Road safety communication campaigns: Research designs and behavioral modeling

Transportation Research Part F 18 (2013) 107–122

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Transportation Research Part F

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Road safety communication campaigns: Research designsand behavioral modeling

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⇑ Corresponding author. Tel.: +30 2421074164; fax: +30 2421074131.E-mail addresses: [email protected] (E. Nathanail), [email protected] (G. Adamos).

Eftihia Nathanail ⇑, Giannis AdamosUniversity of Thessaly, Department of Civil Engineering, Pedion Areos, 38334 Volos, Greece

a r t i c l e i n f o a b s t r a c t

Article history:Received 10 March 2012Received in revised form 23 November 2012Accepted 21 December 2012

Keywords:Road safetyCommunication campaignResearch designBehavioral modeling

Communication campaigns are used as a rather efficient strategy to approach the wideaudience in terms of promoting road safety and improving driving behavior. Incorporationof the evaluation in the campaign design is considered of high importance, since it providesinformation about the effectiveness of the campaign. Literature review on road safety cam-paigns, conducted in the last decade, highlights the importance, as well as the limitedapplication of a well structured theoretical background when designing and implementinga road safety campaign, that could enable predicting possible behavioral changes of theroad users owing to the campaign, and facilitate the assessment of its effectiveness.

The scope of this study is to examine the predictability of alternative research designs asregards driving behavior, when evaluating the effectiveness of road safety campaigns;moreover, to assess the impact of the various parameters and predict behavioral changes.The conclusions drawn rely on the results of the assessment of the impacts of two localcampaigns, one on drink and drive and the other on seat belt usage, both implementedon a university campus, with its 1587 students (drivers and passengers) forming the targetgroup. Both campaigns were designed taking as a premise for design and assessment theTheory of Planned Behavior, and an attempt was made of developing alternative modelsfor correlating behavior and intentions with behavioral beliefs, control beliefs, normativebeliefs, and descriptive norms.

Increase of the predictability of the models was noticed as more constructs were beingadded; especially, when past behavior was added in the models predicting intention,and intention in the models predicting behavior. This demonstrates the high correlationbetween these two constructs. The theoretical and applied implications of the modelsare discussed.

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1. Introduction

Communication campaigns are used as a rather efficient strategy to approach the wide audience in terms of promotingroad safety, improving driving behavior and contributing to less road accidents, injuries and fatalities (Conner & Norman,2005; Marcil, Bergeron, & Audet, 2001). A definition of road safety campaigns is given in the ‘‘Manual for Designing, Imple-menting, and Evaluating Road Safety Communication Campaigns’’ (Delhomme et al., 2009): ‘‘Purposeful attempts to inform,persuade, and motivate a population (or sub-group of a population) to change its attitudes and/or behaviors to improve road safety,using organized communications involving specific media channels within a given time period, often supplemented by other safety-promoting activities (enforcement, education, legislation, enhancing personal commitment, rewards, etc.’’

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108 E. Nathanail, G. Adamos / Transportation Research Part F 18 (2013) 107–122

According to the manual (Delhomme et al., 2009), road safety communication campaigns aim at:

� Providing information regarding new or modified legislation.� Increasing knowledge and awareness as concerns the impact of new technology, equipment, as well as behavior on the

road and associated risks.� Changing the parameters that have been proven scientifically or empirically that affect road user behavior (Ajzen, 1985).� Attempting at changing inappropriate behavior that increases risk or retaining behavior that promotes safety.� Contributing in the reduction of the frequency of road accidents and the minimization of the severity of their impacts.

In parallel, the evaluation of road safety campaigns is considered as important as the design and implementation, since itprovides information about the effectiveness or not of the campaign (appreciation, recognition, driver behavioral change,etc.), and investigates the efficiency of alternative media channels and types of messages to approach the audience, while,through the documentation of the results, contributes to the design, implementation and evaluation of future campaigns(Adamos & Nathanail, 2011).

Several studies have been conducted in the last decade, investigating the implementation of road safety campaigns andassessing their impact on road safety. Woolley’s review on mass media campaigns (Woolley, 2001) indicated that althoughmass media campaigns play a significant role in the improvement of road safety, it is difficult to isolate potential changes indriving behavior. Also, the need that mass media campaigns should accompany other activities, i.e. enforcement or educa-tion, was noticed, as well as the adoption of a social persuasion framework in the design of campaigns that focus on behav-ioral change. Regarding relative gaps, questions such as ‘‘how much advertising is enough’’ or ‘‘how the advertisingeffectiveness could be measured’’ were raised (Woolley, 2001).

Also, Delaney, Lough, Whelan, and Cameron (2004), in ‘‘A review of mass media campaigns in road safety’’, examinedinternational literature on road safety mass media campaigns, the applicable theories of behavioral change, the strategic de-sign of the campaigns and their evaluation. The review showed that the Rogers Protection Motivation Theory (Rogers, 1975,1983; Rogers & Mewborn, 1976), the Extended Parallel Process Model (Witte, 1992, 1998), and the Theory of Planned Behav-ior (Ajzen, 1991) had the highest applicability, when developing road safety mass media campaigns. In addition, the impor-tance of the careful identification of the target audience in the development of the campaign and the characteristics of themessages was also observed. In the same review (Delaney et al., 2004), two meta-analyses are described; one by Delhommeet al. (1999) and the other by Elliott (1993), in which key issues were identified, such as the importance of using a theoreticalmodel, the consideration of relative to the campaign themes and the support of the campaign through legislation, enforce-ment, etc.

Within the framework of the 6th Framework Programme European Research project CAST (Campaigns and Awarenessraising Strategies in Traffic safety), a report was published on meta-analysis of road safety campaigns that have been con-ducted in fifteen European countries (Vaa & Phillips, 2009). The main findings of the analysis showed that road safety cam-paigns work effectively, contributing to a percentage increase of 25% in seat belt use, and a decrease of 9% and 16% inaccidents and speeding, respectively.

The positive impact of identifying a specific target group, using personal communication, combining emotional and ra-tional content and addressing subjective social norms was also indicated. Finally, it was observed that the use of enforce-ment as an accompanying activity works beneficially (Vaa & Phillips, 2009).

In another report, entitled ‘‘Best practice in road safety mass media campaigns: A literature review’’, Wundersitz,Hutchinson, and Woolley (2010) investigated fourteen road safety campaigns that were published from 2001 to 2009,and, though they did not make a comparative analysis, due to the variety in the campaigns’ objectives, methods, outcomesand quality, they highlighted some significant observations, as regards the improvement of the design and evaluation of roadsafety mass media campaigns, among which is the necessity of incorporating a scientific theoretical approach.

The applicability of ‘‘A theoretical approach to assess road safety campaigns – Evidence from seven European countries’’was demonstrated in Nathanail and Adamos (2009), where the results from the evaluation of the effectiveness of seven dif-ferent campaigns were presented. The specific campaigns were local, regional and national, while, in the majority of them,the evaluation was conducted before and after the implementation of the activities or interventions that were foreseen in thedesign of the campaigns. The theoretical models used for the evaluation were an extended or modified version of the Theoryof Planned Behavior (Ajzen, 1985), which argues that the personal decisions to perform a behavior (intentions) are based onattitudes toward the behavior, subjective norms, and perceived behavioral control (Ajzen, 1991), and the TranstheoreticalModel, which outlines six stages (pre-contemplation, contemplation, preparation, action, maintenance and termination) thatpeople go through, before a new behavior can be constantly adopted (Prochaska & DiClemente, 1983). Results showed thatthe use of both primary (behavior) and secondary (attitudes) objectives seems to provide greater accuracy when assessingchanges due to the implementation of the campaign, and thus, the use of a well structured theoretical background for theselection and assessment of the appropriate parameters that predict behavior is a necessity (Nathanail & Adamos, 2009).

