How Demographics Affect Quality of Work Life and Work-Life

Balance

Rina Anindita

, Lindawati

, Taufiqur Rachman

and Hasyim

Management Department, University Esa Unggul, Jakarta

Keywords: Demographics, Quality of Work Life and Work-Life Balance

Abstract: This research aims to identify is there any impact on the quality of work life and work-life balance of

tourism industry workers given by demographics factor (age,gender and marital status). How age gives

impact on quality of work life and work-life balance; how gender gives impact on quality of work life and

work-life balance; and how marital status gives impact on quality of work life and work-lifebalance.

Methods of data analysis used in this research arethe chi-square method with the help of two

categorizations, three and five in all data from all respondents. This research was conducted in some big

cities in Indonesia involving 150 male and female workers, working in the tourism industry. The findings

show that there is no impact on the quality of work life from age, gender and marital status. Age does give

an impact to work-life balance, while gender and marital status do not give any impact to work-life balance.

1 INTRODUCTION

From the time we were born, humansare destined to

work. Law 1945 article 27 verse 2 set a rule that

every citizen shall have the right to work and to earn

a humane livelihood. While seeing ourselves or

other people try as hard as we can to compete to earn

a degree and try to make our dreams true, we may

often wonder what is the purpose of work while at

the same time some of us may think it is because we

would have no choice about what to do. As a

conclusion, we may say that working aims to earn

money (White, 2017).

When the person is working, usually it is done

after they are finished their study either in high

school or university level according to their

tendency or age limitation. For male workers,

working usually becomes a continuous routine

especially after marriage. That is to fulfil their

responsibility either from their perspective, local

norms or by law. On the other side, a Korean

website, Chosun surveyed in April 2017 and

concluded that 46% percent of women quit their job

after marriage. Though there are women who quit

after marriage, some of them still continue.

According to Wolfman B.S. (1992) as cited from

Sumiyatiningsih (2014) motivation for women who

continue working after marriage usually followed by

two factors, motivation to fulfil economic needs and

toactualize themselves.

Quality of work life and work-life balance

studies mainly conducted due to traditional thinking

that women who play two or three roles: as a wife,

mom and career woman may find it difficult to play

all roles in their lives with averagely equal time

spend. This perspective may not wrong eventhough

we can see it happens among us or maybe we

experience it by ourselves. Men are treated and seen

as a strong human who always taught to be the

breadwinner since their young age. In opposite,

women are treated to be able to do domestic chores

to be seen as weak creatures thus make them stay at

home and handle all domestic problems. The

traditional domestic roles like this may still be

applicable for some families. However, in the

modern way of living nowadays, it may not be too

relevant anymore.

Priherdityo (2016) on CNN stated that Indonesia

has the 6th largest career women in the world. With

a percentage for senior position for women is in

34%. It means that by time men and women can

have the same opportunity to climb the company

ladder. Women’s position is getting more and more

attention by studies. It may be based on the thinking

perspective or by another attention. Aside from that,

study about quality of work life for men is not as

much as about women.

2616

Anindita, R., Lindawati, ., Rachman, T. and Hasyim, .

How Demographics Affect Quality of Work Life and Work-Life Balance.

DOI: 10.5220/0009949226162627

In Proceedings of the 1st International Conference on Recent Innovations (ICRI 2018), pages 2616-2627

ISBN: 978-989-758-458-9

In a general perspective, age usually can be seen

as the measurement of how mature someone to see

things in their life. It is seen as how someone can

organize themselves and everything surrounding

them. For example, we may find it is difficult to

balance our school life and family life when we were

still teenagers. However, as we grow older, now we

are facing another problem: how to balancing our

work life with family life, social life and leisure

time. Another complexity is, when talking about

work life, there are times when we feel comfortable

at work,but also there are times when we do notfeel

any positive vibes through work. The problems that

may occur is it may reduce our productivity and

develop negative feelings in the workplace.

While age usually acts as a measurement of

one’s maturity, gender is usually seen from people’s

perspective about how should someone live their

life. Even male and female roles are not only set by

social norm, but also by law. Indonesian law

regulation put men like the one who should protect

his wife and give everything she needs, also women

are the one who is responsible for the domestic affair

(Under Marriage Law of 1974 article 34 verse 1). As

human needs have increased, women’s role in the

workplace cannot be underestimated and make

gender diversity is now a modern issue in the

workplace. While many efforts were made to make

women and men being equal, but there is still

another effort that needs to being put.

We can see that eventhough women roles inthe

workplace have increased but gender equity has not

come out in an equal way between men and women.

