Factor Affecting Individuals’ Investment in Life Insurance in Malaysia

CHAPTER 3
RESEARCH METHODOLOGY
3.1 Introduction:
This chapter provides a discussion of the approach used to achieve the
objectives of the current study. This will be accomplished through
several sub-topics including research methodology, survey instrument,
pilot testing, data collection methods, and sample, data analysis.
3.2 Research methodology
Self administered questionnaires were designed and distributed to 250
participants. The study sample, which was composed of students at SEGi
University and the neighboring estates, was selected as a representative
of the entire population of Malaysian. The questions in the research
questionnaires were designed to assist in investigating the four
hypotheses in chapter two regarding the association of the four
independent variables namely, age, employment status, marriage status,
and education with the individuals’ decision to invest in life
insurance, which was the independent variable.
3.2.1 Research design
The design for the current study involves the collection of data with an
intention of making inferences about the target population at one point
in time. The inference is drawn from the study after the statistical
analysis of the data collected, which helps in identification of the
interrelationships between different variables (Harwell, 2002, p.
147-181).
3.2.1.1 Deductive method and survey
Some of the currently available research works on factors affecting
investment in life insurance have yielded varying opinions. Comparing
the previous findings about the capacity of the independent variable
(age, marital status, employment status, and level of education)
considered in the current research some research identified positive
association while others with individuals’ decision to invest in life
insurance (Min, 2008, p. 3-77 and Dash, & Sood, 2013, p. 36-55). The
current research seeks to study the effect of these factors in the
Malaysian life insurance market and assess if they have positive or
negative association with the decision to invest in life insurance.
Figure 3.2.1.1: Research design
3.2.1.2 Quantitative research
The numerical data obtained from the research helped in identifying the
existing connection between empirical observations and mathematical
expression of the relationships between the research variables (Laidlaw,
Gunn & McManus, 2005, p. 2-27). Different approaches can be used in
quantitative research, but the most common ones include experimental,
descriptive, and association between variables. However, the current
study adopted the inferential approach, which provides significant
statistics to generalize the facts identified about the study samples
and the target population as a whole (Laidlaw, Gunn & McManus, 2005, p.
2-27). This implies that different characteristics of the study sample
were studied, and the research inferred that the entire population had
the same characteristics. This was achieved by controlling the
environment and manipulating the variables to observe their influence on
other variables.
3.3 Questionnaire design
The survey instrument selected for the pursuance of the goal of the
current research is the research questionnaire. To achieve the
objectives of the study, a total of 15 questions was designed and
arranged in two sections to comprise the questionnaire. Two hundred and
fifty questionnaires were then printed and distributed to 250 study
participants in SEGi University and its surrounding estates in the form
of hard copy for the purpose of data collection.
The purpose of questionnaires designed for the current study was to
assist in gathering data from the target population (Colosi, 2006, p.
1-4). The data collected helped in exploring the different factors that
influence the individual decision to invest in life insurance among the
Malaysian population. This was accomplished in respect with the
hypothesis formulated in chapter two following the review of available
literature. The first section of the questionnaire assessed the
demographic features of the population of the study. The second section
assessed their capacity of age, marital status, employment status, and
level of education in to influence their decision to invest in life
insurance.
The standardized questionnaires, as in the current study, are used in
the collection of data that are statistically analyzed to test and
quantify the hypothesis (Brancato, 2005, p. 60-92). The questionnaire
designed to aid in pursuance of the objectives of the current study
consisted of Part A and B. Part A contained ten questions that helped in
identification of demographic characteristics (including gender, age,
education level, employment, occupation, monthly allowance, marital
status, whether the respondent has a life insurance policy, the amount
invested in life insurance, and insurance agents among those operating
in Malaysia) of the study participants. The second section was designed
to assess the impact of the independent variables (age, marital status,
level of education, and employment status) in influencing the
participants’ decision to invest in life insurance. To this end, this
section consisted of five questions, four of them targeting the
independent variables and the last question assessing the intention of
the participants to invest in life insurance.