From the above described literature review, including mostly extensive reports, as well as the research on individualstudies concerning specific road safety communication campaigns, conducted in the last decade, the absence of a well struc-tured theoretical framework is highlighted, when designing and implementing a road safety campaign that could contributein the evaluation of its effectiveness. Especially, in regards to evaluation, problems are indicated, such as the limitation of theassessment of the impact of the campaign only in terms of objective (types of activities) and subjective (recall and recogni-

E. Nathanail, G. Adamos / Transportation Research Part F 18 (2013) 107–122 109

tion) exposures, as well as the weakness to isolate the individual success of the campaign, when running with other accom-panying activities.

For the measurability of its effectiveness, a road safety campaign has to be carefully designed, following a clear, coherentand proper research design that enables measurements and evaluation (Fylan, Hempel, Grunfeld, Conner, & Lawton, 2006).Such measurements are related to the attitudes, subjective and descriptive norms, perceived behavioral control and inten-tions along with behavior (Fylan et al., 2006).

Attitude is essential to explain and predict risk-related behavior, and is an important indicator of road user behavior(Armitage & Conner, 2006). Subjective norms, which reflect the social influence to behave or not under certain ways, ordescriptive norms, that provide information on what significant others do through observation, have been shown to beimportant predictors of road user intentions (Zabukovec et al., 2007; Rivis & Sheeran, 2004).

Road user past behavior and personal/moral norm, which is an individual’s perception about moral correctness of per-forming a specific behavior (Sparks, 1994), make useful contribution to the prediction of intention and behavior (Manstead,2000). The motivation to behave or the confidence of controlling one’s behavior and the actual behavior are strongly relatedto the other (Armitage & Conner, 2006; Zabukovec et al., 2007). Thus, if road safety campaigns succeed in influencing roaduser perceptions of control, they might also lead to significant changes in behavior.

Behavioral beliefs are assumed to provide cognitive and effective foundation for attitudes, normative beliefs provide thefoundation for subjective norms, and control beliefs the foundation for perceptions of control (Ajzen, 1991, 2002). Finally, inten-tions appear to be the strongest predictor of behavior (Armitage & Conner, 2006; Zabukovec et al., 2007; Webb & Sheeran, 2006).

The formulation and examination of an integrated theoretical framework is the scope of the present paper; especially, theconducting of an extended study to assess the predictability of alternative research designs as regards road user behavior,when evaluating the effectiveness of road safety campaigns; moreover, the assessment of the impact of the various param-eters, i.e. behavioral beliefs, intentions, past behavior, etc., on road user behavior and predict possible changes. The studyincludes adoption of a theoretical model, identification of applicable research designs (experimental or quasi-experimental),and selection of the measurement variables (i.e. self-reported measures, observed behavior and accident statistics) and datacollection techniques (i.e. method of asking, observing and document analysis), in the design and implementation of twolocal road safety campaigns.

The objectives of the campaigns were to increase the awareness of the users on the campaigns’ themes, to achieve accep-tance of safe road behavior and to identify the measures that influence awareness and acceptance.

Following the implementation and evaluation of the campaigns, collected data were used for the development of roaduser behavior prediction models. Different sets of data and different constructs were used in each model, so that to revealtheir impact on predictability. Useful conclusions are drawn regarding the types of measurements (data collected and modeof collection, sample grouping, etc.) required, in order to improve model accuracy and reliability. Finally, the analysis of thecampaign results highlights the most appropriate design method, which provides evidence on the campaign efficiency orinefficiency.

2. Method

2.1. Designing and implementing the road safety campaigns

Two local campaigns were designed, implemented and evaluated by the Transportation Engineering Laboratory of theUniversity of Thessaly in Volos, Greece; one campaign addressed the issue of drinking and driving and the other the seat beltusage. The aims of the campaigns were:

– Increasing the awareness rate on the permissible alcohol level while driving, or on the obligation to wear seat belt as adriver or passenger, respectively.

– Increasing the appeal on the permissible alcohol level, or on the seat belt usage, respectively.– Increasing the awareness on the risks of drinking and driving, or of not wearing seat belt, respectively.– Investigating the measures that would influence road users to adopt a safer attitude on the road.– Decreasing the drivers that drink and drive, or do not wear seat belt, respectively.– Increasing the passengers that refrain drivers from drinking while driving, or drivers that ask passengers to wear seat

belts, respectively.– Increasing the passengers that wear seat belts.

Unlike national campaigns that aim to reach wide audiences, the scope of the regional and/or local campaigns is focusedon specific communities. All other or selected communities in the region/area, not reached by the campaign may be used ascontrol group and serve for comparison, under experimental or quasi-experimental design, respectively.

The campaigns were launched simultaneously on April 14th 2008 and implemented for 4 weeks. The scope of the cam-paigns was local; thus, the campus of the University of Thessaly was the test bed of the campaigns, and the target groupcomposed of the 1587 young students (between 18 and 30 years old), both drivers and passengers, as enrolled at the timeof the campaigns.


In order to be able to formulate both intervention and control groups, the students were divided in those of the Civil Engi-neering Department (intervention group), and the rest of the Engineering School (control group). Owing to the layout of thecampus, where the buildings of each department, as well as the other facilities (cafeterias, secretaries) are completely sep-arated such division was feasible. In addition, as educational programs are completely supported by the relevant depart-ment, students do not need to move from one building to the other. Therefore, it was assumed that during the campaignimplementation, the control group was not exposed to the campaign, fact that was supported by a question in the question-naire that investigated in the after measurements whether the subjects were aware of the campaign. The implementation ofthe campaigns was done at the Civil Engineering Department only, through the distribution of 500 brochures and the postingof 50 posters on all the building entrances and announcement boards. An open workshop, regarding the improvement ofroad safety (avoidance of drink and drive and seat belt usage), took place during the same period, and the attendancewas mandatory for the civil engineering students.

Based on the goals of the evaluation and the availability of resources, a face-to-face questionnaire survey was selected asthe data collection method for the campaigns (Logan, Padgett, Thyer, & Royse, 2006). Two surveys were conducted, first thebefore survey that lasted 2 weeks in March 2008, and then, the after survey that lasted another 2 weeks in June 2008.

Assuming a confidence interval of 0.1 and a confidence level of 95%, the minimum sample size was calculated to 309,based on a population of 1587 persons, and 400 questionnaires were distributed, separated to 200 for the before measure-ments and 200 for the after measurements. More specifically, the interviewees were193 drivers and 207 passengers in thedrink and drive campaign, and 209 drivers and 191 passengers in the seat belt campaign. Also, some of the interviewees an-swered both before and after questionnaires. Further breakdown in the respective groups is the following:

(1) Drink and drive campaign – drivers: intervention before: 35, intervention after: 18, control before: 73, control after:67, out of which 4 persons in the intervention group and 8 in the control group answered both before and afterquestionnaires.

(2) Drink and drive campaign – passengers: intervention before: 31, intervention after: 34, control before: 61, controlafter: 81, out of which 7 persons in the intervention group and 6 in the control group answered both before and afterquestionnaires.

(3) Seat belt campaign – drivers: intervention before: 39, intervention after: 36, control before 62, control after: 72, out ofwhich 16 persons in the intervention group and 9 in the control group answered both before and after questionnaires.

(4) Seat belt campaign – passengers: intervention before: 32, intervention after: 34, control before: 67, control after: 58,out of which 15 persons in the intervention group and 1 in the control group answered both before and afterquestionnaires.

