The role of religiosity in product and technology acceptance: Evidence from COVID-19 vaccines

Abstract

Previous research on religion and economic phenomena has suggested that religious attitudes are related to risk aversion. Moreover, risk attitudes play a significant role in the adoption and diffusion of technological innovations. However, the role of religiosity in technology-related phenomena is still relatively unexplored. The present study fills this gap and investigates the impact of religiosity on the acceptance of innovative technologies and products in the context of the COVID-19 pandemic. Specifically, we frame COVID-19 vaccines as new products based on innovative production technologies and show that their acceptance by the general public is negatively associated with country-level religiosity. Furthermore, we investigate the role of religious leaders in endorsing COVID-19 vaccines to their followers. Our hypotheses are empirically tested on 1179 weekly observations of vaccination rates in 22 European countries characterised by different levels of religiosity. The results suggest that religiosity is negatively associated with vaccine rates after controlling for country-level social and economic factors. Conversely, the countries where Roman Catholics are the majority religious group display a positive association between religiosity and vaccine rates, highlighting the role of leaders in endorsing the COVID-19 vaccination campaign.

1. Introduction

The COVID-19 pandemic has led to the introduction and spread of several technological innovations, such as contact tracing apps (Cloos and Mohr, 2022). Moreover, it has induced significant changes in collective behaviours, such as the adoption of social distancing to prevent the diffusion of the virus (Pedersen and Favero, 2020). However, one of the most critical issues faced for “flattening the curve” and redressing the heavy economic consequences of lockdown (Debecker and Modis, 2021) has been the success of the COVID-19 vaccination campaign.

There have been major issues related to individuals' decision to adopt COVID-19 vaccines, which have been produced through innovative technologies, such as mRNA platforms (Ura et al., 2021) and adenovirus DNA vectors (Knoll and Wonodi, 2021). Both technologies are not completely new to academic researchers. Other mRNA treatments have already been tested in recent years (Kowalczyk et al., 2016), and adenovirus DNA vectors have been widely studied in the last three decades (Majhen et al., 2014). However, the first approval for use in humans of a vaccine based on adenovirus DNA vector dates back only to 2019 (Chang, 2021), while mRNA vaccines are considered a completely new pharmaceutical product (Mogaji, 2021). Consequently, both the technologies employed and the resulting products (namely, the Pfizer, Moderna, and AstraZeneca vaccines) were largely unknown to the general public in the earliest stages of the vaccination campaign.

Seminal works aiming to extend the technology acceptance model (Featherman, 2001) suggest that individuals' behavioural intention to adopt a new technology is linked with the perceived associated risks, especially in the early stages of adoption (e.g., Ortega Egea and Román González, 2011). The same concerns about safety and/or risk have been found in adopting new products (Heiman and Muller, 1996; Ta and Prybutok, 2018). Moreover, research on COVID-19 vaccines suggests that the behavioural intention to get vaccinated is strongly related to the relative perception of the risks connected to vaccines and the infection itself (Pelegrín-Borondo et al., 2021; Zhang et al., 2021).

If an innovation to be adopted is perceived by individuals as riskier than average, the role of risk preferences and all factors affecting them should not be underrated. We build on the relationship between religious attitudes and risk aversion and propose religiosity as a factor potentially influencing the adoption of COVID-19 vaccines. In doing so, we aim to fill a relevant gap in the literature on technology (product) acceptance, country-level characteristics, and individual behaviours.

Different disciplines have established an empirical link between religious attitudes and risk aversion. As summarised by Hilary and Hui (2009), the psychology and anthropology literature has consistently shown religiosity (usually measured through church attendance) to be positively correlated with risk aversion (Osoba, 2003) and negatively associated with risk-taking behaviours (e.g., gambling (Diaz, 2000)). Overall, we leverage previous evidence on the relationship between religiosity, risk aversion, and technology (product) intention to adopt and expect a negative association between country-level religiosity and COVID-19 vaccine diffusion.

Other factors could potentially moderate the relationship between country-level religiosity and vaccination rates. Namely, several studies have suggested that religious leaders play a relevant role in pushing their followers to adopt health-related practices (Anshel and Smith, 2014; Cohen-Dar and Obeid, 2017). Consequently, some correspondences from the Journal of Public Health suggest that religious leaders have a critical role in overcoming hesitancy and promoting COVID-19 vaccines (Corpuz, 2021; Gopez, 2021). Therefore, we expect religiosity to be positively associated with COVID-19 vaccine diffusion when the majority religious group in a country has leaders who are publicly and consistently favourable to COVID-19 vaccines.

We test our hypotheses by exploiting the empirical setting of the European Union, in which the European Medicine Agency (EMA), a supranational authority, is responsible for approving vaccines. We observe weekly vaccination rates, computed as the number of first doses administered over the country population, between December 2020 and December 2021 in 22 European countries. Following previous research (Barro and McCleary, 2003), we capture country-level religiosity by leveraging answers to questions in the World Value Survey. We perform our analyses on a panel of 1179 week-country observations.

Results suggest that religiosity is negatively associated with vaccination rates after controlling for other country-level social and economic factors. However, there is a positive association between religiosity and vaccination rates in countries where Roman Catholicism is the predominant religion. This last result supports the role of religious leaders in promoting technology (product) acceptance. For example, Pope Francis1 has frequently publicly endorsed vaccination against COVID-19 (Corpuz, 2021; Gopez, 2021).

