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Risk-mitigating beliefs, risk estimates, and self-reported speeding in a sample of Australian drivers
Journal of Safety Research 34 (2003) 183 – 188 www.nsc.org
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Risk-mitigating beliefs, risk estimates, and self-reported speeding in a sample of Australian drivers Stephen L. Brown *, Amy Cotton Department of Psychology, University of Central Lancashire, Preston PR1 2HE, UK Received 15 October 2001; received in revised form 15 May 2002; accepted 30 September 2002
Abstract Problem: Research suggests that people who engage in risk-taking behaviors often hold specific beliefs that can mitigate or reduce their perceptions of risk associated with those behaviors. Method: A scale was developed (Speeding Risk Belief Scale (SRBS)) to assess beliefs about speeding-related risk and predict self-reported speeding in a random-digit telephone survey of 800 South Australian drivers between the ages of 16 and 50. Results: The scale was internally consistent, and path analyses showed it to be associated with self-reported speeding, both directly and indirectly through participants’ estimates of speeding-related risk. Discussion: Origins of risk-mitigating beliefs and the extent to which they may be causally linked with speeding are discussed, and recommendations are made for future research. Impact on Industry: This research has strong implications for the conduct of countermeasure campaigns that disseminate information on speedingrelated risk. D 2003 National Safety Council and Elsevier Science Ltd. All rights reserved. Keywords: Speeding; Risk perception; Erroneous beliefs; Risk mitigation; Survey research
1. Problem Speeding is a significant risk factor for injurious vehicle collisions. Case-control studies suggest that vehicles involved in crashes are likely to have been traveling at higher speeds than other vehicles using the same roads under the same conditions (Kloeden, MacLean, Moore, & Ponte, 1997; Solomon, 1964). Reductions of posted speed limits can decrease crash frequency (Frith & Toomath, 1982; Garber & Graham, 1990; Nilsson, 1990), while, conversely, increasing limits can have the opposite effect (Wagenaar, Streff, & Schultz, 1989). In addition to the increased probability of crashing, high speeds lead to greater energy exchanges if collisions do occur, which increases the potential for injury (Munden, 1967). Most contemporary models of volitional behavior change suggest that people become motivated to change behaviors, such as speeding, when they realize that those behaviors entail a risk of personal harm (Ajzen, 1985; Janz & Becker, 1984; Rippetoe & Rogers, 1987). Longitudinal research has
* Corresponding author. Tel.: +44-1772-893-875; fax: +44-1772-892925. E-mail address: [email protected] (S.L. Brown).
linked risk estimates to protective behavior, although such relationships can be modest (van der Pligt, 1998). Risk estimates have been more strongly linked to proximal indicators of future behavioral change such as contemplation of change (Velicer, Rossi, Prochaska, & Di Clemente, 1996), willingness to change (Gibbons & Gerrard, 1995), and intention to change (Godin & Kok, 1996). With regard to speeding, a cross-sectional study by Adams-Guppy and Guppy (1995) found modest inverse relationships between self-reported speeding behavior and perceived risk of speeding. Fildes, Rumbold, and Leening (1991) took a series of unobserved speed measurements before stopping and interviewing the drivers concerned. Travel speeds were positively related to drivers’ perceptions of what were safe speeds for those locations. Links between risk perception and future behavioral change provide a theoretical foundation for mass-reach intervention campaigns against smoking, alcohol abuse, high-risk sexual behaviors, drunk driving, and other harmful behaviors. In Australia, drivers are constantly reminded about the relationship between speeding and crash likelihood, and surveys indicate that most drivers accept this (e.g., Guerin, 1994). Indeed, it is difficult to conceive that a rational driver would not perceive any relationship between speeding and crash risk. However, many Australian drivers
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also speed. This contradiction is similar to that faced by people engaged in other forms of risky behavior such as smoking, alcohol abuse, unsafe sexual behavior, and drunk driving where the existence of risk is clear to most participants. Research suggests that people attempt to resolve this contradiction by engaging in beliefs that minimize the perception of personal risk (Chapman, Wong, & Smith, 1993; Christensen, Moran, & Wiebe, 1999). For example, people often create a distinction between a generic conception of risk and the (usually lesser) extent to which that risk applies to them (Weinstein & Klein, 1996), and this is also true of drivers (DeJoy, 1989; DelHomme, 1991; Guerin, 1994). Some evidence suggests that people might maintain this distinction by holding erroneous beliefs about the relevance of health risks to themselves. For example, Chapman et al. (1993) showed that, while smokers are prepared to accept that smoking is a risky behavior in a general sense, they are more likely than nonsmokers or ex-smokers to hold erroneous beliefs, such as ‘‘physical activity gets the tar out of your system’’ or ‘‘if smoking was really harmful, the government would ban it.’’ Christensen et al. (1999) found that erroneous beliefs about a range of health risks constitute a single dimension, upon which high scorers are less likely to engage in health protective behaviors. This finding implies that erroneous beliefs about risk represent a coherent underlying tendency to minimize the perception of personal risk, which contributes to the maintenance of unsafe behaviors by reducing risk estimates. Qualitative research suggests that some drivers possess beliefs that may help to mitigate the perception of risk involved with speeding (Adams Research, 1995; Elliot and Shanahan Research, 1995). Although aware of speedingrelated risk in general terms, many drivers report beliefs pertaining to the acceptability of speeding in specific environments such as on long straight roads or in light traffic conditions. As a large proportion of speed-related crashes occur under these conditions (Hanworth & Rechnitzer, 1993), such beliefs ought to be regarded as both erroneous and potentially dangerous. It is likely that such beliefs will both reflect and cause speeding; they may be reflective in the sense that they are employed by drivers to rationalize speeding behavior and causative in the sense that they may encourage further speeding because they reduce the perception of speeding-related risk. The researchers of this study conducted a preliminary examination into relationships between specific risk-mitigating beliefs, estimates of speeding-related risk, and selfreported speeding behavior. The objective of this study was to develop an instrument that would measure risk-mitigating beliefs specific to speeding and to identify the extent to which it is correlated with estimates of speeding-related risk and self-reported speeding. An instrument was developed to assess erroneous beliefs specific to speeding and used a cross-sectional telephone survey of a large sample of South Australian drivers to investigate its relation to
speeding. Similar to Christensen et al. (1999), it was expected that the scale would show good internal consistency and a unidimensional structure. Although the crosssectional methodology does not permit conclusive causal interpretations, a path analysis was used to investigate the possibility that the Speeding Risk Belief Scale (SRBS) may indirectly influence speeding through its influence on general risk estimates.
