Associations between conspiracism and the rejection of scientific innovations

Abstract

Public opinion regarding scientific developments such as genetically modified food can be mixed. We suggest such science-based technological innovations are rejected by some because they are perceived to be advanced as part of a conspiracy. In nationally representative samples (Australia n = 1011; New Zealand n = 754), we report the associations between five conspiracism facets and anti-science attitudes. Results indicate broad public opposition to genetically modified food and use of nuclear power, but more acceptance of renewable power, potable recycled water, 5G networks, and childhood vaccinations. There were small to moderate associations between the rejection of scientific innovations and conspiracism. Multivariate models estimating unique associations of conspiracism facets with anti-science attitudes suggested several novel and important relationships, particularly for childhood vaccination, genetically modified food, and 5G networks. We discuss the importance of examining factors such as conspiracism in understanding what may motivate and sustain rejection of scientific evidence-based claims about socially contentious technological innovations.

Keywords

conspiracism GM food public opinion science attitudes and perceptions vaccinations

1. Introduction

Scientific knowledge is growing at an exponential rate (Bornmann and Mutz, 2015), bringing with it benefits for human health and prosperity. Advances in wireless data networks are central to development of the Internet of Things (Li et al., 2018), renewable energy technologies play a critical role in mitigating climate change (Edenhofer et al., 2011), and vaccinations are one of the most significant scientific contributions to public health, credited with saving millions of lives (Andre et al., 2008). Paradoxically, humanity is also in the midst of a “post-truth” era where debate on important societal issues is often framed in terms of appeals to emotion and identity, associated with the dismissal of epistemic authority of science and technologies by diverse anti-science movements (Kienhues et al., 2020). Scientific discovery, the dissemination and public reception of scientific findings and technologies are intertwined with attitudes influencing contemporary society, culture, and politics (Suhay and Druckman, 2015). Thus, to understand why people accept or reject science-based claims and associated medical and technological advances, it is important to study how factors such as ideologies—cognitive structures of organized ideas, attitudes, and values around an abstract theme (Converse, 2006) —underpin these attitudes.

There is growing evidence that this rejection of science is motivated by underlying ideologies such as political conservatism and religious identity (e.g. Kraft et al., 2015), but is also issue-dependent (Rutjens et al., 2018), suggesting heterogeneity in the determinants of science rejection (Rutjens and van der Lee, 2020). A recent theoretical model proposed six underlying “attitude roots” that sustain and motivate the rejection of scientific evidence-based claims about socially contentious issues such as vaccination (Hornsey and Fielding, 2017). In this research, we examine one of these underlying motivations of anti-science attitudes: conspiracist ideology, or conspiracism—the general tendency to engage in explanations about the cause, or concealment, of threats to human wellbeing stemming from a secretive network of (purported) malevolent actors (Swami et al., 2011).

Attempts to measure stable individual differences in the propensity to engage in conspiracism have included both unidimensional single-item (Lantian et al., 2016) and multi-item scales (e.g. Bruder et al., 2013). The validated multi-item unidimensional scale developed by Brotherton et al. (2013) identified five conspiracism facets (see Table 2). Several studies have used this scale to investigate the associations between conspiracism and anti-science beliefs and attitudes, but all have treated conspiracism as a unidimensional construct and focused on attitudes toward science in general rather than specific claims (e.g. Dieckmann and Johnson, 2019; Rizeq et al., 2020). However, research focusing on political attitudes and beliefs has profitably drawn on the multi-item nature of Brotherton’s measure, in some cases revealing that facets can have opposing associations. For example, a study reported that the personal wellbeing and control of information facets negatively and positively predict anti-elitist attitudes, respectively (Castanho Silva et al., 2017). The extent to which each of the facets is linked to specific anti-science attitudes remains an important, but unanswered question. Understanding how different sets of conspiracy beliefs are linked to the rejection of science on an issue can inform efforts to shift views, counter misinformation (Cichocka, 2020), and change behavior (Hornsey and Fielding, 2017). Therefore, our research question is: To what extent does each of the interrelated but distinct facets of conspiracism relate to anti-science attitudes?

Few individuals reject science in any absolute sense. Rather, what is observed is the rejection of claims about the nature of particular phenomena (e.g. disease and the human immune system) and consequent courses of action (i.e. vaccination) proposed by authorities on the basis of evidence generated by endeavors characterized as being “scientific.” An important aspect of such rejection is a refusal to concede any special truth value to claims of knowledge generated by methods of inquiry used by scientists. We propose that an important basis for this refusal is invalidation of the legitimacy of any claim of truth because it is being advanced in bad faith—as part of a conspiracy.