2. Theoretical framework

2.1. Religiosity, risk aversion, and innovative technology (product) acceptance

The first COVID-19 vaccines introduced in 2020 are based on innovative production technologies, namely, the mRNA platform (Ura et al., 2021) and adenovirus DNA vectors (Knoll and Wonodi, 2021). Both technologies were not new to academic researchers: treatments based on mRNA had already been studied before (Kowalczyk et al., 2016), and adenovirus DNA vectors were based on a long-standing research stream (Majhen et al., 2014). However, they were largely unknown to the general public in the earliest stages of the vaccination campaign. The same is true for the resulting products, the Pfizer, Moderna, and AstraZeneca vaccines. In fact, the first approval of a vaccine based on adenovirus DNA vector for human use dates back only to 2019 for Ebola (Chang, 2021), while mRNA vaccines are considered an entirely new pharmaceutical product (Mogaji, 2021).

Even if academic research considers vaccines as innovative products (e.g., Li and Qiu, 2013; Mogaji, 2021) based on innovative technological platforms (e.g., Mascola and Fauci, 2020), only one study has analysed them in terms of behavioural acceptance as a function of their technological nature (Pelegrín-Borondo et al., 2021). Indeed, vaccines are very specific products based on complex technologies. For this reason, the decision on whether to get injected can be considered an “extreme case” of product and technology acceptance. Accordingly, the role of risk perception is also taken to the extreme, as vaccines involve health-related concerns.

Compared to average cases, extreme cases in social sciences are considered critical to reveal more about a phenomenon (Chen, 2015; Flyvbjerg, 2006). In some instances, they are the only means through which to understand certain phenomena (McKelvey and Andriani, 2005). Consequently, this study argues that the acceptance and diffusion of COVID-19 vaccines can be framed as an extreme case of technology and product acceptance, as vaccines: (a) incorporate innovative elements into their production technologies, (b) are new products themselves, and (c) are related to personal health, making individual risk perception exceptionally salient in their adoption.

The relative speed of production with respect to traditional vaccines, together with the adoption of a conditional authorisation model by health authorities (Cavaleri et al., 2021), is bound to affect the initial risks' perception of COVID-19 vaccines by the general public, who could consider them “too new” and “not tested properly” (Latkin et al., 2021). Previous research on technology and product acceptance models suggests that individual behavioural intention to adopt new technology (Featherman, 2001) or buy a new product (Heiman and Muller, 1996) is affected by the perception of the risks involved. A higher perception of “experiencing negative consequences or losses in uncertain situations” (Ortega Egea and Román González, 2011, p. 323) decreases individuals' intention to adopt an innovative technology (e.g., Slade et al., 2015) or to buy a new product (Barnes and Ayars, 1977; Cowart et al., 2008). Moreover, the role of risk preferences in technology (product) diffusion has also been considered central in the literature about the “diffusion of innovation,” suggesting that both risk perception (Lilien, 1980) and risk aversion (Chatterjee and Eliashberg, 1990) negatively impact the diffusion of innovative technology (product). Consequently, factors affecting risk preferences, defined as preferences “for options perceived to be more or less risky” (Weber and Milliman, 1997, p. 123), should play a role in the decision of individuals to get vaccinated. We propose religiosity as one of those factors.

Different disciplines have established an empirical link between individual-level religious attitudes and risk aversion. As summarised by Hilary and Hui (2009), the psychology and anthropology literature has consistently shown religiosity (usually measured through church attendance) to be positively correlated with risk aversion (Osoba, 2003) and negatively associated with risk-taking behaviour (i.e., gambling (Diaz, 2000)). Hilary and Hui (2009) further explore this relationship by correlating questions related to religious beliefs and behaviours to questions associated with risk aversion in the European Social Survey and find a significant and positive relationship between their answers (Hilary and Hui, 2009). Moreover, experimental evidence further confirms these results (Hilary and Hui, 2009).

Both Miller and Hoffmann (1995) and Hilary and Hui (2009) provide a theoretical explanation for the positive relationship between risk aversion and religiosity. Risk-averse individuals seek religion to alleviate anxiety caused by uncertainty encountered in their daily life (Hilary and Hui, 2009). The link between anxiety and religiosity is empirically confirmed by evidence from the World Values Survey, and experimental results show how anxiety is positively associated with risk aversion (Lerner and Keltner, 2001). Moreover, the economics literature has directly tested the link between religiosity and innovation. Bénabou et al. (2015a) show how religiosity and attitudes towards innovation and scientific progress at the individual level are negatively correlated. More importantly for our setting, Bénabou et al. (2015b) find that such a relationship also holds at the country level, as highly religious areas exhibit fewer patents per capita. Overall, these results support the idea of religion as a source of (endogenous) persistence and resistance to change in the population of specific areas (Bénabou et al., 2015a, Bénabou et al., 2015b) and are consistent with individual-level evidence on religious people being less willing to take risks (Hilary and Hui, 2009).

This study, taking into consideration the arguments mentioned above, frames COVID-19 vaccines as new products made with innovative production technologies and perceived as involving some degree of risk by the target population. Therefore, it is reasonable to hypothesise that individuals' risk aversion strongly influences their COVID-19 vaccine acceptance, and its consequent country-level diffusion. Risk-averse and highly religious people are more resistant to innovation (Hilary and Hui, 2009; Bénabou et al., 2015a), and this pattern also holds at the country level (Bénabou et al., 2015b). Thus, we would expect to observe lower vaccination rates in areas characterised by high levels of religiosity. Therefore, we formulate the following hypothesis:

H1

Highly religious countries exhibit a lower vaccination rate.