It was observed that 77.8% of the drivers were male (standard deviation 4.15%) and 22.2% female (standard deviation1.43). For passengers, the distribution reverses, and 37.7% of the passengers were male (standard deviation 3.14%) and62.3% female (standard deviation 4.61%). The average age of the sample was 21.65 years old, homogeneous within thegroups. In particular, in the driver group the average age and standard deviation (SD) of the sample was 22.69 (SD 2.33)and 22.38 (SD 2.05) years old for the control and intervention groups, respectively. Passenger age average was 22.19 (SD2.21) and 20.95 (SD 1.84) years old for the control and intervention groups, respectively. Finally, drivers (approximately52% of the sample size) declared a driving experience of 3.77 (SD 1.5) years, and most of them (75%) drive on an almost dailybasis.

2.2. Questionnaire development

The measurements in each survey were obtained from a structured questionnaire (one per campaign) that was addressedto both drivers and passengers, with some common and some designated questions to each type of road users (drivers andpassengers). Distinction between persons who responded to the questionnaire both before and after the campaigns was fea-sible, enabling the testing of models that foresee same subjects in the before and after groups. In such cases, however, thesample size reduced.

Theory of Planned Behavior (TPB) was selected as the theoretical model for the analysis of the results (Ajzen, 1991), as itassociates human behavior with attitude and intention, and it takes into account the effect of social norms in the predictionof the behavior. TPB is based on the principle that a person’s behavior is determined by his/her intention. Intention is deter-mined by a person’s attitude, his or her subjective norms, and perceived behavioral control. Knowledge on such attributesmay predict intention, and consequently behavior (Ajzen, 1991). Its successful implementation in other campaigns (Ajzen,1991) and its high level of applicability when designing, implementing and evaluating road safety campaigns (Delaneyet al., 2004; Forward & Kazemi, 2009) contributed also to the selection of TPB in the present research.

The questionnaires started by asking the participants the type of road users they were, i.e. car drivers, car passengers,motorcycle drivers or motorcycle passengers. Some questions about their background were included such as age, gender,number of years owing a driver’s license, how often they drive and if they had ever been involved in an accident. The corepart of the questionnaires was responded using a 7-point scale (Ajzen, 2002), ranging from 1 (Strongly disagree/very unli-kely/not at all/never) to 7 (Strongly agree/very likely/a lot/always), depending on the topic addressed.


Where applicable, scenarios were examined such as ‘‘Suppose you are driving back home after a party or a club with yourfriends and you have all drunk at least one glass of alcohol’’, and ‘‘Suppose you are going back home from a party or a cluband the driver of your company has drunk at least one glass of alcohol’’, for the drivers and passengers, respectively in thedrink driving campaign. In the seat belt campaign the examined scenarios were formulated depending on the road network’scharacteristics, i.e. urban or interurban area, commuting or rarely used itinerary, long or short distance.

Questions and statements were developed, according to the parameters tested. Examples of them are:

(1) Behavioral beliefs (BBs), were expressed in the drink driving campaign, in questions like ‘‘How much in favor are youof driving while having drunk?’’, continued by ‘‘What is the likeliness of driving after having one drink?’’, or in state-ments such as ‘‘Even if I have drunk, I can still drive safely’’ or ‘‘To drink and drive will increase the likelihood of beinginvolved in an accident’’. Similar questions/statements in the seat belt campaign were ‘‘If I drive a car without wearinga seat belt, I feel unsafe’’, and ‘‘If I do not wear the seat belt, it is likely that I get fined’’.

(2) Normative beliefs (NB), were asked for example ‘‘Would you drive if your passengers believe that you have drunk toomuch for driving?’’, in the drink driving campaign; or a statement was used, such as, ‘‘My family thinks that I should. . . I should not wear a seat belt while driving a car for a short distance in an urban area’’, in the seat belt campaign.

(3) Control beliefs (CB), were indicated from questions like ‘‘What is the probability of getting a ticket if you do not wear aseat belt?’’, in the seat belt campaign, and statements like ‘‘If public transport is available, it is very unlike that I driveafter having drunk’’.

(4) Past behavior (PB), stated for example ‘‘How many times in the past 2 months have you driven while having drunk atleast one drink?’’, in the drink driving campaign.

(5) Intention (INT), was asked in statements like ‘‘I plan to drive after having drunk one drink in the next month’’, in thedrink driving campaign.

(6) Descriptive norm (DN), where the interviewees’ perception on actual situations was addressed, such as ‘‘My friendsdrive back home after they have drunk at least one glass of alcohol’’, analyzed in parallel with normative beliefs‘‘My friends allow me to drive home after I have drunk at least one glass of alcohol’’, in the drink driving campaign.

More specifically, in the case of the drink driving campaign, the precise number of questions measuring each of the aboveparameters is five for BB (BB1–BB5), one for NB, four for CB (CB1–CB4), six for PB (PB1–PB6), one for intention and two for DN(DN1–DN2). In the seat belt campaign, the questionnaire included eight questions measuring BB (BB1–BB8), seven for NB(NB1–NB7), seven for CB (CB1–CB7), six for PB (PB1–PB6), one for intention and six for DN (DN1–DN6). The relevant param-eters were encoded using the construct identification followed by a number in ascending order, e.g. BB1, CB7, etc. (Tables 1aand 1b).

The recall and appreciation of the campaign was also investigated, by asking the participants (both drivers and passen-gers) whether they had seen or heard a drink and drive and/or seat belt campaign in the last couple of weeks, if they couldbriefly describe it and the means they had noticed the campaign (i.e. posters, etc.). In order to cope for possible contamina-

Table 1aVariables classification - Drink driving campaign.

Statements or questions expressing Code Statements or questions expressing Code

Behavioral beliefs BB Past behavior PBHow much in favor are you of driving while having

drunk?BB1 How many times in the past 2 months have you driven in an urban area after

having drunk at least one drink?PB1

What is the likeliness of driving after having onedrink?

BB2 How many times in the past 2 months have you driven on motorway afterhaving drunk at least one drink?

PB2

Even if I have drunk, I can still drive safely BB3 How many times in the past 2 months have you driven on a familiar routeafter having drunk at least one drink?

PB3

To drink and drive will increase the likelihood of beinginvolved in an accident

BB4 How many times in the past 2 months have you driven on an unknown routeafter having drunk at least one drink?

PB4

To drink and drive will increase the likelihood of beingfined

BB5 How many times in the past 2 months have you driven on a short trip afterhaving drunk at least one drink?

PB5

Normative beliefs NB How many times in the past 2 months have you driven on a long trip afterhaving drunk at least one drink?

PB6

My friends allow me to drive home after I have drunkat least one glass of alcohol

NB Intention INT

Control beliefs CB I plan to drive after having drunk one drink in the next month INTIf public transport is available, it is very likely that I

drive after having drunkCB1 Descriptive norms DN

If I have promised somebody a lift, it is very likely thatI drive after having drunk

CB2 My friends drink even if they have to drive DN1

If I need the car for the next day, it is very likely that Idrive after having drunk

CB3 My friends drive back home after they have drunk at least one glass ofalcohol

DN2

If I need to get home late at night, it is very likely that Idrive after having drunk

CB4

Table 1bVariables classification – Seat belt campaign.

Statements or questions expressing Code Statements or questions expressing Code

Behavioral beliefs BB Would a negative experience in your near environment convinceyou to wear seat belt?

CB4

Drivers who wear seat belts are less prone to accidents BB1 Would media campaigns environment convince you to wear seatbelt?

CB5

Front passengers who wear seat belts are less prone to accidents BB2 Would images of crash tests convince you to wear seat belt? CB6Back passengers who wear seat belts are less prone to accidents BB3 Would onboard safety systems (optical or sound notification)

convince you to wear seat belt?CB7

I drive safely, so I don’t really need to wear a seat belt BB4 Past behavior PBWearing seat belt will not make any difference in case of an

accident in the cityBB5 How often in the past 2 months did you wear seat belt in urban

area?PB1

Wearing seat belt will not make any difference in case of anaccident on the motorway

BB6 How often in the past 2 months did you wear seat belt onmotorway?

PB2

If I do not wear the seat belt, it is likely that I get fined BB7 How often in the past 2 months did you wear seat belt on afamiliar route?