Most conspiracy theories advance that malevolent acts of authorities such as governments, corporations, scientific research institutes, and even individual scientists are kept secret. Some of these narratives claim bias in scientific methods and conclusions, or the deliberate distortion, suppression, or falsification of results (Weigmann, 2018)—claims which are intended to undermine support, development, and adoption of emerging technologies and medical advances (Goertzel, 2010). While there may be common conspiracist elements related to the rejection of these scientific actions—such as distrust in the actions of authorities (Uscinski et al., 2017)—there are distinctions among anti-science conspiracy theories. For example, anti-vaccination narratives commonly suggest pharmaceutical companies and governments hide information about adverse vaccine side effects and falsify information about their efficacy (Kata, 2010). Genetically modified organism (GMO) narratives posit malevolent global conspiracies by biotech corporations to take over the agriculture industry (Uscinski and Parent, 2014). Recent 5G conspiracies suggest that governments are covering up health risks (Meese et al., 2020). These narratives impact more than attitudes. Exposure to misinformation, including conspiracy theories, may be a driver of vaccine skepticism responsible for measles outbreaks in countries such as New Zealand (Lee and Sibley, 2020), violent actions in the wake of debunked theories linking the spread of COVID-19 with the advent of 5G (Meese et al., 2020), and have harmful consequences for social and political action related to climate change (e.g. van der Linden, 2015).

Previous research has found conspiracism to be a largely consistent predictor of specific anti-science beliefs across a range of domains (e.g. Landrum and Olshansky, 2019). For example, studies have shown that increased conspiracism is associated with anti-vaccination attitudes (Hornsey et al., 2018; Rutjens and van der Lee, 2020) or the belief that 5G technology is associated with adverse health effects (Miller, 2020). Lewandowsky et al. (2013) reported that conspiracism predicted rejection of scientific claims involving climate change, vaccination, and GM food—all publicly debated topics which have been the subject of specific conspiracy theories, as outlined above. However, there are also areas where scientific consensus and certainty are high but public support is low, which have attracted few, or no, associations with conspiracy theories. For example, Australian research suggests that willingness to use recycled water is low, despite a need to develop future strategies for water security (Etale et al., 2020). Also, public support for wind and solar power technologies in Australia is high, while support for nuclear power is low (Bird et al., 2014). In New Zealand, public support for renewable energy technologies is strong, as is the opposition to nuclear power due to historical concerns of environmental safety (Byrd and Matthewman, 2013). There is little evidence of conspiracism playing a role in debates on these issues.

Examining conspiracism facets’ associations with anti-science attitudes will provide useful knowledge about the specific content of conspiracism that are most important in understanding the rejection of various forms of scientific actions. In this study, we aimed to describe and investigate associations between five conspiracism facets and anti-science attitudes to eight technological and scientific innovations, separately for Australia and New Zealand. Specifically, we focused on scientific actions associated with conspiracism (i.e. GMOs for food, childhood vaccinations, and 5G mobile networks) and others where public support is mixed with few or no known conspiracism associations (i.e. technologies for power including nuclear, solar, and wind; and, recycled water for drinking). While specific content of conspiracism may be associated with anti-science attitudes, it is likely that there are unique social-cultural and historical differences between these countries that may shape the strengths of these associations. To this end, we analyzed relationships across the two countries with multivariate models estimating the unique effect of each conspiracism facet on each science and technology attitude while controlling for the effects of the other conspiracism facets in addition to well-established predictors of anti-science attitudes—political and religious ideology.

2. Method

Participants and procedure

Research company Dynata (dynata.com) recruited participants from its accredited panel between 9 and 20 September 2019. Potential adult participants were directed to an online Qualtrics survey and were asked their age, gender, and state of residence (or region for New Zealand). Interlocking quotas for these demographics, established from the latest available estimates for Australia (2018 Q4; Australian Bureau of Statistics, 2019) and New Zealand (2019 Q2; Stats NZ, 2019), were used to target 1000 Australian and 750 New Zealand participants.¹ A sensitivity analysis delivered 80% power to detect a true correlation ρ > .102 for the New Zealand sample (n = 754) and ρ > .088 for the Australian sample (n = 1011). Demographic information is presented in Table 1.

Table 1.

Demographic characteristics of Australian and New Zealand samples.

