Pub Date : 2023-10-20DOI: 10.1080/13669877.2023.2270669
Zhenhao Yu, Weina Qu, Yan Ge
AbstractThe motorcycle rider behavior questionnaire (MRBQ) is one of the most extensively used questionnaires to explore risky rider behavior worldwide. However, whether previous research adopted other scales or other versions of MRBQ, neither of them fully cover the typicality of the risky behavior in Chinese motorcyclists. Moreover, past research investigated the MRBQ while combining the joint effect of Big Five personality (BFP) and sensation seeking lacks. Our study aims to revise the Chinese version of MRBQ in young riders and explore the relationship among BFP, sensation seeking, MRBQ, and self-reported traffic violations. 278 online participants filled out the Big Five Inventory measuring BFP, the sensation seeking scale, MRBQ items selected from previous versions in other countries, and self-reported traffic violations from the traffic management system (crashes, traffic violation frequency, penalty points, and fines). Exploratory factor analysis suggested 7 factors (safety equipment, traffic errors, speed violations, control errors, stunts, traffic violations, and safety violations), and the internal consistency reliability ranged from 0.58–0.91. The hierarchical linear regression analysis showed that agreeableness and conscientiousness in BFP negatively predicted the total MRBQ score, while openness in BFP and sensation seeking positively predicted the total MRBQ score. In addition, the Poisson regression analysis suggested that all kinds of self-reported traffic violations could be positively predicted by the total MRBQ score. Path analysis suggested the fully mediating role of sensation seeking. In conclusion, the Chinese version of the MRBQ is useful for future studies and the sensation seeking plays a mediating role between the Big Five personality and MRBQ.Keywords: Safety of motorcyclistsmotorcycle rider behavior questionnairebig five personalitysensation seekingmediation model Disclosure statementNo potential conflict of interest was reported by the author(s)Consent to participateInformed consent was obtained from all individual participants included in the study.Data availability statementPlease email the corresponding author for raw data and materials.Additional informationFundingThis study was supported by the National Natural Science Foundation of China under Grants No. 32071064, 32071066, 32271132, 31771225, 71971073.
{"title":"Utilizing MRBQ to investigate risky rider behavior in Chinese young riders: combining the effect of Big Five personality and sensation seeking","authors":"Zhenhao Yu, Weina Qu, Yan Ge","doi":"10.1080/13669877.2023.2270669","DOIUrl":"https://doi.org/10.1080/13669877.2023.2270669","url":null,"abstract":"AbstractThe motorcycle rider behavior questionnaire (MRBQ) is one of the most extensively used questionnaires to explore risky rider behavior worldwide. However, whether previous research adopted other scales or other versions of MRBQ, neither of them fully cover the typicality of the risky behavior in Chinese motorcyclists. Moreover, past research investigated the MRBQ while combining the joint effect of Big Five personality (BFP) and sensation seeking lacks. Our study aims to revise the Chinese version of MRBQ in young riders and explore the relationship among BFP, sensation seeking, MRBQ, and self-reported traffic violations. 278 online participants filled out the Big Five Inventory measuring BFP, the sensation seeking scale, MRBQ items selected from previous versions in other countries, and self-reported traffic violations from the traffic management system (crashes, traffic violation frequency, penalty points, and fines). Exploratory factor analysis suggested 7 factors (safety equipment, traffic errors, speed violations, control errors, stunts, traffic violations, and safety violations), and the internal consistency reliability ranged from 0.58–0.91. The hierarchical linear regression analysis showed that agreeableness and conscientiousness in BFP negatively predicted the total MRBQ score, while openness in BFP and sensation seeking positively predicted the total MRBQ score. In addition, the Poisson regression analysis suggested that all kinds of self-reported traffic violations could be positively predicted by the total MRBQ score. Path analysis suggested the fully mediating role of sensation seeking. In conclusion, the Chinese version of the MRBQ is useful for future studies and the sensation seeking plays a mediating role between the Big Five personality and MRBQ.Keywords: Safety of motorcyclistsmotorcycle rider behavior questionnairebig five personalitysensation seekingmediation model Disclosure statementNo potential conflict of interest was reported by the author(s)Consent to participateInformed consent was obtained from all individual participants included in the study.Data availability statementPlease email the corresponding author for raw data and materials.Additional informationFundingThis study was supported by the National Natural Science Foundation of China under Grants No. 32071064, 32071066, 32271132, 31771225, 71971073.","PeriodicalId":16975,"journal":{"name":"Journal of Risk Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.1080/13669877.2023.2270605
Zhuling Liu, Janet Z. Yang
AbstractApplying the risk information seeking and processing (RISP) model, this study explores the antecedents to information seeking and information avoidance about a relatively novel risk – per- and polyfluoroalkyl substances (PFAS) contamination. Based on an experimental survey, we found that current knowledge, informational subjective norms, and risk perception are positively related to information seeking and information avoidance. Perceived personal control is positively related to information avoidance, but not related to information seeking. Fear is positively related to information seeking, but sadness is not related to either seeking or avoidance. Lastly, information seeking and information avoidance are associated with preventive behaviors related to PFAS contamination in the opposite direction.Keywords: Per- and polyfluoroalkyl substances (PFAS)information seekinginformation avoidancepreventive behaviors Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 All collinearity diagnostics were satisfactory in the regression models.