PB3

If I drive a car without wearing a seat belt, I feel unsafe BB8 How often in the past 2 months did you wear seat belt on anunknown route?

PB4

Normative beliefs NB How often in the past 2 months did you wear seat belt on a shorttrip?

PB5

My family thinks that I should wear a seat belt while driving/riding a car for a short distance

NB1 How often in the past 2 months did you wear seat belt on a longtrip?

PB6

My family thinks that I should wear a seat belt while driving/riding a car for a long distance

NB2 Intention INT

My family thinks that I should wear a seat belt while driving/riding a car on a familiar route

NB3 How often do you intend to wear seat belt in the next month? INT

My family thinks that I should wear a seat belt while driving/riding a car on an unfamiliar route

NB4 Descriptive norms DN

My family thinks that I should wear a seat belt while driving/riding a car in an urban area

NB5 My friends fasten their seat belt in urban area. DN1

My family thinks that I should wear a seat belt while driving/riding a car on the motorway

NB6 My friends fasten their seat belt on a motorway. DN2

My friends believe that everybody in a car should wear a seat belt NB7 My friends fasten their seat belt on a familiar route. DN3Control beliefs CB My friends fasten their seat belt on an unknown route. DN4What is the probability of getting a ticket if you do not wear a seat

belt?CB1 My friends fasten their seat belt on a short trip. DN5

Would you wear a seat belt if enforcement was more intensive? CB2 My friends fasten their seat belt on a long trip. DN6Would you wear a seat belt if fines were higher? CB3


tion, questions such as ‘‘have you seen a road safety campaign on campus’’ were used to exclude, from the control group,students from other departments that had seen the campaigns.

3. Research designs

3.1. Implementing research designs

The need for random sampling dictates the identification of a research design, according to which the groups of measure-ments are defined.

The way that the experiment is set, supports the campaign design and the implemented model, in the sense that it indi-cates the way that the data is collected and further analyzed and/or compared, so that to provide scientific proof on the effec-tiveness of the intervention (campaign).

Both experimental (random sampling) and quasi-experimental methods may be used for the campaign design, depend-ing on the specific requirements of the campaign and the methods. Experimental methods are considered as the mostpowerful methods implemented for evaluating the impact of an intervention. Quasi-experimental methods may also beimplemented, in cases where random sampling is not required (or feasible) and the groups of measurements are pre-se-lected. Special attention has to be paid to the contamination of the control group, when quasi-experimental methods areused. However, in case of an intervention that is implemented on a well defined population, this method may provideuseful data for the evaluation of the effectiveness of the intervention on the perception and behavior. Before and afterdata for the control and intervention groups are compared, using the appropriate statistical analysis (Delhomme et al.,2009).

3.2. Model development and testing

Based on the number of measurements (before and after) and the achieved sample size, three designs were used, as theybest fit the measurements and the available data:


� ‘‘The two group before–after randomized experiment’’, where the ‘after’ measurement scores were adjusted against theirvariability, by using as covariate the ‘before’ measurement, and thus resulted in a more efficient and powerful estimate ofthe intervention’s impact (Trochim, 2006).� ‘‘The Solomon four-group design’’, that is considered as one of the most powerful designs that controls for internal valid-

ity threats, and provides rigorous and valid conclusions about the impact of the intervention (campaign), ruling out otherpossible causes (Zabukovec et al., 2007; Valente, 2001).� ‘‘The separate pre–post samples design’’, where participants in the ‘before’ and ‘after’ measurements differ (Trochim,

2006).

In each campaign, a control and an intervention group were defined, and both before and after measurements were taken.Four groups were formulated:

� Control_before� Control_after� Intervention_before� Intervention_after

The impact of the campaigns was assessed for each of them, as well as for their combinations and further breakdown,according to the design implemented. Combinations assumed a before and an after group including both control and inter-vention groups, whereas subgroups were also created depending on whether the interviewees participated in both beforeand after, before only or after only measurements. In such cases, the groups are distinguished by an index (i.e. _1 and_2), indicating the time of measurement in each group in the parenthesis. For example, intervention_1 (before and after)and control_1 (before and after) refers to the respective group subjects that responded both before and after the campaign(thus, same persons for the before and after measurements) in models 1, 2 and 3 of the Solomon 4-group, and intervention_2and control_2, the subjects that responded only in the after measurements.

Constructs were built by combination of the measured parameters, using alpha test, where a > 0.6 (Cronbach, 1951), andlinear regression analysis (Hankins, French, & Horne, 2000), was used to predict intentions and behaviors of the abovegroups. In the cases where parameter combinations were used for the formulation of a construct, the construct is indicatedwithout an index, otherwise, the index reflects a parameter.

Six experiments were formulated and the predictors and dependent variables were defined in each of them. In the firstthree experiments (models 1, 2 and 3 in Tables 2a, 3a and 4a), Intention (INT) was defined as the dependent variable withpredictors varying from three to five, adding in each subsequent experiment an additional one; thus, starting with behavioralbeliefs (BB), control beliefs (CB) and normative beliefs (NB), in the first experiment, enriched with descriptive norm (DN) inthe second and with past behavior (PB) in the third. Self-reported behavior (B) was used as the dependent variable in theother three experiments (models 4, 5 and 6 in Tables 2b, 3b and 4b), with predictors BB, CB and NB, in the first, enrichedby DN, in the second and INT in the third.

The above experiments run for different sets of data (groups), according to the specifications of the design method imple-mented. Overall, 51 experiments run for each of the two campaigns.

For the ‘‘Two group before–after randomized experiment’’ six groups and group combinations were formulated; the fourbasic groups listed above, and two combinations of them, namely, the control group, including before and after measure-ments (thus, control before and control after), and the before group, including measurements from both the interventionand control groups (thus, intervention before, and control before). All six groups and group combinations were used forthe first three experiments that predict intention, whereas for the last three experiments that predict behavior, only theintervention after group was used.

The ‘‘Solomon four group design’’ assumed four subsets of the previous groups, specifically, the control and interventionsubgroups that responded both before and after the intervention, and the control and intervention subgroups that respondedonly after the intervention. All combinations were used for the models that predict intention, whereas, only the interventiongroup that responded only after the intervention, was used for the models that predict behavior.

In the ‘‘Separate pre–post samples design’’ only subsets of groups that responded only before or only after the interven-tion, were used. Therefore, four subgroups were formulated from the control and intervention groups. All four of them wereused for the models that predict intentions, and only the intervention subgroup that responded after the campaign was usedfor the models that predict behavior, similar to the ‘‘Solomon four group design’’.

4. Results

4.1. Preliminary analysis

Inferential statistics and hypotheses testing were used for examining the strength of association (Logan et al., 2006). Thus,when measuring the effect of a road safety campaign, the first assessment of the magnitude of the effect is testing statisticalsignificance, and furthermore indicating the probability that the null hypothesis is falsely rejected or accepted.

Table 2aSummary of hierarchical regression analyses predicting drivers’ intention to drink and drive.

Model Research design Experiment R2 Adjusted R2 F

1 The two-group before–after randomized experiment Intervention_before 0.281 0.212 4.047Control_before 0.413 0.369 9.425Intervention_after 0.483 0.372 4.356Control_after 0.367 0.337 12.184Control_before + control_after 0.394 0.372 17.459Intervention_before + intervention_after 0.347 0.315 10.835

The Solomon 4-group Intervention_1 (before and after) 0.322 0.335 5.312Control_1 (before and after) 0.403 0.353 9.687Intervention_2 (after only) 0.581 0.544 5.427Control_2 (after only) 0.394 0.348 11.465

The separate pre–post samples design Intervention_1 (before only) 0.267 0.226 5.687Control_1 (before only) 0.378 0.331 8.956Intervention_2 (after only) 0.581 0.544 5.427Control_2 (after only) 0.394 0.348 11.465

2 The two-group before–after randomized experiment Intervention_before 0.419 0341 5.408Control_before 0.468 0.42 9.685Intervention_after 0.711 0.622 8.001Control_after 0.440 0.403 12.159Control_before + control_after 0.454 0.429 18.414Intervention_before + intervention_after 0.434 0.4 12.888






Table 2bSummary of hierarchical regression analyses predicting drivers’ behavior towards drink and drive.


