Asia Pacific Management Review 14(3) (2009) 301-312
www.apmr.management.ncku.edu.tw
How Motivations, Constraints, and Demographic Factors
Predict Seniors’ Overseas Travel Propensity
Ching-Fu Chena,*, Chine-Chiu Wub
a
Department of Transportation and Communication Management Science , National Cheng Kung University,
Taiwan
b
Department of Tourism Department, Nan Hua University, Taiwan
Accepted 30 August 2008
Abstract
This study investigates the influences of travel motivations, travel constraints, and
demographic variables on seniors’ overseas travel intentions. A survey of Taiwanese seniors
was conducted for the purpose of identifying the significant predicators of overseas travel.
Using factor analyses, four motivations (relaxation, novelty, escape, and socialization) and
three travel constraints (perceived risks, time commitments, and personal reasons) are
delineated. The results from the logistic regression analysis suggest that the main factors
predicting seniors’ overseas travel propensity include age, income source, employment status,
relaxation motive, novelty motive, socialization motive, and personal reasons constraint.
Keywords: Seniors, travel motivation, constraint, travel propensity, logistic regression
1. Introduction1
As the trend in aging societies is growing all over the world, the travel and tourism market
for senior citizens has attracted much attention recently from both practitioners and academics.
The market shows great potential, not only because of its considerable population proportion,
but also because of senior citizens’ great contribution to a tourism economy. It is generally
accepted that seniors in developed countries on average possess a relatively large share of
discretionary income and time and are still in good health. These characteristics enable them
to travel more, and many seniors have the time to travel and are willing to spend a significant
amount of their savings on travel (Fleischer and Pizam, 2002). In addition, the travel
behaviour of seniors is characterized as traveling more frequently, going longer distances,
staying away longer, spending more money, and relying more on travel agents (Rosenfeld,
1986). Hence, senior travellers are important to the tourism industry and will grow in
importance as their segment grows in size and wealth (Reece, 2004). Understanding seniors’
travel decision behaviour is a crucial issue to travel marketers who compete for this important
market.
The proportion of Taiwan’s population age over 50 accounted for 23.09% by the year
2004, which is evidence that Taiwan’s population is stepping into the era of an aging society
like other developed countries (Snyder, 2001). Given its potential contribution to the travel
and tourism market, this senior segment should gain more attention in terms of market
research. Unfortunately, to date only a few studies can be found that explore Taiwanese
seniors’ travel behaviors, especially travel motivations (Huang and Tsai, 2003; Jang and Wu,
*
Corresponding author. E-mail: cfchen99@mail.ncku.edu.tw
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2006). However, the issue on how the affecting factors such as travel motivations and travel
constraints influence senior traveler decision behaviors requires more research efforts. This
study attempts to provide insights into this special market segment on this issue in Taiwan.
Many facets of the senior market have been studied, including travel motivations
(Anderson and Langmeyer, 1982; Blazey, 1987; Chun, 1989; Huang and Tsai, 2003; IsoAhola and Crowley, 1991; Guinn, 1980; Romsa and Blenman, 1989; Shoemaker, 1989; 2000),
travel constraints (Fleischer and Pizam, 2002; Hong et al., 1999; Huang and Tsai, 2003;
Romsa and Blenman, 1989), market segmentation (Horneman et al., 2002; Hsu and Lee, 2002;
Mathur et al., 1998; Shoemaker, 1989; 2000), the effects of various senior citizens’
destination choices (Zimmer et al., 1995), travel modes (Baloglu and Shoemaker, 2001), and
vacation behaviors (Romsa and Blenman, 1989).
The theory of tourism demand states that it is influenced by three major determinants:
economic factors, socio-psychological factors, and exogenous factors (Uysal, 1998). The
economic factors include per capita income, cost of living, exchange rate, tourism prices, and
transportation costs, among others. The social-psychological factors are associated with the
decision-making of travelers such as demographic factors, travel motivations, constraints,
images of destinations, and travel preferences, among others. The exogenous factors are
associated with the business environment. This study explores the travel motivations and
constraints of Taiwanese senior travelers and, furthermore, investigates the effects of the main
decision-making factors on seniors’ travel propensity - e.g. demographic factors, travel
motivations, and travel constraints. To the best of our knowledge, this study is the first
attempt to explore the issue in the Taiwanese senior travel context. The results of this study
can provide more understandings of the factors associated with senior travel decision
behaviors, and suggestions for travel marketers to compete for the senior travel market in
Taiwan.
