An investigation of professional drivers: Organizational safety climate, driver behaviours and performance

Transportation Research Part F 16 (2013) 81–91

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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf

An investigation of professional drivers: Organizational safety climate, driver behaviours and performance Bahar Öz a, Türker Özkan b,⇑, Timo Lajunen b a b

Human Factors and Safety Behaviour Group, Institute of Behavioural Sciences, University of Helsinki, PO Box 9, FI-00014, Finland Safety Research Unit, Department of Psychology, Middle East Technical University, 06800 Ankara, Turkey

a r t i c l e

i n f o

Article history: Received 12 January 2011 Received in revised form 20 June 2012 Accepted 16 August 2012

Keywords: Professional drivers Driver behaviours Driver performance Organizational climate Safety climate

a b s t r a c t The aim of this study was to investigate the relationships among organizational safety climate, driver behaviours and performance for a total of 223 male Turkish professional drivers. The participants were asked to fill out the extended Driver Behaviour Questionnaire (i.e. errors, violations and positive behaviours), Driver Skills Inventory (i.e. safety skills and perceptual-motor skills), Transportation Companies’ Climate Scale, which is newly and specially designed for the professional drivers for the first time, and a background information form. Results of the factor analyses conducted for Transportation Companies’ Climate Scale yielded three factors, which were named as general safety management, specific practices and precautions and work and time pressure. After controlling for the effects of age and annual mileage, the results of hierarchical regression analyses revealed significant relationships between work and time pressure and frequencies of violations and errors. Hierarchical regression analyses also showed that general safety management was related to safety skills of professional drivers. Transportation companies’ safety climate was not found to be related to positive driver behaviours or perceptual-motor skills. The results have both theoretical and practical implications by providing additional and new data to the related literature to be used for the future research and providing directions to the organizations in arrangement of safer work settings, respectively. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction A country’s traffic system organizes mobility by taking safety into account and minimizing the risk. Although the main goal in traffic is to maximize mobility and safety at the same time; for all drivers those two aspects might be in conflict in traffic settings (Elvik & Vaa, 2005; Evans, 2004). As proposed by Özkan and Lajunen (2011), traffic safety culture/climate of the whole country might influence driving and safety of both professional and nonprofessional (see Özkan & Lajunen, 2011 for the difference between self-paced vs. forced-paced tasks) drivers who constitute a sample of road users of that particular country. In spite of the number of studies on self-reported attitudes and behaviours that influence accident risk for nonprofessional drivers, relatively little research has examined the self-reported driver behaviours of professional drivers who drive company sponsored vehicles and/or spend long periods of time behind the wheel (Davey, Wishart, Freeman, & Watson, 2007; Sullman, Meadows, & Pajo, 2002; Xie & Parker, 2002). Because of the nature of their task, for the professional drivers, the conflict between mobility and safety might have a different nature with the effects of some additional factors. For the professional drivers, as different from the nonprofessional ones, safe driving is a situation that might be provided or shaped by the organization, at least partly (Caird & Kline, 2004). For the employees working for an organization, there is a ‘system of ⇑ Corresponding author. Tel.: +90 312 2105118; fax: +90 312 2107975. E-mail address: [email protected] (T. Özkan). 1369-8478/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trf.2012.08.005

