Transportation Research Part F: Traffic Psychology and Behaviour
The theory of planned behaviour: The role of descriptive norms and past behaviour in the prediction of drivers’ intentions to violate
Introduction
Several factors contribute to accidents, including driver characteristics, the road layout, the design of the car and the weather, although most road accidents are attributed to the ‘human factor’. In this context Sabey and Taylor (1980) are often quoted. They suggested that 95% is partly due to this factor and 65% wholly. This is interesting but insufficient unless we also try to understand what kinds of factors are responsible. Research has therefore tried to define the different types of failure and the most promising, so far, have been the ones presented by the Manchester team (Reason, Manstead, Stradling, Baxter, & Campbell, 1990). They divide failures into three different groups: violations (e.g. speeding, drinking and driving), errors (e.g. failing to see or misjudgements), and lapses (forgetfulness). With regard to accidents it is violations, rather than error and lapses, which are the main contributing factor (Gras et al., 2004, Parker et al., 1995a, Reason et al., 1990, Sullman et al., 2002). Thus, a wealth of studies trying to understand and predict violations have been produced and the most commonly used theoretical model has been the theory of planned behaviour (TPB) and its predecessor, the theory of reasoned action (TRA).
The historical background to the TPB lies in the TRA (Fishbein & Ajzen, 1975), which in turn is an adaptation of Dulany’s (1961) theory of propositional control. Briefly, the theory states that the attitude towards the act, subjective norm and perceived behavioural control are indirectly linked to behaviour via intention. If an intention should be turned into an action depends on motivation and how much energy the person is willing to invest. According to the TPB, attitude (Aact) refers to evaluations of a behaviour, which could be favourable or unfavourable. Subjective norm (SN) describes the perceived pressure from others to commit the behaviour. Perceived behavioural control (PBC) refers to how easy or difficult it would be to carry out the act. These constructs are also described as direct measures and who in turn are determined by three belief based or indirect measures namely; behavioural beliefs, normative beliefs and control beliefs.
Behavioural beliefs are the antecedent of attitude and deals with the consequences of the act and an evaluation of the same. Subjective norm develops from normative beliefs and the motivation to comply although the latter usually receives weak support (Budd et al., 1984, Miniard and Cohen, 1981) and at times it is excluded from the model (Beck and Ajzen, 1991, Charng et al., 1988). Indirect measures of perceived behavioural control are control belief’s strength and control belief’s power. Control beliefs strength indicates the perceived likelihood (or frequency) of a given factor being present. Control belief power assesses if these factors have the power to facilitate or impede the performance. However, only a few studies have used an indirect measure of perceived behavioural control, instead it is the direct approach which is more commonly found (Ajzen, 2002a). Each component within the model describes different constructs and should correlate more strongly with intention than with each other. Before a decision is made each factor is considered separately although the theory does not suggest that this process is always conscious. It is quite possible for an attitude to be activated automatically, that is, without conscious intent or cognitive effort (Bamberg, Ajzen, & Schmidt, 2003).
The model’s ability to predict intention has been tested in a great number of studies. The results from a meta-analysis found that attitudes and subjective norms explained 33–50% of the variance (Ajzen, 1991, Armitage and Conner, 2001, Sherran and Taylor, 1997). When the model added perceived behavioural control, a further improvement of 5–12% was noted (Armitage and Conner, 2001, Sherran and Taylor, 1997). The variables’ relative importance depends on behaviour plus the population and it is not always the case that each of them makes a significant contribution to the prediction of intention (Ajzen, 1991, Ajzen and Fishbein, 1980). However, a number of studies, including two different meta-analyses (Armitage and Conner, 2001, Hausenblas et al., 1997), have found that a lack of contribution tends to relate more to subjective norms than to attitudes and perceived behavioural control thus presenting itself as the weakest link. Different explanations have been presented and one them refers to individual differences. Terry, Hogg, and White (1999) argued that the effect of subjective norms depends on whether the person identifies him- or herself with the target group or not. Trafimow and Finlay (1996) suggested that people are under normative or attitudinal control.