2. Method All data used in this study were obtained from a broad telephone survey into speeding-related matters. Items were randomly distributed through the entire interview schedule. Drivers below the age of 50 living in metropolitan Adelaide, Australia were surveyed by an independent private market research company. The company had neither any knowledge of the hypotheses nor any professional interest in the results. Telephone numbers were randomly selected from a computerized version of the metropolitan telephone directory. Within each household, the caller requested permission to interview a holder of a current drivers’ license aged 50 or less. In households where there were several eligible respondents, the respondent was selected on the alternating criteria of being either the last or the next to have a birthday. On occasions when this respondent could not be contacted, five callbacks were made before replacement in the sample. The response rate is unknown. The survey was administered to 800 respondents: 396 (49.5%) were male, 404 (50.5%) female, 129 (16.1%) were aged from 16 to 19, 271 (33.9%) were aged from 20 to 29, and 400 (50%) were aged from 30 to 50. 2.1. Measures of speeding Self-report measures of the proportion of time that respondents exceed speed limits were used. Although selfreport measures of speeding may be subject to error through memory, perceptual limitations, or social desirability responding (Lajunen, Corry, Summala, & Hartley, 1997), they show moderate to strong correlations with observational measures (Aberg, Larsen, Glad, & Beilinsson, 1997; West, French, Kemp, & Elander, 1993) and are commonly used in the literature. Three items were used. Respondents were asked to indicate the amount of time that they spent exceeding the speed limit in the Adelaide metropolitan area (a general 60 km/h limit with some 50, 70, and 80 km/h zones), the amount of time they exceeded the limit by 5 km/ h, and the amount of time they exceeded it by 10 km/h. The response format was ‘‘most of the time,’’ ‘‘about half of the time,’’ ‘‘about quarter of the time,’’ ‘‘only occasionally,’’ and ‘‘never.’’ Respondents were given an opportunity to freely respond, and if their response was not compatible with the response format, possible responses were read out and they were asked to select the most appropriate.
S.L. Brown, A. Cotton / Journal of Safety Research 34 (2003) 183–188 Table 1 Mean, S.D., component matrix for principal components analysis, and corrected item – total correlation scores for all risk mitigation items
SRBS OK on familiar roads I can drive safely at speed Only speed when safe OK when no cars around OK on long straight roads Only really high speeds dangerous Speed major contributor to crashesa Exceed limit by small amount dangerousa a
Mean
S.D.
Item – total correlation
Component matrix loading
12.63 0.92 1.10 1.47 1.15 1.05 1.06
3.70 0.67 0.72 0.78 0.66 0.64 0.69
0.64 0.55 0.59 0.62 0.60 0.52
0.75 0.67 0.70 0.73 0.73 0.63
3.11
0.67
0.41
0.53
2.77
0.68
0.51
0.63
These items were reverse scored when scaled.
The speeding measures showed intercorrelations of between .41 and .55. To simplify analyses, a single speeding measure based upon principal components analyses of the three items was constructed. A single factor, with an eigenvalue of 2.01, accounting for 66.93% of variance, was obtained. Frequency of exceeding the limit had a loading of 0.79, frequency of exceeding the limit by 5 km/h had a loading of 0.86, and frequency of exceeding the limit by 10 km/h had a loading of 0.80. Participants’ scores on this factor were used in all analyses. 2.2. Risk estimates Weinstein and Nicolich (1993) draw a distinction between the perceptions of personal risk inherent in specific behaviors and the risk of future harm to an individual practicing them. The latter is in more regular use but represents two entities: (a) a function of inherent risk and (b) the amount of exposure to that risk. The interest of this study is only in perceptions of personal risk inherent in speeding. Respondents were asked to estimate how dangerous exceeding the speed limit is by 10 km/h on main roads (a generic term used locally to refer to arterial and collector roads) and on residential backstreets and a 20 km/h violation on main roads (a corresponding 20 km/h violation on backstreets was not assessed because such violations are quite rare). Possible responses were ‘‘not at all dangerous,’’ ‘‘dangerous,’’ and ‘‘very dangerous.’’ 2.3. SRBS items Available qualitative research on Australian drivers was used to construct eight items representing risk-mitigating beliefs (Adams Research, 1995; Elliot and Shanahan Research, 1995). Items were chosen because they represented beliefs that were judged by the authors to be both erroneous and likely to mitigate estimates of speedingrelated risk. As they reflect attitudes, these items differ
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from risk estimates (van der Pligt, 1998). Respondents indicated the extent of their agreement or disagreement with items on a four-point scale using the terms ‘‘strongly agree,’’ ‘‘agree,’’ ‘‘disagree,’’ and ‘‘strongly disagree.’’ Items were ‘‘It is safe to speed on roads that are familiar,’’ ‘‘I can drive safely at high speeds,’’ ‘‘It is OK to speed when there are no other cars around,’’ ‘‘It is OK to exceed speed limits on long straight roads in the metropolitan area,’’ ‘‘I only speed when it is safe to do so,’’ and ‘‘Only really high speeds are dangerous.’’ Another two items were derived from beliefs that indicated the opposite view that speeding is unsafe under any conditions. These were ‘‘Even exceeding speed limits by small amounts is dangerous’’ and ‘‘People who exceed speed limits are a major contributor to crashes.’’ These items were reverse scored and added to the scale. Items were randomly distributed through the survey to disguise the intent of the questionnaire.