As people always see men likestrong creatures and

women as a weak creature but an in-depth study of

quality of life and work life balance in men

perspective has been rarely found. In this modern

era, good quality of life and work life balance are

not only limited by how a person’s role at the

workplace and their family that most people always

associate it with women as the employee, a mother

and wife. But also, include a person’s social life that

can be associated with a broader circle: singles and

men.

We may wonder now, why the quality of work

life and work-life balance need to be learned again if

they are too common to be learned by scholars? The

answer may vary, but the quality of work life and

work-life balance will always be themain focus by

companies nowadays since many companies are

putting more effort to encourage their employees to

balance their lives domains. For example, PwC

(report) provides some facilities to facilitate their

employees’ work-life balance. They held: PwC Say,

PWC Away day, PwC Outing and PwC Gathering.

Also, they provide lounge, in-house clinic and dental

clinic, nursing room and other facilities to support

their employees’ quality of work life. Some articles

also now encourage the interviewee to ask about the

company’s work-life balance through interview

session.

For this research, the researcherchooses men and

women aged above 25 years old who works in

Jakarta and Tangerang. Jakarta is considered to be

the busiest city in Indonesia as it is the capital city of

Indonesia and Tangerang is seen as the satellite city

that supports workers who work in Jakarta. Reported

by Tranghanda in Hamdani (2017), around four

million people commute from satellite cities such as

Tangerang, Bekasi, Bogor and Depok to work in

Jakarta. Since Jakarta and Tangerang are connecting

and supporting each other in the economic sector,

we consider the workers in these both cities as the

workers whom the quality of life and the work-life

balance may get affected by their condition in their

journey to and from work.

The purpose of this study is to know the impact

of demographics to the quality of work life for

workers in Jakarta and Tangerang, and The impact

of demographics to work-life balance for workers in

Jakarta and Tangerang.

2 HYPOTHESES

DEVELOPMENT

2.1 Demographic to Quality of Work

Life

In her study, Anyaoku (2016) finds that QWL

depends on the gender by using Independent

Samples Test. In addition, also in her study

librarians age 45-60 reported significantly higher

satisfaction in contributing to the growth and

development to the society compared to those aged

20-29. Related to Anyaoku (2016), Amirtash and

Tondnevis (in Mirkamali and Thani, 2011) carried a

study and concluded that there is a significant

relationship between QWL and some of its aspects

with age and number of teaching years in faculty

members.

2.2 Demographic to Work-Life Balance

Thriveni and Rama (2012) bring a conclusion that

demographic variables such as age, income,

experience, marital status influence the women

How Demographics Affect Quality of Work Life and Work-Life Balance

2617

employees in their work-life balance. McMillan et

al. (2011) in Tomaževič et al. (2014) describes

work/life issues impact everyone without seeing

their education level, gender, income level, family

structure, occupation, race, age, job status or

religion. Panisoara and Serban (2013) did a special

study to find a relationship of marital status and

work-life balance. To reduce inequity, they divided

all respondents into four categories: unmarried,

married without children, married with children

under 18 and married with children above 18. All

four categories do not have a significant relationship

to work-life balance.

2.3 Hypothesis

Quality of work life programs mainly focuses on

two sectors, productivity and increases the

satisfaction of employees (Gadon, 1984 in Ahmad,

2017). In his journal, Wright (2002) also depicts that

factors like age, employment, gender, education and

income are very important to relate to the level of

QWL among the employees. Ahmad (2017) in his

study also proved that the study suggested a

statistically significant correlation between the

demographic variables such as age, a period of

service, income and education of employees of

University and QWL. The result excluded gender as

it has no significant correlation to QWL. From the

description above, hence, hypothesis 1 to 3 are

offered.

: Demographic is related to the quality of

work life

: Age is related to the quality of work life

: Gender is related to the quality of work

life

: Marital Status is related to the quality of

work life

Working women at midlife age mostly

experience varieties of challenges such as, caring for

children, parents, or spouse, yet sustaining marriage

in the face of the opposite pulls of overload and

complacency, juggling various rules, and stimulating

(Whelan Berry and Gordon, 2004; Wallen, 2002 in

Marcinkus et al., 2007). Due to the new gender

equity which women nowadays are more likely also

in work population, shifting role expectations (in the

family), and family time scarcity, many men and

women are required to find new ways to balance

their professional and personal lives (Rao and Indla,

2010). From that findings, hypothesis 4 – 6 are

offered.