2. Literature review
2.1 Travel motivation
Ryan (1991) emphasizes the significance of the psychological determinants of demand in
explaining some reasons why tourists travel and select particular destinations. Motivation is
defined as the ‘driving force behind all behavior’ (Fodness, 1994). From the perspective of
the traveler’s decision-making process, travel motivations are seen as the energizers of
demand that promote an individual to decide on a holiday (Page and Hall, 2003). Travel
behavior can be predicted by underlying motivations (Pearce and Caltabiano, 1983). Hence,
those who are highly motivated might be those who are most likely to overcome constraints
and participate in more leisure activities (Fredman and Heberlein, 2005).
The concept of travel motivations is based on the existence of “push” and “pull” factors.
This has been extensively discussed and is a generally accepted concept (Crompton, 1979;
Dann, 1981; Jang and Cai, 2002; Pearce and Caltabiano, 1983). Push motivation refers to an
individual’s internal energy and an increase in the desire for people to travel, while pull
motivation refers to a force external to an individual that influences where people travel,
given the initial desire to travel (Dann, 1981).
Ryan (1991) finds that tourist travel motivations could be identified as wish fulfillment,
shopping, escaping from a mundane environment, rest and relaxation, an opportunity for play,
strengthening family bonds, prestige, social interaction, and educational opportunities. The
most common motivations identified by related research regarding seniors’ travel motivation
are rest and relaxation, escaping from a mundane environment, social interaction, physical
exercise, learning, nostalgia, visiting friends and relatives, and excitement (Fleischer and
Pizam, 2002; Horneman et al., 2002; Shoemaker, 1989; 2000). Shoemaker (1989) notes that
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among fourteen motivations, rest and relaxation as well as escaping from a mundane
environment are the most salient two motivations, while knowing friends of a different gender
and playing golf are the least important two motivations for senior travelers in Pennsylvania.
Interestingly, after ten years, Shoemaker (2000) finds that the main motivations of senior
travelers shifted to visiting new places and experiencing new things.
Regarding seniors’ travel motivation, some main classifications can be identified in the
literature such as rest/relaxation, social interaction, health, learning, seeking, escaping,
attracting, cost, nostalgia and visiting historical sites (Huang and Tsai, 2003). Huang and Tsai
(2003) reported that ‘Get rest and relaxation’ (35.6%) and ‘Meet people and socialization’
(20.1%) are found as the main travel motivation of Taiwanese seniors. In addition,
considering both push and pull dimensions of Taiwanese senior travel motivations, Jang and
Wu (2006) found the push motivations include ‘ego-enhancement’, ‘self-esteem’, ‘knowledge
seeking’, ‘relaxation’, and ‘socialization’ while the pull motivations encompass ‘cleanliness
and safety’, ‘facility, event and cost’, and ‘natural and historical sites’.
2.2 Travel constraints
Unlike motivations that serve as energizers, constraints towards traveling function as
filters for tourism demand, preventing the decision makers from engaging in travel even
though the motivation may exist (Page and Hall, 2003). Leisure constraints can provide a
conceptual framework that may help understand why individuals do not participate in specific
tourism activities. The hierarchical model proposed by Crawford and Godbey (1987) and
Crawford et al. (1991), is the most accepted theoretical framework of leisure constraints
(Nyaupane et al., 2004). Their studies categorize leisure constraints into three hierarchically
organized levels: intrapersonal, interpersonal, and structural constraints. Furthermore,
participation in leisure activities is seen as a process of overcoming the various constraints.
The intrapersonal constraints are defined as individual psychological states and attributes,
such as stress, anxiety, attitudes, and perceived self skill, that might inhibit one from
participating in leisure activities (Fredman and Heberlein, 2005). They exist when people fail
to develop leisure preferences due to problems or misconceptions associated with personality
needs, prior socialization, personal ability, and perceptions of reference group attitudes. The
interpersonal barriers result from social interactions with friends, family, and others. The
structural constraints include factors that block people’s intentions from taking actions such as
economic resources, availability of time, and accessibility (Fredman and Heberlein, 2005;
Jackson and Scott, 1999). The most often cited constraints to travel in related research are a
lack of time, financial considerations, physical and emotional costs, health status (objective
and self-reported), perceived disability, age, security concerns, lack of information, family
approval, and family responsibilities (Blazey, 1987; Fleischer and Pizam, 2002; Mcguire,
1984; Mcguire et al., 1986; Romsa and Blenman, 1989 ).
