Temporal stability of public acceptability of novel and established energy technologies

Open Access
01-03-2025
Original Article

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Authors: Robert Görsch; Goda Perlaviciute; Linda Steg
Published in: Energy Efficiency | Issue 3/2025

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Abstract

The article examines the temporal stability of public acceptability of energy technologies, highlighting the importance of this stability for long-term policy decisions. It introduces a theoretical framework based on the Elaboration Likelihood Model (ELM) to explain differences in stability between novel and established technologies. The study presents two longitudinal surveys that compare the stability of acceptability judgements of established (wind and nuclear power) and novel (geothermal energy and CCS) technologies. Key factors influencing stability, such as knowledge, ambivalence, importance, and personal values, are investigated. The findings reveal that acceptability judgements of novel technologies are less stable and are influenced by factors like ambivalence and importance. The article also explores the relationship between stability of acceptability judgements and behavior towards energy technologies, providing practical implications for policymakers.

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Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1007/s12053-025-10305-5.

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Introduction

Transitioning from fossil fuels towards low-carbon and energy-efficient systems is central to mitigating climate change (IPCC, 2023). This shift involves implementing energy sources and technologies that would reduce GHG emissions (IEA, 2023). The implementation and use of these technologies hinges not only on economic, technical, and ecological factors, but also critically on public acceptability, because changes are less likely to be implemented if the public opposes them. We define public acceptability judgements as people’s positive or negative evaluations (or: attitudes) towards energy technologies, which might affect behavioral responses, such as voting or protest in favor or against policies¹ (Perlaviciute & Steg, 2014). Like all attitudes, acceptability judgements may change over time (Petty & Krosnick, 1995). Acceptability judgements and stability of acceptability judgements are conceptualized as continuous ranging from low (stability of) acceptability to high (stability of) acceptability. It is important to understand how stable acceptability judgements of energy technologies are, as decisions about implementing energy technologies have long-term consequences spanning years or even decades. If acceptability judgements about a technology are very unstable, then acceptability of energy technologies might shift during their implementation, potentially creating conflict over the technology. Next, understanding what factors make acceptability judgements more stable might inform interventions that can help to create a more reliable basis for decision-making on technologies. Therefore, the research questions that guide our work are: i) to what extent are acceptability judgements of different energy technologies stable over time, ii) which factors are related to the stability of acceptability judgements, and iii) is stability of acceptability judgements linked to the likelihood of engaging in related behavior, such as signing petitions or supporting political candidates and organizations?

Our paper is the first to investigate and compare the stability of acceptability judgments regarding different energy technologies over longer periods of time and without targeted interventions or contextual changes (including policies). We present two longitudinal survey experiments in which we compare the stability of acceptability judgements of established versus novel energy technologies, and examine which factors affect stability of acceptability judgements. Unlike previous research that focused mostly on short-term changes in acceptability judgements following interventions (Daamen et al., 2006; Mastop et al., 2014), our study examines acceptability judgements over longer timeframes and without purposely trying to influence acceptability judgements (e.g., via policy interventions). This avoids potential confounds in previous studies, in which brief measurement intervals or information provision might have introduced demand effects, prompting participants to adjust their responses to what they think might have been expected from them, for example by changing their acceptability judgments after an intervention or by trying to answer consistently when being asked the same questions twice. Moreover, we examine stability of acceptability judgements over periods that are more relevant for policymaking in real-world contexts, thereby increasing external validity. We further extend previous studies by introducing a theoretical framework that may explain any differences in stability of acceptability judgements, based on the elaboration likelihood model (ELM; Petty & Cacioppo, 1986) and research on attitude stability, offering new insights into the factors related to stability of acceptability judgements.

This paper is structured as follows: the next section presents the theoretical background of our study, indicating which factors are likely to influence stability of acceptability judgements. Further, we explain why stability of acceptability judgements may affect behavioral responses towards energy technologies. Next, we present the methods and results of Study 1 and Study 2, respectively, followed by a discussion of the results of both studies. The final section gives a conclusion of our research.

Factors influencing stability of energy technology acceptability judgements

While acceptability judgements regarding energy technologies may change through changes in the technology or society, or due to exposure to new or persuasive information, our research specifically focuses on changes that occur without any planned intervention (Petty & Krosnick, 1995). The ELM (Petty & Cacioppo, 1986) proposes that attitudes are likely to be more stable when they are formed through more elaboration, i.e., when people have thought more and more deeply about them. This implies that acceptability of energy technologies is more likely to be stable when people have thought more about them. Based on the ELM and literature on attitude stability,² we argue that the stability of acceptability judgements is likely to depend on four factors that we link to the elaboration underlying acceptability judgements: How novel the technology is, how much knowledge people have about it, how ambivalent their opinion on a technology is, and how important the technology is to them personally (see also Fig. 1). We will introduce each of them in detail below.

Fig. 1

Conceptual model of antecedents and consequences of stability of acceptability judgements

Full size image

Novelty of the energy technology

First, novelty of a technology may affect how stable acceptability judgements are. Novel technologies, such as deep geothermal energy and carbon capture and storage (CCS), are not yet widely used in Europe and globally, and therefore, the general public likely had limited exposure to them (European Geothermal Energy Council, 2023; Global CCS Institute, 2023). As a result, people have had less opportunities to engage with these technologies directly or via media and public discourse, resulting in lower familiarity of the technologies (Ashworth et al., 2013; Malo et al., 2015; Mastop et al., 2014; Merk et al., 2023; Whitmarsh et al., 2015). Based on the ELM (Petty & Cacioppo, 1986), we argue that when people form acceptability judgements about technologies, these can vary in depth of elaboration, which is likely to impact the stability of acceptability judgements. For novel technologies, people have had fewer opportunities for thinking carefully about their pros and cons. Thus, people likely form their acceptability judgements very spontaneously, relying more on heuristics and cues than on detailed elaboration (Daamen et al., 2006; Malone et al., 2010). This likely results in less stable acceptability judgements, as judgements based on less elaboration are more strongly affected by changing situational influences and cues, for instance the way questions are framed or the context in which they are asked (Converse, 1970; Malone et al., 2010).

In contrast, people’s acceptability judgements regarding established energy technologies such as wind or nuclear energy tend to be based on higher familiarity with the technology, its pros and cons, and more encounters with the technology through media or other exposure (Ashworth et al., 2013; Whitmarsh et al., 2015). Direct experience with an object has been shown to lead to more stable attitudes (Doll & Ajzen, 1992), probably as this has prompted deeper elaboration of beliefs about the technology. Elaboration also prepares individuals to counterargue against information that are not in line with their acceptability judgements, possibly stabilizing their acceptability judgements regarding technologies (cf. Holbrook et al., 2005; Krosnick, 1988). This suggests that people likely elaborated more on their acceptability judgements about established technologies based on their beliefs and experiences, possibly making these acceptability judgements more stable over time.

There is some initial evidence to suggest that acceptability judgements regarding novel energy technologies can be relatively unstable. A study by Mastop et al. (2014) compared acceptability of established technologies (i.e., wind and nuclear energy) with acceptability of less established technologies (CCS and CCS with biomass) before and after receiving balanced information about the consequences of each technology in a representative Dutch sample. Correlations between acceptability judgements regarding CCS before and after receiving information were lower than for acceptability judgements regarding wind or nuclear energy (\({r}_{CCS}\) = 0.36–0.37; \({r}_{wind}\)= 0.51; \({r}_{nuclear}\)= 0.63), suggesting that acceptability judgments regarding novel technology might be less stable than regarding established technologies (Mastop et al., 2014). Another study that tested the effect of information provision on CCS acceptability judgements showed that CCS acceptability judgements changed substantially within a very short time. Specifically, people’s evaluations of six CCS technologies at two time points twelve minutes apart had an average correlation of only r = 0.35 (Daamen et al., 2006). The study also showed that the provision of information on the usefulness of CCS increased its acceptability, while people who received an unrelated, slightly annoying task between the two ratings showed decreased acceptability of CCS. This susceptibility to persuasive information and situational influences can be seen as another indicator of unstable acceptability judgements. However, assessing acceptability judgements after information provision might evoke a demand effect that prompts participants to adjust their evaluations. Moreover, since the authors did not test how resistant the acceptability judgements towards other technologies are against new information, it is possible that information provision changes acceptability judgements also for established technologies (Bidwell, 2016; Feldman & Hart, 2018; Pidgeon et al., 2008). In addition, it is important to study how likely acceptability judgements change in absence of any intervention, such as information provision. Hence, we extend this initial research by investigating the temporal stability of acceptability judgements regarding energy technologies that differ in how novel they are without any intervention and over a longer period of time. Specifically, we will compare the stability of the acceptability judgements of two established technologies (wind energy and nuclear power) with the stability of acceptability judgements of deep-geothermal energy and CCS, which are not yet widely used in the Netherlands and thus rather novel to the public. This enables us to test our first hypothesis: Acceptability judgements regarding novel energy technologies like geothermal energy or CCS are less stable over time compared to acceptability judgements regarding established energy technologies like wind or nuclear energy (H1).

Knowledge about technologies

Second, knowledge about a technology might be linked to stability of acceptability judgements, and could explain why acceptability judgements regarding novel technologies is less stable compared to established technologies, thus acting as a mediator for the effects of novelty on stability of acceptability judgements. Knowledge about and awareness of novel energy technologies is lower compared to knowledge about more established technologies (DECC, 2016; Fischer et al., 2022; Leiss & Larkin, 2019; Malo et al., 2015). If people acquire more knowledge about a technology, this can help them to elaborate on the pros and cons of the technology, resulting in more stable acceptability judgements (cf. Petty & Cacioppo, 1986). Furthermore, acceptability of technologies people know more about tends to be better integrated with cognitions on how the technology works and beliefs about what positive and negative consequences it has, which may stabilize acceptability judgements.

The relationship between knowledge and stability of acceptability judgements regarding energy technologies (i.e., wind, nuclear, geothermal and CCS) has not been studied yet, to our knowledge. Therefore, we discuss the empirical findings regarding the stability of attitudes more generally. In contrast to theorizing that more knowledge is related to higher attitude stability, evidence is mixed. For example, a positive relationship was found between subjective knowledge (i.e., people’s self-assessed knowledge) and stability of attitudes toward Covid safety behaviors, where participants who knew more about safety behaviors had more stable attitudes (Conner et al., 2022b). Yet, in another study, subjective knowledge was not related to the stability of attitudes towards certain brands (Rocklage & Luttrell, 2021). Despite the mixed evidence from research on attitudes towards different issues, based on our reasoning above, we hypothesize that higher knowledge about a technology is associated with higher stability of acceptability judgements regarding that technology (H2). Moreover, we propose that knowledge mediates the influence of technology novelty on stability of acceptability judgements (H3).

