Examining behavioral and attitudinal differences among groups in their traffic safety culture

https://doi.org/10.1016/j.trf.2014.03.005Get rights and content

Highlights

  • We establish a theory of risky driving behavior based on ten factors.

  • We segment the sample into four clusters, three of which reflect risky driving groups.

  • One cluster of high risk drivers is associated with excitement seeking and optimism.

  • One cluster is associated with denial of basic societal values.

  • One cluster is associated with rationalization of excess speed.

Abstract

The paper explores the concept that, for a given population, there is not a single “traffic safety culture,” but rather a set of alternative cultures in which the individual driver might belong. There are several different cultures of dangerous driving behavior and each might need a separate strategy for intervention or amelioration. First, the paper summarizes the over-arching theory explored in the research, which applies Multi-group Structural Equation Modeling (MSEM) in a modification of the Theory of Planned Behavior (TPB) in the explanation of Risky Driving Behavior, based on ten observed explanatory factors. Second, we apply Latent Class Cluster (LCC) segmentation to the full sample, revealing four segments: one cluster reflecting a “Low Risk Driving Safety Group” and three clusters describing three different groups of problematic drivers. We first apply MSEM to two groups; the “Low Risk Driving Safety Group,” and the “High Risk Driving Safety Group,” defined as the members of the three problematic clusters together, revealing how a “Low Risk” culture differs from the “High Risk” culture, with the relative importance of the TPB explanatory factors varying sharply between the two groups. Finally, the three problematic clusters are profiled for demographics and their mean scores for the ten observed explanatory factors. Each of the clusters is reviewed in terms of responses to selected survey questions. Three separate and distinct dangerous traffic safety cultures emerge: first, a culture of risky driving dominated by excitement seeking and optimism bias; a second dominated by denial of societal values; and a third characterized by its propensity to find rational justifications for its speeding behavior. The paper applies two research methods together: LCC segmentation divides our sample into meaningful subgroups, while MSEM reveals both within-group analysis of variance and between-group differences in safety attitudes and outcomes. The paper concludes that the combination of the segmentation powers of the LCC and the analysis powers of the MSEM provides the analyst with an improved understanding of the attitudes and behaviors of the separate groups, all tied back to the over-arching theory underlying the research.

Introduction

The objective of this paper is to improve the understanding of how attitudes, beliefs, and values toward driving behavior influence different subgroups of the driving population in different ways. It is based on a survey of 990 residents of three northeastern states in the United States. The paper utilizes Latent Class Cluster (LCC) methods to segment the full sample into four clusters. One cluster had attitudes and behaviors toward traffic safety that were markedly better than the rest of the sample, and was labeled the “Low Risk Driving Safety Group” (n = 501) while the rest of the sample was labeled the “High Risk Driving Safety Group” (n = 489) in order to compare the two using the tools of Multi-group Structural Equation Modeling (MSEM). Secondly, the paper applies LCC segmentation analysis procedures to better understand the three distinct clusters of problematic drivers within the High Risk Driving Safety Group. The three problematic clusters of drivers are profiled in terms of attitudes, beliefs and behaviors, noting their demographics. The paper posits that there does not exist a single, monolithic culture of dangerous driving behavior, but rather three separate problematic cultures, each of which would require a separate set of strategies and actions to improve driving behavior. Importantly, these three clusters of drivers are defined on the basis of the similarity of their attitudes, beliefs and driving behaviors, and not on any a priori categorization, such as male/female, urban/rural or rich/poor.

The market segmentation described in this paper has been applied to a model based on established theories in the field, using data collected to test an over-arching theory diagrammed in Fig. 1. That theory is a modification of the Theory of Planned Behavior (TPB) (Ajzen, 1991) which states that there are three proximal antecedents (attitude, subjective norm and perceived behavioral control) which predict a person’s intention to commit and act. In recent years the theory has been successfully used to predict a range of different behaviors, many in the field of public health (Conner & Armitage, 1998), and many concerning traffic violations (e.g., Elliott et al., 2003, Elliott et al., 2005, Forward, 2009, Forward, 2010, Letirand and Delhomme, 2005, Parker et al., 1992, Parker et al., 1992, Wallén Warner and Åberg, 2005, Wallén Warner and Åberg, 2008).

In an effort to operationalize this theory, Coogan, Forward, Assailly, and Adler (2012a) created a MSEM in which Risky Driving Behavior was predicted by three proximal latent factors (shown as ovals in Fig. 1); each of the latent unobserved factors was derived from two observed factors (shown as rectangles). In the theory behind structural equation modeling (Byrne, 2001, Kline, 2005, Schumacker and Lomax, 2004), the unobserved factors (ovals) are scaleless and unobservable, the model having derived them from measurement of the observed factors (rectangles) (Brown, 2006). Table A.1 shows how each of the observed factors were created from the survey items. The outcome latent factor, Risky Driving Behavior, is derived from the observed factors Speed, and Aberrant Driving. Each of the observed factors takes the form of a summed scale of responses to survey items, whose internal reliability is reflected in the Chronbach’s alpha value (Kline, 2005) shown within each rectangle. The explanatory portion of the model is based on ten observed factors; six for the three proximal latent factors from the TPB, and four for the two latent factors representing the personality traits. These five latent explanatory factors are summarized here, with reference to key articles in the literature which influenced the present analysis:

  • The latent factor Attitude toward Speeding is derived from the observed factors “Instrumental Attitude” and “Affective Attitude,” both which are defined in Table A.1. These concepts have been explored by Fishbein and Ajzen, 1975, Fishbein and Ajzen, 2010, Forward, 2010, Stradling and Parker, 1997, and others.

