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The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
How to cite this article:
Alfi, C. F. (2023). A meta-analysis of the relationship between religiosity and saving
behaviour. International Journal of Banking and Finance, 18(1), 67-94. https://doi.
org/10.32890/ ijbf2023.18.1.4
A META-ANALYSIS OF THE RELATIONSHIP BETWEEN
RELIGIOSITY AND SAVING BEHAVIOUR
Coky Fauzi Alfi
State Polytechnic of Sriwijaya, Indonesia
coky.fauzi.alfi@polsri.ac.id
Received: 2/2/2022 Revised: 2/3/2022 Accepted: 30/3/2022 Published: 5/1/2023
ABSTRACT
The purpose of this study was to synthesize the findings of previous
studies on the relationship between religiosity and saving behaviour
by using a meta-analysis approach. It also sought to determine the
strength of the relationship, besides its direction. Eleven studies which
met the five criteria and four techniques used in the study were used as
samples for the meta-analytic analysis. The size of the effect in each
study was then determined by Pearson’s product-moment correlations
(r). To estimate the average distribution of relationship true effects, the
Fisher r-to-z transformation and random-effects methods were used.
The empirical evidence showed that there was a positive correlation
between religiosity and saving behaviour. However, according to
Guilford’s convention, the true effect size (r = 0.303) would mean
that religiosity had a weak correlation with saving behaviour. It is
recommended that authorities and financial institutions use the
findings of this study to develop plans focused on advocating and
facilitating saving behaviour among religious people.
https://e-journal.uum.edu.my/index.php/ijbf
INTERNATIONAL JOURNAL
OF BANKING AND FINANCE
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The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
Keywords: Meta-analysis, religiosity, saving behaviour, Fisher r-to-z
transformation, random-effects method.
JEL Classification: Z120, G410.
INTRODUCTION
It appears that the global community is becoming more religious.
Based on a Pew Research Center (2015) survey, all major religions
are estimated to show a rise in the number of followers by 2050. The
survey found that 84 percent of the world population was religiously
affiliated in 2010, with projections predicting that this share would
rise to 87 percent by 2050. These findings and projections appear to
contradict the views of several influential scholars, such as Karl Marx,
Emile Durkheim, and Max Weber, who predicted that religion would be
less important in various socioeconomic activities as industrialization
progressed, economic markets expanded, and science, technology,
and education advanced rapidly (Basedau et al., 2018).
Furthermore, the findings of various studies in economics (e.g. Azzi
& Ehrenberg, 1975; Iannaccone, 1998; Iyer, 2016), sociology (e.g.
Geertz, 1973; Inglehart, 2018; Lenski, 1961), and psychology (e.g.
Allport & Ross, 1967; Berry et al., 2002; Pargament, 1999) have
acknowledged the importance of religion in human society. For
example, it plays an important role in, energising people to work for
social change, promoting mental well-being, or acting as a social
control agent.
The investigation into the relationship between religion and economic
growth has received considerable attention, ever since Max Weber
(1905) recognized the significance of religious affiliations in economic
performance. He argued that the values in Protestant teachings would
shape their adherents’ work ethic, resulting in professionalism and
efficiency in economic activities. More than a century after Weber’s
thesis, a large body of literature has noticed a link between religion
and macroeconomic prosperity. For instance, it has been discovered
that religious beliefs, particularly beliefs in hell and heaven, have a
positive effect on economic attitudes, leading to higher incomes and
Gross Domestic Product (GDP), and Christianity is the religion with
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the greatest impact on economic growth, with Protestants being more
capitalists than other Christians (Barro & McCleary, 2003; Filipova,
2012; Guiso et al., 2003; Hayward & Kemmelmeier, 2011).
Moreover, religion has long been associated with the teachings of
thriftiness and customary living. The research findings however, show
that there are differences in which religions adhere to the most frugal
and conventional ways of life. According to Keister (2003), Guiso et
al. (2003), and Renneboog and Spaenjers (2012), Catholics appeared
to value frugality and convenient living more than Protestants,
whereas Arruñada (2010) and Filipova (2012) discovered the opposite.
Although many studies found a correlation between religious belief
and thriftiness, their findings are less convincing when used to explain
a link between religious belief and saving decisions. This is due to the
distinction between thriftiness and saving decisions. Thriftiness is the
trait to try and reduce spending, whereas saving decisions are initiated
by residual income. Therefore, research into how religion influences
economic behaviour and financial decisions at the microeconomic
level seems to remain limited (see Klaubert, 2010; Yayeh, 2014). In
terms of empirical assessments that link individual saving attitudes
to religious preferences or practices, it needs to be explored further.
This investigation should be beneficial because it could help us to
solve pressing issues in the national economy, for instance, pressing
concerns such as wealth inequality (Bilen, 2016; Keister, 2003) and
consumerism (Tjahjono, 2014), or even the issues of conserving
energy and natural resources (Singh et al., 2021).
Since the investigation of religiosity on saving behaviour is an
emerging research area, the present study is interested in knowing
the ‘true’ effect size of the relationship between these variables.
Therefore, this study has performed a meta-analysis to gain a more
objective, robust, and less biased understanding of the relationship
between the variables by investigating the distribution of effect sizes.
A meta-analysis is an approach for aggregating effect-size indices from
multiple studies (Borenstein et al., 2011). It contributes to answering
the question of whether the observed variations in effect sizes across
studies are due to a single population effect size (Law, 1995).
To date, there has been no other study examining religiosity and saving
behaviour across samples, methodologies, and time. This study has
utilized meta-analysis as a quantitative tool to synthesize the findings
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of previous studies, and to determine the strength of the relationship
between religiosity and saving behaviour, as well as the direction of
that relationship. As a result, the strength of the correlation between
previous findings and their direction, whether positive or negative,
has been held to the same standards, as long as they meet the inclusion
criteria set for the study sample. One of the inclusion criteria, for
example, was that the study sample would include religiosity, religious
belief, or religious faith as an independent variable. In addition, the
other objective is to contribute to the growth of the literature in this
area of study. This study can provide a retrospective summary of the
existing literature and provide further empirical evidence of the true
effect of religiosity on saving behaviour. It could help shape new
research by describing what was already known and synthesizing the
new body of evidence.
After reviewing previous studies and establishing the inclusion
criteria, eleven journal articles were identified as the study sample; all
together these sources provided a total of 1,063 participants coming
from various locations. More specifically, Yayeh (2014) collected
samples in Ethiopia, while Ababio and Mawutor (2015) did so in
Ghana. Satsios and Hadjidakis (2017) gathered data in Greece. In
Indonesia, questionnaires were administered by Murdayanti et al.
(2020); Prastiwi (2021); Priyo Nugroho et al. (2017); Wijaya et al.
(2019). Meanwhile, data was collected in Malaysia by Abdullah and
Abd. Majid (2001); Ismail et al. (2018); Kassim et al. (2019); Mei Teh
et al. (2019). As a result, this meta-analytic study was able to generate
numerous plot functions, such as the forest plot, standardized residual
plot, and Cook’s Distance plot, as well as measurements, such as the
random-effect model, heterogeneity statistics, outliers, and influential
case diagnostics.
LITERATURE REVIEW
The relationship between religiosity and saving behaviour is typically
measured using one of two methods: methods which are either
economically or psychologically oriented. In the economic approach,
the goal is to create forecasts about behaviour as accurately as
possible. However, this approach often neglects to explore the true
underlying causes of why individuals behave the way they do (Nyhus,
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2017). Researchers who employ this approach rely on secondary data
surveys, such as the World Values Survey (WVS), the International
Social Survey Program (ISSP), the Gallup Millennium Survey, the
Panel Study of Income Dynamics (PSID), or the Konda Araştrma ve
Danşmanlk, to determine an individual’s religious behaviour. From
these data sources, researchers discovered that the ‘average’ person’s
religiosity could be measured in the following five ways, namely
participation in religious services, belief in heaven and hell, belief in
the afterlife, faith in God, and self-identification as a religious person
(Barro & McCleary, 2003). These religious aspects are then examined
in relation to the adherents’ amount of income or consumption using
various econometric methodologies so as to understand the significance
of religiosity in saving behaviour. Klaubert (2010), for example, used
the PSID to investigate the link between individual saving decisions
and religiosity, as measured by church attendance, in the United
States. Using the Konda data survey, Davutyan and Öztürkkal (2016)
investigated the effect of religious affiliation on financial behaviour
in Turkey. They discovered however, weak evidence that religious
people have distinct preferences for saving decisions. This was due to
there being no difference between religious and non-religious people
when it comes to saving decisions. Meanwhile, Guiso et al. (2003)
discovered a link between religious intensity and thriftiness in a cross-
national study using the WVS sample statistics.
On the other hand, the psychological viewpoint begins from a different
place. This approach frequently concentrates on psychological factors,
and examines individual differences rather than average human
behaviour. Therefore, various explanatory variables and methods have
been employed in the analysis of the relationship between religiosity
and saving behaviour, which makes it different from the economic
approach. The psychological viewpoint often employs primary data
sources and applies behavioural theories, for example the theory of
planned behavior (Ajzen, 1991) or social learning theory (Bandura,
1977). The theory of planned behavior identifies specific factors,
namely intentions and perceived behavioral control, that can be
utilised to estimate and describe human behavior in various contexts.