4 The two-group before–after randomized experiment Intervention_after 0.461 0.461 5.852The Solomon 4-group Intervention_2 (after only) 0.657 0.595 6.214The separate pre–post samples design Intervention_2 (after only) 0.657 0.595 6.214




If there is no difference between the two measurements in the control group while a significant difference is observedin the intervention group, then it can be concluded that the campaign probably has been efficient (for a given confidencelevel).

Especially when using quasi-experimental design, the similarity of the control and intervention groups have to be proven,otherwise, existing differences may affect the conclusions on the impact of the campaign. ‘‘Contamination’’ of the control

Table 3aSummary of hierarchical regression analyses predicting drivers’ intention to wear seat belt.








3 The two-group before–after randomized experiment Intervention_before 0.868 0.848 4347Control_before 0.776 0.752 31.79Intervention_after 0.87 0.802 12.806Control_after 0.825 0.809 51.116Control_before + control_after 0.795 0.785 82.135Intervention_before + intervention_after 0.787 0.776 70.191



Table 3bSummary of hierarchical regression analyses predicting drivers’ behavior towards wearing seat belt.






group is also a factor that requires attention, and may be controlled when asking respondents if they have seen the roadsafety campaign.

From the analysis of the before-after responses, assuming a 95% confidence level (Wholey, 2004), it was indicated that78.85% of the intervention group had seen the campaign. Their awareness of the upper alcohol consumption rate increasedby 20%, whereas, it stayed at the same level in the control group. The vast majority of the responses (approximately 89% inboth drivers and passengers) indicated knowledge of the obligation to wear a seat belt in the front seats. Knowledge on the

Table 4aSummary of hierarchical regression analyses predicting passengers’ intention to wear seat belt.



The Solomon 4-group Intervention_1 (before and after) 0.937 0.852 11.112Control_1 (before and after) 0.849 0.806 19.656Intervention_2 (after only) 0.785 0.672 6.913Control_2 (after only) 1 – –








Table 4bSummary of hierarchical regression analyses predicting passengers’ behavior towards wearing seat belt.






seat belt usage in the back seats increased after the campaign in the passengers of the intervention group from 47% before to70% after the intervention. No change in the before and after measurements of the drivers or the control group was observed.

In most answers by the intervention group, a better appreciation of the risk associated to drink driving was noted in theafter measurements, as compared to the before. However, this improved attitude did not seem to be statistically significant,except of the case of passengers that seemed to be more reluctant to return home with a driver who had consumed at leastone drink and would discourage their driver of having a drink. It is important to note here, that the sample indicated a high


appreciation of the risk of drink driving already in the before measurements, factor that explains the insignificant change inthe after measurements. In the case of seat belt usage, significant increase was noted on both drivers and passengers’ inten-tion to ask passengers to wear, and wear, respectively, seat belt in the front and back seat. Similar increase was indicated inthe intention to wear seat belt in both short and long distance traveling.

In the control group before and after measurements, no significant change of the measured parameters was observed; norin the control and intervention before measurements. Thus, credibility of the sample distribution amongst the groups is pro-ven (Nathanail & Adamos, 2009).

4.2. Drink driving campaign

As the measurements that concern passengers were very limited and inadequate for being correlated to the dependentvariables, prediction models were only developed for drivers for the drink and drive campaign. Furthermore, in the nextparagraphs, only models with constructs or items, where combinations were not feasible (Cronbach alpha < 0.6) of highercorrelation to the dependent variable are presented. Many more models and possible combinations were tested withinthe context of this research.

Assessing the predictability of the ‘‘Two-group before–after randomized experiment’’ design, the results of the regressionanalysis showed that both descriptive norm (DN), added in model 2 as compared to model 1 and past behavior (PB), added inmodel 3 increased the predictability of the models for the dependent variable intention (INT). Indicatively, regarding theexperiment ‘‘intervention_after’’, it was observed that the addition of DN in model 2 and PB in model 3 increased the ex-plained variance (adjusted R2) as compared to model 1 from 37.2% to 62.2% and 76.2%, respectively (Table 2a). Similar resultswere observed, when behavior (B) was the dependent variable in models 4, 5 and 6. DN and INT improved the predictabilityof the models, when added to BB, NB and CB. More specifically, results showed that the explained variance of the parameterbehavior when drink and driving in model 4 was 46.1%, was increased to 59.8% in model 5 (adding DN) and 74.6% in model 6(adding INT) (Table 2b).

As regards the testing of the ‘‘The Solomon 4 group design’’, the analysis showed that the addition of DN in the predictorsof model 2 resulted in a significant increase of adjusted R2 when predicting intention, as compared to model 1. A higher in-crease of adjusted R2 was observed in model 3, in which PB was added. Specifically for the experiment ‘‘intervention_2’’, thevalue of adjusted R2 was increased from 54.4% to 62.3%, when the parameter DN was added and 80.6%, when PB was added(Table 2a). Similar results were indicated, when predicting behavior, in models 4, 5 and 6, for ‘‘intervention_2’’ group, inwhich the prediction power was increased in models 5 and 6 as compared to model 4, with the addition of the parametersDN and INT, respectively. The relevant values of adjusted R2 were 59.5%, 67.4% and 79.5%, respectively (Table 2b). Similarresults were identified in the rest groups.

Finally, the investigation of ‘‘The separate pre–post samples design’’ showed that the addition in the prediction param-eters of DN and PB when predicting intention (models 1, 2 and 3) and DN and INT when predicting behavior (models 4, 5and 6) resulted in an increase of the adjusted R2 in all groups. The relevant figures in ‘‘intervention_2’’ group, are similarto the ‘‘Solomon 4 group design’’ (Tables 2a and 2b). Similar results were identified in the rest groups of the analysis.

Based on the values of the indicator Beta, behavioral beliefs (BB) seem to be the strongest, in terms of prediction in models1, 2 and 3 of the ‘‘Two-group before–after randomized experiment’’ design, normative beliefs (NB), also, contribute signifi-cantly to the total variances, while PB was the weakest parameter. Similarly, the strongest parameter in model 4 was CB andthe weakest NB. In model 5, NB remains the weakest parameter, and DN the strongest one, while in model 6, INT was thestrongest and NB remained the weakest parameter, respectively.

In the ‘‘The Solomon 4 group design’’, the strongest parameters for models 1, 2 and 3 were NB, DN and PB, respectively.The strongest parameter in model 4 was CB, in model 5, DN and in model 6, DN and INT.

In the ‘‘The separate pre–post samples design’’ the strongest parameters for models 1, 2 and 3 were NB, DN and DN andINT, respectively, and in models 4, 5 and 6, same as in the Solomon 4 group design.

The summary of the all the hierarchical regression analyses conducted are presented in Tables 2a and 2b. It should benoted, that only the models demonstrating some kind of predictability are presented here. Other parameter combinationswere also tested within the research. The model with the highest predictability in the drink and drive campaign was model3 of the Solomon 4-group design for the ‘‘intervention_2’’ group (after only). Its specific parameters and their coefficientsappear in Table 5. The parameters applying in the rest of the models and their coefficients, as well as further informationon them, may be found in Nathanail and Adamos (2009).

