Post on 30-Jul-2021
Quality of Life (QoL) Survey for Expatriates
Student Name
Institution Affiliation
Introduction
Quality of Life (QoL) essentially encompasses an individual’s well-being in terms of
their social, physical and spiritual aspects. However, as Theofilou (2013) argues, attempts to
define or measure the QoL concept are often challenging as it not only is multifaceted and
complex but also, requires evaluation from multiple theoretical angles. As a result, diverse
approaches of measuring QoL have emerged, ranging from economics, medicine and
sociology to political science and psychology. On the same note, different definitions of QoL
have also emerged over the years in the different fields. The World Health Organisation
identifies QoL as the individual’s perception of their position in life and in the context of
their culture and value systems in relation to their expectations, standards, goals and concerns
(The Whoqol Group, 1998). The evaluation of the definition highlights its influence in this
research as it emphasizes that the quality of life of individuals is related to their mental well-
being and how they relate to the environments they live in and their relation to the standards.
This research specifically focuses on expatriates as a class of workers who face
diverse challenges in international work environments owing to the increasing effects of
globalization on business (Shaffer et al., 2012). The researchers report that as businesses
grow to operate on a global basis, employees are often called upon to work in international
assignments in order to ensure survival of the firms. However, according to research by Aracı
(2015), one of the most common challenges faced by multinationals in undertaking work in
international environments stems from the high failure of the expatriates. In explanation, the
researchers report that the high failure of expatriates is driven by the complexity in managing
cultural differences within the local countries. Subsequently, there has been significant
interest in investigating how to enhance the success of expatriates in the international
environments due to its direct impact on the performance of multinational firms.
As Quality of Life is considered to be influenced by the nature of environments that
individuals live in (The Whoqol Group, 1998), this research particularly directs its attention
to the housing facilities provided for expatriate workers. The motivation for undertaking the
research stems from findings by Howden-Chapman, Roebbel, & Chisholm (2017) which
revealed that the provision of well-designed housing was important in promoting overall
quality of life. Therefore, by extension, the research argues that by providing high quality
housing and residential environments for expatriate workers, this is anticipated to improve
their success rates in international assignments, thereby, enhancing performance of global
firms. The high-quality housing and residential environments are further contextualized in
terms of the expatriates’ satisfaction towards daily commute, home environment, safety and
utilities or maintenance.
Research Question
i. What is the impact of the housing environment and residential needs on the quality of
life perception by expatriate workers?
Research Objectives
i. To identify the impact of commute satisfaction on quality of life perception by
expatriate workers.
ii. To investigate the impact of home satisfaction on quality of life perception by
expatriate workers.
iii. To understand the impact of safety satisfaction on quality of life perception by
expatriate workers.
iv. To identify the impact of maintenance satisfaction on quality of life perception by
expatriate workers.
Literature Review
Over the years, diverse researchers have examined the contribution of housing and
residential satisfaction on the overall quality of life satisfaction. In the study by Peck & Kay
Stewart (2009), for instance, findings reported indicated that an increase in housing
satisfaction was associated with a substantial increase in overall life satisfaction. In addition,
the findings also reported that an increase in housing satisfaction was associated with various
characteristics of the dwelling unit such as neighbourhood satisfaction, improved structural
quality, high number of years of residence, lower perceived housing costs, ownership and
lower person-to-room ratios. Directly, the findings suggested that satisfaction with housing
contributed to overall life satisfaction where the qualities of dwelling units were argued to
influence satisfaction.
The findings from the study resonated with Aminian (2019) who also observed that
housing satisfaction, as mediated by variables such as ownership, housing type and dwelling
design, had a significant and direct impact on overall quality of life in Netherlands. Similarly,
Zhang, Zhang, & Hudson (2018) also reported that in urban China, home ownership and the
sizes of houses influenced the levels of happiness that respondents had in living in the urban
environments. The suggestion from these studies (Peck & Kay Stewart, 2009; Zhang, Zhang,
& Hudson, 2018; Aminian, 2019) is that, the nature of housing or dwelling units and their
features are influential in influencing overall life satisfaction and happiness of individuals. As
such, in the context of this research, it would be argued that the management of multinational
companies can positively enhance quality of life of their expatriate workers by providing
them with homes or housing them in establishments that are of high quality. In turn, this
would be expected to influence their performance at work assignments and the overall
success of the organisation.
