopted .843
Impediments of time management .444
Personal time planning -.626
Professional development outcomes of it
professional
.790
Source: Primary data
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179
The results of the above tables reveal that, the most important factors
contain the variables like, techniques of time management, level of time
management techniques adopted, personal time planning, measurement of
professional development outcomes and impediments of time management
which explain 51.745% of variance and has been given an appropriate name for
this group of IT professionals as ‘time value conscious group’. As such, this is
the only prominent group emerged from the study.
The factor analysis shows an interesting phenomenon wherein five main
variables are significant in the context of the realisation of time management
and work values of executives. If these IT professional lack in any one
faculties of the main variables, it seem that they did not attain the realisation of
time management as perceive by them and this may result in the decline of
their time management competency levels. Statistical analysis of the present
study explain the influence of affecting variables as presented in the table
below.
Figure 4.2
SIGNIFICANT VARIABLES OF IT PROFESSIONALS
TIME VALUE CONSCIOUS GROUP
Techniques of time management
Level of time management
techniques adopted
Measurement of professional
development outcomes of IT
professional and
Impediments of time
management
Time value
conscious group
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180
The techniques of time management and level of time management
practiced highly influences the ‘time value conscious group’ thereby increase
professional job outcomes. Impediments of time management moderately
influence the time value conscious group which means the respondents are able
to reduce the impediments only to some extent and they feel that they are
wasting their time which confirms both negative and positive relationship of
the factor ‘impediments of time management’. Regarding personal time
planning, the respondents are not able to allocate required time for personal life
as there is a need to work nearly seventy hours in a week in their office. Thus,
the personal time planning is negatively correlated to the time value conscious
group. To conclude the sample professionals under ‘time value conscious
group’ are able to achieve their career and professional development outcomes
due to their pragmatic time management capacity.
4.4 CLUSTER ANALYSIS
Cluster Analysis is an advanced statistical technique used to identify
relatively homogeneous groups of cases or variables based on selected
characteristics. This technique uses either distance or similarity measures to
identify the groups. Distance or similarity measures are generated by the
proximities between individuals based on multivariate data of the sample
respondents. If the distance is low between any two individuals, they are said
to belong to one group or cluster and whenever the distance between any two
objects is maximum, the two entities are said to be apart from one another. .
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Similarly two individuals are said to belong to one cluster, if the similarity
between the two is the highest.
There are several distance measures listed in the literature. Some of the
distance measures are Euclidean distance, Mahalanobis distance, City Block
distance and so on.
A number of clustering procedures have been evolved ever since the
concept has been introduced in the data analysis. Some of the methods are
single linkage, complete linkage, average linkage, Ward’s method,
MacQueen’s K-means method etc.
For the present analysis, ‘Mac Queeen’s K-means method’ and Squared
Euclidean distance have been used. The method and the measure used in this
analysis are the most commonly used clustering method to measure for
continuous data.
The basic data used for the analysis contains all the five variables like,
1. Techniques of time management
2. Level of time management techniques adopted
3. Impediments of time management
4. Personal time planning
5. Measurement of professional development outcomes of IT personnel.
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182
TABLE-4.10
TABLE SHOWING THE NUMBER OF CASES IN EACH CLUSTER
Particulars Number
Cluster1 329
Cluster 2 193
TOTAL 522
Source: Primary data
The above table indicated the presence of three clusters in the data set.
Total 522 executives have been classified in to the two distinctive clusters. The
first cluster consists of 329 executives; the second cluster has 193 executives.
TABLE-4.11
TABLE SHOWING THE FINAL CLUSTER CENTERS
Particulars
Cluster
1 2
Techniques of time management 84.02 74.07
Level of time management techniques
adopted78.70 68.60
Impediments of time management 68.77 63.18
Personal time planning 60.71 74.32
Measurement of professional development
outcomes of IT personnel 82.52 69.69
Source: Primary data
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183
The statistical analysis shows that, the average values of the two clusters
differ in all the five aspects considered in the present study. The mean value of
first cluster is found to be higher in the techniques of time management, level
of time management techniques adopted, impediments of time management
and measurement of professional development outcomes when compared to
that of the second cluster. The mean values of the second cluster are found to
be higher in personal time planning when compared to that of the first cluster.