4.3. Seat belt campaign

When assessing the predictability of the ‘‘The two-group before–after randomized experiment’’, results showed that theaddition of the parameters descriptive norm (DN) and past behavior (PB) in the predictors increased the value of adjusted R2

in almost all groups. More specifically, regarding the experiment ‘‘intervention_after’’ group, the adjusted R2 was modifiedfrom 53% in model 1 to 52% in model 2 (when DN was added) and 80.2% in model (when past behavior (PB) was added)(Table 3a). It has to be noted that in this case, DN causes a decrease of adjusted R2 in model 2. Similar results were indicatedin models 4, 5 and 6, in which behavior was the dependent parameter, and an increase of the predictability of the models wasobserved when DN and intention INT were added, with an adjusted R2 of 85.6% in model 6 (Table 3b).


In the case of evaluating the ‘‘The Solomon 4 group design’’, results showed that the addition of DN, PB and INT caused anincrease of the values of adjusted R2 in all models. Indicatively, for the ‘‘intervention_2’’ group, the increase was from 88.4%in model 1 to 86.5% in model 2 and 93.1% in model 3 was observed, respectively (Table 3a). Same results were indicated inmodels 4, 5 and 6, with adjusted R2 values 71.4%, 78.6% and 86.6%, respectively, when adding DN and INT to the ‘‘interven-tion_2’’ group (Table 3b).

Finally, the testing of the ‘‘The separate pre–post samples design’’, showed, also, as in the other research designs, that theaddition of the parameters DN, PB and INT in the predictors increased the value of R2 in almost all groups. For example, refer-ring to the ‘‘intervention_2’’ experiment, the values of adjusted R2 were 42.5% in model 1, 49% in model 2 and 71.9% in model3, respectively (Table 3a). The adjusted R2 in models 4, 5 and 6, were 58.3%, 61.9% and 74.9%, respectively, in the ‘‘interven-tion_2’’ experiment (Table 3b).

Similar tendencies, with increasing predictability of the models, were observed when testing the relative experimentsreferring to passengers, when DN, PB and INT were added as predicting parameters in the intention and behavior models.These models indicated higher adjusted R2 values than driver models, with highest value 99.7% in model 3 of the ‘‘interven-tion_1’’ group (Tables 4a and 4b).

Based on the values of Beta, the strongest parameter in model 1 of the ‘‘The two-group before–after randomized exper-iment’’, was behavioral beliefs (BB), in model 2, BB and normative beliefs (NB), in model 3, past behavior (PB), in model 4 CB,in model 5 DN and in model 6 INT.

In the case of evaluating the ‘‘The Solomon 4 group design’’, and according to the Beta values, the strongest parameter inmodel 1 and 2 seem to be the BB, and the PP, respectively and in model 3 the PB. Here also, the strongest parameter in model4 was CB, in model 5, DN and in model 6, DN and INT.

In ‘‘The separate pre–post samples design’’, the strongest parameter in model 1 seems to be the BB, in model 2 the PB andin model 3 the PB. Strongest parameters in models 4, 5 and 6 are CB, DN and DN and INT, respectively.

As there is no indication of increasing predictability by adding a construct in the passenger models, all parameters arealmost similarly significant in the passenger prediction models.

Table 5Selected models predicting intentions.

Design Campaign Group Construct Standardized coefficients Unstandardized coefficients t p

B Std. error Beta

The Solomon 4-group Constant �2.739 2.637 �1.284 0.193Drink and drive campaign BB2 0.073 0.31 0.029 0.319 0.839Intervention after only group CB 0.094 0.284 0.219 0.749 0.376

NB 0.216 0.129 0.253 1.164 0.169DN 0.562 0.328 0.438 1.849 0.084PB 0.639 0.295 0.732 3.784 0.002a

The Solomon 4-group Constant 0.119 1.115 0.107 0.919Seat belt campaign BB1 0.073 0.054 0.139 1.356 0.224Intervention after only group (drivers) BB2 0.474 0.273 0.263 1.734 0.134

BB3 �0.273 0.205 �0.165 �1.336 0.23BB4 0.01 0.06 0.017 0.168 0.872BB5 �0.102 0.073 �0.199 �1.408 0.209BB6 0.094 0.047 0.187 2.019 0.09BB7 �0.022 0.088 �0.023 �0.249 0.812CB 0.056 0.05 0.104 1.12 0.305NB1 0.16 0.077 0.243 2.096 0.081NB2 0.032 0.04 0.059 0.792 0.459DN 0.048 0.05 0.087 0.962 0.373PB 0.518 0.101 0.679 5.119 0.002a

The separate pre–post samples design Constant �58.68 3.215 �18.252 0a

Seat belt campaign BB1 0.033 0.023 0.039 1.446 0.244Intervention after only group (passengers) BB2 4.862 0.217 0.683 22.357 0a

BB3 0.357 0.177 0.05 2.013 0.138BB4 0.044 0.028 0.048 1.567 0.215BB5 0.101 0.055 0.119 1.828 0.165BB6 0.015 0.02 0.014 0.771 0.497BB7 �0.124 0.044 �0.068 �2.839 0.066CB 0.011 0.016 0.013 0.666 0.553NB1 0.03 0.034 0.037 0.875 0.446NB2 1.013 0.171 0.256 5.916 0.01NB6 2.852 0.309 0.4 9.22 0.003DN 0.099 0.032 0.085 3.129 0.052PB 0.124 0.071 0.103 1.749 0.179

Note 1: BB: behavioral beliefs, CB: control beliefs, NB, normative beliefs, DN: descriptive norms, PB: past behavior.Note 2: The index in the construct indicates different grouping of initial parameters.

a p-Values < 5%.


The model with the highest predictability in the seat belt campaign was model 3 of the Solomon 4-group design for thedrivers’ ‘‘intervention_2’’ group (after only) and model 3 of the separate pre–post samples design for the passengers’ ‘‘inter-vention_2’’ group (after only). The specific parameters and coefficients of these models appear in Table 5. The parametersapplying in the rest of the models and their coefficients, as well as further information on them, may be found in Nathanailand Adamos (2009).

5. Discussion

Communication campaigns are used as a means for improving driving behavior and contributing to less road accidents,injuries and fatalities (Conner & Norman, 2005; Marcil et al., 2001). Evaluating the impact of the campaigns on roadsafety is essential. However, it is difficult to isolate potential changes in driving behavior owing to a campaign, apart frommeasurements such as recall and recognition, and only a well structured evaluation methodology, based on scientificgrounds can provide the measurements that assess and may predict the campaign’s effectiveness on behavior, andintention.

Many theories have been developed that explain human intentions and behavior on road safety mass media campaigns,such as the Rogers Protection Motivation Theory (Rogers, 1975, 1983; Rogers & Mewborn, 1976), the Extended ParallelProcess Model (Witte, 1992, 1998), and the Theory of Planned Behavior (Ajzen, 1991). The importance of the careful iden-tification of the target audience in the development of the campaign and the characteristics of the messages was also ob-served (Delaney, 2004), as well as the necessity to use both primary (behavior) and secondary (attitudes) objectives, as itseems to provide greater accuracy when assessing changes due to the implementation of the campaign (Nathanail & Ada-mos, 2009).

For the measurability of its effectiveness, a road safety campaign has to be carefully designed, following a clear, coherentand proper research design that enables measurements and evaluation (Fylan et al., 2006). Such measurements are related tothe attitudes, subjective and descriptive norms, perceived behavioral control and intentions along with behavior (Fylan et al.,2006).

Selection of the proper campaign evaluation, affects the campaign design. The measurement variables as well as the datacollection methods and techniques must be foreseen. It is always advised to consider the possible optimum performance ofthe evaluation outcome, thus data validity and reliability, in conjunction to the time and costs. However, even if the one-group tests are quicker and less expensive, using a control and an intervention group and before and after measurementsprovide higher internal validity.

Interviewing is considered as one of the most successful data collection techniques, however, they increase the time andcost of the campaign. As compared to postal survey, the response rate is increased when a face-to-face questionnaire surveyis conducted. On-site observations provide high validity in actual behavior measurements, however it is more expensive andtime consuming than interviewing.