	Australia (n = 1011)	New Zealand (n = 754)
	Percentage	Percentage
Gender: female	50.94	50.00
Area
Metropolitan/urban	79.23	82.23
Rural	19.68	17.24
Remote	1.09	.53
Annual income
Less than $10,000	8.21	5.70
$10,000–$19,999	9.59	9.68
$20,000–$29,999	13.45	17.37
$30,000–$39,999	8.70	7.29
$40,000–$49,999	8.61	8.49
$50,000–$59,999	8.61	9.15
$60,000–$69,999	7.02	7.29
$70,000–$79,999	4.75	5.84
$80,000–$89,999	4.85	4.38
$90,000–$99,999	3.56	4.11
$100,000–$149,999	8.90	7.29
More than $150,000	3.46	2.25
Rather not to say	10.39	11.14
Education level (highest attained)
High school not completed	11.93	12.33
High school graduate, or the equivalent	28.03	22.65
Advanced diploma	15.21	16.09
Bachelor’s degree, graduate diploma, and certificate	31.51	35.52
Postgraduate diploma and certificate, bachelor honors degree	5.07	8.04
Master’s degree	6.76	4.69
Doctorate degree	1.39	.80
	M (SD)	M (SD)
Age (years)	47.22 (18.06)	47.67 (17.94)
Importance of religion/spirituality	3.70 (2.19)	3.74 (2.25)
Political ideology
Economic	3.85 (1.20)	3.96 (1.20)
Social	3.98 (1.31)	4.13 (1.37)

SD: standard deviation.

Weighted N for all variables. Four participants identifying as gender non-binary omitted from analyses for each sample. Five cases of unspecified education level not reported for Australia; eight unspecified education level cases not reported for New Zealand. All percentages reported are valid percentages. Range for age is 18–90 years for Australia and 18–96 years for New Zealand. Range for importance of religion/spirituality is 1 = not at all to 7 = extremely. Political ideology is 1 = extremely conservative, 2 = very conservative, 3 = somewhat conservative, 4 = neither conservative nor progressive, 5 = somewhat progressive, 6 = very progressive, and 7 = extremely progressive.

Participants responded to the scales and items in the order presented below. Following the completion of these materials, participants reported on demographics, including economic and social political ideology (see Talhelm et al., 2015) and importance of religion/spirituality. This research was approved by the Human Ethics Committee of La Trobe University (reference HEC19372). Materials, data, and syntax are available (https://osf.io/npzmh/).

Materials

Conspiracism

Conspiracism was assessed with the Generic Conspiracist Belief Scale (Brotherton et al., 2013). Five facets, each operationalized as the mean of three items, underpin a coherent network of conspiracism thought to form a monological belief system. Items and reliability estimates of each facet are presented in Table 2. The scale has been validated and used as a measure of general tendency toward conspiracy thinking (see Douglas et al., 2019).

Table 2.

Individual items of the five facets of conspiracism, including Cronbach’s alpha for Australian and New Zealand samples.

	Cronbach’s αAustralia	Cronbach’s αNew Zealand
Government malfeasance (i.e. criminal conspiracy within governments)	.88	.88
The government is involved in the murder of innocent citizens and/or well-known public figures, and keeps this a secret
The government permits or perpetrated acts of terrorism on its own soil, disguising its involvement
The government uses people as patsies to hide its involvement in criminal activity
Malevolent global conspiracies (i.e. cabals who secretly control global events)	.88	.87
The power held by the heads of state is second to that of small unknown groups who really control world politics
A small, secret group of people is responsible for making all major world decisions, such as going to war
Certain significant events have been the result of the activity of a small group who secretly manipulate world events
Extra-terrestrial cover-up (i.e. concealment of the existence of aliens)	.85	.86
Secret organizations communicate with extraterrestrials, but keep this fact from the public
Evidence of alien contact is being concealed from the public
Some UFO sightings and rumors are planned or staged to distract the public from real alien contact
Personal wellbeing (i.e. conspiracies relating to aspects of personal health and liberty)	.84	.82
The spread of certain viruses and/or disease is the result of the deliberate, concealed efforts of some organization
Technology with mind-control capacities is used on people without their knowledge
Experiments involving new drugs or technologies are routinely carried out on the public without their knowledge or consent
Control of information (i.e. suppression of information by organizations)	.74	.79
Groups of scientists manipulate, fabricate, or suppress evidence to deceive the public
New and advanced technology which would harm current industry is being suppressed
A lot of important information is deliberately concealed from the public out of self-interest

Participants rated these 15 items on a 5-point scale (1 = definitely not true, 2 = probably not true, 3 = not sure/cannot decide, 4 = probably true, and 5 = definitely true). UFO = Unidentified Flying Object.