The first section of the questionnaire had close ended questions, where
the respondents were restricted to selecting the response from the
multiple choices given. In the Likert questions, respondents were
required to rate various aspects of life insurance from strongly
disagree, disagree, neutral, agree, or strongly agree. Table 1 below
shows the Likert questions and sources of the questionnaire used in the
current study.
Table 3.3.2.1: Questionnaire sources and questions
Construct Source
Age
Fear of reduced income at retirement age influence people to invest in
life insurance.
Aged people find satisfaction in life insurance packages compared to the
youths.
In overall, the desire to invest in life insurance increase with the
advancement in age.
Dash & Soon J., 2013, p. 36-55 Tulika & Ganesh, 2013, p. 1-9 Danylchuk
& Butler, 2010, p. 1-2
Employment status
Employed people have a tendency to invest in life insurance compared
to the unemployed.
The employed people invest in life insurance to ensure a consistent
source income even after retirement.
Employed people are financially sophisticated and future oriented, thus
understand the need for life insurance.
In overall, employment status influences the individuals’ decision to
invest in life insurance. Ariad Custom Communication, 2011, p. 1-4
Gerry, 2001, p. 2-12
Marital status
Couples purchase life insurance packages to hedge their dependants
against loss of income should they die.
Married couples invest in life insurance to protect beneficiaries and
not for their personal interests.
Couples in marriage invest more in life insurance than the single
individuals.
In overall, marital status influence individuals’ decision to invest
in life insurance. Hong & Rio-Rull, 2006, p. 1-28 Dash & Sood, 2013,
p. 36-55 Hartman , 2010, 10-28 Shakti, 2010, p. 3-12
Education level
Elite people understand the importance and invest in life insurance
than an uneducated lot.
Individuals desire to invest in life insurance as they climb the
educational ranks.
Elite people are more informed about the current and emerging
uncertainties of life.
In overall, level of education influences the individuals’ decision to
invest in life insurance. Loke & Goh, 2012, p. 415-419 Shakti, 2010,
p. 3-12 Neelaveni, 2012, p. 223-258.
Intention to invest in life insurance
Life insurance is a good retirement plan
Life insurance is a reliable means of investment.
Life insurance hedges against the uncertainties of life for the insured
and their dependants.
I will invest in life insurance now and in the future. Ballsum, Collins
& Jurkat, 2006, p. 280-301 Bicker, Hollanders, & Ponds, 2009, p. 2-21.
3.4 Pilot testing
Pilot testing is the exercise that is conducted to evaluate the data
collection tools for reliability and validity before the actual study
(Center for Evaluation and Research, 2011, p. 1-2). This is achieved by
simulation of actual data collection process on a small scale to assess
whether the data collection instruments will meet the expectations in
the actual study. The pilot test for the current study was conducted
among 20 participants (who were students at SEGi University) in order to
identify whether the questions designed would help in the collection of
reliable data, and thus the validity of the conclusion drawn from the
research regarding the factors that influence an individual’s decision
to invest in life insurance. The pilot test assessed the reliability of
the five constructs (four independent variables and one dependent
variable) in collecting valid data during the actual study. The data
collected was then analyzed using the SPSS software and the results of
the Cronbach’s Alpha were as shown in Table 4.2 and Table 4.3
3.4.1 Reliability statistics
Reliability statistics assist the researcher in determining whether the
scale used in the research can yield consistent results if the
measurements are repeated (Tavakol & Dennick, 2011, p. 52-53). This is
achieved through careful examination of the proportionality of
systematic variations within the scale. There are several approaches
that can be used in the determination of reliability statistics, but
Cronbach’s Alpha was selected to assess the internal consistency of
variations in the current study. Cronbach’s Alpha is a coefficient
used to represent the internal consistence or degree of relationship of
items in a group. A high value (closer to one) of Cronbach’s Alpha
indicates that the items measure an underlying construct, thus high
consistency and reliability (Dean, 2002, p. 17-18). The Cronbach’s
Alpha coefficient of 0.7 and above is a suggestion of sufficient
consistence within the constructs. The more the coefficient approaches
the value of 1.0 the better the prediction of consistency within the
constructs and hence the reliability of the data collection instrument
(Dean, 2002, p. 17-18).