When a before measurement is taken, the after measurement is highly dependent on it, and optimally, similar questions/statements as in the before study should be used. New measurement variables that may be collected are restricted to obser-vations occurring after the intervention.

When selecting randomly before and after samples, history threat can be reduced. In a limited area of implementing acampaign (e.g. local campaign) such a random sampling may be difficult owing to the limited population.

To capture the multiple dimensions of a campaign impact, multiple measurement variables better be collected, althoughincreasing the complexity of the design, as well the easiness of data collection process. However, more variables may com-pensate for the inefficiency of some variables. The possibility for combining variables into a construct increases its power topredict the dependent variable (e.g. behavior). Especially, when measuring socio cognitive variables, such as attitudes, onemeasured item does not cope for reliability, and the latter improves when multiple items are combined (Armitage and Con-ner, 2001), as random errors seem to be removed (Kerlinger & Lee, 2000). This is achieved with the usage of coefficient alpha(Cronbach, 1951).

Another threat to the validity of measurement variables owing to social desirability (Lajunen, Corry, Summala, & Hartley,1997), where the responses may be influenced by the respondents’ perception of researchers’ expectations, and further bythe desire to protect their own image (Anastasi & Urbina, 1997), seems to be outraged as some studies have proven thatobservations are correlated to self reported behavior (e.g., Aberg, Larsen, Glad, & Beilinsson, 1997; Dalziel & Job, 1997; Par-ker, Reason, Manstead, & Stradling, 1995). This situation is assumed to apply in the two local campaigns, as the research hasbeen conducted by college mates, and thus no different image than the real one was intended to be presented by theinterviewees.

A threat to the validity appears, when testing before and after occurs, which may introduce testing or sensitization threat(Snow & Tebes, 1991). The way to cope with this disadvantage of designs, such as the two-group before–after randomizedexperiment, is to test other variable except of participant/subject dependant variables (e.g. knowledge, attitudes, intended orself-reported behavior). Solomon 4-group and separate pre–post samples design control for this latter threat as they arecomposed of multiple measurements, however contamination may be observed. In the case of local campaigns, contamina-tion can be controlled if the samples are well separated (as in the case of the different departments in the two localcampaigns).


6. Conclusions

The present research demonstrated the implementation of alternative research designs in the development of intentionand behavior prediction models. The conclusions drawn rely on the results of the assessment of the impacts of two localcampaigns, one on drink and drive and the other on the seat belt usage. Both campaigns were realized taking as a basisthe Theory of Planned Behavior, according to which behavioral beliefs, control beliefs, normative beliefs, descriptive norms,past behavior and intentions were measured. An attempt was made to correlate past behavior and intention with combina-tions of the above constructs, based on three designs, the ‘‘Two-group before–after randomized experiment’’, the ‘‘Solomon 4group design’’ and the ‘‘separate pre–post samples design’’.

The high appreciation of the risk of drink driving, indicated already in the before measurements, explains the insignificantimprovement, in the after measurements of the intervention group. Exception is the case of passengers that seemed to bemore reluctant to return home with a driver who had consumed at least one drink and would discourage their driver of hav-ing a drink. Significant increase was noted on both drivers’ and passengers’ intention, in the intervention after group, to askpassengers to wear, and wear, respectively, seat belt in the back seat, as well as to wear seat belt in both short and long dis-tance traveling. Seatbelt usage by drivers or passengers in the front seat did not gain any substantial increase in the aftermeasurements, attributed to the knowledge already owned by the subjects.

Homogeneity of the groups was proven by the insignificant change of the measured parameters in the control and inter-vention before measurements. Insignificant differences were also observed between the control group before and aftermeasurements.

Six models were formulated and tested on control and intervention groups and the main conclusions that were drawnand regard both campaigns are:

(1) Models considering the intervention after group in the drink driving campaign demonstrated the best predictability ascompared to the other models (i.e. control after, intervention_1, etc.) using data from other groups. As more constructsare added to the models, the adjusted R2 increases (from 37.2% to 80.6% when predicting intention and 46.1% to 79.5%when predicting behavior), however no significant predominance is observed between the two groups of models. The‘‘Solomon 4 group design’’ and the ‘‘separate pre–post samples design’’ seem slightly stronger as compared to the‘‘two-group before–after randomized experiment’’ (for example, in model 6 for the intervention after group, theadjusted R2 for the first two designs is 79.5% and for the latter 74.6%).

(2) Models used for predicting driver intention and past behavior as regards the seat belt usage do not vary significantlydepending on the group, expect of the models predicting intention, that appear to give a higher adjusted R2 for theintervention after group under all designs, as compared to models developed from other groups. Here too, as the con-structs increase, the adjusted R2 increases (from 53% to 93.1% when predicting intention and 65.8% to 86.6% when pre-dicting past behavior), but no significant predominance is observed between the two groups of models.

(3) Seat belt usage intention and behavior are better predicable in the case of passengers (up to 99.7% probability), ascompared to drivers (up to 95% probability). Intentions and past behavior related to drink driving are more difficultto predict (up to 80.6% probability).

Furthermore,(1) Increase of the predictability of the models is noticed as more constructs are added. What others of significance to the

subjects do (DN), affects the subjects’ intentions (Zabukovec et al., 2007; Rivis & Sheeran, 2004). Especially when pastbehavior is added in the models predicting intention, and intention in the models predicting behavior, which demon-strates the high correlation between these two constructs, and is in accordance with the Theory of Planned Behavior(Ajzen, 1991), where intention or even decision to perform a behavior is based on attitudes towards behavior, and theTranstheoretical Model (Prochaska & DiClemente, 1983), where a new behavior is built on previous stages, includingpast behavior. It is more expected for a subject to intent to cope with a safety regulation (e.g. wear seat belt), when he/she already does, in most of the cases (Sparks, 1994).

(2) This is also indicated by the high correlation coefficient of PB in the models predicting intentions of the drivers in bothcampaigns (0.639, and 0.518, respectively, in Table 5). Possible threat to the validity owing to social desirability, thatwould explain also this high interrelation, is assumed to be outraged, as the interviewers were college mates with theinterviewees, and self-reported behavior was considered similar to the actual (observed) behavior (e.g. Aberg et al.,1997).

(3) Descriptive norms seem very significant predictors of intentions to drink and drive, as compared to the seat belt usage.As alcohol consumption is a social phenomenon, and in parallel with driving affects not only the driver’s but also thesurrounding’s safety, what other significant persons do is a strong intention and behavior predictor.

(4) The belief of the possibility of the impact of an accident, in case that the driver or a passenger sitting in the front seat isa very strong predictor in the intentions for seat belt usage, in both target groups (drivers and passengers). Especiallyfor the passengers, seat belt usage is affected by Passengers seem to be less affected by past behavior, as, in most cases,their intentions are affected by the they may comply with what their environment thinks, mainly when going on along trip and/or on a motorway. This is attributed to the fact that enforcement for passengers is not so strict in theurban areas of the case study.


(5) As intentions and behavior are closely correlated (Manstead, 2000), not significant differences have been observed inthe level of predictability, provided by the models, when predicting either intention or past behavior, with a slighthigher adjusted R2 in the case of intention being the dependent variable.

(6) ‘‘Solomon 4 group design’’ demonstrated better predictability than the other designs, especially in the data obtainedfrom the intervention group after the implementation of the campaign, followed by the ‘‘separate pre–post samplesdesign’’ and the ‘‘two-group before–after randomized experiment’’, for the same group. The first two designs, almostsimilar in some experiments (groups), cope for the threat to the validity owing to testing or sensitization threat, asmore measurements are being taken (Snow & Tebes, 1991). On the other hand, possible contamination is consideredto have been avoided, as the control and intervention population were physically separated.

