A Gender Gap? Public Trust in Regional Policymaking and Citizen Demands for Engagement

A Gender Gap? 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Public Trust in Regional Policymaking and Citizen Demands for Engagement Katharina Fellnhofer, Emilia Vähämaa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6831450/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in npj Urban Sustainability → Version 1 posted 10 You are reading this latest preprint version Abstract Trust acts as both a social clue and an alternative governance mechanism that operates partly by increasing confidence in other partners’ commitment to a greater good. From April to July 2020, we surveyed 7,729 individuals, broadly representative for age and gender, in four European regions. Our results highlight that trust indicators like transparency and trust in regional organizations significantly drive citizen demands to be engaged in policymaking: the greater the trust, the higher the demand for engagement. At the same time, citizen engagement increases trust. Thus, the greater the correlation between trust and citizen engagement for tackling grand societal challenges, the greater the value for all. However, despite indicating higher levels of trust in regional organizations, women do not demand greater engagement in regional policymaking. This gender gap calls for careful engagement strategies by policymakers to increase the equity and effectiveness of policy interventions for tackling grand societal challenges. Social science/Psychology/Human behaviour Social science/Social policy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Trust is an underlying psychological condition in which we accept vulnerability because we expect positive intentions or behavior from another (Rousseau, Sitkin, Burt, & Camerer, 1998). Trust also acts as a social glue that keeps partners in a partnership (e.g., Faems et al., 2008). Its multidimensional conceptualization highlights its key role in policymaking (Rousseau et al., 1998). Trust also represents an alternative governance mechanism partly because it increases confidence in other parties’ commitment to a greater good (Faems et al., 2008). We trust one another when we believe that others are interacting authentically, when we have faith in their judgment and competence, and when we think that those other people care (Jones & George, 1998). Thus, trust in groups or organizations is crucial for tackling the grand societal challenges that we are facing (Nature, 2010; Tollefson, 2010). Trust is also very important from the corporate responsibility point of view as the four building blocks of corporate responsibility are ethics, leadership, personal responsibility, and trust (Mostovicz, Kakabadse, & Kakabadse, 2011). It is reported that organizations that are responsible and comply with principles of accountability, transparency, and disclosure will enjoy higher levels of trust (Child & Rodrigues, 2004). Moreover, trust violations disrupt the social norms, and the society as a whole suffers if there is a loss of trust. Previous research has highlighted the innovative impact of trust at the individual level of analysis (Clegg, Unsworth, Epitropaki, & Parker, 2002) in such a way that trust in an organization predicted innovative behavior and at the organizational level (Baer & Frese, 2003) in such a way that a trust-filled climate positively affects personal initiative. Given that trust embodies risk taking (McAllister, 1995), we expect that individuals who feel trust in their regional organizations and regional policymaking are more likely to take the risk of taking ownership of these aspects (Parker, Williams, & Turner, 2006). This ownership and willingness to engage are crucial for tackling grand societal challenges. Thus, citizen engagement has consistently been promoted in research and policymaking. However, to our knowledge, no study to date has focused on the specific relationship between trust and citizen demands for engagement in regional policymaking, with a particular focus on tackling grand societal challenges. Thus, the aim of this research report is to summarize the results from a European Commission-funded project dedicated to answering following research question: How does trust shape citizen engagement in regional policymaking for tackling grand societal challenges and vice versa? We hypothesize that trust in regional organizations supports citizen engagement in tackling complex societal challenges and vice versa: the greater the demand for citizen engagement, the higher the trust in regional organizations to solve complex societal challenges. Furthermore, we explore how trade-off dilemmas between innovation and societal challenges related to energy, smart cities, and transportation are perceived by different cohorts of individuals. However, as studies have found that women show different levels of trust (e.g., Haselhuhn et al., 2015; van den Akker et al., 2020), we also expect gender differences in this context. Moreover, literature on gender-based behavioral differences is unanimous in suggesting that women are more risk averse than men and exhibit less risky behavior in decision-making (e.g., Eckel & Grossman, 2002; Fehr-Duda et al, 2006; Johnson & Powell, 1994; Levin, Snyder & Chapman, 1988; Powell & Ansic, 1997; Watson & McNaughton, 2007.) Consequently, we hypothesize that trust and gender significantly impact demand for engagement in regional policymaking. With respect to practical implications, we also analyze different gender perspectives in greater detail, as recent research has found that gender quotas can significantly increase the equity and effectiveness of policy interventions for tackling grand societal challenges like climate change (Cook, Grillos, & Andersson, 2019). The motivation of our study stems from the well-documented importance of trust in organizations, combined with the existing behavioral gender-based differences for example in trust, risk aversion, and networking. Our research findings may be of interest to legislators when setting future policies for promoting gender equality and the advancement of women in the organizations. Finally, our manuscript will contribute to the scientific debate in the fields of organizational studies, corporate governance, and gender studies as follows: first, our theoretical contribution focuses on mapping how individuals at the micro level trust organizations as macro level entities, second, our research contributes to the theory of gendered trust in organizations by investigating the gender-based differences in trust behavior and, finally we also contribute to the timely corporate responsibility discussion since trust is considered as one of the components forming the core of social responsibility in an organizational context. Theoretical Background Although the study of trust has long been an area of inquiry in organization studies, the recent COVID-19 pandemic has shaken trust in organization research and challenged the accepted answers to age-old questions (Meyer & Quattrone, 2021). In addition, the war in Ukraine, the general political instability, and the ongoing energy crisis have caused severe turbulence in the economy, thereby highlighting the importance of trust in organizations. Trust and its repair during and after crises is a multi-faceted phenomenon across individual, organizational, institutional, and societal levels. According to the Neo-Classical Theory (also known as Human Relations Theory) that focuses on examining how people behave in a particular situation, trust is the foundation of the human relations (Shorthose, 1996) and, consequently, a crucial part of a well-functioning organization. Trust and its dynamics at the macro and micro levels have already been conceptualized (e.g., Rousseau et al., 1998). However, most theory on trust repair has focused on micro-level phenomena, with only a few attempts to study it on the macro level (Dirks & Ferrin, 2001; Kramer & Lewicki, 2010). At the same time, earlier studies indicate that trust is a relational construct that has social and affective elements (Bachmann et al., 2015). Therefore, solutions to the questions of trust repair at the micro and macro level may be very different. In this regard, prior work highlights that the processes of trust repair are fundamentally different at the macro organizational level than at the interpersonal micro level (Gillespie & Dietz, 2009). Related to trust repair, earlier literature suggests that trust may be repaired by improving transparency in organizations (e.g., Rawlins, 2009). Auger (2014) reports that organizations demonstrating transparency both related to the organization’s reputation for transparency and its efforts to communicate transparency achieved significantly higher levels of trust than non-transparent organizations. Trust among network members makes the network effective. Thus, social networks are important regarding building and maintaining trust. Earlier literature indicates that women and men build and manage their social networks in different ways (e.g., Ibarra, 1993; McPherson, Smith-Lovin & Cook, 2001; Moore, 1990). An early study by Schein (1978) suggests that women don’t network as well as men. This may hurt their performance at different social contexts, including the work environment. Prior studies also indicate that trust among networks is gendered, which may limit the effectiveness of the networks (Merluzzi, 2017; Song, Vernet & Pryke, 2022). Interestingly, Bevelander and Page (2011) report that the social network size varies with level of trust between parties. Moreover, they suggest that women network in a manner that disadvantage them. To conclude, the earlier literature indicates that 1) trust is crucial for the optimal functioning of an organization and that 2) trust is gendered. We combine these two strands of literature by examining the possible existence of a gender gap in public trust. The present study draws on theory from prior trust research, with a focus on trust repair (Bachmann, 2001; Bachmann, Gillespie, & Priem, 2015; Bachmann & Inkpen, 2011; Gillespie, Hurley, Dietz, & Bachmann, 2012; Owen & Currie, 2021; Pfarrer, Decelles, Smith, & Taylor, 2008). In this context, our theoretical contribution centers on how individuals at the micro level trust organizations as entities at the macro level. Moreover, our research also contributes to the theory of gendered trust by documenting the gender-based differences in trust behavior. Our findings complement theory on trust building in organization studies in two key ways. First, we identify how micro-level trust indicators drive trust development at the organizational level and show how they differ between individuals. Second, our findings regarding those individual differences also show differences when facing trade-off dilemmas between innovation and societal challenges, which are crucial steps to maintain trust across individuals after a crisis. Finally, our theory of trust differences between the micro and macro levels stresses the importance of engaging individuals in order to maintain trust at the organizational level. Methods All data, analysis code, and research materials are available at https://osf.io/zj2w3/?view_only=5391ff03d3844960bd5114e483a111ed . Data were analyzed using R version 4.1.3 and JASP version 0.16.2. This study’s design and analysis were not preregistered. However, due to applied Bayesian inference, preregistration is not necessary (Dienes, 2014; Etz & Wagenmakers, 2017; Ly, Verhagen, & Wagenmakers, 2016; Rouder & Morey, 2012; van Doorn et al., 2021). Data collection was conducted by regional survey companies. Where required, regional data collection was approved by relevant ethics committees (e.g., University of Twente BMS Ethical Committee, no. 200184). We obtained electronic informed consent from all participants. Our methodological approach was supported by four regional survey companies (Palmos Analysis P.C., Newcom Research and Consultancy B.V., Newton Research Europe d.o.o., and Kantar A.S.) that used local languages to collect data from April to July 2020 by conducting online surveys of a total of 7,729 individuals in Greece’s Kriti region, the Netherlands’s Overijssel region, Spain’s Galicia region, and Norway’s Vestland region. These four regions were selected due to resource availability and network, as regional partners were available in these areas. All the examined areas are in highly developed industrial countries with high gender equality. Thus, the data from these four areas are comparable and can be examined as one data set. We did not exclude any subjects from our analysis. However, for gender analysis between women and men, we excluded the 38 subjects who did not share their gender [1] . We did not exclude outliers. The data were collected from respondents of at least 18 years of age. We deliberately recruited samples broadly representative for age and gender (3,861 men; 3,830 women) in