2.2. Religious leaders' role in the health-related behaviour of religious followers

The role of religious leaders in influencing followers' opinions and behaviours is a long-standing topic in social science (Mckeown and Carlson, 1987; Pinto, 1964). Even if early experimental results have suggested a nonsignificant effect of the influence of religious leaders on followers' political opinions (Mckeown and Carlson, 1987), subsequent research has conceptualised and empirically supported the role of religious leaders in shaping their followers' opinions about political and ethical matters such as arms race (Wald, 1992), immigration (Nteta and Wallsten, 2012), the death penalty, and abortion (Mulligan, 2006). More recently, several studies have analysed the role of religious leaders in endorsing and promoting health-related behaviour. Religious leaders can raise awareness about health-related issues (Cohen-Dar and Obeid, 2017) and promote virtuous behaviour in their religious communities (Anshel and Smith, 2014). Overall, there exists empirical evidence supporting the effectiveness of religious leaders' involvement in promoting health-related practices (Toni-Uebari and Inusa, 2009).

Among all possible health-related concerns, vaccine acceptance and hesitance within religious communities have also been addressed (Wombwell et al., 2015). Likewise, the role of religious leaders in hampering or prompting vaccine diffusion has also been discussed (Williams et al., 2020). An in-depth analysis of religious scriptures and reports on vaccine attitudes in major religious communities found no strong canonical or doctrinal basis for refusing vaccines (Grabenstein, 2013). Moreover, calls for preserving life and caring for others in the community may encourage vaccination (Grabenstein, 2013). However, religious leaders' attitudes towards vaccines vary from full acceptance to outright refusal (Ruijs et al., 2013), presumably leading to different vaccine-related attitudes in their respective communities (Williams and O'Leary, 2019).

During the COVID-19 pandemic, academic research has directly called for religious leaders to endorse COVID-19 vaccines to increase vaccine coverage inside religious communities (Corpuz, 2021; Galang, 2021; Nagar and Ashaye, 2022). However, to the best of our knowledge, no published scientific studies have empirically verified the conceptualised positive relationships between religious leaders' support for vaccines and the effective vaccination rate. Therefore, building on the arguments above, we formulate the following hypothesis:

H2

Countries in which the religious leaders of majority religious groups have publicly endorsed COVID-19 vaccines exhibit a higher vaccination rate.

3. Research methodology

3.1. Data and sample

Our dataset combines information from various sources. First, we obtain weekly data on vaccination rates and COVID-19 cases in the European Union from the European Centre for Disease Prevention and Control. Our timeframe starts in late December 2020, when the first doses were administered, and ends in December 2021.2 We capture religiosity at the country level drawing from data from the seventh wave of the Joint European and World Values Survey. Finally, we obtain social and economic data on education, gender, health status, foreign population, national net income, and unemployment rates at the country level from Eurostat. The final sample consists of 22 European countries and 1179 week-country observations.3

3.2. Variable definitions

Our outcome variable is the rate of vaccination against COVID-19 (Vaccinated (1st dose)), measured by the weekly amount of first doses of all types of vaccine administered, standardised by the country population.

Our variable of interest is country-level religiosity. Following previous research (Barro and McCleary, 2003; Bénabou et al., 2015b; Chen et al., 2016), we capture religiosity at the country level with five measures corresponding to the answers to five questions in the last wave of the joint World and European Values Survey.

One section of the World (European) Values Survey tracks the religious beliefs and practices of the respondents (Chen et al., 2016), and these data are consistently used in the literature to compare religious beliefs between societies. Member of Religion is computed as the number of respondents for each country that answered “A religious person” to the question: “Independently of whether you attend religious services or not, would you say you are: Missing; Unknown; Not asked; Not applicable; No answer; Do not know; A religious person; Not a religious person; A convinced atheist; other answer” (Chen et al., 2016). Importance of Religion is the number of respondents for each country that answered “Very important” or “Rather important” to the question: “Would you say religion is: Missing; Unknown; Not asked; Not applicable; No answer; Don't know; Very important; Rather important; Not very important; Not at all important” (Chen et al., 2016). Religious Services is the number of respondents for each country that declared to attend a religious service more than once a year when asked: “Apart from weddings and funerals, about how often do you attend religious services these days? 1: More than once a week; 2: Once a week; 3: Once a month; 4: Only on special holy days; 5: Once a year; 6: Less often; 7: Never, practically never” (Chen et al., 2016). Belief in God is computed as the number of respondents for each country that answered “Yes” to the question: “Do you believe in God?” (Bénabou et al., 2015a). Importance of God is computed as the number of respondents for each country who answered, “Very important” to the question “Importance of God in your life?” (Bénabou et al., 2015a). All the previous variables are standardised by the number of total respondents in each country. Following Chen et al. (2016), we then built a country-level index of religiosity by extracting the first principal component of the five variables (Religiosity Index). Finally, Catholic, Orthodox, and Protestant are dummy variables equal to one if the predominant religion of the country is, respectively, Roman Catholic, a branch of the Orthodox Churches, or Protestantism, as reported on the CIA Factbook.4

We control for several country-level characteristics that could affect individual willingness to be vaccinated, the speed of national-level vaccination campaigns, and individual risk aversion. The first set of controls aims to account for local differences in the (potential) severity of the pandemic. We include the weekly COVID-19 contagion rate (Contagion) to account for differences in the stage of the pandemic each country is experiencing each week. Depending on the level of contagion, people could feel compelled to get vaccinated due to fear (Salali and Uysal, 2021). Then, we factor in country-level differences in the general health of the population through Healthy population, computed as the number of expected years without activity limitation. Especially in the first phases of the pandemic, attention was drawn to the correlation between health problems and severe or lethal cases of COVID-19 (Yang et al., 2020).

The second set of controls accounts for differences in wealth and economic conditions across countries. For this purpose, we include Unemployment rate (or is the fraction of people unemployed over the labour force) and the national net income in EUR millions (Income).5

Finally, we follow Hilary and Hui (2009) and Iannaccone (1998) by including some demographic characteristics as possible individual-level determinants of religious participation. We include Education, measured as the fraction of the country's population with a bachelor's degree or higher. We control for gender-related risk aversion through the fraction of males over country population (Male). Finally, we include the fraction of the foreign population belonging to the EU27 countries over the country population (Foreign (EU27)) as a measure of minorities in each country. All socioeconomic controls apart from Contagion rates were measured in 2019.