Acknowledgements

The authors would like to thank the Laboratory of Transportation Engineering of the University of Thessaly for permissionto use in this study data collected during the campaigns organized in cooperation with the consortium of the European re-search project CAST (Campaigns and Awareness raising Strategies in Traffic safety). Support from the European CommissionDG-TREN is gratefully acknowledged.

References

Aberg, L., Larsen, L., Glad, A., & Beilinsson, L. (1997). Observed vehicle speed and drivers’ perceived speed of others. Applied Psychology: An InternationalReview, 46(3), 287–302.

Adamos, G., & Nathanail, E. (2011). An experimental approach towards the evaluation of mass media safety campaigns – A comparative analysis of two casestudies. Transportation Research Board 90th annual meeting, January 23–27, 2011, Washington, DC.

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action-control: From cognition to behavior.Heidelberg: Springer.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.Ajzen, I. (2002). Constructing a TPB questionnaire: Conceptual and methodological considerations. <http://www.people.umass.edu/aizen/pdf/

tpb.measurement.pdf> Retrieved 09.11.06.Anastasi, A., & Urbina, S. (1997). Psychological testing (7th ed.). Upper Saddle River, New Jersey: Prentice-Hall Inc..Armitage, C. J., & Conner, M. (2006). Efficacy of the theory of planned behavior: A meta-analytic review. British Journal of Social Psychology, 40, 471–499.Conner, M., & Norman, P. (2005). Predicting health behavior: A social cognition approach. In M. Conner & P. Norma (Eds.), Predicting health behavior: Research

and practice with social cognition models (2nd ed., pp. 1–27). Maintenhead: Open University Press.Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrical, 16(3), 297–334.Dalziel, J. R., & Job, R. F. S. (1997). Motor vehicle accidents, fatigue and optimism bias in taxi drivers. Accident Analysis and Prevention, 29(4), 489–494.Delaney, A., Lough, B., Whelan, M., & Cameron, M. (2004). Review of mass media campaigns in road safety (No. 220). Clayton, Victoria: Monash University

Accident Research Centre.Delhomme, P., Vaa, T., Meyer, T., Harland, G., Goldenberg, C., & Jarmark, S., et al. (1999). Evaluated road safety media campaigns: An overview of 265 evaluated

campaigns and some meta-analysis on accidents (EC, Deliverable 4. Gadget project. Contract No: RO-97-SC.2235). Arcueil: INRETS.Delhomme, P., De Dobbeleer, W., Forward, S., Simões, A. (Eds.). (2009). Manual for Designing, Implementing, and Evaluating Road Safety Communication

Campaigns. CAST project. Belgian Road Safety Institute (IBSR-BIVV), Brussels, Belgium.Elliott, B. (1993). Road safety mass media campaigns: A meta analysis (CR 118). Canberra: Federal Office of Road Safety.Fylan, F., Hempel, S., Grunfeld, B., Conner, M., & Lawton, R. (2006). Effective interventions for speeding motorists. Road Safety Research Report No. 66.

Department for Transport: London.Hankins, M., French, D., & Horne, R. (2000). Statistical guidelines for studies on the theory of reasoned action and the theory of planned behaviour.

Psychology and Health, 15, 151–161.Kerlinger, F. N., & Lee, H. B. (2000). Foundations of behavioral research (4th ed.). New York: Harcourt College Publishers.Lajunen, T., Corry, A., Summala, H., & Hartley, L. (1997). Impression management and self-deception in traffic behaviour inventories. Personality and

Individual Differences, 22(3), 341–352.Logan, T. K., Padgett, D. K., Thyer, B. A., & Royse, D. (2006). Program evaluation: An introduction. Belmont, California: Thomson Brooks/Cole.Manstead, A. S. R. (2000). The role of moral norm in the attitude–behavior relation. In D. J. Terry & M. A. Hogg (Eds.), Attitudes, behavior and social context.

New Jersey: Lawrence Erlbaum.Marcil, I., Bergeron, J., & Audet, T. (2001). Motivational factors underlying the intention to drink and drive in young male drivers. Journal of Safety Research, 3,

363–376.Nathanail, T., & Adamos, G. (2009). A theoretical approach to assess road safety campaigns: Evidence from seven European countries. In Forward, S., &

Kazemi, A. (Eds.), CAST project. Brussels, Belgium: Belgian Road Safety Institute (IBSR-BIVV).Parker, D., Reason, J. T., Manstead, A. S. R., & Stradling, S. G. (1995). Driving errors, driving violations and accident involvement. Ergonomics, 38, 1036–1048.Prochaska, J. O., & DiClemente, C. C. (1983). Stages and processes of self-change of smoking. Toward an integrative model of change. Journal of Consulting and

Clinical Psychology, 51, 390–395.Rivis, A., & Sheeran, P. (2004). Descriptive norms as an additional predictor in the theory of planned behavior: A meta-analysis. Current Psychology, 22(3),

218–233.Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology, 91(1), 93–114.Rogers, R. W. (1983). Cognitive and physiological process in fear appeals and attitude change: A revised theory of protection motivation. In J. Cacioppo & R.

E. Petty (Eds.), Social psychophysiology (pp. 153–176). New York: Guilford Press.Rogers, R. W., & Mewborn, C. R. (1976). Fear appeals and attitude change: Effects of a threat’s noxiousness, probability of occurrence, and the efficiency of

coping responses. Journal of Personality & Social Psychology, 34(1), 54–61.Snow, D. L., & Tebes, J. K. (1991). Experimental and quasi-experimental designs inprevention research. In C. G. Leukefeld & W. Bukoski (Eds.), Drug abuse

prevention intervention research: Methodological issues (pp. 140–158). (NIDA Research Monograph 107), Washington, DC: U.S. Government PrintingOffice.

Sparks, P. (1994). Attitudes towards food: Applying, assessing and extending the theory of planned behaviour. In D. R. Rutter & L. Quine (Eds.), Socialpsychology and health: European perspectives (pp. 25–46). Avebury: Aldershot.

Trochim, W. M. (2006). Research methods knowledge base. 2nd ed. <http://trochim.human.cornell.edu/kb/index.htm> (version current as 20.10.06).Vaa, T., & Phillips, R. (Eds.). (2009). Deliverable D1.3.Effects of road safety campaigns. CAST project.

http://www.people.umass.edu/aizen/pdf/tpb.measurement.pdf

http://www.people.umass.edu/aizen/pdf/tpb.measurement.pdf

http://trochim.human.cornell.edu/kb/index.htm


Valente, T. W. (2001). Evaluating communication campaigns. In R. E. Rice & C. K. Atkin (Eds.), Public communication campaigns (3rd ed., pp. 105–124).California: Sage Publications, Inc..

Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence.Psychological Bulletin, 132(2), 249–268.

Wholey, J. S. (2004). Handbook of practical program evaluation. San Francisco: Jossey-Bass.Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model. Communication Monographs, 59(4), 329–349.Witte, K. (1998). Fear as a motivator, fear as a inhibitor: Using the extended parallel process model to explain fear appeal successes and failures. In P.A.

Andersen, & L.K. Guerrero (Eds.), Handbook of communication and emotion: Research, theory, applications and contexts.Woolley, J. E. (2001). Literature review on best practice with mass media. Adelaide: Transport Systems Centre, University of South Australia.Wundersitz, L. N., Hutchinson, T. P., & Woolley, J. E. (2010). Best practice in road safety mass media campaigns: A literature review. Adelaide: Centre for

Automotive Safety Research, University of Australia.Zabukovec, V. Divjak, M., Nathanail, T., Delhomme, P., Deugnier, M., Eyssartier, C. et al. (2007). Evaluation designs. In CAST consortium, Deliverable 2.2

Comparison of research designs. CAST project, 2007.

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Road safety communication campaigns: Research designs and behavioral modeling

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