those four regions, with the following stakeholder distribution: 5.6% academics, 5.2% policymakers, 18.1% entrepreneurs, 51.5% in civil society, and 19.7% others. While demographic questions were asked (gender, age, educational level, activity status, and country), we focus in this study on central questions related to regional dilemmas, trust indicators, trust in organizations, and citizen demands for policymaking engagement. All reported p -values are two-tailed. Table 1 outlines the descriptive statistics of each region and across regions. Table 1 Sample descriptive statistics Spain: Galicia ( 1 ) Greece: Kriti ( 2 ) Norway: Vestland ( 3 ) Netherlands: Overijssel ( 4 ) Overall (N = 2006) (N = 2010) (N = 2053) (N = 1660) (N = 7729) Stakeholder Academic community ( 1 ) 58 (2.9%) 100 (5.0%) 48 (2.3%) 227 (13.7%) 433 (5.6%) Government ( 2 ) 220 (11.0%) 3 (0.1%) 17 (0.8%) 159 (9.6%) 399 (5.2%) Business ( 3 ) 595 (29.7%) 216 (10.7%) 47 (2.3%) 541 (32.6%) 1399 (18.1%) Civil society ( 4 ) 192 (9.6%) 1691 (84.1%) 1920 (93.5%) 175 (10.5%) 3978 (51.5%) Other ( 5 ) 941 (46.9%) 0 (0%) 21 (1.0%) 558 (33.6%) 1520 (19.7%) Profession status activity Employee ( 1 ) 1242 (61.9%) 963 (47.9%) 1268 (61.8%) 810 (48.8%) 4283 (55.4%) Unemployed ( 2 ) 325 (16.2%) 251 (12.5%) 34 (1.7%) 79 (4.8%) 689 (8.9%) Retired ( 3 ) 176 (8.8%) 600 (29.9%) 504 (24.5%) 356 (21.4%) 1636 (21.2%) Pupil or Student ( 4 ) 116 (5.8%) 86 (4.3%) 107 (5.2%) 214 (12.9%) 523 (6.8%) Household ( 5 ) 64 (3.2%) 106 (5.3%) 32 (1.6%) 81 (4.9%) 283 (3.7%) Other ( 6 ) 83 (4.1%) 4 (0.2%) 108 (5.3%) 120 (7.2%) 315 (4.1%) Education No or primary education ( 1 ) 44 (2.2%) 207 (10.3%) 91 (4.4%) 21 (1.3%) 363 (4.7%) Secondary education ( 2 ) 628 (31.3%) 886 (44.1%) 228 (11.1%) 996 (60.0%) 2738 (35.4%) Bachelor’s degree or equivalent ( 3 ) 998 (49.8%) 702 (34.9%) 679 (33.1%) 445 (26.8%) 2824 (36.5%) Postgraduate, doctoral, or equivalent ( 4 ) 336 (16.7%) 215 (10.7%) 497 (24.2%) 166 (10.0%) 1214 (15.7%) Other 0 (0%) 0 (0%) 558 (27.2%) 32 (1.9%) 590 (7.6%) Age 18–24 ( 1 ) 119 (5.9%) 101 (5.0%) 92 (4.5%) 293 (17.7%) 605 (7.8%) 25–34 ( 2 ) 450 (22.4%) 180 (9.0%) 274 (13.3%) 208 (12.5%) 1112 (14.4%) 35–65 ( 3 ) 1329 (66.3%) 1248 (62.1%) 1233 (60.1%) 762 (45.9%) 4572 (59.2%) 65 ≤ ( 4 ) 108 (5.4%) 481 (23.9%) 454 (22.1%) 397 (23.9%) 1440 (18.6%) Gender Male (0) 1000 (49.9%) 971 (48.3%) 1026 (50.0%) 864 (52.0%) 3861 (50.0%) Female ( 1 ) 1001 (49.9%) 1039 (51.7%) 1027 (50.0%) 763 (46.0%) 3830 (49.6%) Other ( 2 ) 5 (0.2%) 0 (0%) 0 (0%) 33 (2.0%) 38 (0.5%) Notes. In Vestland, the categories of higher general education ( 5 ) and higher vocational education ( 6 ) were also available; for the present study, they were merged into higher education (Bachelor’s degree or equivalent) ( 3 ). Table 1 HERE Measurements We employ the following four measures for examining the possible gender gap in public trust: ( 1 ) Trust indicators, ( 2 ) Trust in regional organizations, ( 3 ) Demand in engagement, and ( 4 ) Dilemmas. We present these measures in detail in the following. Trust indicators. We used a five-point Likert scale from strongly disagree ( 1 ) to strongly agree ( 5 ) to quantify trust indicators, using a composite measure that averaged each individual’s answer to the following four questions: In terms of general trust, I trust organizations or groups of people when they: ( 1 ) assess the effects of innovation in an independent way (autonomy); ( 2 ) look at the effects of innovation from different angles (diversity); ( 3 ) clearly indicate which interests they have in innovation (interest); and ( 4 ) communicate in an open way about innovation (transparency). Cronbach’s alpha was .849. Trust in regional organizations. We used a five-point Likert scale from not at all ( 1 ) to very much ( 5 ) to quantify trust indicators, using a composite measure that averaged each individual’s levels of agreement with the following eight statements: I trust the following in my region: ( 1 ) regional government, ( 2 ) local government, ( 3 ) civil society organizations, ( 4 ) non-governmental organizations, ( 5 ) researchers, ( 6 ) small- and medium-sized businesses, ( 7 ) large companies, ( 8 ) gender-balanced governing bodies. Cronbach’s alpha was .764. Demand in engagement. We used a five-point Likert scale from strongly disagree ( 1 ) to strongly agree ( 5 ) to quantify demand in engagement in regional policymaking, using a composite measure that averaged each individual’s levels of agreement with two statements: ( 1 ) I believe that citizens should be actively involved in helping to design regional innovation policies; ( 2 ) I believe that citizens should be actively involved in helping to evaluate regional innovation policies. Cronbach’s alpha was .821. Dilemmas. We used a five-point Likert scale from strongly disagree ( 1 ) to strongly agree ( 5 ) to quantify regional dilemmas using each individual’s levels of agreement with the following statements: Please indicate your agreement with the following arguments: ( 1 ) I believe that promoting innovation should be a higher priority than citizens’ well-being (such as jobs, income, housing, health, safety); ( 2 ) I believe that innovation should be boosted even though it might create gender inequalities in my region; ( 3 ) I believe that it is good to support innovation when it has a positive impact on smart cities, energy, and transport, even if it requires access to my personal data; ( 4 ) I believe that innovation outcomes for facilitating smart cities, energy, and transport should be boosted, even if I might not have all the necessary skills to use them. Figure 1 presents a correlation matrix indicating a significant relationship between ( 2 ) age, ( 3 ) trust indicators, and ( 4 ) trust in regional organizations with ( 5 ) demands for policy involvement. FIGURE 1 HERE Results In light of the advantages of Bayesian inference (Dienes, 2014; Etz & Wagenmakers, 2017; Ly et al., 2016; Rouder & Morey, 2012; van Doorn et al., 2021), we tested our hypothesis by conducting a Bayesian linear mixed‑effect model of the effects of trust indicators, trust in regional organizations, and gender, including the control variables education, employment status, stakeholder, and age as fixed effects and region as a random intercept, with participants nested within nationalities. As recommended (Gelman, Jakulin, Pittau, & Su, 2008), we used default non‑informative priors for all coefficients that represent a lack of knowledge about the effect size under examination (Gönen, Johnson, Lu, & Westfall, 2005; Liang, Paulo, Molina, Clyde, & Berger, 2008; Rouder, Speckman, Sun, Morey, & Iverson, 2009). For our test, we plotted the parameter estimation with posterior distribution with credible intervals and accepted or rejected the null hypothesis following the highest density interval (HDI) with the region of practical equivalence (ROPE) decision (Kruschke, 2018). In addition, we provide violin plots to compare gender depicting the Mann‑Whitney U tests for trust indicators, trust in regional organizations, and demand for engagement. In this context, we also provide Bayesian tests and Yuen t ‑tests for our variable under investigation to check robustness. Additionally, we conducted further robustness and sequential analysis with JASP version 0.16.2. (see the supplementary material at https://osf.io/zj2w3/?view_only=5391ff03d3844960bd5114e483a111ed). We report decisions regarding whether the regression coefficients are effectively zero or non‑zero. Decisions were made by using the posterior estimated magnitudes of regression coefficients. We employ the decision rule using the HDI of the posterior distribution for accepting or rejecting null values of parameters and the ROPE (Kruschke, 2010, 2013, 2018; Kruschke, Aguinis, & Joo, 2012). The range of parameter values that is good enough for practical purposes is expressed by the ROPE. The range of values of θ (demand for engagement in regional policymaking) that includes the 95% most credible values is marked in the posterior distribution as the 95% HDI that refers to the probability density. In other words, every parameter value within the HDI has a higher probability density and thus credibility than any parameter outside the HDI. The 95% HDI contains the 95% most credible values of the parameter. The HDI limits are computed from the Markov chain Monte Carlo chain using Kruschke’s method (2014) and exceeding an effective sample size of 10,000, as recommended. Following the recommendations in the literature (Etz & Wagenmakers, 2017; Kruschke, 2021; Ly et al., 2016; van Doorn et al., 2021), we determine the size of the effect by plotting the posterior distributions marked with their mean values and their 95% HDIs and decide to accept or reject the null hypotheses by using the HDI‑with‑ROPE decision rule (Kruschke, 2018): the null value is declared rejected if the 95% HDI falls completely outside the ROPE, and the null value is declared accepted for practical purposes if the 95% HDI falls completely inside the ROPE. In other words, a parameter value is rejected when its ROPE falls entirely outside the 95% HDI. If the entire HDI—that is, all the most credible values—falls within the ROPE, then we accept the target value for practical purposes; in other words, a parameter value is accepted when its ROPE completely contains the 95% HDI. We also present visual figures and numerical tables with posterior modes, medians and means, standard deviations, posterior distributions, 95% HDI, ROPE, and percentage within ROPE; this will allow readers to decide for themselves whether the HDI is or is not fully inside or outside the ROPE. We conducted the Bayesian analysis based on the following linear mixed‑effect model equation: ( 1 ) H A : y i = β 0 + β 1 TI i + β 2 TO i + β 3 TO i G i + control variables + ϵ i ; versus ( 2 ) H 0 : y i = β 0 + β 1 TI i + β 2 TO i + control variables + ϵ i , where y i represents the demand for policymaking engagement of individual I , TI i the trust indicator, and TO i this individual’s trust in regional organizations. Figure 2 presents the results of our Bayesian linear mixed-effect model, which highlights that trust indicators and trust in regional organizations significantly impact the demands for citizen engagement in regional policymaking: based on the HDI+ROPE decision rule (Kruschke, 2018; Edwards & Berry, 2010), the null values of trust indicators ( 0 % in ROPE, M = 0.18, SD = 0.01, 95% CI [ 0.15, 0.2 ]) and trust in regional organizations ( 0 % in ROPE, M = 0.23, SD = 0.02, 95% CI [0.19, 0.27]) are declared rejected because the 95% HDI falls completely outside the ROPE (see table 2). In other words, trust indicators and trust in regional organizations drive citizen demands for engagement in public policymaking. The same holds true for gender; as figure 2 shows, our Bayesian analysis reveals that for gender, only 17.86% of the HDI falls within the conventional ROPE of Cohen’s d of [−0.09, 0.09], which provides evidence in favor of the alternative hypothesis that gender shows a significant effect on demand for engagement in policymaking ( M = -0.16, SD = 0.08, 95% CI [ -0.32,0 ]). However, the interaction of gender and trust in organization can be considered not significant, as the 95% HDI falls completely inside the ROPE ( 100 % in ROPE, M = 0.04, SD = 0.02, 95% CI [-0.01, 0.08]; see table 2). In other words, although women display greater trust in others’ intentions to perform according to their positive expectations, they do not demand significantly more active engagement in policymaking to monitor those they trust. Furthermore, the null values of control variables such as education, employment status, and stakeholder are declared accepted for practical purposes because the 95% HDI falls completely inside the ROPE. Finally, we also declare age, with 72.82% inside the ROPE, as not significant for our results. FIGURE 2 HERE Using the HDI‑with‑ROPE decision rule for hypothesis testing has been promoted as increasing the predictive precision of theories in the organizational sciences (Edwards & Berry, 2010). The ROPE represents a decision threshold, and its limits are always chosen in the context of current theory and measurement precision. As recommended (Kruschke, 2018), we use conventional parameters and the limits typically observed in social and behavioral research, such as Cohen’s d , to measure small, medium, and large effect sizes (Cohen, 1988, 2013). Referring to our research framework, we use effect size as a parameter that corresponds to the convention that 0.2 is a “small” effect size for Cohen’s d (2013). In line with recommendations (Kruschke, 2018), we set the ROPE for linear models to ‑0.1 * Sdy , 0.1 * Sdy . In line with Cohen’s d for small effect at 0.2, the effect size of a mean as δ = (μ‑µ 0 )/σ is practically equivalent to zero if it is less than half of the small effect and falls within the ROPE. This decision regarding the ROPE has been made with respect to state-of-the-art