A close examination of the table indicates that the two clusters differ from
one another in all the parameters, viz.,. techniques of time management, level
of time management techniques adopted, personal time planning, impediments
of time management and Measurement of professional development outcomes.
IT professionals in the first cluster have significantly higher values in four
parameters than the second cluster. The second cluster has significantly higher
values in one parameter than the IT professionals belonging to the first cluster.
TABLE 4.12
TABLE SHOWING THE ANALYSIS OF VARIANCE
Variables
Cluster Error
F Sig. Mean
Square
df
Mean
Square
Df
Techniques of time
management
11999.479 1 48.007 519 249.951 .000
Level of time 12386.211 1 49.672 519 249.362 .000
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management
techniques adopted
Impediments of
time management
3785.963 1 80.594 519 46.976 .000
Personal time
planning
22453.719 1 67.956 519 330.416 .000
Professional
development
outcomes of IT
professional
19964.302 1 49.204 519 405.742 .000
Source: Primary data
The data analysis indentified the existence of two groups of IT
professionals. The first group of IT professionals have high perceptions of
techniques of time management, level of time management techniques adopted,
personal time planning, impediments of time management and measurement of
professional development outcomes. These variables significantly influence
their measurement of professional development outcomes, than the second
group. First group of IT professionals have extremely higher perception values
in all the four variables i.e., techniques of time management, level of time
management techniques adopted, personal time planning, impediments of time
management, on professional development outcomes, whereas the second
group is moderately high perception values on these aspects. The entire five
variables are statistically significant. Hence, the two clusters have been given
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an appropriate name as, first cluster is called ‘Time Value Preference Group’,
second cluster is called ‘ Moderates Group’.
Conclusion:
Based on empirical results and interpretations, the following profiles of I.T.
professionals are developed with regard to influence of time management in
their professional development outcomes.
Specific inferences drawn on the basis of statistical analysis are presented
below:
Overall perceptions of I.T. professionals on techniques of time management
are:-
Level of time management techniques and personal time management are
positive and high wherein I.T. professionals are almost adopting the time
management techniques at high levels, though they adopt the personal time
planning at moderate levels. Professional development outcomes of I.T.
personnel are more than 50 % which explains that perceptions of techniques of
time management and level of time management adopted, personal time
planning and professional development outcomes are positive and significantly
related with each of the above factors.
Significant outcomes of results are, techniques of time management
correlated with all other variables. But inversely related to personal time
planning criteria. The level of time management techniques adopted positively
correlated with all other variables, but negatively correlated with personal time
planning efforts. The impediments of time management measure is
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significantly related with all variables, but personal time management is
negatively related. Personal time planning has negatively significant
relationship with all other variables. The Professional development outcomes
of IT employees is correlated significantly with all other variables but inverse
related with personal time planning.
The results of stepwise regression analysis explain that, the regression
model classified main variables and interim variables. The main variables are
techniques of time management, level of time management techniques adopted,
impediments of time management and measurement of professional
development outcomes and interim variable is personal time planning.
Factor analysis reveals that the IT professionals are significantly
influenced by techniques of time management, level of time management
techniques adopted, impediments of time management and measurement of
professional development outcomes of IT professionals and personal time
planning variables and has been named as ‘time value conscious group’.
Cluster analysis clearly explained that, first group of IT professionals
show high level of level of time management techniques adopted, impediments
of time management and in measurement of professional development
outcomes than the second group. The second group of IT professionals are
highly influenced, when analysed on their perceptions of personal time
management. These two groups were appropriately named as ‘time value
preference group’ and ‘moderately group’s respectively.