3.3. Econometric model

Our econometric approach is based on a panel fixed effect technique to estimate the impact of religiosity on vaccination rates in the European Union. We include weekly fixed effects in all regressions to account for time confounding effects. To test our first hypothesis, we estimate the following equation:

$\mathit{Vaccinated}{\left(1\mathit{st}\mathit{dose}\right)}_{c,t}=\alpha +{\beta }_1{\mathit{Religiosity}}_c+{\beta }_i{\mathit{Controls}}_{c,t}+{\mu }_t+{\epsilon }_{c,t}$ (1)

Vaccinated (1st dose) is the weekly vaccination rate of country c at time t. Religiosity is, alternatively, one of our religiosity-related variables in country c ((1) Religiosity Index, (2) Member of Religion, (3) Importance of Religion, (4) Religious Services, (5) Belief in God, (6) Importance of God). Controls _{c, t} is a vector of country-level socioeconomic control variables (Contagion, Healthy population, Unemployment rate, Income, Education, Male, Foreign (EU27)). The key coefficient is β ₁. If the coefficient is negative and statistically significant, then we can conclude that the level of religiosity observed in a country negatively impacts willingness to be vaccinated against COVID-19. μ _t represents weekly fixed effects, and ε _{c, t} represents an error term. To account for the risk of serial correlations, we use clustered heteroscedasticity-adjusted standard errors at the country level.

We are also interested in understanding the possible moderating role of the leaders of majority religious groups that have endorsed COVID-19 vaccines on the relationship between country-level religiosity and vaccination rates (H2). To test H2, we compare predominantly Roman Catholic countries, where the clear and public endorsement of COVID-19 vaccines by Pope Francis (Corpuz, 2021; Gopez, 2021) should have a significant effect, with countries with a different majority religious group. First, we compare majority Roman Catholic countries with majority Orthodox countries as a benchmark of unclear endorsement because some published academic studies suggest that anti-vaccine movements could be stronger within Orthodox communities (Mărcău et al., 2022) and very few Orthodox Churches' religious leaders have endorsed COVID-19 vaccines (Dascalu et al., 2021). Second, we compare majority Roman Catholic countries with majority Protestant countries as a neutral benchmark because we did not find published studies indicating positive or negative attitudes towards COVID-19 vaccines or endorsements of Protestant religious leaders.

We estimated the following equation:

$\mathit{Vaccinated}{\left(1\mathit{st}\mathit{dose}\right)}_{c,t}=\alpha +{\beta }_1{\mathit{Religiosity\; Index}}_c+{\beta }_2{\mathit{Major\; Religious\; Group}}_c+{\beta }_3{\mathit{Religiosity\; Index}}_c\ast {\mathit{Type\; of\; Religion}}_c+{\beta }_i{\mathit{Control}}_{c,t}+{\mu }_t+{\epsilon }_{c,t}$ (2)

Where Religiosity Index is our country-level index of religiosity. Major Religious Group _c takes the value of one if the country's predominant religion is either Roman Catholic (Catholic), a branch of the Orthodox Churches (Orthodox), or Protestant (Protestant). The coefficient of the interaction between these dummies and country-level religiosity (Religiosity Index _c ∗ Type of Religion _c) aims to explore the moderating effect of majority religious groups with leaders endorsing COVID-19 on the relationship between religiosity and vaccination rates.

4. Empirical analysis

4.1. Descriptive statistics

Table 1 presents the summary statistics. The mean (median) weekly vaccination rate is 1.19 % (0.7 %). In terms of religiosity indicators, Religiosity Index, Member of Religion, Importance of Religion, Religious Services, Belief in God, and Importance of God average 0.000, 0.581, 0.466, 0.254, 0.634, and 0.193, respectively. The predominant religion dummies show that, approximately 50 % of countries included in the sample are predominantly Roman Catholic (Catholic), 13.3 % of countries are predominantly Orthodox (Orthodox), and 23 % of countries are mainly Protestant (Protestant).

Table 1.

Summary statistics (countries included are Austria, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Lithuania, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, and Sweden). Vaccinated (1st dose) is the weekly amount of first doses of all types of vaccine injected, standardised by the country's population. The measures of religiosity are proxied by the (1) Religiosity Index, (2) Member of Religion, (3) Importance of Religion, (4) Religious Services, (5) Belief in God, and (6) Importance of God. Catholic, Orthodox, and Protestant are dummy variables equal to one if the predominant religion of the country is Roman Catholicism, a branch of the Orthodox church, or Protestantism, respectively, as reported on the CIA Factbook. Contagion is the weekly contagion rate. Healthy population is the number of expected years without activity limitation. Unemployment rate is the fraction of people unemployed over the labour force. Income is the national net income in EUR millions. Education is the fraction of the population with a bachelor's degree or higher in 2019. Male is the fraction of males over the population in 2019. Foreign (EU27) is the fraction of foreign population belonging to the EU27 countries over the country population.