The dependent variable considered in the research was the
individuals’ decision to invest in life insurance among the Malaysian
population while the independent variable include the age, marital
status, level of education, and employment status of the study subjects.
The reliability statistics for all the constructs indicated a positive
Cranach’s Alpha of more than seven. This suggests that all the
constructs had a high level of internal consistency. Therefore, the
questionnaires were reliable to be used in the actual study.
3.5 Data collection methods
The standard questionnaires were distributed randomly to the to the
study participants by hand. However, some copies of questionnaires were
distributed to respondents through online survey tool, kwiksurveys.com,
in order to fast tract the process of data collection and recruit more
participants. The respondents issued with hard copies of questionnaires
were requested to complete and submit immediately.
3.6 Sample
The sampling techniques used in research depend on the type of data that
the researcher targets to collect (Gary, 2010, p. 11-51). The rationale
for the selection of random sampling includes its capacity to reduce
bias in research. Random selection method offered every entity in the
study population an equal chance of being recruited. A sample of 250
participants from SEGi University, and the neighboring estates were
selected as a representative of the entire population of Malaysia and
supplied with research questionnaires through handouts and online.
However, a total of two hundred and one (201) returned their
questionnaires, thus the remaining fourth nine (49) questionnaires were
not returned. This was a reasonable response, which resulted in data
collection from 80 % of the study sample.
3.7 Data analysis
Statistical data analysis is a significant part of research that helps
in processing, transforming, and modeling the raw data in order to
highlight useful information, supporting decision making process, and
suggesting a conclusion (Myers, Arnold & Lorch, 2010, p. 188-224).
Similarly, the data collected using the question for the accomplishment
of the current research was analyzed in order to assess the hypotheses
formulated in chapter two. The data were analyzed using the Statistical
Package for Social Sciences (SPSS) software (version 16.0). The key
functions of the software used to explore the data include the Frequency
Distribution and Chi-square test of association between variables, which
are considered below
3.7.1 Frequency distribution
Frequency distribution analysis involves the computation of the
frequency (number of respondents that selected a choice) or relative
frequency, which helps in the determination of the percentage of the
respondents that selected a given choice (Myers, Arnold & Lorch, 2010,
p. 188-224). In the current research, frequency distribution was
conducted for demographic analysis of the respondents’
characteristics, which included age, education level, employment status,
and marital status among other variables.
3.7.2 Chi-square statistics
Chi-square statistic is applied in assessing whether distributions of
categorical variables differ from one another. It is a non-parametric
test that does not require assumptions about different parameters of the
population. This implies that the nature of the data used in Chi-square
test is frequencies rather than numerical score. It is a suitable method
for testing the hypothesis because it specifies the proportion of the
population that should appear in each category. These help in the
determination of the expected frequencies that describe how the sample
would come into view if it were in perfect agreement with the null
hypothesis. In addition, Chi-square is used in testing for independence.
Chi-square model
χ2 = [Σ (fo – fe) 2 / fe]
Where
F0 = the frequencies observed
Fe = the frequencies expected
Σ = the sum of
However, the Chi-square test result of the current research was
generated using the analytical program SPSS. The key important figures
in the SPSS result where the Chi-square value and significance. The
null hypothesis stated that there was a significant difference between
the actual and anticipated. The result was interpreted by taking the p
value (probability that the deviation of observed from expected is
caused by chance alone) was taken as > 0.05. This implies that if the p
< 0.05 meant that there was no significant association between the two
variables while a P>0.05 meant a statistically significant association
between the variables (Garczynski, 2011, p. 1-4).
