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Numerous theories in social and health psychology assume that intentions cause behaviors. However, most tests of the intention- behavior relation involve correlational studies that preclude causal inferences. In order to determine whether changes in behavioral intention engender behavior change, participants should be assigned randomly to a treatment that significantly increases the strength of respective intentions relative to a control condition, and differences in subsequent behavior should be compared. The present research obtained 47 experimental tests of intention-behavior relations that satisfied these criteria. Meta-analysis showed that a medium-to-large change in intention (d = 0.66) leads to a small-to-medium change in behavior (d = 0.36). The review also identified several conceptual factors, methodological features, and intervention characteristics that moderate intention-behavior consistency.

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The present meta-analysis investigated the effectiveness of strategies derived from the process model of emotion regulation in modifying emotional outcomes as indexed by experiential, behavioral, and physiological measures. A systematic search of the literature identified 306 experimental comparisons of different emotion regulation (ER) strategies. ER instructions were coded according to a new taxonomy, and meta-analysis was used to evaluate the effectiveness of each strategy across studies. The findings revealed differences in effectiveness between ER processes: Attentional deployment had no effect on emotional outcomes (d(+) = 0.00), response modulation had a small effect (d(+) = 0.16), and cognitive change had a small-to-medium effect (d(+) = 0.36). There were also important within-process differences. We identified 7 types of attentional deployment, 4 types of cognitive change, and 4 types of response modulation, and these distinctions had a substantial influence on effectiveness. Whereas distraction was an effective way to regulate emotions (d(+) = 0.27), concentration was not (d(+) = -0.26). Similarly, suppressing the expression of emotion proved effective (d(+) = 0.32), but suppressing the experience of emotion or suppressing thoughts of the emotion-eliciting event did not (d(+) = -0.04 and -0.12, respectively). Finally, reappraising the emotional response proved less effective (d(+) = 0.23) than reappraising the emotional stimulus (d(+) = 0.36) or using perspective taking (d(+) = 0.45). The review also identified several moderators of strategy effectiveness including factors related to the (a) to-be-regulated emotion, (b) frequency of use and intended purpose of the ER strategy, (c) study design, and (d) study characteristics.

Protection motivation theory (PMT) was introduced by Rogers in 1975 and has since been widely adopted as a framework for the prediction of and intervention in health‐related behavior. However. PMT remains the only major cognitive model of behavior not to have been the subject of a meta‐analytic review. A quantitative review of PMT is important to assess its overall utility as a predictive model and to establish which of its variables would be most useful to address health‐education interventions. The present paper provides a comprehensive introduction to PMT and its application to health‐related behavior, together with a quantitative review of the applications of PMT to health‐related intentions and behavior. The associations between threat‐ and coping‐appraisal variables and intentions, and all components of the model and behavior were assessed both by meta‐analysis and by vote‐count procedures. Threat‐ and coping‐appraisal components of PMT were found to be useful in the prediction of health‐related intentions. The model was found to be useful in predicting concurrent behavior, but of less utility in predicting future behavior. The coping‐appraisal component of the model was found to have greater predictive validity than was the threat‐appraisal component. The main findings are discussed in relation to theory and research on social cognition models. The importance of the main findings to health education is also discussed, and future research directions are suggested.