{"title":"Information seeking and information avoidance about per- and polyfluoroalkyl substances (PFAS) contamination: knowledge, personal control, or affect?","authors":"Zhuling Liu, Janet Z. Yang","doi":"10.1080/13669877.2023.2270605","DOIUrl":"https://doi.org/10.1080/13669877.2023.2270605","url":null,"abstract":"AbstractApplying the risk information seeking and processing (RISP) model, this study explores the antecedents to information seeking and information avoidance about a relatively novel risk – per- and polyfluoroalkyl substances (PFAS) contamination. Based on an experimental survey, we found that current knowledge, informational subjective norms, and risk perception are positively related to information seeking and information avoidance. Perceived personal control is positively related to information avoidance, but not related to information seeking. Fear is positively related to information seeking, but sadness is not related to either seeking or avoidance. Lastly, information seeking and information avoidance are associated with preventive behaviors related to PFAS contamination in the opposite direction.Keywords: Per- and polyfluoroalkyl substances (PFAS)information seekinginformation avoidancepreventive behaviors Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 All collinearity diagnostics were satisfactory in the regression models.","PeriodicalId":16975,"journal":{"name":"Journal of Risk Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1080/13669877.2023.2259402
Mikel Subiza-Pérez, Aiora Zabala, Daniel Groten, Laura Vozmediano, César San Juan, Jesús Ibarluzea
Where strategies to reduce and recycle urban solid waste are insufficient, waste incineration is proposed as second-best management. Waste-to-energy facilities often raise remarkable public controversy, which the Not-In-My-Backyard effect does not explain sufficiently. Heterogeneous concerns lead to diverse risk perception profiles that standard psychometric scales cannot uncover. We explore this diversity of profiles by analyzing risk perceptions about a recently built waste-to-energy facility in Gipuzkoa (Spain), a case underlined by a decades-long public debate about waste management alternatives. Using Q, a semi-qualitative method, we identify risk perceptions within a diverse sample of fifty participants, including residents at different distances to the facility. We identify three main types of risk perception based on the relative importance respondents gave to 26 possible perceived risks of the facility. We define risk perception types according to the concerns that respondents with similar views emphasized most: human health, politics and institutions, and local social-ecological impacts. Whereas human-health and social-ecological concerns could be partially addressed with information—including timely and accessible reporting of effluent monitoring—and improved safety, building institutional trust to mitigate the concerns in the second risk perception type requires longer-term dynamics. Understanding heterogeneous risk profiles as done in this study can support adequate communication strategies and help policymakers prioritize governance areas to improve. Our results contribute to understanding social-environmental risk perceptions associated with controversial facilities. Using an approach that is new in this domain, these results add nuanced understanding that complements the quantitative profiling prevalent in the literature on risk perceptions and about waste-to-energy plants.