7.8
Chapter three outlines the approach followed in the assessment of the
research hypothesis. Most importantly, the research design methodology
selected for the current research was the cross-sectional. The data
collection exercise was done using a questionnaire as the primary tool
for data collection. The pilot testing assisted in the determination of
the reliability of the questionnaire. Two aspects of the SPSS software
(frequency distribution and Chi-square test of association) were used in
data analysis and testing of the research hypothesis. This was done to
determine the existence of a causal effect of independent variables
(age, marital status, employment status, and level of education) on
dependent variables.
Chapter 4
Data Analysis Result
4.1 Introduction
This chapter presents the results of analyzing data in two parts. The
first section of the chapter presents the demographic details of the
respondents, which were mainly analyzed with the frequency distribution.
The second section presents the results of data analysis for the
independent variables, which include age, marital status, employment
status, and level of education. The results of the analysis presented in
this section provide the assessment of the hypothesis outlined in
chapter two.
4.2 Pilot test results
The results of statistical analysis, indicated in Table 4.2.1 and Table
4.2.2, indicated that all the constructs could be reliable in the
collection of data in the actual research. The overall Cronbach’s
Alpha coefficient of 0.802 as shown in Table 3.4.1 suggests that the
questionnaire was, in overall, reliable for the study. In addition,
Table 4.2.2 indicates that all the individual constructs have
Cronbach’s Alpha coefficient values above seven, thus suggesting that
they were all reliable for the actual study (Dean, 2002, p. 17-18).
Table 3.4.1: Reliability statistics results
Cronbach`s Alpha N of Items
.802 5
Source: Author construction
Table 4.2.2: Reliability statistics for the five constructs
Item-Total Statistics
Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected
Item-Total Correlation Cronbach`s Alpha if Item Deleted
Employment influence decision to invest in life insurance 16.1000 5.463
.452 .807
The education level influence decision to invest in life insurance
15.9000 4.832 .726 .720
Marital status influence decision to invest in life insurance 15.7000
4.853 .762 .711
The age influence decision to invest in life insurance 16.0000 6.211
.420 .809
Intention to invest in life insurance 15.9000 4.411 .628 .757
Source: Author construction
4.3 Demographic details
The current research was based on the data obtained from the SEGi
University and the surrounding estates. Two hundred and fifty (250)
persons were recruited as the study participants. Two hundred and one
respondents returned the completed questionnaires, which provided
sufficient data analyzed in this section. This represented an 80.4 %
response.
4.3.1 Gender analysis
Table 4.3.1: Frequency table on the gender of respondents
Gender Frequency Percentage
Male 96 47.8
Female 105 52.2
Source: Authors construction
Table 4.3.1 shows that 47.8 % of the respondents were male while 52.2 %
were female. This implies that the proportion of female participants
exceeded that of their male counterparts by 4 %. This difference may be
explained by the 49 respondents (19.6 %) who failed to return the
questionnaires.
4.3.2 Age analysis
Table 4.3.2: Frequency table for the age of respondents
Age (Years) Frequency Percentage
20-40 184 91.5
41-60 17 8.5
Table 4.3.2 show that all the 91.5 % out of 201 respondents belonged to
the age group 20-40 years. Eight percent point five (8.5 %) belonged to
the age group 41-60 years. This may be explained by the geographical
coverage of the study, which was mainly conducted within the university
and its surroundings that are mainly patronized by young students.
4.3.3 Education level analysis
Table 4.3.3: Education level of respondents
Level Frequency Percentage
STPM / A Level 12 6
Undergraduate 165 82.1
Postgraduate 24 11.9
Table 4.3.3 show that the 6 % of the respondents had attained STPM or A
level academic level of education. Eighty five (82 %) of respondents
were undergraduate students. This may be explained by the large number
undergraduate students around Kota Damansara, which was the target area
of study. In addition, the Table 4.3.3 shows that 11.9 % had attained a
postgraduate level of education. This population of persons with
tertiary level of education may comprise the lecturers and other elites
working the university and other institutions within Kota Damansara.