Anti-science attitudes

Attitudes were conceptualized as an affective evaluation of an attitude object, indicated by a generalized feeling of comfort or discomfort (Eagly and Chaiken, 1993). Eight science and technology attitude objects were each assessed by a single 7-point item of comfort (1 = not at all comfortable, 7 = very comfortable). Participants rated the following in order, “Genetically modified plants for food,” “Genetically modified animals for food,” “Childhood vaccinations,” “Nuclear power in [country],” “Wind farms in [country],” “Solar farms in [country],” “Recycled water for drinking,” and “New mobile phone networks (e.g. 5G),” where [country] was specified for each sample. Comfort has been previously used to assess science and technology attitudes in areas such as gene editing (Critchley et al., 2019) and GMOs (Marques et al., 2015).

Statistical analysis

All data were analyzed with R version 3.6.0 (R Core Team, 2020). All path analysis models were run with the lavaan package version 0.6-7 (Rosseel, 2012), using maximum likelihood estimation with robust standard errors to account for any skewness in the data (Yuan and Bentler, 2000). Due to the number of specified regressions on each attitude, we used a more stringent criteria for interpretation (p < .01). Models report standardized effects with frequentist 99% confidence intervals. Tables were generated using apaTables package version 2.0.5 (Stanley, 2018).

3. Results

Descriptive statistics and zero-order correlations between conspiracism facets and science attitudes are presented in Table 3.

Table 3.

Summary of intercorrelations, means, and standard deviations for facets of general conspiracist belief, attitude items, political identification, and religious/spiritual importance for Australia and New Zealand.

Australia and New Zealand
Variable	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	M	SD
1. GovMalf	2.46	1.06	–	.75*	.69*	.79*	.70*	−.14*	−.08	−.38*	.03	−.21*	−.15*	−.10*	−.22*	−.07	−.10*	.17*	2.24	1.09
2. MalGlob	2.66	1.08	.77*	–	.66*	.77*	.72*	−.16*	−.13*	−.31*	−.06	−.16*	−.11*	−.09	−.24*	−.07	−.13*	.23*	2.59	1.10
3. ExtraTerr	2.46	1.07	.74*	.69*	–	.73*	.62*	−.07	−.03	−.28*	−.05	−.17*	−.13*	−.05	−.19*	−.04	−.04	.12*	2.34	1.07
4. PersWB	2.51	1.03	.81*	.78*	.76*	–	.78*	−.17*	−.11*	−.40*	−.04	−.19*	−.15*	−.11*	−.26*	−.13*	−.16*	.21*	2.45	1.03
5. ContrInfo	3.06	0.92	.70*	.72*	.64*	.73*	–	−.12*	−.12*	−.29*	−.02	−.11*	−.06	−.07	−.23*	−.06	−.10*	.17*	3.07	0.98
6. GMOp	3.47	1.82	−.14*	−.18*	−.11*	−.14*	−.14*	–	.77*	.30*	.38*	.02	.03	.21*	.35*	.14*	.11*	−.20*	3.56	1.92
7. GMOa	2.89	1.75	−.06	−.12*	−.06	−.06	−.12*	.76*	–	.19*	.38*	−.04	−.06	.23*	.29*	.09	.01	−.15*	2.82	1.81
8. Vacc	5.73	1.59	−.39*	−.35*	−.31*	−.42*	−.27*	.24*	.16*	–	.14*	.21*	.29*	.17*	.35*	.08	.15*	−.21*	5.90	1.60
9. Nuclear	3.91	1.97	−.21*	−.14*	−.14*	−.16*	−.10*	.30*	.32*	.24*	–	−.05	−.04	.18*	.23*	.01	−.04	−.01	2.89	1.95
10. Wind	5.55	1.66	−.10*	−.12*	−.12*	−.15*	−.09*	.11*	.03	.32*	−.04	–	.76*	.18*	.19*	.10*	.14*	−.05	6.09	1.29
11. Solar	5.86	1.45	−.14*	−.15*	−.16*	−.18*	−.07	.08	< .01	.37*	−.03	.79*	–	.21*	.20*	.10*	.13*	−.08	6.23	1.19
12. RecWat	4.70	1.92	−.19*	−.19*	−.15*	−.17*	−.15*	.36*	.28*	.34*	.23*	.31*	.34*	–	.28*	.12*	.11*	−.08	4.55	1.88
13. 5G	4.81	1.70	−.28*	−.28*	−.24*	−.31*	−.28*	.32*	.26*	.45*	.28*	.30*	.29*	.41*	–	.09	.09	−.09	4.64	1.76
14. Pol_eco	3.85	1.20	< .01	−.11*	−.08	−.07	−.05	.06	.03	.11*	−.18*	.21*	.20*	.13*	.11*	–	.63*	−.13*	3.97	1.20
15. Pol_soc	3.98	1.31	−.06	−.13*	−.10*	−.12*	−.09*	.10*	.03	.15*	−.21*	.27*	.27*	.19*	.11*	.69*	–	−.16*	4.14	1.37
16. Religion	3.69	2.19	.08	.14*	.11*	.15*	.07	−.14*	−.12*	−.15*	< .01	−.10*	−.11*	−.06	−.06	−.17*	−.21*	–	3.71	2.25