of theory and the best measuring device available (Serlin & Lapsley, 1985). Table 2 Bayesian analysis for HDI-with-ROPE decision rule Variable Mean Median Mode SD HDI ROPE p (ROPE) in % 5% 95% low high (Intercept) 2.32 2.31 2.57 0.18 1.94 2.64 -0.09 0.09 0 Trust indicators 0.18 0.18 0.18 0.01 0.15 0.2 -0.09 0.09 0 Trust in organizations 0.23 0.23 0.21 0.02 0.19 0.27 -0.09 0.09 0 Gender -0.16 -0.16 -0.25 0.08 -0.32 0 -0.09 0.09 17.86 Age 0.08 0.08 0.07 0.01 0.06 0.11 -0.09 0.09 72.59 Education -0.02 -0.02 -0.02 0.01 -0.04 0 -0.09 0.09 100 Activity -0.01 -0.01 -0.02 0.01 -0.02 0.01 -0.09 0.09 100 Stakeholder 0.01 0.01 0.03 0.01 0 0.03 -0.09 0.09 100 Trust in organizations x Gender 0.04 0.04 0.07 0.02 -0.01 0.08 -0.09 0.09 100 Notes. Means, medians, and modes correspond to unstandardized betas on the dependent variable demand in engagement; SD = standard deviation, HDI = highest density interval, and ROPE = region of practical equivalence. We set a default non-informative prior (Stan Development Team, 2022) and, as recommended (Kruschke, 2018), for HDI we used conventional parameters such as Cohen’s d (1988, 2013) to measure small effect sizes. The HDI limits were computed from the Markov chain Monte Carlo using Kruschke’s method (2014) and exceeding an effective sample size of 10,000, as recommended. TABLE 2 HERE To explore gender differences between the 3,830 women and 3,861 men, we performed a Mann-Whitney test (two-sided testing), because our data are not normally distributed. All reported p ‑values are two-tailed. Our results, which are illustrated in violin plots in figure 3, reveal the following gender differences: As shown in figure 3a, women ( M = 3.50, SD = 0.95) and men ( M = 3.50, SD = 0.94) rely on trust indicators like transparency, autonomy, interest, and diversity similar ( W Mann-Whitney = 7.41e 6 , p = .84, 95% CI = [-0.02, 0.03], n = 7,691). The Bayes factor (BF) for the same analysis revealed that the data were 3.66 times (δ = 1.27e -3 , 95% CI = [-0.02, 0.03], r = .71) more probable under the null hypothesis (BF 01 ) than the alternative hypothesis (BF 10 ). This can be considered moderate evidence (Jeffreys, 1961) in favor of the null hypothesis. Next, and by contrast, women displayed significant higher trust ( M = 3.42, SD = 0.73) than men ( M = 3.34, SD = 0.71) in different regional organizations ( W Mann-Whitney = 6.80e 6 , p = 1.20e -9 , 95% CI = [-0.11, -0.05], n = 7,691). The BF for the same analysis revealed that the data were 8.35 times (δ = -0.08, 95% CI = [-0.11, -0.05], r = .71) more probable under the alternative hypothesis (BF 10 ) than the null hypothesis (BF 01 ). This can be considered moderate evidence (Jeffreys, 1961) in favor of the alternative hypothesis. Although women trust organizations significantly more than men, there is not enough evidence to indicate that women demand ( M = 3.90, SD = 0.87) significantly higher citizen involvement than men ( M = 3.91, SD = 0.87) in the design and implementation of regional policymaking ( W Mann-Whitney = 7.45e 6 , p = .55, 95% CI = [-0.02, 0.03], n = 7,691). The BF for the same analysis was not performed because the distribution of the variable is too sparse. We also performed a robustness check: t Yuen (7612.76) = 0.492, p = .623, 95% CI = [-0.023, 0.060], n = 7,691. FIGURE 3 HERE Because women exhibit greater trust in organizations without demanding more citizen engagement in policymaking, we further explore trade-off dilemmas between innovation and societal challenges. The results presented in figure 4 highlight how men and women perceive trade-off decisions regarding innovation differently and to a significant degree. The results illustrate a rather traditional mindset among respondents, with women significantly more averse to innovation than men. Our evidence indicates that men tend to be more innovation-oriented than women when it comes to trade-off decisions; however, this comes at the cost of societal challenges. In our survey, self-reporting dilemmas across genders were non-normally distributed according to a Kolmogorov–Smirnov test, so we report non-parametric statistics for all trade-off comparisons. A Mann-Whitney U test revealed that, overall, female participants showed a significantly higher tendency against innovation and toward well-being ( W Mann-Whitney = 8.01e 6 , p = 5.72e -11 , 95% CI = [0.06, 0.11], n = 7,691). The BF for the same analysis revealed strong evidence (Jeffreys, 1961) that the data were 15.55 (δ = 0.18, 95% CI = [0.13, 0.24], r = .71) times more probable under the alternative hypothesis (BF 10 ) than the null hypothesis (BF 01 ). These results, which are illustrated in figure 4a, stress first that women demonstrate a significantly different point of view regarding promoting innovation vis-à-vis prioritizing citizen well-being. It is thus not surprising that female participants showed a significantly higher tendency against innovation and toward gender equality ( W Mann-Whitney = 8.49e 6 , p = 2.17e -31 , 95% CI = [0.12, 0.17], n = 7,691). The BF for the same analysis revealed very strong evidence (Jeffreys, 1961) that the data were 65.77 (δ = 0.33, 95% CI = [0.28, 0.38], r = .71) times more probable under the alternative hypothesis (BF 10 ) than the null hypothesis (BF 01 ). Second, as illustrated in figure 4b, women disagreed more strongly than men with the statement that innovation should be boosted, even though that might create gender inequalities in their region. Third, as illustrated in figure 4c, women showed a significantly stronger rejection of innovation that has positive impacts on smart cities, energy, and transport when it requires access to personal data or comes at a cost to privacy. Again, female participants showed a significantly higher tendency against innovation and toward data protection ( W Mann-Whitney = 8.11e 6 , p = 3.21e ‑14 , 95% CI = [0.07, 0.12], n = 7,691). The BF for the same analysis revealed very strong evidence (Jeffreys, 1961) that the data were 25.35 (δ = 0.22, 95% CI = [0.17, 0.28], r = .71) times more probable under the alternative hypothesis (BF 10 ) than the null hypothesis (BF 01 ). Finally, as illustrated in figure 4d, women tend to have a significantly different view than men of innovation outcomes for facilitating smart cities, energy, and transport when they do not have the skills needed to use them. Overall, female participants also showed a significantly higher tendency against innovation and toward accepting a lack of skills ( W Mann-Whitney = 5.49e -13 , p = 2.17e -31 , 95% CI = [0.07, 0.12], n = 7,691). The BF for the same analysis revealed strong evidence (Jeffreys, 1961) that the data were 22.75 (δ = 0.20, 95% CI = [0.15, 0.26], r = .71) times more probable under the alternative hypothesis (BF 10 ) than the null hypothesis (BF 01 ). FIGURE 4 HERE Discussion Our empirical study results stress that trust in organizations that are involved in regional innovation strategies for tackling grand societal challenges is significantly higher among women than among men. Despite prior research reporting a tendency for men to exhibit more trust than women (e.g., Falk and Hermle, 2018), our study’s empirical findings indicate that women trust organizations involved in regional innovation strategies more than men. Although women display greater trust in others’ intention to perform according to their positive expectations (e.g., Cook et al., 2019), they do not demand significantly more active citizen engagement in policymaking to monitor those they trust. Violating this trust would occur when those females’ positive expectations are not met. Violations harm women’s trust less than men’s trust, and trust among women has been found to be more resistant to change in the face of untrustworthy behavior (Haselhuhn et al., 2015). Overall, it is unclear why women do not demand greater engagement in regional innovation policymaking to accompany, much less validate, their trust. Our results show that the gender difference in trade-off decision-making perceptions turns out to be especially strong when it comes to gender equality. This gap is particularly important because decision makers who could be authorized to redistribute resources during crises may face trade-offs that support traditional gender-inequality mindsets. In particular, as COVID-19 has had a more negative social and economic impact on women than on men (Wenham et al., 2020), we need to consider different gendered positive expectations about other partners’ capabilities in future perilous innovative-driven situations and avoid violating this trust. The COVID-19 crisis has endangered gender equality; women have been more vulnerable throughout the pandemic (Utoft, 2020). The innovation potential of women represents an underexploited source of value creation and economic growth (Strohmeyer, Tonoyan, & Jennings, 2017). Policymakers and companies making investments in developing an ecosystem that encourages women to boost innovation (EC, 2020). For decades, the United Nations has called for women to be engaged in environmental decision making at all policy levels (Buckingham, 2010), and the European Union has committed to implementing gender equality as one of its sustainable development goals (European Commission, 2016). It is clear that underutilizing the capability of the females, that is, approximately half of the population, is not optimal from the society’s point of view. At the same time, women themselves must also demonstrate more attention to and interest in being engaged in policymaking. Any effort toward gender equality begins with endeavors to adjust deep-rooted mindsets, especially during crises. Paradoxically, women’s high trust and low policy engagement expectations appear contrary to their self-interest, especially as more women serve as leading decision makers. This desire among women to maintain relationships even at the cost of gender equality (Buchan et al., 2008; Haselhuhn et al., 2015) calls for more and better practices and policy interventions to boost women’s desire for and comfort with engagement in regional policymaking. This is especially important for tackling grand societal challenges, for it has been shown that gender quotas can significantly increase the equity and effectiveness of policy interventions for tackling challenges like climate change (Cook et al., 2019). Conclusion From April to July 2020, we surveyed 7,729 individuals, broadly representative for age and gender, in four European regions to explore how trust can provide an alternative governance mechanism across diverse citizens to increase commitment to tackling societal challenges at the regional level. The research topic is particularly timely and important considering the ongoing COVID-19 pandemic conditions and the political turmoil. The results of this study have important implications for policymakers, as our results highlight that trust indicators like transparency and trust in regional organizations significantly drive citizen demands to be engaged in policymaking: the greater the trust, the higher the demand for engagement. Similarly, citizen engagement increases trust. Thus, the greater the correlation between trust and citizen engagement for tackling grand societal challenges, the greater the value for all. However, women exhibit significantly higher trust in regional organizations than men without demanding greater engagement in regional policymaking. Practical and Theoretical Implications Our findings highlight the importance of trust in organizations and provide new evidence on gendered trust. The reported findings may have important implications for policymakers and they can serve as a point of reference for legislators when setting future policies for promoting gender equality and the advancement of women in the organizations. We contribute to the theoretical debate in the fields of organizational studies, corporate governance, and gender studies by three ways: ( 1 ) we investigate how individuals at the micro level trust organizations as macro level entities, ( 2 ) we contribute to the theory of gendered trust in organizations by investigating the gender-based differences in trust behavior and, ( 3 ) we also contribute to the timely corporate responsibility discussion, as trust is considered as one of the components forming the core of social responsibility in organizational context. Our research also contributes to the theory of gendered trust by documenting the gender-based differences in trust behavior. This gender gap calls for careful engagement strategies by policymakers at the regional, national, and supranational levels to increase the equity and effectiveness of policy interventions for tackling grand societal challenges. Cultivating and enhancing citizens’ trust by engagement principles that work well with both men and women will help tackle complex societal challenges. Promoting engagement to create sustainable ecosystems requires a solid governmental regulatory environment around