Importance of technologies

People are more likely to have more stable attitudes about issues that are important to them (Conner et al., 2022b; Krosnick, 1988). Research suggests that people have higher motivation to think and elaborate more deeply about issues important to them, and that they integrate acceptability judgements about important issues more strongly into a larger cognitive network of related attitudes, beliefs, and values (cf. Holbrook et al., 2005; Krosnick, 1988). For instance, if an individual believes wind energy is very acceptable and wind energy is very important for them, then their acceptability judgement is likely stabilized through its connection with beliefs about the positive consequences of wind energy, few negative consequences, and related to their values or identity. Thus, acceptability judgements of this person would only change when their associated beliefs, values and identities change in the same direction as well, because conflicting beliefs or values would otherwise cause cognitive dissonance (Festinger & Carlsmith, 1959), which people are motivated to avoid. Additionally, individuals are more likely to seek out and form social connections with people who share their important attitudes (Krosnick, 1988), and the resulting shared social norm is likely to stabilize acceptability judgements. Furthermore, people are more likely to publicly commit to attitudes on issues they find important (Krosnick, 1988), strengthening their temporal stability through their need for consistency and avoidance of social disapproval.

So far, no studies investigated whether the perceived importance of a technology is related to the stability of acceptability judgements of that technology, but empirical evidence from research on other attitudes supports our theorizing. For example, people’s attitudes towards Covid safety behaviors were more stable if they considered Covid safety behaviors more important (Conner et al., 2022b). Similar results were found in a study on various US policy issues (Krosnick, 1988).

Perceived importance could also be a mediator that explains why acceptability judgements regarding novel technologies are less stable than acceptability judgements regarding more established ones. Research suggests that policy issues people are less familiar with are deemed less important (Prislin, 1996). Novel technologies, being less familiar, are less likely to be immediately recognized as important, affecting the stability of acceptability judgments. This is likely influenced by the main causes of importance, namely self-interest, social identification, and personal values (Boninger et al., 1995; Howe & Krosnick, 2017), which are more readily applied to established technologies. Self-interest increases the perceived importance of someone’s acceptability judgement because people tend to be more concerned with a technology if they perceive it as having positive or negative consequences for themselves or important others. This is more likely for established than for novel technologies, as for the latter, there has not been sufficient opportunity for in-depth consideration of their implications. In a similar vein, individuals may be more likely to identify with others based on their acceptability judgements regarding established technologies, because to associate with others, acceptability judgments have to exist and be known within a group. Given these insights, we hypothesize that higher perceived importance of an energy technology is associated with higher stability of acceptability judgements regarding the technology (H4), and that perceived importance of a technology mediates the relationship between technology novelty and stability of acceptability judgements regarding that technology (H5).

Ambivalence towards technologies

Acceptability judgements regarding an energy technology may be less stable if people feel conflicted in their evaluation of the technology. We refer to this as ambivalence of acceptability judgements, reflecting the degree to which positive and negative evaluations of the same technology are present simultaneously within a person (cf. Armitage, 2003). For example, an individual might feel positive about wind turbines because they produce clean energy, but also dislike the visual impact of the turbines on the landscape (Scheer et al., 2017; Warren et al., 2005), resulting in the feeling of ambivalence towards wind energy.

Ambivalent acceptability judgements may be less stable over time for two reasons. First, ambivalent acceptability judgements might be more sensitive to contextual influences, because cues in a situation might make either positive or negative aspects about the technology more salient (Jonas et al., 2000). For instance, encountering a dead bird close to a wind park might make an ambivalent acceptability judgement about wind energy more negative, as the person gets reminded of the danger that wind turbines pose to birds, while a hot day might make the person’s acceptability judgement more positive because it reminds them of the positive impact wind energy has on climate change. Moreover, the experience of ambivalence can create feelings of being conflicted and uncertain about the technology, which can make people more prone to change their acceptability judgements over time to reduce the unpleasant feeling of cognitive dissonance (Festinger & Carlsmith, 1959). Indeed, higher ambivalence about Covid protection behaviors has been found to be linked to less stable attitudes towards these behaviors (Conner et al., 2022b).

Ambivalence could also be another mediator of the link between novelty of an energy technology and lower stability of acceptability judgements. If people have not yet encountered a technology, they might be more ambivalent and less certain about their evaluation of pros and cons. In contrast, people who elaborated a lot on their acceptability judgements towards a technology might have bolstered their own position with beliefs consistent with those acceptability judgements, as attitudes have been shown to guide congruent information seeking and information processing (Hart et al., 2009; Munro & Ditto, 1997). Indeed, people were more ambivalent about Covid safety behaviors if this topic was more novel for them (Conner et al., 2022b). Thus, we propose that higher ambivalence of acceptability judgements is associated with lower stability of acceptability (H6) and that ambivalence mediates the relationship between technology novelty and stability of acceptability judgements (H7).

Personal values

Values are transsituational goals that serve as a guiding principle in a person’s life (Schwartz, 1992). Values might make acceptability judgements regarding energy technologies more stable because they can influence how people evaluate various aspects of the technology and how people process information. Indeed, people evaluate energy technologies that align with their values as more acceptable, and technologies that threaten their values as less acceptable (Perlaviciute & Steg, 2015; Perlaviciute et al., 2021). Given that values are seen as relatively stable (Stern, 2000; Vecchione et al., 2016), once they are linked to acceptability judgements, they might make those acceptability judgements more stable as well. In a set of studies on the stability of attitudes towards various issues ranging from alcohol to gun control, individuals indeed showed more stable attitudes if they thought that these attitudes are a reflection of their core moral beliefs and convictions (Luttrell & Togans, 2021). Therefore, we propose that acceptability judgements are more stable when they are more strongly associated with individuals’ values (H8).

Stability of acceptability judgements and behavior

The next question is how stability of acceptability judgements affects citizenship behaviors towards energy technologies that can be seen as expressions of people’s acceptability judgements, such as voting or demonstrating in favor or against the relevant technology. Acceptability judgements that are more stable could be better predictors of such citizenship behaviors than unstable acceptability judgements because they affect behaviors more consistently (Petty & Krosnick, 1995). Stable acceptability judgements are likely to be more accessible and rehearsed in an individual’s mind (cf. Doll & Ajzen, 1992; Fazio, 1995), which increases the likelihood that they are translated into action when relevant situations arise.

So far, no studies investigated the link of stability of acceptability judgements and behaviors towards technologies, but research on other attitudes provide some support for our arguments. Attitudes have been shown to be better predictors of behavior such as Covid safety behaviors and volunteering if they are stable (Conner et al., 2022a, b; Glasman & Albarracín, 2006; Schwartz, 1978). In addition, some meta-analytic evidence found that the more stable a person’s attitude, the better attitudes predict related behavior (Cooke & Sheeran, 2004; Glasman & Albarracín, 2006; Kraus, 1995). While suggestive, these meta-analyses were based on a limited number of original studies, none of which dealt with acceptability judgements regarding energy technologies. Therefore, we extend previous work by testing if the stability of acceptability judgements moderates the relationship between acceptability judgements regarding energy technologies and related behavior. Thus, we argue that the more stable peoples’ acceptability judgements, the more likely people will act according to their acceptability judgements (H9). Figure 1 shows our conceptual model that summarizes our hypotheses on variables predicting the stability of acceptability judgements and the effects of stability of acceptability on behavior.

Study objectives and design

We tested our reasoning in two longitudinal studies that compared the stability of acceptability judgements regarding novel (Study 1: geothermal energy and CCS; Study 2: geothermal energy) and established (Study 1: wind and nuclear power, Study 2: nuclear power) technologies in the Netherlands. To combat climate change, the Dutch government aims to reduce the Netherlands' greenhouse gas emissions by 49% by 2030, compared to 1990 levels, and by 95% till 2050. Geothermal energy, CCS, wind and nuclear power all play a role in the Dutch Energy and Climate Plan 2021–2030 (Ministerie van Economische Zaken en Klimaat, 2019) and are thus relevant to Dutch citizens, which makes public acceptability of these energy technologies crucial.

In both studies, we next tested whether acceptability judgements are more stable when people have higher knowledge (subjective knowledge in both studies as well as objective knowledge in Study 2) about the technology, when they evaluate the technology as more important, and when their acceptability judgements are less ambivalent. In addition, we investigate if acceptability judgements are more stable the more it is rooted in people’s values. Moreover, we investigate if acceptability judgements predict citizenship behaviors related to energy technologies better when acceptability judgements are more stable. Specifically, in both studies, participants are asked to sign a (hypothetical) petition about stopping the increased use of the relevant energy technology in the Netherlands. In Study 2 we additionally ask participants to indicate their willingness to support each energy technology through different citizenship behaviors (e.g., voting for politician who expresses the same opinion about a technology as oneself, donating money to a political group that advocates for the technology).

Study 1

Method

Participants and Procedure

Data for this study were collected via an online questionnaire using the software Qualtrics (Qualtrics, Provo, UT) and was distributed in Dutch and English through an online platform for first year psychology students of the University of Groningen. Responses of both language versions were pooled after data collection. Students received course credit for their participation. Data were collected online with two measurement time points (first survey = T1, second survey = T2) between October 2021 till April 2022. The mean time span between participating in T1 and T2 was approximately four and a half months (M = 131.9 days, SD = 42.42). We employed a within-subjects experimental design in which each participant evaluated four technologies, namely wind power, nuclear power, geothermal energy and CCS, in both waves. Wind and nuclear energy were selected to represent established technologies that we expected participants to be relatively familiar with, while geothermal energy and CCS were assumed to be novel and rather unfamiliar to the participants (Ashworth et al., 2013; Malo et al., 2015; Merk et al., 2023).

At both times, participants first provided informed consent. At T1, participants then filled in the personal values measure.³ Next, at both T1 and T2, participants were asked to indicate to what extend they find each technology acceptable. The order of the technologies was randomized to control for potential order effects. At T2, after rating each technology, participants next filled in the measures of ambivalence about the technology, subjective knowledge about the technology, and personal importance of each technology. Moreover, participants were introduced to a petition that urged the Dutch government to limit the use of the respective technology in the Netherlands. After reading the petition, participants indicated their support for the petition. Both surveys ended by giving participants the opportunity to make comments on the study, before thanking and debriefing them. The median completion time of T1 was 8.80 min and of T2 6.65 min.