Intentions are motivational variables that demonstrate how far
individuals are willing to go and how much effort they intend to put in,
whereas perceived behavioral control refers to the perception of how
easy or difficult it is to control an interest (Ajzen, 1991). Meanwhile,
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attitudes toward behavior, subjective norms, and perceived behavioral
control can all have an impact on intentions. Furthermore, social
learning theory is often used as a base theory to describe the role of
financial literacy in saving behavior. The theory hypothesizes that the
cognitive abilities of individuals, i.e., knowledge and skills, impact
on changing their behaviors. This cognitive ability can be learned by
seeing, imitating, practicing, and processing information from the
behaviour of others and its environments, including families, friends,
neighbours, the workplace, or the media.
The psychological viewpoint also employs religiosity measurement
scales, such as the orthodoxy measurement (Glock, 1962) or the
religious orientations (Allport & Ross, 1967). The orthodoxy
measurement uses the following five scales: belief, practice,
knowledge, experience, and consequences, and these would inform
the preferred faith. Belief is an ideological dimension that a religious
person will adhere to. Prayer, fasting, involvement in special
sacraments, worship, and other ritualistic activities are included in the
practice. Knowledge refers to the understanding of the fundamental
tenets of a religious person’s faith and its sacred scriptures. Experience
gives a religious emotional experience, and consequences are all of
the religious prescriptions for what a religious person should do.
Meanwhile, the measurement of religious orientation uses the
following two dimensions: intrinsic and extrinsic, and they would
inform us of the primary motive for life in religion. Those who are
intrinsically motivated find that their primary motive in religion is
to live according to their religious convictions and prescriptions.
However, extrinsically oriented people may find religion useful in a
variety of ways, including stability and reassurance, social connection
and diversionary tactics, status, and self-justification. Few researchers
have adopted this approach in their studies. For example, Priyo
Nugroho et al. (2017) expanded the theory of planned behaviour by
including two new variables: religiosity and self-efficacy. They then
employed Allport and Ross’s (1967) scale for measuring religiosity
to investigate the saving behaviour of Islamic bank customers. In the
meantime, Kassim et al. (2019) who used the social learning theoretical
framework and the religiosity measurement scale which had its root
in Glock’s (1962) work, discovered that whereas religiosity had no
effect on saving behaviour, financial literacy did.
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METHODOLOGY
Criteria and Search Procedure
The samples for the present meta-analytic study were selected because
they had discussed the influence of religiosity on saving behaviour
directly. To be included in the meta-analytic sample, the studies must
fulfil five criteria. They are as follows:
• The studies used religiosity, religious belief, or religious faith
as an independent variable.
• The studies used saving behaviour or saving habits, saving
money, or saving decisions as a dependent variable.
• The studies used a quantitative research approach.
• The studies used primary data at a micro analytical level.
• The studies presented the Pearson’s r effect size clearly or
could be processed using another statistical method.
Studies would be excluded if they had found a relationship between
religiosity and saving behaviour indirectly.
Finding studies from various journals, such as journals on economics,
business, management, finance, marketing, religion, culture, and
social science, that fit the inclusion and exclusion criteria for a meta-
analysis study was challenging. For example, to avoid the possibility
that this might turn out to be a time-consuming process, an effective
search strategy was used from start to finish. The present study has
implemented four techniques to conduct a wide-ranging literature
search. They were as follows: (1) deciding search terms and keywords,
(2) searching for specific phrases, (3) using truncated and wildcard
searches as well as Boolean logic, and (4) using citation searching.
Firstly, these terms and keywords were applied in the search process:
religiosity, religious belief, religious faith, saving behaviour, saving
habits, saving money, and saving decisions. Secondly, quotation
marks were used for words which appear next to each other, e.g.,
“religious belief,” “religious faith,” “saving behaviour,” “saving
habits,” “saving money,” “saving decisions.” Thirdly, the search
used combined truncation and wildcard searches with Boolean logic,
e.g., “religio*” AND “saving behavio?r”. Fourthly, articles that were
cited in other publications were also included in the search. These
techniques were then employed to search for studies in the various
research search engines and databases, such as Semantic Scholar,
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Google Scholar, Research Gate, EBSCOhost, ProQuest, JSTOR, and
Wiley Online Library.
All potentially relevant titles and abstracts were then saved and
managed systematically for the next stage, which was screening.
The first stage of the screening was to import all the references into
a reference management software package and de-duplicate them. In
this case, a software called EndNote was used. The next stage was to
read and identify all the saved articles of study, and filter them out if
they were irrelevant. The stages of screening resulted in 11 journal
articles identified as the relevant data selected for the meta-analysis.
Data Extraction
Once screening has been done and all relevant articles selected for
the study have been identified, the next step is the data extraction (see
Teshome et al., 2018; Zuckerman et al., 2013). In this process, the key
aspects that will be used for the statistical meta-analysis will have to
be extracted from the articles. Some key aspects of the articles are set,
namely the authors’ name and year of publication, sampling methods,
measurement techniques, variables identification (independent
and dependent), methods of statistical analysis, and a summary of
the results (see Table 1). The characteristics of each article that met
the criteria for inclusion were also highlighted. For example, the
eleven studies used various themes related to religiosity and saving
behaviour as independent and dependent variables, respectively.
These are described in the column on variables. In another column,
such as the measurement technique, it is stated that all studies applied
a questionnaire survey to ensure that primary data was used. The most
useful information, however, is in the results column. It discusses
the significance of the relationship between religiosity and saving
behaviour, as well as various goodness of fit tests, e.g., chi-square,
odds ratio, or t-statistic, that can be used to compute the effect size r.
Effect Size Computation
Following data extraction, the next task was to determine the size of
the effect in each study and ensure that this effect size was expressed
in the same way. The effect sizes are used to describe the strength of
the relationship between the variables. There are two common types
of effect size: the r type and the d type. The two most commonly
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used of the r type are Pearson’s product-moment correlations (r) and
Fisher’s r-to-z transformation (Zr), whereas the three most commonly
used d type are Cohen’s d, Hedges’s g, and Glass’s D (Rosenthal,
1995). In this study, Pearson’s r was the preferred effect size. In this
regard, it involved calculating the r value for each study carrying out
the meta-analysis. There was no need to do anything if a study had
used the r value. However, because some studies had no effect size
value and only provided various fit test indicators (e.g., chi-square,
odds ratio, t-test statistic), a conversion to Pearson’s r was performed
via an online calculator at www.psychometrica.de/effect_size.html
(Lenhard & Lenhard, 2016). Meanwhile, if the authors did not provide
the indicators and could not compute a conversion to the r value, they
were contacted via email to gain the relevant information. A reminder
was sent if they did not reply.
Method of Analysis
The analysis is carried out using the Fisher r-to-z transformed
correlation coefficient as the outcome measure. The Fisher’s r-to-z
transformation is commonly used because samples from a meta-
analysis contain a variety of effect sizes. It is also to achieve normality
in the effect sizes (Cheung et al., 2012). There are three steps in
implementing this method (Borenstein et al., 2011; Field & Gillett,
2010). To begin, use Fisher’s r-to-z transformation to convert the
effect size in each study into a standard normal metric. The Fisher’s
r-to-z transformation formula is given as where
is the effect size in each study. After that, for each study, a weighted
average of scores are computed by the formula
where is the number of studies and is the sample size. Finally, it
should be converted back to using the formula
In addition, the random-effects statistical model was applied to
estimate the average distribution of true effects. The random-effects
method was chosen because the effect size was extracted from a
series of studies conducted by various authors in various populations
at various times. The present analysis also reported on the estimate
of the index, the H2 index, the I2 index, and the Q-test (Cochran,
1954) with a p-value as the heterogeneity statistics outcome. The
Q-test was used to assess the null hypothesis that all effect sizes
A META-ANALYSIS OF THE RELATIONSHIP BETWEEN
RELIGIOSITY AND SAVING BEHAVIOUR
Method of Analysis
𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑒𝑒 �
1+𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
1−𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
�,
𝑧𝑧𝑧𝑧�̅�𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
,
𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖−1
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖+1
.
τ2
A META-ANALYSIS OF THE RELATIONSHIP BETWEEN
RELIGIOSITY AND SAVING BEHAVIOUR
Method of Analysis
𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑒𝑒 �
1+𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
1−𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
�,
𝑧𝑧𝑧𝑧�̅�𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
,
𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖−1
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖+1
.
τ2
A META-ANALYSIS OF THE RELATIONSHIP BETWEEN
RELIGIOSITY AND SAVING BEHAVIOUR
Method of Analysis
𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑒𝑒 �
1+𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
1−𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
�,
𝑧𝑧𝑧𝑧�̅�𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
,
𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖−1
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖+1
.
τ2
A META-ANALYSIS OF THE RELATIONSHIP BETWEEN
RELIGIOSITY AND SAVING BEHAVIOUR
Method of Analysis
𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑒𝑒 �
1+𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
1−𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
�,
𝑧𝑧𝑧𝑧�̅�𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
,
𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖−1
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖+1
.
τ2
A META-ANALYSIS OF THE RELATIONSHIP BETWEEN
RELIGIOSITY AND SAVING BEHAVIOUR
Method of Analysis
𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑒𝑒 �
1+𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
1−𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
�,
𝑧𝑧𝑧𝑧�̅�𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
,
𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖−1
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖+1
.