3. Results Table 1 shows means and standard deviations (S.D.) for SRBS items measured on a four-point scale with a midpoint of 2.5. Item means ranged between 0.79 and 1.47, while the two negatively worded items had relatively high means (2.77 and 3.11). The Cronbach a was good at .83. Corrected item – total correlations for each item ranged from .52 to .64 and are displayed in Table 1. To examine the structure of the SRBS, items were subjected to a principal components analysis with eigenvalues of greater than 1.00 being treated as significant. One factor with an eigenvalue of 3.66, which accounted for 45.7% the variance, was extracted. Component matrix scores ranged from 0.53 to 0.75 and are also
Table 2 Breakdown of SRBS scores by gender, age, educational attainment, and employment status Mean
S.D.
Significance
Males (396) Females (404)
22.27 24.17
3.56 3.59
t = 7.49, P < .001
Age 16 – 19 (129) 20 – 29 (271) 30 – 50 (400)
22.99 22.76 23.63
3.59 3.72 3.68
F = 4.82, P=.008
Educational attainment Primary (72) Secondary (356) Diploma or certificate (153) Tertiary (218)
23.13 23.22 23.03 23.41
3.48 3.77 3.83 3.55
F = 0.34, P=.794
Employment status Full time (422) Part time (164) Unemployed or pension (38) Student (127)
22.99 23.61 23.00 23.03
3.67 3.70 3.85 3.14
F = 1.18, P=.318
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Table 3 Correlations between study variables 2 1. Speeding 2. Danger at 10 km/h over limit-main roads 3. Danger at 20 km/h over limit-main roads 4. Danger at 10 km/h over limit-backstreets 5. SRBS
3 .36
4 .30 .54
5 .20 .21
.44 .44
.36
.38 .28
All correlations are statistically significant at P < .001.
displayed in Table 1. The principal components analysis strongly suggests that these items represent a unidimensional scale. To test the hypothesis that SRBS items are empirically distinct from risk estimates, the eight SRBS and three risk estimate items were subjected to a principal components analysis. The analysis showed two factors. The first (eigenvalue = 4.25, 38.6% of variance) was defined by loadings of between 0.52 and 0.72 for the SRBS items. The three risk estimates items showed lower loadings (between 0.41 and 0.61). The second factor was defined by risk estimates with loadings of between 0.49 and 0.59, but none of the SRBS items were related to this factor. To enhance the solution, the analysis was repeated using a maximum likelihood extraction technique (this technique maximizes higher factor loadings at the expense of lower loadings) and an oblique rotation using Kaiser normalization (oblique rotation maximizes the separation of factors by allowing correlation between them). The solution converged after five iterations, showing a correlation of .56 between the two factors. The pattern matrix showed a clear discrimination between the factors, the first being defined by SRBS items (loadings between 0.68 and 0.27) with no contribution from the risk estimates items (loadings between 0.02 and 0.07). The second factor was defined by the risk estimates (loadings of between 0.42 and 0.74) but not SRBS items (loadings between 0.00 and 0.28). It was concluded that, although strongly correlated, SRBS and risk perception items are empirically distinct. Table 2 shows breakdowns of SRBS scores by gender, age category, educational attainment, and employment status. Females showed slightly higher SRBS scores than males, scores were marginally higher in the 30– 50 age group, but there were no significant differences for educational attainment or employment status.
Pearson correlation coefficients were used to assess relationships between all variables and are presented in Table 3. All variables were associated with self-reported speeding in predicted directions. As predicted, the SRBS showed a moderate correlation with speeding. Also, as predicted, the SRBS was negatively correlated with risk estimates, which were, themselves, negatively correlated with speeding. Researchers predicted that risk estimates would mediate the relationship between SRBS and speeding. A path analyses was conducted to test this hypotheses. As the three risk estimate variables were interrelated, a composite variable was constructed by regressing them onto speeding and using the standardized predicted values in the analysis. This simplified the path analysis without losing the predictive power of the risk estimate variables. Outcomes of the path analysis are presented in Fig. 1 and show support for the hypothesis that relations between SRBS and self-reported speeding are mediated by risk estimates. However, a direct link between SRBS and speeding was also observed. Thus, mediation by risk estimates only partially explains the correlation between SRBS and speeding.
4. Discussion Prior research has suggested that people who engage in risk-taking behaviors often hold erroneous beliefs about the risks involved with those behaviors. These beliefs play a role in reducing estimates of risk associated with behavior (Christensen et al., 1999). The SRBS was developed to measure risk-mitigating beliefs about speeding and identify cross-sectional correlations between these beliefs, risk estimates, and speeding. The SRBS directly predicted selfreported speeding. As predicted, this relationship was partly mediated through generic risk estimates. These findings open the possibility that risk-mitigating beliefs play a causal role in speeding. However, further work is required to strengthen these findings, to test causal hypotheses, and to understand precisely how risk-mitigating beliefs are related to speeding. The SRBS was internally consistent and unidimensional. This finding is similar to that of Christensen et al. (1999) who found a measure of erroneous beliefs about general health risk to be unidimensional. Such coherence of item
Fig. 1. Path analysis showing significant multivariate predictors of self-reported speeding.