Regarding the dimensionality of leisure constraint measurement, Jackson (1993) identifies
six dimensions of leisure constraints from eight studies: social isolation, accessibility,
personal reasons, costs, time commitments, and facilities. The social isolation dimension can
be deemed as an interpersonal dimension that is based on characteristics involving interaction
between/among people. The accessibility dimension includes factors such as a lack of or
limited access to transportation or ‘getting there’. The personal reasons dimension is an
intrapersonal dimension that includes items pertaining directly to an individual’s abilities or
motivations. The cost dimension covers items related to the outlay of money. The time
dimension represents a collection of items referred to as reasons that affect levels and
intensity of participation among adults. Finally, the facility dimension relates specifically to
leisure settings and individuals’ perceptions (Hultsman, 1995).
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3. Method
3.1 Sample design and data collection
To collect the data, this study employs a non-probability sampling method called
convenience sampling. It is frequently used in research when probability sampling is not a
feasible option. Although various definitions of a senior tourist are found in past studies, in
order to cover the growing trend of pre-retirement (Tsai and Huang, 2003), we operationalize
the senior tourist in this study as being 50 years old and above. Due to the cost and time
constraints, a self-administered questionnaire was distributed to individuals at five senior
community centres and senior societies in Kaohsiung city in southern Taiwan who are over 50
years old.
The purpose of the survey was explained to the participants and agreement to participate
in the survey was obtained before the survey was given. The interviewers only provided
assistance to respondents with difficulty in reading by filling in the questionnaire based on
their opinions. The data collection was conducted from November to December in 2004.
Three hundred surveys were distributed during the data collection period, and 224 (i.e. 74.7%)
usable samples were obtained after eliminating incomplete questionnaires.
3.2 Survey instrument
The survey instrument is a four part questionnaire. The questions in the questionnaire are
based on a review of the related literature. The questionnaire was pre-tested by 30 respondents
to uncover any potential problems. Some revisions were made based on the pre-test result and
respondents’ comments. Hence, the content validity of the questionnaire was deemed
adequate.
Section 1 of the questionnaire contains two items pertinent to overseas travel experience
in the past three years and the propensity to travel overseas in the following year. A binary
categorical scale is employed to measure both questions. Section 2 deals with the
measurement of travel motivations with 23 items. Section 3 deals with the measurement of
the constraint to travel with 15 items. Respondents are asked to indicate their agreement level
of each item in Sections 2 and 3 on a five-point Likert scale anchored by ‘strongly disagree (=
1)’ to ‘strongly agree (= 5)’. The final section presents a respondent’s demographic
information with six items, such as gender, age, marital status, and education level, by means
of a categorical scale.
3.3 Data analysis
The data analysis was carried out in three main stages. First, descriptive statistics analyzed
the profile of respondents. Second, a principal factor analysis with a varimax rotation method
is conducted on travel motivations and travel constraints for data reduction purposes. Third, a
binary logistic regression is employed to investigate the predict utility of travel motivations,
travel constraints, and demographic variables on the intention to travel for seniors. This study
specifies future travel propensity (0 = no, 1 = yes) as the dependent variable for the logistic
regression, while travel motivations, travel constraints, and demographic variables are the
independent variables.
4. Results
4.1 Respondents’ profile
Table 1 shows the demographic profile of respondents. Among the 224 usable
questionnaires, females make up the great majority of respondents (57.6%), a slight majority
of them are ages 50-54 (39.3%); and 22.3%, 20.1%, and 18.3% respectively represent
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respondents of ages 55-59, 60-64, and 65 or older. In all, 41.9% hold a college or university
degree and higher, while 41% have achieved the education level of high school (junior or
senior). The dominant majority are married (91.5%), slightly over half of the respondents are
retired, while 34.3% are full-time workers and 15.2% are part-time workers. Regarding
income source, 48.7% state they have their own salary, while 23.7% of respondents indicate
their source is from their savings, followed by pensions (14.3%) and donations from their
children (14.3%).