In total, 482 participants completed the first questionnaire (T1), of which we excluded 15 participants (3%) who did not give their consent to participate and two (0.4%) for failing an attention check.⁴ Of the remaining 467 participants, 216 gave consent and completed the second questionnaire (T2, 46.3%). After excluding eleven participants for failing the attention check, our final sample includes 205 participants..⁵ In our sample, 149 participants identified as female (72.7%), 55 as male (26.8%), and 1 (0.5%) chose the option ‘other’ to indicate their gender. The mean age of the participants was 20 years (SD = 2.3). The ethical review board of the University of Groningen approved the research. The preregistration of this study, the questionnaires, anonymized datasets, and analysis code, can be found on our OSF project page https://osf.io/dvgfz/.

Measures

Acceptability

We assessed acceptability judgements regarding each technology in both survey waves by asking the participants to complete the statement ‘[technology] is…’ on three seven-point semantic differential scales (1 = very unacceptable – 7 = very acceptable; 1 = very unnecessary – 7 = very necessary; 1 = very bad – 7 = very good) (Perlaviciute et al., 2021). We computed mean scores of the three items that formed reliable scales for all technologies in both waves (Cronbach’s α = 0.83–0.94). Table 1 contains means, standard deviations, range, and internal consistency of all measures.

Table 1

Means, standard deviations, minimum and maximum scores, and reliability (Cronbach’s Alpha or r) of measures (study 1)

Variable	Mean	SD	Min	Max	Cronbach’s α	r
Acceptability wind T1	6.07	0.99	2.00	7.00	0.85
Acceptability nuclear T1	3.79	1.67	1.00	7.00	0.92
Acceptability geothermal T1	4.93	1.54	1.00	7.00	0.94
Acceptability CCS T1	4.48	1.31	1.00	7.00	0.91
Acceptability wind T2	6.10	0.97	1.00	7.00	0.83
Acceptability nuclear T2	3.91	1.59	1.00	7.00	0.91
Acceptability geothermal T2	4.96	1.18	1.00	7.00	0.89
Acceptability CCS T2	4.40	1.16	1.33	7.00	0.88
Stability of wind acceptability	5.35	0.77	0.00	6.00
Stability of nuclear acceptability	5.23	0.75	1.33	6.00
Stability of geothermal acceptability	4.92	0.98	1.33	6.00
Stability of CCS acceptability	4.95	1.05	1.00	6.00
Subjective knowledge wind	6.66^a	1.84	2.50	10.00		0.59
Subjective knowledge nuclear	5.44^b	2.09	0.00	10.00		0.68
Subjective knowledge geothermal	3.23^c	2.49	0.00	9.20		0.79
Subjective knowledge CCS	2.65^d	2.20	0.00	8.25		0.76
Ambivalence wind	2.66^a	1.33	1.00	7.00	0.80
Ambivalence nuclear	4.05^b	1.53	1.00	7.00	0.82
Ambivalence geothermal	3.82^bc	1.32	1.00	7.00	0.76
Ambivalence CCS	4.20^bd	1.18	1.00	6.67	0.64
Importance wind	4.48^a	1.33	1.00	7.00		0.57
Importance nuclear	3.99^b	1.41	1.00	7.00		0.52
Importance geothermal	3.34^c	1.27	1.00	7.00		0.51
Importance CCS	3.26^c	1.25	1.00	6.00		0.57
Altruistic values	5.53	0.96	1.25	7.00	0.64
Biospheric values	4.71	1.36	1.25	7.00	0.85
Egoistic values	2.40	1.07	−0.40	5.00	0.62
Hedonic values	5.15	1.29	1.33	7.00	0.83

We tested if subjective knowledge, ambivalence, and importance significantly differ between technologies, tested via follow-up contrasts using Tukey HSD. Means with different superscripts (a, b, c or d) differ statistically significantly from each other. If two technologies do not share the same superscript letter, this means their mean scores are significantly different, p < 0.05, two-sided

We report Cronbach’s alpha as a measure of internal consistency of our multi-item scales. For scales consisting of only two items, the correlation coefficient r is given instead of Cronbach’s α

T1 and T2 refer to the first and second survey wave in which acceptability judgements were measured

Stability of acceptability judgements

Stability of acceptability judgements over time was assessed by calculating the absolute difference between participant’s acceptability judgements at T1 and T2. We subtracted the difference score from a constant so that a higher score represents higher stability. This measure follows common practice and allows to quantify not only if, but also to which degree acceptability changed (Conner et al., 2022a; Rocklage & Luttrell, 2021; Schwartz, 1978). Moreover, this measure of stability has been shown to be highly correlated with other attitude stability measures (Campbell, 1990; Conner et al., 2000, 2022a, b).

Petition support

We measured participant’s willingness to sign a petition urging the Dutch government to prevent further implementation of the four mentioned technologies: wind energy, nuclear energy, deep-geothermal energy, and CCS. To this end, we constructed one petition per technology (see Supplementary Materials). The four petitions were worded almost identically, except for the name of the technology and small adjustments in the CCS petition, as CCS is not an energy source. The petitions claimed that the given technology is harmful and any new implementation should be stopped. The petitions were designed to look like actual petitions that can be found on online petition websites such as change.org or petities.nl. Participants were asked to indicate if they would like to sign this petition against the technology with the two answer options 0 = No, I would NOT like to sign the petition and 1 = Yes, I would like to sign the petition.

Subjective knowledge

We constructed a three-item measure of subjective knowledge based on previous studies (Berger et al., 1994; Bidwell, 2016; Priester & Petty, 1996; Smith et al., 2008), i.e., ‘I know a lot about [technology]’, ‘I understand what [technology] is’, and ‘I don’t really know how [technology] works’ (reversed). Participants indicated their agreement with each statement using an eleven point slider ranging from 0 = strongly disagree to 10 = strongly agree). Reliability analysis suggested that internal consistency was somewhat reduced by the third item, thus we decided to drop it from the scale. The remaining items were highly correlated for all technologies, thus formed a reliable scale (r = 0.59–0.79).

Ambivalence

We measured ambivalence about the technology by asking participants how they felt when thinking about their opinions regarding a respective technology. We calculated the mean score of three seven-point semantic differential scales (1 = I feel no conflict at all – 7 = I feel very conflicted; 1 = I feel very decisive – 7 = I feel not decisive at all; 1 = I have a completely one-sided reaction – 7 = I have a completely mixed reaction). This ambivalence measure was taken from previous studies (Luttrell et al., 2016; Priester & Petty, 1996; Wallace et al., 2020) and showed sufficient internal consistency for all technologies (Cronbach’s α = 0.64–0.82).

Importance

Perceived personal importance of the technology was measured with three items per technology. Participants were asked how much they agreed with each statement on a seven-point scale (1 = strongly disagree to 7 = strongly agree): ‘I feel very strongly about [technology]’, ‘The issue of [technology] is very important to me’, and ‘I don't care if the government has the same position on [technology] as myself’ (reverse coded). These items were based on measures from earlier studies (Krosnick et al., 1993; Visser et al., 2003). Reliability analysis suggested that internal consistency was somewhat reduced by the third item, thus we decided to drop it from the scale. The remaining items were highly correlated for all technologies (r = 0.51–0.57).

Personal values

We used an established and validated value scale (Steg et al., 2014) that contains 16 items (four items each for altruistic and biospheric values, five items for egoistic values and three items for hedonic values), where participants receive a list of values with short descriptions and rate the importance of each value as a guiding principle in their life on a 9-point scale ranging from −1 = opposed to my guiding principles, 0 = not important, to 7 = extremely important. The importance ratings of the respective items were averaged to form reliable composite scales of altruistic, biospheric, egoistic, and hedonic values (Cronbach’s α = 0.62–0.85).

Analysis plan

To investigate whether acceptability judgements regarding established technologies are more stable than acceptability judgements regarding novel technologies (H1), we used a multilevel model analysis with two levels to account for the repeated measures structure of our data (Snijders & Bosker, 2012). In our model, technologies (level 1) are nested within participants (level 2). Here, we tested if the mean stability of acceptability judgements differs between novel and established technologies using planned contrasts (wind and nuclear energy vs. geothermal energy and CCS). In the next step, to test whether subjective knowledge, importance, and ambivalence regarding the technology relate to how stable acceptability judgements are (H2, H4 and H6), those variables were added to the model as predictors on level 1. The multilevel models used fixed slopes and variables were grand-mean centered. For each predictor that was associated with stability of acceptability judgements and differed statistically significantly between novel and established technologies, we then tested if they mediate the effects of technology novelty on stability of acceptability judgements (H3, H5, H7) using parallel mediation following Montoya and Hayes (2017) procedure for estimating mediation models for within-subjects designs. Bootstrap confidence intervals (10.000 bootstrap samples) were used to establish the existence of mediation effects. Next, we tested the relationship of personal values with stability of acceptability judgements (H8) in four separate stepwise multiple regressions. Specifically, we tested whether the relationship between acceptability judgements at T1 and T2 weakens when we control for personal values. Lastly, we tested if more stable acceptability judgements will lead people to behave more in accordance with their acceptability judgements by engaging in related citizenship behaviors (H9). We ran four logistic regressions (one for each technology) and regressed petition support onto technology acceptability at T2, stability of acceptability judgements, and the interaction between acceptability judgements at T2 and stability of acceptability judgements. To avoid issues of collinearity, acceptability and stability scores were centered before the analysis. Most analyses were done in RStudio, version 4.2.0, using the packages lmerTest (Kuznetsova et al., 2017) for multilevel modelling and MASS (Venables & Ripley, 2002) for data transformation. Within-subject mediation was calculated with the MEMORE macro in SPSS (Montoya & Hayes, 2017).

Results study 1

Descriptive statistics

Figures 3, 4, 5 and 6 in the Appendix depict scatterplots that show how acceptability judgements of each technology changed from T1 to T2 for each participant. It can be seen that acceptability of wind energy is very high, while acceptability of the other technologies are lower and more varied. More importantly, for most participants, acceptability judgements of established technologies only slightly changed and most individuals remained either rather negative or positive about the technology. In contrast, for novel technologies, larger changes in acceptability judgements can be observed, suggesting for some people, acceptability judgments changed from positive to negative or vice versa.

Tables 9, 10, 11 and 12 in the Appendix show correlations of study variables for wind energy, nuclear energy, geothermal energy and CCS. Higher stability of acceptability judgements was correlated with lower ambivalence in case of wind and nuclear energy (p < 0.01), and higher perceived importance in case of wind and geothermal energy (p < 0.01). No other variables were significantly correlated with stability of acceptability judgements (all p > 0.05).