Another reason for the poor effect of subjective norms could be that the normative measure is too narrow (Rivis, Sheeran, & Armitage, 2006) and that other norms should be included, such as moral norm (e.g. Beck and Ajzen, 1991, Gorsuch and Ortberg, 1983, Jackson et al., 2003, Parker et al., 1995b), personal norm (e.g. Harland et al., 1999, Parker et al., 1995b) and descriptive norms (e.g. Conner and McMillan, 1999, Grube et al., 1986, Heath and Gifford, 2002, McMillan and Conner, 2003, Norman et al., 2005, Rivis and Sheeran, 2003a, Rivis et al., 2006, Sherran and Orbell, 1999).1
Descriptive norms measure an individual’s beliefs about other people’s behaviour. It has been described as something which is done rather than, as is the case with subjective norms, something which ought to be done (injunctive). Deutsch and Gerard (1955) added that it represents something which is seen as normal, regardless if it is morally correct or not. The TPB would also acknowledge the effect of descriptive norms since later versions of the model combine subjective norm with descriptive norm (Ajzen & Fishbein, 2005). However, a number of studies have not been able to support this combination, instead they have found that the two variables are distinct from each other (e.g. Cialdini et al., 1990, Conner and McMillan, 1999, Deutsch and Gerard, 1955, Grube et al., 1986) and at times descriptive norm is a better predictor of intention than subjective norm (Rivis et al., 2006). In a meta-analysis based on 14 studies descriptive norm was generally successful and increased the variance with 5% over and above the variables already in the model (Rivis & Sheeran, 2003b). Nevertheless, in the same meta-analysis some conflicting findings were also reported. For instance, descriptive norm was successful in predicting intention to diet, binge drinking, play the lottery but not to eat healthily and use a condom. Various reasons have been presented to explain contradictory evidence; one refers to the behaviour itself and that it would be more important when assessing those behaviours which carry some form of risk. The argument is that a risky behaviour is more salient and that it is in those situations when we are more influenced by others (Rivis & Sheeran, 2003b).
According to the model, past behaviour relates to intention but the effect is indirect and is mediated by the variables already included in the model. Despite this, Fishbein and Ajzen (1975) recognized the effect of habit and that it may interfere with the intention-behaviour relationship although from their point of view automatic responses such as habits is not of interest to the social scientists. Nevertheless, this would not appear to have dampened the interest in this variable and a large number of studies using the TRA presented confirmative results arguing for an extension (e.g. Bentler and Speckart, 1979, Budd et al., 1984, Charng et al., 1988, Finlay et al., 2002, Hom and Hulin, 1981, Mullen et al., 1987, Rutter and Bunce, 1989). It has also been argued that the effect of past behaviour depends on whether the behaviour in question has become habitual or not. Habit could be defined as a semi-automatic performance of a well-learned behaviour (Charng et al., 1988) which may or may not be the same as past behaviour. It has been proposed that when the behaviour has become habitual then it is mostly guided by environmental cues which in turn elicit a more or less automatic response – a response not guided by the more cognitively driven components of the model (e.g. Ronis, Yates, & Kirscht, 1989).
The TPB has also been used to predict speeding in two different contexts; rural areas (e.g. Letirand and Delhomme, 2005, Wallén Warner and Åberg, 2008) and urban areas (e.g. Elliott et al., 2003, Elliott et al., 2005, Newnam et al., 2004, Parker et al., 1992, Wallén Warner and Åberg, 2008). In the studies by Parker et al., 1992, Wallén Warner and Åberg, 2008, which both used indirect measures, the model explained 47% and 31%, respectively, of the intention to speed in an urban area. The most important variable was either perceived behavioural control (Parker et al., 1992) or subjective norms (Wallén Warner & Åberg, 2008).
Factors determining dangerous overtaking have also been studied but to a much lesser degree. Parker et al. (1992), who used the TPB, found that the model explained 32% of the variance and the most important predictor was subjective norm followed by perceived behavioural control.
Studies on driver behaviour have also looked at the effect of descriptive norms although the term used has been different (e.g. perceived behaviour of others and perceived consensus) and generally not in combination with the TPB. Rothengatter (1988), for instance, suggested that drivers want to behave as other drivers and Connolly and Åberg (1993) argued that drivers adjust their own speed according to the speed of nearby drivers implying that speeding had a contagious effect. In addition to this, Manstead, Parker, Stradling, Reason, and Baxter (1992) investigated if drivers seek consensus for their chosen behaviour. They found that drivers who regularly carried out different behaviours defined as violations and errors tended to overestimate the percentage of other drivers who would do the same. In contrast, the group defined as irregulars underestimated rather than overestimated consensus for their own position. One possible reason proposed by the authors was that negative behaviours were more salient and therefore more accessible.