S.L. Brown, A. Cotton / Journal of Safety Research 34 (2003) 183–188
responses is suggestive of a systematic tendency to mitigate speeding-related risk, consistent with a unitary motivation, such as the wish to reduce speeding-related risk perceptions. It is inconsistent with the view that drivers make independent and balanced appraisals of each SRBS item. The theoretical model states that risk-mitigating beliefs reduce the general perception of speeding-related risk estimates, thus rationalizing past speeding and possibly encouraging speeding in the future. SRBS scores were good predictors of speeding, and the path analyses showed that some mediation of the relationship between SRBS and speeding by risk estimates are possible. It is suspected that risk-mitigating beliefs are part of a broader self-deceptive process whereby speeding drivers attempt to rationalize risks associated with speeding. Although these findings are consistent with a self-deception explanation, it is not possible to definitively rule out the interpretation that SRBS scores represent honestly held beliefs about risk. Job (1990) noted that successful performance of risk-taking activities reduces perceptions of risk associated with them. As crashing is a comparatively rare event, many consistent speeders have not suffered adverse consequences. Risk-mitigating beliefs may, thus, appear to be realistic to them. In light of this explanation, correlations between SRBS scores and speeding may reflect honest, but erroneous, judgments about risk, not self-deceptions. As this process involves a broad tendency toward risk mitigation, it would explain the unidimensionality of the SRBS. However, this explanation is not fully convincing because the tendency to adopt risk-mitigating beliefs should increase with driver experience (Job, 1990). SRBS scores increased only slightly with age. Moreover, further analysis showed that the correlation between SRBS scores and speeding remained approximately equal across the three age groups [16 – 19 group, r(127)=.45; 20– 29 group, r(269)=.50; 30 – 50 group, r(127)=.39]. Another possible interpretation of the findings is methodological. Social desirability responding is known to reduce self-reports of speeding behavior (Lajunen et al., 1997) and may have affected the SRBS in the same way, thus creating a correlation between the two variables. Precautions were taken to minimize this problem. An independent market research company conducted surveys anonymously over the telephone, and SRBS items were distributed through the survey to disguise their intent. Moreover, SRBS items were deliberately phrased in a moderate way. However, these precautions do not fully eliminate the possibility of a social desirability bias. Even if one accepts that the SRBS scores represent deliberate self-deception about speeding-related risk, this does not mean that risk-mitigating beliefs play a causal role in speeding. The findings are consistent with mediation of the relationship between SRBS and speeding by risk estimates, which are known to be predictive of future behavior (van der Pligt, 1998). This suggests that causality is likely. However, when used in a cross-sectional study, path analytic techniques do not demonstrate causality. A stronger case for
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a causal relationship requires a longitudinal study design, where SRBS scores can be shown to predict changes in speeding behavior. This study demonstrates a close association between irrational beliefs about speed-related risk and speeding. Currently, the SRBS is the only measure of speedingspecific irrational beliefs. Its brevity, unidimensional structure, and association with speeding suggest the SRBS potential for wider use in further theoretically based investigations into risk-mitigating beliefs and for studies directed toward targeting segments of the population for countermeasures. However, future users of the SRBS need be aware that work is required to assess further psychometric properties such as stability and criterion validity. This represents a fruitful avenue for future research. This research has implications for mass-reach intervention campaigns based upon the communication of speedingrelated risk. SRBS scores may represent the outcome of a unitary self-deceptive process. Thus, the content of SRBS items is likely to be of secondary importance to the process itself. Self-deceptive processes are known to be flexible (Baumeister & Cairns, 1992), and drivers who are motivated to minimize their perceptions of risk probably possess a wider range of risk-mitigating strategies and beliefs than are measures in the SRBS. However, as Liberman and Chaiken (1992) point out, people who engage in self-deceptive strategies to minimize the perception of risk are, by definition, sensitive to risk information. One strategy is to design risk communications that avoid self-deceptive processes. For example, there is evidence that communication techniques that employ highly fear-arousing stimuli, which ask people to perform difficult behaviors and those which ask people to forego preferred activities (Brown, 2001; Job, 1988; La Tour & Zahra, 1988), may elicit a defensive approach. Job (1988) and La Tour and Zahra (1988) provide recommendations for reducing defensiveness. These include inducing moderate rather than high levels of fear, providing easily implemented behavioral alternatives, and providing support for attempts to change behavior.
5. Summary This study found robust relationships between risk-mitigating beliefs, speeding-related risk estimates, and selfreported speeding in a large cross-sectional sample of drivers between the ages of 16 and 50. It is suggested that these associations represent a reciprocal process whereby risk-mitigating beliefs are used to rationalize speeding by reducing the level of perceived risk, thus encouraging further speeding. Although these findings are consistent with this view, they must be regarded as preliminary because alternative interpretations are possible. Further research is required to establish that SRBS scores represent a process of self-deception, requiring, for example, demonstration of links with instruments designed to measure self-
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deceptive processes. Consolidation of these findings also requires the establishment that SRBS scores predict future changes in speeding behavior. A further goal is to design and test interventions that can change risk-mitigating beliefs and show causal relationships between such beliefs and speeding.