Differences in knowledge, importance, and ambivalence between novel and established technologies

To test whether the level of knowledge about a technology, personal importance of the technology, and ambivalence towards a technology differs across novel and established technologies, we tested three multilevel models to regress subjective knowledge (Model 1), personal importance (Model 2) and ambivalence (Model 3) on the contrast between established (nuclear, wind) and novel (geothermal, CCS) technologies (Tables 13, 14 and 15 in Appendix). As predicted, participants indicated to have more knowledge about established technologies than about novel technologies, b = −3.11, p < 0.001, R² = 0.331, found established technologies more important than novel technologies b = −0.94, p < 0.001, R² = 0.111, and were less ambivalent about established technologies b = 0.65, p < 0.001, R² = 0.049 compared to novel technologies. Results of post-hoc tests using Tukey’s HSD are shown in Table 1.

Predictors of stability of energy technology acceptability judgements

Table 2 reports the results of the multilevel regression analyses of the associations between stability of acceptability judgements and its predictors: technology novelty, subjective knowledge, ambivalence, and importance. As can be seen in Model 1, stability of acceptability judgements is significantly higher for established than for novel technologies (b = −0.35, p < 0.001, R² = 0.037). Post-hoc tests using Tukey’s HSD indicated that acceptability judgements regarding wind were more stable (M = 5.35) than acceptability judgements regarding geothermal energy (M = 4.92, b = 0.42, p < 0.001) and CCS (M = 4.95, b = 0.40, p < 0.001). Also, acceptability judgements regarding nuclear energy were more stable (M = 5.35) than acceptability judgements regarding geothermal energy (b = 0.31, p = 0.002) and CCS (b = 0.28, p = 0.006), while stability of acceptability judgements did not significantly differ between wind and nuclear energy (b = −0.12, p = 0.49) and between geothermal energy and CCS (b = −0.03, p = 0.99).

Table 2

Multilevel regression model for stability of acceptability judgements (study 1)

	Model 1		Model 2
	Estimate	(SE)	Estimate	(SE)
Fixed effects
Intercept	5.11	(0.04)	5.11	(0.04)
Level 1 variables
Novelty of technology	−0.35***	(0.06)	−0.32***	(0.08)
Level 2 variables
Subjective knowledge			−0.03	(0.02)
Importance			0.07**	(0.03)
Ambivalence			−0.06*	(0.02)
Random effects
Level-2 variance \({\tau }_{0}^{2}\)	0.08		0.08
Level-1 variance σ²	0.73		0.71
R²	3.7%		5.7%
Deviance	2145.7		2146.9

N = 205. Novelty of technology is coded 0 = established technologies 1 = novel technologies

For level 1 and 2 variables: *p < 0.05, **p < 0.01, ***p < 0.001, two-tailed

Model 2 depicts the final model with subjective knowledge, ambivalence, and importance added as predictors of stability of acceptability judgements. As expected, stability was higher the more personal importance people attached to the technology (b = 0.07, p = 0.007) and the less ambivalent they felt about it (b = −0.07, p = 0.018). Contrary to our expectation, subjective knowledge was not significantly associated with stability of acceptability judgements (b = −0.03, p = 0.13). Together, the predictors explain 5.7% of variance in stability of acceptability judgements.

We ran within-subjects mediation analysis to test if the novelty of a technology affects stability of acceptability judgements via ambivalence and importance of technologies (Fig. 2). Knowledge was excluded from the mediation model due to the lack of association with stability of acceptability judgements in the previous analysis. Contrary to our expectations, no evidence of a mediation effect was found, as all 95% bootstrap CI of the indirect effects of technology novelty on stability of acceptability judgements contained 0. The total effect of technology novelty on stability was c = −0.35, p < 0.001 and the direct effect of novelty on stability c’ = −0.30, p < 0.001, indicating that stability of acceptability judgements is higher for established than for novel technologies even after controlling for ambivalence and importance. Noteworthy, ambivalence and importance were significant predictors of stability in our multilevel model but not in the mediation analysis. This might be due to the fact that the multilevel analysis shows that in general (between and within-participants), acceptability judgements are more stable the more important and the less ambivalent they are, while the mediation specifically tests if people’s within-subjects difference in stability between novel and established technologies is explained by how much novel and established technologies differ in importance and ambivalence for that individual (Montoya & Hayes, 2017).

Fig. 2

Mediation model to explain stability of acceptability judgements (study 1). Note. Novelty of technology is coded 0 = established technologies; 1 = novel technologies.*p < 0.05, **p < 0.01, ***p < 0.001, two-tailed; N = 205

Full size image

Relationship between personal values and stability of acceptability judgements

Next, we tested the relationship of personal values with stability of acceptability judgements (H8) in four separate stepwise multiple regressions (Table 3). Specifically, we tested whether the relationship between acceptability judgements at T1 and T2 (step 1) weakens when we control for personal values (step 2). Acceptability judgements at T1 were positively related to acceptability judgements at T2 for all four technologies (all p > 0.001). Interestingly, T1 acceptability judgements explained most variance in T2 acceptability for nuclear energy (R² = 0.592), followed by wind (R² = 0.217) and geothermal energy (R² = 0.211), while T1 acceptability judgements regarding CCS only explained 7.4% of the variation in acceptability judgements regarding CCS at T2. This supports our finding that acceptability judgements are more stable for established than for novel technologies. Adding altruistic, biospheric, egoistic and hedonic values to the model in step 2 did not, however, lead to a significant improvement in model fit (all p > 0.9) and the relationship between acceptability judgements at T1 and T2 remained significant and practically unchanged for all technologies, suggesting that values were not significantly related to stability of acceptability judgements.

Table 3

Multiple regression models of acceptability judgements at time 1 and personal values predicting acceptability judgements at time 2 (study 1)

	B	95% CI for B		R²adj	p
		LL	UL
Nuclear energy
Step 1				0.592	< 0.001
Intercept	1.12	0.78	1.46		< 0.001
Acceptability T1	0.73	0.65	0.82		< 0.001
Step 2				0.588	< 0.001
Intercept	0.69	−0.41	1.78		< 0.001
Acceptability T1	0.75	0.66	0.84		< 0.001
Altruistic values	0.07	−0.10	0.23		0.432
Biospheric values	0.02	−0.09	0.14		0.701
Egoistic values	−0.05	−0.18	0.09		0.478
Hedonic values	0.01	−0.11	0.11		0.935
Wind energy
Step 1				0.217	< 0.001
Intercept	3.32	2.61	4.04		< 0.001
Acceptability T1	0.46	0.34	0.58		< 0.001
Step 2				0.212	< 0.001
Intercept	2.72	1.66	3.78		< 0.001
Acceptability T1	0.46	0.34	0.57		< 0.001
Altruistic values	0.10	−0.03	0.24		0.130
Biospheric values	−0.03	−0.12	0.07		0.592
Egoistic values	0.00	−0.11	0.11		0.966
Hedonic values	0.05	−0.06	0.12		0.450
Geothermal energy
Step 1				0.211	< 0.001
Intercept	3.31	2.73	3.68		< 0.001
Acceptability T1	0.36	0.26	0.45		< 0.001
Step 2				0.200	< 0.001
Intercept	3.36	2.17	4.55		< 0.001
Acceptability T1	0.35	0.26	0.45		< 0.001
Altruistic values	−0.01	−0.18	0.16		0.913
Biospheric values	0.00	−0.12	0.12		0.985
Egoistic values	0.06	−0.08	0.19		0.423
Hedonic values	−0.04	−0.16	0.07		0.450
CCS
Step 1				0.074	< 0.001
Intercept	3.29	2.76	3.83		< 0.001
Acceptability T1	0.25	0.13	0.36		< 0.001
Step 2				0.074	< 0.001
Intercept	2.63	1.38	3.88		< 0.001
Acceptability T1	0.26	0.14	0.38		< 0.001
Altruistic values	−0.01	−0.19	0.17		0.942
Biospheric values	0.00	−0.13	0.13		0.982
Egoistic values	0.05	−0.10	0.19		0.515
Hedonic values	0.10	−0.02	0.22		0.097

CI confidence interval, LL lower limit, UL upper limit

Relationship between stability of acceptability judgements and behavior

We ran four multiple regression models, one for each technology, to investigate if more stable acceptability judgements were more predictive of signing a petition against a technology than unstable acceptability judgements (see Table 4). In each regression model, we included acceptability at T2, stability of acceptability judgements, and their interaction as predictors. For each technology except wind energy, we found significant main effects of acceptability, p < 0.01, indicating that individuals reporting lower acceptability were more inclined to sign a petition against the respective technology. Yet, the main effect of stability of acceptability judgements on petition support was not statistically significant, except for CCS, where a negative association between stability of acceptability judgements and petition support was observed, β = −0.44, p = 0.008. Contrary to our expectations, the interaction between acceptability at T2 and stability was not significant for any of the technologies (all p > 0.17), yielding no support for our hypothesis that more negative acceptability judgements would increase the likelihood of signing the petition when acceptability judgements were more stable.

Table 4

Logistic regressions for signing petition (Study 1)

	Estimate (b)	SE	z	p	R²
Nuclear
Intercept	−0.73	0.20	−3.61	< 0.001
Acceptability T2	−1.26	0.18	−7.20	< 0.001
Stability	0.10	0.25	0.38	0.705
Acceptability T2*Stability	−0.26	0.19	−1.37	0.171
					0.530
Wind
Intercept	−3.26	0.40	−8.23	< 0.001
Acceptability T2	−0.37	0.37	−1.00	0.320
Stability	0.48	0.77	0.62	0.533
Acceptability T2*Stability	0.33	0.28	1.16	0.245
					0.134
Geothermal
Intercept	−2.73	0.35	−7.89	< 0.001
Acceptability T2	−1.10	0.30	−3.70	< 0.001
Stability	0.18	0.39	0.47	0.640
Acceptability T2*Stability	0.27	0.32	0.85	0.393
					0.244
CCS
Intercept	−1.72	0.21	−8.08	< 0.001
Acceptability T2	−0.55	0.20	−2.61	0.009
Stability	−0.44	0.17	−2.66	0.008
Acceptability T2*Stability	−0.15	0.13	−1.11	0.265
					0.107

Dependent variable was coded so that positive b values indicate a variable is associated with a higher likelihood to sign the petition and negative b values indicate lower likelihood to sign the petition. R² was calculated as Nagelkerke’s R², a common measure of model fit for logistic regression

Study 2

We conducted Study 2 to replicate and extend the findings of Study 1 in a general population sample. For Study 2, in addition to measuring people’s subjective knowledge, we also include a measure of objective knowledge, i.e., people’s knowledge of facts on a technology. Subjective and objective knowledge do not always overlap (Carlson et al., 2009; Radecki & Jaccard, 1995). Objective knowledge might be a more important predictor of stability, as actually knowing more about a technology tends to be associated with higher cognitive elaboration of attitudes, which is a process by which attitudes are likely to become more stable (Conner et al., 2022b; Haugtvedt & Petty, 1992). On the contrary, subjective knowledge has been found to be associated with less information seeking (Radecki & Jaccard, 1995). This could prevent deeper elaboration of acceptability judgements, and thus inhibit the formation of more stable acceptability judgements.