Other researchers extend the discussion further to also consider the influence of
participants’ satisfaction towards other residential characteristics such as neighbourhood and
commute satisfaction on overall quality of life satisfaction (Mouratidis, 2020). In the study,
the researcher revealed that commute satisfaction, neighbourhood satisfaction and housing
satisfaction were reliable indicators of the liveability in urban areas and directly, subjective
well-being. A different study by Lorenz (2018) however revealed that there was no evidence
that showed that commuting was linked to lower life satisfaction. Instead, the researcher
reported that longer commutes were mainly associated with lower life satisfaction in specific
domains such as leisure time and family life. The study reiterated Wang, Yin, & Shao (2021)
who also reported that longer commutes led to a reduction in the levels of life satisfaction
among respondents from 92 Chinese cities. The evaluation of the different studies (Lorenz,
2018; Mouratidis, 2020; Wang, Yin, & Shao, 2021) suggests that aside from the qualities of
dwelling units such as the size of houses and ownership, additional residential features such
as the ease of commuting and quality of neighbourhoods also influence the overall
perceptions of QoL. Therefore, this would further indicate that the management of global
multinationals should also emphasize on identifying suitable residential locations for their
expatriate workers that allow them to commute easily to their workplace.
Methodology
The core objective of this research is to investigate the impact of the housing
environment and residential needs on the quality of life perception by expatriate workers. As
a result, the dependent or explanatory variable is the quality of life perception while
independent variables are the housing environment and residential needs. To address the
research objective, the researcher begins by collecting data from 238 countries across the
globe by administering a survey to expatriates. The questionnaire comprises of 41 close-
ended questions which is further categorised into various topical areas that customers are
satisfied with. The table 1 below further outlines the dependent and independent variables.
Table 1. Dependent and independent research variables
Dependent variable Independent variables
Overall satisfaction Housing satisfaction
Maintenance satisfaction
Security measures satisfaction
Commute satisfaction
Upon identification of the dependent and independent variables, the next phase
involves the data analysis in SPSS software. The researcher imports the data from Excel
spreadsheets and proceeds to undertake the analysis in order to address the research
objectives. However, two main types of statistics will be employed in the analysis;
descriptive and inferential statistics.
Findings and Interpretation
Descriptive analysis
To begin with, it was important to run descriptive statistics in order to understand how
the expatriates ranked satisfaction levels towards various residential needs in the housing
environment. The descriptive analysis regarded calculation of the mean satisfaction rates for
the various sub-elements within each of the independent variables.
The first aspect examined regarded the satisfaction levels towards commuting to
different neighbourhood locations such as work, grocery store, school and shopping center
among others. Table 2 below displays the satisfaction levels towards commuting to the
various locations.
Table 2. Satisfaction levels towards commuting
Commuting location Mean
satisfaction
Standard
deviation
Valid Missing Total
Work 4.11 0.608 195 43 238
Grocery store 4.32 0.417 195 43 238
School 3.85 0.768 195 43 238
Community store 4.06 0.735 195 43 238
Shopping center 4.07 0.514 195 43 238
Results in table 2 showed that the expatriates were highly satisfied with their
commuting to the grocery store at a mean of 4.32 while they had low satisfaction towards
going to school at a mean rank of 3.85. To better understand the distribution of the
satisfaction levels for the two variables, histograms were also extrapolated. Refer to the
appendix where histograms of the satisfaction towards commuting to the grocery store and
commuting to school displayed in figure 1 and figure 2.
Descriptive statistics were also extrapolated for the satisfaction towards home
amenities. Table 3 below details the mean and standard deviations obtained for the home
amenities.
Table 3. Satisfaction levels towards home amenities
Housing amenities Mean
satisfaction
Standard
deviation
Valid Missing Total
Overall interior layout 3.90 0.532 238 - 238
Appliances (kitchen,
laundry, etc.)
3.68 0.585 238 - 238
Furniture 3.42 0.607 238 - 238
Storage space 3.74 0.588 238 - 238
Additional storage
space
3.55 0.703 238 - 238
Results in table 3 revealed that the expatriates were highly satisfied with the overall
interior layout at a mean score of 3.90 while they had lower satisfaction towards furniture at a
score of 3.42. Refer to the appendix where histograms of the satisfaction towards overall
interior layout and furniture are displayed in figure 3 and figure 4.
The satisfaction levels of the participants towards housing utilities were also
determined as shown in table 4 below.
Table 4. Satisfaction levels towards housing utilities
Housing utility Mean
satisfaction
Standard
deviation
Valid Missing Total
Physical infrastructure 3.68 0.597 238 - 238
HVAC 3.50 0.554 238 - 238
Plumbing 3.53 0.541 238 - 238
Trash collection and
sewage
3.94 0.469 238 - 238
Electrical (wires,
plugs, etc)
3.69 0.616 238 - 238
Electrical service 3.82 0.59 238 - 238
Water reliability 3.88 0.605 238 - 238
Water quality / safety 3.90 0.611 238 - 238
Results in table 4 showed that the expatriates ranked trash collection and sewage
highly (mean 3.94) compared to HVAC at a mean of 3.50. Refer to the appendix where
histograms of the satisfaction towards trash collection and sewage and HVAC is displayed in
figure 5 and figure 6.