SD: standard deviation; GovMalf: government malfeasance; MalGlob: malevolent global conspiracies; ExtraTerr: extra-terrestrial cover-up; PersWB: personal wellbeing; ContrInfo: control of information; GMOp: genetically modified plants for food; GMOa: genetically modified animals for food; Vacc: childhood vaccinations; Nuclear: nuclear power in Australia/New Zealand; Wind: wind farms in Australia/New Zealand; Solar: solar farms in Australia/New Zealand; RecWat: recycled water for drinking; 5G: new mobile phone networks (e.g. 5G); Pol_eco, economic political ideology; Pol_soc: social political ideology; Religion: importance of religion/spirituality

Conspiracist ideation ranges from 1 to 5. Attitude, political ideology, and religion variables range from 1 to 7. Lower scores reflect lower levels of the construct. Range for importance of religion/spirituality is 1 = not at all to 7 = extremely. Political ideology is 1 = extremely conservative, 2 = very conservative, 3 = somewhat conservative, 4 = neither conservative nor progressive, 5 = somewhat progressive, 6 = very progressive, and 7 = extremely progressive. Lower triangles are values for Australia. Upper triangles are values for New Zealand.

Australia n = 1011; New Zealand n = 754. Lower triangle are values for Australia. Upper triangle are values for New Zealand.

p < .01.

Next, we specified a path analysis model where each science and technology attitude was regressed on each facet of conspiracism, including covariates of political ideology and importance of religion/spirituality, for each sample. A strength of using this approach was that we specified covariances between predictors as well as outcomes, thus allowing us to investigate the unique relationships between conspiracism and a constellation of anti-science attitudes. The models are presented in Figure 1a and b with estimates in Table 4 (see Supplemental Tables S1a and S1b for all model estimates).

Figure 1.

Associations between facets of conspiracism and anti-science attitudes in Australia and New Zealand.

Table 4.

Standardized estimates of effects of facets of general conspiracist belief on all attitude items for Australia and New Zealand.