One important step in the advancement of transportation psychology would be to compare and contrast road user behaviours across different contexts and cultures using a theoretical model which is clearly defined and which has been empirically tested. Indeed, and as indicated above, attempts have been made to investigate driving violations in a more systematic way and a growing number are using the TRA or the TPB. However, to date it is still difficult to draw any firm conclusions from these studies. Some of the above use direct measures and others indirect ones, something that Wallén Warner and Åberg (2008) demonstrated can present very different results. Furthermore, only a handful studies analyze the data as suggested by Ajzen (1991). If the focus is on speeding in an urban setting using indirect measure we are left with the studies carried out by Parker et al., 1992, Wallén Warner and Åberg, 2008. With regard to dangerous overtaking the same applies although in this case only one study can be found (Parker et al., 1992). This can then be contrasted with the intention to predict healthy behaviour such as taking exercise where Hausenblas et al. in 1997 conducted a meta-analysis of 31 studies, all of them complying with some clearly specified criteria. The first aim of this study was therefore to use the TPB to predict two different driving violations: speeding in an urban area and dangerous overtaking. Contrary to the suggestion by Ajzen and Fishbein (2005), a number of studies have found that it is a mistake to treat subjective norm and descriptive norm as a unitary concept and therefore suggested that they should be entered into the model separately. Descriptive norms have also been found to be an important factor with regard to road user behaviour but to this author’s knowledge its contribution to the TPB has not been assessed after the variables within the model have been considered. Furthermore, there is some evidence within mainstream psychology which suggests that descriptive norms are especially important in a situation perceived as risky. The second aim of this study was therefore to first assess the contribution of descriptive norms but also determine if the effect is greater in a situation described as risky. The effect of past behaviour has also been shown to be important for at least two reasons: The first to determine if the behaviour has become automatic or not and second, how much residue it picks up, which could indicate that the model still lacks some important constructs. The third aim of this study was therefore to assess the unique effect of past behaviour entered after all the other variables have been controlled for. Demographic variables have also been found to predict intention to violate, with young male drivers being more prone to this than female and older drivers (Parker et al., 1992). Although from a practical perspective this information is not very productive, especially not if the aim is to change behaviour, it could however help in defining target groups. The fourth aim of this study was to assess the effect of demographic variables.
To summarise, the present study have four aims:
- 1.
Examine the intention to violate in two different contexts using the TPB, namely speeding in a built up area and dangerous overtaking.
- 2.
Assess the unique effect of descriptive norm and determine its possible relationship with risk.
- 3.
Assess the unique effect of past behaviour after controlling for the variables already included in the TPB.
- 4.
Assess the effect of age, sex and annual mileage.
Section snippets
Participants
The participants consisted of 275 people with a current driving licence. The participants were between 20 and 75 years (mean = 44 years; standard deviation of age (14.72), 132 (48%) were females and 143 males (52%). Nearly half of the sample (42%) had been involved in at least one accident and 64% would use their car daily, 27% a couple of times a week, 4% a couple of times per month and 5% very rarely. Fifty-one people (18.5%) had previous traffic convictions.
Procedure
A questionnaire was mailed to 500
Results
Initially some of the scores were recoded such that a higher score always indicated a more positive stance towards the intention to violate. To form an aggregate measure of attitude all behavioural beliefs were multiplied by an evaluation of those outcomes, the resulting product was then summed across the number of salient beliefs. Subjective norm was a summation of normative beliefs divided by the number of salient beliefs. Analytical methods used in this study were descriptive statistics
Discussion
In this study two different driving violations were examined by the use of the theory of planned behaviour. The theory explained 47% of the variance with regard to intention to speed in an urban area. This was the same as the result presented by Parker et al. (1992) but greater than the one presented by Wallén Warner and Åberg (2008) (31%). In the present study attitudes made the largest contribution which is contrary to both Wallén Warner and Åberg, 2008, Parker et al., 1992 who either found
Conclusion
In conclusion, the present study showed that the theory of planned behaviour was able to predict drivers´ intention to commit two different driving violations: speeding in an urban area and dangerous overtaking. When an extended version of the model was tested the results showed that descriptive norm contributed to the prediction of the same over and above the variables already included in the model. This would then support the evidence for a distinction between descriptive norm and subjective
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