sustainable value creation that includes diversity among stakeholders, such as gender equality. The greater the correlation between trust and citizen engagement across gender for tackling grand societal challenges, the greater the value for all, regardless of gender. Limitations and Directions for Future Research Despite the additional tests for robustness, several limitations need to be considered in interpreting the results presented in this paper. First, the sample consists of individuals residing in gender equal highly developed countries. Thus, the results may not be applicable to developing countries or to areas with lower levels of gender equality. As an avenue for future research, it would be interesting to examine if a similar gendered trust phenomenon can be found in developing countries or in less gender-equal countries. Second, as explained on p. 7, we acknowledge that gender is a continuum but, for statistical modeling purposes, we employ it as a binary variable (female/male) in this study. Thus, our research might not capture the total spectrum of genders. We leave this analysis for the future research to cover. Third, since the data are collected during the COVID-19 pandemic in 2020, the results may not be fully applicable to other time periods. Considering the COVID-19 pandemic, the war in Ukraine, and ongoing the energy crisis, it is clear that the societies have changed significantly during the past few years. Thus, it would be interesting to study the effect of these events on trust, in particular the effects on the gender-based differences in trust that we document in this paper. Finally, in addition to the examined features, other, non-examined individual-specific characteristics may have an impact on trust. Due to data constraints, additional analyses with other characteristic variables cannot be conducted here. Thus, these topics are left for future studies to address. Declarations Competing Interests The Author is the owner of the Research and Innovation Management GmbH which received funding for the RRI2SCALE project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 872526. Thus, the Author received support from her company such as her salary for producing this publication which was paid with this funding. Author Contribution K.F. wrote the main manuscript text and E.V. reviewed the manuscript. 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Footnotes While acknowledging that gender is a continuum, we measure it as a binary variable (female/male) in this study. Additional Declarations Competing interest reported. The Author is the owner of the Research and Innovation Management GmbH which received funding for the RRI2SCALE project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 872526. Thus, the Author received support from her company such as her salary for producing this publication which was paid with this funding. Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in npj Urban Sustainability → Version 1 posted Editorial decision: Revision requested 13 Oct, 2025 Reviews received at journal 11 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers invited by journal 18 Sep, 2025 Submission checks completed at journal 18 Sep, 2025 Editor assigned by journal 18 Jul, 2025 First submitted to journal 05 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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16:58:16","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161532,"visible":true,"origin":"","legend":"","description":"","filename":"6c658bd151c147d386ea4653555883221structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-6831450/v1/c2d62651ed326c761d128e3f.xml"},{"id":92434811,"identity":"782a3787-2732-4a6c-960c-5a7f41b23409","added_by":"auto","created_at":"2025-09-29 16:50:16","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185291,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6831450/v1/0fe3952085c3585c39564490.html"},{"id":92434786,"identity":"c62a3645-445b-4343-8555-4b86a18da2a9","added_by":"auto","created_at":"2025-09-29 16:50:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":284495,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes. *** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. Variables are (1) gender, (2) age, (3) trust indicators, (4) trust in regional organizations, and (5) demand for engagement in regional policymaking. We did not include education, stakeholder type, and employment or activity status as those are not continuous data.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6831450/v1/066d538503f3a84005abe328.png"},{"id":92434784,"identity":"a4d67336-3041-4b9e-a320-364097117714","added_by":"auto","created_at":"2025-09-29 16:50:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBayesian analysis and results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes. Data and (marginal) posterior predictions with distributions of the individual parameters. The posterior distributions are marked with their mean values and their 95% highest density intervals (HDIs). The region of practical equivalence (ROPE) is marked in blue and based on the convention for Cohen’s \u003cem\u003ed\u003c/em\u003eindicating small effect sizes (Cohen, 1988, 2013). As recommended (Kruschke, 2018), we use that small effect size, ranging from ‑0.09 to 0.09. The HDI limits were computed from the Markov chain Monte Carlo using Kruschke’s (2014) method and exceed an effective sample size of 10,000, as recommended. Our decisions to accept or reject our null hypotheses were based on the HDI+ROPE decision rule (Kruschke, 2018; Edwards \u0026amp; Berry, 2010): the null value is declared rejected if the 95% HDI falls completely outside the ROPE, and the null value is declared accepted for practical purposes if the 95% HDI falls completely inside the ROPE.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6831450/v1/58a7e0b7546638a399dcef4e.png"},{"id":92435680,"identity":"b2763bf5-b5f3-4125-93d1-33dc0ec33fc4","added_by":"auto","created_at":"2025-09-29 16:58:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":234508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eViolin plots of results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes. Gender differences among trust indicators, trust in regional organizations, and demand for policymaking engagement. Across the plots, blue indicates women and red men; natural logarithms for Bayes factor (BF) values are used (loge\u003csup\u003e(BF01)\u003c/sup\u003e=−loge\u003csup\u003e(BF10)\u003c/sup\u003e) with 95% CIs selected. We used default effect size priors (Cauchy scale 0.707). Bars correspond to median scores, and lower and upper hinges correspond to the first and third quartiles, respectively. The dots reflect the central tendency measure, whether mean or median, as indicated in the plot.\u003cstrong\u003e a) \u003c/strong\u003eResults for the Mann‑Whitney test for trust indicators; \u003cstrong\u003eb) \u003c/strong\u003eResults for the Mann‑Whitney test for trust in regional organizations; \u003cstrong\u003ec) \u003c/strong\u003eResults for the Mann‑Whitney test for demand for engagement. Further sequential analysis performed with JASP version 0.16.2., Bayesian robustness checks with Yuen’s (1974) \u003cem\u003et\u003c/em\u003e‑test for trimmed means and \u003cem\u003ep\u003c/em\u003e‑values based on Hochberg’s method are provided in the supplementary material.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6831450/v1/4832ce20bae0d347274889f6.png"},{"id":92434791,"identity":"3a3e2925-7dda-40aa-b826-2a6cd745b1f8","added_by":"auto","created_at":"2025-09-29 16:50:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGender differences in trade-offs in decision making\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes. Results for the Mann‑Whitney tests between genders for each of four dilemmas. Red points represents the median for each gender. For more details regarding BFs and Yuen’s \u003cem\u003et\u003c/em\u003e-tests for robustness, please refer to the supplementary material. \u003cstrong\u003ea) \u003c/strong\u003eGender differences regarding prioritizing innovation in relation to citizen well-being; \u003cstrong\u003eb)\u003c/strong\u003e gender differences regarding prioritizing innovation in relation to gender inequality; \u003cstrong\u003ec)\u003c/strong\u003e gender differences regarding prioritizing innovation in relation to personal data and privacy; \u003cstrong\u003ed) \u003c/strong\u003egender differences regarding between prioritizing innovation despite a lack of skills regarding its use.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6831450/v1/aa812a0dcd163a3156c4b3b3.png"},{"id":98244962,"identity":"6addd2d6-5d76-4297-b156-791cab2c45ec","added_by":"auto","created_at":"2025-12-15 16:16:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2653258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6831450/v1/d86c0cc0-0eee-4965-87a8-4a491d5fa4dd.pdf"}],"financialInterests":"Competing interest reported. The Author is the owner of the Research and Innovation Management GmbH which received funding for the RRI2SCALE project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 872526. Thus, the Author received support from her company such as her salary for producing this publication which was paid with this funding.","formattedTitle":"A Gender Gap? Public Trust in Regional Policymaking and Citizen Demands for Engagement","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTrust is an underlying psychological condition in which we accept vulnerability because we expect positive intentions or behavior from another (Rousseau, Sitkin, Burt, \u0026amp; Camerer, 1998). Trust also acts as a social glue that keeps partners in a partnership (e.g., Faems et al., 2008). Its multidimensional conceptualization highlights its key role in policymaking (Rousseau et al., 1998). Trust also represents an alternative governance mechanism partly because it increases confidence in other parties’ commitment to a greater good (Faems et al., 2008). We trust one another when we believe that others are interacting authentically, when we have faith in their judgment and competence, and when we think that those other people care (Jones \u0026amp; George, 1998). Thus, trust in groups or organizations is crucial for tackling the grand societal challenges that we are facing (Nature, 2010; Tollefson, 2010).\u003c/p\u003e\n\u003cp\u003eTrust is also very important from the corporate responsibility point of view as the four building blocks of corporate responsibility are ethics, leadership, personal responsibility, and trust (Mostovicz, Kakabadse, \u0026amp; Kakabadse, 2011). It is reported that organizations that are responsible and comply with principles of accountability, transparency, and disclosure will enjoy higher levels of trust (Child \u0026amp; Rodrigues, 2004). Moreover, trust violations disrupt the social norms, and the society as a whole suffers if there is a loss of trust.\u003c/p\u003e\n\u003cp\u003ePrevious research has highlighted the innovative impact of trust at the individual level of analysis (Clegg, Unsworth, Epitropaki, \u0026amp; Parker, 2002) in such a way that trust in an organization predicted innovative behavior and at the organizational level (Baer \u0026amp; Frese, 2003) in such a way that a trust-filled climate positively affects personal initiative. Given that trust embodies risk taking (McAllister, 1995), we expect that individuals who feel trust in their regional organizations and regional policymaking are more likely to take the risk of taking ownership of these aspects (Parker, Williams, \u0026amp; Turner, 2006). This ownership and willingness to engage are crucial for tackling grand societal challenges. Thus, citizen engagement has consistently been promoted in research and policymaking. However, to our knowledge, no study to date has focused on the specific relationship between trust and citizen demands for engagement in regional policymaking, with a particular focus on tackling grand societal challenges. Thus, the aim of this research report is to summarize the results from a European Commission-funded project dedicated to answering following research question: \u003cem\u003eHow does trust shape citizen engagement in regional policymaking for tackling grand societal challenges and vice versa?