Conclusion:-
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The concept of ‘Time’ valued as money explains it being a resource that
can be measured in real value terms to individuals and organisations. While the
amount of money that can be made in a life time is not necessarily bounded,
time is naturally limited. Hence “Saving time” can be accomplished through
better time management. To understand the association between time
management and professional and career development, empirical analyses were
done, which concludes that most clearly time management helps to improve job
efficiency by enabling professionals to allocate adequate time to their job’s
most important tasks. This greater attention to high priority work area will
improve professional and career development.
Statistical analyses of this study confirms that efficient time management
reduces job stress, which can be an important impediment to job performance.
Thus, job induced stress was negatively correlated with self assessed
professionals and career development. Evan.C14
documented similar path from
time management to perceived time control to reduced work strain and higher
job performance. Professionals who manage time better report lower emotional
exhaustion and less job burn out of job burn out is already confirmed in this
study by Peters and Ruth 200515
. Sample IT professionals of this study had
reported that they have achieved their work life balance in a better manner
through proper management of time planning and proper execution of work,
ultimately leading to professional excellence. This study concludes that the IT
professionals’ time management practices are associated with more productive
work behaviour and more positive work outcomes which provide initial
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evidence that time management highly influence their professional and career
development.
Efficient and effective use of time is an unavoidable necessity for work
and life success. Work life balance is a condition in which there should be
harmony between individual life and work demands. Having a balanced life
means to achieve satisfactory experience in all areas of life and the need for
good distribution of time energy in all facets of life of professionals.
As the work life conflicts affects all aspects of professionals’ life, their
families, work places and even their mental physical health, steps should be
taken by the organizations for the professionals to have satisfaction, health and
productivity in the life which includes work leisure and love.
Work life imbalance is one of the great stressors in work place due to high
work hours, having multiple jobs, not using annual leave and being in the work
place during week ends and holidays, constant pressure to end work, long trips
for doing work etc.
The next chapter explain the findings, implications, interpretations and
suggestions for future research.
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END NOTES
1. Rezaeian Ali: “Complete system of time management” Samt publicaction,
1984, 45
2. Lewis David: “Time management” Kamaran Rooh translation 7th
printing
Phoenix publishing (1985), 143
3. Adams, G.A., & Jex, S.M. (1999) Relationships between time management,
control, work-family conflict, and strain. Journal of Occupational Health
Psychology, 1999.4(1), 72-77.
4. Nick & Steve Jacob, Mrowsky.J & Ross.C.E.1990 Control or defense.
Depression and the sense of control over good and bad outcomes. Journal
of Health and Social Behavior 31. 79-86.
5. Limon Cells T.2006 Time management for System administration O Reilly,
75
6. Hassanzabeh, R., & Ebadi, a.G. (2007). Measure the share of the effective
factors and time management. World Applied Science Journal, 2(3), 168-174
7. Schummacker, R.E., Michael, S., & Bembry, K.L. (1995) Identifying at-risk
gifted students in an early college entrance program, Roeper Review 18(2),
126-129.
8. Prevatt, F., Petscher, Y., Proctor, B.E., Hurst, A., & Adams, K. (2006) The
revised learning and study strategies inventory: An evaluation of
competing models. Educational and Psychological Measurement, 66(3), 448-
458.
9. Karim, Sevari, Mitrakandy(2007) – Time management skills Impact on Self
efficacy & academic performance Journal of American Science – 2011 7
(12).
10.Classens, C, Van Eerde, Rutte & Roe, A. (2007) A review of the time
management literature.Personal review, 36, 2, 255-276.
11.Oye.et al (2007) Influence of work motivation Leadership effectiveness on
time management and on employees’ performance in some selected
industries in Ibadan, vol.12(3),24-43
12.Karaoglan & Yaman (2009) The relationship between the success and time
management of executives. G.V.Journal of Science, 22(4), 287-295
Page 43
13.Hamid Sina Islamic Azad University, A survey of relationship between
organizational time management skills and work stress among principals
in Marvdasht Schools” Siyasal Kitabevi Ankara Turkey – 2011 – ISBN
9786053641049.
14.Evan.C. (2008) Time management for Dummies John Wiley & Sons, 39
15. Peters & Ruth (2005) The good research guide for small scale social
research project 4th
edition, 74
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