	Mean	Median	SD	p25	p75
Vaccinated (1st dose)	0.0119	0.0070	0.0121	0.0028	0.0178
Religiosity Index	0.0006	−0.0047	0.0212	−0.0176	0.0176
Member of Religion	0.5812	0.5990	0.1625	0.4322	0.7360
Importance of Religion	0.4658	0.3995	0.1793	0.3445	0.5958
Religious Services	0.2536	0.1914	0.1493	0.1187	0.3506
Belief in God	0.6340	0.6519	0.1759	0.4914	0.7606
Importance of God	0.1934	0.1382	0.1292	0.1020	0.2127
Catholic	0.4996	0.0000	0.5002	0.0000	1.0000
Orthodox	0.1329	0.0000	0.3397	0.0000	0.0000
Protestant	0.2303	0.0000	0.4212	0.0000	0.0000
Contagion	0.0855	0.0807	0.0440	0.0543	0.1081
Healthy population (age)	62.1506	61.0000	4.8767	57.5000	66.3000
Unemployment rate	0.0564	0.0500	0.0263	0.0370	0.0670
Income (EUR millions)	608,237.6859	241,065.0000	892,261.4848	60,246.0000	511,239.0000
Education	0.5274	0.5076	0.1665	0.3918	0.6250
Male	0.4881	0.4884	0.0098	0.4839	0.4935
Foreign (EU27)	0.0292	0.0218	0.0312	0.0076	0.0387

Considering other country-level characteristics, the mean rate of Contagion is 8.6 %, and on average, people reach 62 years of age before incurring into any activity-limiting health problem (Healthy population). The average Unemployment rate equals 5.6 %, while the mean Income is 608,237.686 EUR million. Regarding sociodemographic controls, mean Education is 52.7 %, average Male equals 48.8 %, and the foreign population belonging to the EU27 countries averages to 2.9 % (Foreign).

Panel A (Panel B) of Fig. 1 shows the geographical distribution of vaccination rates (religiosity) across the 22 European countries included in the sample, based on the average value of Vaccinated (1st dose) (Religiosity Index) by country from December 2020 to December 2021. The figure presents four categories obtained using a quantile methodology, with darker blue (red) representing higher (lower) average levels of vaccination rates (Panel A) and religiosity (Panel B). The highest vaccination rates are observed in Italy, Germany, Spain, and Portugal. The lowest vaccination rates are observed in Romania, Bulgaria, Poland, and Croatia. Regarding the Religiosity index, the highest values are observed in Romania, Poland, Cyprus, and Italy, while the smallest values are for Sweden, Norway, Netherlands, and France.

Fig. 1.

This figure presents the geographical distribution of the vaccination rate (Panel A) and religiosity (Panel B) across the 22 EU countries analysed. The distribution is based on the mean value for the period Dec. 2020–Dec. 2021. The figure presents four categories obtained based on quantile methodology with darker blue colours representing higher vaccination rates (Panel A) and religiosity (Panel B). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 2 displays the correlation matrix. As expected, the vaccination rate is negatively correlated with our proxies of religiosity. Overall, the correlation among our variables of interest is low (<50 %).

Table 2.

Correlations between vaccinated population and various measures of religiosity. This table reports the pairwise correlations between the following variables at the country level: Vaccinated (1st dose), Religiosity Index, Member of Religion, Importance of Religion, Religious Services, Belief in God, Importance of God, Catholic, Orthodox, Protestant, Education, Male, Healthy population (age), Contagion, Foreign (EU27), Unemployment rate and Income.

	Vaccinated (1st dose)	Religiosity Index	Member of Religion	Importance of Religion	Religious Services	Belief in God	Importance of God
Vaccinated (1st dose)	1.000
Religiosity Index	−0.059	1.000
Member of Religion	−0.056	0.900	1.000
Importance of Religion	−0.061	0.967	0.792	1.000
Religious Services	−0.042	0.943	0.830	0.905	1.000
Belief in God	−0.043	0.969	0.903	0.928	0.872	1.000
Importance of God	−0.074	0.897	0.683	0.891	0.801	0.821	1.000
Catholic	0.018	0.402	0.513	0.329	0.447	0.440	0.149
Orthodox	−0.103	0.454	0.248	0.545	0.294	0.455	0.575
Protestant	0.064	−0.459	−0.408	−0.452	−0.469	−0.418	−0.398
Education	−0.005	0.218	0.062	0.298	0.218	0.180	0.257
Male	0.021	−0.368	−0.428	−0.332	−0.278	−0.410	−0.272
Healthy population	0.037	−0.211	−0.400	−0.058	−0.139	−0.223	−0.179
Contagion	−0.182	−0.035	−0.004	−0.065	0.055	−0.101	−0.041
Foreign (EU27)	0.089	−0.082	−0.242	−0.034	−0.024	−0.008	−0.084
Unemployment rate	0.080	0.099	0.075	0.126	0.103	0.214	−0.071
Income	0.081	−0.180	−0.218	−0.146	−0.110	−0.159	−0.210

	Catholic	Orthodox	Protestant	Education	Male %	Healthy population	Contagion	Foreign (EU27)	Unempl. rate	Income
Catholic	1.000
Orthodox	−0.391	1.000
Protestant	−0.547	−0.214	1.000
Education	0.223	0.195	−0.287	1.000
Male %	−0.435	−0.020	0.566	−0.101	1.000
Healthy population	−0.166	0.074	0.308	−0.043	0.449	1.000
Contagion	0.213	−0.087	−0.438	0.219	−0.152	−0.155	1.000
Foreign (EU27)	−0.286	0.224	0.219	0.289	0.363	0.218	−0.096	1.000
Unemployment rate	0.401	−0.077	−0.120	0.106	−0.108	0.294	−0.013	0.125	1.000
Income	0.030	−0.224	0.239	0.172	0.144	0.433	−0.199	0.143	0.215	1.000

4.2. Baseline results: Religiosity and COVID-19 vaccine

Table 3 tabulates the main results for Eq. (1). In Column 1, we show the results for Religiosity Index, and in Columns 2–6 for each of the components of Religiosity Index (Member of Religion, Importance of Religion, Religious Services, Belief in God, Importance of God).

Table 3.