τ2
A META-ANALYSIS OF THE RELATIONSHIP BETWEEN
RELIGIOSITY AND SAVING BEHAVIOUR
Method of Analysis
𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑒𝑒 �
1+𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
1−𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
�,
𝑧𝑧𝑧𝑧�̅�𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
,
𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖−1
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖+1
.
τ2
three steps to implementing this method (Borenstein et al., 2011; Field & Gillett, 2010). To begin, use
Fisher's r-to-z transformation to convert the effect size in each study into a standard normal metric. The
Fisher's r-to-z transformation formula is given by 𝑧𝑧𝑟𝑟𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒 (
1+𝑟𝑟𝑖𝑖
1−𝑟𝑟𝑖𝑖
), where 𝑟𝑟𝑖𝑖 is the effect size in each
study. After that, for each study, a weighted average of 𝑧𝑧𝑟𝑟 scores are computed by �̅�𝑧𝑟𝑟𝑖𝑖 =
∑ 𝑛𝑛𝑖𝑖𝑧𝑧𝑟𝑟𝑖𝑖
𝑘𝑘
𝑖𝑖=1
∑ 𝑛𝑛𝑖𝑖𝑘𝑘𝑖𝑖=1
, where 𝑘𝑘
is the number of studies and 𝑛𝑛𝑖𝑖 is the sample size. Finally, it should be converted back to 𝑟𝑟𝑖𝑖 using the
formula 𝑟𝑟𝑖𝑖 =
𝑒𝑒2�̅�𝑧𝑟𝑟𝑖𝑖−1
𝑒𝑒2�̅�𝑧𝑟𝑟𝑖𝑖+1
.
In addition, the random-effects statistical model is applied to estimate the average distribution of true
effects. The random-effects method was chosen because the effect size was extracted from a series of
studies conducted by various authors in various populations at various times. The analysis also reports the
estimate of the 2 index, the H2 index, the I2 index, and the Q-test (Cochran, 1954) with a p-value as the
heterogeneity statistics outcome. The Q-test is used to assess the null hypothesis that all effect sizes from
all studies are homogenous (Chen & Peace, 2021). If the p-value is less than (the typical significance
level is 0.05), the null hypothesis should be rejected, indicating that the effect sizes from all studies are not
homogenous. Meanwhile, 2, H2, and I2 are used to determine the strength of the distribution of true effect
sizes. The 2 index is estimated using the Hedges’ estimator (Hedges & Olkin, 1985) to measure the variance
of the true effect sizes, and the index should be greater than zero. The H2 index is quantified using Higgins
and Thompson’s (2002) formula to inform the relative extent of heterogeneity in comparison to all studies,
and the index should be greater than 1. The I2 index is also calculated using Higgins and Thompson's (2002)
formula to determine the percentage of observed heterogeneity versus real heterogeneity. As a rule of
thumb, the I2 index could be considered as having low heterogeneity (I2 = 25%), moderate heterogeneity
(I2 = 50%), and high heterogeneity (I2 = 75%). To display the conclusions of meta-analyses, forest plots are
generated. Forest plots provide information about each study’s effect size and confidence interval, as well
as the average distribution of true effects.
The analysis also examines whether studies may be outliers and/or influential in the random-effect model.
They can have a significant impact on the value of the estimated random-effect model coefficients, i.e., the
intercept. If they had remained in the analysis, they could have changed the entire outcome. The
standardized residuals are used to detect outliers, while the Cook's distances (Cook, 1977) and DFFITS
(Difference in Fits) are applied to diagnose the influential studies. Studies are considered as potential
outliers if they have a standardized residual larger than 3 or smaller than -3 (rstudent > ± 3), while they are
considered to be influential if the Cook's distance value is more than 1 (cook.D > 1) and DFFITS is larger
than 2 (dffits > 2) (Gerbing, 2014).
The meta-analysis was carried out with the help of open-source statistical software Jamovi version 1.6.23
(The Jamovi Project, 2021). The MAJOR meta-analysis module library was used to compute r-to-z
transformations, as well as to generate a random-effect model, heterogeneity statistics, a forest plot, and
outlier and influential case diagnostics.
RESULTS
Table 1 shows the results of data extraction processing. The eleven studies were published between 2001
and 2021, with Abdullah and Abd. Majid (2001) is the longest and Prastiwi (2021) is the most recent. The
studies used two types of sampling methods: probability sampling and non-probability sampling. Ababio
and Mawutor (2015); Kassim et al. (2019); Murdayanti et al. (2020); and Yayeh (2014) applied the
probability sampling method, whereas Ismail et al. (2018); Mei Teh et al. (2019); Priyo Nugroho et al.
three steps to implementing this method (Borenstein et al., 2011; Field & Gillett, 2010). To begin, use
Fisher's r-to-z transformation to convert the effect size in each study into a standard normal metric. The
Fisher's r-to-z transformation formula is given by 𝑧𝑧𝑟𝑟𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒 (
1+𝑟𝑟𝑖𝑖
1−𝑟𝑟𝑖𝑖
), where 𝑟𝑟𝑖𝑖 is the effect size in each
study. After that, for each study, a weighted average of 𝑧𝑧𝑟𝑟 scores are computed by �̅�𝑧𝑟𝑟𝑖𝑖 =
∑ 𝑛𝑛𝑖𝑖𝑧𝑧𝑟𝑟𝑖𝑖
𝑘𝑘
𝑖𝑖=1
∑ 𝑛𝑛𝑖𝑖𝑘𝑘𝑖𝑖=1
, where 𝑘𝑘
is the number of studies and 𝑛𝑛𝑖𝑖 is the sample size. Finally, it should be converted back to 𝑟𝑟𝑖𝑖 using the
formula 𝑟𝑟𝑖𝑖 =
𝑒𝑒2�̅�𝑧𝑟𝑟𝑖𝑖−1
𝑒𝑒2�̅�𝑧𝑟𝑟𝑖𝑖+1
.
In addition, the random-effects statistical model is applied to estimate the average distribution of true
effects. The random-effects method was chosen because the effect size was extracted from a series of
studies conducted by various authors in various populations at various times. The analysis also reports the
estimate of the 2 index, the H2 index, the I2 index, and the Q-test (Cochran, 1954) with a p-value as the
heterogeneity statistics outcome. The Q-test is used to assess the null hypothesis that all effect sizes from
all studies are homogenous (Chen & Peace, 2021). If the p-value is less than (the typical significance
level is 0.05), the null hypothesis should be rejected, indicating that the effect sizes from all studies are not
homogenous. Meanwhile, 2, H2, and I2 are used to determine the strength of the distribution of true effect
sizes. The 2 index is estimated using the Hedges’ estimator (Hedges & Olkin, 1985) to measure the variance
of the true effect sizes, and the index should be greater than zero. The H2 index is quantified using Higgins
and Thompson’s (2002) formula to inform the relative extent of heterogeneity in comparison to all studies,
and the index should be greater than 1. The I2 index is also calculated using Higgins and Thompson's (2002)
formula to determine the percentage of observed heterogeneity versus real heterogeneity. As a rule of
thumb, the I2 index could be considered as having low heterogeneity (I2 = 25%), moderate heterogeneity
(I2 = 50%), and high heterogeneity (I2 = 75%). To display the conclusions of meta-analyses, forest plots are
generated. Forest plots provide information about each study’s effect size and confidence interval, as well
as the average distribution of true effects.
The analysis also examines whether studies may be outliers and/or influential in the random-effect model.
They can have a significant impact on the value of the estimated random-effect model coefficients, i.e., the
intercept. If they had remained in the analysis, they could have changed the entire outcome. The
standardized residuals are used to detect outliers, while the Cook's distances (Cook, 1977) and DFFITS
(Difference in Fits) are applied to diagnose the influential studies. Studies are considered as potential
outliers if they have a standardized residual larger than 3 or smaller than -3 (rstudent > ± 3), while they are
considered to be influential if the Cook's distance value is more than 1 (cook.D > 1) and DFFITS is larger
than 2 (dffits > 2) (Gerbing, 2014).
The meta-analysis was carried out with the help of open-source statistical software Jamovi version 1.6.23
(The Jamovi Project, 2021). The MAJOR meta-analysis module library was used to compute r-to-z
transformations, as well as to generate a random-effect model, heterogeneity statistics, a forest plot, and
outlier and influential case diagnostics.