\u0026nbsp;\u003c/em\u003eWe hypothesize that trust in regional organizations supports citizen engagement in tackling complex societal challenges and vice versa: the greater the demand for citizen engagement, the higher the trust in regional organizations to solve complex societal challenges. Furthermore, we explore how trade-off dilemmas between innovation and societal challenges related to energy, smart cities, and transportation are perceived by different cohorts of individuals. However, as studies have found that women show different levels of trust (e.g., Haselhuhn et al., 2015; van den Akker et al., 2020), we also expect gender differences in this context. Moreover, literature on gender-based behavioral differences is unanimous in suggesting that women are more risk averse than men and exhibit less risky behavior in decision-making (e.g., Eckel \u0026amp; Grossman, 2002; Fehr-Duda et al, 2006; Johnson \u0026amp; Powell, 1994; Levin, Snyder \u0026amp; Chapman, 1988; Powell \u0026amp; Ansic, 1997; Watson \u0026amp; McNaughton, 2007.) Consequently, we hypothesize that trust and gender significantly impact demand for engagement in regional policymaking. With respect to practical implications, we also analyze different gender perspectives in greater detail, as recent research has found that gender quotas can significantly increase the equity and effectiveness of policy interventions for tackling grand societal challenges like climate change (Cook, Grillos, \u0026amp; Andersson, 2019).\u003c/p\u003e\n\u003cp\u003eThe motivation of our study stems from the well-documented importance of trust in organizations, combined with the existing behavioral gender-based differences for example in trust, risk aversion, and networking. Our research findings may be of interest to legislators when setting future policies for promoting gender equality and the advancement of women in the organizations. \u0026nbsp;Finally, our manuscript will contribute to the scientific debate in the fields of organizational studies, corporate governance, and gender studies as follows: first, our theoretical contribution focuses on mapping how individuals at the micro level trust organizations as macro level entities, second, our research contributes to the theory of gendered trust in organizations by investigating the gender-based differences in trust behavior and, finally we also contribute to the timely corporate responsibility discussion since trust is considered as one of the components forming the core of social responsibility in an organizational context. \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTheoretical Background\u003c/h3\u003e\n\u003cp\u003eAlthough the study of trust has long been an area of inquiry in organization studies, the recent COVID-19 pandemic has shaken trust in organization research and challenged the accepted answers to age-old questions (Meyer \u0026amp; Quattrone, 2021). In addition, the war in Ukraine, the general political instability, and the ongoing energy crisis have caused severe turbulence in the economy, thereby highlighting the importance of trust in organizations. Trust and its repair during and after crises is a multi-faceted phenomenon across individual, organizational, institutional, and societal levels. According to the Neo-Classical Theory (also known as Human Relations Theory) that focuses on examining how people behave in a particular situation, trust is the foundation of the human relations (Shorthose, 1996) and, consequently, a crucial part of a well-functioning organization.\u003c/p\u003e\u003cp\u003eTrust and its dynamics at the macro and micro levels have already been conceptualized (e.g., Rousseau et al., 1998). However, most theory on trust repair has focused on micro-level phenomena, with only a few attempts to study it on the macro level (Dirks \u0026amp; Ferrin, 2001; Kramer \u0026amp; Lewicki, 2010). At the same time, earlier studies indicate that trust is a relational construct that has social and affective elements (Bachmann et al., 2015). Therefore, solutions to the questions of trust repair at the micro and macro level may be very different. In this regard, prior work highlights that the processes of trust repair are fundamentally different at the macro organizational level than at the interpersonal micro level (Gillespie \u0026amp; Dietz, 2009).\u003c/p\u003e\u003cp\u003eRelated to trust repair, earlier literature suggests that trust may be repaired by improving transparency in organizations (e.g., Rawlins, 2009). Auger (2014) reports that organizations demonstrating transparency both related to the organization\u0026rsquo;s reputation for transparency and its efforts to communicate transparency achieved significantly higher levels of trust than non-transparent organizations.\u003c/p\u003e\u003cp\u003eTrust among network members makes the network effective. Thus, social networks are important regarding building and maintaining trust. Earlier literature indicates that women and men build and manage their social networks in different ways (e.g., Ibarra, 1993; McPherson, Smith-Lovin \u0026amp; Cook, 2001; Moore, 1990). An early study by Schein (1978) suggests that women don\u0026rsquo;t network as well as men. This may hurt their performance at different social contexts, including the work environment. Prior studies also indicate that trust among networks is gendered, which may limit the effectiveness of the networks (Merluzzi, 2017; Song, Vernet \u0026amp; Pryke, 2022). Interestingly, Bevelander and Page (2011) report that the social network size varies with level of trust between parties. Moreover, they suggest that women network in a manner that disadvantage them.\u003c/p\u003e\u003cp\u003eTo conclude, the earlier literature indicates that 1) trust is crucial for the optimal functioning of an organization and that 2) trust is gendered. We combine these two strands of literature by examining the possible existence of a gender gap in public trust. The present study draws on theory from prior trust research, with a focus on trust repair (Bachmann, 2001; Bachmann, Gillespie, \u0026amp; Priem, 2015; Bachmann \u0026amp; Inkpen, 2011; Gillespie, Hurley, Dietz, \u0026amp; Bachmann, 2012; Owen \u0026amp; Currie, 2021; Pfarrer, Decelles, Smith, \u0026amp; Taylor, 2008). In this context, our theoretical contribution centers on how individuals at the micro level trust organizations as entities at the macro level. Moreover, our research also contributes to the theory of gendered trust by documenting the gender-based differences in trust behavior. Our findings complement theory on trust building in organization studies in two key ways. First, we identify how micro-level trust indicators drive trust development at the organizational level and show how they differ between individuals. Second, our findings regarding those individual differences also show differences when facing trade-off dilemmas between innovation and societal challenges, which are crucial steps to maintain trust across individuals after a crisis. Finally, our theory of trust differences between the micro and macro levels stresses the importance of engaging individuals in order to maintain trust at the organizational level.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAll data, analysis code, and research materials are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/zj2w3/?view_only=5391ff03d3844960bd5114e483a111ed\u003c/span\u003e\u003cspan address=\"https://osf.io/zj2w3/?view_only=5391ff03d3844960bd5114e483a111ed\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Data were analyzed using R version 4.1.3 and JASP version 0.16.2. This study\u0026rsquo;s design and analysis were not preregistered. However, due to applied Bayesian inference, preregistration is not necessary (Dienes, 2014; Etz \u0026amp; Wagenmakers, 2017; Ly, Verhagen, \u0026amp; Wagenmakers, 2016; Rouder \u0026amp; Morey, 2012; van Doorn et al., 2021). Data collection was conducted by regional survey companies. Where required, regional data collection was approved by relevant ethics committees (e.g., University of Twente BMS Ethical Committee, no. 200184). We obtained electronic informed consent from all participants.\u003c/p\u003e\u003cp\u003eOur methodological approach was supported by four regional survey companies (Palmos Analysis P.C., Newcom Research and Consultancy B.V., Newton Research Europe d.o.o., and Kantar A.S.) that used local languages to collect data from April to July 2020 by conducting online surveys of a total of 7,729 individuals in Greece\u0026rsquo;s Kriti region, the Netherlands\u0026rsquo;s Overijssel region, Spain\u0026rsquo;s Galicia region, and Norway\u0026rsquo;s Vestland region. These four regions were selected due to resource availability and network, as regional partners were available in these areas. All the examined areas are in highly developed industrial countries with high gender equality. Thus, the data from these four areas are comparable and can be examined as one data set.\u003c/p\u003e\u003cp\u003eWe did not exclude any subjects from our analysis. However, for gender analysis between women and men, we excluded the 38 subjects who did not share their gender\u003csup\u003e[1]\u003c/sup\u003e. We did not exclude outliers. The data were collected from respondents of at least 18 years of age. We deliberately recruited samples broadly representative for age and gender (3,861 men; 3,830 women) in those four regions, with the following stakeholder distribution: 5.6% academics, 5.2% policymakers, 18.1% entrepreneurs, 51.5% in civil society, and 19.7% others. While demographic questions were asked (gender, age, educational level, activity status, and country), we focus in this study on central questions related to regional dilemmas, trust indicators, trust in organizations, and citizen demands for policymaking engagement. All reported \u003cem\u003ep\u003c/em\u003e-values are two-tailed. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the descriptive statistics of each region and across regions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample descriptive statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpain: Galicia (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreece: Kriti \u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNorway: Vestland \u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNetherlands: Overijssel \u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;2006)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;2010)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;2053)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1660)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;7729)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStakeholder\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic community (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e227 (13.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e433 (5.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220 (11.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e159 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e399 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusiness (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e595 (29.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e541 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1399 (18.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCivil society (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1691 (84.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1920 (93.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e175 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3978 (51.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e941 (46.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e558 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1520 (19.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProfession status activity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployee (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1242 (61.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e963 (47.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1268 (61.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e810 (48.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4283 (55.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e325 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e251 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e689 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e600 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e504 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e356 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1636 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePupil or Student (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e214 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e523 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e283 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e315 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEducation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo or primary education (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e363 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary education (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e628 (31.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e886 (44.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e228 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e996 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2738 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s degree or equivalent (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e998 (49.