Australia	GMO plants		GMO animals		Vaccination		Nuclear power		Wind power		Solar power		Recycled water		5G networks
Australia	β (CI)	p	β (CI)	p	β (CI)	p	β (CI)	p	β (CI)	p	β (CI)	p	β (CI)	p	β (CI)	p
Government malfeasance	−0.041 (–0.332, 0.250)	.714	0.058 (–0.224, 0.34)	.596	–0.304 (–0.555, –0.053)	.002	–0.419 (–0.717, –0.121)	<.001	0.025 (–0.235, 0.285)	.807	−0.064 (–0.293, 0.166)	.476	−0.270 (–0.572, 0.031)	.021	−0.119 (–0.383, 0.145)	.246
Malevolent global conspiracy	–0.308 (–0.578, –0.038)	.003	−0.250 (–0.514, 0.015)	.015	−0.031 (–0.254, 0.192)	.719	0.004 (–0.277, 0.286)	.968	0.016 (–0.227, 0.258)	.869	−0.025 (–0.227, 0.177)	.748	−0.188 (–0.475, 0.100)	.093	−0.066 (–0.313, 0.180)	.488
Extra-terrestrial cover-up	0.091 (–0.151, 0.334)	.331	0.029 (–0.200, 0.257)	.747	0.097 (–0.112, 0.307)	.232	−0.046 (–0.300, –0.209)	.644	−0.052 (–0.274, 0.169)	.543	−0.097 (–0.29, 0.096)	.196	0.015 (–0.255, 0.284)	.889	0.106 (–0.116, 0.328)	.217
Personal wellbeing	0.023 (–0.274, 0.321)	.840	0.236 (–0.065, 0.537)	.043	–0.568 (–0.805, –0.331)	<.001	−0.104 (–0.421, 0.214)	.400	−0.221 (–0.484, 0.042)	.031	–0.257 (–0.489, –0.025)	.004	0.039 (–0.284, 0.363)	.753	–0.323 (–0.584, –0.061)	.001
Control of information	−0.050 (–0.284, 0.184)	.581	–0.242 (–0.477, –0.006)	.008	0.174 (–0.011, 0.359)	.015	0.161 (–0.099, 0.421)	.111	0.052 (–0.179, 0.284)	.559	0.244 (0.044, 0.444)	.002	0.037 (–0.213, 0.287)	.702	−0.172 (–0.391, 0.047)	.043
Pol_eco	−0.050 (–0.251, 0.152)	.527	−0.021 (–0.218, 0.177)	.786	0.048 (–0.107, 0.202)	.427	−0.123 (–0.331, 0.084)	.126	0.088 (–0.08, 0.256)	.175	0.053 (–0.102, 0.208)	.379	0.007 (–0.198, 0.211)	.933	0.133 (–0.054, 0.32)	.067
Pol_soc	0.128 (–0.076, 0.332)	.106	0.006 (–0.196, 0.207)	.941	0.115 (–0.045, 0.274)	.064	–0.367 (–0.582, –0.152)	<.001	0.350 (0.172, 0.528)	<.001	0.324 (0.163, 0.484)	<.001	0.330 (0.122, 0.538)	<.001	0.036 (–0.154, 0.225)	.627
Religion	–0.200 (–0.355, –0.045)	.001	–0.207 (–0.355, –0.059)	<.001	−0.106 (–0.230, 0.017)	.026	−0.062 (–0.227, 0.103)	.333	−0.038 (–0.175, 0.099)	.475	−0.028 (–0.144, 0.088)	.537	0.006 (–0.156, 0.169)	.918	0.002 (–0.138, 0.141)	.976
New Zealand
Government malfeasance	−0.043 (–0.361, 0.274)	.725	0.050 (–0.244, 0.344)	.660	–0.357 (–0.633, –0.082)	.001	0.444 (0.099, 0.789)	.001	–0.232 (–0.456, –0.008)	.008	−0.141 (–0.334, 0.052)	.060	−0.109 (–0.467, 0.248)	.431	−0.010 (–0.312, 0.291)	.929
Malevolent global conspiracy	−0.143 (–0.451, 0.165)	.231	−0.192 (–0.485, 0.101)	.092	0.119 (–0.11, 0.348)	.181	−0.284 (–0.610, 0.041)	.025	0.012 (–0.191, 0.215)	.879	0.045 (–0.138, 0.228)	.526	−0.061 (–0.385, 0.264)	.630	−0.137 (–0.43, 0.157)	.231
Extra-terrestrial cover-up	0.205 (–0.071, 0.481)	.056	0.261 (0.006, 0.517)	.008	0.052 (–0.192, 0.296)	.582	−0.122 (–0.400, 0.156)	.259	−0.074 (–0.268, 0.120)	.328	−0.065 (–0.235, 0.104)	.323	0.169 (–0.115, 0.453)	.126	0.038 (–0.23, 0.307)	.712
Personal wellbeing	−0.275 (–0.666, 0.117)	.071	−0.103 (–0.463, 0.257)	.461	–0.532 (–0.821, –0.242)	<.001	−0.206 (–0.615, 0.202)	.193	−0.136 (–0.380, 0.108)	.151	−0.160 (–0.375, 0.055)	.055	−0.201 (–0.584, 0.181)	.175	−0.294 (–0.643, 0.054)	.029
Control of information	0.058 (–0.249, –0.365)	.629	−0.159 (–0.454, 0.136)	.166	0.130 (–0.081, 0.340)	.112	0.096 (–0.218, 0.409)	.431	0.175 (–0.009, 0.359)	.015	0.175 (–0.002, 0.352)	.011	0.069 (–0.237, 0.374)	.562	−0.064 (–0.329, 0.200)	.530
Pol_eco	0.207 (–0.024, 0.437)	.021	0.237 (0.014, 0.459)	.006	−0.066 (–0.237, 0.105)	.319	0.088 (–0.151, 0.328)	.343	0.028 (–0.122, 0.179)	.625	0.017 (–0.131, 0.166)	.762	0.137 (–0.108, 0.382)	.150	0.105 (–0.115, 0.325)	.219
Pol_soc	−0.009 (–0.247, 0.229)	.923	−0.197 (–0.418, 0.024)	.022	0.158 (–0.015, 0.330)	.018	−0.140 (–0.377, 0.096)	.126	0.128 (–0.016, 0.273)	.022	0.118 (–0.005, 0.241)	.013	0.074 (–0.180, 0.329)	.453	0.021 (–0.199, 0.241)	.806
Religion	–0.290 (–0.475, –0.105)	<.001	–0.216 (–0.393, –0.039)	.002	–0.202 (–0.349, –0.055)	<.001	−0.006 (–0.194, 0.182)	.931	0.002 (–0.12, 0.125)	.959	−0.052 (–0.169, 0.064)	.247	−0.076 (–0.265, 0.114)	.303	−0.050 (–0.225, 0.125)	.463