Table 1. Respondents’ profiles (N = 224).
Demographic variables
Frequency
Percentage (%)
Gender
Male
95
42.4
Female
129
57.6
Age
50-54
88
39.3
55-59
51
22.3
60-64
45
20.1
65 and older
40
18.3
Education
Illiteracy
17
7.7
Primary
21
9.4
Junior high
31
13.8
Senior high
61
27.2
College
37
16.5
University and higher
57
25.4
Martial status
Single
3
1.4
Couple
205
91.5
Widow
13
5.8
Divorced
3
1.4
Employment
Work full-time
97
34.3
Work part-time
14
15.2
Unemployed or retired
113
50.5
Income source
Pension
32
14.3
Own savings
53
23.7
Children’s donation
30
14.3
Own salary
109
48.7
4.2 Factor analysis of travel motivations
The principal component factor analysis with a varimax rotation was used to generate the
factors underlying the 23 travel motivation items. Those factors with an eigenvalue greater
than 1 were chosen. The eigenvalues suggest that a five-factor solution explains 58.9% of the
total variance. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is 0.88, and
the value of Bartlett’s test of sphericity is 2307.1 (d.f. = 253, p = 0.00), which suggests that
the factor analysis is appropriate and the variables show good predictive power for the
dimensions (Hair, Anderson, Tatham and Black, 1995).
Five items with a factor loading value of less than 0.5 (the cutoff value) were removed.
Cronbach's α was calculated to test the reliability of each factor. Based upon the minimum
value of 0.5 as an indication of reliability (Nunnally, 1978), one two-item factor with α less
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than 0.5 was removed for further analysis. In the end, the α values for all remaining factors
range from 0.52 to 0.85. Table 2 summarizes the factor analysis results of travel motivations.
According to the characteristics of the items under each factor, the four motivation factors are
named as: relaxation, novelty, escape, and socialization. The factor scores of the four
motivation factors are calculated and used in the logistic regression analysis.
Table 2. Factor analysis of travel motivation.
Motivation factor/
item
Relaxation (3.80)*
Having opportunities
for doing sports
Resting and relaxing
Spending some time
with family
Visiting local
museums and
historical sights
Being close to nature
Wanting to have a
sense of discovery
Novelty (3.66)
Increasing knowledge
of new things
Exploring new places
Experiencing different
ways of life
Experiencing different
cultures
Being curious and
trying new things
Escape (2.66)
Getting away from
work
Escaping from daily
routine
Socialization (2.70)
Visiting relatives and
friends
Meeting new people
Traveling while health
is good
Factor
loading
Eigenvalue
Variance
explained
(%)
Cumulative
Cronbach’s α
variance
explained (%)
5.98
32.9
32.9
.85
2.03
11.3
44.2
.77
1.41
7.8
52.0
.83
1.24
6.9
58.9
.52
.827
.783
.698
.648
.571
.551
.799
.758
.584
.573
.572
.867
.768
.833
.634
.523
Note: * The value in the parenthesis is the factor mean.
4.3 Factor analysis of travel constraints
Applying the same method and criteria in the motivation factor analysis, three constraint
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factors were extracted from 15 constraint items. The eigenvalues suggest that the three factors
explain 65.3% of the total variance. The Kaiser-Meyer-Olkin overall measure of sampling
adequacy is 0.85 and the value of Bartlett’s test of sphericity is 1182.9 (d.f. = 66, p = 0.00),
indicating that the factor analysis is appropriate and the variables have good predictive power
for the dimensions. Three items were removed due to each factor loading value being less
than 0.5. Cronbach’s α value of the three factors range from 0.72 to 0.86, well above the
minimum acceptable value of 0.5.
Table 3 summarizes the factor analysis results of the travel constraints. According to the
characteristics of items under each factor, the four motivation factors are labeled as:
perceived risks, time commitments, and personal reasons. The factor scores of the three
constraint factors are used in the logistic regression analysis.
Table 3. Factor analysis of constraints to travel.