Moreover, in Study 1 we measured subjective knowledge, importance and ambivalence regarding each technology at T2 after assessing acceptability judgements, which means that we could test whether they are related to past, but not future, stability of acceptability judgements. Further, in Study 2 we measured all potential predictors of temporal stability at T1 to test whether these factors predict future stability of acceptability judgements.

Method

Participants and Procedure

Data were collected online with two measurement time points (first survey = T1, second survey = T2) via a panel provider Panel Inzicht (https://panelinzicht.nl/) from September till December 2023. The mean time span between participating in T1 and T2 was 59.1 days (SD = 8.99). We aimed to collect data from at least 400 participants with complete and valid data sets after accounting for attrition. Due to the longitudinal character of our study, we aimed to recruit 600 participants at T1, because we expected a dropout rate of approximately 30–35% based on our previous experiences with attrition in longitudinal research. We used a within-subjects experimental design in which each participant evaluated nuclear and geothermal energy, in both waves. We narrowed the focus in Study 2 to two relevant energy technologies instead of four to address potential participant burden from having to answer the same questions for four technologies.

The study procedure was very similar to Study 1. In the first wave, participants were asked to provide informed consent and demographic data, after which they filled in the personal values measure. Next, both at T1 and T2, participants were presented with a brief neutral description of each technology (see Supplementary Materials) in randomized order and rated its acceptability. We added technology descriptions because in Study 1, some participants reported that they were very unfamiliar with some of the novel technologies and looked up some information while answering the survey. Therefore, we introduced each technology with a brief and neutral description of how the technology works, but we did not add any information about advantages and disadvantages of the technology to avoid influencing participants’ acceptability judgements. We consulted technical experts about nuclear and geothermal energy to check our descriptions for accuracy and unbiased presentation of the information. Next, we assessed subjective knowledge, objective knowledge, technology importance, ambivalence, and personal values. At T2, after rating a technology, participants were asked to indicate their willingness to engage in citizenship behaviors such as voting or demonstrating to express what they think about the technology. Then, participants were offered to sign a petition that urged the Dutch government to limit the use of the respective technology in the Netherlands. Lastly, at T1 and T2, participants were asked to briefly describe the topic of the questionnaire and whether they answered all questions truthfully, after which they were thanked, and debriefed. The median completion time of T1 was 7.4 min and of T2 2.8 min.

We employed quotas to recruit a T1 sample that is representative of the adult Dutch population in terms of age, gender, and education. In total, 667 participants filled in the first questionnaire (T1). Fifty-seven (8.5%) failed the data quality test and were thus excluded. The data quality test involved passing at least three out of four checks, namely reasonable response times (> 1/4 of the median survey duration),⁶ no straight-lining, confirming that all questions were truthfully answered, and giving a sensible answer to an open question about the topic of the survey. Thirteen (2.1%) participants did not complete one or both acceptability measures and were thus excluded. Of the remaining 597 participants, 420 gave consent and completed the second questionnaire (T2, 70.4%). After excluding 24 (5.6%) participants for failing the same data quality test as in T1, our final sample are 396 participants. Participant demographics of our final sample are presented in Table 5.

Table 5

Sample description study 2

	Count	%
Gender
Female	216	54.5
Male	180	45.5
Age
18–24	34	8.6
25–34	60	15.2
35–44	66	16.7
45–54	63	15.9
55–64	68	17.2
65 +	105	26.5
Education
Primary	12	3.0
Secondary	95	24.0
Vocational	159	40.2
Bachelor	85	21.5
Master or higher	45	11.4

Respondents received a small monetary compensation for completing the study. Given no major ethical risks were anticipated, the study followed the fast-track ethics procedure where the researchers check the study against a set of criteria set by the ethics committee of the university. We registered relevant research documents (research plan, data management plan, participant information forms, consent forms) prior to the start of the study. The preregistration of this study with a complete overview of deviations from the pre-registration, the questionnaires, anonymized datasets, and analysis code, can be found on our OSF project page https://osf.io/dvgfz/.

Measures

Citizenship behavior

Extending Study 1, we employed two different measures of citizenship behavior. First, we measured people’s willingness to engage in citizenship behavior to express what they think about nuclear or geothermal energy. We used four items reflecting environmental activism (adapted from Steg et al., 2011). Participants read “Below you find a number of things people could do to show how they feel about [technology]. Would you be willing to do any of these things?” The items were: “I would vote for a political party that has the same opinion on [technology] as I do.”, “I would donate money to an organization that expresses what I think about [technology]”, “I would become a member of an organization that expresses what I think about [technology]”, and “I would join a demonstration to expresses what I think about [technology]”. Respondents answered the items on a Likert scale ranging from 1 = totally disagree to 5 = totally agree. The items formed reliable scales for both technologies (Cronbach’s α = 0.77–0.80). Second, we used petitions similar to those in Study 1, but we modified them so that they reflect actual policy plans discussed by policymakers, the media or other stakeholders in the Netherlands to make them more realistic and credible. Both petitions urged the Dutch government to stop increasing the use of the technology. Participants were asked to indicate if they would like to sign this petition against the technology with the two answer options Yes, I would like to sign the petition and No, I would NOT like to sign the petition. The petitions can be found in the Supplementary Materials. Table 6 contains means, standard deviations, range and internal consistency of all measures.

Table 6

Means, standard deviations, minimum and maximum scores, and reliability (Cronbach’s Alpha or r) of measures (study 2)

Variable	Mean	SD	Min	Max	Cronbach’s α	r
Acceptability nuclear T1	4.54	1.58	1.00	7.00	0.93
Acceptability geothermal T1	4.81	1.43	1.00	7.00	0.93
Acceptability nuclear T2	4.80	1.57	1.00	7.00	0.94
Acceptability geothermal T2	5.08	1.30	1.00	7.00	0.92
Stability of nuclear acceptability	5.19	0.94	0.00	6.00
Stability of geothermal acceptability	5.01	0.97	0.00	6.00
Citizenship behavior willingness nuclear	2.63	0.85	1.00	5.00	0.77
Citizenship behavior willingness geothermal	2.55	0.86	1.00	5.00	0.80
Subjective knowledge nuclear	5.05	2.42	0.00	10.00		0.73
Subjective knowledge geothermal	4.45	1.70	0.00	9.87		0.67
Objective knowledge nuclear	4.33	1.78	0.00	7.00	0.62
Objective knowledge geothermal	2.05	1.14	1.00	4.00	0.34
Ambivalence nuclear	3.70	1.45	1.00	7.00	0.82
Ambivalence geothermal	3.82	1.31	1.00	7.00	0.76
Importance nuclear	4.26	1.32	1.00	7.00		0.65
Importance geothermal	3.80	1.22	1.00	7.00		0.68
Altruistic values	4.64	1.48	−1.00	7.00	0.83
Biospheric values	4.49	1.67	−1.00	7.00	0.93
Egoistic values	2.28	1.67	−1.00	7.00	0.86
Hedonic values	4.85	1.55	−1.00	7.00	0.91

We used Cronbach’s alpha as a measure of internal consistency of our multi-item scales. For scales consisting of only two items, the correlation coefficient r is given instead of Cronbach’s α. T1 and T2 refer to the first and second measurement of acceptability judgements

Objective knowledge

We assessed objective knowledge about nuclear and geothermal energy. We consulted technical experts about both energy sources to validate our items and modified our initial items based on their feedback. Additionally, we conducted a pre-test (N = 11) amongst our colleagues to check for item clarity and distribution of responses to ensure that each scale has enough variance and items within each scale differ in their difficulty. We included seven knowledge items per technology. Four items per scale were multiple choice items (four response options). The other three items contained statements, which participants had to rate as either true or false, e.g., “The electricity production of geothermal power plants is relatively stable over the course of the day”. Due to the low associations with the overall scale, we decided to drop 3 items in the geothermal energy knowledge scale. We employed the same measures as in Study 1 for acceptability, subjective knowledge, ambivalence, importance, and personal values.

Analysis plan

We followed the same analytical strategy to test our hypotheses as in Study 1 and added the analysis of the willingness to perform citizenship behaviors as additional dependent variable for H9. In addition to the analyses described for Study 1, we ran two linear regressions (one for each technology) in which we regressed willingness to engage in citizenship behaviors on stability of acceptability judgements. Additionally, we ran two logistic regressions and regressed petition support onto acceptability judgements at T2, stability of acceptability judgements, and the interaction between acceptability at T2 and stability of acceptability judgements. To avoid issues of collinearity, acceptability judgements and stability scores were centered before including them in the analysis. Analyses were done in RStudio, version 4.2.0, using the packages lmerTest (Kuznetsova et al., 2017) for multilevel modelling and MASS (Venables & Ripley, 2002) for data transformation.

Results study 2

Descriptive statistics

Figures 7 and 8 in the Appendix depict scatterplots that give an impression of how acceptability judgements regarding each technology changed from T1 to T2 for each participant. For most participants, acceptability judgements regarding both technologies moderately changed but many individuals remained either rather negative or positive about the technology. Similar to Study 1, somewhat bigger changes from positive to negative or vice versa can be seen for geothermal energy compared to nuclear energy. Tables 16 and 17 in the Appendix show correlations between study variables for nuclear and geothermal energy. Higher stability of acceptability judgements regarding nuclear energy was correlated with lower ambivalence about nuclear energy (r = −0.14, p < 0.01), and higher objective knowledge about the technology (r = 0.12, p < 0.05). No variables were significantly correlated with stability of acceptability judgements regarding geothermal energy (all p > 0.05).

Differences in knowledge, importance, and ambivalence between novel and established technologies

To test whether there are any differences in knowledge, personal importance, and ambivalence about geothermal and nuclear energy, we ran three multilevel models in which we regressed subjective knowledge (Model 1), personal importance (Model 2) and ambivalence (Model 3) on the contrast between nuclear and geothermal energy (Tables 18, 19 and 20 in Appendix). As expected, participants indicated to have more knowledge about nuclear (M = 5.33) than geothermal energy (M = 4.59), (b = −0.74, p < 0.001, R² = 0.032), and found nuclear energy more personally important than geothermal energy (M = 4.26 vs. M = 3.79, b = −0.48, p < 0.001, R² = 0.044). In contrast to our expectation, we found no significantly difference in ambivalence regarding both technologies, b = −0.05, p = 0.559, R² = 0.000).

