Individual Perception vs. Structural Context: Searching for Multilevel Determinants of
Science-Technology Acceptance across 34 Countries
Abstract
Our study analyzes the variations between individual and contextual factors in individual‘s acceptance
of science-technology. Acceptance of science-technology is not fully determined by individual thought
but socially context. Hence individual‘s acceptance could be explained by both individual and contextual
predictors, not by just either one.
Based on data covering 31,390 respondents in 34 countries, we apply the multilevel modeling to test
effects of individual and contextual factors on individuals‘ acceptance of science-technology. The
multilevel adopted, as predictors, perceived risk/benefit, knowledge and affective image at individuallevel and such as economic state (GDP per capita), religiosity, and post-materialism at contextual-level.
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Individual Perception vs. Structural Context: Searching for Multilevel Determinants of
Science-Technology Acceptance across 34 Countries
Ⅰ. Introduction: Missing Context in Science-Technology Acceptance and Risk Studies
Over the past decade, psychometric paradigms in risk studies have expanded the domain and have
gained the power of explanation or relevance. Started by Fischhoff and colleagues (1978), the
psychometric paradigm as the most popular and dominant approach in risk studies has contributed to
finding out the universal structure of risk perception which individuals generally hold. The psychometric
paradigm has used psycho-physical scaling and multivariate analysis techniques to produce quantitative
representations or cognitive maps for risk attitudes and perception (Slovic 1987). In this view of judging
the risk, the subjective perceived degree of riskiness is more important to people than the objective
expected number of fatalities from risk. Also, since psychometric paradigms mainly have focused on the
risk characteristics that determine the risk perception at the individual level, they proved that people‘s
risk attitudes are explained by the variety of qualitative characteristics of risk, such as being dread, known
and afraid (Slovic 1987).
Despite those contributions, the psychometric paradigm faces criticism in terms of conceptual and
methodological aspects. Conceptually, the psychometric paradigm has placed much emphasis on the
individual predictors (e.g, perceived risk and benefit) in explaining individual‘s risk judgment, while it
has not sufficiently reflected the social, cultural and institutional contextual determinants which are even
important determinants of risk perception (Frewer et al. 1998; Marris et al. 1998).
Methodologically, its heavily dependence on psychometric scale at the individual level has limited
explanatory power to account for the cause and effect of risk judgments. For example, several studies
show that the psychometric approach explains a little variance of how and why people regard something
as risky or hazardous (Sjöberg 2000; 2004).
All those limits of psychometric paradigm seem to be based on methodological individualism in which
individual perception is just regarded as by-products of rational thinking or perception at the individual
levels. Hence, psychometric paradigm partially explains the individual‘s risk perception only by
individual factors, e.g., perceived risk and benefit, without considering the contextual factors, e.g. nation,
neighborhood, or contextual variables, e.g., national economic welfare and social capital.
Of course, there are a lot of comparative risk studies across countries at the macro-level beyond
individual level. Those studies have contributed to finding out the cause and effect of risk perceptions at
the macro-level, especially national level. After reviewing comparative studies of risk perception, Boholm
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(1998) concluded that even if the relative risk rankings seem to be similar, there are differences in risk
concern and the magnitude of risk rating across countries.
However, although comparative studies have tried to show different results, e.g., contextual effect, from
the psychometric paradigm, they did failed this work because they were still used the same or similar
concepts and methods that psychometric paradigm has usually adopted. Even if making comparison
between countries, they still mainly analyzed the effect of not contextual but individual variables on risk
judgment within specific country. Hence, most comparative studies have confirmed the effect of
individual factor under each country, not testing the effects of context itself in explaining the risk
perception and acceptance of science-technology. As a result, these individual-oriented comparative
studies have reached the same conclusions as the psychometric paradigm has suggested. Therefore there
are very few comparative studies that go beyond Kleinhesselink & Rosa‘s (1994) general conclusion that
cross-cultural perception of risk appears both uniform (related to the common cognitive structure by using
similar empirical methods) and variability (related to cultural variations in risk) (Hohl & Gaskell 2008).
In other words, comparative studies, even if they had the macro-orientation, did not really consider the
context effect, nor compare the effect between ‗individual‘ and ‗context‘ variables on risk judgement.
In risk judgment, individual‘s acceptance of science-technology is not fully determined by individual
thought but rather socially constituted. Hence individual‘s risk acceptance should be explained by both
individual and contextual predictors. Recently, several studies attempted to integrate or compare the
effect of individual and context variables in risk studies. For example, Hohl and Gaskell (2008)
empirically showed that the perceived food risk depends on not only individual level such as risk
sensitivity, gender, and age, but also on risk sensitivity at the contextual level. Clearly, multilevel analysis
can provide the tools to link or compare the effect from macro level with one from micro level. This study
argues that the social acceptance of science-technology can be fully predicted by not only individualistic
characteristics at the micro level but also social contextual variables at the macro level.
We argue that the social acceptance of science-technology should be predicted by not only individual
psychometric factors but also social context or emergent contextual variables. Based on survey data
collected from 31,390 respondents across 34 countries, we applied the multilevel modeling to test the
model for individual‘s acceptance of science-technology, which was nested with national context and
other contextual variables, not only with individual factors. Before conducting analysis of the multilevel
modeling, we reviewed the key theoretical background and concepts about not only the individual factors,
such as perceived risk/benefit, knowledge, and affective image, but also the contextual variables, such as
economic state (GDP per capita), religiosity, and materialism/post-materialism.
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In the data analysis, first, within each country, we uncovered the difference in individuals‘ attitudes
toward and determinants of science-technology acceptance. Second, by analyzing the aggregated data
across countries, we checked the significant impact of individual and context variables on acceptance at
the national level. Finally we analyze the dividend of variations between individual and contextual
variables in explaining individual‘s acceptance of science-technology.
Ⅱ. Theoretical Background
1. Individual Determinants
Perceived Benefit & Risk
The perceived benefit and risk are essential variables for explaining risk judgments (Fischhoff et al.
1978; Alhakami and Slovic 1994; Frewer et al. 1998). Perceived risk usually has a negative impact on the
acceptance of science-technology, whereas perceived benefit has a positive effect. Concerned with
relationships between perceived benefit and risk, Fischhoff et al. (1978) and Alhakami and Slovic (1994)
confirmed the existence of a strong inverse interdependence between risk and benefit judgments: the
higher the perceived benefit, the lower the perceived risk (Frewer et al. 1998). Those propositions related
to perceived risk and benefit have been empirically tested in acceptance of sciences-technologies, such as
gene technology (Siegrist 1999), gene-recombination technology (Tanaka 2004), and nanotechnology
(Siegrist et al. 2007). Recently, Brossard et al. (2009) demonstrated that perception of risk and benefit
was closely related to the public support for funding nanotechnology. Siegrist (1999) and Tanaka (2004)
found the indirect effect of trusts on acceptance about gene technology by way of perceived risk and
benefit.
However, those inverse or causal relationships tend to be usually tested regardless with contextual
effects. A few empirical studies showed whether this inverse relationship could be varied by ‗macrocontext‘. Kim (2009) empirically showed that the inverse relationships can be changed according to local
contexts to which respondents were belonged. Other studies also show that different social contexts based
on different social relationships bring out different risk perceptions; egalitarians perceived risk highly,
whereas individualism and hierarchy underestimated the risk (Dake 1990; Marris et al. 1998). However,
such contextual effects on individual risk perception have rarely been verified, especially under existence
of individual-level predictors.
Knowledge
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Knowledge has been regarded as a main factor to influence the risk perception about science-technology.
The so-called science literary model assumed that the deficit knowledge is a crucial factor to block
interactive communications about science-technology. The science literacy and public understanding of
science model have assumed a public state of knowledge deficiency; citizens lack either enough or the
right kind of knowledge, and thus fail to display sufficiently "reasonable" attitudes or risk perceptions
(Bauer 2009). Hence, the literature suggests that basic scientific knowledge and ideas are a necessary
requirement for securing support for science (Miller, Pardo, & Niwa 1997; Lee et al. 2005; Nisbet 2005).
Concisely, higher scientific literacy is linked with positive views of science-technology, and a lower level
of knowledge produces the misconception of science-technology‘s objective benefit or risk.
However, knowledge is not the direct factor to determine the acceptance of technology; the relationship
between technical knowledge and support for technology is tentative, limited, or not even statistically
significant (Brossard et al. 2009). Lee et al. (2005) demonstrate that the role of knowledge has a weaker
indirect effect on attitudes for people who show strong emotional reactions to the nanotechnology.