4.3.4 Income of respondents
Table 4.3.4: Monthly salary scale for respondents
Income (RM) Frequency Percentage
Less than 5,000 140 69.7
5,100-10,000 45 22.4
10,100-15,000 16 8.0
Table 4.3.4 show that 69.7 % of the 201 respondents earned less than RM
5,000 per month. This is reasonable given that the area of study is
mainly patronized by the students with the majority being the
undergraduates. In addition, the analysis indicates that 22.4 % of the
respondents earned RM 5,100-10,000. This may have comprised the students
working part-time and other medium income earning residents of Kota
Damansara. Moreover, the analysis indicates that 8 % of the study
participants earned RM 10,100-15,000.
4.2.5 Employment status of respondents
Table 4.3.5: Employment status of respondents
Employment status Frequency Percentage
Employed 55 72.6
Unemployed 146 72.6
Table 4.3.5 show that 27.4 % of the 201 respondents participated in
active employment. The rest of the respondents 72.6 % were unemployed.
This large population of the respondents may comprise of the
undergraduate students who took part in the current study.
Table 4.3.6: Occupation of respondents
Occupation Frequency Percentage
Government employee 14 7
Private job employee 23 11.4
Self employed 18 9
Student 146 72.6
Table 4.3.6 show that the highest percentage (72.6 %) of the respondents
was students. This may again be explained by the target area of the
study, which was within and around the university. In addition, 11.4 %
of the respondents were employees in the private sector, 9.0 % were self
employed while 7.0 % were government employees.
4.3.7 Marital status of respondents
Table 4.3.7: Marital status of respondents
Marital status Frequency Percentage
Married 37 18.4
Single 163 81.1
Others 1 5
Table 4.3.7 show that most of the study participants (81.1 %) were
singles, probably the undergraduate students. Additionally, 18.4 % of
respondents were married persons while 0.5 % of respondents in a
relationship. The high number of the single respondents corresponds with
the high proportion of students who participated in the study.
4.3.8 Respondents with life insurance policy
Table 4.3.8: investment in life insurance
Investment in life insurance Frequency Percentage
Yes 67 33.3
No 134 66.7
Table 4.3.8 show that most of the study respondents (66.7 %) had no life
insurance policy. The analysis indicates that only 33.3 % of the
respondents owned life insurance policy. However, the proportions were
reasonable given that the majority of the participants were
undergraduate students.
4.3.9 The amount invested in life insurance
Table 4.3.9: Amount of RM invested in life insurance by respondents who
owned life insurance cover
Amount (RM) Frequency Percentage
Less than 20,000 40 19.9
21,000-40,000 15 7.5
41,000-60,000 11 5.5
61,000-80,000 1 0.5
Table 4.3.9 show that 19.9 % of the study respondents invested less than
RM 20,000 in life insurance. In addition, the analysis indicates that
7.5 % of them invested RM 21,000-40,000 in life insurance. The analysis
also indicates that 0.5 % of the study respondents invested RM
61,000-80,000 in life insurance. Moreover, the analysis indicates a
missing value of 66.7 %, which represents the proportion of respondents
who did not have the insurance cover and thus invested zero amounts in
life insurance.
4.3.10 Insurance agents of respondents
Table 4.3.10: Insurance agents who assured respondents
Agent Frequency Percentage
AXA Affin Life Insurance Berhad 13 6.5
CIMB Aviva Assurance Berhad 9 4.5
Great Eastern Life Assurance (Malaysia) Berhad 22 10.9
Manulife Insurance Berhad 8 4.0
Maybank Life Assurance Berhad 9 4.5
MCIS Zurich Insurance 3 1.5
Others 3 1.5
Table 4.3.10 show that most of the respondents (10.9 %) who owned life
assurance cover purchased their policies from Great Eastern Life
Assurance (Malaysia). A proportion of 1.5 % purchased the policy from
other insurance companies. Other key agents include the AXA Affin Life
Insurance Berhad (6.5 %), Maybank Life Assurance Berhad, and CIMB Aviva
Assurance Berhad both with 4.5 % clientele of the respondents.