RESULTS
Table 1 shows the results of data extraction processing. The eleven studies were published between 2001
and 2021, with Abdullah and Abd. Majid (2001) is the longest and Prastiwi (2021) is the most recent. The
studies used two types of sampling methods: probability sampling and non-probability sampling. Ababio
and Mawutor (2015); Kassim et al. (2019); Murdayanti et al. (2020); and Yayeh (2014) applied the
probability sampling method, whereas Ismail et al. (2018); Mei Teh et al. (2019); Priyo Nugroho et al.
three steps to implementing this method (Borenstein et al., 2011; Field & Gillett, 2010). To begin, use
Fisher's r-to-z transformation to convert the effect size in each study into a standard normal metric. The
Fisher's r-to-z transformation formula is given by 𝑧𝑧𝑟𝑟𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒 (
1+𝑟𝑟𝑖𝑖
1−𝑟𝑟𝑖𝑖
), where 𝑟𝑟𝑖𝑖 is the effect size in each
study. After that, for each study, a weighted average of 𝑧𝑧𝑟𝑟 scores are computed by �̅�𝑧𝑟𝑟𝑖𝑖 =
∑ 𝑛𝑛𝑖𝑖𝑧𝑧𝑟𝑟𝑖𝑖
𝑘𝑘
𝑖𝑖=1
∑ 𝑛𝑛𝑖𝑖𝑘𝑘𝑖𝑖=1
, where 𝑘𝑘
is the number of studies and 𝑛𝑛𝑖𝑖 is the sample size. Finally, it should be converted back to 𝑟𝑟𝑖𝑖 using the
formula 𝑟𝑟𝑖𝑖 =
𝑒𝑒2�̅�𝑧𝑟𝑟𝑖𝑖−1
𝑒𝑒2�̅�𝑧𝑟𝑟𝑖𝑖+1
.
In addition, the random-effects statistical model is applied to estimate the average distribution of true
effects. The random-effects method was chosen because the effect size was extracted from a series of
studies conducted by various authors in various populations at various times. The analysis also reports the
estimate of the 2 index, the H2 index, the I2 index, and the Q-test (Cochran, 1954) with a p-value as the
heterogeneity statistics outcome. The Q-test is used to assess the null hypothesis that all effect sizes from
all studies are homogenous (Chen & Peace, 2021). If the p-value is less than (the typical significance
level is 0.05), the null hypothesis should be rejected, indicating that the effect sizes from all studies are not
homogenous. Meanwhile, 2, H2, and I2 are used to determine the strength of the distribution of true effect
sizes. The 2 index is estimated using the Hedges’ estimator (Hedges & Olkin, 1985) to measure the variance
of the true effect sizes, and the index should be greater than zero. The H2 index is quantified using Higgins
and Thompson’s (2002) formula to inform the relative extent of heterogeneity in comparison to all studies,
and the index should be greater than 1. The I2 index is also calculated using Higgins and Thompson's (2002)
formula to determine the percentage of observed heterogeneity versus real heterogeneity. As a rule of
thumb, the I2 index could be considered as having low heterogeneity (I2 = 25%), moderate heterogeneity
(I2 = 50%), and high heterogeneity (I2 = 75%). To display the conclusions of meta-analyses, forest plots are
generated. Forest plots provide information about each study’s effect size and confidence interval, as well
as the average distribution of true effects.
The analysis also examines whether studies may be outliers and/or influential in the random-effect model.
They can have a significant impact on the value of the estimated random-effect model coefficients, i.e., the
intercept. If they had remained in the analysis, they could have changed the entire outcome. The
standardized residuals are used to detect outliers, while the Cook's distances (Cook, 1977) and DFFITS
(Difference in Fits) are applied to diagnose the influential studies. Studies are considered as potential
outliers if they have a standardized residual larger than 3 or smaller than -3 (rstudent > ± 3), while they are
considered to be influential if the Cook's distance value is more than 1 (cook.D > 1) and DFFITS is larger
than 2 (dffits > 2) (Gerbing, 2014).
The meta-analysis was carried out with the help of open-source statistical software Jamovi version 1.6.23
(The Jamovi Project, 2021). The MAJOR meta-analysis module library was used to compute r-to-z
transformations, as well as to generate a random-effect model, heterogeneity statistics, a forest plot, and
outlier and influential case diagnostics.
RESULTS
Table 1 shows the results of data extraction processing. The eleven studies were published between 2001
and 2021, with Abdullah and Abd. Majid (2001) is the longest and Prastiwi (2021) is the most recent. The
studies used two types of sampling methods: probability sampling and non-probability sampling. Ababio
and Mawutor (2015); Kassim et al. (2019); Murdayanti et al. (2020); and Yayeh (2014) applied the
probability sampling method, whereas Ismail et al. (2018); Mei Teh et al. (2019); Priyo Nugroho et al.
76
The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
from all studies were homogenous (Chen & Peace, 2021). If the
p-value was less than (the typical significance level is 0.05), the null
hypothesis should be rejected, indicating that the effect sizes from all
studies were not homogenous. Meanwhile, , H2, and I2 were used
to determine the strength of the distribution of true effect sizes. The
index was estimated using the Hedges’ estimator (Hedges & Olkin,
1985) to measure the variance of the true effect sizes, and the index
should be greater than zero. The H2 index was quantified using the
Higgins and Thompson’s (2002) formula to inform the relative extent
of heterogeneity in comparison to all studies, and the index should
be greater than 1. The I2 index was also calculated using the Higgins
and Thompson’s (2002) formula to determine the percentage of
observed heterogeneity versus real heterogeneity. As a rule of thumb,
the I2 index could be considered as having low heterogeneity (I2 =
25%), moderate heterogeneity (I2 = 50%), and high heterogeneity (I2
= 75%). To display the conclusions of the meta-analyses, forest plots
were generated. Forest plots provided information about each study’s
effect size and confidence interval, as well as the average distribution
of true effects.
The analysis also examined whether studies may be outliers and/or
influential in the random-effect model. They can have a significant
impact on the value of the estimated random-effect model coefficients,
i.e., the intercept. If they had remained in the analysis, they could have
changed the entire outcome. The standardized residuals are used to
detect outliers, while the Cook’s distances (Cook, 1977) and DFFITS
(Difference in Fits) are applied to diagnose the influential studies.
Studies are considered as potential outliers if they have a standardized
residual larger than 3 or smaller than -3 (rstudent > ± 3), while they
are considered to be influential if the Cook’s distance value is more
than 1 (cook.D > 1) and DFFITS is larger than 2 (dffits > 2) (Gerbing,
2014).
The meta-analysis was carried out with the help of an open-source
statistical software the Jamovi version 1.6.23 (The Jamovi Project,
2021). The MAJOR meta-analysis module library was used to compute
r-to-z transformations, as well as to generate a random-effect model,
heterogeneity statistics, a forest plot, and outlier and influential case
diagnostics.
three steps to implementing this method (Borenstein et al., 2011; Field & Gillett, 2010). To begin, use
Fisher's r-to-z transformation to convert the effect size in each study into a standard normal metric. The
Fisher's r-to-z transformation formula is given by 𝑧𝑧𝑟𝑟𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒 (
1+𝑟𝑟𝑖𝑖
1−𝑟𝑟𝑖𝑖
), where 𝑟𝑟𝑖𝑖 is the effect size in each
study. After that, for each study, a weighted average of 𝑧𝑧𝑟𝑟 scores are computed by �̅�𝑧𝑟𝑟𝑖𝑖 =
∑ 𝑛𝑛𝑖𝑖𝑧𝑧𝑟𝑟𝑖𝑖
𝑘𝑘
𝑖𝑖=1
∑ 𝑛𝑛𝑖𝑖𝑘𝑘𝑖𝑖=1
, where 𝑘𝑘
is the number of studies and 𝑛𝑛𝑖𝑖 is the sample size. Finally, it should be converted back to 𝑟𝑟𝑖𝑖 using the
formula 𝑟𝑟𝑖𝑖 =
𝑒𝑒2�̅�𝑧𝑟𝑟𝑖𝑖−1
𝑒𝑒2�̅�𝑧𝑟𝑟𝑖𝑖+1
.
In addition, the random-effects statistical model is applied to estimate the average distribution of true
effects. The random-effects method was chosen because the effect size was extracted from a series of
studies conducted by various authors in various populations at various times. The analysis also reports the
estimate of the 2 index, the H2 index, the I2 index, and the Q-test (Cochran, 1954) with a p-value as the
heterogeneity statistics outcome. The Q-test is used to assess the null hypothesis that all effect sizes from
all studies are homogenous (Chen & Peace, 2021). If the p-value is less than (the typical significance
level is 0.05), the null hypothesis should be rejected, indicating that the effect sizes from all studies are not
homogenous. Meanwhile, 2, H2, and I2 are used to determine the strength of the distribution of true effect
sizes. The 2 index is estimated using the Hedges’ estimator (Hedges & Olkin, 1985) to measure the variance
of the true effect sizes, and the index should be greater than zero. The H2 index is quantified using Higgins
and Thompson’s (2002) formula to inform the relative extent of heterogeneity in comparison to all studies,
and the index should be greater than 1. The I2 index is also calculated using Higgins and Thompson's (2002)
formula to determine the percentage of observed heterogeneity versus real heterogeneity. As a rule of
thumb, the I2 index could be considered as having low heterogeneity (I2 = 25%), moderate heterogeneity
(I2 = 50%), and high heterogeneity (I2 = 75%). To display the conclusions of meta-analyses, forest plots are
generated. Forest plots provide information about each study’s effect size and confidence interval, as well
as the average distribution of true effects.
The analysis also examines whether studies may be outliers and/or influential in the random-effect model.
They can have a significant impact on the value of the estimated random-effect model coefficients, i.e., the
intercept. If they had remained in the analysis, they could have changed the entire outcome. The
standardized residuals are used to detect outliers, while the Cook's distances (Cook, 1977) and DFFITS
(Difference in Fits) are applied to diagnose the influential studies. Studies are considered as potential
outliers if they have a standardized residual larger than 3 or smaller than -3 (rstudent > ± 3), while they are
considered to be influential if the Cook's distance value is more than 1 (cook.D > 1) and DFFITS is larger
than 2 (dffits > 2) (Gerbing, 2014).