Recently several studies at the individual level have focused on the role of sociological or structural
attributes such as religiosity or ideology. Ho et al. (2008) showed that, compared to the impact of
structural value variable (i.e., religiosity, ideology, and defense of scientific authority), scientific
knowledge played a minor role in influencing attitudes toward stem cell research. Brossard et al. (2009)
empirically showed that knowledge does not have a direct effect on support for nanotechnology but an
indirect effect that is filtered through the lens of religiosity, a macro structural variable. However, there
are very few studies to examine how not only individual perception but also religiosity, a socialcontextual variable, could affect individual‘s risk judgment.
Affective Image
Affective image or stigma has a direct or indirect effect on risk judgment (Slovic et al. 1991; Alhakami
& Slovic 1994; Slovic 1999; Lee et al. 2005). Affect image is defined as the first positive (like) or
negative (dislike) ones which arose in mind upon judging the object (e.g., green color or Chernobyl in
thinking nuclear power). Lee et al. (2005) argued that affective judgment often precedes the cognitive
evaluation, and people‘s judgments about science and technology are sometimes based not on analytical
thinking but on feeling or emotional thinking about science and technology. People generally judge the
risk objects by not considering all of the knowledge but using information just available to themselves,
what we called the heuristic judgment. For example, if some technologies are related with positive image,
they are evaluated as having higher benefits and lower risks, regardless of actual benefits and risks.
However, those studies have often disregarded the effect of macro-contexts in risk judgment. Kim et al.
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(2007) demonstrated that macro-local context has significant impacts on experienced affective judgment
about acceptance of nuclear power station. There are very few studies that test the affect‘s effect on
individual‘s risk judgment after considering other contextual effects.
2. Contextual Determinants
Religiosity
Recently several studies show that religious values have a significant impact on public attitudes toward
science and technology (Nisbet 2005; Brossard 2009; Scheufele et al. 2008). Regarding conflicts between
religiosity and science and technology, Gaskell et al. (2005) explained that, since the unnaturalness of
science and technology is liable to disturb the natural order, those who have religious beliefs tend to
express opposition to scientific research related to genetically modified organisms (GMO). Scheufele et al.
(2008) argued that science is incompatible with strong religious belief, and that if specific technologies
are concerned with moral issues, there will be more negative feeling toward them. Brossard et al. (2009)
empirically showed that strong religious belief is negatively related to support for funding technology. In
more integrated studies between the micro and macro levels, Scheufele et al. (2008) found a robust
relationship between levels of religiosity (measured by how much religious guidance was ‗not at all
important‘ or ‗very important‘ in respondents‘ lives on a 10-point scale) and individual‘s moral support
for nanotechnology both at the individual level and the national level. At the national level, for instance,
the more religious countries are (i.e., Italy, Austria, and Ireland), the less the moral acceptance of
nanotechnology exists, whereas the more secular countries (i.e., Denmark, Sweden, France, and
Germany) see more agreement. However, a few studies demonstrate that religiosity gives positive impacts
on judging technologies. Costa-Font and Mossialos (2005) showed that self-perception of individual
religiosity increases the support for biotech related to GMO food and medicine. Further systematic
examination of between religiosity and risk judgment at both the individual and contextual levels would
be necessary.
Post-materialism
People have different value structure and system, based on their experiences and concerns. Inglehart
(1971, 1990, 1995) and Abramson and Inglehart (1986) argue that because a new generation experiences
different social-historical events and tends to have a different need structure, they seek for different values,
such as post-materialism. Post-materialists give less value to the old political agenda, such as economic
development, which is considered important by conventional politics. Rather, they are more likely to be
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concerned about new values, such as the environment, women‘s movement, and the peace movement.
Such post-materialism is connected with risk judgment. Lima et al. (2005) asserted that societal
sensitivity to risk is associated with higher levels of environmental awareness, which is a value that postmaterialists hold.
Perhaps the anti-science value belongs to post-materialism. Frewer et al. (1997) stated that the rise of
post-material values and increased environmental concern are more likely to increase public resistance to
emerging technologies. Following Frewer et al. (1997), Knight (2011) hypothesizes that people who hold
post-materialist values have lower levels of support for biotechnology applications than those who hold
materialist values. Gooch (1995) showed that through correlation analysis, there are weak negative
relationships between post-materialism and support for science and technology. He empirically
demonstrated that in Sweden, people having post-materialist values had positive attitudes toward
environmentalism, which usually has the negative association with support for science and technology.
However, there are few empirical studies over the relationship between post-materialism and sciencetechnology acceptance, under considering both the individual and contextual levels.
Wealth of a Nation
A nation‘s wealth is a key variable to influence science-technology acceptance. Few studies have tested
the argument at the national level, whereas much researches have focused on income‘s effect at the
individual level: The higher the individual‘s income increases, the lower the perceived risk becomes
(Slovic 1999; Dosman et al. 2001). It finally may reduce an individual‘s acceptance level of sciencetechnology. At the individual level, regarding the effect of economic condition on risk perception, the
poor tend to feel more risk and deny the hazard technology because they have few resources to mobilize
for the sake of decreasing the risk (Graham et al. 1992).
However, at the macro-national level, since people in a more advanced (rich) country have more
experience with the hazard of science-technology, they show less support for science-technology. Bauer
et al. (1994) empirically tested a post-industrialism effect: knowledge, interest, and attitudes toward
science show a curvilinear relationship with levels of industrialization. In the macro-level studies, Bauer
(2009) demonstrated that there are negative relationships between GDP and endorsing the ‗myth
of science‘; the more wealthy the country, the less trust in science. The literature suggests that the decline
of interest in science and the less positive attitudes in highly developed countries require further study.
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Ⅲ. Research Design
1. Sample and Data
We use the Eurobarometer 63.1 social survey data regarding science and technology, social values, and
services of general interest. The total number of respondents interviewed was 31,390 across 34 countries.
The social survey was carried out between 3 January 2005 and 15 February 2005 by the personal face-toface interview method. The Eurobarometer 63.1 survey covers the population of 27 European Union
members, four candidate countries (Bulgaria, Romania, Croatia, and Turkey) and three European Free
Trade Association countries (Iceland, Norway, and Switzerland). The basic sample design adopted the
multistage, random probability method. In each country, a number of sampling points were drawn with
the probability proportional to population size and population density. For detailed information about the
survey, please refer to ICPSR (Inter-University Consortium for Political and Social Research 2011).
2. Measures
Except religiosity and GDP per capita, all of the measurements were drawn from the Eurobarometer
63.1 survey questionnaires (see Table1). For controlling for confounding effects, three demographic
variables—gender, age and ideology—were included in the model. At the individual level, we set the
acceptance of science-technology as the outcome variables and perceived benefit, perceived risk, affective
image, and knowledge as the predictor variables. For the reliability and validity of those five measures at
the individual level, we used composite scales based on the mean of the responses to several questions. At
the national level, we selected three predictor variables, post-materialism, religiosity, and GDP per capita.
We calculated at-the-national-level mean value for post-materialism, from Eurobarometer 63.1 and for
religiosity (the degree of belief in the importance of God) from the European Value Study 2000,
European Value Study 2008, and Fifth Wave of World Value Survey. GDP per capita was calculated
from Penn Table Data (mean of five year from 2000 to 2004). Even if the religiosity and the postmaterialism were measured at the individual level, they were, after being transformed into aggregated
variable by compositing the individual data, used at the context level by Gaskell et al. (2005), Scheufele
et al. (2008) in former and by Franzen & Meyer (2010), Mostafa (2011) in the latter. Those transformed
aggregate variables possessed the emergent variations at the national level, which are different from
variance at the individual level.
Table 1: Question Structure
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3. Multilevel Analysis
Recently multilevel methods have been used increasingly. Multilevel analysis enables to link or compare
the effect from macro level with one from micro level. In fact, the methodological individualism has
dismissed contextual or interactive effects between micro and macro levels. Moreover, individuals in
social surveys and experiments are usually regarded as atomic units, which have full information at both
individual and aggregate levels. However, those individual-oriented methods have a high risk of having
the atomistic fallacy where inferences about groups are falsely drawn from individual level information
(Hox 2002). Traditional statistical methods, such as regression analysis, have assumed that residuals have
attributes of independence, normal distribution, and homosecedasity. However, since individuals under
the same context may share some common attributes, those false assumptions usually increase the risk of
making the Type I error, i.e., the estimation did not occur in more conservative ways (Luke 2004).