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e702 (34.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e679 (33.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e445 (26.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2824 (36.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostgraduate, doctoral, or equivalent (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e336 (16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e215 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e497 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e166 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1214 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e558 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e590 (7.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;24 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e293 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e605 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e450 (22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e274 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e208 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1112 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;65 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1329 (66.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1248 (62.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1233 (60.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e762 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4572 (59.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65 \u0026le; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e481 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e454 (22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e397 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1440 (18.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000 (49.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e971 (48.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1026 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e864 (52.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3861 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1001 (49.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1039 (51.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1027 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e763 (46.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3830 (49.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes. In Vestland, the categories of higher general education (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and higher vocational education (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) were also available; for the present study, they were merged into higher education (Bachelor\u0026rsquo;s degree or equivalent) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e HERE\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMeasurements\u003c/h2\u003e\u003cp\u003eWe employ the following four measures for examining the possible gender gap in public trust: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Trust indicators, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Trust in regional organizations, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Demand in engagement, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Dilemmas. We present these measures in detail in the following.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTrust indicators.\u003c/em\u003e We used a five-point Likert scale from \u003cem\u003estrongly disagree\u003c/em\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to \u003cem\u003estrongly agree\u003c/em\u003e (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) to quantify trust indicators, using a composite measure that averaged each individual\u0026rsquo;s answer to the following four questions: In terms of general trust, I trust organizations or groups of people when they: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) assess the effects of innovation in an independent way (autonomy); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) look at the effects of innovation from different angles (diversity); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) clearly indicate which interests they have in innovation (interest); and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) communicate in an open way about innovation (transparency). Cronbach\u0026rsquo;s alpha was .849.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTrust in regional organizations.\u003c/em\u003e We used a five-point Likert scale from \u003cem\u003enot at all\u003c/em\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to \u003cem\u003every much\u003c/em\u003e (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) to quantify trust indicators, using a composite measure that averaged each individual\u0026rsquo;s levels of agreement with the following eight statements: I trust the following in my region: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) regional government, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) local government, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) civil society organizations, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) non-governmental organizations, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) researchers, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) small- and medium-sized businesses, (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) large companies, (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) gender-balanced governing bodies. Cronbach\u0026rsquo;s alpha was .764.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDemand in engagement.\u003c/em\u003e We used a five-point Likert scale from \u003cem\u003estrongly disagree\u003c/em\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to \u003cem\u003estrongly agree\u003c/em\u003e (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) to quantify demand in engagement in regional policymaking, using a composite measure that averaged each individual\u0026rsquo;s levels of agreement with two statements: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) I believe that citizens should be actively involved in helping to design regional innovation policies; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) I believe that citizens should be actively involved in helping to evaluate regional innovation policies. Cronbach\u0026rsquo;s alpha was .821.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDilemmas.\u003c/em\u003e We used a five-point Likert scale from \u003cem\u003estrongly disagree\u003c/em\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to \u003cem\u003estrongly agree\u003c/em\u003e (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) to quantify regional dilemmas using each individual\u0026rsquo;s levels of agreement with the following statements: Please indicate your agreement with the following arguments: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) I believe that promoting innovation should be a higher priority than citizens\u0026rsquo; well-being (such as jobs, income, housing, health, safety); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) I believe that innovation should be boosted even though it might create gender inequalities in my region; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) I believe that it is good to support innovation when it has a positive impact on smart cities, energy, and transport, even if it requires access to my personal data; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) I believe that innovation outcomes for facilitating smart cities, energy, and transport should be boosted, even if I might not have all the necessary skills to use them.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a correlation matrix indicating a significant relationship between (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) age, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) trust indicators, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) trust in regional organizations with (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) demands for policy involvement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e HERE\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn light of the advantages of Bayesian inference (Dienes, 2014; Etz \u0026amp; Wagenmakers, 2017; Ly et al., 2016; Rouder \u0026amp; Morey, 2012; van Doorn et al., 2021), we tested our hypothesis by conducting a Bayesian linear mixed‑effect model of the effects of trust indicators, trust in regional organizations, and gender, including the control variables education, employment status, stakeholder, and age as fixed effects and region as a random intercept, with participants nested within nationalities. As recommended (Gelman, Jakulin, Pittau, \u0026amp; Su, 2008), we used default non‑informative priors for all coefficients that represent a lack of knowledge about the effect size under examination (G\u0026ouml;nen, Johnson, Lu, \u0026amp; Westfall, 2005; Liang, Paulo, Molina, Clyde, \u0026amp; Berger, 2008; Rouder, Speckman, Sun, Morey, \u0026amp; Iverson, 2009). For our test, we plotted the parameter estimation with posterior distribution with credible intervals and accepted or rejected the null hypothesis following the highest density interval (HDI) with the region of practical equivalence (ROPE) decision (Kruschke, 2018). In addition, we provide violin plots to compare gender depicting the Mann‑Whitney U tests for trust indicators, trust in regional organizations, and demand for engagement. In this context, we also provide Bayesian tests and Yuen \u003cem\u003et\u003c/em\u003e‑tests for our variable under investigation to check robustness. Additionally, we conducted further robustness and sequential analysis with JASP version 0.16.2. (see the supplementary material at https://osf.io/zj2w3/?view_only=5391ff03d3844960bd5114e483a111ed).\u003c/p\u003e\n\u003cp\u003eWe report decisions regarding whether the regression coefficients are effectively zero or non‑zero. Decisions were made by using the posterior estimated magnitudes of regression coefficients. We employ the decision rule using the HDI of the posterior distribution for accepting or rejecting null values of parameters and the ROPE (Kruschke, 2010, 2013, 2018; Kruschke, Aguinis, \u0026amp; Joo, 2012). The range of parameter values that is good enough for practical purposes is expressed by the ROPE. The range of values of \u0026theta; (demand for engagement in regional policymaking) that includes the 95% most credible values is marked in the posterior distribution as the 95% HDI that refers to the probability density. In other words, every parameter value within the HDI has a higher probability density and thus credibility than any parameter outside the HDI. The 95% HDI contains the 95% most credible values of the parameter. The HDI limits are computed from the Markov chain Monte Carlo chain using Kruschke\u0026rsquo;s method (2014) and exceeding an effective sample size of 10,000, as recommended.\u003c/p\u003e\n\u003cp\u003eFollowing the recommendations in the literature (Etz \u0026amp; Wagenmakers, 2017; Kruschke, 2021; Ly et al., 2016; van Doorn et al., 2021), we determine the size of the effect by plotting the posterior distributions marked with their mean values and their 95% HDIs and decide to accept or reject the null hypotheses by using the HDI‑with‑ROPE decision rule (Kruschke, 2018): the null value is declared rejected if the 95% HDI falls completely outside the ROPE, and the null value is declared accepted for practical purposes if the 95% HDI falls completely inside the ROPE. In other words, a parameter value is \u003cem\u003erejected\u003c/em\u003e when its ROPE falls entirely outside the 95% HDI. If the entire HDI\u0026mdash;that is, all the most credible values\u0026mdash;falls within the ROPE, then we accept the target value for practical purposes; in other words, a parameter value is \u003cem\u003eaccepted\u003c/em\u003e when its ROPE completely contains the 95% HDI. We also present visual figures and numerical tables with posterior modes, medians and means, standard deviations, posterior distributions, 95% HDI, ROPE, and percentage within ROPE; this will allow readers to decide for themselves whether the HDI is or is not fully inside or outside the ROPE.