{"title":"Waste-to-energy risk perception typology: health, politics and environmental impacts","authors":"Mikel Subiza-Pérez, Aiora Zabala, Daniel Groten, Laura Vozmediano, César San Juan, Jesús Ibarluzea","doi":"10.1080/13669877.2023.2259402","DOIUrl":"https://doi.org/10.1080/13669877.2023.2259402","url":null,"abstract":"Where strategies to reduce and recycle urban solid waste are insufficient, waste incineration is proposed as second-best management. Waste-to-energy facilities often raise remarkable public controversy, which the Not-In-My-Backyard effect does not explain sufficiently. Heterogeneous concerns lead to diverse risk perception profiles that standard psychometric scales cannot uncover. We explore this diversity of profiles by analyzing risk perceptions about a recently built waste-to-energy facility in Gipuzkoa (Spain), a case underlined by a decades-long public debate about waste management alternatives. Using Q, a semi-qualitative method, we identify risk perceptions within a diverse sample of fifty participants, including residents at different distances to the facility. We identify three main types of risk perception based on the relative importance respondents gave to 26 possible perceived risks of the facility. We define risk perception types according to the concerns that respondents with similar views emphasized most: human health, politics and institutions, and local social-ecological impacts. Whereas human-health and social-ecological concerns could be partially addressed with information—including timely and accessible reporting of effluent monitoring—and improved safety, building institutional trust to mitigate the concerns in the second risk perception type requires longer-term dynamics. Understanding heterogeneous risk profiles as done in this study can support adequate communication strategies and help policymakers prioritize governance areas to improve. Our results contribute to understanding social-environmental risk perceptions associated with controversial facilities. Using an approach that is new in this domain, these results add nuanced understanding that complements the quantitative profiling prevalent in the literature on risk perceptions and about waste-to-energy plants.","PeriodicalId":16975,"journal":{"name":"Journal of Risk Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136115974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1080/13669877.2023.2264314
Adam Mayer, Ellison Carter
AbstractMultiple municipalities in the U.S. have banned natural gas hook-ups in new home construction. Several state governments have pre-empted those bans. Yet little is known about public perceptions of natural gas appliances in the home. We used survey data to evaluate risk perceptions associated with natural gas appliances and investigate potential demographic and ideological effects on risk perceptions. We find little political polarization or “white male” effects, but those who are dissatisfied with indoor air quality and concerned about climate change have heightened risk perceptions. Overall, natural gas risk perceptions are low. However, as of late 2022, the health implications of natural gas use in the home and potential mitigation policy have entered public discourse, implying that these risks may become more salient and politically charged. We conclude by discussing implications for indoor environments and policy.Keywords: natural gasindoor airrisk perception AcknowledgementWe acknowledge the city of Fort Collins, the Bloomberg Foundation Mayors Challenge, and the JPB Foundation Harvard Environmental Health Fellowship for providing funding for this research.Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Should we use natural gas in our homes? Risk perceptions from the U.S","authors":"Adam Mayer, Ellison Carter","doi":"10.1080/13669877.2023.2264314","DOIUrl":"https://doi.org/10.1080/13669877.2023.2264314","url":null,"abstract":"AbstractMultiple municipalities in the U.S. have banned natural gas hook-ups in new home construction. Several state governments have pre-empted those bans. Yet little is known about public perceptions of natural gas appliances in the home. We used survey data to evaluate risk perceptions associated with natural gas appliances and investigate potential demographic and ideological effects on risk perceptions. We find little political polarization or “white male” effects, but those who are dissatisfied with indoor air quality and concerned about climate change have heightened risk perceptions. Overall, natural gas risk perceptions are low. However, as of late 2022, the health implications of natural gas use in the home and potential mitigation policy have entered public discourse, implying that these risks may become more salient and politically charged. We conclude by discussing implications for indoor environments and policy.Keywords: natural gasindoor airrisk perception AcknowledgementWe acknowledge the city of Fort Collins, the Bloomberg Foundation Mayors Challenge, and the JPB Foundation Harvard Environmental Health Fellowship for providing funding for this research.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":16975,"journal":{"name":"Journal of Risk Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1080/13669877.2023.2259406
Harishankar Vasudevanallur Subramanian, Casey Canfield, Daniel B. Shank, Matthew Kinnison
AbstractThe use of Artificial Intelligence (AI) decision support is increasing in high-stakes contexts, such as healthcare, defense, and finance. Uncertainty information may help users better leverage AI predictions, especially when combined with their domain knowledge. We conducted a human-subject experiment with an online sample to examine the effects of presenting uncertainty information with AI recommendations. The experimental stimuli and task, which included identifying plant and animal images, are from an existing image recognition deep learning model, a popular approach to AI. The uncertainty information was predicted probabilities for whether each label was the true label. This information was presented numerically and visually. In the study, we tested the effect of AI recommendations in a within-subject comparison and uncertainty information in a between-subject comparison. The results suggest that AI recommendations increased both participants’ accuracy and confidence. Further, providing uncertainty information significantly increased accuracy but not confidence, suggesting that it may be effective for reducing overconfidence. In this task, participants tended to have higher domain knowledge for animals than plants based on a self-reported measure of domain knowledge. Participants with more domain knowledge were appropriately less confident when uncertainty information was provided. This suggests that people use AI and uncertainty information differently, such as an expert versus second opinion, depending on their level of domain knowledge. These results suggest that if presented appropriately, uncertainty information can potentially decrease overconfidence that is induced by using AI recommendations.Keywords: Overconfidenceartificial intelligenceuncertaintyhuman-AI teamsrisk communication AcknowledgmentsWe thank Cihan Dagli, Krista Lentine, Mark Schnitzler, and Henry Randall for their insights on the design of AI decision support systems.Disclosure statementThe authors report that there are no competing interests to declare.Additional informationFundingThis work was supported by a National Science Foundation Award #2026324.