: Demographic is related to work-life

balance

: Age is related to work-life balance

: Gender is related to work-life balance

: Marital Status is related to work-

lifebalance

3 RESEARCH METHOD

3.1 Sampling Method

The method of this research is purposive sampling

that is a type of non-probability sampling technique.

Non-probability sampling focuses on units that are

investigated based on the judgement by the

researcher. This means, before the research has

started, the researcher has classified which group of

respondents that meet the characteristics the

researcher needs. The goal of purposive sampling is

to focus on particular characteristics of a population

which will enable us to answer the questions (Lund,

2012).

These are some characteristics of the respondents

for this research:

a) Workers aged >21

b) Workers with minimum one year of work

c) Workers that work in a formal sector (not civil

workers)

3.2 Data Analysis Technique

3.2.1 Validity Test

The importance of validating research instruments

especially questionnaires are spread through most

studies. Validity test expresses the stage or degree in

which the measurements in the research instrument

measure the purpose of the research, it varies

depending on which instrument the researcher is

using when the research is going. Several varieties

that can be usedare faced validity, construct validity,

content validity and criterion validity. Validity tests

are categorized into two components, internal and

external validities. Internal validity refers to how

accurately the measures obtained from the what the

research is quantifying what it was designed to

measure, while external validity refers to on what

stage the measures can obtain from the sample that

describes the population general (Bolarinwa, 2015).

Questionnaire validity test was conducted by

Pearson Product Moment Correlations using SPSS

(SPSS, 2015). The validity test by Pearson was done

by seeing the correlation on each item on the

ICRI 2018 - International Conference Recent Innovation

2618

questionnaire in a total score. Here is the basic

making decision when using validity test. The

Pearson Product Moment formula is as shown

below:





∑



∑



∑









∑

.

∑







∑











∑







∑









(1)

Source: Siregar (2016)

Where:

rxy = Items correlation coefficient

n = total subject

∑x = total score from each item

∑y = total multiplication from each item

When the scoring is done, we can decide the next

action that we should do after we read the score.

These rules applied after we get the score.

a. Seeing the value of significance:

i. If the significance value < 0.05, then the

instrument is declared invalid

ii. If the significance value > 0.05, then the

instrument is declared invalid

b. Comparing the value of rxy table with r product

moment:

i. If the value of rxy> r table product moment,

then the instrument is declared invalid

ii. If the value of rxy< r table product moment,

then the instrument is declared invalid

3.2.2 Reliability Test

Reliability can be established by using a pilot test

with 20 to 30 respondents outside the sample

(Bolarinwa, 2015). This step is used basically to

check the consistency of the respondents

(Collingridge, 2014). Cronbach Alpha is the most

common test used to measure internal consistency

reliability. Cronbach Alpha values range from 0-1.0

that the acceptable value range starts from 0.70. In

most cases, the lowest value is 0.60 to make the

score acceptable.

The formula of Cronbach Alpha can be seen

below:









1





1

∑













(2)

Where:

= the coefficient of instrument reliability

k = number of questions

= score variants from all questions

St = deviation standard from all instrument

Some conditions could affect Cronbach values,

they are:

a. Numbers of items, the scale of <10 variables

could cause Cronbach alpha to be low;

b. Distribution of score, normality increases

Cronbach alpha value while skewed data

reduces it;

c. Timing, Cronbach alpha does not indicate the

stability or consistency of the test over time;

d. The wording of the items, the negative-worded

questionnaire should be reversed before scoring;

e. Items with 0. 1 and negative scores; Ensure that

items/statements that have 0 s, 1 s and negatives

are eliminated.

3.3 Score Interpretation

Psychology score interpretation is normative, means

that all scores that have been collected measure to

the relative position of theoretic population score

mean as a parameter. In the end, the quantitative

score that is still being a number can be interpreted

qualitatively (Azwar, 2015). To interpret the score

thoroughly, we may make categorization to put each

unit to its group which has an elevating position

based on the attribute that measured. This research

then measures the impact of demography onthe

quality of work life and demography to work-life

balance with two tests. The first one in three

categories and the second one in five categories.

Three categories measure each relationship as

follows:

μ1,0 Low



μ1,0







μ1,0



Medium



μ1,0



 High

While five categories measure each relationship as

follows:

μ1,5 Very low

μ1,5 μ0,5 Low

μ0,5 μ0,5 Medium

μ0,5 μ1,5 High

μ1,5 Very High

Where:

X = Total score of questionnaires

µ = Mean

 = Deviation standard

Since this research used three variables which

demographic has three its variables, in total,six

categories are measuring applied in this research to

find the precise answer.