The meta-analysis was carried out with the help of open-source statistical software Jamovi version 1.6.23
(The Jamovi Project, 2021). The MAJOR meta-analysis module library was used to compute r-to-z
transformations, as well as to generate a random-effect model, heterogeneity statistics, a forest plot, and
outlier and influential case diagnostics.
RESULTS
Table 1 shows the results of data extraction processing. The eleven studies were published between 2001
and 2021, with Abdullah and Abd. Majid (2001) is the longest and Prastiwi (2021) is the most recent. The
studies used two types of sampling methods: probability sampling and non-probability sampling. Ababio
and Mawutor (2015); Kassim et al. (2019); Murdayanti et al. (2020); and Yayeh (2014) applied the
probability sampling method, whereas Ismail et al. (2018); Mei Teh et al. (2019); Priyo Nugroho et al.
A META-ANALYSIS OF THE RELATIONSHIP BETWEEN
RELIGIOSITY AND SAVING BEHAVIOUR
Method of Analysis
𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑒𝑒 �
1+𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
1−𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
�,
𝑧𝑧𝑧𝑧�̅�𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
,
𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖−1
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖+1
.
τ2
A META-ANALYSIS OF THE RELATIONSHIP BETWEEN
RELIGIOSITY AND SAVING BEHAVIOUR
Method of Analysis
𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 0.5𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑒𝑒 �
1+𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
1−𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
�,
𝑧𝑧𝑧𝑧�̅�𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑧𝑧𝑧𝑧𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
∑ 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖
𝑘𝑘𝑘𝑘
𝑖𝑖𝑖𝑖=1
,
𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖−1
𝑒𝑒𝑒𝑒2𝑧𝑧𝑧𝑧�𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖+1
.
τ2
77
The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
Ta
bl
e
1
O
ve
rv
ie
w
o
f S
tu
di
es
In
cl
ud
ed
in
M
et
a-
A
na
ly
si
s
A
ut
ho
rs
(y
ea
r)
Sa
m
pl
in
g
M
et
ho
d
M
ea
su
re
m
en
t
Te
ch
ni
qu
e
V
ar
ia
bl
es
St
at
is
tic
al
A
na
ly
si
s
R
es
ul
t
I
nd
ep
en
de
nt
D
ep
en
de
nt
A
bd
ul
la
h
an
d
A
bd
. M
aj
id
(2
00
1)
N
ot
m
en
tio
ne
d
Q
ue
st
io
nn
ai
re
su
rv
ey
R
el
ig
io
si
ty
in
de
x
an
d
in
co
m
e
Sa
vi
ng
M
ul
tip
le
li
ne
ar
re
gr
es
si
on
“…
th
er
e
ex
is
t a
c
on
cl
us
iv
e
re
la
tio
ns
hi
p
be
tw
ee
n
sa
vi
ng
an
d
R
el
ig
io
si
ty
In
de
x
…
”
(t
-s
ta
tis
tic
s
=
1.
99
3,
p
<
0
.0
5)
(p
. 7
5)
.
Y
ay
eh
(2
01
4)
M
ul
tis
ta
ge
cl
us
te
r s
am
pl
in
g
an
d
pr
ob
ab
ili
ty
pr
op
or
tio
na
l t
o
si
ze
(P
PS
) s
am
pl
in
g
Q
ue
st
io
nn
ai
re
su
rv
ey
re
lig
io
n
af
fil
ia
tio
n,
re
lig
io
us
at
te
nd
an
ce
, r
el
ig
io
n
id
en
tit
y,
ho
us
eh
ol
d
ne
t i
nc
om
e
pe
r
m
on
th
, g
en
de
r,
ho
us
eh
ol
d
ac
ce
pt
in
g
in
te
re
st
p
ay
m
en
t,
le
ve
l o
f e
du
ca
tio
n,
fa
m
ily
si
ze
, a
ge
, m
ar
ita
l s
ta
tu
s,
w
ea
lth
, a
nd
k
no
w
le
dg
e
ab
ou
t
sa
vi
ng
in
te
re
st
p
ay
m
en
t.
sa
vi
ng
L
in
ea
r a
nd
p
ro
bi
t
re
gr
es
si
on
“…
th
e
m
or
e
of
te
n
pe
op
le
go
in
g
to
c
hu
rc
h/
m
os
qu
e,
i.
e.
th
e
m
or
e
re
lig
io
us
th
ey
a
re
,
th
e
lo
w
er
is
th
ei
r
pr
op
en
si
ty
to
s
av
e
m
on
ey
.”
(W
al
d
ch
i-
sq
ua
re
o
f 6
2.
58
w
ith
p
-v
al
ue
of
0
.0
00
)
A
ba
bi
o
an
d
M
aw
ut
or
(2
01
5)
Si
m
pl
e
ra
nd
om
sa
m
pl
in
g
an
d
co
nv
en
ie
nc
e
sa
m
pl
in
g
Q
ue
st
io
nn
ai
re
su
rv
ey
re
lig
io
si
ty
, u
nc
er
ta
in
ty
,
liq
ui
di
ty
c
on
st
ra
in
t,
st
ag
e
in
lif
e,
a
nd
in
te
rg
en
er
at
io
na
l
ef
fe
ct
in
co
m
e
sa
vi
ng
L
og
it
m
od
el
C
hu
rc
h
at
te
nd
an
ce
v
er
y
si
gn
ifi
ca
nt
ly
e
xp
la
in
s
th
at
re
lig
io
si
ty
e
ff
ec
ts
s
av
in
g
be
ha
vi
or
(o
dd
s
ra
tio
=
0
.8
22
, p
<
0.
05
) (
p.
5
5)
.
(c
on
tin
ue
d)
78
The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
A
ut
ho
rs
(y
ea
r)
Sa
m
pl
in
g
M
et
ho
d
M
ea
su
re
m
en
t
Te
ch
ni
qu
e
V
ar
ia
bl
es
St
at
is
tic
al
A
na
ly
si
s
R
es
ul
t
I
nd
ep
en
de
nt
D
ep
en
de
nt
Pr
iy
o
N
ug
ro
ho
et
a
l.
(2
01
7)
Pu
rp
os
iv
e
sa
m
pl
in
g
Q
ue
st
io
nn
ai
re
su
rv
ey
Se
lf
-e
ffi
ca
cy
, R
el
ig
io
si
ty
,
A
tti
tu
de
, a
nd
S
ub
je
ct
iv
e
no
rm
In
te
nt
io
n
an
d
B
eh
av
io
r
Si
m
ul
ta
ne
ou
s
E
qu
at
io
n
M
od
el
in
g
“…
r
el
ig
io
si
ty
h
as
a
p
os
iti
ve
an
d
si
gn
ifi
ca
nt
in
flu
en
ce
o
n
cu
st
om
er
b
eh
av
io
r
us
in
g
pr
od
uc
ts
a
nd
s
er
vi
ce
s
of
Is
la
m
ic
b
an
ks
”
(i
.e
. b
an
k
sa
vi
ng
s
or
d
ep
os
its
) (
p.
4
4)
.
Sa
ts
io
s
an
d
H
ad
jid
ak
is
(2
01
7)
Sn
ow
ba
ll
sa
m
pl
in
g
Q
ue
st
io
nn
ai
re
su
rv
ey
re
lig
io
si
ty
a
nd
s
el
f-
m
as
te
ry
fiv
e
in
te
nt
io
ns
to
sa
vi
ng
s
ub
sc
al
es
:
th
ri
ft
, s
av
in
g
in
vo
lv
em
en
t,
sa
vi
ng
ha
bi
ts
, s
ha
m
e
of
de
bt
a
nd
n
o
ne
ed
to
s
av
e
Pe
ar
so
n
co
rr
el
at
io
n
“…
, r
el
ig
io
si
ty
is
s
ig
ni
fic
an
tly
po
si
tiv
el
y
co
rr
el
at
ed
w
ith
al
l 5
in
te
nt
io
n
su
bs
ca
le
s,
...
”
(p
. 2
0)
. T
he
s
ub
sc
al
e
of
sa
vi
ng
h
ab
its
is
s
ig
ni
fic
an
tly
po
si
tiv
el
y
co
rr
el
at
ed
w
ith
re
lig
io
si
ty
(r
(1
00
) =
0
.3
32
, p
<
0.
01
).
Is
m
ai
l e
t a
l.
(2
01
8)
Pu
rp
os
iv
e
sa
m
pl
in
g
Q
ue
st
io
nn
ai
re
su
rv
ey
Se
rv
ic
e
qu
al
ity
, r
el
ig
io
us
be
lie
f,
an
d
kn
ow
le
dg
e.
sa
vi
ng
b
eh
av
io
r
M
ul
tip
le
li
ne
ar
re
gr
es
si
on
“…
r
el
ig
io
us
b
el
ie
f i
s
si
gn
ifi
ca
nt
ly
r
el
at
ed
to
s
av
in
g
be
ha
vi
ou
r
(t
=
4
.6
0,
p
=
0.