{"title":"Combining uncertainty information with AI recommendations supports calibration with domain knowledge","authors":"Harishankar Vasudevanallur Subramanian, Casey Canfield, Daniel B. Shank, Matthew Kinnison","doi":"10.1080/13669877.2023.2259406","DOIUrl":"https://doi.org/10.1080/13669877.2023.2259406","url":null,"abstract":"AbstractThe use of Artificial Intelligence (AI) decision support is increasing in high-stakes contexts, such as healthcare, defense, and finance. Uncertainty information may help users better leverage AI predictions, especially when combined with their domain knowledge. We conducted a human-subject experiment with an online sample to examine the effects of presenting uncertainty information with AI recommendations. The experimental stimuli and task, which included identifying plant and animal images, are from an existing image recognition deep learning model, a popular approach to AI. The uncertainty information was predicted probabilities for whether each label was the true label. This information was presented numerically and visually. In the study, we tested the effect of AI recommendations in a within-subject comparison and uncertainty information in a between-subject comparison. The results suggest that AI recommendations increased both participants’ accuracy and confidence. Further, providing uncertainty information significantly increased accuracy but not confidence, suggesting that it may be effective for reducing overconfidence. In this task, participants tended to have higher domain knowledge for animals than plants based on a self-reported measure of domain knowledge. Participants with more domain knowledge were appropriately less confident when uncertainty information was provided. This suggests that people use AI and uncertainty information differently, such as an expert versus second opinion, depending on their level of domain knowledge. These results suggest that if presented appropriately, uncertainty information can potentially decrease overconfidence that is induced by using AI recommendations.Keywords: Overconfidenceartificial intelligenceuncertaintyhuman-AI teamsrisk communication AcknowledgmentsWe thank Cihan Dagli, Krista Lentine, Mark Schnitzler, and Henry Randall for their insights on the design of AI decision support systems.Disclosure statementThe authors report that there are no competing interests to declare.Additional informationFundingThis work was supported by a National Science Foundation Award #2026324.","PeriodicalId":16975,"journal":{"name":"Journal of Risk Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1080/13669877.2023.2264301
Branden B. Johnson, Byungdoo Kim
AbstractAlthough early concepts of risk perception measures distinguished cognitive from affective items, until recently multi-dimensional taxonomies were absent from risk perception studies, and even more from tests of their association with behavior or policy support. Six longitudinal panel surveys on U.S. COVID-19 views (n = 2004 February 2020, ending April 2021) allowed testing of these relationships among ≤ 10 risk perception items measured in each wave. Confirmatory factor analyses revealed consistent distinctions between personal (conditioning perceived risk on taking further or no further protective action), collective (U.S., global), affective (concern, dread), and severity (estimates of eventual total U.S. infections and deaths) measures, while affect (good-bad feelings) and duration (how long people expect the outbreak to last) did not fit with their assumed affective and severity (respectively) parallels. Collective and affective/affect risk perceptions most strongly predicted both behavioral intentions and policy support for mask wearing, avoidance of large public gatherings, and vaccination, controlling for personal risk perception (which might be partly reflected in the affective/affect effects) and other measures. These findings underline the importance of multi-dimensionality (e.g. not just asking about personal risk perceptions) in designing risk perception research, even when trying to explain personal protective actions.Keywords: behavioral intentionsCOVID-19policy supportRisk perceptiontaxonomy Disclosure statementNo potential conflict of interest was reported by the authors.Notes1 A corollary might be that the global risk perception measure also belongs in this cluster, particularly for duration, which does impose a geographical limit on the area where the pandemic “ends.” A separate analysis (unreported here) showed results similar to those for this fifth model.2 Backup exploratory factor analyses for Waves 2-6 identified six factors out of the 10 items: collective, severity (infection, deaths), personal, affect, duration, and dread. Concern loaded on both collective and personal factors (> .49 and > .41, respectively). The personal connection might be prompted by the measure’s reference to “where you live”; its association with collective measures is unclear. Models clustering personal, collective, and concern measures, including affect and duration as single-item factors, had poor fit (e.g. Wave 2: chi-square/df = 26.849; RMSEA = .127 [.118, .135]; CFI = .928; AIC = 42,991.443).Additional informationFundingThe work contributing to this article was funded by the United States National Science Foundation under Grant No. 2022216.
