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 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 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.

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,

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, 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.

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 Method contacted of Analysis via email to gain the relevant information. A reminder A META-AN was sent if they did not reply. REL

processing. Theare eleven considered studies to were be influential publishedif between the Cook’s 2001 distance value is more than 1 (cook.D 001) is the longest and Prastiwi (2021) is the most recent. The > 1) and DFFITS is larger than 2 (dffits > 2) (Gerbing, 2014). : probability sampling and non-probability sampling. Ababio ; Murdayanti et al. (2020); and Yayeh (2014) applied the The meta-analysis was carried out with the help of an open-source il et al. (2018); Mei Teh et al. (2019); Priyo Nugroho et al. 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.

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 metaanalytic 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).

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.

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). Intercept 0.303. 0.0587 5.17 < .001 0.188 0.418

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

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, 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).

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 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.

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.