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Janz, N., & Becker, M. (1984). The health-belief model: a decade later. Health Education Quarterly, 11, 1 – 47. Job, R. F. S. (1988). Effective and ineffective use of fear in health promotion campaigns. American Journal of Public Health, 78, 163 – 167. Job, R. F. S. (1990). The application of learning theory to driver confidence: the effect of age and the impact of random breath testing. Accident Analysis and Prevention, 22, 97 – 107. Kloeden, C. N., MacLean, A. J., Moore, V. M., & Ponte, G. (1997). Travelling speed and the risk of crash involvement. Canberra, Australia: Federal Office of Road Safety (CR172). Lajunen, T., Corry, A., Summala, H., & Hartley, L. (1997). Impression management and self-deception in traffic behaviour inventories. Personality and Individual Differences, 22, 341 – 353. La Tour, M. S., & Zahra, S. A. (1988). Fear appeals as advertising strategy: should they be used? Journal of Services Marketing, 2, 3 – 14. Liberman, A., & Chaiken, S. (1992). Defensive processing of personallyrelevant health messages. Personality and Social Psychology Bulletin, 18, 669 – 679. Munden, J. W. (1967). The relation between a driver’s speed and his accident rate. Crowthorne, UK: Road Research Laboratory. Nilsson, G. (1990). Reduction of the speed limit from 110 km/h to 90 km/h during summer 1989: effects on personal injury accidents, injured and speeds. Stockholm: Swedish Road and Traffic Institute. Rippetoe, P. A., & Rogers, R. W. (1987). Effects of components of protection-motivation theory on adaptive and maladaptive coping with a health threat. Journal of Personality and Social Psychology, 52, 596 – 604. Solomon, D. (1964). Accidents on main rural highways related to speed, driver and vehicle. Washington, DC: Department of Commerce, Bureau of Public Roads. van der Pligt, J. (1998). Perceived risk and vulnerability as predictors of precautionary behaviour. British Journal of Health Psychology, 3, 1 – 14. Velicer, W. F., Rossi, J. S., Prochaska, J. O., & Di Clemente, C. C. (1996). A criterion measurement model for health behaviour change. Addictive Behaviors, 21, 555 – 584. Wagenaar, A. C., Streff, F. M., & Schultz, R. H. (1989). The 65 mph speed limit in Michigan: effects on injury and death. Michigan: University of Michigan, Traffic Research Institute. Weinstein, N. D., & Klein, W. M. (1996). Unrealistic optimism: present and future. Journal of Social and Clinical Psychology, 15, 1 – 8. Weinstein, N. D., & Nicolich, M. (1993). Correct and incorrect interpretations of correlations between risk perception and risk behaviors. Health Psychology, 12, 235 – 245. West, R. J., French, D., Kemp, R., & Elander, J. (1993). Direct observation of driving, self-reports of driver behavior and accident involvement. Ergonomics, 36, 557 – 567. Stephen Brown graduated from Adelaide University with a PhD in 1994 and has worked with the South Australian Department of Transport and Monash University in Australia. He is now employed at the University of Central Lancashire in the United Kingdom, where he conducts research on drunk driving and speeding. His theoretical interests lie in risk perception, self-deception about health risks, and social norms and behavior. Amy Cotton graduated from the Flinders University of South Australia with an Honours degree in psychology. She is now employed by the Department of Transport in South Australia, examining population trends in unsafe driving and pedestrian behavior.
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Risk-mitigating beliefs, risk estimates, and self-reported speeding in a sample of Australian drivers Stephen L. Brown *, Amy Cotton Department of Psychology, University of Central Lancashire, Preston PR1 2HE, UK Received 15 October 2001; received in revised form 15 May 2002; accepted 30 September 2002
Abstract Problem: Research suggests that people who engage in risk-taking behaviors often hold specific beliefs that can mitigate or reduce their perceptions of risk associated with those behaviors. Method: A scale was developed (Speeding Risk Belief Scale (SRBS)) to assess beliefs about speeding-related risk and predict self-reported speeding in a random-digit telephone survey of 800 South Australian drivers between the ages of 16 and 50. Results: The scale was internally consistent, and path analyses showed it to be associated with self-reported speeding, both directly and indirectly through participants’ estimates of speeding-related risk. Discussion: Origins of risk-mitigating beliefs and the extent to which they may be causally linked with speeding are discussed, and recommendations are made for future research. Impact on Industry: This research has strong implications for the conduct of countermeasure campaigns that disseminate information on speedingrelated risk. D 2003 National Safety Council and Elsevier Science Ltd. All rights reserved. Keywords: Speeding; Risk perception; Erroneous beliefs; Risk mitigation; Survey research
1. Problem Speeding is a significant risk factor for injurious vehicle collisions. Case-control studies suggest that vehicles involved in crashes are likely to have been traveling at higher speeds than other vehicles using the same roads under the same conditions (Kloeden, MacLean, Moore, & Ponte, 1997; Solomon, 1964). Reductions of posted speed limits can decrease crash frequency (Frith & Toomath, 1982; Garber & Graham, 1990; Nilsson, 1990), while, conversely, increasing limits can have the opposite effect (Wagenaar, Streff, & Schultz, 1989). In addition to the increased probability of crashing, high speeds lead to greater energy exchanges if collisions do occur, which increases the potential for injury (Munden, 1967). Most contemporary models of volitional behavior change suggest that people become motivated to change behaviors, such as speeding, when they realize that those behaviors entail a risk of personal harm (Ajzen, 1985; Janz & Becker, 1984; Rippetoe & Rogers, 1987). Longitudinal research has
* Corresponding author. Tel.: +44-1772-893-875; fax: +44-1772-892925. E-mail address: [email protected] (S.L. Brown).
linked risk estimates to protective behavior, although such relationships can be modest (van der Pligt, 1998). Risk estimates have been more strongly linked to proximal indicators of future behavioral change such as contemplation of change (Velicer, Rossi, Prochaska, & Di Clemente, 1996), willingness to change (Gibbons & Gerrard, 1995), and intention to change (Godin & Kok, 1996). With regard to speeding, a cross-sectional study by Adams-Guppy and Guppy (1995) found modest inverse relationships between self-reported speeding behavior and perceived risk of speeding. Fildes, Rumbold, and Leening (1991) took a series of unobserved speed measurements before stopping and interviewing the drivers concerned. Travel speeds were positively related to drivers’ perceptions of what were safe speeds for those locations. Links between risk perception and future behavioral change provide a theoretical foundation for mass-reach intervention campaigns against smoking, alcohol abuse, high-risk sexual behaviors, drunk driving, and other harmful behaviors. In Australia, drivers are constantly reminded about the relationship between speeding and crash likelihood, and surveys indicate that most drivers accept this (e.g., Guerin, 1994). Indeed, it is difficult to conceive that a rational driver would not perceive any relationship between speeding and crash risk. However, many Australian drivers