  • The latent factor Subjective Norm is derived from the observed factors labeled as “Descriptive Norm” and “Injunctive Norm.” These two concepts have been shown to be distinct from each other (e.g., Cialdini et al., 1990, Conner and McMillan, 1999, Deutsch and Gerard, 1955, Forward, 2009, Grube et al., 1986) as they measure two different constructs; Descriptive Norm reflects what is done, while our Injunctive Norm reflects something which ought to be done (Forward, 2009, Rivis et al., 2006). The latent factor thus reflects both, consistent with the recommendation of Ajzen (Website).

  • The latent factor Assessment of Risk and Consequences is derived from the observed factors “Deny Consequences” and “Optimism Bias/Deny Risk.” This latent factor functions in the same role as the TPB’s perceived behavioral control to the extent that it reflects the difficulty associated with the adoption of the behavior based on “beliefs about the presence of factors that may facilitate or impede performance of the behavior” (Ajzen, Website). Concepts reflected in the observed factor, “Optimism Bias/Deny Risk” have been explored in a number of studies including DeJoy, 1989, Meskin et al., 2005, Sticher, 2005, Forward, 2006, Chan et al., 2010, Brown and Cotton, 2003, Elliott, 2012a, Elliott, 2012b. The observed factor “Deny Consequences” is similar to concepts explored in work measuring denial of consequences and anticipated regret by Conner et al., 2007, Elliott, 2012a, Elliott, 2012b, Parker et al., 1995.

  • The latent factor Personality Type: Excitement Seeking is derived from the observed factors “Sensation-Intensity” and “Sensation-Novelty,” which have been reviewed by Stephenson, Hoyle, Palmgreen, and Slater (2003) based on many works by Zuckerman, 1991, Zuckerman, 1994, Zuckerman, 2006, Horvath and Zuckerman, 1992, Jonah, 1997. A significant relationship between risky driving behavior and sensation seeking has also been presented in a number of studies (e.g., Delhomme et al., 2009, Greaves and Ellison, 2011, Jonah et al., 2001). The observed factor representing “Novelty” also includes the concept of mild social deviance, as recommended by Vassalo et al. (2007).

  • The latent factor Personality Type: Altruistic/Confident is derived from the observed factors “Altruism” and “Confident.” These two concepts were analyzed by Machin and Sankey (2008), who developed an altruism factor in a SEM model for variance in speeding in young people, which also included a factor for Excitement Seeking. They reported an interest in developing an additional factor reflecting confidence and competence. Similar concepts were explored in Ulleberg and Rundmo (2003). Issues about control have also been explored by Horswill and McKenna (1999).

In sum, the model to be applied in this study of segmentation integrates ten observed explanatory factors, each of which has a solid base in the existing literature. In addition, one latent factor representing the outcome variable was created:

  • The latent factor Risky Driving Behavior is derived from two observed factors, one concerning speed (“Speed”) and one concerning other problematic driving behavior (“Aberrant Driving Behavior”). A process of confirmatory factor analysis was applied to several questions adapted from the Driver Behavior Questionnaire, resulting in the use of three survey questions concerning speeding on a rural interstate, on a two-lane highway and on a residential street, summed to create the observed factor, “Speed.” A similar process resulted in the selection of “race away from the lights with the intention of beating the driver next to you,” and “pass a slow driver on the right” in order to explore negative patterns beyond just speeding in the observed factor, “Aberrant Driving.”

Section snippets

Approach

In the spring of 2009, a study was conducted to investigate the driving behaviors and attitudes of New England residents. The study explored risky driving behaviors and attitudes to better understand the driving safety culture of the targeted areas. Care was given to ensure that differences in attitudes and behaviors associated with age, gender and geographic locations could be analyzed based on the sample created.

Participants

The survey covered 1033 residents in three US states: Maine, New Hampshire and

Latent Class Clusters revealed

Table 1 shows the list of indicators derived in the LCC modeling process. The original model specification included 87 variables and an interactive process was used to eliminate variables with an R2 of less than 0.15 from each subsequent model. Table 1 shows the results of this process, showing the indicator variables used in the final model developed for this analysis, sorted by their associated R2 values. This resulted in a core set of variables that have significant effects on cluster

Segmentation into two groups

Our two efforts at market segmentation show that, indeed, meaningful groupings of the driver population can be defined by their separate and distinct sets of attitudes and behaviors. Simply by bifurcating the population, it becomes clear how different is the explanation of variance in Risky Driving Behavior between the Low Risk Driving Safety Group, and the High Risk Driving Safety Group. The structural model for the High Risk Driving Group suggests that a group member’s excitement-seeking

Conclusions

The development of a new paradigm of a traffic safety culture to better understand “the attitudes, beliefs, values, and knowledge about safe driving behavior shared within a meaningfully defined group” (McDonald & Arthur, 2014) will require the development of a research process that is both data driven, and consistent with foundational theory. If we are to develop a “proactive vision of transforming the social processes that influence behavioral choice in order to achieve sustainable

Acknowledgements

The data used in this paper were collected through funding from the United States Department of Transportation Federal Highway Administration to the New England Transportation Institute (NETI, 2010). Additional support for the on-going activities of NETI has been provided by the Transportation Research Center of the University of Vermont, and a generous grant for transportation studies from the Green Mountain Coffee Roasters, Inc.

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