Constraint Factor/
Item
Perceived Risks (3.24)
Factor
loading
*
Perceived safety
about destination
Worry about
healthcare resource
at destination
Perceived risk about
destination
Foreign language skill
in communication
Product failing
Variance
Cumulative
explained (%) variance
explained (%)
Cronbach’s α
5.14
42.8
42.8
.861
1.43
11.9
54.7
.724
1.25
10.4
65.3
.758
.845
.844
.721
.695
.611
Time Commitments
(2.96)
Lack of time
Lack of family
support
Family commitment
Financial
consideration
Eigen
-value
.775
.734
.666
.514
Personal Reasons
(3.06)
Fear of feeling unease
away from home
Physical ability
.821
Age problem
.580
.829
Note: * The value in the parenthesis is the factor mean.
4.4 Logistic regression analysis
A logistic regression is a suitable technique to predict the likelihood of an event to occur
and uses a dichotomous dependent variable. Likewise, it can accommodate independent
variables that are measured on either a continuous or categorical scale. In this study future
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travel intention is expressed as a discrete variable Y with a value 1 if the respondent is likely
to travel in the next year, and a 0 if not - that is:
Yi * = α ' Z i + β i ,
where Yi * is an unobservable variable reflecting the likelihood of travel participation. The
observed counterpart of Yi * is Yi with an observed value.
Y = 0 if Yi * ≦ 0,
Y = 1 if Yi * ≧ 0.
Among the 224 usable questionnaires, 77 respondents report that they intend to travel in
the future (i.e. Y = 1), while 147 respondents indicate no future travel intention. Four factors
of travel motivations, three factors of travel constraints, and selected demographic variables
(age, education level, martial status, income source, and employment) are specified as the
dependent variables. The factors of motivations and constraints are continuous measures
while the demographic variables are discrete measures. For simplicity purposes, the
demographics were converted to dichotomous measures (as shown in Table 4) before the
logistic regression analysis.
Table 4. Definitions of dichotomous demographic variables.
Variables
Dichotomous value
Gender
female = 1
male = 0
over 60 = 1
other = 0
college or over = 1
other = 0
couple = 1
other = 0
own salary = 1
other = 0
retired = 1
other = 0
Age
Education
Marital status
Income source
Employment
Table 5 reports the results of the logistic regression. To assess the model’s goodness of fit,
the chi-square value and the Hosmer and Lemeshow goodness-of-fit index are computed. As
shown in Table 4, the model chi-square is significant (i.e. χ2 = 96.900, d.f .= 12, p = 0.000),
indicating that the classification of seniors into intent to travel overseas and not to travel
overseas could be predicted from the study’s independent variables. In addition, the Hosmer
and Lemeshow goodness-of-fit index is 15.215 (d.f. = 8, p = 0.055), meaning that the model
fits quite well.
The second column of Table 5 shows the logit estimates of the parameter β for all
independent variables. The fifth column shows the significant p-value for these sample
statistics. Three out of four motivation factors - namely, relaxation, novelty, and socialization
- are significant at p < .01. Here, the βs of relaxation (0.850) and novelty (0.561) are positive,
while the βof socialization (-0.520) is negative. This implies that the more travel motivations
of relaxation and novelty that respondents have, the more likely they are to travel. However,
the more travel motivations of external interaction/socialization the respondents have, the less
likely they are to travel.
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Although the theoretical hypothesis assumes a positive impact of travel motivation on
travel propensity, it is acceptable to see a negative impact of socialization motivation on
travel intention in this study. The decision of whether to travel overseas or to travel
domestically involves different decision-making considerations. The push power of travel
motivation, such as socialization in general, can motivate seniors to travel in the domestic
context due to familiar cultural and/or social customs, languages, etc. However, traveling
overseas might be a barrier for seniors due to an unfamiliar or foreign environment. This is
indirectly supported by about 70% of retirees choosing group package tours as their overseas
travel mode in Taiwan (Wang, 2006).
The odds ratios of both relaxation and novelty are greater than 1, revealing that they
predict which type of respondent is more likely to travel. On the other hand, the odd ratio of
socialization is less than 1, indicating that it predicts the type of respondents who are less
likely to travel.
Regarding travel constraints, only the personal reasons constraint is significant at p <. 05.
In addition, the β of personal reasons (-0.468) is negative, and its odds ratio is less than 1,
showing that the greater the intrapersonal constraint is that a respondent perceives, the less
likely the respondent will travel.