4.4 Chi-square analysis: Hypothesis testing
The Chi square test for the capacity of education level to influence the
decision of individuals to invest in life insurance indicated a
significant relationship between age and individuals’ decision to
invest in life insurance where the Pearson Chi-Square X 2 (16, N = 201)
= 38.69, p = 0.005. In addition, the study identified a positive
association between the education level and individuals’ decision to
invest in life insurance where X 2 (16, N = 201) = 31.07, p = 0.006.
Additionally, the study identified a positive association between
marital status and individuals’ decision to invest in life insurance
where X 2 (16, N = 201) = 33.82, p= 0.003. The study also identified a
positive association between employment status and individuals’
decision to invest in life insurance where X 2 (16, N = 201) = 35.91, p
= 0.006.
The results indicated that the p value for the test of association
between age, marital status, level of education, and marital status with
the individuals’ decision to invest in life insurance were all less
than 0.05 thus the null hypothesis were rejected and alternative
hypothesis accepted. This implied that all the independent variable
investigated in the current research influence the Malaysian people when
deciding to invest in life insurance. The results of the Chi – square
test of association are presented in the following tables
Table 4.4.1: Chi-square test for association between age and
individuals’ decision to invest in life insurance
Chi-Square Tests
Value df Asymp. Sig. (2-sided) Asymp. Sig. (1-sided)
Pearson Chi-Square 38.693a 16 .001 .0005
Likelihood Ratio 36.525 16 .002
Linear-by-Linear Association 6.820 1 .009 .005
N of Valid Cases 201
a. 4 cells (16.0%) have expected count less than 5. The minimum
expected count is 4.48.
Table 4.4.2: Chi-square test for association between education level
and individuals’ decision to invest in life insurance
Chi-Square Tests
Value df Asymp. Sig. (2-sided) Asymp. Sig. (1-sided)
Pearson Chi-Square 31.074a 16 .013 .0065
Likelihood Ratio 30.817 16 .014
Linear-by-Linear Association 2.755 1 .097
.060
N of Valid Cases 201
a. 6 cells (24.0%) have expected count less than 5. The minimum
expected count is 4.48.
Table 4.4.3: Chi-square test for association between marital status and
individuals’ decision to invest in life insurance
Chi-Square Tests
Value df Asymp. Sig. (2-sided) Asymp. Sig. (1-sided)
Pearson Chi-Square 33.827a 16 .006 .003
Likelihood Ratio 32.740 16 .008
Linear-by-Linear Association 6.333 1 .012 . 005
N of Valid Cases 201
a. 5 cells (20.0%) have expected count less than 5. The minimum
expected count is 4.78.
Table 4.4.4: Chi-square test for association between employment status
and individuals’ decision to invest in life insurance
Chi-Square Tests
Value df Asymp. Sig. (2-sided) Asymp. Sig. (1-sided)
Pearson Chi-Square 35.913a 16 .003 .006
Likelihood Ratio 36.489 16 .002
Linear-by-Linear Association 4.859 1 .028 . 020
N of Valid Cases 201
a. 0 cells (.0%) have expected count less than 5. The minimum expected
count is 5.22.
Hypothesis 1: Findings and analysis of the relationship between age and
individuals’ decision to invest in life insurance
The study identified a positive association between age and the decision
to invest in life insurance. This implies that an increase in age
motivated the people of Malaysia to invest in life insurance. The
findings were consistent with the previous study conducted by Yadav &
Tiwari, 2012, p. 10, which concluded that age was one the key factors
that influence individuals to invest in life insurance. The study had
established that as the family heads aged, they became more informed
about the need for protection against financial loss in case they die
and leave their loved ones. To this end, the insurance companies should
target the aged people as the key market segment. A similar study
conducted by Praveen Kumar Tripathi (2008, p. 12) established that the
urge to invest in life insurance increased with age, but it declines at
a certain age. Therefore, insurance companies should market life
insurance packages as a sufficient hedge against financial loss, but
should target the middle aged population. Therefore, the current study
upheld the alternative hypothesis, which states
H1: There is a positive and statistically significant relationship
between the age of an individual and the need for investment in life
insurance.