How Demographics Affect Quality of Work Life and Work-Life Balance

2619

3.4 Chi-Square

Chi-Square is a statistical measure used in the

context of sampling analysis for comparing a

variance to a theoretical variance (Kothari, 2004).

As a non-parametric test, chi-square can be used as a

test of goodness of fit and as a test of independence.

Before applying the chi-square method, some

conditions shouldbe applied:

a. Observations recorded and used are collected on

a random basis.

b. All the items in the sample must be

independent, means no relation between items

c. A group should not contain very few items (less

than 10).

d. The overall number of items must also be

reasonably large. It should normally be at least

50, howsoever small the number of groups may

be.

e. The constraints must be linear. Constraints

which involve linear equations in the cell

frequencies of a contingency table (i.e.,

equations containing no squares or higher

powers of the frequencies) are known are

known as linear constraints

Chi-squareis then calculated as follows:



















(3)

Where:



= chi square

= observed frequency of the cell in i

row and

column

= expected frequency of the cell in i

row and j

column.

∑ = Summation

Observed frequency can be defined as counts

made from experimental data. In other words, the

observed frequency is obtained after the experiment

happen. In another side, expected frequency is the

number from calculations made by using theory

(Statisticshowto, 2017). In chi-square, both units are

said having a relationship if the significant value is

under 0,05. Alternatively, if it is written in number

sentence, the statement becomes, sig < 0,05.

4 RESULTS AND DISCUSSION

4.1 Results

4.1.1 Quality of Work Life (QWL)

Quality of Work Life uses two categorization, three

and five. For each different categorization

calculation applied. The calculation is done using

SPSS with score result, minimum = 22; maximum =

80, mean = 42.87 and deviation standard = 6.68.

From this calculation, all 150 questionnaires were

used and classified into each suitable category as

shown in Table 1.

Table 1: Quality of Work Life 3 Categorization

No. Formula Interpretation Total Percentage

1. X < 38.9 Low 15 10%

38.9 ≤ x <

52.723

Medium 120 80%

3. 52.723 ≤ x High 15 10%

From the table, it shows that 15 respondents have

a low level, 120 respondents have a medium

level,and 15 respondents have a high level of quality

of work life. The interesting part is, the majority of

respondents fall in the medium level of quality of

work life. For the next step, the researcher has

broken down the categories from three to five

categories to get the detail result.

Table 2: Quality of Work Life 5 Categorization

No. Formula Interpretation Total Percentage

1. X ≤ 35.45 Very low 7 5%

35.45 < x ≤

42.36

Low 38 25%

42.36 < x ≤

49.26

Medium 72 48%

49.26 < x ≤

56.17

High 28 19%

5. 56.17 < x Very high 5 3%

Source: Researcher’s own SPSS Result, 2018

According to Table 2, in this categorization, we

have found that 7 respondents can be classified in

the very low level of QWL, 38 respondents in low

level, 72 respondents in medium level, 28

respondents in high level and 5 respondents in very

high level. Compare to results in three

categorization; the finding is still related that

respondents vastly have a medium level of quality of

work life.

ICRI 2018 - International Conference Recent Innovation

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4.1.2 Work-Life Balance (WLB)

There are two categorizations for Work-Life

Balance (WLB), three and five categorizations. The

calculation is done using SPSS with the following

result: minimum = 16.00, maximum = 39.00, mean

= 27.24 and deviation standard = 3.60. The same

questionnaires (respondents) were used for this

calculation. The result for three categorisations can

be seen in Table 3.

Table 3: Work-Life Balance 3 Categorizations

No. Formula Interpretation Total Percentage

1. X < 23.63 Low 8 5.34%

23.63 ≤ x <

27.23

Medium 50 33.34%

3. 27.23 ≤ x High 92 61.32%

From the table shown above, there are 8

respondents fall in low level, 50 respondents fall in

medium level,and 92 respondents fall in the high

level of work-life balance. Moreover, from the

results majority fall in high level. To get the detail

result, the researcher did another test of WLB in five

categorizations. The result can be seen in Table 4.

Table 4:Work-Life Balance 5 Categorizations

No. Formula Interpretation Total Percentage

1. X ≤ 21.84 Very low 3 2%

21.84 < x ≤

25.44

Low 9 6%

25.44 < x ≤

29.04

Medium 105 70%

29.04 < x ≤

32.64

High 31 20.7%

5. 32.64 < x Very high 4 1.3%

Source: Researcher’s own SPSS result, 2018

Among those 150 respondents, there are 3

respondents with very low WLB level, 9

respondents fall in low level, 105 respondents in

medium level, 31 respondents in high level and 4

respondents fall in the very high level of WLB.