00
).”
(p
. 1
07
6)
K
as
si
m
e
t a
l.
(2
01
9)
D
is
pr
op
or
tio
na
te
st
ra
tifi
ed
s
am
pl
in
g
Q
ue
st
io
nn
ai
re
su
rv
ey
Fa
m
ily
b
ac
kg
ro
un
d,
R
el
ig
io
si
ty
, A
tti
tu
de
, L
ite
ra
cy
,
H
ou
se
ho
ld
In
co
m
e,
A
ge
,
L
ev
el
o
f e
du
ca
tio
n,
a
nd
L
oc
al
ity
.
Sa
vi
ng
b
eh
av
io
r
M
ul
tip
le
li
ne
ar
re
gr
es
si
on
“…
th
e
re
su
lts
d
em
on
st
ra
te
th
at
r
el
ig
io
si
ty
, …
a
re
n
ot
si
gn
ifi
ca
nt
ly
r
el
at
ed
to
s
av
in
g
be
ha
vi
or
.”
(p
. 2
48
) (
t-
st
at
is
tic
s
=
1.
41
8)
(c
on
tin
ue
d)
79
The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
A
ut
ho
rs
(y
ea
r)
Sa
m
pl
in
g
M
et
ho
d
M
ea
su
re
m
en
t
Te
ch
ni
qu
e
V
ar
ia
bl
es
St
at
is
tic
al
A
na
ly
si
s
R
es
ul
t
I
nd
ep
en
de
nt
D
ep
en
de
nt
M
ei
T
eh
e
t a
l.
(2
01
9)
C
on
ve
ni
en
ce
sa
m
pl
in
g
Q
ue
st
io
nn
ai
re
su
rv
ey
In
di
vi
du
al
c
ha
ra
ct
er
is
tic
,
So
ci
al
is
at
io
n,
C
og
ni
tiv
e
ab
ili
ty
, R
el
ig
io
n
fa
ith
, a
nd
Se
lf
-e
ffi
ca
cy
.
Pr
iv
at
e
sa
vi
ng
L
og
is
tic
re
gr
es
si
on
“A
s
fo
r
re
lig
io
us
fa
ith
, d
iv
in
e
gu
id
an
ce
(o
dd
s
ra
tio
=
6
.5
1)
si
gn
ifi
ca
nt
ly
p
re
di
ct
ed
a
n
in
di
vi
du
al
’s
lik
el
ih
oo
d
to
s
av
e
m
on
ey
.”
(p
. 1
0)
W
ija
ya
e
t a
l.
(2
01
9)
C
on
ve
ni
en
ce
sa
m
pl
in
g
Q
ue
st
io
nn
ai
re
su
rv
ey
R
el
ig
io
si
ty
le
ve
l
Sa
vi
ng
d
ec
is
io
ns
C
hi
-s
qu
ar
e
te
st
“…
a
c
hi
-s
qu
ar
e
te
st
b
et
w
ee
n
re
lig
io
si
ty
le
ve
l a
nd
s
av
in
g
de
ci
si
on
s
cr
ite
ri
a,
w
hi
ch
sh
ow
ed
th
er
e
is
a
s
ig
ni
fic
an
t
di
ffe
re
nc
e
(p
<
0
.0
1)
. M
or
e
th
an
6
0
pe
r
ce
nt
o
f t
he
re
sp
on
de
nt
s
de
ci
de
d
to
s
av
e
m
on
ey
in
B
M
Ts
b
ec
au
se
of
th
ei
r
pr
od
uc
ts
b
ei
ng
in
ac
co
rd
an
ce
w
ith
S
ha
ri
a.
”
(p
.
14
75
) (
ch
i-
sq
ua
re
=
6
.4
63
67
)
M
ur
da
ya
nt
i e
t
al
. (
20
20
)
Pr
op
or
tio
na
te
st
ra
tifi
ed
ra
nd
om
sa
m
pl
in
g
Q
ue
st
io
nn
ai
re
su
rv
ey
Fi
na
nc
ia
l k
no
w
le
dg
e,
s
el
f-
co
nt
ro
l,
an
d
re
lig
io
us
b
el
ie
fs
.
Sa
vi
ng
b
eh
av
io
r
Pa
rt
ia
l L
ea
st
Sq
ua
re
“…
r
el
ig
io
us
b
el
ie
fs
h
av
e
a
si
gn
ifi
ca
nt
p
os
iti
ve
e
ffe
ct
o
n
sa
vi
ng
s
be
ha
vi
or
, …
”
(p
. 8
)
(t
-s
ta
tis
tic
s
=
6.
77
, p
<
0
.0
01
)
Pr
as
tiw
i (
20
21
)
N
ot
m
en
tio
ne
d
Q
ue
st
io
nn
ai
re
su
rv
ey
R
el
ig
io
si
ty
, E
nv
ir
on
m
en
t,
an
d
R
ep
ut
at
io
n.
Sa
vi
ng
d
ec
is
io
n
M
ul
tip
le
li
ne
ar
re
gr
es
si
on
“…
R
el
ig
io
si
ty
, .
..
ha
ve
a
si
gn
ifi
ca
nt
p
os
iti
ve
e
ffe
ct
o
n
sa
vi
ng
d
ec
is
io
ns
.”
(p
. 2
22
)
(t
-s
ta
tis
tic
s
=
2.
16
1,
p
<
0
.0
5)
(c
on
tin
ue
d)
80
The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
Ta
bl
e
2
O
ve
rv
ie
w
o
f D
es
cr
ip
tiv
e
St
at
is
tic
s
an
d
E
ffe
ct
S
iz
e
A
ut
ho
rs
(y
ea
r)
N
A
ge
(y
/o
)
G
en
de
r
M
ar
ita
l s
ta
tu
s
R
el
ig
io
n
L
oc
at
io
n
(C
ou
nt
ry
)
Pe
ar
so
n
r
A
bd
ul
la
h
an
d
A
bd
. M
aj
id
(2
00
1)
16
0
78
.1
3%
1
8
to
2
3
19
.3
8%
2
4
to
2
9
2.
50
%
3
0
to
3
5
34
.3
8%
M
al
e
65
.6
3%
F
em
al
e
95
.6
3%
S
in
gl
e
4.
38
%
M
ar
ri
ed
M
us
lim
In
te
rn
at
io
na
l I
sl
am
ic
U
ni
ve
rs
ity
M
al
ay
si
a
(I
IU
M
)-
Se
la
ng
or
(M
al
ay
si
a)
0.
15
76
Y
ay
eh
(2
01
4)
38
4
42
(M
ea
n)
N
ot
m
en
tio
ne
d
74
%
M
ar
ri
ed
16
%
W
id
ow
ed
67
.5
%
O
rt
ho
do
x
C
hr
is
tia
n
30
.8
%
M
us
lim
1.
7%
P
ro
te
st
an
t
W
es
t A
m
ha
ra
n
at
io
na
l r
eg
io
na
l
st
at
e
(E
th
io
pi
a)
0.
40
37
A
ba
bi
o
an
d
M
aw
ut
or
(2
01
5)
20
0
N
ot
m
en
tio
ne
d
N
ot
m
en
tio
ne
d
N
ot
m
en
tio
ne
d
C
hr
is
tia
n
A
cc
ra
M
et
ro
po
lit
an
(G
ha
na
)
0.
05
4
Pr
iy
o
N
ug
ro
ho
et
a
l.
(2
01
7)
22
0
42
%
le
ss
3
1
45
%
3
1
to
4
0
13
%
a
bo
ve
4
0
N
ot
m
en
tio
ne
d
N
ot
m
en
tio
ne
d
M
us
lim
Y
og
ya
ka
rt
a
(I
nd
on
es
ia
)
0.
56
6
Sa
ts
io
s
an
d
H
ad
jid
ak
is
(2
01
7)
10
0
N
ot
m
en
tio
ne
d
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81
The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
A
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The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
RESULTS
Table 1 shows the results of data extraction processing. The 11 studies
were published between 2001 and 2021, with Abdullah and Abd.
Majid (2001) being the oldest and Prastiwi (2021) is the most recent.
The studies used two types of sampling methods: probability sampling
and non-probability sampling. Ababio and Mawutor (2015); Kassim
et al. (2019); Murdayanti et al. (2020); and Yayeh (2014) applied the
probability sampling method, whereas Ismail et al. (2018); Mei Teh et
al. (2019); Priyo Nugroho et al. (2017); Satsios and Hadjidakis (2017);
and Wijaya et al. (2019) employed the non-probability sampling
method. Meanwhile, Abdullah and Abd. Majid (2001); and Prastiwi
(2021) there was no mention of the method used in their studies.
To collect primary data, all studies developed a self-administered
questionnaire. Furthermore, various themes of religiosity and saving
behaviour, such as religious attendance (Yayeh, 2014), religious belief
(Ismail et al., 2018; Murdayanti et al., 2020), religion faith (Mei Teh
et al., 2019), saving habits (Satsios & Hadjidakis, 2017), and saving
decisions (Prastiwi, 2021; Wijaya et al., 2019), were used to as the
independent and dependent variables.