{"title":"COVID-19 risk perception measures: factoring and prediction of behavioral intentions and policy support","authors":"Branden B. Johnson, Byungdoo Kim","doi":"10.1080/13669877.2023.2264301","DOIUrl":"https://doi.org/10.1080/13669877.2023.2264301","url":null,"abstract":"AbstractAlthough early concepts of risk perception measures distinguished cognitive from affective items, until recently multi-dimensional taxonomies were absent from risk perception studies, and even more from tests of their association with behavior or policy support. Six longitudinal panel surveys on U.S. COVID-19 views (n = 2004 February 2020, ending April 2021) allowed testing of these relationships among ≤ 10 risk perception items measured in each wave. Confirmatory factor analyses revealed consistent distinctions between personal (conditioning perceived risk on taking further or no further protective action), collective (U.S., global), affective (concern, dread), and severity (estimates of eventual total U.S. infections and deaths) measures, while affect (good-bad feelings) and duration (how long people expect the outbreak to last) did not fit with their assumed affective and severity (respectively) parallels. Collective and affective/affect risk perceptions most strongly predicted both behavioral intentions and policy support for mask wearing, avoidance of large public gatherings, and vaccination, controlling for personal risk perception (which might be partly reflected in the affective/affect effects) and other measures. These findings underline the importance of multi-dimensionality (e.g. not just asking about personal risk perceptions) in designing risk perception research, even when trying to explain personal protective actions.Keywords: behavioral intentionsCOVID-19policy supportRisk perceptiontaxonomy Disclosure statementNo potential conflict of interest was reported by the authors.Notes1 A corollary might be that the global risk perception measure also belongs in this cluster, particularly for duration, which does impose a geographical limit on the area where the pandemic “ends.” A separate analysis (unreported here) showed results similar to those for this fifth model.2 Backup exploratory factor analyses for Waves 2-6 identified six factors out of the 10 items: collective, severity (infection, deaths), personal, affect, duration, and dread. Concern loaded on both collective and personal factors (> .49 and > .41, respectively). The personal connection might be prompted by the measure’s reference to “where you live”; its association with collective measures is unclear. Models clustering personal, collective, and concern measures, including affect and duration as single-item factors, had poor fit (e.g. Wave 2: chi-square/df = 26.849; RMSEA = .127 [.118, .135]; CFI = .928; AIC = 42,991.443).Additional informationFundingThe work contributing to this article was funded by the United States National Science Foundation under Grant No. 2022216.","PeriodicalId":16975,"journal":{"name":"Journal of Risk Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1080/13669877.2023.2249927
Rebekka Schwesig, Irina Brich, Jürgen Buder, Markus Huff, Nadia Said
AbstractSurveys worldwide show that the public perceives artificial intelligence (AI) as a double-edged sword: A risk and an opportunity. However, how this ambiguous perception of AI is related to people’s willingness to use AI-based applications has yet to be investigated. To this end, two online experiments were conducted, including two samples, N = 246 and N = 495 (quota-sample, representative for age and gender). As hypothesized, people’s risk-opportunity perception of AI applications correlated positively with the probability of using AI. Exploratory analyses indicated that people’s willingness to use AI significantly depended on the context of AI use (medicine vs. transport vs. media vs. psychology). This research expands existing behavioral research by investigating ambiguous and not solely risk-taking behavior for different AI application contexts. Study results motivate the investigation of causal-effect relations and underline the need to understand risk and opportunity perception stability across different contexts of AI use.Keywords: Risk perceptionopportunity perceptionartificial intelligencebehaviorconfidence Ethical approvalAPA ethical standards were followed in the conduct of both studies reported in this article and informed consent was collected from the participants at the beginning of the study. Both studies were approved by the ethics committee of the Leibniz-Institut für Wissensmedien, Tübingen.Authors’ contributionsNS, IB, JB, and MH developed the research idea. NS administered the whole project and developed the study concepts. Both, RS and NS developed the methodology of the studies and analyzed the data. RS was responsible for data collection and data visualization. RS and NS wrote the original draft. All authors were responsible for reviewing and editing the original draft. All authors approved the final version of the manuscript for submission.