Compare to the previous Table 4.6 there is a

different result that from all respondents that

classified to a high level of WLB in three

categorizations, when it is all broken down to five

categories, most of all respondents that classified to

the high level had been classified to medium level of

WLB.

4.1.3 Chi-square Test

In this research, the researcher uses three

demographics factor: age, gender and marital status

as an X and both QWL and WLB as Y. The impact

level from X to both Ys is seen from the result of

Chi-square test using SPSS software. The rule is if

the Pearson chi-square test shows result score >0.05

it means X has an impact to Y. In contrast, if the

Pearson chi-square test shows result score <0.05 it

means X does not have an impact to Y. Since there

are two categorizations for both Y variables, each

demographic factor will be tested separately with

each Y categorization.

4.1.4

Demographics Crosstabulation Age

Two tests were done for both variables,

demographic (age) to QWL. each test shows the

impact of age to QWL in three or five

categorizations. In age classifications, there are

originally six age classifications, but since there are

two classifications that did not get any respondents

thus both of them will not be shown in this table or

any test.

Table 5: Crosstabulation Age to QWL 3 Categorizations

Age

Quality of Work Life

(Categorization)

Total

1 2 3

21 – 25 8 57 4 69

26 – 35 6 54 9 69

36-45 1 7 2 10

46-55 0 2 0 2

Total 15 120 15 150

Source: Researcher’s own SPSS Result, 2018

Based on Table 5, the score of sig. 



test is

0.696 that rejects the hypothesis and has a meaning

that there is no relationship between age and quality

of work life. After finished doing this test, the

researcher did another test for QWL in five

categorizations. The result shows in Table 6.

Table 6: Crosstabulation Age to QWL 5 Categorizations

Age

Quality of Work Life

(Categorization)

Total

1 2 3 4 5

21 - 25 4 18 35 10 2 69

26 - 35 3 17 31 15 3 69

36-45 0 3 4 3 0 10

46-55 0 0 2 0 0 2

Total 7 38 72 28 5 150

Source: Researcher’s own SPSS Result, 2018

In this test, QWL has divided into five

categorizations which makes the researcher able to

analyze deeper. The sig. 



score is known at 0.935

which also rejects the hypothesis from the researcher

How Demographics Affect Quality of Work Life and Work-Life Balance

2621

and confirm the finding from the previous table that

these two variables have no relationship which age

has no impact on QWL of the respondents. On the

next step, two tests were done between age and

WLB (three and five categorizations).

Table 7 :Crosstabulation Age to WLB

Age

Work-LifeBalance (Categorization)

Tot

1 2 3 4 5

21 - 25 3 1 49 12 4 69

26 - 35 0 7 45 16 0 68

36-45 0 1 9 1 0 11

46-55 0 0 0 2 0 2

Total 3 9 103 31 4 150

Source: Researcher’s own SPSS Result, 2018

Based on Table 7, in this test sig. 



= 0.036

confirms that there is an impact on age to WLB.

This brings hypothesis 2a can be accepted and

rejects H0. Even though most of the respondents fall

in the third category, but compare to older

respondents (aged >35) younger respondents have

more tendency to have higher work life balance.

Gender

Gender only falls into two classifications: male

and female. In this section, gender will be tested

with QWL and WLB. For each Y variable, there will

be 2 tests. Each table shows the connection between

two variables in three and five categories.

Table 8: Crosstabulation Gender to QWL 3

Categorizations

Gender

Work-Life Balance

(Categorization)

Total

1 2 3

Male 5 44 10 59

Female 10 76 5 91

Total 15 120 15 150

Source: Researcher’s own SPSS Result, 2018

According to Table 8, this test shows there is no

relationship between gender to WLB from the test

score, sig. 



= 0.71, while in the chi-square

method, two variables are said having a relationship

if the significance test is < 0.05. To confirm the

finding in this test, the researcher did another test

with QWL in five categorizations.

Table 9: Crosstabulation Gender to QWL 5

Categorizations

Gender

Quality of Work Life

(Categorization)

Total

1 2 3 4 5

Male 2 17 25 11 4 59

Female 5 21 47 17 1 91

Total 7 38 72 28 5 150

Source: Researcher’s own SPSS Result, 2018

Similar to the previous table, according to Table

9, the sig. 



score also rejects the relationship and

any impacts from gender to QWL by scoreof 0.296.

This means for this research, all respondents come

from both gender (male and female) tend to have a

balance level of QWL.

