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.

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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