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData of both studies are freely accessible under http://dx.doi.org/10.6084/m9.figshare.20589597 (Schwesig and Said 2021, dataset). The analysis code (R) that produces all results and figures of this article are available at http://dx.doi.org/10.6084/m9.figshare.20589597. Before data collection, both experimental studies were preregistered: https://aspredicted.org/CVC_CRQ (study 1) and https://aspredicted.org/SR3_8Q3 (study 2).Notes1 Note, that we preregistered that people’s AI knowledge works as a moderator on the association of risk and opportunity perception and behavior towards AI as hypothesis 2 for the second study.2 Note, that for study 2 there was a significant main effect of knowledge when we did not control for age, gender, and education, and entering knowledge as main effect only: Χ2Study2(2) = 4.36, p = .037, OR =7.42, 95% CI [1.12, 49.37].3 Note, that for study 2 there was a significant main effect of confidence when we did not control for age, gender, and ed
摘要全球调查显示,公众认为人工智能(AI)是一把双刃剑:既是风险,也是机遇。然而,这种对人工智能的模糊认知与人们使用基于人工智能的应用程序的意愿之间的关系还有待调查。为此,我们进行了两次在线实验,包括两个样本,N = 246和N = 495(配额样本,年龄和性别具有代表性)。根据假设,人们对人工智能应用的风险-机会感知与使用人工智能的概率正相关。探索性分析表明,人们使用人工智能的意愿在很大程度上取决于人工智能使用的背景(医学、交通、媒体、心理学)。本研究扩展了现有的行为研究,研究了不同人工智能应用环境下的模糊行为,而不仅仅是冒险行为。研究结果激发了对因果关系的调查,并强调了在不同的人工智能使用背景下理解风险和机会感知稳定性的必要性。关键词:风险感知机会感知人工智能行为自信伦理认可本文报道的两项研究均遵循apa伦理标准,并在研究开始时收集了参与者的知情同意。这两项研究都得到了宾根市莱布尼茨研究所(Leibniz-Institut fr Wissensmedien)伦理委员会的批准。作者的贡献(sns, IB, JB, MH)发展了研究思路。NS管理了整个项目并制定了研究概念。RS和NS都制定了研究方法并分析了数据。RS负责数据收集和数据可视化。RS和NS撰写了最初的草案。所有作者都负责审稿和编辑初稿。所有作者都同意提交最终版本的手稿。披露声明作者未报告潜在的利益冲突。数据可用性声明两项研究的数据均可在http://dx.doi.org/10.6084/m9.figshare.20589597 (Schwesig and Said 2021,数据集)免费获取。生成本文所有结果和图表的分析代码(R)可从http://dx.doi.org/10.6084/m9.figshare.20589597获得。在收集数据之前,两项实验研究都进行了预注册:https://aspredicted.org/CVC_CRQ(研究1)和https://aspredicted.org/SR3_8Q3(研究2)。注1注意,我们在第二项研究的假设2中预注册了人们的人工智能知识在风险和机会感知与人工智能行为之间的关联中起调节作用注意,在研究2中,当我们不控制年龄、性别和教育程度,只将知识作为主要影响因素时,知识的主效应显著:Χ2Study2(2) = 4.36, p = 0.037, OR =7.42, 95% CI [1.12, 49.37] 3值得注意的是,在研究2中,当我们不控制年龄、性别和教育程度,并仅将信心作为主要影响因素时,信心的主效应显著:Χ2Study2(2) = 5.12, p = 0.024, OR =1.22, 95% CI[1.02, 1.44]。其他信息资金数据收集由德国宾根莱布尼茨研究所(Leibniz-Institut fr Wissensmedien)的内部资金资助。
{"title":"Using artificial intelligence (AI)? Risk and opportunity perception of AI predict people’s willingness to use AI","authors":"Rebekka Schwesig, Irina Brich, Jürgen Buder, Markus Huff, Nadia Said","doi":"10.1080/13669877.2023.2249927","DOIUrl":"https://doi.org/10.1080/13669877.2023.2249927","url":null,"abstract":"AbstractSurveys worldwide show that the public perceives artificial intelligence (AI) as a double-edged sword: A risk and an opportunity. However, how this ambiguous perception of AI is related to people’s willingness to use AI-based applications has yet to be investigated. To this end, two online experiments were conducted, including two samples, N = 246 and N = 495 (quota-sample, representative for age and gender). As hypothesized, people’s risk-opportunity perception of AI applications correlated positively with the probability of using AI. Exploratory analyses indicated that people’s willingness to use AI significantly depended on the context of AI use (medicine vs. transport vs. media vs. psychology). This research expands existing behavioral research by investigating ambiguous and not solely risk-taking behavior for different AI application contexts. Study results motivate the investigation of causal-effect relations and underline the need to understand risk and opportunity perception stability across different contexts of AI use.Keywords: Risk perceptionopportunity perceptionartificial intelligencebehaviorconfidence Ethical approvalAPA ethical standards were followed in the conduct of both studies reported in this article and informed consent was collected from the participants at the beginning of the study. Both studies were approved by the ethics committee of the Leibniz-Institut für Wissensmedien, Tübingen.Authors’ contributionsNS, IB, JB, and MH developed the research idea. NS administered the whole project and developed the study concepts. Both, RS and NS developed the methodology of the studies and analyzed the data. RS was responsible for data collection and data visualization. RS and NS wrote the original draft. All authors were responsible for reviewing and editing the original draft. All authors approved the final version of the manuscript for submission.