Predictors of stability of acceptability judgements

Table 7 reports the results of the multilevel regression analyses of the associations between stability of acceptability judgements and technology novelty, subjective knowledge, objective knowledge, ambivalence, and importance. As can be seen in Model 1, stability of acceptability judgements was significantly higher for nuclear (M = 5.19) than for geothermal (M = 5.01) energy (b = −0.17, p = 0.006, R² = 0.008), suggesting that acceptability judgements are indeed more stable for established than for novel technologies, but the effect size is very small.

Table 7

Multilevel regression model for stability of acceptability judgements (study 2)

	Model 1		Model 2
	Estimate	(SE)	Estimate	(SE)
Fixed effects
Intercept	5.36	(0.10)	5.37	(0.10)
Level 1 variables
Novelty of technology	−0.17**	(0.06)	−0.18**	(0.06)
Level 2 variables
Subjective knowledge			0.02	(0.02)
Objective knowledge^a			0.06	(0.04)
Importance			−0.04	(0.04)
Ambivalence			−0.07**	(0.03)
Random effects
Level-2 variance \({\tau }_{0}^{2}\)	0.16		0.16
Level-1 variance σ²	0.76		0.75
R²	0.8%		2.8%
Deviance	2174.2		2160.4

N = 395. Novelty of technology is coded 0 = nuclear (established technology);

1 = geothermal (novel technology). For level 1 and 2 variables: *p < 0.05, **p < 0.01, ***p < 0.001, two-tailed

^aObjective knowledge scores were standardized via z-scores within each condition before centering

Model 2 depicts the final model with subjective knowledge, objective knowledge, ambivalence, and importance added as predictors of stability of acceptability judgements.⁷ As expected, higher ambivalence was associated with lower stability of acceptability (b = −0.07, p = 0.009). Contrary to our expectation, subjective knowledge, objective knowledge, and importance of the technology were not significantly associated with stability of acceptability judgements (all p > 0.09). Together, the predictors explain only 2.8% of variance in stability of acceptability judgements.

Since subjective knowledge and personal importance were not significantly related to stability of acceptability judgements, and ambivalence did not differ between geothermal and nuclear energy, we did not test whether these three variables mediated the relationship between novelty and stability of acceptability judgements.

Relationship between personal values and stability of acceptability judgements

We tested whether personal values are related to stability of acceptability judgements (H8) via two separate stepwise multiple regressions (Table 8). Specifically, we tested whether the relationship between acceptability judgements at T1 and T2 (step 1) weakens when we control for personal values (step 2). Acceptability judgements at T1 were positively related to acceptability judgements at T2 for nuclear energy (b = 0.70, p < 0.001) and for geothermal energy (b = 0.46, p < 0.001). T1 acceptability judgements appeared to explain a larger share of variance in T2 acceptability judgements for nuclear energy (R² = 0.493) compared to geothermal energy (R² = 0.257), further supporting that acceptability judgements are more stable for established than for novel technologies. Adding altruistic, biospheric, egoistic and hedonic values to the model in step 2 did not, however, lead to a significant improvement in model fit (all p > 0.109) and the relationship between acceptability judgements at T1 and T2 remained significant and practically the same for both technologies, suggesting that values were not related to stability of acceptability.

Table 8

Multiple regression models of acceptability at time 1 and personal values predicting acceptability judgements at time 2 (study 2)

	B	95% CI for B		R²adj	p
		LL	UL
Nuclear energy
Step 1				0.492
Intercept	1.65	1.31	1.98		< 0.001
Acceptability T1	0.70	0.63	0.77		< 0.001
Step 2				0.491	< 0.001
Intercept	1.97	1.45	2.50		< 0.001
Acceptability T1	0.69	0.62	0.77		< 0.001
Altruistic values	0.01	−0.11	0.13		0.867
Biospheric values	−0.04	−0.15	0.06		0.359
Egoistic values	−0.01	−0.08	0.06		0.866
Hedonic values	−0.03	−0.12	0.07		0.572
Geothermal energy
Step 1				0.255	< 0.001
Intercept	2.89	2.50	3.28		< 0.001
Acceptability T1	0.46	0.38	0.53		< 0.001
Step 2				0.261	< 0.001
Intercept	2.75	2.23	3.27		< 0.001
Acceptability T1	0.44	0.36	0.52		< 0.001
Altruistic values	0.09	−0.03	0.21		0.159
Biospheric values	0.04	−0.06	0.14		0.397
Egoistic values	−0.03	−0.10	0.04		0.399
Hedonic values	−0.06	−0.15	0.03		0.166

CI confidence interval, LL lower limit, UL upper limit. T1 refers to the first survey wave in which acceptability judgements were measured

Relationship between stability of acceptability judgements and behavior

Next, we ran logistic regression models for nuclear and geothermal energy, to investigate if more stable acceptability judgements are more predictive of signing a petition against a technology than unstable acceptability judgements. In each regression model, we included acceptability judgements at T2, stability of acceptability judgements, and their interaction as predictors. For nuclear energy, the model revealed a significant main effect of acceptability at T2, β = −1.23, p < 0.001, indicating that the less acceptable respondents found nuclear energy at T2, the more they were inclined to sign a petition against the construction of new nuclear power plants. The main effect of stability of acceptability judgements was not statistically significant, β = −0.10, p = 0.475. In line with our hypothesis, the interaction between acceptability judgements regarding nuclear energy at Time 2 and stability of acceptability judgements was significant and negative, β = −0.22, p = 0.005, indicating that people who find nuclear energy less acceptable were more inclined to sign the petition against nuclear energy when their acceptability judgements were more stable. The overall model fit for nuclear energy was Nagelkerke’s R² = 0.423. The less acceptable participants find geothermal energy at T2, the more they were likely to sign a petition against it, β = −0.58, p < 0.001. However, stability of acceptability judgements and the interaction between acceptability judgements regarding geothermal at T2 and the stability of acceptability judgements were not significantly related to signing the petition (both p > 0.16), indicating that the strength of the relationship between acceptability of geothermal energy and the likelihood of signing the petition did not depend on the stability of acceptability judgements regarding geothermal energy. The overall model fit for geothermal energy was Nagelkerke’s R² = 0.139.

Lastly, we conducted two linear regressions to test if higher stability of acceptability judgements was associated with higher willingness to engage in citizenship behaviors that express support or opposition towards the respective technology, such as voting or demonstrating. Neither the stability of acceptability judgements about nuclear energy (β = −0.08, p = 0.08) nor the stability acceptability judgements about geothermal energy (β = −0.05, p = 0.24) was significantly associated with willingness to engage in citizenship behaviors regarding the respective technologies.

Discussion

Public acceptability has been recognized as an important factor that affects the likelihood that low-carbon and energy-efficient technologies are implemented (Steg et al., 2022). Since such technology implementation takes time and results in long-term consequences, it is important to understand how stable public acceptability judgements about these technologies are, as unstable acceptability judgments would make it more challenging to base policy decisions on. We aimed to understand to what extent acceptability judgements of different energy technologies are stable over time without targeted intervention or contextual changes, and to investigate what factors underlie stability of such acceptability judgements. We argued that public acceptability judgements are less stable for novel technologies than for established technologies, because individuals are less likely to have encountered and thought about novel technologies. Relatedly, we expected that individuals who know less about a technology, feel more ambivalent towards it, and who find a technology less important would have less stable acceptability judgments. We theorized that knowledge, ambivalence, and importance would mediate the relationship between novelty of a technology on stability of acceptability judgements about the relevant technology: people will know less and feel more ambivalent about novel technologies, and find them less important than established technologies, which in turn is associated with less stable acceptability judgements. In addition, we proposed that acceptability judgements are more likely to be stable when they are rooted in values that are known to be relatively stable over time. Lastly, we argued that the more stable one’s acceptability judgements, the more likely it is to influence one’s behaviors towards energy technologies.

Novelty and stability of acceptability judgements

In line with our hypothesis, stability of acceptability judgements of two novel technologies—geothermal energy and CCS—were indeed less stable over time compared to the stability of acceptability of more established technologies—nuclear power and wind energy. These findings support our reasoning based on the elaboration likelihood model (ELM; Petty & Cacioppo, 1986): people probably did not think and elaborate as much about novel technologies than established technologies, which results in less stable acceptability judgements. Indeed, most people have had hardly any direct experience with or exposure to geothermal energy and CCS (European Geothermal Energy Council, 2023; Global CCS Institute, 2023), which may limit the likelihood that they elaborate on the various aspects of these technologies (cf. Doll & Ajzen, 1992). Many people have had more opportunities to encounter wind energy and nuclear power through media, conversations with peers, or other exposure, which increases the chances that they have elaborated on various aspects of the technology, and have formed more stable acceptability judgements.

Knowledge and stability of acceptability judgements

Contrary to our expectations, knowing more about a technology was not associated with the stability of acceptability judgements, and knowledge did not mediate the effect of novelty on the stability of acceptability judgements. While people did indicate to know more about established than about novel technologies, knowledge did not affect the stability of acceptability judgements. Previous research found that people with higher knowledge on politics did have more stable attitudes towards different policies (Bartle, 2000), suggesting that the influence of knowledge on the stability of acceptability judgments and attitudes more generally may vary across domains. Future research is needed to understand when knowledge is most likely to be related to attitude stability.

Importance of technologies and stability of acceptability judgements

We found that the more important people evaluate an energy technology, the more stable their acceptability judgements in the student sample (Study 1), but not in the general population sample (Study 2), partly supporting our hypothesis. Additionally, different from what we expected based on the ELM, the perceived importance of energy technologies did not mediate the relationship between novelty and the stability of acceptability judgements. Future research is needed to investigate under what circumstances the importance of a technology is related to the stability of acceptability judgements.

Ambivalence and stability of acceptability judgements

As expected, feeling ambivalent about an energy technology was associated with lower stability of acceptability judgements. Ambivalent acceptability judgements are likely more sensitive to situational influences such as information or cues that make either positive or negative aspects of the technology more salient (cf. Jonas et al., 2000). Additionally, experiencing ambivalence can make people want to resolve the experienced conflict of having both negative and positive evaluations of a technology, resulting in less stable acceptability judgements. Individuals were more ambivalent towards novel technologies compared to established technologies in Study 1 but not in Study 2, where participants regarded geothermal and nuclear energy similarly ambivalent. However, ambivalence did not mediate the relationship between technology novelty on stability of acceptability judgments. This could be partially due to our choice to investigate acceptability of nuclear energy, which is an established energy source that seems to evoke rather ambivalent responses, as people tend to believe nuclear energy has both positive and negative aspects (Corner et al., 2011; Pidgeon et al., 2008). Apparently, people’s ambivalence towards a technology is not systematically related to its novelty, but ambivalence still explains stability of acceptability judgements.