Moreover, since important levels, i.e., context, cause the largest extent effects on specific fixed
coefficients, variance components, and their corresponding standard errors, ignoring them can lead to
different research conclusions in statistical inference (Opdenakker & Van Damme 2000)
Ⅳ. Analysis
1. Descriptive overview: Variations in Science and Technology Acceptance across 34 Countries
We examine whether or not variations of science and technology acceptance exist among the 34
countries. Acceptance of science-technology at all the samples shows mean=3.141, SD=.45915,
Min.=1.00, Max.=4.00. Figure 1 provides the simple mean, ordered by its magnitude, of acceptance
across 34 countries: a larger value means more acceptance of science-technology.
Figure 1.Variation of the Acceptance Level of Science-Technology across 34 Countries
As shown in Figure 1, we find out two noticeable facts. First, it demonstrates that there are considerable
variations across the 34 European countries on acceptance of science-technology. As an extreme case,
Turkey revealed the highest positive attitude toward acceptance of science-technology, whereas
Switzerland expressed the lowest one. Moreover, an ANOVA test for differences across 34 countries and
within them reveals a significant statistic (F-value=65.214, P<.01). Second, according to the rule of
thumb, we find out some systemic clustering among countries. At first, Western-Northern European
countries, such as Switzerland, Germany, Finland, Netherlands, France, Austrian, and Norway show more
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a negative position than Eastern European countries. Hohl and Gaskell‘s (2008) research already reported
such a geographic divide: North-South divide in which people in Northern countries expressed worry less
often than those in Southern countries. Two facts suggest the possibility of contextual effects at the
national level. Since most of scholars have focused on individuals within nations, they can rarely answer
the inquiries about the contextual effects across nations. Different levels of acceptance across countries
implied that there are some variations coming from the contexts.
Next, to get the basic information about what kinds of predictors have relations with the variations in
acceptance of science-technology across the 34 countries, we calculate the mean of social acceptance
according to demographic or psychometric variables. For this work, as shown in Table2, we divide the
respondents into two or three groups within each country. Group categories of psychometric variables are
based on mean value of perceived risk/benefit, knowledge, and image. If respondents show a higher (or
lower) score than the mean in each country, they are classified into higher (or lower) groups. Moreover, to
highlight the difference between higher and lower groups, as shown at Table 2, we calculated the gap
scores by making minus the score at the first row (lower group) from one at the second row (higher
group) at each variable except age at which gap score means the difference between the first row and third
row.
Table 2 displays several meaningful findings about acceptance across countries. First, the several
systemic patterns of acceptance across countries were observed. Among demographic variables, the male
generally shows higher social acceptance than the female. Female‘s acceptance of risks may be explained
by several hypotheses, such as women‘s social or biological weakness or their lack of knowledge and
familiarity (Slovic 1999). Regarding the age, as people become older, they show a more positive attitude
toward social acceptance. In terms of ideology, the right-oriented group showed higher acceptance of
science-technology than the left-oriented group did. Among psychometric variables, the higher group in
perceived benefit expresses more support for science and technology than the lower group did, whereas
the former in perceived risk showed less support than the latter did. Those who have more knowledge
have more a positive attitude toward science-technology. Finally, respondents in the group with a
negative image revealed less support for science-technology than those in the positive image group.
Second, although such an overall systemic pattern can be observed across countries, they were not
always revealed in the same direction and applied to every country. The minus or plus sign in the gap
score displays the similarity of direction of support/oppose across countries. The same direction in gap
scores were shared among gender (-), perceived benefit (+), perceived risk (-), knowledge (+), and image
(-). However, the age gap score did not have the same direction. In the Netherlands, Luxembourg, and
Turkey, the older people show more positive attitude toward science-technology than the younger people
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do, whereas in Italy, Spain, Austria, Hungary, and Poland, the older people reveal less positive attitude
than the younger people do. In the case of ideology, contrasting with the general trend of ‗right has more
acceptances‘, the left in Portugal, Austria, Cyprus, Hungary, Poland, and Slovenia has a more positive
acceptance of science-technology than the right does.
Third, variations in gap score across countries reflect the degree of cleavages in individual‘s attitudes
toward science-technology within the 34 countries. For example, some countries, i.e., -.121 in Austria,
have the larger gap score in gender, whereas others, e.g., -.001 in Luxemburg, have the smaller one. In
short, the seven predictors have an effect in different ways under different national contexts. Even if there
are shared systemic patterns among countries, there are also contrasting attitudes and different sizes in
gap scores at the national level, which implies that the national boundary may take a significant role in
forming any contextual effects.
Table2. ANOVA-Test
2. Individual Determinants at the Individual Level
Before comparing effects of individual and contextual factors on technology-science acceptance at the
individual level, we regressed this individual acceptance on perceived benefit/risk, knowledge, and image
after controlling for socio-demographic variables, including gender (dummy variable, male=0, female=1),
age, education, and ideology (1=left, 10=right).
The whole model in the second column of Table 3 represents the standardized regression coefficients of
independent variables across all 34 countries. Four socio-demographic variables have significant
relationships with technology acceptance. Female shows more negative attitude toward technology
acceptance than male does. This result is consistent with Flynn et al.(1994) and Slovic (1999) that (white)
male tends to judge risks as smaller and less problematic than (white) female does. Age has a negative
relationship with technology acceptance. We infer that since older people, based on more a conservative
stance, are relatively opposed to new ideas and developments, they may be cautious about science and
technology. Related with ideology, our data did not prove Ho et al.‘s (2008) empirical finding that
conservatives show lower support for science, such as human embryonic stem cell research. This
contrasting result may come from the fact that conservatives have ethical sensitivity toward stem cell
research whereas they favor of benefit from science-technology.
Among the psychometric paradigm variables, perceived benefit increases the acceptance whereas
perceived risk decreases it. More knowledge increases the acceptance level of science-technology. This
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confirmed the axiom: ‗the more you know, the more you love it‘ (Bauer 2009). Negative image to the
largest extent decreases the acceptance of science-technology. The image‘s largest beta coefficients
verified the power of image thinking. Slovic et al.(2004) explained that image thinking based on the
―experiential system,‖ which is intuitive, fast, mostly automatic, and not very accessible to conscious
awareness, was fairly effective.
However, such results from the whole model did not apply to those from the individual model in each
country. Each country has its own variation, differing from the whole trend. Such differences might be
found in terms of variables, models, and explanatory power as follows.
First, in terms of variables, they revealed the difference in both ‗frequencies‘ in the statistical
significance and ‗degree/direction‘ of the standardized coefficient. The image variable is to the largest
extent significantly effective across all 34 countries, whereas ideology appears significant only in 17
countries. Moreover, size and order of a specific variable‘s standardized coefficient varies across
countries. The image variable‘s standardized coefficient in Cyprus recorded the largest values with the
highest rank among independent variables, whereas in Norway, perceived benefit substitutes for the rank
of image. Also if we examined the direction of the slope in the standardized coefficient, it revealed the
exceptional cases, contrasting with the overall trend. For example, even if generally the right shows a
more positive attitude toward science-technology than the left does, this does not apply in Northern
Ireland and Slovenia where the left reveals a more positive stance than the right does. Those exceptions
are also found in the case of older people in Malta, who expressed a positive orientation to acceptance of
science-technology, in contrast with older people in other countries.
Second, in terms of modeling, the F-value notifies that there are the largest variations in present model‘s
statistical power and significance. F-value in Turkey recorded the largest value, 54.024, which is greatly
contrasted with the smallest one, 9.824 in Northern Ireland. Such differences imply that statistical
effectiveness of the present model, consisting of seven variables, could change according to each county.
Third, in terms of
-value, even if the same model based on seven independent variables is applied to
34 countries, its size varies by country. In Turkey, the present model explains 40% of acceptance variance,
which is large contrast with 10.6% in Greece. In case of the lower F-value and
–value, it does not need
a general model to be effective to explain all the countries‘ variance, but other specific model to be
applied to a particular country‘s case.
In short, even if the general model using seven individual-oriented independent variables explains the
larger portion of science and technology acceptances across countries, it cannot explain all of the
variation in the acceptance. Moreover different structures of determinants between nations require future
searching for a new model, for example, by reflecting the effects of contextual factor at the national level.