Relation between religiosity and vaccination. This table reports estimates from a panel regression model where the impact of religiosity on vaccination rate is analysed. All models include time fixed effect. The estimation period is Dec. 2020-Dec2021. *, **, and *** indicate significance at the 10 %, 5 %, and 1 % level, respectively.

	(1)	(2)	(3)	(4)	(5)	(6)
	Model	Model	Model	Model	Model	Model
Religiosity Index	−0.0321**
	(−2.8290)
Member of Religion		−0.0033**
		(−2.1194)
Importance of Religion			−0.0041***
			(−2.8575)
Religious Services				−0.0040**
				(−2.7222)
Belief in God					−0.0043**
					(−2.6459)
Importance of God						−0.0050**
						(−2.5575)
Contagion	0.0065	0.0084	0.0047	0.0106	0.0042	0.0060
	(0.7660)	(0.9728)	(0.5991)	(1.2165)	(0.5114)	(0.6484)
Healthy population	−0.0083	−0.0099	−0.0063	−0.0083	−0.0088	−0.0075
	(−1.2810)	(−1.5621)	(−0.9924)	(−1.2221)	(−1.4492)	(−1.0417)
Unemployment rate	0.0254**	0.0263*	0.0249*	0.0244*	0.0291**	0.0210*
	(2.1180)	(1.9651)	(2.0284)	(1.9779)	(2.4859)	(1.8198)
Income	0.0010***	0.0010***	0.0009***	0.0010***	0.0009***	0.0009**
	(3.0996)	(3.3868)	(3.0894)	(3.3077)	(3.2172)	(2.7282)
Education	−0.0029*	−0.0036**	−0.0023	−0.0033**	−0.0031*	−0.0026
	(−1.8895)	(−2.2351)	(−1.5502)	(−2.0974)	(−2.0747)	(−1.6228)
Male	−0.0620	−0.0545	−0.0660*	−0.0575	−0.0685	−0.0556
	(−1.5706)	(−1.2969)	(−1.7618)	(−1.4529)	(−1.6823)	(−1.4309)
Foreign (EU27)	0.0438***	0.0426***	0.0433***	0.0457***	0.0461***	0.0428***
	(4.3484)	(4.2298)	(4.5105)	(4.3166)	(4.7429)	(3.8620)
Constant	0.0629**	0.0675**	0.0590**	0.0604**	0.0712**	0.0581**
	(2.3612)	(2.4704)	(2.2165)	(2.2147)	(2.6876)	(2.0962)
Observations	1.179	1.179	1.179	1.179	1.179	1.179
R-squared	0.648	0.647	0.648	0.647	0.648	0.648
Time FE	YES	YES	YES	YES	YES	YES

The coefficient of Religiosity Index is negative and statistically significant at the 5 % level (β = −0.0321, t-statistic = −2.8290), which suggests that in countries where religiosity is higher, the vaccination rate is lower. In Column 2, we find that being a member of a religion is negatively associated with vaccination rates (β = −0.0033, t-statistic = −2.1194). Similarly, for the other religiosity measures (Importance of Religion, Religious Services, Belief in God, Importance of God), we find a consistently negative relationship between religiosity and vaccination rates. These results confirm that religiosity and its related risk aversion can be negatively associated with individuals' acceptance of innovative technology (product); in fact, the results show that religiosity is generally associated with a lower willingness to take COVID-19 vaccines.6

4.3. The moderation of majority religious groups with religious leaders that have publicly endorsed COVID-19 vaccines

Our next step is to assess whether different attitudes towards COVID-19 vaccines among religious leaders have affected vaccination rates. Empirically, we construct three dummies indicating each country's predominant religion (Roman Catholicism, a branch of the Orthodox Churches, or Protestantism). Then, we interact these dummies with Religiosity Index. We expect that the observed impact of each religious leader's endorsement of vaccines will be especially strong in countries where most of the population belongs to the religious group of reference. If the interaction coefficient (β ₃ in Eq. (2)) is positive (negative), then we can conclude that the public stance of different religious leaders positively (negatively) mediates the relationship between religiosity and the level of vaccination against COVID-19. The results are reported in Table 4 .

Table 4.

Relation between types of religions and vaccination. This table reports estimates from a panel regression model where the impact of types of religions on vaccination rate is analysed. We analyse three different religious groups: (1) Catholic (2) Orthodox and (3) Protestant. All models include time fixed effect. The estimation period is Dec. 2020-Dec2021. *, **, and *** indicate significance at the 10 %, 5 %, and 1 % level, respectively.

	(1)	(2)	(3)
	Model	Model	Model
Religiosity Index	−0.0725***	−0.0113	−0.0104
	(−3.8685)	(−0.8300)	(−0.5801)
Catholic	0.0010
	(1.4319)
Religiosity Index*Catholic	0.0856**
	(2.4407)
Orthodox		0.0006
		(1.0999)
Religiosity Index*Orthodox		−0.0729**
		(−2.5495)
Protestant			−0.0004
			(−0.7137)
Religiosity Index*Protestant			−0.1504**
			(−2.1539)
Contagion	−0.0029	0.0016	0.0172
	(−0.4680)	(0.1878)	(1.4603)
Healthy population	−0.0001	−0.0001	−0.0002
	(−1.1455)	(−0.8265)	(−1.5275)
Unemployment rate	0.0294**	0.0282**	0.0331***
	(2.6210)	(2.2648)	(2.9463)
Income	0.0000***	0.0000*	0.0000**
	(3.3337)	(2.7478)	(3.9982)
Education	−0.0032*	−0.0034*	−0.0041**
	(−1.9197)	(−1.9975)	(−2.5483)
Male	−0.0173	−0.0174	−0.0611
	(−0.4342)	(−0.4269)	(−1.2734)
Foreign (EU27)	0.0475***	0.0439***	0.0380***
	(4.4296)	(4.6750)	(4.3274)
Constant	0.0240	0.0246	0.0482*
	(1.3789)	(1.4205)	(2.0772)
Observations	1179	1179	1179
R-squared	0.649	0.646	0.647
Time FE	YES	YES	YES