GM: genetically modified; CI: confidence interval; Pol_eco: economic political ideology; Pol_soc: social political ideology; Religion: importance of religion/spirituality.

Conspiracist ideation ranges from 1 to 5. Attitude, political ideology, and religion variables range from 1 to 7. Lower scores reflect lower levels of the construct. Range for importance of religion/spirituality is 1 = not at all to 7 = extremely. Political ideology is 1 = extremely conservative, 2 = very conservative, 3 = somewhat conservative, 4 = neither conservative nor progressive, 5 = somewhat progressive, 6 = very progressive, and 7 = extremely progressive. β = standardized beta, with frequentist 99% confidence intervals reported within parentheses. Australia n = 1011; New Zealand n = 754. Estimates in bold indicate p ⩽ .01.

Anti-science attitudes toward GMO animals and plants for food were moderately associated with increased malevolent global conspiracy and less so with other conspiracism facets in Australia. Vaccination anti-science attitudes were strongly associated with increased government malfeasance and personal wellbeing conspiracy beliefs in both Australia and New Zealand. 5G anti-science attitudes were moderately associated with increased personal wellbeing in Australia and marginally so in New Zealand. Increased importance of Religion/Spirituality was uniquely associated with increased GMO anti-science attitudes in both samples, and with Vaccination anti-science attitudes in New Zealand. Only economic, not social conservatism was uniquely associated with GMO anti-science attitudes in New Zealand. Overall, these models explained a small amount of the variance in attitudes toward GMOs (; $R_{New Zealand}^{2} = 5.2 % - 7.2 %$ ), 5G ( $R_{Australia}^{2} = 11.6 %$ ; $R_{New Zealand}^{2} = 7.7 %$ ), and a larger amount of vaccination attitudes ( $R_{Australia}^{2} = 20.7 %$ ; $R_{New Zealand}^{2} = 19.8 %$ ).

Anti-science attitudes toward recycled water were not uniquely associated with any conspiracism facets, however, there were some unique associations with solar power. Solar anti-science attitudes were moderately associated with increased personal wellbeing and decreased control of information in Australia, and in New Zealand only marginally with decreased control of information. Anti-science wind power attitudes, however, were associated with increased government malfeasance in New Zealand, and marginally with increased personal wellbeing in Australia. Nuclear power anti-science attitudes were associated with both increased and decreased government malfeasance in Australia and New Zealand, respectively.² Increased social conservatism was uniquely associated with increased wind, solar, recycled water, and decreased nuclear anti-science attitudes in Australia only. These models explained small proportion of the variance in recycled water ( $R_{Australia}^{2} = 7.2 %$ ; $R_{New Zealand}^{2} = 3.0 %$ ), solar ( $R_{Australia}^{2} = 11.1 %$ ; $R_{New Zealand}^{2} = 5.0 %$ ), wind ( $R_{Australia}^{2} = 8.8 %$ ; $R_{New Zealand}^{2} = 6.7 %$ ), and nuclear ( $R_{Australia}^{2} = 9.8 %$ ; $R_{New Zealand}^{2} = 6.7 %$ ).

4. Discussion

Although previous research has described levels of conspiracism and anti-science attitudes, few studies have examined the association between the two, and none have examined the distinctive content of conspiracy theories in relation to anti-science attitudes. In the present research, we aimed to describe and investigate associations between five conspiracism facets and anti-science attitudes to eight science and technology innovations in samples from Australia and New Zealand.

In general, anti-science attitudes were heterogeneous, consistent with previous research (e.g. Rutjens and van der Lee, 2020). Both samples were opposed toward GMOs for food (Marques et al., 2015), more positive toward renewable energy sources, but less positive toward nuclear power (Bird et al., 2014)—especially in New Zealand. Attitudes toward recycled water, 5G networks, and vaccinations were somewhat positive on average. Furthermore, while anti-science attitudes were generally associated with increased conspiracism across each facet at the zero-order level (Lewandowsky et al., 2013), our findings shed light on the associations between the unique content of conspiracism and the rejection of science and technology.