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Table 3. Regression Analysis across 34 Countries
2. Contextual Determinants at the National Level
Before comparing between individual and contextual effects on individual acceptance of sciencetechnology by multilevel modeling, we check the effects of three contextual predictors, i.e., GDP per
capital, religiosity and post-materialism, on outcome variables (the acceptance of science-technology),
besides individual variables, at the national level by using aggregated data.
Figure 2. X: GDP per capita-Y: Tech. Acceptance
Figure 3. X: Religiosity-Y: Tech. Acceptance
Figure 4. X: PM-Y: Tech. Acceptance
Figure 2 shows the simple regression line in which, as GDP per capital increases, the support for science
technology decreases. Based on the general assumption that science-technology developments usually
contribute to enhancing the economic condition in a given country, it is unexpected results that sciencetechnology‘s contribution has received a negative evaluation in rich countries..
Second, as demonstrated in Figure 3, when acceptance of science-technology was regressed on the
country-level‘s average means of religiosity, the higher religiosity decreases the support rates. Such
positive relationships sharply contrast with Scheufele et al.‘s (2008) research in which religiosity takes a
role in decreasing the moral support for nanotechnology research. Such a difference might be the result of
several reasons; the number of country samples (34 in our study vs. 13 in Schuefele et al. (2008)), objects
for judgment (14 science-technologies vs. just nanotechnology), and the focus of judgment (future impact
on life vs. moral evaluation).
The dotted average and simple regression line in Figure 4 confirm that there are somewhat weak
positive relationships between post-materialism and positive evaluations about science-technology at the
country level. Those relationships contrasted with the hypothesis of Frewer et al.(1997) and Knight(2011)
that post-materialists have lower levels of support for emerging technologies than materialists do.
However, such relationships are not statistically significant in regression analysis (see Table 4) and
reversed in multilevel analysis (see Table 5).
At the aggregate level, to know the comparative power of each independent variable, we regressed the
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aggregated acceptance on both micro and macro independent variables (see Table 4). Model 1 shows the
significant impact of perceived benefit/risk and image on acceptance with higher R-square (70.6 percent).
The model 2 is statically significant (F=6.244, p<0.01) and explains 23.2% of the variance. Among the
three variables, only GDP per capita significantly decreased the acceptance. However, coefficients of both
religiosity and post-materialism appear not to be significant. Final full model shows two microindependent variables, i.e., the perceived benefit and image, influence the aggregated value for acceptance.
Table 4. Regression Analysis at the Aggregate Level
4.Multilevel Determinants
To compare the effects of individual and contextual factors on acceptance of science technology, we
construct the multilevel modeling. Table 5 shows the results from multilevel modeling, the coefficients
and (co)variance parameter and fit index by using Restricted Maximum Likelihood (REML). Full
Information Maximum Likelihood (FIML) and REML are usually used as an estimation method in
multilevel analysis. Although each model has its own reason for its function in multilevel analysis, under
the fairly large samples, two methods give rise to substantially equal estimates. Moreover, compared with
FIML, REML provides the more accurate variance estimates in the case of smaller sample sizes,
especially when the number of estimated parameters increases (Peugh 2010). Table 5 presents statistical
results of four multilevel models. Following is more detailed results and discussions of each model.
Model 1: Unconditional Model
The unconstrained or null model, the so-called unconditional means model (Peugh & Enders 2005), is
similar to a one-way ANOVA with random effects because it separates the total variability of science and
technology acceptance into within-group and between-group factors. This model was mainly applied to
compute the proportion of variance in dependent variables (e.g. in our model, level of science-technology
acceptance) that exists between second level units (e.g., in our model, countries) (Luke 2004).
According to Peugh (2010), prior to analyzing any nested data set, we should answer the question
whether or not to use multilevel modeling because all of the nested datasets do not automatically require
multilevel modeling. If there is relatively little variance at level 2, i.e. contextual effects from nations in
our study, there is no need to proceed to a further step for multilevel analysis. Below, Model 1 provides
the variation in response variable scores across level 2 units, countries before executing the multilevel
analysis. Unconditional Model 1 also provides basic information for calculating intra-class correlation
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coefficient (ICC), which compares the variance between level 1 and level 2. ICC is a useful statistical
indicator to provide an answer to whether multilevel modeling is needed. ICC is similar to the R2effect
size from regression and the eta-squared effect size from ANOVA (Peugh 2010). The basic structure of
the null model is as follows:
Model1: Unconditional Model as a Null Model
Level 1:
(1)
Level 2:
(2)
Combined Model:
(3)
In the null model, the technology acceptance of people i in country j (
acceptance for the
country (
) and a residual ( ). The residual represents the individual differences
around a given country‘s (j) mean (1).
country specific deviation
) is a function of the mean of
in level 1 can be explained by the grand mean (
) and
). As Raudenbush and Bryk(1992) explained, the key feature of the null
model is that only the intercept parameter in the level 1 model is assumed to vary at level 2. The intercept
is regarded as a parameter that varies across countries as a function of grand mean (
term
) (2). We get the combined model (3) by substituting the level 2 equation into the level 1. The
combined model estimates the three parameters, a fixed effect (the grand mean,
residual ( ) at level 1 (denoted by
by
) and a random
).
), and variance of the intercept deviation (
), variance of the
) at level 2(denoted
is the average variance of an individual‘s score within the country, and
represents the
variation in mean science and technology acceptance across countries.
The second column at table 5 shows the results of multilevel analysis. The estimate for the grand mean
of social acceptance (
), 3.148, is interpreted as the average value of the dependent variable across all
subjects. Variability recorded at the within-country (
), .197, and one at the between-country (
), .013,
appear statistically significant. The variance at the country level appears much smaller than that one at the
individual level. However, the Wald Z test for the estimated variable component between countries
appears to be highly significant (Wald Z=3.998, 0.01 > P-value). This indicates significant existence of
variation among nations about the support for the acceptance of science-technology.
This significance of variance (
) at science and technology acceptance across countries urges us to
analyze upper-level contextual effects. In particular, the latter two estimates (
,
) of variance
(variation across individual and variation across countries) could be used for calculating ICC. If ICC has
15
zero-value, this means that there is no variation at the mean of social acceptance across countries at level
2. In this model, ICC was computed as .0631 (.013/[.197+.013], which means 6.31% of science and
technology acceptance variance could be explained by contextual effects which occurred between
countries at level 2. Since Muthén (1994) suggests ICC of 5% as the threshold value for statistical
judgment, this demands further work by multilevel modeling including both level 1 and level 2; 6.31% of
variability at respondent‘s rating of acceptance is a function of national context for each respondent.
However, the ICC estimate does not only indicate the need for multilevel analyses. The design effect
(=1+ (nc-1)∙ICC; nc is the mean of the number individuals per country) is an indicator used to judge the
effect of independence violations on standard error estimates. It is an estimate of the multiplier that needs
to be applied to standard errors to correct for the negative bias that results from nested data (Peugh 2010).
The critical value of design effect at greater than 2.0 notifies a need for MLM (Muthén 1994). The value
of the design effect, 58.193 (=1+ (923.23-1)∙.631), appears beyond the critical value of 2.0. By analyzing
Model 1, since we come to the conclusion that variance at the national context exists, it is worth of the
next multilevel modeling.
Model 2: Random Intercept Model I
The significant estimates of deviation (
) variance of model 1 ushers in the Random Intercept Model.
The random intercept model is functionally used for calculating the increase of explanation by adding the
covariate at level 2 to the null model. It notifies the effectiveness of contextual variables in explaining the
unexplained variance. Shown below in Model 2, we add the three covariate variables at level 2, such as
GDP per capita (
, religiosity
, and post-materialism
, to model 1. In the random intercept model,
three contextual variables as fixed factors, not random ones, influence the mean of social acceptance. We
use the deviance to test whether or not the more general model 2 fits significantly better than the simple
model 1.
Model 2: Random Intercept Model I
Level 1:
Level 2:
Combined:
(5)
(6)
(7)
Table 5 provides the estimates of both estimated parameter and fit index. The most critical judgment in
16
Model 2 is about whether or not the variation of the contextual variables, after excluding the variance at
the individual level, contributes to explaining the acceptance of science-technology. This judgment is
based on variance change after adding three covariates at level 2. In Table 5, two variance components,
and
, are statistically significant. Residual variance (
of means of science and technology
acceptance decreased by 38.5% ([.013-.008]/.013) after adding three contextual variables, which means
contextual variables make a considerable contribution to reducing the unexplained variance for
acceptance of science-technology.