Notably, from Column 1, the interaction term Religiosity Index*Catholic is positive and statistically significant at the 5 % level (β = 0.07856, t-statistic = 2.4407), suggesting that, for higher levels of religiosity, countries where the predominant religion is Roman Catholic have a higher vaccination rate than other countries. Indeed, when we consider the interaction term Religiosity Index*Orthodox in Column 2, we find a negative and statistically significant effect on vaccination rates (β = −0.0729, t-statistic = −2.5495). Finally, in Column 3, we find that the interaction term Religiosity Index*Protestant is negative and significant at the 5 % level (β = −0.1504, t-statistic = −2.1539). Evidence from Columns 2 and 3 points towards a reinforcing role of Orthodox and Protestant on the already negative relationship between religiosity and vaccination rates.

Overall, we find support for an active role of religious leaders in endorsing vaccines. Our findings are in line with that suggested by Corpuz (2021) and Gopez (2021) as we find a positive moderating effect of religiosity on vaccination in countries where the predominant religious group is Catholic. Unlike other religious leaders, Pope Francis has been a very vocal supporter of vaccination against COVID-19 (Corpuz, 2021; Gopez, 2021). Conversely, for higher levels of religiosity, countries where majority religious communities are guided by leaders who have not clearly endorsed COVID-19 vaccines exhibit even lower vaccination rates than what the baseline coefficient would suggest.

4.4. Robustness

In this section, we report two additional robustness tests. First, we re-estimated all our models using an alternative measure of religiosity. More specifically, we use data from the 2008 European Values Survey to construct our religiosity variables. Cultural attributes are generally characterised by temporal and spatial stickiness. However, using an older wave can ensure that our results are not affected by measurement error potentially correlated with a single wave. The results are reported in Panel A of Table 5 and are in line with those previously shown in Tables 2 (Models 1 to 6 of Table 5, Panel A) and Table 3 (Models 7 to 9 of Table 5, Panel A). Second, to account for possible autocorrelation within standard errors at the country and week level, we cluster standard errors at the country times week level. All results align with our baseline ones and are reported in Panel B of Table 5.

Table 5.

Robustness tests. Panel A reports the results using alternative measures of religiosity. Panel B reports the results with standard errors clustered at the country times week level. All models include time fixed effects. The estimation period is Dec. 2020-Dec2021. *, **, and *** indicate significance at the 10 % level, 5 %, and 1 % level, respectively.

Panel A	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Model	Model	Model	Model	Model	Model	Model	Model	Model
Religiosity Index (2008)	−0.0003***						−0.0610***	−0.0177*	−0.0100
	(−3.2773)						(−3.1324)	(−1.9818)	(−1.1264)
Member of Religion (2008)		−0.0025**
		(−2.6227)
Importance of Religion (2008)			−0.0033***
			(−3.2220)
Religious Services (2008)				−0.0026***
				(−2.8723)
Belief in God (2008)					−0.0042***
					(−3.4821)
Importance of God (2008)						−0.0059***
						(−3.1934)
Religiosity Index (2008)*Catholic							0.0725**
							(2.4931)
Catholic							0.0068
							(0.4910)
Religiosity Index (2008)*Orthodox								−0.0504*
								(−1.7986)
Orthodox								−0.0743***
								(−2.6822)
Religiosity Index (2008)*Protestant									−0.1641***
									(−3.7850)
Protestant									−0.1690***
									(−3.7489)
Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES
Observations	1179	1179	1179	1179	1179	1179	1179	1179	1179
R-squared	0.648	0.646	0.648	0.647	0.648	0.649	0.645	0.649	0.651
Time FE	YES	YES	YES	YES	YES	YES	YES	YES	YES

Panel B	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Model	Model	Model	Model	Model	Model	Model	Model	Model
Religiosity Index	−0.0321***						−0.0617***	−0.0167*	−0.0123
	(−2.9900)						(−3.1117)	(−1.9289)	(−1.4023)
Member of Religion		−0.0033**
		(−2.5754)
Importance of Religion			−0.0041***
			(−3.0694)
Religious Services				−0.0040***
				(−3.0937)
Belief in God					−0.0043***
					(−3.1641)
Importance of God						−0.0050**
						(−2.2836)
Religiosity Index*Catholic							0.0734**
							(2.5791)
Catholic							0.0034
							(0.7163)
Religiosity Index*Orthodox								−0.0556
								(−1.6033)
Orthodox								−0.0022
								(−1.4968)
Religiosity Index*Protestant									−0.1503***
									(−3.5130)
Protestant									0.0051
									(0.8077)
Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES
Observations	1179	1179	1179	1179	1179	1179	1179	1179	1179
R-squared	0.648	0.647	0.648	0.647	0.648	0.648	0.650	0.649	0.651
Time FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
Standard Errors	Country*Week	Country*Week	Country*Week	Country*Week	Country*Week	Country*Week	Country*Week	Country*Week	Country*Week

5. Discussion and contributions

Our study aims to contribute to the literature on technology (product) acceptance and religiosity's socioeconomic impact. The ongoing debate about technology (product) acceptance has increasingly highlighted the critical role of risk perception and attitudes by potential adopters (Cloos and Mohr, 2022). However, scant research has addressed the potential role of religiosity, as related to risk aversion, in influencing individuals' adoption of innovative technologies or new products. This issue is even more critical in a context in which a collective effort to adopt an innovation is instrumental for the common good, such as the serious concerns for public health during the COVID-19 pandemic.