\u003c/p\u003e\n\u003cp\u003eWe conducted the Bayesian analysis based on the following linear mixed‑effect model equation: (\u003cem\u003e1\u003c/em\u003e) H\u003csub\u003eA\u003c/sub\u003e: \u003cem\u003ey\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e= \u0026beta;\u003csub\u003e0\u0026nbsp;\u003c/sub\u003e+ \u0026beta;\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eTI\u003c/em\u003e\u003csub\u003ei\u0026nbsp;\u003c/sub\u003e+ \u0026beta;\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eTO\u003c/em\u003e\u003csub\u003ei\u0026nbsp;\u003c/sub\u003e+ \u0026beta;\u003csub\u003e3\u003c/sub\u003e\u003cem\u003eTO\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003eG\u003csub\u003ei\u0026nbsp;\u003c/sub\u003e+\u003csub\u003e\u0026nbsp;\u003c/sub\u003econtrol variables + ϵ\u003csub\u003ei\u003c/sub\u003e; versus\u003csub\u003e\u0026nbsp;\u003c/sub\u003e(\u003cem\u003e2\u003c/em\u003e) H\u003csub\u003e0\u003c/sub\u003e: \u003cem\u003ey\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e= \u0026beta;\u003csub\u003e0\u0026nbsp;\u003c/sub\u003e+ \u0026beta;\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eTI\u003c/em\u003e\u003csub\u003ei\u0026nbsp;\u003c/sub\u003e+ \u0026beta;\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eTO\u003c/em\u003e\u003csub\u003ei\u0026nbsp;\u003c/sub\u003e+\u003csub\u003e\u0026nbsp;\u003c/sub\u003econtrol variables + ϵ\u003csub\u003ei\u003c/sub\u003e, where \u003cem\u003ey\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e represents the demand for policymaking engagement of individual \u003cem\u003eI\u003c/em\u003e, \u003cem\u003eTI\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e the trust indicator, and \u003cem\u003eTO\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e this individual\u0026rsquo;s trust in regional organizations. Figure 2 presents the results of our Bayesian linear mixed-effect model, which highlights that trust indicators and trust in regional organizations significantly impact the demands for citizen engagement in regional policymaking: based on the HDI+ROPE decision rule (Kruschke, 2018; Edwards \u0026amp; Berry, 2010), the null values of trust indicators (\u003cem\u003e0\u003c/em\u003e% in ROPE, \u003cem\u003eM\u003c/em\u003e = 0.18, \u003cem\u003eSD\u003c/em\u003e = 0.01, 95% CI [\u003cem\u003e0.15, 0.2\u003c/em\u003e]) and trust in regional organizations (\u003cem\u003e0\u003c/em\u003e% in ROPE, \u003cem\u003eM\u003c/em\u003e = 0.23, \u003cem\u003eSD\u003c/em\u003e = 0.02, 95% CI [0.19, 0.27]) are declared rejected because the 95% HDI falls completely outside the ROPE (see table 2). In other words, trust indicators and trust in regional organizations drive citizen demands for engagement in public policymaking. The same holds true for gender; as figure 2 shows, our Bayesian analysis reveals that for gender, only 17.86% of the HDI falls within the conventional ROPE of Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e of [\u0026minus;0.09, 0.09], which provides evidence in favor of the alternative hypothesis that gender shows a significant effect on demand for engagement in policymaking (\u003cem\u003eM\u003c/em\u003e = -0.16, \u003cem\u003eSD\u003c/em\u003e = 0.08, 95% CI [\u003cem\u003e-0.32,0\u003c/em\u003e]). However, the interaction of gender and trust in organization can be considered not significant, as the 95% HDI falls completely inside the ROPE (\u003cem\u003e100\u003c/em\u003e% in ROPE, \u003cem\u003eM\u003c/em\u003e = 0.04, \u003cem\u003eSD\u003c/em\u003e = 0.02, 95% CI [-0.01, 0.08]; see table 2). In other words, although women display greater trust in others\u0026rsquo; intentions to perform according to their positive expectations, they do not demand significantly more active engagement in policymaking to monitor those they trust. Furthermore, the null values of control variables such as education, employment status, and stakeholder are declared accepted for practical purposes because the 95% HDI falls completely inside the ROPE. Finally, we also declare age, with 72.82% inside the ROPE, as not significant for our results.\u003c/p\u003e\n\u003cp\u003eFIGURE 2 HERE\u003c/p\u003e\n\u003cp\u003eUsing the HDI‑with‑ROPE decision rule for hypothesis testing has been promoted as increasing the predictive precision of theories in the organizational sciences (Edwards \u0026amp; Berry, 2010). The ROPE represents a decision threshold, and its limits are always chosen in the context of current theory and measurement precision. As recommended (Kruschke, 2018), we use conventional parameters and the limits typically observed in social and behavioral research, such as Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e, to measure small, medium, and large effect sizes (Cohen, 1988, 2013). Referring to our research framework, we use effect size as a parameter that corresponds to the convention that 0.2 is a \u0026ldquo;small\u0026rdquo; effect size for Cohen\u0026rsquo;s \u003cem\u003ed\u0026nbsp;\u003c/em\u003e(2013). In line with recommendations (Kruschke, 2018), we set the ROPE for linear models to ‑0.1 * \u003cem\u003eSdy\u003c/em\u003e, 0.1 * \u003cem\u003eSdy\u003c/em\u003e. In line with Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e for small effect at 0.2, the effect size of a mean as \u0026delta; = (\u0026mu;‑\u0026micro;\u003csub\u003e0\u003c/sub\u003e)/\u0026sigma; is practically equivalent to zero if it is less than half of the small effect and falls within the ROPE. This decision regarding the ROPE has been made with respect to state-of-the-art of theory and the best measuring device available (Serlin \u0026amp; Lapsley, 1985).\u0026nbsp;\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBayesian analysis for HDI-with-ROPE decision rule\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eROPE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e(ROPE) in %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust in organizations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStakeholder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust in organizations x Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eNotes. Means, medians, and modes correspond to unstandardized betas on the dependent variable demand in engagement; SD\u0026thinsp;=\u0026thinsp;standard deviation, HDI\u0026thinsp;=\u0026thinsp;highest density interval, and ROPE\u0026thinsp;=\u0026thinsp;region of practical equivalence. We set a default non-informative prior (Stan Development Team, 2022) and, as recommended (Kruschke, 2018), for HDI we used conventional parameters such as Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e (1988, 2013) to measure small effect sizes. The HDI limits were computed from the Markov chain Monte Carlo using Kruschke\u0026rsquo;s method (2014) and exceeding an effective sample size of 10,000, as recommended.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTABLE 2 HERE\u003c/p\u003e\n\u003cp\u003eTo explore gender differences between the 3,830 women and 3,861 men, we performed a Mann-Whitney test (two-sided testing), because our data are not normally distributed. All reported \u003cem\u003ep\u003c/em\u003e‑values are two-tailed. Our results, which are illustrated in violin plots in figure 3, reveal the following gender differences: As shown in figure 3a, women (\u003cem\u003eM\u003c/em\u003e = 3.50, \u003cem\u003eSD\u003c/em\u003e = 0.95) and men (\u003cem\u003eM\u003c/em\u003e = 3.50, \u003cem\u003eSD\u003c/em\u003e = 0.94) rely on trust indicators like transparency, autonomy, interest, and diversity similar (\u003cem\u003eW\u003c/em\u003e\u003csub\u003eMann-Whitney\u003c/sub\u003e = 7.41e\u003csup\u003e6\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e = .84, 95% CI = [-0.02, 0.03], \u003cem\u003en\u003c/em\u003e = 7,691). The Bayes factor (BF) for the same analysis revealed that the data were 3.66 times (\u0026delta; = 1.27e\u003csup\u003e-3\u003c/sup\u003e, 95% CI = [-0.02, 0.03], \u003cem\u003er\u003c/em\u003e = .71) more probable under the null hypothesis (BF\u003csub\u003e01\u003c/sub\u003e) than the alternative hypothesis (BF\u003csub\u003e10\u003c/sub\u003e). This can be considered moderate evidence (Jeffreys, 1961) in favor of the null hypothesis.\u003c/p\u003e\n\u003cp\u003eNext, and by contrast, women displayed significant higher trust (\u003cem\u003eM\u003c/em\u003e = 3.42, \u003cem\u003eSD\u003c/em\u003e = 0.73) than men (\u003cem\u003eM\u003c/em\u003e = 3.34, \u003cem\u003eSD\u003c/em\u003e = 0.71) in different regional organizations (\u003cem\u003eW\u003c/em\u003e\u003csub\u003eMann-Whitney\u003c/sub\u003e = 6.80e\u003csup\u003e6\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e = 1.20e\u003csup\u003e-9\u003c/sup\u003e, 95% CI = [-0.11, -0.05], \u003cem\u003en\u003c/em\u003e = 7,691). The BF for the same analysis revealed that the data were 8.35 times (\u0026delta; = -0.08, 95% CI = [-0.11, -0.05], \u003cem\u003er\u003c/em\u003e = .71) more probable under the alternative hypothesis (BF\u003csub\u003e10\u003c/sub\u003e) than the null hypothesis (BF\u003csub\u003e01\u003c/sub\u003e). This can be considered moderate evidence (Jeffreys, 1961) in favor of the alternative hypothesis. Although women trust organizations significantly more than men, there is not enough evidence to indicate that women demand (\u003cem\u003eM\u003c/em\u003e = 3.90, \u003cem\u003eSD\u003c/em\u003e = 0.87) significantly higher citizen involvement than men (\u003cem\u003eM\u003c/em\u003e = 3.91, \u003cem\u003eSD\u003c/em\u003e = 0.87) in the design and implementation of regional policymaking (\u003cem\u003eW\u003c/em\u003e\u003csub\u003eMann-Whitney\u003c/sub\u003e = 7.45e\u003csup\u003e6\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e = .55, 95% CI = [-0.02, 0.03], \u003cem\u003en\u003c/em\u003e = 7,691). The BF for the same analysis was not performed because the distribution of the variable is too sparse. We also performed a robustness check: \u003cem\u003et\u003c/em\u003e\u003csub\u003eYuen\u003c/sub\u003e(7612.76) = 0.492, \u003cem\u003ep\u003c/em\u003e = .623, 95% CI = [-0.023, 0.060], \u003cem\u003en\u003c/em\u003e = 7,691.\u003c/p\u003e\n\u003cp\u003eFIGURE 3 HERE\u003c/p\u003e\n\u003cp\u003eBecause women exhibit greater trust in organizations without demanding more citizen engagement in policymaking, we further explore trade-off dilemmas between innovation and societal challenges. The results presented in figure 4 highlight how men and women perceive trade-off decisions regarding innovation differently and to a significant degree. The results illustrate a rather traditional mindset among respondents, with women significantly more averse to innovation than men. Our evidence indicates that men tend to be more innovation-oriented than women when it comes to trade-off decisions; however, this comes at the cost of societal challenges. In our survey, self-reporting dilemmas across genders were non-normally distributed according to a Kolmogorov\u0026ndash;Smirnov test, so we report non-parametric statistics for all trade-off comparisons. A Mann-Whitney U test revealed that, overall, female participants showed a significantly higher tendency against innovation and toward well-being (\u003cem\u003eW\u003c/em\u003e\u003csub\u003eMann-Whitney\u003c/sub\u003e = 8.01e\u003csup\u003e6\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e = 5.72e\u003csup\u003e-11\u003c/sup\u003e, 95% CI = [0.06, 0.11], \u003cem\u003en\u003c/em\u003e = 7,691). The BF for the same analysis revealed strong evidence (Jeffreys, 1961) that the data were 15.55 (\u0026delta; = 0.18, 95% CI = [0.13, 0.24], \u003cem\u003er\u003c/em\u003e = .71) times more probable under the alternative hypothesis (BF\u003csub\u003e10\u003c/sub\u003e) than the null hypothesis (BF\u003csub\u003e01\u003c/sub\u003e). These results, which are illustrated in figure 4a, stress first that women demonstrate a significantly different point of view regarding promoting innovation vis-\u0026agrave;-vis prioritizing citizen well-being. It is thus not surprising that female participants showed a significantly higher tendency against innovation and toward gender equality (\u003cem\u003eW\u003c/em\u003e\u003csub\u003eMann-Whitney\u003c/sub\u003e = 8.49e\u003csup\u003e6\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e = 2.17e\u003csup\u003e-31\u003c/sup\u003e, 95% CI = [0.12, 0.17], \u003cem\u003en\u003c/em\u003e = 7,691). The BF for the same analysis revealed very strong evidence (Jeffreys, 1961) that the data were 65.77 (\u0026delta; = 0.33, 95% CI = [0.28, 0.38], \u003cem\u003er\u003c/em\u003e = .71) times more probable under the alternative hypothesis (BF\u003csub\u003e10\u003c/sub\u003e) than the null hypothesis (BF\u003csub\u003e01\u003c/sub\u003e). Second, as illustrated in figure 4b, women disagreed more strongly than men with the statement that innovation should be boosted, even though that might create gender inequalities in their region.