Table 10: Crosstabulation Gender to WLB 3

Categorizations

Gender

Work-Life Balance

(Categorization)

Total

1 2 3

Male 3 20 36 59

Female 5 30 56 91

Total 8 50 92 150

Source: Researcher’s own SPSS Result, 2018

Based on Table 10, the researcher finds that

gender does not give any impacts to WLB based on

the sig. 



score, 0.989. To support this finding, the

researcher did another test which between gender to

WLB in five categorizations.

Table 11: Crosstabulation Gender to WLB 5

Categorizations

Gender

Work-Life Balance (Categorization)

Total

1 2 3 4 5

Male 0 6 38 14 1 59

Female 3 3 65 17 3 91

Total 3 9 103 31 4 150

Source: Researcher’s own SPSS Result, 2018

Based on Table 11, this test was done by

compiling data of gender variable to WLB,and the

categorization has been spread into five to see the

relationship from both variables thoroughly. The sig.





score for this test is at 0.214 which support the

previous test and rejects the researcher’s hypothesis.

This means, ones’ work life balance does not

affected by gender.

Marital Status

This research divides marital status into three

classifications: single, married, widowed. The

researcher will do four test for this variable. Two

tests to know the connection (impact) between

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2622

marital status to QWL (3 and 5 categorizations) also

another to between marital status to WLB (3 and 5

categorizations).

Table 12: Crosstabulation Marital Status to QWL 3

Categorizations

Marital

Status

Quality of Work Life

(Categorization)

Total

1 2 3

Single 13 84 12 109

Married 2 31 3 36

Widowed 0 5 0 5

Total 15 120 15 150

Source: Researcher’s own SPSS Result, 2018

As shown in Table 12, this test was done by

compiling data from the marital status variable and

quality of work life. The sig. 



score for this test is

at 0.581 which shows there is no relationship

between marital status and QWL and rejects the

researcher’s hypothesis

Table 13: Crosstabulation Marital Status to QWL 5

Categorizations

Marital

Status

Quality of Work Life (Categorization)

Total

1 2 3 4 5

Single 7 23 52 23 4 109

Married 0 12 19 4 1 36

Widowed 0 3 1 1 0 5

Total 7 38 72 28 5 150

Source: Researcher’s own SPSS Result, 2018

To support the previous finding, the researcher

did another test which broadensthe quality of work

life variable to five categorizations as shown in

Table 13. According to sig. 



test, the researcher got

the score at 0.313 which rejects the researcher’s

hypothesis. In another word, marital status does not

give any impact to ones’ work life balance.

Table 14: Crosstabulation Marital Status to WLB 3

Categorizations

Marital

Status

Work-Life Balance (Categorization)

Total

1 2 3

Single 7 34 68 109

Married 1 13 22 36

Widowed 0 3 2 5

Total 8 50 92 150

Source: Researcher’s own SPSS Result, 2018

After compiling data and did test to marital status

and quality of work life, the researcher did two more

tests. This test is between marital status to work-

lifebalance in three categorizations as shown in

Table 14. From sig. 



score, 0.620 it shows that

marital status does not give any impact to work life

balance and rejects the researcher’s hypothesis.

Table 15:Crosstabulation Marital Status to WLB 5

Categorizations

Marital

Status

Work-Life Balance (Categorization)

Total

1 2 3 4 5

Single 3 6 73 23 4 109

Married 0 2 26 8 0 36

Widowed 0 1 4 0 0 5

Total 3 9 103 31 4 150

Source: Researcher’s own SPSS Result, 2018

This last test was done to support the previous

finding. Based on Table 15, from sig. 



test, the

researcher found the score is at 0.690. This test

result is similar to the previous test and also rejects

the researcher’s hypothesis which means marital

status does not give any impacts to ones’ work life

balance.

Results Analysis

In this research, demographics are presented by

three variables: age, gender and marital status while

quality and work life and work-life balance present

themselves. In the calculation method, all

demographics were tested to each Y variable with

two categorizations (three and five). Results show

that from 12 tests, 11 tests indicate invalidation of

the hypotheses (sig. 



> 0.05) and only one test

appears to confirm the researcher’s hypothesis (sig.





<0.05).