In sum, we found that technology novelty was associated with less stable acceptability judgements, and that lower ambivalence and higher importance of the technology (but the latter only in Study 1) were associated with higher stability of acceptability judgements. No evidence was found that acceptability judgements would be more stable if people have more knowledge about the relevant technology. In addition, we did not find evidence that knowledge, importance and ambivalence mediated the effects of novelty on stability of acceptability judgements. Future research could investigate other processes through which novelty may affect the stability of acceptability judgements. For instance, future studies could examine the stability of factors influencing acceptability judgements, such as beliefs about consequences of an energy technology, and which factors affect the temporal stability of beliefs about those consequences. Interestingly, we found that acceptability judgments were overall rather stable, and most individuals remained either rather favorable, unfavorable or neutral about a technology, in particular in case of established technologies. This suggests that without targeted interventions or contextual changes (e.g., technological and societal changes, specific events like an accident at a nuclear power plant; Bird et al., 2014; Poortinga et al., 2013), acceptability judgements are not likely to change radically in the general public.

Values and stability of acceptability judgements

In contrast to our reasoning, controlling for personal values did not reduce the strength of the association of acceptability judgements at T1 and T2. Our results indicate that acceptability of wind energy was rather high, which may explain why personal values were not significantly related to acceptability judgements regarding wind energy. In case of novel technologies such as CCS, people might have not yet connected considered the consequences of the technologies for their values, which may explain why values were not related to acceptability judgements regarding novel technologies such as CCS.

Stability of acceptability judgements and behavior

Contrary to our expectations, more stable acceptability judgements were not more predictive of citizenship behaviors to express support or opposition towards a technology than less stable acceptability judgments. The only instance where we found support for this hypothesis was in case of petition support for nuclear energy in Study 2, but this may be due to chance. It may be that when participants rated their acceptability for each technology, their acceptability judgements became salient, which might have affected behavior regardless of stability of their acceptability judgements.

Limitations of our study

Based on the ELM, we reasoned that acceptability judgements of established energy technologies are likely to be more stable because people have elaborated more on the pros and cons of such technologies. Yet, we did not assess how much people have elaborated on each technology. Future studies could assess the level of elaboration, for instance by asking people how much they thought about a technology or to list all thoughts they have about a technology, and count the number of thoughts they come up with (cf. Barden & Petty, 2008; Conner et al., 2022b; Petty & Cacioppo, 1986), and test whether the level of elaboration explains why acceptability judgements regarding novel technologies are less stable compared to acceptability judgements regarding established technologies.

We relied on a student sample in the Study 1, which is not representative of the Dutch population. On one hand, attitudes might fluctuate more as people move into adulthood (like students) than later in life (Petty & Cacioppo, 1986), while on the other hand, higher education is generally associated with more motivation and abilities to critically analyze issue-relevant information (Cacioppo et al., 1996), potentially leading to more stable acceptability judgements. We addressed this potential shortcoming in Study 2, in which we employed demographic quotas to collect data from a sample that is representative of the adult Dutch population, and found overall very consistent results.

Since our aim was to provide insights that can be generalized to the broader population, our samples also included people who are not strongly engaged in energy issues. Hence, it might have been difficult for some respondents to answer questions on citizenship behaviors regarding technologies that they do not know much about or are not interested in. Yet, respondents who are not interested in a technology can still give meaningful answers to questions about citizenship intentions, as they would likely indicate that they would not participate in those actions. Consistent with our reasoning, people for whom an energy technology was more important were more willing to engage in these citizenship behaviors.

Both of our studies fall within the timeframe of important (geo)political events, namely Russia’s invasion of Ukraine in February 2022 and an international energy crisis, which resulted in increased energy prices and various political responses and societal debates on energy in the Netherlands and other countries. Russia invaded Ukraine during data collection of Study 1, and the war as well as the energy crisis were still salient issues during the time we collected data for our Study 2 between September and December 2023. These events may have affected people’s perceptions and acceptability judgements regarding energy technologies. Impactful events might change people’s acceptability judgements (Bird et al., 2014; Poortinga et al., 2013), and might also have led to an overall lower stability of acceptability judgements for the energy technologies in our studies, particularly in Study 1, during which the war and the energy crises were dominating topics in the media. Yet, as we were particularly interested in comparing stability of acceptability judgements across technologies and in factors that explain the stability of acceptability judgements, it is unlikely that these external events substantially influenced the relationships observed in our studies.

Practical implications

Our findings have important practical implications for energy policy. Overall, we only observed minor changes in acceptability judgements, and few people who initially were supporters of a technology became opponents, and vice versa. Still, we found that acceptability judgements do change over time, especially acceptability judgements regarding novel technologies, when people feel more ambivalent about technologies, and if the technology is less important to them. Policymakers could consider the stability of acceptability judgements and encourage or enable people to think about the pros and cons of energy technologies before making decisions on their implementation, as this may yield more stable acceptability judgements which are a better basis for decision-making. This could be done by providing citizens with information on the advantages and drawbacks of (novel) energy technologies, and by highlighting why energy technologies are important to people. Our findings further imply that acceptability of technologies may guide citizenship behaviors such as voting or protesting, regardless of how (un)stable those acceptability judgments are, which implies that any future changes in acceptability judgements will likely also impact people’s behaviors towards these technologies.

Conclusion

Public acceptability of energy technologies is crucial for their successful implementation. It is important to understand how stable acceptability judgements are, as decisions about implementing energy technologies have long-term consequences spanning years or even decades. Our results indicate that acceptability judgements of energy technologies were overall relatively stable. However, as expected, acceptability judgements changed more for novel technologies, when people felt more ambivalent about a technology, and when the technology was less important to them. Yet, knowledge about technologies was not associated with the stability of acceptability judgements. Knowledge, ambivalence and importance did not mediate the effects of technology novelty on stability of acceptability judgements. Practitioners could undertake efforts to strengthen stability of acceptability judgments, as to ensure a solid basis to make decisions on which energy technologies to implement in the short and long term.

Acknowledgements

This work was supported by the Netherlands Organization for Scientific Research (NWO) as part of the NWO Stevin Prize awarded to Linda Steg and via the Internet Research Fund of the Heymans Institute for Psychological Research at the University of Groningen.

Declarations

Conflict of interest

The authors have no conflicts of interest to disclose.

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Metadata
Appendix
Footnotes
Literature

Title: Temporal stability of public acceptability of novel and established energy technologies
Authors: Robert Görsch
Goda Perlaviciute
Linda Steg
Publication date: 01-03-2025
Publisher: Springer Netherlands
Published in: Energy Efficiency / Issue 3/2025
Print ISSN: 1570-646X
Electronic ISSN: 1570-6478
DOI: https://doi.org/10.1007/s12053-025-10305-5

Appendix

Tables 9, 10, 11 and 12.

Table 9

Correlations between study variables wind energy (study 1)

	1	2	3	4	5	6	7	8	9
1. Stability of acceptability	-
2. Knowledge	0.02	-
3. Ambivalence	-0.23***	-0.19**	-
4. Importance	0.19**	0.41***	-0.36***	-
5. Altruistic values	0.07	-0.07	-0.03	0.13	-
6. Biospheric values	-0.02	0.04	0.04	0.20**	0.45***	-
7. Egoistic values	0.04	0.00	0.12	0.01	-0.06	-0.07	-
8. Hedonic values	0.10	0.06	0.00	0.07	0.04	0.13	0.21**	-
9. Acceptability T1	0.29***	0.15*	-0.41***	0.32***	0.06	0.11	-0.04	0.02	-
10. Acceptability T2	0.46***	0.28***	-0.50***	0.48***	0.10	0.07	-0.04	0.07	0.47***

*p < 0.05, **p < 0.01, ***p < 0.001, two-tailed. T1 and T2 refer to the first and second survey wave in which acceptability judgements were measured

Table 10

Correlations between study variables nuclear energy (study 1)

	1	2	3	4	5	6	7	8	9
1. Stability of acceptability	-
2. Knowledge	-0.02	-
3. Ambivalence	-0.18**	-0.27***	-
4. Importance	0.11	0.50***	-0.27***	-
5. Altruistic values	-0.04	-0.04	0.04	0.06	-
6. Biospheric values	0.03	0.02	0.11	0.20**	0.45***	-
7. Egoistic values	0.01	0.08	0.04	0.06	-0.06	-0.07	-
8. Hedonic values	-0.01	-0.01	0.00	-0.01	0.04	0.13	0.21**	-
9. Acceptability T1	0.04	0.13	0.01	0.07	-0.17*	-0.15*	0.19*	-0.06	-
10. Acceptability T2	-0.07	0.18**	0.09	0.03	-0.08	-0.06	0.14	-0.03	0.79***

*p < 0.05, **p < 0.01, ***p < 0.001, two-tailed. T1 and T2 refer to the first and second survey wave in which acceptability judgements were measured

Table 11

Correlations between study variables geothermal energy (study 1)

	1	2	3	4	5	6	7	8	9
1. Stability of acceptability	-
2. Knowledge	0.09	-
3. Ambivalence	0.00	-0.44***	-
4. Importance	0.22**	0.31***	-0.26***	-
5. Altruistic values	0.06	-0.07	0.07	0.08	-
6. Biospheric values	0.09	0.08	-0.02	0.20**	0.45***	-
7. Egoistic values	0.04	0.10	-0.06	0.12	-0.06	-0.07	-
8. Hedonic values	-0.01	-0.03	-0.05	0.09	0.04	0.13	0.21**	-
9. Acceptability T1	0.18*	0.35***	-0.29***	0.05	-0.14*	0.02	-0.01	-0.07	-
10. Acceptability T2	0.22***	0.56***	-0.47***	0.23***	-0.08	0.01	0.03	-0.05	0.45***

*p < 0.05, **p < 0.01, ***p < 0.001, two-tailed. T1 and T2 refer to the first and second survey wave in which acceptability judgements were measured

Table 12

Correlations between study variables CCS (study 1)

	1	2	3	4	5	6	7	8	9
1. Stability of acceptability	-
2. Knowledge	-0.10	-
3. Ambivalence	0.04	-0.24***	-
4. Importance	-0.07	0.29***	-0.23***	-
5. Altruistic values	-0.05	0.00	0.00	0.10	-
6. Biospheric values	-0.09	-0.05	0.02	0.05	0.45***	-
7. Egoistic values	0.07	-0.02	0.01	0.06	-0.06	-0.07	-
8. Hedonic values	0.02	-0.01	0.02	0.11	0.04	0.13	0.21**	-
9. Acceptability T1	-0.05	0.00	-0.10	-0.03	-0.05	0.05	0.01	-0.13	-
10. Acceptability T2	-0.08	0.30***	-0.22**	0.17*	-0.01	0.04	0.06	0.07	0.28***

*p < 0.05, **p < 0.01, ***p < 0.001, two-tailed. T1 and T2 refer to the first and second survey wave in which acceptability judgements were measured

Figs. 3, 4, 5 and 6.