Descriptive statistics of the satisfaction towards the responsiveness to maintenance
requests were also outlined as shown in table 5 below.
Table 5. Satisfaction levels towards responsiveness to maintenance requests
Housing amenities Mean
satisfaction
Standard
deviation
Valid Missing Total
Physical
infrastructure
3.91 0.541 204 - 238
HVAC 3.88 0.529 204 - 238
Plumbing 3.91 0.456 204 - 238
Trash collection and
sewage
4.03 0.456 204 - 238
Electrical 4.00 0.468 204 - 238
Mildew / Rust 3.56 0.674 204 - 238
Dust 3.61 0.716 204 - 238
Pests 3.62 0.681 204 - 238
Results in table 5 revealed that the guests were most satisfied with the responsiveness
towards trash collection and maintenance at a mean score of 4.03 while being less satisfied
with mildew or rust with a score of 3.56. Refer to the appendix where histograms of the
satisfaction towards trash collection and sewage maintenance and mildew or rust is displayed
in figure 7 and figure 8.
Finally, descriptive statistics of satisfaction towards security and safety provided in
the residential sector were generated as summarized in table 6 below.
Table 6. Satisfaction levels towards safety and security
Housing utility Mean
satisfaction
Standard
deviation
Valid Missing Total
Intruder prevention 4.12 0.514 238 - 238
Accident prevention 3.73 0.518 238 - 238
Health hazard
prevention
3.50 0.603 238 - 238
Extreme weather
countermeasures
3.64 0.539 238 - 238
Safety during commute 3.91 0.509 238 - 238
Crime and theft,
relative to other
neighbourhoods
4.04 0.462 238 - 238
Results in table 6 showed that the expatriates were highly satisfied with the safety
provided against intruder prevention at a mean score of 4.12 while they displayed least
satisfaction for health hazard prevention at a mean of 3.50. Refer to the appendix where
histograms of the satisfaction towards intruder prevention and health hazard prevention is
displayed in figure 9 and figure 10.
Inferential analysis
As the descriptive analysis revealed the mean satisfaction scores for the different
independent variables, correlation analysis was thereafter carried out in order to evaluate the
strength of the association between study variables. The rule of thumb adopted was that the
values of the correlation coefficient ranged from -1 (perfect negative correlation) to +1
(perfect positive correlation) and correlation values closer to -1 or +1 indicated stronger
relationships between the variables. However, in order to correlate the variables, there was
need to first calculate the average mean scores for the sub-groups within independent
variables. Thereafter, the correlation was conducted against the overall average satisfaction.
The table 7 below displays the results of the correlation analysis.
Table 7. Correlation analysis
Correlation AnalysisOverall_ave_satisfactio
n
Commute_satisfaction
Home_amenities_satisfa
ction
Housing_utilities_satisfaction
Maintenance_satisfactio
n
Security_satisfa
ction
Overall_ave_satisfaction
Pearson Correlation
1 .648** .807** .836** .760** .818**
Sig. (2-tailed)
.000 .000 .000 .000 .000
N 238 238 238 238 235 238Commute_satisfaction
Pearson Correlation
.648** 1 .412** .411** .290** .430**
Sig. (2-tailed)
.000 .000 .000 .000 .000
N 238 238 238 238 235 238Home_amenities_satisfaction
Pearson Correlation
.807** .412** 1 .659** .626** .616**
Sig. (2-tailed)
.000 .000 .000 .000 .000
N 238 238 238 238 235 238Housing_utilities_satisfaction
Pearson Correlation
.836** .411** .659** 1 .632** .720**
Sig. (2-tailed)
.000 .000 .000 .000 .000
N 238 238 238 238 235 238Maintenance_satisfaction
Pearson Correlation
.760** .290** .626** .632** 1 .669**
Sig. (2-tailed)
.000 .000 .000 .000 .000
N 235 235 235 235 235 235Security_satisfaction
Pearson Correlation
.818** .430** .616** .720** .669** 1
Sig. (2-tailed)
.000 .000 .000 .000 .000
N 238 238 238 238 235 238**. Correlation is significant at the 0.01 level (2-tailed).
From the results in table 7, it emerged that the overall average satisfaction was
positively and strongly correlated to; i) commute satisfaction (r = 0.648, p < 0.01), ii) home
amenities satisfaction (r = 0.807, p < 0.01), iii) housing utilities satisfaction (r = 0.836, p <
0.01), iv) maintenance satisfaction (r = 0.760, p < 0.01), v) security satisfaction (r = 0.818, p
< 0.01). Further analysis also revealed that housing utilities satisfaction exerted the highest
impact on overall satisfaction due to its high correlation coefficient (r = 0.846). This was
followed by the security satisfaction (r = 0.818) and home amenities satisfaction (r = 0.807).
Secondly, linear regression analysis was also undertaken in order to test the strength
of the relationship between the study variables. The model summary of the R and R square
was generated as shown in table 8 below.