Hypothesis 2: Findings and analysis of the relationship between
employment status and individuals’ decision to invest in life
insurance in the Malaysian population
The study established a positive relationship between employment status
and individuals’ decision to invest in life insurance. This was
consistent with most of the previous studies, which suggest that
employment status is a suitable predictor of individuals’ decision to
invest in life insurance (Gandolfi & Miners, 1996, p. 45 & Duker, 1969,
p. 67). The two studies suggested that availability of employment
opportunities elevates the demand for insurance policies especially the
life insurance policy. Moreover, a study by Redzuan (2011, p. 16-53)
suggested that the employment status of a wife in the family influences
the decision for investment in life insurance positively. The findings
inform the stakeholders in the insurance industry in Malaysia (including
the government and life assurance companies) about the potential effect
of job creation in the life insurance market. The government of Malaysia
can strengthen the life insurance market through job creation.
Additionally, the life insurance companies should target the employed
people in marketing the life insurance packages. Therefore, the study
upholds the hypothesis
H2: There is a positive and statistically significant relationship
between employment status and demand for life insurance.
Hypothesis 3: Findings and analysis of the relationship between marital
status and individuals’ decision to invest in life insurance in the
Malaysian population
The findings of the current study identified that marital status
influence individuals’ decision to invest in life insurance. This
reaffirmed most of the previous findings that suggested a positive
association between investment in life insurance and marital status.
Hong & Rio-Rull (2006, p. 1-28) suggested that married couples prefer
investing in life insurance in order to provide income security for
their loved ones. This implies that marital status increases the utility
for life insurance. This implies that the life insurance companies and
agents should market life insurance packages as a means of financial
security when targeting the married couples. Similarly, a research by
Wang (2010, p. 2-21) concluded that most of the married individuals
invest in life insurance especially the whole life packages to hedge the
income of their dependants. Therefore, the results of the current study
inform the insurance companies in Malaysia that marketing their
insurance package as a means of hedging income for the dependants are
the most effective strategy. Identification of couples as a major market
segment for whole life insurance policies is a viable business idea in
Malaysia. Thus, the following hypothesis is proposed with:
H3: There is a positive and statistically significant relationship
between marital status of individuals and demand for life insurance.
Hypothesis 4: Findings and analysis of the relationship between level of
education and individuals’ decision to invest in life insurance in the
Malaysian population
The result of the current research indicated that there is a positive
association between the individuals’ decision to invest in life
insurance and education level. This was consistent with the findings of
a research conducted by Loke & Goh (2012, p. 415-419), which suggested
that utility for life insurance increases with an increase with the
level of education. In addition, Negi & Singh (2012, p. 169-180)
identified that, at a high level of education, individuals are able to
comprehend the large number of risks and uncertainties in life and hence
the significance of insurance cover. To this end, the current study
informed the government and other stakeholders in the insurance sector
that academic advancement encourages the Malaysian society to acquire
financial protection through life insurance packages. Moreover, the
government of Malaysia and insurance packages should aim at enlightening
the society about life`s uncertainties and the significance of life
insurance packages in hedging them against the unexpected risks.
Therefore, the study proposed the alternative hypothesis, which states
H4: There is a positive and statistically significant relationship
between level of education of individuals and demand for life insurance.
4.4
The chapter covered the statistical analysis and test of the four
hypotheses as proposed in chapter two. The analyzed data had been
collected from 201 participants from SEGi University, Kota Damansara.
The data processing involved for demographic Chi-square test of
association. The statistical analysis suggested a positive association
between the individuals’ decision to invest in life insurance and the
independent variables (including education level, marital status, and
employment status).
CHAPTER 5
CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
Chapter five provides the conclusion, recommendations and self
reflection of the entire research.