In the fixed effect, the expected mean of science and technology acceptance is
=3.122 in the case of
a country having the value of zero at the GDP per capita, religiosity, and post-materialism. Three
contextual variables at the level 2 significantly influence the mean of science and technology acceptance
with each coefficient,
=-.000,
=.037,
=-.062. GDP per capita is not significant. The religiosity
increases the acceptance whereas post-materialism decreases it. This implies that two variables can
explain a significant proportion of the between-country variance in the intercept. Contrasting with nonsignificant figures in Table 4, covariates of religiosity and post-materialism in Model 2 appear significant.
Such results may be caused by difference of estimation methods (regression analysis vs. multilevel
modeling) or outcome variables (i.e., aggregated mean at the national level in <Table 4> vs. grand mean
of technology acceptance across countries in Model 2).
Model 3: Random Intercept Model II
In both model 1 and model 2, statistically significant residual variance at the level 1 requires us to build
the causal model to explain it. The random intercept model, i.e., one-way random effects ANCOVA
(Luke 2007) in model 3 is used, first, to calculate to what extent variables at the individual level
contribute to explaining the social acceptance after controlling the variance at the context level and,
second, to examine whether or not variance at the context level still significantly remains after inputting
individual variables into the model.
As shown in the box below, Model 3 adds seven individual variables (i.e., age, gender, ideology, benefit
and risk, knowledge and stigma) at level 1, all of which were frequently regarded as independent
variables to explain the technology acceptance in existing research. In Model 3, Yij (ith individual‘s social
acceptance in given jth country) is a function of the mean of social acceptance in a given country (
unique
intercept (
),
, attributed to each country, and residuals ( ). At level 2, the random
the grand mean (
) and country-specific residual
). Each slope
17
of predictor from
to
variable (
was decided by each specific slope of predictors, i.e., fixed influence of each
) and residuals, i.e., random deviations of coefficient variable across
countries(
.
Model 3: Random Intercept Model II
Level
1:
Level 2:
,
,
,
,
,
,
,
Combined:
Model 3 in Table 5 provides the estimate of one fixed intercept and seven fixed coefficients. The
expected mean of social acceptance for an individual whose value is zero in the country is
=3.665.
Seven fixed coefficients seem to be significant, having the exact same direction as those in Table 3.
Moreover, the residual among random effect estimates is statistically significant. The residual ( )
variance component at level 1 was
=.141, compared to .197 in Model 1 and 2. Including seven
variables at level 1 reduces the variance by 28.4% ([.197-.141]/.197). This figure implies that individual
variables continue to make a sufficient contribution to explaining the variance of individual acceptance
across countries.
A key issue in Model 3 is whether or not variance across countries still remains after adding the
individual variable to the model. Since the intercept (
)
appears statistically significance, context
variables are still alive, which demonstrates the independent share of contextual covariates in acceptance
of science-technology.
Model 4: Intercepts (with Slope) as Outcome Model
Compared to Model 3, Model 4, i.e., ‗intercepts as outcome model‘, highlights the significance of
context variables and the overall model. As the matrix shown in box below indicates, the final model adds
not only individual predictors at level 1 but context covariates at level 2to the multilevel model. The
combined Model 4 contains11 fixed variables in which fixed variables do not vary across countries under
varying nine random estimates.
In building the level-2 model, we should consider how to add the level-2 predictor to level 2: whether
context predictor adds to (1) only the intercept or both the intercept and (2) slope equations. Since our
18
research questions focus on the main effects, not the interaction between level 1 and level 2 covariates,
we add context predictors at level 2 only to intercepts as follows:
Model 4: Intercepts (with slope) as outcome model
Level 1:
Level 2:
,
,
,
,
,
,
Combined:
About statistically significant testing of full model 4, alike F-test in multi-regression, the multilevel
model generally depends on comparing deviance between the unconditional model with one at the model
having the predictors. A transformation of the likelihood called the deviance is obtained by multiplying
the natural log of the likelihood by minus two (-2LL). Deviance is a measure of the lack of fit between the
data and the model (Luke 2004). As Hox (2002) and Peugh (2010) explained, the difference of deviance
between model 1 and model 4 provides approximates of chi-square test statistics with degrees of freedom,
the difference in the number of parameters of both models. Between Model 1 and Model 4, difference in
deviance appears statistically significant,
2
=16566.022(37417.052-20851.030) with degree of
freedom=11, P<.01. This means that model 4 with 11 predictors at level 2 is more fitted with the data than
unconditional Model 1. Also lower Akaike Information Criterion (AIC) and Schwarz‘s Bayesian
Information Criterion (BIC) confirm such fitness from the deviance. The expansion of the model by
including context variables provides a significant improvement in the goodness of fit. The variance
component of intercept decreases from .061 in Model 3 to .049 in Model 4, which means, compared with
Model 3, a 19% ((.061-.049)/.061)increase of explanation in Model 4.
From Table 5, for the fixed effects at the individual-level variables, the results are similar to the findings
from the fixed effects in Model 3. All of covariates at level 1 significantly influence the outcomes of
social acceptance. The individual-level effects are unchanged after introducing context effects, which
means the robust power of individual variables at level 1.
At the national level, the effects of contextual variables on science and technology acceptance are not
fully supported by the present data set; after controlling the predictors at the individual level, GDP per
capita and post-materialism have significant negative impacts on the acceptance, whereas religiosity
shows non-significant coefficients with positive impact.
Why don‘t wealthy countries support science-technology? We could suggested the hypothesis of the
19
byproduct effect from science-technology that even if people in wealthy nations have experienced, in the
course of economic development, a lot of benefit from development of science-technology, they also have
faced the negative byproducts from science-technology, for example, environmental problems and
hazards or accidents from technological products. Negative association between post-materialism and
science and technology acceptance might reflect the results from new post-materialistic values, i.e.,
environmentalism or non-economic value. Such the hypotheses demand the empirical validation in the
future research.
To test the null hypothesis for variance component, which was assumed to be equal to zero, we applied
the Wald test. As show in Table 5, the Wald test rejects the null hypothesis; Even if there was little
variance association, two variance components of residual and intercept still keep their statistical
significance after including 11 predictors. This confirms the necessity of multilevel model constructing
for the sake of explaining acceptance of science-technology. However, one should be cautious in
interpreting the context effect because first, the contextual variables are minimally or insignificantly
associated with the respondent variables, and second variance components have been decreased by a
small value in Model 4.
Table 5.Mutilevel Analysis
Ⅴ. Summary and Discussion
Since there is dominant power of psychometric studies under which risk perception has heavily
dependent on individual predictors, structural-contextual factors have not been seriously considered. With
balanced approaches by adopting both individual and structural context variables through using multilevel
modeling, we examined the variation between them in explaining the acceptance of science-technology.
Based on data from 34 countries and 31,390 samples, we empirically confirmed the impact of macro
contextual variables on individual‘s acceptance of science-technology, even if there is significant variance
from micro-individual predictors. The key findings from the analysis include the following.
First, using descriptive analysis, we confirmed that there are cleavages in social acceptance of sciencetechnology at the national level, not at the individual level. In particular, variation in acceptance gaps
implied that the country can be a kind of context or context-breeding factor in influencing the acceptance
of science-technology.
Second, at the micro level, through analyzing the individual data within a country using ANOVA and
regression analysis, we found significant determinants of science-technology acceptance. All of the
20
results confirmed the findings from existing researches; negative effects by gender, perceived risk,
affective image whereas positive effects by perceived benefit and knowledge. Since those effects at the
micro level still continued after considering the context variables, we recognized that individual variables,
i.e., perceived risk/benefit and affective image, possessed the great robust power of explaining the
acceptance of science-technology.
Third, the simple regression analysis at the aggregated level confirmed that GDP per capita has a
significant effect on acceptance of science and technology at the national level. Moreover, micro
individual variables maintained strong power in explaining the aggregated-level acceptance.
Finally, using multilevel modeling, we confirmed the significant effect of context and contextual
variables, besides individual-level factors, on individual‘s acceptance of science-technology by using four
multilevel models. At the first, by the null model, we examined whether or not national contexts, has
significant variance at science and technology acceptance, besides individual share of variance. Findings
indicate that the nation as a contextual factor has a significant share in variance; variance exists not only
within a country but also between countries. From the second model, we found that the three contextual
variables contributed to increasing the explained outcome variance. From the third model, including the
demographic and psychometric determinants at the individual level, we confirmed not only the robustness
of variables at the individual level but also the significant existence of context effect at the country level.