Bitter personal experience and meta-analysis converge on the conclusion that people do not always do the things that they intend to do. This paper synthesizes research on intention- The Intention-Behavior GapGoal intentions are people's self-instructions to achieve desired outcomes (e.g., "I intend to finish this paper before I die!"; Triandis, 1980) and behavioral intentions are self-instructions to perform particular actions directed towards attaining these outcomes (e.g., "I intend to spend Monday morning working on this paper!"). Intentions capture both the level of the set goal or behavior (e.g., the number of hours that the person intends to spend working on their paper), and the person's level of commitment (e.g., how determined they are to devote that number of hours to working on the paper). Although most behavior is habitual or involves responses that are triggered automatically by situational cues (e.g., Bargh, 2006;Wood & Neal, 2007), forming intentions can be crucial for securing long-term goals (Baumeister & Bargh, 2014;Kuhl & Quirin, 2011). The concept of intention has thus proved invaluable for researchers concerned with behavior change, and interventions designed to promote public health, energy conservation, and educational and organizational outcomes generally rely on frameworks that construe intentions as a key determinant of behavior change (e.g., Ajzen, 1991;Bandura, 1996;Locke & Latham, 1992;Rogers, 1983).Numerous correlational studies indicate that intentions predict behavior. For instance, Sheeran (2002) meta-analyzed 10 previous meta-analysis (422 studies in total) and found a 'large' sample-weighted average correlation between intentions measured at one time-point and measures of behavior taken at a subsequent time-point (r + = 0.53). Moreover, intention offers superior prediction of behavior in correlational tests compared to other cognitions including (explicit and implicit) attitudes, norms, self-efficacy, and perceptions of risk and severity (e.g., McEachan et al., 2011; Sheeran, Harris, & Epton, 2014; Sheeran et al., in press) as well as personality factors (e.g., Chiaburu et al., 2011;Poropat, 2009;Rhodes & Smith, 2006). These findings would seem to suggest that forming an intention to change is vital if people are to initiate new behaviors or to alter courses of action that are no longer seen as desirable. The Intention-Behavior Gap: The Proverbial 'Road to Hell' is Well PavedHow well a variable predicts behavior in correlational studies does not indicate how much change in behavior accrues from manipulating that variable, however (Sheeran, Klein, & Rothman, in press). A meta-analysis of experiments that manipulated intention showed that a medium-to-large-sized change in intentions led to only a small-to-medium-sized change in behavior (d + = .36; Webb & Sheeran, 2006; see also Rhodes & Dickau, 2012). Findings from statistical simulations also converge on the conclusion that changing intentions does not guarantee behavior change (Fife-Schaw, Sheeran, & Norman, 2007)...

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This study used meta-analysis: (a) to quantify the relationship between descriptive norms and intentions, and (b) to determine the increment in variance attributable to descriptive norms after variables from the theory of planned behaviour (TPB; Ajzen, 1991) had been controlled. Literature searches revealed twenty-one hypotheses based on a total sample of N = 8097 that could be included in the review. Overall, there was a medium to strong sample-weighted average correlation between descriptive norms and intentions (r+ = .44). Regression analysis showed that descriptive norms increased the variance explained in intention by 5 percent after attitude, subjective norm, and perceived behavioural control had been taken into account. Moderator analyses indicated that younger samples and health risk behaviours were both associated with stronger correlations between descriptive norms and intentions. Implications of the findings for the conceptualization of social influences in the TPB are discussed.T he theory of planned behaviour (TPB; Ajzen, 1991) is perhaps the most influen tial theory for the prediction of social and health behaviours. This model is an extension of the theory of reasoned action (TRA; Ajzen & Fishbein, 1980) and incorporates both social influences and personal factors as predictors. Social influences are conceptualized in terms of the pressure that people perceive from important others to perform, or not to perform, a behaviour (subjective norm). Subjective norm is determined by beliefs about the extent to which important others want them to perform a behaviour (normative beliefs, e.g., "My friends think that I should engage in a binge drinking session") multiplied by one's motivation to comply with those people's views (e.g., "I generally want to do what my friends think I should do"). Subjective norms are proposed to influence behaviour through their impact upon intentions, (e.g., "I intend to engage in a binge drinking session"). Intentions summarize a person's motivation to act in a particular manner and indicate how hard the person is willing to try and how much time and effort he or she is willing to devote in order to perform a behaviour. Also important in the prediction of intentions are people's positive or negative evaluations of their performing a behaviour (attitudes, e.g., "For me, engaging in a binge drinking session would be wise/foolish") and the degree of control that they believe they have over performing the behaviour (perceived behavioural control, e.g., "Engaging in a binge drinking session is entirely under/outside my control"). Like subjective norms, attitudes and perceived behavioural control are determined by cer-