Fig. 3

Scatter plot acceptability of nuclear energy T1 and T2 (study 1)

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Fig. 4

Scatter plot acceptability of wind energy T1 and T2 (study 1)

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Fig. 5

Scatter plot acceptability of geothermal energy T1 and T2 (study 1)

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Fig. 6

Scatter plot acceptability of CCS T1 and T2 (study 1)

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Tables 13, 14, 15, 16 and 17.

Table 13

Multilevel regression of technology novelty predicting subjective knowledge (study 1)

	Estimate	(SE)	t	p
Fixed effects
Intercept	4.50	(0.11)	40.34	< 0.001
Level 1 variables
Novelty of technology	-3.11	(0.12)	-25.11	< 0.001
Random effects
Level-2 variance \({\tau }_{0}^{2}\)	1.76
Level-1 variance σ²	3.15
R²	33.1%
Deviance	3510.7

N = 205. Novelty of technology is coded 0 = established technologies and 1 = novel technologies

Table 14

Multilevel regression of technology novelty predicting personal importance (study 1)

	Estimate	(SE)	t	p
Fixed effects
Intercept	3.77	(0.07)	56.97	< 0.001
Level 1 variables
Novelty of technology	-0.94	(0.08)	-12.47	< 0.001
Random effects
Level-2 variance \({\tau }_{0}^{2}\)	0.61
Level-1 variance σ²	1.15
R²	11.1%
Deviance	2681.8

N = 205. Novelty of technology is coded 0 = established technologies and 1 = novel technologies

Table 15

Multilevel regression of technology novelty predicting ambivalence (study 1)

	Estimate	(SE)	t	p
Fixed effects
Intercept	3.68	(0.06)	62.01	< 0.001
Level 1 variables
Novelty of technology	0.65	(0.09)	6.99	< 0.001
Random effects
Level-2 variance \({\tau }_{0}^{2}\)	0.28
Level-1 variance σ²	1.79
R²	4.9%
Deviance	2906.1

N = 205. Novelty of technology is coded 0 = established technologies and 1 = novel technologies

Table 16

Correlations between study variables nuclear energy (study 2)

	1	2	3	4	5	6	7	8	9	10	11
1. Stability of acceptability	-
2. Subj. knowledge	0.10	-
3. Obj. knowledge	0.12*	0.34***	-
4. Ambivalence	-0.14**	-0.22***	-0.26***	-
5. Importance	-0.01	0.49***	0.24***	-0.27***	-
6. Altruistic values	-0.03	0.03	0.08	0.17***	0.08	-
7. Biospheric values	-0.02	0.06	0.10	0.21**	0.13*	0.75***	-
8. Egoistic values	0.00	0.05	-0.20***	0.17***	0.18***	0.16**	0.17***	-
9. Hedonic values	0.10	-0.03	0.01	0.00	0.05	0.59***	0.52***	0.22***	-
10. Acceptability T1	0.27***	0.17***	0.20***	-0.32***	0.08	-0.09	-0.14**	0.07	0.09	-
11. Acceptability T2	0.00	0.15***	0.17***	-0.32***	0.11*	-0.10*	-0.16**	0.03	0.02	0.70***	-
12. Citizenship willingness	-0.09	0.20***	-0.05	0.09	0.30***	0.05	0.08	0.29***	-0.07	-0.08	0.02

*p < 0.05, **p < 0.01, ***p < 0.001, two-tailed. T1 and T2 refer to the first and second survey wave in which acceptability judgements were measured

Table 17

Correlations between study variables geothermal energy (study 2)

	1	2	3	4	5	6	7	8	9	10	11
1. Stability of acceptability
2. Subj. knowledge	0.01
3. Obj. knowledge	0.07	0.15**
4. Ambivalence	-0.06	-0.24***	-0.29***
5. Importance	0.00	0.37***	-0.03	-0.17***
6. Altruistic values	-0.01	-0.01	0.05	-0.05	0.09
7. Biospheric values	-0.03	0.04	0.04	-0.01	0.18***	0.75***
8. Egoistic values	0.02	0.17**	-0.17***	0.28***	0.19***	0.16**	0.17***
9. Hedonic values	-0.06	-0.07	0.04	0.04	-0.06	0.59***	0.52***	0.22***
10. Acceptability T1	0.32***	0.23***	0.28***	-0.36***	0.11*	0.15**	0.17***	0.04	0.06
11. Acceptability T2	0.02	0.16**	0.18***	-0.28***	0.03	0.17***	0.17***	-0.01	0.04	0.51***
12. Citizenship willingness	-0.06	0.23***	-0.19***	0.12*	0.27***	0.09	0.18***	0.29***	-0.07	0.11*	0.26***

*p < 0.05, **p < 0.01, ***p < 0.001, two-tailed. T1 and T2 refer to the first and second survey wave in which acceptability judgements were measured

Figs. 7 and 8.

Fig. 7

Scatter plot acceptability of nuclear energy T1 and T2 (study 2)

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Fig. 8

Scatter plot acceptability of geothermal energy T1 and T2 (study 2)

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Tables. 18, 19 and 20.

Table 18

Multilevel regression of technology novelty predicting subjective knowledge (study 2)

	Estimate	(SE)	t	p
Fixed effects
Intercept	6.07	(0.18)	34.59	< 0.001
Level 1 variables
Novelty of technology	-0.74	(0.10)	-7.37	< 0.001
Random effects
Level-2 variance \({\tau }_{0}^{2}\)	2.19
Level-1 variance σ²	1.99
R²	3.2%
Deviance	3244

N = 396. Novelty of technology is coded 0 = nuclear energy and 1 = geothermal energy

Table 19

Multilevel regression of technology novelty predicting personal importance (study 2)

	Estimate	(SE)	t	p
Fixed effects
Intercept	4.74	(0.11)	44.27	< 0.001
Level 1 variables
Novelty of technology	-0.48	(0.07)	-7.39	< 0.001
Random effects
Level-2 variance \({\tau }_{0}^{2}\)	0.41
Level-1 variance σ²	0.83
R²	3.2%
Deviance	2374.5

N = 396. Novelty of technology is coded 0 = nuclear energy and 1 = geothermal energy

Table 20

Multilevel regression of technology novelty predicting ambivalence (study 2)

	Estimate	(SE)	t	p
Fixed effects
Intercept	3.75	(0.13)	27.88	< 0.001
Level 1 variables
Novelty of technology	-0.05	(0.08)	-0.59	0.559
Random effects
Level-2 variance \({\tau }_{0}^{2}\)	0.62
Level-1 variance σ²	1.30
R²	0.0%
Deviance	2723.6

N = 396. Novelty of technology is coded 0 = nuclear energy and 1 = geothermal energy

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The term acceptability is sometimes used interchangeably with acceptance in psychology and related fields (for a review, see Busse & Siebert, 2018). We define acceptability as people’s attitude towards an energy technology in general, while acceptance refers to the attitude towards a specific technology project that is proposed or implemented (Upham et al., 2015).

While we focus on the stability of acceptability judgements, we draw on research on the stability of attitudes more broadly, including those unrelated to energy technologies (e.g., attitudes toward behaviors or objects). For readability, we refer to stability of acceptability when discussing energy technologies, and to stability of attitudes when discussing theories and findings regarding other attitude objects.

Measures at T1 were part of a larger questionnaire, which also included questions unrelated to this study and will therefore not be discussed in this article.

The attention check asked people to select a certain response for an item. We ran the main analyses with and without the participants who failed the attention check. The analysis that includes the responses of inattentive participants yielded very similar results. Therefore, we report the results when excluding the inattentive respondents.

Due to a translation inaccuracy of the attention check item in the Dutch questionnaire version, we only excluded participants for failing the attention check in the English version. In the Dutch version, the attention check item instructed participants to select "zeer oneens" (very much disagree) but the respective response option was inaccurately translated into “sterk oneens” (strongly disagree), which holds a very similar meaning. Some participants remarked that this caused confusion about which answer to choose here.

Our preregistration stated that participants would be flagged if their response times would be smaller than 3SD from the median. However, due to some respondents taking longer times than we expected to submit their survey, SD was actually higher than the median. Thus, we could not flag any respondents as speeders based on this criterion, and decided that ¼ median (approximately 2 min) was a reasonable cut-off for flagging too short response times.

Due to violations of normality and homoscedasticity, we also ran the analysis using several ways to transform the acceptability scale. Box-Cox transformation reduced assumption violations the most, and lead to essentially the same results. We therefore report results for the untransformed scale, as these are more easy to interpret.

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Springer Professional

Abstract

Supplementary Information

Publisher's Note

Introduction

Factors influencing stability of energy technology acceptability judgements

Novelty of the energy technology

Knowledge about technologies

Importance of technologies

Ambivalence towards technologies

Personal values

Stability of acceptability judgements and behavior

Study objectives and design

Study 1

Method

Participants and Procedure

Measures

Analysis plan

Results study 1

Descriptive statistics

Differences in knowledge, importance, and ambivalence between novel and established technologies

Predictors of stability of energy technology acceptability judgements

Relationship between personal values and stability of acceptability judgements

Relationship between stability of acceptability judgements and behavior

Study 2

Method

Participants and Procedure

Measures

Analysis plan

Results study 2

Descriptive statistics

Differences in knowledge, importance, and ambivalence between novel and established technologies

Predictors of stability of acceptability judgements

Relationship between personal values and stability of acceptability judgements

Relationship between stability of acceptability judgements and behavior

Discussion

Novelty and stability of acceptability judgements

Knowledge and stability of acceptability judgements

Importance of technologies and stability of acceptability judgements

Ambivalence and stability of acceptability judgements

Values and stability of acceptability judgements

Stability of acceptability judgements and behavior

Limitations of our study

Practical implications

Conclusion

Acknowledgements

Declarations

Conflict of interest

Publisher's Note

Appendix

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