The rejection of childhood vaccinations was associated with beliefs involving a government criminal conspiracy and the infringement of liberties, commensurate with vaccination misinformation (Kata, 2010). GMO rejection for Australians, but not New Zealanders,³ was associated with increased beliefs in a group of “puppeteers” manipulating world events and the suppression of information by organizations, consistent with narratives involving the takeover of the agriculture industry by biotech corporations (Uscinski and Parent, 2014). Personal wellbeing beliefs, which included mind-control used on an unsuspecting public, was associated with the rejection of 5G networks, consistent with emerging narratives in Australia and New Zealand about the unsupported harmful effects this technology (Meese et al., 2020).⁴ Overall, associations between the conspiracism facets and these science and technology attitudes were stronger, as compared with the other anti-science attitude targets.

Our study findings are consistent with theories on the motivated rejection of science (Hornsey and Fielding, 2017; Kraft et al., 2015), highlighting the associations with conspiracism and underscoring the distinct content central to anti-science attitudes. Partitioning the multidimensional aspects of conspiracism may afford promising avenues for future research, such as developing topic relevant content to counter misinformation through pre-bunking (Cichocka, 2020). Our data also suggest that in contrast to previous research importance of religion/spirituality was negatively associated with GMO attitudes (Rutjens et al., 2018), whereas we found a statistically significant association with vaccination anti-science attitudes, in New Zealand only (Rutjens et al., 2018; Rutjens and van der Lee, 2020). Importantly, social conservatism was associated with the rejection of renewable technologies in Australia only, potentially as a result of the local political climate around climate change policies (Hornsey and Fielding, 2020).

Strengths of this research include describing the levels and associations between conspiracism and anti-science attitudes in two representative samples of New Zealanders and Australians. However, the study has some limitations. Our samples comprised online panels which are typically more educated and have higher income than the general population.⁵ Although representative, our study is correlational and cannot establish the causal nature of these associations. Future studies could seek to establish the temporal stability and ordering of these variables.

While increased conspiracism was associated with most of the eight anti-science attitudes, our investigation of the unique relationship of each facet revealed some novel and important associations, such as childhood vaccination, GMOs, and 5G networks. This suggests the importance of examining conspiracism as a whole, as these beliefs may cohere consistent with a monological belief system (Brotherton et al., 2013), and also speaks to the heterogeneity of anti-science attitudes (Rutjens and van der Lee, 2020). Future research may benefit by investigating the origins of, and what sustains conspiracies to combat critical issues, such as vaccine skepticism, opposition against emerging technologies, and the rejection of well-evidenced science-based innovations more broadly.

Supplemental Material

sj-rtf-1-pus-10.1177_09636625211007013 – Supplemental material for Associations between conspiracism and the rejection of scientific innovations

Supplemental material, sj-rtf-1-pus-10.1177_09636625211007013 for Associations between conspiracism and the rejection of scientific innovations by Mathew D. Marques, John R. Kerr, Matt N. Williams, Mathew Ling and Jim McLennan in Public Understanding of Science

Footnotes

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: School of Psychology and Public Health, La Trobe University. Grant Number: Small grants scheme.

ORCID iDs

Mathew D. Marques

John R. Kerr

Matt N. Williams

Mathew Ling

Jim McLennan

Supplemental material

Supplemental material for this article is available online.

Notes

Author biographies

Mathew D. Marques is a Lecturer in Psychology at the School of Psychology and Public Health, La Trobe University, Melbourne, Australia. His research uses social psychology to understand the role of psychological mechanisms such as trust, conspiracy theories, and ideologies in the formation of attitudes toward socially contentious scientific research, such as genetically modified foods and vaccinations.

John R. Kerr is a Research Associate at the Winton Centre for Risk and Evidence Communication, University of Cambridge, and an affiliate of the Cambridge Social Decision Making Lab. His research focuses on how people form and change beliefs about debated scientific issues and the communication scientific evidence.

Matt N. Williams is a Senior Lecturer at the School of Psychology, Massey University, New Zealand. His research interests include the psychology of climate change, beliefs in conspiracies, and quantitative methodology.

Mathew Ling is a member of the Misinformation Lab at Deakin University, Melbourne, Australia. His research focuses on susceptibility to and remedy of misinformation particularly in the health domain.

Jim McLennan (PhD Monash University, Melbourne, Australia) is an Adjunct Professor in the School of Psychology and Public Health at La Trobe University, Melbourne, Australia. Since 2009, he has been conducting research into environmental hazards risk perception and mitigation.

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