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData of both studies are freely accessible under http://dx.doi.org/10.6084/m9.figshare.20589597 (Schwesig and Said 2021, dataset). The analysis code (R) that produces all results and figures of this article are available at http://dx.doi.org/10.6084/m9.figshare.20589597. Before data collection, both experimental studies were preregistered: https://aspredicted.org/CVC_CRQ (study 1) and https://aspredicted.org/SR3_8Q3 (study 2).Notes1 Note, that we preregistered that people’s AI knowledge works as a moderator on the association of risk and opportunity perception and behavior towards AI as hypothesis 2 for the second study.2 Note, that for study 2 there was a significant main effect of knowledge when we did not control for age, gender, and education, and entering knowledge as main effect only: Χ2Study2(2) = 4.36, p = .037, OR =7.42, 95% CI [1.12, 49.37].3 Note, that for study 2 there was a significant main effect of confidence when we did not control for age, gender, and ed","PeriodicalId":16975,"journal":{"name":"Journal of Risk Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.1080/13669877.2023.2259411
Sarah Duckett, George Warren
"Foolproof: why we fall for misinformation and how to build immunity by Sander Van Der Linden book review." Journal of Risk Research, ahead-of-print(ahead-of-print), pp. 1–2
{"title":"Foolproof: why we fall for misinformation and how to build immunity by Sander Van Der Linden book review <b>Foolproof: Why We Fall for Misinformation and How to Build Immunity</b> by Sander van der Linden, 4th Estate, London","authors":"Sarah Duckett, George Warren","doi":"10.1080/13669877.2023.2259411","DOIUrl":"https://doi.org/10.1080/13669877.2023.2259411","url":null,"abstract":"\"Foolproof: why we fall for misinformation and how to build immunity by Sander Van Der Linden book review.\" Journal of Risk Research, ahead-of-print(ahead-of-print), pp. 1–2","PeriodicalId":16975,"journal":{"name":"Journal of Risk Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135859782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.1080/13669877.2023.2259415
Giulia Priolo, Martina Vacondio, Stephan Dickert, Nicolao Bonini
AbstractWe investigated whether different Mortality Rate Formats used to express the same objective probability affected people’s Emotional reactions, Risk perception, and protective behavioral intentions. A sample from the Italian population (N = 604) was exposed to six different formats (i.e. Absolute value; Raw ratio; 1 in X; Verbal; Percentage; Probability) to report the mortality rate of COVID-19 in a between-subject design. In line with expectations, the Probability format led to lower emotional reactions compared to all the other formats. Moreover, results from a path analysis revealed that emotional reactions predicted risk perception. The Mortality Rate Formats also had an indirect effect on Behavioral Intentions to protect oneself, which was mediated by emotional reactions and risk perception. The effect sizes of these indirect effects ranged from small to medium. The direct effect of risk on intentions was found to differ among two dimensions of risk. Affective Risk led to higher Behavioral Intentions, while Deliberative Risk had the opposite effect. We discuss these results in line with the ongoing debate regarding the role played by risk scientists during the pandemic and offer practical implications for risk management during health crises like COVID-19.Keywords: Risk perceptionemotionsBehavioral IntentionscommunicationCOVID-19 Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe dataset is available on the OSF platform and accessible through the following link: https://osf.io/x49uy/Additional informationFundingThis work was supported by the University of Trento under Grant Bando di Ateneo COVID-19.
