The Tripartite Model of Risk Perception (TRIRISK): Distinguishing Deliberative, Affective, and Experiential Components of Perceived Risk

Ferrer, Rebecca A.; Klein, William M. P.; Persoskie, Alexander; Avishai-Yitshak, Aya; Sheeran, Paschal

doi:10.1007/s12160-016-9790-z

Cited by 199 publications

(229 citation statements)

References 52 publications

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“…Although previous research has shown that cognitive and affective risk items load on different factors (Ferrer, Klein, Persoskie, Avishai-Yitshak, & Sheeran, 2016; Janssen et al, 2012) and are more strongly associated with other affective constructs (e.g., worry; Janssen et al, 2012) suggesting conceptual distinctiveness, it might be interesting to investigate if affective risk questions indeed reflect affective processes using objective measures (e.g., fMRI technology) and are not merely different from the cognitive questions due to specificity differences (i.e. cognitive questions require more specific and objective information).…”

Section: Discussionmentioning

confidence: 98%

Don't know responses to cognitive and affective risk perception measures: Exploring prevalence and socio‐demographic moderators

Janssen

Verduyn

Waters

2018

British J Health Psychol

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Lower uncertainty for affective (vs. cognitive) absolute perceived risk items is consistent with research stating: (1) Risk perceptions are grounded in people's feelings about a hazard, and (2) feelings are easier for people to access than facts. Including affective perceived risk items in health behaviour surveys may reduce missing data and improve data quality. Statement of contribution What is already known on this subject? Many people report that they don't know their risk (i.e., risk uncertainty). Evidence is growing for the importance of feelings of risk in explaining health behaviour. Feelings are easier for people to access than facts. What does this study add? Don't know responding is higher for absolute cognitive than absolute affective risk questions. This difference does not vary in magnitude by demographic characteristics. Affective perceived risk questions in surveys may reduce missing data and improve data quality.

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Section: Discussionmentioning

confidence: 98%

Don't know responses to cognitive and affective risk perception measures: Exploring prevalence and socio‐demographic moderators

Janssen

Verduyn

Waters

2018

British J Health Psychol

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“…Because item wording was guided by the GRFM, which also includes a more affectively-charged prevention scale, this discrepancy was by design. Health research in other domains suggests that affectively-laden constructs may better predict behavior than more deliberative or cognitive constructs (e.g., Ferrer et al, 2016; Leventhal et al, 2003; Power et al, 2011). Future research may examine whether prevention focus can be assessed with a less affective scale, or whether promotion focus can be assessed with a more affective scale.…”

Section: Discussionmentioning

confidence: 99%

“…Participants (n = 500) were recruited via Amazon mTurk (https://www.mturk.com/) and completed the HRFS (as part of a larger survey on cancer-related health cognitions; Ferrer et al, 2016). Recruitment materials indicated that enrollees had never had cancer.…”

Section: Methodsmentioning

confidence: 99%

Developing a scale to assess health regulatory focus

Ferrer

Lipkus

Cerully

et al. 2017

Social Science & Medicine

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Rationale: Regulatory focus (i.e., focus on motivation to achieve gains or avoid losses and non-gains) is used to tailor health behavior change interventions, improving efficacy, but is currently assessed by scales that are not health-specific and may capture a version of the construct that is not ideally matched to the rationale for tailoring. Objective: We developed and validated a Health Regulatory Focus Scale (HRFS), which assesses tendencies to avoid negative health consequences (prevention focus) or achieve positive health outcomes (promotion focus). Methods: Across four studies (and a scale development study in supplementary online materials), we established convergent, discriminant, and predictive validity for the HRFS. In studies examining predictive validity, main outcome measures were health behavior intentions, including intentions to reduce alcohol use, quit smoking, eat a healthy diet, exercise, be screened for cancer, and engage in general cancer preventive behaviors. Results: The promotion and prevention sub-scales performed well in confirmatory factor analyses. Single-factor models had significantly poorer fit than models delineating promotion and prevention. The sub-scales were differentially (and only modestly) correlated with related constructs (anxiety, optimism, information avoidance, ambiguity/fatalism). Higher HRFS-Promotion focus generally corresponded with greater health behavior intentions. Conversely, higher HRFS-Prevention focus corresponded with lower health behavior intentions. Associations were largely maintained even when controlling for established regulatory focus measures, supporting the assertion that the HRFS would predict unique variance in health behavior intentions. Conclusion: The HRFS has the potential to improve the precision with which framed health messages change behavior, as it may assess a version of regulatory focus that is more ideally matched to rationale for tailoring interventions.

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“…Risk perceptions assessed in previous studies were deliberatively (rather than affectively) derived—that is, participants were asked about the likelihood of a risk, rather than about how they felt about the risk. Deliberatively and affectively derived risk perceptions do not always correspond (e.g., Ferrer et al, ; Ferrer, Klein, Persoskie, Avishai‐Yitshak, & Sheeran, in press; Portnoy, Ferrer, Bergman, & Klein, ), and affectively derived risk perceptions are often more predictive of risk‐taking than deliberatively derived risk perceptions (e.g., Dillard, Ferrer, Ubel, & Fagerlin, ; Janssen, van Osch, de Vries, & Lechner, ). Indeed, advantageous decisions on tasks involving experientially derived risk rely heavily on affectively laden “somatic markers” that highlight the best choice (Damasio, ).…”

Section: Discussionmentioning

confidence: 99%

Revisiting the Effects of Anger on Risk‐Taking: Empirical and Meta‐Analytic Evidence for Differences Between Males and Females

Ferrer

Maclay

Litvak³

et al. 2016

Behavioral Decision Making

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That anger elicited in one situation can carry over to drive risky behavior in another situation has been described since the days of Aristotle. The present studies examine the mechanisms through which and the conditions under which such behavior occurs. Across three experiments, as well as a meta‐analytic synthesis of the data, results reveal that incidental anger is significantly more likely to drive risky decision making among males than among females. Moreover, the experiments document that, under certain circumstances, such risk‐taking pays off financially. Indeed, the present experiments demonstrate that, because the expected‐value‐maximizing strategy in these studies rewarded risk‐taking, angry‐male individuals earned more money than did both neutral‐emotion males and angry females. In sum, these studies found evidence for robust disparities between males and females for anger‐driven risk‐taking. Importantly, although men did not experience more anger than women, they did show a heightened tendency to respond to anger with risk‐taking. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

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The Tripartite Model of Risk Perception (TRIRISK): Distinguishing Deliberative, Affective, and Experiential Components of Perceived Risk

Cited by 199 publications

References 52 publications

Don't know responses to cognitive and affective risk perception measures: Exploring prevalence and socio‐demographic moderators

Don't know responses to cognitive and affective risk perception measures: Exploring prevalence and socio‐demographic moderators

Developing a scale to assess health regulatory focus

Revisiting the Effects of Anger on Risk‐Taking: Empirical and Meta‐Analytic Evidence for Differences Between Males and Females

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