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also speed. This contradiction is similar to that faced by people engaged in other forms of risky behavior such as smoking, alcohol abuse, unsafe sexual behavior, and drunk driving where the existence of risk is clear to most participants. Research suggests that people attempt to resolve this contradiction by engaging in beliefs that minimize the perception of personal risk (Chapman, Wong, & Smith, 1993; Christensen, Moran, & Wiebe, 1999). For example, people often create a distinction between a generic conception of risk and the (usually lesser) extent to which that risk applies to them (Weinstein & Klein, 1996), and this is also true of drivers (DeJoy, 1989; DelHomme, 1991; Guerin, 1994). Some evidence suggests that people might maintain this distinction by holding erroneous beliefs about the relevance of health risks to themselves. For example, Chapman et al. (1993) showed that, while smokers are prepared to accept that smoking is a risky behavior in a general sense, they are more likely than nonsmokers or ex-smokers to hold erroneous beliefs, such as ‘‘physical activity gets the tar out of your system’’ or ‘‘if smoking was really harmful, the government would ban it.’’ Christensen et al. (1999) found that erroneous beliefs about a range of health risks constitute a single dimension, upon which high scorers are less likely to engage in health protective behaviors. This finding implies that erroneous beliefs about risk represent a coherent underlying tendency to minimize the perception of personal risk, which contributes to the maintenance of unsafe behaviors by reducing risk estimates. Qualitative research suggests that some drivers possess beliefs that may help to mitigate the perception of risk involved with speeding (Adams Research, 1995; Elliot and Shanahan Research, 1995). Although aware of speedingrelated risk in general terms, many drivers report beliefs pertaining to the acceptability of speeding in specific environments such as on long straight roads or in light traffic conditions. As a large proportion of speed-related crashes occur under these conditions (Hanworth & Rechnitzer, 1993), such beliefs ought to be regarded as both erroneous and potentially dangerous. It is likely that such beliefs will both reflect and cause speeding; they may be reflective in the sense that they are employed by drivers to rationalize speeding behavior and causative in the sense that they may encourage further speeding because they reduce the perception of speeding-related risk. The researchers of this study conducted a preliminary examination into relationships between specific risk-mitigating beliefs, estimates of speeding-related risk, and selfreported speeding behavior. The objective of this study was to develop an instrument that would measure risk-mitigating beliefs specific to speeding and to identify the extent to which it is correlated with estimates of speeding-related risk and self-reported speeding. An instrument was developed to assess erroneous beliefs specific to speeding and used a cross-sectional telephone survey of a large sample of South Australian drivers to investigate its relation to
speeding. Similar to Christensen et al. (1999), it was expected that the scale would show good internal consistency and a unidimensional structure. Although the crosssectional methodology does not permit conclusive causal interpretations, a path analysis was used to investigate the possibility that the Speeding Risk Belief Scale (SRBS) may indirectly influence speeding through its influence on general risk estimates.
2. Method All data used in this study were obtained from a broad telephone survey into speeding-related matters. Items were randomly distributed through the entire interview schedule. Drivers below the age of 50 living in metropolitan Adelaide, Australia were surveyed by an independent private market research company. The company had neither any knowledge of the hypotheses nor any professional interest in the results. Telephone numbers were randomly selected from a computerized version of the metropolitan telephone directory. Within each household, the caller requested permission to interview a holder of a current drivers’ license aged 50 or less. In households where there were several eligible respondents, the respondent was selected on the alternating criteria of being either the last or the next to have a birthday. On occasions when this respondent could not be contacted, five callbacks were made before replacement in the sample. The response rate is unknown. The survey was administered to 800 respondents: 396 (49.5%) were male, 404 (50.5%) female, 129 (16.1%) were aged from 16 to 19, 271 (33.9%) were aged from 20 to 29, and 400 (50%) were aged from 30 to 50. 2.1. Measures of speeding Self-report measures of the proportion of time that respondents exceed speed limits were used. Although selfreport measures of speeding may be subject to error through memory, perceptual limitations, or social desirability responding (Lajunen, Corry, Summala, & Hartley, 1997), they show moderate to strong correlations with observational measures (Aberg, Larsen, Glad, & Beilinsson, 1997; West, French, Kemp, & Elander, 1993) and are commonly used in the literature. Three items were used. Respondents were asked to indicate the amount of time that they spent exceeding the speed limit in the Adelaide metropolitan area (a general 60 km/h limit with some 50, 70, and 80 km/h zones), the amount of time they exceeded the limit by 5 km/ h, and the amount of time they exceeded it by 10 km/h. The response format was ‘‘most of the time,’’ ‘‘about half of the time,’’ ‘‘about quarter of the time,’’ ‘‘only occasionally,’’ and ‘‘never.’’ Respondents were given an opportunity to freely respond, and if their response was not compatible with the response format, possible responses were read out and they were asked to select the most appropriate.
S.L. Brown, A. Cotton / Journal of Safety Research 34 (2003) 183–188 Table 1 Mean, S.D., component matrix for principal components analysis, and corrected item – total correlation scores for all risk mitigation items
SRBS OK on familiar roads I can drive safely at speed Only speed when safe OK when no cars around OK on long straight roads Only really high speeds dangerous Speed major contributor to crashesa Exceed limit by small amount dangerousa a
Mean
S.D.
Item – total correlation
Component matrix loading
12.63 0.92 1.10 1.47 1.15 1.05 1.06
3.70 0.67 0.72 0.78 0.66 0.64 0.69
0.64 0.55 0.59 0.62 0.60 0.52
0.75 0.67 0.70 0.73 0.73 0.63
3.11
0.67
0.41
0.53
2.77
0.68
0.51
0.63
These items were reverse scored when scaled.