摘要本研究探讨了表达相同客观概率的不同死亡率格式是否会影响人们的情绪反应、风险感知和保护行为意图。来自意大利人群的样本(N = 604)暴露于六种不同的格式(即绝对值;原始比例;1 in X;口头的;百分比;概率)在受试者间设计中报告COVID-19的死亡率。与预期一致,与所有其他格式相比,概率格式导致了较低的情绪反应。此外,通径分析的结果显示,情绪反应可以预测风险感知。死亡率格式对自我保护行为意图也有间接影响,这种影响是由情绪反应和风险感知介导的。这些间接效应的效应大小从小到中等不等。风险对意向的直接影响在风险的两个维度上是不同的。情感性风险导致较高的行为意向,而审慎风险则相反。我们根据正在进行的关于风险科学家在大流行期间所发挥作用的辩论来讨论这些结果,并为COVID-19等健康危机期间的风险管理提供实际意义。关键词:风险感知情绪行为意向沟通covid -19披露声明作者未报告潜在利益冲突。数据可用性声明该数据集可在OSF平台上获得,并可通过以下链接访问:https://osf.io/x49uy/Additional information资助本工作由特伦托大学在Grant Bando di Ateneo COVID-19下支持。
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Pub Date : 2023-09-22DOI: 10.1080/13669877.2023.2259399
Soo Jung Hong, Yungwook Kim
AbstractThis study investigates the effect of dread and unknown risks on individuals’ cognitive and affective responses and decision-making related to particulate matter (PM) air pollution. In particular, we investigate how dread and unknown risks play different roles in forming the cognitive and affective routes that affect the South Korean public’s intentions to reduce PM air pollution by adopting risk mitigation behaviors. One thousand South Korean adults participated in the study via a professional research company in 2022. Statistical analysis was performed using PROCESS Marco. Indirect effects and their significance were estimated using bias-corrected bootstrap (n = 5,000 resampling) confidence intervals (CIs). According to the results, perceived dread of PM air pollution had significant and positive associations with perceived health risks and negative emotion, and perceived risks had a significant and positive association with negative emotion. Moreover, the perceived unknown-ness of PM air pollution had a significant and positive association with perceived uncertainty, which had a significant and negative association with negative emotion. Our mediation models demonstrate that the cognitive and affective routes associated with the two risk dimensions had different effects on behavioral intentions to reduce PM air pollution. These distinct cognitive and affective routes have significant theoretical implications for the effective application of the psychometric paradigm in addressing various risk-related issues. The findings also imply that an appropriate level of negative emotion is crucial to motivate the public’s engagement in risk-reduction behaviors. While employing campaign messages that integrate perceived risk and negative emotional appeals derived from dread can be effective, caution should be taken not to diminish the public’s negative emotions when addressing the risk-related uncertainty in campaigns or interventions. Overall, our findings not only have several practical implications for environmental communication strategies but also make important theoretical contributions to the literature on risk perceptions and the psychometric paradigm.Keywords: Particulate matter (PM)air pollution preventionthe psychometric paradigmdread riskunknown risknegative emotion Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2023-00217228).
{"title":"Dread and unknown characteristics of particulate matter pollution: cognitive and affective routes to air pollution prevention","authors":"Soo Jung Hong, Yungwook Kim","doi":"10.1080/13669877.2023.2259399","DOIUrl":"https://doi.org/10.1080/13669877.2023.2259399","url":null,"abstract":"AbstractThis study investigates the effect of dread and unknown risks on individuals’ cognitive and affective responses and decision-making related to particulate matter (PM) air pollution. In particular, we investigate how dread and unknown risks play different roles in forming the cognitive and affective routes that affect the South Korean public’s intentions to reduce PM air pollution by adopting risk mitigation behaviors. One thousand South Korean adults participated in the study via a professional research company in 2022. Statistical analysis was performed using PROCESS Marco. Indirect effects and their significance were estimated using bias-corrected bootstrap (n = 5,000 resampling) confidence intervals (CIs). According to the results, perceived dread of PM air pollution had significant and positive associations with perceived health risks and negative emotion, and perceived risks had a significant and positive association with negative emotion. Moreover, the perceived unknown-ness of PM air pollution had a significant and positive association with perceived uncertainty, which had a significant and negative association with negative emotion. Our mediation models demonstrate that the cognitive and affective routes associated with the two risk dimensions had different effects on behavioral intentions to reduce PM air pollution. These distinct cognitive and affective routes have significant theoretical implications for the effective application of the psychometric paradigm in addressing various risk-related issues. The findings also imply that an appropriate level of negative emotion is crucial to motivate the public’s engagement in risk-reduction behaviors. While employing campaign messages that integrate perceived risk and negative emotional appeals derived from dread can be effective, caution should be taken not to diminish the public’s negative emotions when addressing the risk-related uncertainty in campaigns or interventions. Overall, our findings not only have several practical implications for environmental communication strategies but also make important theoretical contributions to the literature on risk perceptions and the psychometric paradigm.Keywords: Particulate matter (PM)air pollution preventionthe psychometric paradigmdread riskunknown risknegative emotion Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2023-00217228).","PeriodicalId":16975,"journal":{"name":"Journal of Risk Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136016711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}