Various statistical analyses were also applied, namely Pearson
correlation (Satsios & Hadjidakis, 2017), chi-square test (Wijaya
et al., 2019), multiple linear regression (Abdullah & Abd. Majid,
2001; Ismail et al., 2018; Kassim et al., 2019; Prastiwi, 2021),
logit regression (Ababio & Mawutor, 2015; Mei Teh et al., 2019),
probit regression (Yayeh, 2014), partial least square (Murdayanti
et al., 2020), and simultaneous equation modelling (Priyo Nugroho
et al., 2017). On the other hand, various fit test indicators, such as
chi-square (Wijaya et al., 2019; Yayeh, 2014), odds ratio (Ababio &
Mawutor, 2015; Mei Teh et al., 2019), and t-statistic (Abdullah &
Abd. Majid, 2001; Ismail et al., 2018; Kassim et al., 2019; Prastiwi,
2021), have been used to assess the significance of the relationship
between religiosity and saving behaviour. These indicators had to be
converted into Pearson’s r before they are used in a meta-analysis.
Meanwhile, Priyo Nugroho et al. (2017) have provided another fit test
indicator, namely the critical ratio (8.395) (personal communication).
Table 2 summarizes the descriptive statistics from each study,
including the number of observations, age, gender, marital status,
religion, and location, as well as the estimated effect sizes. As
previously stated, 11 articles were used as samples for the meta-
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analytic study, and these gave a total of 1,063 observations. Kassim
et al. (2019) observed 531 people, making it the largest population
sample, while Prastiwi (2021); as well as Satsios and Hadjidakis
(2017) had 100 observations, making it the smallest. Meanwhile, the
age range varied from 13 to more than 40 years. Furthermore, only
four studies provided gender information, i.e., Abdullah and Abd.
Majid (2001); Ismail et al. (2018); Prastiwi (2021); and Wijaya et al.
(2019), in which most of their respondents were women. Similarly,
marital status was only provided by three studies, i.e., Abdullah and
Abd. Majid (2001); Kassim et al. (2019); and Yayeh (2014), with the
majority of their respondents being married. On the other hand, all
studies provided religious information, with Muslims representing the
majority of their respondents. Furthermore, the study locations reveal
that respondents were from a variety of countries, including Ethiopia,
Ghana, Greece, Malaysia, and Indonesia. Meanwhile, all studies were
ready to use the same method to express effect sizes in Pearson’s r.
The study conducted by Priyo Nugroho et al. (2017) had the largest
effect size (0.566), while the study performed by Ababio and Mawutor
(2015) had the smallest one (0.054).
Table 3
Random-effect Model
Estimate se z P CI Lower Bound CI Upper Bound
Intercept 0.303 0.0587 5.17 < .001 0.188 0.418
Table 4
Heterogeneity Statistics
I² H² Df Q P
0.181
0.0328
(SE= 0.0171)
89.08% 9.155 10 108.574 < .001
Table 3 provides the random-effects model, and Table 4 presents
the heterogeneity statistics. Based on the random-effects model, the
estimated average Fisher r-to-z transformed correlation coefficient
was 0.303. (95% CI: 0.188 to 0.418) and was statistically significant
(z = 5.17, p < 0.001). It showed that statistically there was a positive
correlation between religiosity and saving behaviour, with a true effect
Table 3
Random-effect model
Estimate se z P CI Lower Bound CI Upper Bound
Intercept 0.303. 0.0587 5.17 < .001 0.188 0.418
Table 4
Heterogeneity statistics
² I² H² Df Q P
0.181
0.0328
(SE= 0.0171)
89.08% 9.155 10 108.574 < .001
Note: Random-effects model (k = 11); ² estimator: Hedges
Table 5
The source of heterogeneity analysis
Heterogeneity
source
Coefficients T P
95% Confidence Interval
Lower Upper
Publication year -0.131 -0.395 0.703 -0.895 0.634
Sample size -0.334 -1.007 0.343 -1.098 0.431
Muslim religion -0.535 -1.79 0.111 -1.22 0.153
Figure 1 Forest plot
Table 3
Random-effect model
Estimate se z P CI Lower Bound CI Upper Bound
Intercept 0.303. 0.0587 5.17 < .001 0.188 0.418
Table 4
Heterogeneity statistics
² I² H² Df Q P
0.181
0.0328
(SE= 0.0171)
89.08% 9.155 10 108.574 < .001
Note: Random-effects model (k = 11); ² estimator: Hedges
Table 5
The source of heterogeneity analysis
Heterogeneity
source
Coefficients T P
95% Confidence Interval
Lower Upper
Publication year -0.131 -0.395 0.703 -0.895 0.634
Sample size -0.334 -1.007 0.343 -1.098 0.431
Muslim religion -0.535 -1.79 0.111 -1.22 0.153
Figure 1 Forest plot
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size of 0.303 and a significance value less than 0.001. Meanwhile,
according to heterogeneity statistics, the true effects appear to be non-
homogenous (Q(10) = 108.574, p < 0.001). By using the Hedges’
estimator, the index (0.0328) agreed with the Q-test result, indicating
that there was some between-study heterogeneity in the data, whereas
= 0.181 indicated that the true effect sizes had an estimated standard
deviation of SD = 0.181. In addition, the H2 (9.155) and I2 (89.08%)
indices confirmed that true effect size differences account for more
than half of the variation in the studies, which meant that the level of
heterogeneity was high.
Table 5
The Source of Heterogeneity Analysis
Heterogeneity
source
Coefficients T P
95% Confidence Interval
Lower Upper
Publication year -0.131 -0.395 0.703 -0.895 0.634
Sample size -0.334 -1.007 0.343 -1.098 0.431
Muslim religion -0.535 -1.79 0.111 -1.22 0.153
Based on a random-effects model, Figure 1 depicts correlation
coefficients with corresponding 95 percent confidence intervals
for each study graphically. The estimated r from each study ranged
from 0.0541 (Ababio & Mawutor, 2015) to 0.6416 (Priyo Nugroho
et al., 2017). Meanwhile, the weights ranged from 7.99 percent to
9.92 percent, in Prastiwi (2021), with Satsios and Hadjidakis (2017)
having the lowest and Kassim et al. (2019) having the highest. On the
other hand, with Kassim et al. (2019) having the shortest interval and
Prastiwi (2021) having the longest interval, the 95 percent confidence
intervals ranged from (-0.02, 0.15) to (0.02, 0.42). Furthermore, the
majority of confidence intervals were completely positive of zero,
indicating that the majority of studies had a statistically significant
positive effect. However, in some studies, i.e., Ababio and Mawutor
(2015) and Kassim et al. (2019), the confidence intervals were
not entirely positive of zero, indicating that these studies had a
statistically insignificant positive effect (0.05 and 0.06). Thus, all
observed dispersions reflected genuine differences in effect size and
p-value between studies. However, a meta-analysis method only uses
the effect size from each study rather than the p-value (Borenstein et
al., 2011).
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Figures 2, 3, and 4 provide the outcomes of outlier and influential case
diagnostics. Figure 2 shows the standardized residual values, which
are used to identify outliers, while Figures 3 and 4 show the DFFITS
values and Cook’s distances, which are used to detect influential cases.
A look at the studentized residuals revealed that none of the studies
had a value greater than ±3, indicating that there were no outliers in
the random-effect model. Meanwhile, based on the DFFITS values of
no more than 2 and Cook’s distances of no more than 1, none of the
studies could be considered overly influential.
Figure 1
Forest Plot
Figure 2
Standardized Residual
Table 3
Random-effect model
Estimate se z P CI Lower Bound CI Upper Bound
Intercept 0.303. 0.0587 5.17 < .001 0.188 0.418
Table 4
Heterogeneity statistics
² I² H² Df Q P
0.181
0.0328
(SE= 0.0171)
89.08% 9.155 10 108.574 < .001
Note: Random-effects model (k = 11); ² estimator: Hedges
Table 5
The source of heterogeneity analysis
Heterogeneity
source
Coefficients T P
95% Confidence Interval
Lower Upper
Publication year -0.131 -0.395 0.703 -0.895 0.634
Sample size -0.334 -1.007 0.343 -1.098 0.431
Muslim religion -0.535 -1.79 0.111 -1.22 0.153
Figure 1 Forest plot
14
Figure 2
Standardized Residual
Figure 3
DFFITS Values
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Figure 3
DFFITS Values
Figure 4
Cook’s Distances
The current study’s goal was to examine the existing empirical
evidence on the relationship between religiosity and saving behaviour
through a meta-analysis approach. The findings show that religiosity
has a positive, but weak correlation with saving behavior with r =
0.303 (p < 0.001; 95% CI = (0.188; 0.418)). As a rule of thumb, the
correlation strength is described as negligible (r < 0.2), low (0.2 ≤ r
< 0.4), moderate (0.4 ≤ r < 0.7), high (0.7 ≤ r < 0.9), or very high (r
≥ 0.9) (Guilford & Fruchter, 1973). According to this rule of thumb,
the correlation will be ignored when its strength is less than 0.2.
This finding implies that changes in religiosity have little impact on
changes in saving behaviour. Moreover, the low r value was caused
by the fact that the r values of the observed studies were mostly low
(see Figure 1), and even two of them were negligible, i.e., Ababio and
14
Figure 2
Standardized Residual
Figure 3
DFFITS Values
15
Figure 4
Cook's Distances
The current study's goal was to examine the existing empirical evidence on the relationship between
religiosity and saving behaviour through a meta-analysis approach. The findings show that religiosity has
a positive, but weak correlation with saving behavior with r = 0.303 (p < 0.001; 95% CI = (0.188; 0.418)).