5.2 Conclusion
The current study assessed the capacity of the age, marital status,
employment status, and level of education to influence individuals’
decisions to invest in life insurance. The findings were consistent
with most of the previous research work, which suggested a positive
association between independent variables and dependent variables under
investigation. This was caused by the constant age of the respondents,
where all respondents belonged to the age group between 20-40 years. The
study identified a statistically significant association between all the
independent variable (including age, employment status, marital status,
and education level) considered in the current study and the dependent
variable. The analysis showed Pearson Chi-Square p values less than
0.005 for all the association.
The positive association between age and individuals decision to invest
in life insurance in Malaysia was established with the Pearson
Chi-Square X 2 (16, N = 201) = 38.69, p = 0.005. The results supported
the alternative hypothesis, which proposed that age was a determining
factor of investment in life insurance in Malaysia. In addition, the
study established appositive association between employment status and
individuals’ decision to invest in life insurance. This was confirmed
by the Pearson Chi-Square X 2 (16, N = 201) = 0.006. Similarly, the
study suggested that there is a positive association between the
individuals’ decision to invest in life insurance and marital status.
The Chi-test of association indicated Pearson Chi-Square value of X 2
(16, N = 201) = 0.003, thus proving that the association was
statistically significant. In addition, the study identified a positive
association between the individuals’ decision to invest in life
insurance and level of education. Statistical analysis produced a
Pearson Chi-Square value of X 2 (16, N = 201) = 0.006, thus proving the
capacity of education level and individuals’ decision to invest in
life insurance.
5.3 Recommendation
The current study was conducted in SEGi University, which is located in
Kota Damansara and the surrounding estates. The, the research recommends
a future study that will expand the geographical coverage and recruit
population beyond Kota Damansara. This is because confining of study
within the University students in the current study may have reduced the
effectiveness of the study in assessing the stated hypothesis. Secondly,
the study recommends a future research that will recruit a diverse
population that will balance between students and persons of other
occupations. This will provide a fair representation of the population
of Malaysia in the study. Moreover, the current study proposed a future
study that will recruit the rural population to assess whether the
independent factors have a positive association with the decision to
invest in life insurance among the Malaysian people living in the rural
areas.
5.4 Limitation
There were several limitations encountered in the current study. First,
majority most of the study subjects had similar demographic features
including the age group, marital status, income, and education level.
This might have affected the diversity of opinion collected during the
data collection process. Secondly, the data collection process had
limited geographical coverage. This is because most of the primary data
were collected within the university and the surrounding estates. This
left out the opinion of Malaysians from other sectors apart from
education.
5.5 Self reflection
The current study aimed at determining if age, marital status, education
level, and employment status influence the individuals’ decision to
invest in life insurance. I feel that, although the study considered a
few demographic factors of age, education, marital status, and
employment status, these are the most crucial variables. All stages of
the study were successful, although it was a challenging exercise. The
process of administering questionnaires resulted in the collection of
reliable and valid data, which then aided in the successful statistical
analysis. The structure simplified structure of the questionnaire and
simple, but objective questions enhanced the strengths of the study
through the collection of relent data. In addition, the literature
review process unveiled many facts about the topic study, thus
facilitating the informed practical exercise (data collection and
analysis).
The study offered an opportunity to learn through challenges. The use of
SPSS software to analyze the data was a challenge especially in the
determination of the Chi-square and Cronbach’s alpha. However, the
study recruited many young participants of the age group 20-40 years.
This was a significant limitation of the study to assess the impact of
age on individuals’ decision to purchase a life insurance policy.
Given that age was one of the independent factors of consideration, it
was necessary to assess participants of varying age groups. Moreover,
the study was confined within the university and its surroundings where
most of the study participants were undergraduate, unemployed, and
single. This was yet another challenge to the assessment of other
independent variables. To this end, there were chances for the
respondents to give an incorrect response while completing the
questionnaires.
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PAGE * MERGEFORMAT 1
Identification of research problem
Problem statement and research objectives
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Data collection
Data analysis

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