For the final full model, among the seven predictors, individual characteristics still provided a more
powerful explanation for outcome variance after adding the context variables to the model. Even if the
inclusion of three contextual variables did not substantially change the effect of the individual factor on
outcome variables, covariates at the national level, such as GDP per capita and post-materialism,
significantly maintained their explanation power, notifying the significant effect of contextual factors.
In short, by adopting multi-level modeling, we conclude that not only variables at the individual level
but also ones at the national level contribute to explaining individual‘s acceptance of science-technology.
This indicates that the acceptance of science-technology significantly differs by both individual and
structural context factors. We argued, since the country is one of loci that make emergent contextual
effects that individuals cannot provide, it needs to find out new contextual variables which well represent
the attributes of country.
Our findings suggest the need to distinguish the significant determinant variables at the micro-individual
level from those at the macro-national level for the sake of explaining individual‘s acceptance. If we
neglected the effect from the national level, estimated coefficient at the individual level might have given
rise to estimation errors, especially overestimation. Second, the present study provides the psychometric
paradigm with more understanding of risk perception which could be interpreted as results from not only
21
individual‘s perceptions but also structural contexts. Finally on the practical side, to increase the
acceptance of science-technology, both individual and contextual variables should be considered in policy
alternatives.
The present study provides the usefulness of synthetic approaches in risk studies by adopting balanced
stances between individual and contextual predictors. However, our research has several limits. First,
there is still an unexplained much amount of variance in the intercept. As this suggests that there were
other micro or macro factors having an impact on the differences between countries, it may be worthwhile
to improve the model by adding or finding new predictors. In particular, because the contextual data or
variables at the national level are not fully available, our study cannot sufficiently test the contextual
effects. Hence model re-specification by including new contextual variables, e.g., science-technology
optimism or skepticism, contributes to finding out the role of macro-factors in acceptance of sciencetechnology. Second, our data partially covered the sample from advanced European countries. Insufficient
coverage narrows the variation of variables. Hence, if more countries are used in the sample, this would
lead to more generalizable results. Third, when using multi-items for one concept, operationalization and
composite method could change the significance in models or variables. Hence, we expect that the
refinements of composite or measurement will bring out better results than the findings in the present
study. Since this is the exploring research examining technology acceptance by using the multilevel
methods, we believe that our findings are not conclusive, but just a burgeoning foundation for future
researches.
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25
Table 1: Question Structure
Variable
Question Statement
Response Scale
s
Social
Acceptan
ce
of
ScienceTechnolo
gy
QB13) I am going to read out a list of areas in which new technologies are currently developing.
For each of these, do you think it will have a positive, a negative, or no effect on our way of life in
the next 20 years?
① solar energy, ② computers and information technology, ③ biotechnology and
A five-point Likerttype scale from 1 (very
negative effect) to 5
(very positive effect)
genetic engineering, ④ space exploration, ⑤ the internet, ⑥ nuclear energy for electricity
production, ⑦ nanotechnology, ⑧ mobile phones, ⑨ new energy sources to power cars, ⑩
air transport, ⑪ military and security equipment, ⑫ high speed trains, ⑬high-tech
agriculture, ⑭ energy saving measures in the home
`Percei
ved
Benefit
Percei
ved
Risk
Knowle
dge
QA12) I would like to read out some statements that people have made about science, technology,
or the environment. For each statement, please tell me how much you agree or disagree.
a2) V232 Thanks to scientific and technological advances, the Earth‘s natural resources will be
inexhaustible
a3) V233 Science and technology can sort out any problem
b2)V238 The application of science and new technologies will make peoples‘ work more
interesting
b6) V242 Science and technology will help eliminate poverty and hunger around the world
QA13) I would like to read out some other statements. For each of them, please tell me how much
you agree or disagree.
a3) Taking everything into account, computers and factory automation will create more jobs
than they will eliminate
a6) V248 New inventions will always be found to counteract any harmful effect of scientific
and technological developments
b5) Science and technology are responsible for most of the environmental problems we have
today
b6) Food made from genetically modified organisms is dangerous
QA3) I would like you to tell me for each of the following issues in the news if you feel very well
informed, moderately well informed, or poorly informed about it?
① New medical discoveries ②Environmental Pollution ③New Inventions and technologies
④New scientific discoveries
(Negati
ve)
Affective
Image
QB14) For each of these different people and groups involved in science and technology, do you
think that what they do has a positive or a negative effect on society?
(Object: ①Newspapers and magazines reporting on science and technology, ②Television and
radio reporting on science and technology, ③Industry developing new products, ④Public
A five-point Likerttype scale from 1
(strongly disagree) to
5 (strongly agree)
A five-point Likerttype scale from 1
(strongly disagree) to
5 (strongly agree)
A three-point scale
from
1(poorly
informed),
2(moderately
well
informed), and 3(very
well informed)
Four-point
scale
from 1 (very
positive effect to
4(very
negative effect).
authorities regulating science and technology, ⑤Scientists in industry doing research, ⑥Public
Postmaterialis
m
Religios
ity
authorities assessing the risks that may come from new technologies)
QB15) For each of the following, how important do you think it will be for our society in ten years
time?
a4) Reducing economic inequalities among people living in the European Union
a5) Giving people more say in important government decisions
b1) Protecting freedom of speech and information
b3) Passing on a sound environment to the next generation
How important is God in your life (Q33).
Four-point
scale
from 1 (not important
at all) to 4 (very
important).
Ten-point scale from
1(not at all) to 10
(very important)
26
Table2. ANOVATest
Total
France
Netherland
Belgium
E-Germ
Italy
Luxem.
Demark
Ireland
UK
N. Ireland
Greece
Spain
Portugal
E-Germ.
Norway
Finland
Sweden
s
Male
3.181
3.052
3.131
3.052
3.033
3.240
3.154
3.168
3.216
3.260
3.178
3.121
3.242
3.223
3.199
3.080
3.049
3.103
Female
3.108
2.996
3.058
2.972
2.934
3.186
3.153
3.063
3.128
3.171
3.167
3.009
3.149
3.178
3.077
3.053
2.953
3.034
GAP
-.073
-.056
-.073
-.080
-.098
-.055
-.001
-.105
-.088
-.088
-.012
-.112
-.092
-.045
-.122