Table 16: Sig. Chi-square Test Result Demographics to

QWL

Hypothesis

Statement

Sig.





score

Interpretation

H1a

Age is related to

the quality of

work life

0.696 Age is not related

to the quality of

work life

0.935

H1b

Gender is

related to the

quality of work

life

0.71 Gender is not

related to the

quality of work

life

0.296

H1c

Marital status is

related to the

quality of work

life

0.581 Marital status is

not related to the

quality of work

life

0.313

Source: Researcher’s own SPSS Result, 2018

As shown in Table 16, the data show based on 



test all hypotheses are rejected (



> 0.05). H1a is

rejected from this test based on the 



test. Age,

gender, and marital status are not related to ones’

quality of work life. This score means that no matter

how young or old is someone, their quality of work

How Demographics Affect Quality of Work Life and Work-Life Balance

2623

life will not be affected by it and contrast with

Anyaoku’s (2016) study that states about QWL is

significantly related to age. Although, in five

categorization the results come in more detail which

shows respondents also have a tendency to move

from medium to low level or medium to high level.

After finding the relationship between age and

QWL, the researcher did a test to find the

relationship between gender and QWL. This

research starts with the initiative about how women

often become the object of QWL and WLB studies

with low attention to men. The table above shows

that there is no relationship between gender to QWL

(



> 0.05). Which means, men and women do not

have a significant different level of QWL thus, make

H1b is rejected. This may be explained from

respondents’ job that does not provide any

differences between male and female workers. Since

all workers have the same responsibilities, it makes

employees feel that they are equal and make a good

relationship between all workers.

Another test is to find a relationship between

marital status and QWL. It appears that marital

status is not related to QWL (



> 0.05), rejects

H1c. This shows that in every status each respondent

has, it does not have any impact on their quality of

work life level. The results can be happened since

the respondents taken for this research is too

homogenous (single) that makes the data calculation

be tendentious to one data and make the result

unrelated to another one.

Table 17: Sig. Chi-square Test Result Demographics to

WLB

Hypothesis

Statement





score

Interpretation

H2a

Age is related

to work-life

balance

0.036

Age is related

to work-life

balance

H2b

Gender is

related to

work-life

balance

0.989 Gender is not

related to the

quality of work

life

0.214

H2c

Marital status

is related to

work-life

balance

0.620 Marital status

is not related to

the quality of

work life

0.690

Source: Researcher’s own SPSS Result, 2018

The latest tests were done to find the result of

those three hypotheses given by the researcher.

From the Table 17, p-value only confirms H2a

which stated age is related to work-life balance.

From this research, it shows that the younger

respondents tend to have high work-life balance

level than the older respondents. This against the

perception that older respondents may have a high

level of work-life balance.

In contrast to Poulouse and Sudarsan (2014)

study which shows gender is related to WLB, this

research shows that gender is not related to WLB

(



> 0.05), rejects H2b. The rejection may come

from many aspects. The respondents used in this

research may not come in the balance amount thus

make the information given becomes more

tendentios into only one gender. In another side, the

contrast result may also come from the balance work

and no discrimination applied at a workplace that

makes workload between male and female workers

are no far from different. Thus, make all workers

have a possible chance to spend their after-work

time with their personal life longer.

From gender, the latest test is done between

marital status and work-life balance. The 



score

for this test is > 0.05 which shows there is no

relationship between marital status and work life

balance. The rejection of H2c may come from some

causes for the example, the homogenous data from

the respondents and there is a significant difference

from one cluster to others. Data used in this research

comes from 73% single respondents. This means,

the score from the calculation may not represent the

whole respondents since there is a significant

difference of three clusters used in this research.

This make this research’s finding contrast with

Thriveni and Rama (2012) that states demographics

(marital status) has a significant relationship with

work life balance. Instead, this research matches the

finding from Panisoara and Serban (2013) that states

marital status has no relationship with work life

balance.

The results of this study are not consistent with

the result of the previous study, Anyaoku (2016) that

shows there is a relationship of age to QWL. Also is

not consistent with Poulouse and Sudarsan (2014)

about their review that states there is a relationship

between demographics to WLB. Moreover, the

cause of major rejection in this study may come

from the majority of answers from respondents. In

QWL part, the average of respondents answer agree

95 throughout all statements that over 100

respondents agree they work in a nice building

company, get good facilities from their workplace,

they participated in sounding their opinion, satisfied

at their work, and have good coworkers.

While in WLB part, the answers vary between

disagree and agree,but the majority is still in “agree”

ICRI 2018 - International Conference Recent Innovation

2624

part with 70 respondents. From that, over 100

respondents agree they work,or they job encourage

them to enjoy their personal life.Although, this study

may be consistent with other studies. The different

results of this study with other may come from the

characteristics of the respondents used that is too

homogenous, the psychological condition of the

respondents when filling in this questionnaire, and

also how the respondents react to the questionnaire.