As a rule of thumb, the correlation strength is described as negligible (r < 0.2), low (0.2 ≤ r < 0.4),
moderate (0.4 ≤ r < 0.7), high (0.7 ≤ r < 0.9), or very high (r ≥ 0.9) (Guilford & Fruchter, 1973).
According to this rule of thumb, the correlation will be ignored when its strength is less than 0.2. This
finding implies that changes in religiosity have little impact on changes in saving behaviour. Moreover,
the low r value was caused by the fact that the r values of the observed studies were mostly low (see
Figure 1), and even two of them were negligible, i.e., Ababio and Mawutor (2015), and Kassim et al.
(2019). Meanwhile, there were only four studies that provided empirical evidence that religiosity had a
moderate impact on saving behaviour, namely Mei Teh et al. (2019), Murdayanti et al. (2020), Priyo
Nugroho et al. (2017), and Yayeh (2014). On the other hand, Ababio and Mawutor (2015) obtained the
lowest r value (0.05), showing that religious belief had no effect on household savings. They assumed that
religious beliefs that promoted values like frugality, hard work, and honesty did not increase the savings
habits of households in Ghana. Likewise, Kassim et al. (2019) found that religiosity had no effect on
saving behaviour (r = 0.06). They assumed that an individual's level of religiosity would lead to the
preference to spend money in God's name rather than saving it. Unfortunately, neither of them tried to dig
a bit deeper into this finding, such as linking it to the lowest frequency value of the saving questions in
their questionnaire. In fact, it could be a clue to crucial evidence showing the low impact of the
relationship between religiosity and saving behaviour.
The result obtained also supports the notion that religious people have varying understandings of saving
behaviour. These understandings stem from two opposing perspectives on saving money taught by
various religions, including Christianity and Islam. These religions consider saving to be either a positive
or negative practice (Yayeh, 2014). In terms of saving as a positive practice, Christianity and Islam, for
example, teach saving when there is enough to go around in times of scarcity (Genesis 37-50; Sahih al-
Bukhari 5357). Religious people who put into practice this belief will also use their saving behaviour to
support their frugal lifestyle (Agarwala et al., 2019). Meanwhile, in terms of saving as a negative practice,
religions such as Christianity and Islam, for example, warn against the dangers of hoarding wealth (Luke
12:16-21; Quran 9:34). Religious people who engage in this practice are motivated by fears that if saving
money becomes a passion, they will become trapped in a cycle of amassing wealth and being stingy one
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The International Journal of Banking and Finance, Vol. 18, Number 1 (January) 2023, pp: 67–94
Mawutor (2015), and Kassim et al. (2019). Meanwhile, there were
only four studies that provided empirical evidence that religiosity had
a moderate impact on saving behaviour, namely Mei Teh et al. (2019),
Murdayanti et al. (2020), Priyo Nugroho et al. (2017), and Yayeh
(2014). On the other hand, Ababio and Mawutor (2015) obtained the
lowest r value (0.05), showing that religious belief had no effect on
household savings. They assumed that religious beliefs that promoted
values like frugality, hard work, and honesty did not increase the
savings habits of households in Ghana. Likewise, Kassim et al. (2019)
found that religiosity had no effect on saving behaviour (r = 0.06).
They assumed that an individual’s level of religiosity would lead to
the preference to spend money in God’s name rather than saving it.
Unfortunately, neither of them tried to dig a bit deeper into this finding,
such as linking it to the lowest frequency value of the saving questions
in their questionnaire. In fact, it could be a clue to crucial evidence
showing the low impact of the relationship between religiosity and
saving behaviour.
The result obtained also supports the notion that religious people have
varying understandings of saving behaviour. These understandings
stem from two opposing perspectives on saving money taught by
various religions, including Christianity and Islam. These religions
consider saving to be either a positive or negative practice (Yayeh,
2014). In terms of saving as a positive practice, Christianity and Islam,
for example, teach saving when there is enough to go around in times
of scarcity (Genesis 37-50; Sahih al-Bukhari 5357). Religious people
who put into practice this belief will also use their saving behaviour
to support their frugal lifestyle (Agarwala et al., 2019). Meanwhile, in
terms of saving as a negative practice, religions such as Christianity
and Islam, for example, warn against the dangers of hoarding wealth
(Luke 12:16-21; Quran 9:34). Religious people who engage in this
practice are motivated by fears that if saving money becomes a
passion, they will become trapped in a cycle of amassing wealth and
being stingy one day (Sawyer, 1954). As a result, in studies where
there is empirical evidence that religiosity has a moderate impact
on saving behaviour, their respondents are likely to view saving as
a positive practice. In contrast, respondents in studies with empirical
evidence that religiosity has a low impact on saving behaviour may be
less open to the idea of saving as a positive practice.
In addition, the current study discovered heterogeneity at a high level
(Q(10) = 108.574, p < 0.001; I2 = 89.08%). Heterogeneity may exist,
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because of differences in study quality (e.g., values for effect size and
significance), methodology, sample size, demographic factors, and
respondent characteristics. From a statistical perspective, quantifying
heterogeneity can be used to determine whether or not population
effect sizes are likely to be consistent or varying (Borenstein et al.,
2011; Hedges & Olkin, 1985). Meanwhile, the summary information
shown in Tables 1 and 2 demonstrates that the characteristics of
studies were not all the same. There were differences even within the
studies themselves, like variation in religious factors (see Table 2).
For instance, Ismail et al. (2018); and Yayeh (2014) collected data
from a variety of religious adherents, whereas other studies only
collected data from a single religious adherent. As a result, the present
study was motivated to investigate the cause of heterogeneity using
a technique known as meta-regression analysis. In meta-regression,
the effect size of each study as the independent variable is regressed
on the study characteristics as the dependent variable (Chen & Peace,
2021). Furthermore, the year of publication, sample size, and Muslim
religion were investigated to determine the source of heterogeneity.
They acted as independent variables in the meta-regression analysis
because they were relatively complete data. The results of meta-
regression analysis then revealed that publication year, sample size,
and Muslim religion were not statistically significant for the presence
of heterogeneity (see Table 5).
CONCLUSION AND FUTURE STUDIES
The current study used a meta-analysis to synthesize the findings
of previous studies to determine the effect of religiosity on saving
behaviour. The meta-analysis of 11 journal articles and 1,063
respondents revealed that religiosity has a low impact on saving
behaviour. The current study also confirmed the notion that religious
people have two different perspectives on saving behaviour, holding
the divergent view that saving can be either a negative or positive
practice. Because the study’s findings indicated that religiosity has
little influence on saving behaviour, it is possible that people with a
high level of religiosity have a less rigid perspective on saving as a
positive behaviour.
The findings have important implications for the development of
theories in the field of saving behaviour. In researching religiosity and
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saving behaviour, this study has pioneered a new analytic approach
known as meta-analysis. It has provided a retrospective summary
of the existing literature on the relationship between religiosity and
saving behaviour. It could help researchers make a more informed
decision about which variables to use. Furthermore, this study has
added to the body of knowledge about the true effect of the relationship
between religiosity and saving behaviour. It could be used as a model
for future research as well as a tool for additional analysis.
The findings also have practical implications for increasing public
awareness. This study has provided insights for authorities and financial
institutions interested in encouraging religious people to save. More
understanding of the findings can aid them in the improvement of
plans focused on savings advocacy and savings facilitation. Savings
advocacy would help religious households and individuals understand
the importance of saving and resource management. It would be a
critical step in developing their saving habits. Meanwhile, financial
institutions can create a type of savings account that corresponds to
the religious predisposition. It would promote the notion that saving
money is a good deed. The plans are beneficial in encouraging
religious people to engage in responsible financial behaviour.
Collective savings will have a significant impact on the economy if
these individuals have a strong savings predisposition.
However, a number of important limitations of the study have to be
considered. First, the current meta-analytic method has synthesized
only a few studies. Although the limited sample used in the present
study was acceptable, the findings would be more reliable if a larger
number of studies could be synthesized. Second, of the 11 previous
studies that were synthesized, Islam was the religion which had the
highest proportion of adherents, with only a small proportion of
adherents from other religions. Although that was the nature of the
meta-analytic samples, the current pattern of results could be more
generalizable if the proportion of adherents was more balanced.
Third, the current study synthesized 11 studies had used primary data
and measured variables with a questionnaire. This technique opens
the possibility of a person’s proclivity to respond to a questionnaire
with a positive self-image, also known as a socially desirable response
(Van de Mortel, 2008). Although bias has been reduced by selecting
studies from reputable journals, it is still possible to be biased when
answering sensitive questions, such as religious ones.
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Furthermore, more research is needed to examine the relationship
between religiosity and saving behaviour, more specifically, the
importance of using longitudinal studies at multiple points in
time. This type of research could shed light on the minor impact
of the relationship and contribute to a critical explanation for this
phenomenon.
ACKNOWLEDGMENT
This research received no specific grant from any funding agency in
the public, commercial, or not-for-profit sectors.
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