The speeding measures showed intercorrelations of between .41 and .55. To simplify analyses, a single speeding measure based upon principal components analyses of the three items was constructed. A single factor, with an eigenvalue of 2.01, accounting for 66.93% of variance, was obtained. Frequency of exceeding the limit had a loading of 0.79, frequency of exceeding the limit by 5 km/h had a loading of 0.86, and frequency of exceeding the limit by 10 km/h had a loading of 0.80. Participants’ scores on this factor were used in all analyses. 2.2. Risk estimates Weinstein and Nicolich (1993) draw a distinction between the perceptions of personal risk inherent in specific behaviors and the risk of future harm to an individual practicing them. The latter is in more regular use but represents two entities: (a) a function of inherent risk and (b) the amount of exposure to that risk. The interest of this study is only in perceptions of personal risk inherent in speeding. Respondents were asked to estimate how dangerous exceeding the speed limit is by 10 km/h on main roads (a generic term used locally to refer to arterial and collector roads) and on residential backstreets and a 20 km/h violation on main roads (a corresponding 20 km/h violation on backstreets was not assessed because such violations are quite rare). Possible responses were ‘‘not at all dangerous,’’ ‘‘dangerous,’’ and ‘‘very dangerous.’’ 2.3. SRBS items Available qualitative research on Australian drivers was used to construct eight items representing risk-mitigating beliefs (Adams Research, 1995; Elliot and Shanahan Research, 1995). Items were chosen because they represented beliefs that were judged by the authors to be both erroneous and likely to mitigate estimates of speedingrelated risk. As they reflect attitudes, these items differ
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from risk estimates (van der Pligt, 1998). Respondents indicated the extent of their agreement or disagreement with items on a four-point scale using the terms ‘‘strongly agree,’’ ‘‘agree,’’ ‘‘disagree,’’ and ‘‘strongly disagree.’’ Items were ‘‘It is safe to speed on roads that are familiar,’’ ‘‘I can drive safely at high speeds,’’ ‘‘It is OK to speed when there are no other cars around,’’ ‘‘It is OK to exceed speed limits on long straight roads in the metropolitan area,’’ ‘‘I only speed when it is safe to do so,’’ and ‘‘Only really high speeds are dangerous.’’ Another two items were derived from beliefs that indicated the opposite view that speeding is unsafe under any conditions. These were ‘‘Even exceeding speed limits by small amounts is dangerous’’ and ‘‘People who exceed speed limits are a major contributor to crashes.’’ These items were reverse scored and added to the scale. Items were randomly distributed through the survey to disguise the intent of the questionnaire.
3. Results Table 1 shows means and standard deviations (S.D.) for SRBS items measured on a four-point scale with a midpoint of 2.5. Item means ranged between 0.79 and 1.47, while the two negatively worded items had relatively high means (2.77 and 3.11). The Cronbach a was good at .83. Corrected item – total correlations for each item ranged from .52 to .64 and are displayed in Table 1. To examine the structure of the SRBS, items were subjected to a principal components analysis with eigenvalues of greater than 1.00 being treated as significant. One factor with an eigenvalue of 3.66, which accounted for 45.7% the variance, was extracted. Component matrix scores ranged from 0.53 to 0.75 and are also
Table 2 Breakdown of SRBS scores by gender, age, educational attainment, and employment status Mean
S.D.
Significance
Males (396) Females (404)
22.27 24.17
3.56 3.59
t = 7.49, P < .001
Age 16 – 19 (129) 20 – 29 (271) 30 – 50 (400)
22.99 22.76 23.63
3.59 3.72 3.68
F = 4.82, P=.008
Educational attainment Primary (72) Secondary (356) Diploma or certificate (153) Tertiary (218)
23.13 23.22 23.03 23.41
3.48 3.77 3.83 3.55
F = 0.34, P=.794
Employment status Full time (422) Part time (164) Unemployed or pension (38) Student (127)
22.99 23.61 23.00 23.03
3.67 3.70 3.85 3.14
F = 1.18, P=.318
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Table 3 Correlations between study variables 2 1. Speeding 2. Danger at 10 km/h over limit-main roads 3. Danger at 20 km/h over limit-main roads 4. Danger at 10 km/h over limit-backstreets 5. SRBS
3 .36
4 .30 .54
5 .20 .21
.44 .44
.36
.38 .28
All correlations are statistically significant at P < .001.
displayed in Table 1. The principal components analysis strongly suggests that these items represent a unidimensional scale. To test the hypothesis that SRBS items are empirically distinct from risk estimates, the eight SRBS and three risk estimate items were subjected to a principal components analysis. The analysis showed two factors. The first (eigenvalue = 4.25, 38.6% of variance) was defined by loadings of between 0.52 and 0.72 for the SRBS items. The three risk estimates items showed lower loadings (between 0.41 and 0.61). The second factor was defined by risk estimates with loadings of between 0.49 and 0.59, but none of the SRBS items were related to this factor. To enhance the solution, the analysis was repeated using a maximum likelihood extraction technique (this technique maximizes higher factor loadings at the expense of lower loadings) and an oblique rotation using Kaiser normalization (oblique rotation maximizes the separation of factors by allowing correlation between them). The solution converged after five iterations, showing a correlation of .56 between the two factors. The pattern matrix showed a clear discrimination between the factors, the first being defined by SRBS items (loadings between 0.68 and 0.27) with no contribution from the risk estimates items (loadings between 0.02 and 0.07). The second factor was defined by the risk estimates (loadings of between 0.42 and 0.74) but not SRBS items (loadings between 0.00 and 0.28). It was concluded that, although strongly correlated, SRBS and risk perception items are empirically distinct. Table 2 shows breakdowns of SRBS scores by gender, age category, educational attainment, and employment status. Females showed slightly higher SRBS scores than males, scores were marginally higher in the 30– 50 age group, but there were no significant differences for educational attainment or employment status.
Pearson correlation coefficients were used to assess relationships between all variables and are presented in Table 3. All variables were associated with self-reported speeding in predicted directions. As predicted, the SRBS showed a moderate correlation with speeding. Also, as predicted, the SRBS was negatively correlated with risk estimates, which were, themselves, negatively correlated with speeding. Researchers predicted that risk estimates would mediate the relationship between SRBS and speeding. A path analyses was conducted to test this hypotheses. As the three risk estimate variables were interrelated, a composite variable was constructed by regressing them onto speeding and using the standardized predicted values in the analysis. This simplified the path analysis without losing the predictive power of the risk estimate variables. Outcomes of the path analysis are presented in Fig. 1 and show support for the hypothesis that relations between SRBS and self-reported speeding are mediated by risk estimates. However, a direct link between SRBS and speeding was also observed. Thus, mediation by risk estimates only partially explains the correlation between SRBS and speeding.