4.2 Research Findings

Based on this research, there are some findings can

be found from each Y variable. Quality of work life

and work-life balance may be affected by

demographics if some certain conditions applied to

the respondents and also the researcher uses certain

measurements to classify which respondents can be

used as the respondent in the research. Thus, data

used in this research did not support the hypotheses,

certain conditions such as time and the current

condition when the respondents answer the

questionnaire, the level of understanding when the

respondent read the questionnaire and also honesty

level of what the respondent truly feel may affect the

answers given. Certain measurements that may be

needed can be about how many respondents needed

for each cluster.

Age is not related to the quality of work life from

the 



score result, 0.696 and 0.935. Gender also is

not related to the quality of work life from the





score result 0.71 and 0.296. The last, marital

status is also not related to the quality of work life

from the 



score 0.581 and 0.313. From all the tests

score above H1 is rejected. In contrast, age is related

to work life balance with 



score 0.045 that

confirms H2a. But the following score tests for

gender to work life balance are at 0.433 and

0.821,also another latest score tests marital status to

work life balance are at 0.098 ad 0.894. In another

word, H2b and H2c are rejected.

4.3 Results Limitations

There are some limitations for this research that may

give some insights for the next research as

corrections, those are:

a. There are only 150 respondents used in this

research.

b. This research used too homogenous

respondents, means majority respondents used

in this research are either in one or two clusters

of demographic variables. This research used

respondents majority age 21-25, female, and

single.

c. Respondents may respond to the questionnaire

with anunclear statement. This may happen if

the respondents do not understand the

statement.

d. Variables used in this research are only two

without intervening connection.

5 CONCLUSIONS

5.1 Summary

This study observed how demographics related to

the quality of work life and work-life balance.

Alternatively, in another word, is there any impact

from demographics of ones’ life to their quality of

work life and work-life balance. Literature review

for these variables may vary with a different

result,but most of them are a study about women.

Based on the tests had taken, there are six

findings that among those six, five of them rejects

the hypotheses.The results are explained as follow:

a. Age is not related to the quality of work life.

This means, whether the person is relatively

young or old, their quality of work life will not

be affected. Younger people can have a high

level of quality of work life,and older people also

can have the same level as younger people.

b. Gender is not related to the quality of work life.

This result shows that male and female workers

can have the same level of quality of work life.

c. Marital status is not related to the quality of work

life. People with single, married or widowed

status can have the same level of quality of work

life. This means, having own family does not

give any impact of ones’ quality of work life.

d. Age is related to work-life balance. In this result,

Age does give any impact of ones’ work-life

balance. For this research, it shows that younger

people tend to have a high level of work-life

balance. While in contrast, older people tend to

have a lower level of work-life balance.

e. Gender is not related to work-life balance. In this

category, the researcher does not find any impact

from gender to work-life balance.

f. Marital status is not related to work-life balance.

This means, in any status of a person it does not

give any impact to ones’ work-life balance.

How Demographics Affect Quality of Work Life and Work-Life Balance

2625

5.2 Recommendations

Based on the results had shown above, the

researcher has some recommendations as follow:

a. For employer around Jakarta and Tangerang

Quality of work life and work-life balance are

two important things that caught more and more

attention lately. This means, in years to come

employee will not only looking for work with a

goodsalary but also with good facilities and

flexibilities to the employee. To attract high

potential employees, the employerneeds to be

more flexible to give an adequate salary, fair

bonus and at least a basic facility to all

employees. For the example, nursery room, fair

leaves days for all employees and vehicle to rent

or borrow for employees who urgently needed.

b. For the successor of this research

This research used respondents that being a

majority in this research. This makes this

research’s P-value be affected. Thus, to have a

fair result, diversity of respondents may need. In

another option, a balance amount of respondent

can produce a fair result.

5.3 Implications

There are two implications can be used from this

research, they are:

a. Practical Implication

This research may be implied in daily life by the

employer to increase their employees quality of

work life and work-life balance. Readers also can

imply this research findings by trying to put all

lives domains in an equal balance. In another

side, many employers prefer to hire single

candidates as an employee because most

employers are afraid that married employees may

put their personal life as a priority of work. Since

this research shows that marital status is not

related to either quality of work life and work-

life balance, thus, employers may need not to

doubt the integrity of married workers.

b. Theoretical Implication

The researcher hopes that the result of this

research may bring new insight to employers

about how the quality of work life and work-life

balance level of employees in the hospitality

industry in Jakarta and Tangerang.

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