Regarding the demographic variables, only age, income, and employment are significant
at p < .01. As a reminder, this study assigns a value of 1 to those seniors over 60 years of age,
whose income comes from their own salary, and those who are retired. The βs of these
variables are positive and their odds ratios are greater than 1. The odds of propensity to travel
increase if the respondent is older (over 60), supports him/herself financially (own salary),
and has more time (retired).
Table 5. Results of binary logistic regression.
(Dependent variable: travel intention, n = 224).
Variables (1)
SE (3)
Z (4)
β(2)
Relaxation
0.850**
0.254
3.35
Novelty
0.561**
0.192
2.92
Escape
0.081
0.186
0.44
Socialization
-0.520**
0.201
-2.59
Perceived risks
-0.196
0.182
-1.08
-0.060
0.208
-0.29
Time
commitments
-0.468*
0.207
-2.26
Personal
reasons
Age
1.475**
0.467
3.16
Education
0.437
0.533
0.82
Marital status
0.440
0.768
0.57
Income source
3.142**
0.675
4.65
Employment
1.731**
0.637
2.72
Constant
-4.74
1.047
-4.53
Log-Likelihood = -95.692
Goodness-of-fit
Chi-Square : 96.900 (DF = 12, p-value = 0.000)
Hosmer-Lemeshow: 15.215 (d.f.= 8, p = 0.055)
Sig.(5)
0.001
0.003
0.663
0.010
0.282
0.770
Exp(β) (6)
2.34
1.75
1.08
0.59
0.82
0.94
0.024
0.63
0.002
0.412
0.566
0.000
0.007
0.00
4.37
1.55
1.55
23.16
5.65
--
Notes: (a). Age (> 60) = 1, education (college or higher) = 1, marital status (couple) = 1, income source
(own money) = 1, employment (retired) = 1.
(b). **: p < 0.01, *: p < 0.05.
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5. Conclusions
This study has empirically investigated the influences of travel motivations, leisure
constraints, and socio-demographic variables on seniors’ overseas travel propensity. A survey
of Taiwanese seniors was conducted for the purpose of identifying the significant predicators.
Four motivations are delineated - relaxation, novelty, escape, and socialization - that are also
frequently cited in previous studies (Fleischer and Pizam, 2002; Horneman et al., 2002,
Shoemaker, 1989; 2000). The results of factor analysis can provide clearer dimensions of
Taiwanese senior travel motivation by considering multiple motivation items. Furthermore, it
helps overcome the reliability problem of motivation measurement scales (Jang and Wu,
2006). In addition, three constraints to travel are identified - perceived risks, time
commitments, and personal reasons constraints - that are in generally consistent with those
major dimensions in past studies as reported by Crawford and Godbey (1987) and Jackson
(1991).
By applying a binary logistic regression analysis, this study contributes to seniors’ travel
choice model by incorporating travel motivations, leisure constraints, and socio-demographic
variables. The results from the binary logistic regression analysis reveal that age, income
source, employment status, relaxation motive, novelty motive, socialization motive, and the
personal reasons constraint are the main factors affecting seniors’ overseas travel propensity.
This shows that in order to understand seniors’ overseas travel propensity, one should take
into account not only socio-demographic, but also psychological factors such as motivations
and perceived constraints. This study also concludes that seniors who are older (over 60),
retired and financially self supporting, are more likely to travel. Both relaxation and novelty
motivations have significantly positive effects on travel likelihood, while external interaction
and socialization motivation as well as intrapersonal constraint have negative influences.
It is worthwhile noting that the negative influence of external interaction and socialization
motivation might result from the nature of overseas travel in this study. To travel overseas for
seniors means to go to a relatively or completely foreign environment and be away from one’s
own daily environment. Due to language barriers and different customs and culture, the
intuitive push factors of external interaction and socialization do not facilitate the propensity
to travel overseas, specifically for Taiwanese seniors. We believe that this motivation has a
positive influence on seniors’ propensity to travel domestically. Hence, a comparative study
on the effects of the determinants on travel propensity in the context of domestic or overseas
destinations will attract further research attention.
The findings herein are also limited to the location scope in this study. The sample of this
study is only obtained from urban seniors. However, whether or not there is a significant
difference in travel motivations and constraints between urban seniors and rural seniors, and
in turn to what extent such a difference would affect their travel propensity, is an interesting
issue for further investigation. Last but not least, empirical data by employing a probability
sampling method rather than convenience sampling method can improve the
representativeness of research objects.
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