-.027
-.096
-.069
F-Value
194.771***
5.375**
8.618***
11.141***
12.285*** 3.167*
.001
19.084*** 9.273***
11.546***
.060
11.041***
9.198***
1.870
11.649***
1.410
17.392***
10.639***
20-39
3.185
3.001
3.099
3.009
3.040
3.259
3.088
3.129
3.191
3.252
3.191
3.102
3.226
3.284
3.149
3.116
3.015
3.073
40-59
3.122
3.038
3.074
2.981
2.945
3.185
3.149
3.112
3.147
3.183
3.207
3.043
3.222
3.201
3.165
3.056
2.954
3.037
Over 60‘s
3.102
3.027
3.130
3.061
2.951
3.091
3.228
3.099
3.171
3.187
3.116
3.003
3.101
3.099
3.108
3.022
3.027
3.108
GAP
-.083
+.026
+.031
+.052
-.089
.168
+.140
-.030
-.020
-.066
-.075
-.098
-.126
-.186
-.042
-.093
+.012
+.035
F-Value
92.199*** .832
1.517
3.730**
5.070***
8.113***
4.123**
.496
.964
3.233
1.386
3.048**
6.807***
11.471***
.858
5.317***
3.949** 3.733**
Left
3.117
2.997
3.089
2.988
2.974
3.192
3.128
3.062
3.169
3.200
3.144
3.020
3.187
3.247
3.127
3.041
2.959
3.030
Right
3.170
3.114
3.131
3.043
3.022
3.334
3.166
3.161
3.176
3.214
3.197
3.122
3.271
3.175
3.224
3.101
3.037
3.106
GAP
+.053
+.117
+.042
+.055
+.048
+.141
+.039
+.100
+.007
+.014
+.052
+.103
+.084
-.072
+.098
+.060
+.078
+.076
F-Value
85.376*** 16.926*** 2.333
4.959**
2.376
15.091***
.615
17.260*** .044
.248
.933
7.007***
4.034**
3.337*
3.811*
6.687*
10.761***
12.602***
Lower
3.062
2.971
3.043
2.950
2.956
3.118
3.108
3.051
3.133
3.159
3.184
2.973
3.145
3.114
3.090
3.008
2.945
3.025
Higher
3.182
3.072
3.137
3.056
3.000
3.233
3.188
3.160
3.191
3.240
3.166
3.093
3.214
3.230
3.171
3.107
3.037
3.105
GAP
+.120
+.102
+.094
+.106
+.044
+.115
+.080
+.109
+.058
+.081
.019
+.120
+.069
+.116
+.081
+.099
+.092
+.079
F-Value
476.899***
18.010*** 14.414***
11.141***
2.326
10.790***
4.053**
20.332*** 3.603*
9.084***
.144
10.732***
3.859*
8.613***
4.567**
19.019***
15.899***
14.126***
Lower
3.184
3.100
3.151
3.057
3.018
3.237
3.206
3.194
3.196
3.251
3.202
3.098
3.206
3.215
3.178
3.107
3.052
3.110
Higher
3.103
2.976
3.042
2.946
2.952
3.177
3.112
3.053
3.149
3.172
3.139
3.032
3.172
3.180
3.105
3.028
2.956
3.031
GAP
-.081
-.124
-.109
-.111
-.065
-.061
-.094
-.141
-.047
-.079
-.063
-.066
-.034
-.035
-.073
-.080
-.096
-.080
F-Value
236.692***
24.701*** 19.328***
21.234*** 5.374**
4.132**
5.682**
34.423*** 2.614
9.267***
1.777
3.641*
1.208
1.160
4.076**
12.515***
17.050***
Lower
3.124
2.992
3.062
2.994
2.956
3.187
3.090
3.095
3.143
3.184
3.156
3.017
3.171
3.185
3.109
3.045
2.978
3.054
Higher
3.215
3.077
3.189
3.057
3.066
3.322
3.263
3.199
3.297
3.303
3.244
3.173
3.333
3.311
3.258
3.141
3.118
3.123
GAP
+.091
+.085
+.127
+.063
+.110
+.135
+.173
+.104
+.154
+.119
+.088
+.156
+.162
+.125
+.149
+.097
+.141
+.069
F+Value
185.304***
11.332***
20.392***
5.429**
10.924*** 20.200***
18.963*** 11.482***
17.748*** 14.488***
2.059
16.911***
13.202*** 5.022**
12.255*** 12.871***
17.932***
7.478***
Lower
3.337
3.237
3.328
3.139
3.228
3.448
3.347
3.274
3.370
3.379
3.306
3.178
3.457
3.348
3.320
3.150
3.121
3.174
Higher
3.016
2.960
2.965
2.931
2.878
3.030
3.039
3.025
3.074
3.074
3.053
2.939
3.040
3.118
3.043
3.000
2.924
3.005
GAP
-.321
-.277
-.364
-.208
-.350
-.418
-.308
-.249
-.296
-.305
-.253
-.240
-.417
-.230
-.277
-.150
-.197
-.169
F-Value
4099.745*** 107.296***
237.430***
76.607*** 145.305***
257.947***
65.239*** 111.425***
73.412***
65.934***
Gender
Age
Ideology
Perceived
Benefit
Perceived
Risk
Knowledg
e
Image
98.522*** 159.327***
31.124*** 53.717*** 224.794***
51.188*** 60.617*** 35.158***
14.390***
*p <.1; ** p < .05; ***p <.01
27
Continued
Total
Austria
Cyprus
Czech
Estonia
Hungary
Latvia
Lithuania
Malta
Poland
Slovakia
Slovenia
Bulgaria
Romania
Turkey
Croatia
Iceland
Switzerla
nd
Male
3.181
3.103
3.258
3.224
3.231
3.221
3.096
3.287
3.341
3.263
3.138
3.161
3.359
3.360
3.477
3.208
3.211
2.947
Female
3.108
2.983
3.214
3.089
3.108
3.171
3.021
3.193
3.327
3.195
3.047
3.060
3.348
3.338
3.488
3.108
3.111
2.873
GAP
-0.073
-.121
-.044
-.135
-.124
-.050
-.075
-.095
-.014
-.068
-.091
-.101
-.010
-.022
+.010
-.100
-.100
-.074
F-Value
194.771***
16.241*** .862
23.668*** 17.049*** 2.349
4.575**
9.973***
.123
7.560***
11.258***
13.899***
.147
.448
.078
10.848*** 9.677***
8.268***
20-39
3.185
3.135
3.189
3.196
3.218
3.251
3.075
3.290
3.322
3.286
3.096
3.147
3.408
3.399
3.478
3.194
3.174
2.938
40-59
3.122
3.008
3.270
3.140
3.153
3.191
3.092
3.217
3.340
3.181
3.085
3.094
3.328
3.305
3.467
3.113
3.085
2.874
Over 60‘s
3.102
2.915
3.230
3.099
3.067
3.140
2.966
3.158
3.333
3.184
3.060
3.062
3.297
3.334
3.555
3.132
3.205
2.905
GAP
-.083
-.219
+.041
-.097
-.151
-.111
-.109
-.132
+.010
-.102
-.036
-.086
-.111
-.065
+.077
-.061
+.030
-.033
F-Value
92.199*** 16.785*** 1.024
3.565**
9.414***
4.138**
4.939***
7.389***
.073
8.898***
.457
3.562**
5.948***
3.376**
.855
2.958*
4.207**
2.176
Left
3.117
3.078
3.263
3.094
3.127
. 3.234
3.069
3.226
3.315
3.252
3.085
3.146
3.324
3.317
3.411
3.126
3.102
2.854
Right
3.170
3.075
3.174
3.192
3.215
3.196
3.076
3.283
3.384
3.237
3.116
3.084
3.367
3.390
3.460
3.208
3.225
2.967
GAP
+.053
-.003
-.089
+.098
+.087
-.038
+.006
+.057
+.069
-.014
+.031
-.062
+.043
+.073
+.049
+.082
+.122
+.113
F-Value
85.376*** .009
2.744*
11.147***
8.316***
.1.106
.027
2.524
1.694
.236
1.039
4.078**
1.776
3.661*
1.147
5.435**
13.313*** 17.381***
Lower
3.062
2.951
3.165
3.121
3.164
3.174
3.018
3.192
3.308
3.210
3.024
3.072
3.277
3.308
3.081
3.074
3.043
2.853
Higher
3.182
3.096
3.262
3.177
3.150
3.198
3.074
3.246
3.353
3.231
3.126
3.129
3.372
3.366
3.545
3.191
3.196
2.947
GAP
+.120
+.146
+.096
+.056
-.014
+.025
+.055
+.054
+.045
+.021
+.102
+.056
+.095
+.058
+.463
+.116
+.154
+.094
F-Value
476.899***
20.971*** 3.457*
3.792*
.208
.483
2.354
2.542
1.216
.628
13.281*** 4.070**
7.238***
1.871
77.129*** 13.177*** 20.519*** 13.356***
Lower
3.184
3.122
3.224
3.210
3.173
3.231
3.060
3.206
3.330
3.255
3.134
3.145
3.367
3.383
3.491
3.194
3.183
2.963
Higher
3.103
2.985
3.239
3.096
3.137
3.154
3.041
3.244
3.336
3.200
3.042
3.088
3.340
3.311
3.463
3.131
3.111
2.849
GAP
-.081
-.137
+.015
-.114
-.037
-.076
-.018
+.038
+.006
-.055
-.092
-.057
-.027
-.071
-.028
-.063
-.072
-.114
F-Value
236.692***
20.728*** .098
17.077*** 1.548
5.672**
.292
1.741
.023
5.009**
11.690***
3.809*
1.029
4.892**
.526
3.893**
5.140**
20.188***
Lower
3.124
3.021
3.204
3.142
3.141
3.183
3.039
3.223
3.297
3.206
3.069
3.095
3.340
3.340
3.438
3.126
3.123
2.868
Higher
3.215
3.153
3.290
3.211
3.289
3.238
3.174
3.314
3.459
3.356
3.232
3.150
3.468
3.466
3.593
3.257
3.322
2.970
GAP
+.091
+.133
+.086
+.069
+.148
+.055
+.135
+.091
+.162
+.149
+.163
+.055
+.129
+.127
+.154
+.131
+.198
+.102
F-Value
185.304***
12.842*** 3.064*
3.399*
6.836***
1.298
5.851**
1.804
12.898*** 18.037***
13.615*** 2.485
9.909***
4.387**
13.71***7 12.595*** 17.844*** 14.787***
Lower
3.337
3.260
3.439
3.345
3.379
3.354
3.263
3.471
3.494
3.408
3.317
3.320
3.566
3.519
3.676
3.347
3.296
3.077
Higher
3.016
2.903
2.954
3.044
3.028
3.107
2.946
3.063
3.200
3.111
2.993
2.977
3.246
3.178
3.148
3.013
3.071
2.832
GAP
-.321
-.357
-.485
-.301
-.350
-.247
-.317
-.408
-.294
-.297
-.324
-.343
-.319
-.341
-.528
-.334
-.224
-.245
F-Value
4099.745*** 158.109***
136.928***
120.795***
184.152***
58.186***
87.757*** .237.089***
138.960***
174.455***
134.103***
131.127***
224.116***
135.321***
48.546*** 82.129***
Gender
Age
Ideology
Perceived
Benefit
Perceived
Risk
Knowledg
e
Image
61.258*** 168.279***
*p <.1; ** p < .05; ***p <.01
28
Table 3. Regression Analysis across 34 Countries
Gender(1=Women)
Whole Model
-.070***
France
-.363***
Belgium
-.042
Netherlands
-.061**
E-Germ
-.135***
Italy
-.016
Age
-.066***
-0.014
-.013
.010
-.140***
-.141***
Luxem.