\u003c/p\u003e\n\u003cp\u003eThird, as illustrated in figure 4c, women showed a significantly stronger rejection of innovation that has positive impacts on smart cities, energy, and transport when it requires access to personal data or comes at a cost to privacy. Again, female participants showed a significantly higher tendency against innovation and toward data protection (\u003cem\u003eW\u003c/em\u003e\u003csub\u003eMann-Whitney\u003c/sub\u003e = 8.11e\u003csup\u003e6\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e = 3.21e\u003csup\u003e‑14\u003c/sup\u003e, 95% CI = [0.07, 0.12], \u003cem\u003en\u003c/em\u003e = 7,691). The BF for the same analysis revealed very strong evidence (Jeffreys, 1961) that the data were 25.35 (\u0026delta; = 0.22, 95% CI = [0.17, 0.28], \u003cem\u003er\u003c/em\u003e = .71) times more probable under the alternative hypothesis (BF\u003csub\u003e10\u003c/sub\u003e) than the null hypothesis (BF\u003csub\u003e01\u003c/sub\u003e). Finally, as illustrated in figure 4d, women tend to have a significantly different view than men of innovation outcomes for facilitating smart cities, energy, and transport when they do not have the skills needed to use them. Overall, female participants also showed a significantly higher tendency against innovation and toward accepting a lack of skills (\u003cem\u003eW\u003c/em\u003e\u003csub\u003eMann-Whitney\u003c/sub\u003e = 5.49e\u003csup\u003e-13\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e = 2.17e\u003csup\u003e-31\u003c/sup\u003e, 95% CI = [0.07, 0.12], \u003cem\u003en\u003c/em\u003e = 7,691). The BF for the same analysis revealed strong evidence (Jeffreys, 1961) that the data were 22.75 (\u0026delta; = 0.20, 95% CI = [0.15, 0.26], \u003cem\u003er\u003c/em\u003e = .71) times more probable under the alternative hypothesis (BF\u003csub\u003e10\u003c/sub\u003e) than the null hypothesis (BF\u003csub\u003e01\u003c/sub\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFIGURE 4 HERE\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur empirical study results stress that trust in organizations that are involved in regional innovation strategies for tackling grand societal challenges is significantly higher among women than among men. Despite prior research reporting a tendency for men to exhibit more trust than women (e.g., Falk and Hermle, 2018), our study\u0026rsquo;s empirical findings indicate that women trust organizations involved in regional innovation strategies more than men. Although women display greater trust in others\u0026rsquo; intention to perform according to their positive expectations (e.g., Cook et al., 2019), they do not demand significantly more active citizen engagement in policymaking to monitor those they trust. Violating this trust would occur when those females\u0026rsquo; positive expectations are not met. Violations harm women\u0026rsquo;s trust less than men\u0026rsquo;s trust, and trust among women has been found to be more resistant to change in the face of untrustworthy behavior (Haselhuhn et al., 2015). Overall, it is unclear why women do not demand greater engagement in regional innovation policymaking to accompany, much less validate, their trust. Our results show that the gender difference in trade-off decision-making perceptions turns out to be especially strong when it comes to gender equality. This gap is particularly important because decision makers who could be authorized to redistribute resources during crises may face trade-offs that support traditional gender-inequality mindsets. In particular, as COVID-19 has had a more negative social and economic impact on women than on men (Wenham et al., 2020), we need to consider different gendered positive expectations about other partners\u0026rsquo; capabilities in future perilous innovative-driven situations and avoid violating this trust. The COVID-19 crisis has endangered gender equality; women have been more vulnerable throughout the pandemic (Utoft, 2020).\u003c/p\u003e\u003cp\u003eThe innovation potential of women represents an underexploited source of value creation and economic growth (Strohmeyer, Tonoyan, \u0026amp; Jennings, 2017). Policymakers and companies making investments in developing an ecosystem that encourages women to boost innovation (EC, 2020). For decades, the United Nations has called for women to be engaged in environmental decision making at all policy levels (Buckingham, 2010), and the European Union has committed to implementing gender equality as one of its sustainable development goals (European Commission, 2016). It is clear that underutilizing the capability of the females, that is, approximately half of the population, is not optimal from the society\u0026rsquo;s point of view. At the same time, women themselves must also demonstrate more attention to and interest in being engaged in policymaking. Any effort toward gender equality begins with endeavors to adjust deep-rooted mindsets, especially during crises. Paradoxically, women\u0026rsquo;s high trust and low policy engagement expectations appear contrary to their self-interest, especially as more women serve as leading decision makers. This desire among women to maintain relationships even at the cost of gender equality (Buchan et al., 2008; Haselhuhn et al., 2015) calls for more and better practices and policy interventions to boost women\u0026rsquo;s desire for and comfort with engagement in regional policymaking. This is especially important for tackling grand societal challenges, for it has been shown that gender quotas can significantly increase the equity and effectiveness of policy interventions for tackling challenges like climate change (Cook et al., 2019).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFrom April to July 2020, we surveyed 7,729 individuals, broadly representative for age and gender, in four European regions to explore how trust can provide an alternative governance mechanism across diverse citizens to increase commitment to tackling societal challenges at the regional level. The research topic is particularly timely and important considering the ongoing COVID-19 pandemic conditions and the political turmoil. The results of this study have important implications for policymakers, as our results highlight that trust indicators like transparency and trust in regional organizations significantly drive citizen demands to be engaged in policymaking: the greater the trust, the higher the demand for engagement. Similarly, citizen engagement increases trust. Thus, the greater the correlation between trust and citizen engagement for tackling grand societal challenges, the greater the value for all. However, women exhibit significantly higher trust in regional organizations than men without demanding greater engagement in regional policymaking.\u003c/p\u003e\n\u003ch3\u003ePractical and Theoretical Implications\u003c/h3\u003e\n\u003cp\u003eOur findings highlight the importance of trust in organizations and provide new evidence on gendered trust. The reported findings may have important implications for policymakers and they can serve as a point of reference for legislators when setting future policies for promoting gender equality and the advancement of women in the organizations. We contribute to the theoretical debate in the fields of organizational studies, corporate governance, and gender studies by three ways: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) we investigate how individuals at the micro level trust organizations as macro level entities, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) we contribute to the theory of gendered trust in organizations by investigating the gender-based differences in trust behavior and, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) we also contribute to the timely corporate responsibility discussion, as trust is considered as one of the components forming the core of social responsibility in organizational context.\u003c/p\u003e\u003cp\u003eOur research also contributes to the theory of gendered trust by documenting the gender-based differences in trust behavior. This gender gap calls for careful engagement strategies by policymakers at the regional, national, and supranational levels to increase the equity and effectiveness of policy interventions for tackling grand societal challenges. Cultivating and enhancing citizens\u0026rsquo; trust by engagement principles that work well with both men and women will help tackle complex societal challenges. Promoting engagement to create sustainable ecosystems requires a solid governmental regulatory environment around sustainable value creation that includes diversity among stakeholders, such as gender equality. The greater the correlation between trust and citizen engagement across gender for tackling grand societal challenges, the greater the value for all, regardless of gender.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Directions for Future Research\u003c/h2\u003e\u003cp\u003eDespite the additional tests for robustness, several limitations need to be considered in interpreting the results presented in this paper. First, the sample consists of individuals residing in gender equal highly developed countries. Thus, the results may not be applicable to developing countries or to areas with lower levels of gender equality. As an avenue for future research, it would be interesting to examine if a similar gendered trust phenomenon can be found in developing countries or in less gender-equal countries.\u003c/p\u003e\u003cp\u003eSecond, as explained on p. 7, we acknowledge that gender is a continuum but, for statistical modeling purposes, we employ it as a binary variable (female/male) in this study. Thus, our research might not capture the total spectrum of genders. We leave this analysis for the future research to cover.\u003c/p\u003e\u003cp\u003eThird, since the data are collected during the COVID-19 pandemic in 2020, the results may not be fully applicable to other time periods. Considering the COVID-19 pandemic, the war in Ukraine, and ongoing the energy crisis, it is clear that the societies have changed significantly during the past few years. Thus, it would be interesting to study the effect of these events on trust, in particular the effects on the gender-based differences in trust that we document in this paper.\u003c/p\u003e\u003cp\u003eFinally, in addition to the examined features, other, non-examined individual-specific characteristics may have an impact on trust. Due to data constraints, additional analyses with other characteristic variables cannot be conducted here. Thus, these topics are left for future studies to address.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Author is the owner of the Research and Innovation Management GmbH which received funding for the RRI2SCALE project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 872526. Thus, the Author received support from her company such as her salary for producing this publication which was paid with this funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eK.F. wrote the main manuscript text and E.V. reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe RRI2SCALE project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 872526. For more details regarding the project and partners please visit https://rri2scale.eu/. Special thanks to RRI2SCALE consortium partners for their support in data collection and survey design. The author thanks SCANCOR fellows, participants of the Harvard Business School’s Organizational Behavior Laboratory, Lars Coenen, and Frank Dobbin for feedback on previous drafts.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll data, analysis code, and research materials are available at [https://osf.io/zj2w3/?view\\_only=5391ff03d3844960bd5114e483a111ed] .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA question of trust. 2010. 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K. 1974.\u003c/strong\u003e \u0026ldquo;The two-sample trimmed t for unequal population variances.\u0026rdquo; \u003cem\u003eBiometrika\u003c/em\u003e, 61(1), 165\u0026ndash;170.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e While acknowledging that gender is a continuum, we measure it as a binary variable (female/male) in this study.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6831450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6831450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTrust acts as both a social clue and an alternative governance mechanism that operates partly by increasing confidence in other partners\u0026rsquo; commitment to a greater good. From April to July 2020, we surveyed 7,729 individuals, broadly representative for age and gender, in four European regions. Our results highlight that trust indicators like transparency and trust in regional organizations significantly drive citizen demands to be engaged in policymaking: the greater the trust, the higher the demand for engagement. At the same time, citizen engagement increases trust. Thus, the greater the correlation between trust and citizen engagement for tackling grand societal challenges, the greater the value for all. However, despite indicating higher levels of trust in regional organizations, women do not demand greater engagement in regional policymaking. This gender gap calls for careful engagement strategies by policymakers to increase the equity and effectiveness of policy interventions for tackling grand societal challenges.\u003c/p\u003e","manuscriptTitle":"A Gender Gap? 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