As with other innovative technologies or products, those related to health can generate concerns about the risks involved in adoption (Ortega Egea and Román González, 2011). Especially in the early phases of the vaccination campaign, concerns about the safety of COVID-19 vaccines spread among the general population (Latkin et al., 2021). Consequently, the COVID-19 vaccination campaign provides an ideal context in which to test the relationship between religiosity and acceptance of innovative technology (product).

While religion can be seen as a source of doctrinal or scriptural motivations that could push towards vaccine hesitancy, a more in-depth study on this issue demonstrates that the majority of religions support the values of preserving life and caring for others (Grabenstein, 2013). Even in the presence of objectionable components or production processes, there should be more arguments favourable to acceptance (Grabenstein, 2013). However, if the innovation to be adopted is perceived by individuals as riskier than average, then the role of risk preferences and all factors affecting them should not be underestimated. Given the acknowledged empirical link between religious attitudes and risk aversion (Hilary and Hui, 2009), we argue that religiosity could be one of the factors potentially negatively affecting vaccination rates.

Our results seem to confirm this assertion. We find an overall negative and significant association between country-level religiosity and vaccination rates in the European Union after controlling for socioeconomic factors potentially related to vaccine acceptance.

We also aim to contribute to the literature on religiosity's socioeconomic consequences by analysing the impact of religious leaders in endorsing innovative technologies or new products among their communities. Previous research has shown that religious leaders can influence followers' opinions and behaviours. However, most studies are qualitative and mainly investigate followers' attitude changes on political or ethical topics. Conversely, our study conceptualises and empirically tests the effect of religious leaders' endorsement on an effective behaviour that can be quantitatively measured and perceived as potentially risky, e.g., vaccination. Our results confirm that in countries where the majority religious group has a leader who has publicly and consistently supported vaccination against COVID-19, religiosity is positively related to vaccination rates. Conversely, in countries where the leaders of major religious communities have a neutral or an unclear position towards vaccination, religiosity remains negatively related to the vaccination rate at the country level.

Our study may have some policy implications. First, it highlights that religiosity affects the level of adoption of innovative technologies or products at the country level, plausibly through a risk aversion channel. Therefore, when a technological innovation or new product must be diffused at the country level, religiosity should be taken into consideration, and efforts should be focused on avoiding an incorrect perception of the risks associated with adoption. Such efforts should be especially targeted towards the spread of misinformation, which has a crucial role in individual perception of the risk involved in decision-making. Second, our study highlights the role of religious leaders' endorsement in supporting this process. An early and public showing of strong support for the innovative technology or product by a religious leader, such as Pope Francis's framing of vaccination as an “act of love,” seems to reverse the relationship between religiosity and the level of acceptance and diffusion of vaccines at the country level.

6. Limitations and future research direction

Even if we try to control for possible confounding effects in our econometric models, individual willingness to be vaccinated and the consequent diffusion of COVID-19 vaccines is a complex phenomenon, and other unobserved factors could have influenced our results. Religiosity proxies are time-invariant, and this prevents us from including country fixed effects. Nevertheless, the inclusion of socioeconomic controls and weekly fixed effects should reduce the risk of confounding effects. Moreover, available data allow empirical testing of the relationship between religiosity and acceptance of vaccines at the national or subnational level. Future research could confirm this relationship at the individual level, even if such an approach could generate other methodological and privacy concerns, especially about how to measure actual vaccine-related behaviour.

In addition, we choose the peculiar empirical setting of COVID-19 vaccines. We argue that this is a suitable choice, as such vaccines can be framed as new products that involve innovative production technologies. Moreover, their perceived riskiness is a major factor impacting country-level vaccination campaigns. Nonetheless, future studies can investigate the relationships between religiosity and the acceptance of other technologies and/or products not necessarily related to health issues (e.g., digital innovations). However, in different contexts, it could be unlikely to observe public endorsement or disapproval by religious leaders. Therefore, despite the possible limitations of our study, we argue that it can provide a relevant contribution.

CRediT authorship contribution statement

Ludovico Bullini Orlandi: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. Valentina Febo: Conceptualization, Data curation, Writing – original draft, Writing – review & editing. Salvatore Perdichizzi: Methodology, Formal analysis, Writing – original draft, Writing – review & editing.

Declaration of competing interest

None.

Biographies

Ludovico Bullini Orlandi is an Assistant Professor in Organization and HRM at the University of Bologna and co-director of the Master in HR and Organization at the Bologna Business School. He holds a Ph.D. in Economics and Management from the University of Verona. His research interests revolve around digital transformation's impact on HR and organizations. He teaches Organization Theory at the University of Bologna. He has held visiting and research collaborations at the University of Lund and the Karlsruhe Institute of Technology. He has also taught at the CUOA Business School, at the Catholic University of Lille, and the University of Verona.

Salvatore Perdichizzi is an Assistant Professor of Financial Markets and Institutions at University of Bologna, Italy. He received his PhD degree from the University of Milan - Bicocca. His research interests cover monetary policy and empirical banking, with a focus on the effectiveness of unconventional and conventional monetary policies on economic development, financial markets and banking stability, green and sustainable finance, bank lending conditions. He is Research Associate at the Yunus Social Business Centre of Bologna, and also served as a Research fellow at the University of Exeter Business School (England) and Catholic University of Sacred Heart - Milan.

Valentina Febo is a Ph.D. Candidate in Management (Banking and Finance track) at the University of Bologna. Her main areas of research are corporate finance, finance and culture, and corporate risk management. Her research interests revolve around how decision-makers' cultural, social, and individual characteristics affect investment choices, risk-taking behaviour, and corporate hedging.

Data availability

Data are publicly available from the described sources