0.01
Demark
-0.09***
0.061
-0.068**
Ireland
UK
N. Ireland
-.115*** -.086***
-.034
-.014
-.061**
-.115**
Greece
-.098***
-.144***
Ideology(Right)
.052***
0.091***
.026
.100***
.094***
.101***
0.047
0.09***
.051
.001
.102*
.089**
Perceived Benefit2
.173***
0.099***
.138
.183***
.147***
.126***
0.154*
0.128***
.118***
.115***
-.029
.115***
Perceived Risk2
-.091***
-0.131***
-.126***
-.147***
-.034
-.123***
-0.093***
-0.141***
-.077**
-.100***
-.139**
-.073**
Knowledge
.064***
0.089***
.133***
.062**
.074**
.072**
0.132***
0.076**
.138***
.108***
.149**
.037
Image
-.383***
-0.309***
-.452***
-.308***
-.401***
-.452***
-0.377***
-0.382***
-.280***
-.381***
-.409***
-.238***
F-Value
923.678***
24.947***
48.726***
27.652***
35.017***
35.487***
15.349***
40.881*** 17.380*** 33.488***
9.824***
13.149***
Adjusted R-Square
.217
.166
.272
.165
.214
.304
..212
.226
.139
.207
.202
.106
Gender(1=Women)
Spain
-.074**
Portugal
-.030
E-Germ.
-.104**
Norway
-.020
Finland
-.116***
Sweden
-.097***
Austria
-.070**
Cyprus
-.067
Czech
-.179***
Estonia
-.140***
Hungary
-.019
Latvia
-.099**
Age
-.077**
-.045
-.064
-.134***
.014
-.002
-.079**
.029
-.059**
-.109***
-.136***
.021
Ideology(Right)
.066**
-.026
.071
.074**
.117***
.087***
.002
-.088**
.061**
.046
-.064*
.002
Perceived Benefit2
.143***
.168***
.230***
.220***
.106***
.213***
.155***
.072
.163***
.097***
.120***
.130***
Perceived Risk2
-.081**
-.058
-.077*
-.111***
-.129***
-.094***
-.124***
.024
-.165***
-.030
-.146***
-.024
Knowledge
.046
.135***
.145***
.181***
.027
.060**
.083**
.031
.030
.047
.050
.052
Image
-.408***
-.294***
-.357***
-.211***
-.357***
-.349***
-.427***
-.566***
-.382***
-.474***
-.319***
-.299***
F-Value
30.031***
16.809***
17.470***
22.068***
31.446***
30.167***
43.009***
20.553***
11.708***
Adjusted R-Square
.220
.159
.209
.146
.191
.176
.298
.351
.247
.283
.156
.110
Lithuania
-.057
Malta
-.034
Poland
-.080**
Slovakia
-.093***
Slovenia
-.098***
Bulgaria
.038
Romania
.039
Turkey
.004
Croatia
-.108***
Iceland
-.073
Switzerland
-.070**
-
Gender(1=Women)
Age
-.131***
.114*
-.093***
-.032
-.083**
-.002
-.052
-.017
-.079**
-.073
-.001
-
Ideology(Right)
.063*
-.011
-.008
.017
-.061*
.105***
.055
.083**
.048
.137***
.137***
-
Perceived Benefit2
.141***
.042
.204***
.175***
.159***
.106***
.117***
.233***
.166***
.173***
.122***
-
Perceived Risk2
-.014
.013
-.193***
-.134***
-.058*
-.069*
-.073**
.000
-.065**
-.101*
-.100***
-
Knowledge
.060
.144**
.048
.108***
.050
.058
.036
.103***
.167***
.120***
.130***
-
Image
-.454***
-.381***
-.391***
-.403***
-.386***
-.454***
-.431***
-.477***
-.317***
-.312***
-.280***
-
F-Value
26.699***
9.852***
26.261***
4.667***6
27.699***
25.997***
27.983***
23.918***
-
Adjusted R-Square
.251
.196
.216
.244
.190
.246
.230
.166
-
28.280*** 41.857*** 37.604***
54.024*** 27.776*** 16.761***
.400
.196
.211
-
*p <.1; ** p < .05; ***p <.01
29
Table 4. Regression Analysis at the Aggregate Level
Model 1
B
Micro
Model 2
Beta
Model 3
B
Beta
B
Beta
1.963(.804)**
-
2.923(.1.315)**
-
(Constant)
3.577(.733)***
Perceived Benefit
.257(.078)***
.570
-
-
.271(.093)***
.601
Perceived Risk
-.141(.077)*
-.195
-
-
-.123(.094)
-.170
Knowledge
.064(.114)
.078
-
-
.054(.143)
.066
Image
-.495(.182)**
-.378
-
-
-.456(.233)*
-.348
Macro
GDP per Capita
-
-
-.000(.000)**
.-.431
.000(.000)
-.044
Variables
Religiosity
-
-
.018(.012)
.264
-.005(.010)
-.069
Post-materialism
-
-
.330(.234)
.225
.148(.209)
.101
Variables
F-Value
17.395***
6.244***
9.247***
R-Square
.706
.384
.713
Adjusted R-Square
.665
.232
.636
*p <.1; ** p < .05; ***p <.01
Note:The numbers in parentheses are standard errors.
30
Table 5.Mutilevel Analysis
Model 1
Model 2
Model 3
Model 4
3.665(.046)***
3.815 (.080) ***
-.058(.007)***
-.058 (.007) ***
-.001(.000)***
-.001 (.000) ***
.010(.002)***
.010 (.002) ***
.051(.003)***
.051 (.003) ***
-.039(.004)***
-.039 (.003) ***
.075(.006)***
.076 (.007) ***
-.325(.013)***
-.324 (.013) ***
Regression Coefficient (Fixed effect)
Intercept
Level
1:
3.148(.201)***
3.0789(.0913)***
Gender
-
-
Age
-
-
Ideology (Right)
-
-
Perceived Benefit
-
-
Perceived Risk
-
-
Knowledge
-
-
Negative Image
-
-
GDP per Capita
-
-.000(.000)
-
Religiosity
-
.028(.011)**
-
Post-Materialism
-
-.071(.0.30)**
-
Individual
Predictors
Level 2: Contextual
Predictors
-.000 (.000)**
.004 (.008)
-.059 (.022)**
Variance Components (random effects)
Variance of Residual(
) (at the individual level)
.197***
.197***
.141***
.141***
Variance of Intercept(
) (at the country level)
.013***
.008***
.055***
.042***
-
-
.006
.007
6.31%
3.91%
30.47%
25.88%
Deviance Statistic(-2 log
37417.052
37434.763
20823.411
20851.030
likelihood)
Akaike's
Information
Criterion (AIC)
37421.052
37438.763
20897.411
20925.030
Schwarz‘s
37437.716
37455.426
21195.469
21223.083
Sum of variance & covariance of slope‘s deviation
Model Summary
Effect
Index
Intraclass
Correlation
(ICC)
Model Fit Index
Bayesian
Criterion(BIC)
31