Table 8. Model summary from linear regression
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .968a .937 .936 .09142
a. Predictors: (Constant), Security_satisfaction, Commute_satisfaction,
Home_amenities_satisfaction, Maintenance_satisfaction, Housing_utilities_satisfaction
From the results in table 8, the model summary revealed an R square of 0.937 which
suggested that the various independent variables (commute satisfaction, home amenities
satisfaction, maintenance satisfaction, security satisfaction and housing utilities satisfaction)
explained 93.7% of the overall satisfaction rate.
The ANOVA model also generated as shown in table 9 below.
Table 9. ANOVA model
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 28.551 5 5.710 683.295 .000b
Residual 1.914 229 .008
Total 30.464 234
a. Dependent Variable: Overall_ave_satisfaction
b. Predictors: (Constant), Security_satisfaction, Commute_satisfaction,
Home_amenities_satisfaction, Maintenance_satisfaction, Housing_utilities_satisfaction
Results in the ANOVA model showed that a significance value of (p < 0.05) which
was also indicative that the regression model was statistically significant and independent
variables predicted the values of the outcome variable. Finally, the unstandardized
coefficients of the regression model were generated as shown in table 10 below.
Table 10. Unstandardized coefficients
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .058 .068 .857 .392
Commute_satisfaction .217 .015 .279 14.943 .000
Home_amenities_satisfaction .202 .019 .254 10.597 .000
Housing_utilities_satisfaction .229 .022 .277 10.555 .000
Maintenance_satisfaction .141 .017 .204 8.352 .000
Security_satisfaction .196 .025 .212 7.956 .000
a. Dependent Variable: Overall_ave_satisfaction
From the results in table 10, a constant value of 0.058 was reported. The coefficients
of all independent variables were also significant given their value of (p < 0.05). As such, a
regression equation could be plotted to explain the relationship between the dependent and
independent variables.
The line plots of each of the independent variables against the overall average
satisfaction are further displayed below. First, the line plot of the overall average satisfaction
against the commute satisfaction average was generated as shown in figure 11 below.
Figure 11. Line plot of overall average satisfaction against commute satisfaction
From the results, a linear plot was identified, with an equation detailed below.
y = 3.74 + 9.39E – 4x
The line plot for the overall average satisfaction against home amenities satisfaction was also
generated as shown in figure 12.
Figure 12. Line plot of overall average satisfaction against home amenities satisfaction
The equation obtained was y = 1.54 + 0.63x
In figure 13, the line plot for the overall average satisfaction against housing utilities
satisfaction was generated as shown below.
Figure 13. Line plot of overall average satisfaction against housing utilities satisfaction
The equation obtained was y = 1.24 + 0.7x
In figure 14, the line plot for the overall average satisfaction against maintenance satisfaction
was generated as shown below.
Figure 13. Line plot of overall average satisfaction against maintenance satisfaction
The equation obtained was y = 1.79 + 0.53x
Finally, the line plot for the overall average satisfaction against security satisfaction was
generated as shown below.
Figure 14. Line plot of overall average satisfaction against security satisfaction
The equation obtained was y = 0.91 + 0.77x
Conclusion
From the analysis of the results undertaken, it emerged that overall average
satisfaction was positively associated with all the independent variables (commute
satisfaction, home amenities satisfaction, maintenance satisfaction, security satisfaction and
housing utilities satisfaction). However, results of the correlation analysis showed that
housing utilities satisfaction exerted the highest impact on overall satisfaction, followed by
the security satisfaction and home amenities satisfaction. Such findings are suggestive that
management of expatriates should focus on ensuring that their employees live in residential
houses with adequate housing utilities and amenities in addition to security and safety. On the
same note, the results showed that factors such as commute satisfaction and maintenance
satisfaction did not exert a strong impact on overall satisfaction as expected. Furthermore, the
evaluation of the home utilities and amenities sub-groups showed that overall interior layout
and trash collection and sewage were highly ranked by the expatriates in terms of the
satisfaction rates. Therefore, the QoL survey advocates for the development of programs
which emphasize on enhancing the quality of housing utilities, home amenities and
security/safety of the premises given to expatriates in order to enhance their quality of life in
international environments.
References
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Appendix
Figure 1. Satisfaction for commuting to the grocery store
Figure 2. Satisfaction for commuting to school
Figure 3. Satisfaction towards overall interior layout
Figure 4. Satisfaction towards furniture provided
Figure 5. Satisfaction towards trash collection and sewage
Figure 6. Satisfaction towards HVAC
Figure 7. Satisfaction towards trash collection and sewage maintenance
Figure 8. Satisfaction towards mildew/rust maintenance
Figure 9. Satisfaction towards intruder prevention
Figure 10. Satisfaction towards health hazard prevention
SPSS Output