4. Discussion Prior research has suggested that people who engage in risk-taking behaviors often hold erroneous beliefs about the risks involved with those behaviors. These beliefs play a role in reducing estimates of risk associated with behavior (Christensen et al., 1999). The SRBS was developed to measure risk-mitigating beliefs about speeding and identify cross-sectional correlations between these beliefs, risk estimates, and speeding. The SRBS directly predicted selfreported speeding. As predicted, this relationship was partly mediated through generic risk estimates. These findings open the possibility that risk-mitigating beliefs play a causal role in speeding. However, further work is required to strengthen these findings, to test causal hypotheses, and to understand precisely how risk-mitigating beliefs are related to speeding. The SRBS was internally consistent and unidimensional. This finding is similar to that of Christensen et al. (1999) who found a measure of erroneous beliefs about general health risk to be unidimensional. Such coherence of item
Fig. 1. Path analysis showing significant multivariate predictors of self-reported speeding.
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responses is suggestive of a systematic tendency to mitigate speeding-related risk, consistent with a unitary motivation, such as the wish to reduce speeding-related risk perceptions. It is inconsistent with the view that drivers make independent and balanced appraisals of each SRBS item. The theoretical model states that risk-mitigating beliefs reduce the general perception of speeding-related risk estimates, thus rationalizing past speeding and possibly encouraging speeding in the future. SRBS scores were good predictors of speeding, and the path analyses showed that some mediation of the relationship between SRBS and speeding by risk estimates are possible. It is suspected that risk-mitigating beliefs are part of a broader self-deceptive process whereby speeding drivers attempt to rationalize risks associated with speeding. Although these findings are consistent with a self-deception explanation, it is not possible to definitively rule out the interpretation that SRBS scores represent honestly held beliefs about risk. Job (1990) noted that successful performance of risk-taking activities reduces perceptions of risk associated with them. As crashing is a comparatively rare event, many consistent speeders have not suffered adverse consequences. Risk-mitigating beliefs may, thus, appear to be realistic to them. In light of this explanation, correlations between SRBS scores and speeding may reflect honest, but erroneous, judgments about risk, not self-deceptions. As this process involves a broad tendency toward risk mitigation, it would explain the unidimensionality of the SRBS. However, this explanation is not fully convincing because the tendency to adopt risk-mitigating beliefs should increase with driver experience (Job, 1990). SRBS scores increased only slightly with age. Moreover, further analysis showed that the correlation between SRBS scores and speeding remained approximately equal across the three age groups [16 – 19 group, r(127)=.45; 20– 29 group, r(269)=.50; 30 – 50 group, r(127)=.39]. Another possible interpretation of the findings is methodological. Social desirability responding is known to reduce self-reports of speeding behavior (Lajunen et al., 1997) and may have affected the SRBS in the same way, thus creating a correlation between the two variables. Precautions were taken to minimize this problem. An independent market research company conducted surveys anonymously over the telephone, and SRBS items were distributed through the survey to disguise their intent. Moreover, SRBS items were deliberately phrased in a moderate way. However, these precautions do not fully eliminate the possibility of a social desirability bias. Even if one accepts that the SRBS scores represent deliberate self-deception about speeding-related risk, this does not mean that risk-mitigating beliefs play a causal role in speeding. The findings are consistent with mediation of the relationship between SRBS and speeding by risk estimates, which are known to be predictive of future behavior (van der Pligt, 1998). This suggests that causality is likely. However, when used in a cross-sectional study, path analytic techniques do not demonstrate causality. A stronger case for
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a causal relationship requires a longitudinal study design, where SRBS scores can be shown to predict changes in speeding behavior. This study demonstrates a close association between irrational beliefs about speed-related risk and speeding. Currently, the SRBS is the only measure of speedingspecific irrational beliefs. Its brevity, unidimensional structure, and association with speeding suggest the SRBS potential for wider use in further theoretically based investigations into risk-mitigating beliefs and for studies directed toward targeting segments of the population for countermeasures. However, future users of the SRBS need be aware that work is required to assess further psychometric properties such as stability and criterion validity. This represents a fruitful avenue for future research. This research has implications for mass-reach intervention campaigns based upon the communication of speedingrelated risk. SRBS scores may represent the outcome of a unitary self-deceptive process. Thus, the content of SRBS items is likely to be of secondary importance to the process itself. Self-deceptive processes are known to be flexible (Baumeister & Cairns, 1992), and drivers who are motivated to minimize their perceptions of risk probably possess a wider range of risk-mitigating strategies and beliefs than are measures in the SRBS. However, as Liberman and Chaiken (1992) point out, people who engage in self-deceptive strategies to minimize the perception of risk are, by definition, sensitive to risk information. One strategy is to design risk communications that avoid self-deceptive processes. For example, there is evidence that communication techniques that employ highly fear-arousing stimuli, which ask people to perform difficult behaviors and those which ask people to forego preferred activities (Brown, 2001; Job, 1988; La Tour & Zahra, 1988), may elicit a defensive approach. Job (1988) and La Tour and Zahra (1988) provide recommendations for reducing defensiveness. These include inducing moderate rather than high levels of fear, providing easily implemented behavioral alternatives, and providing support for attempts to change behavior.
5. Summary This study found robust relationships between risk-mitigating beliefs, speeding-related risk estimates, and selfreported speeding in a large cross-sectional sample of drivers between the ages of 16 and 50. It is suggested that these associations represent a reciprocal process whereby risk-mitigating beliefs are used to rationalize speeding by reducing the level of perceived risk, thus encouraging further speeding. Although these findings are consistent with this view, they must be regarded as preliminary because alternative interpretations are possible. Further research is required to establish that SRBS scores represent a process of self-deception, requiring, for example, demonstration of links with instruments designed to measure self-
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deceptive processes. Consolidation of these findings also requires the establishment that SRBS scores predict future changes in speeding behavior. A further goal is to design and test interventions that can change risk-mitigating beliefs and show causal relationships between such beliefs and speeding.
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