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organization’ as well, where different structured/unstructured or formal/informal organizational characteristics and aspects might play roles and have effects on its employees. However, the role of organizational culture and climate in professional driving and safety, by considering their relationship with other driving related factors as well, remained mainly unexamined (see Öz, 2011). 1.1. The concept of organizational safety climate Organizations are complex systems having values, principles, attitudes and viewpoints making them different from others (Arnold, 1998). In his recent work, Guldenmund (2010) mentioned about three major organizational forces interaction of which determines behaviour within the organizations. These forces are structure (i.e. the formal organization); process (i.e. the primary processes exist in the organization, including communication); and culture (i.e. basic assumptions). It has been a discussion topic that whether culture which can be defined as ‘collective programming of the human mind distinguishing the members of one group from those of another’ (Hofstede, 2001); and climate which can be defined as ‘a summary of molar perceptions that employees share about their work environments’ (Zohar, 1980, p. 96) are the same or two different concepts (e.g. Glick, 1985; Guldenmund, 2010; James et al., 2008; Schein, 1992). After investigating the characteristics of culture and climate concepts in detail, Guldenmund (2000) ended up that culture research is mainly based on qualitative methods (e.g. field notes, quotes), whereas climate research is conducted mostly by using quantitative methods (e.g. questionnaires) that share a lot of similarities with attitude measurements. According to Denison (1996), culture research aims at achieving a deep understanding of the underlying mechanisms, whereas climate research deals with organizational members’ perception of organizational practices and how these practices and perceptions are categorized into the analytical dimensions defined by the researchers. James et al. (2008) stated that organizational climate is a property of individual, whereas culture is a property of organization. Although in the literature, some different characteristics of culture and climate concepts were mentioned, as listed above, it is still possible to argue that there is not a general consensus on the definitions of and differences between these two concepts, as well as the models to develop to understand and explore their relationships with other variables (e.g. Glick, 1985; Guldenmund, 2000; James et al., 2008; Schein, 1992). In his review, Schneider (1975) indicated organizational climate as an amorphous and inclusive concept having amorphous measurements as a result. Climate concept has been mentioned about as having many potential faces causing not to have a specific focus. Thus, it is argued that climate research has to focus on something. In support of this, Zohar (1980) indicated that within a single organization, different climates are created, and James et al. (2008) mentioned that the recent trend in the organizational climate research is to focus on ‘climate for something’ (p. 20), like team climate, safety climate or creativity climate. By this way, the construct of organizational climate would not be treated as an all-inclusive concept but describes a specific area of research, as well as becoming more narrow and tangible (Guldenmund, 2000). In the scope of the present study, organizational climate is specifically investigated as organizational safety climate. Zohar (2010) proposed that since the beginning of the studies of safety climate in organizational settings, which might be traced back to his study in 1980, a considerable amount of development has been observed in the field. In such a way that the studies conducted so far were successful in validating safety climate as a leading indicator of safety outcomes across industries and countries. There have been many studies to investigate the multidimensional nature and structure of this concept (Guldenmund, 2010; Parker, Lawrie, & Hudson, 2006; Zohar, 1980). The attempts to end up with the same factor structures as a result of the studies conducted in similar kind of organizations were not successful (Coyle, Sleeman, & Adams, 1995). According to Guldenmund (2010) ending up with different factor structures might be a consequence of conducting the studies in different sectors, because employees from different sectors might have different objects for their attitudes. Thus, it is not surprising to have different dimensions as a result of the studies conducted in different sectors like construction, energy and service, which are not much similar in terms of their content (see Cabrera, Isla, & Vilela, 1997; Cox & Cox, 1991; Coyle et al., 1995). Zohar (2010) emphasized the need to develop industry specific scales offering a variety of climate dimensions to make the shared perceptions emerge. According to the researcher, this is the way to determine the detailed indicators of safety climate specific to that sector in addition to the general indicators focusing on the core themes, like ‘managements’ commitment to safety’. In the transportation literature, some safety climate scales have been used so far. However, to the knowledge of the authors of the present study, there has not been any safety climate scale, which was developed from scratch and specifically with the purpose of gathering information on professional drivers’ safety related evaluation regarding the organization that they are working for. For example, in their study, investigating safety climate in road construction, Glendon and Litherland (2001) used a modified safety climate scale which was originally developed by Glendon, Stanton, and Harrison (1994). The participants of the Glendon and Litherland’s (2001) study were construction and maintenance crew. The original version of the scale used in that study was developed by converting the factors that influence performance and might be common to many organizations, into safety statement perceptions and organizational climate aspects (Glendon et al., 1994). At a later time, Wills, Watson, and Biggs (2006, 2009) conducted research studies on safety climate and work-related driving. They used the same safety climate questionnaire originally developed by Glendon et al. (1994) in which the items were modified to increase applicability to the context of work-related vehicle driving. Investigating the management practices as antecedents of safety culture within the trucking industry, Arboleda, Morrow, Crum, and Shelley (2003) measured the drivers’ level of concern that their organization demonstrated concerning by using a four-item scale. All the mentioned examples show that although there have been different scales or questions used to gather safety climate information from professional drivers,

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it is not possible to say that there is a scale developed only for that purpose. However, the need for such scales to be more specific on a sector or job while measuring and evaluating safety climate has been emphasized in the previous literature (e.g. Flin, Mearns, O’Conor, & Bryden, 2000; Zohar, 2010). For this reason, administration of a safety climate questionnaire which is developed for the first time and being specific to the professional drivers is especially emphasized through the present study. 1.2. Human factors in driving: driver behaviour and performance Most road traffic accidents can directly be attributed to human factors as a sole or a contributory factor (Lewin, 1982). Human factors in driving can be investigated under two separate components: driver behaviours/style and performance/ skills (Elander, West, & French, 1993). Driver behaviour refers to the ways drivers choose to drive or habitually drive, including, for example, the choice of driving speed, habitual level of general attentiveness and gap acceptance (Elander et al., 1993). In other words, it explains what drivers usually ‘do’. Although they become established over a period of years, driver behaviours do not necessarily get safer with driving experience (Elander et al., 1993). Driver performance includes information processing and motor and safety skills, which improve with practice and training, that is, with driving experience. In other words, it explains what drivers ‘can’ do. 1.2.1. Driver behaviours According to Reason (1990), driver behaviours can be roughly divided into two categories: errors and violations. This differentiation provided a base for the development of the Manchester Driver Behaviour Questionnaire (the DBQ; Reason, Manstead, Stradling, Baxter, & Campbell, 1990). Research using the DBQ has suggested that driver errors, violations and slips and lapses are three empirically distinct classes of behaviour. Reason et al. (1990) defined errors as ‘the failure of planned actions to achieve their intended consequences’, violations as ‘deliberate deviations from those practices believed necessary to maintain the safe operation of a potentially hazardous system’ and slips and lapses as attention and memory failures. Unlike errors, violations were seen as deliberate behaviours, although both errors and violations are potentially dangerous and might lead to a crash. Parker, West, Stradling, and Manstead (1995) indicated that slips and lapses might cause embarrassment but are unlikely to have an impact on driving safety. Lawton, Parker, Manstead, and Stradling (1997) extended the DBQ by adding more items into the violations scale and split it into two distinct scales, as ordinary violations and aggressive violations, according to the reason why drivers violate. Ordinary violations are deliberate deviations from safe driving without a specifically aggressive aim (e.g. speeding for saving time), whereas aggressive violations contain overtly aggressive acts (e.g. showing hostility by chasing other vehicles). Even though this addition of items has resulted in different factor solutions, the distinction between errors and violations, first shown by Reason et al. (1990), seems to be robust for private and professional drivers alike, both within and across different countries and cultures (Wallén Warner (2006) for an overview). The distinction between violations and errors is also supported by the findings showing that this two-factor solution was the most stable solution (among possible solutions with two to six factors) over a three-year follow-up study in Finland (Özkan, Lajunen, & Summala, 2006). Both violations and errors were labelled as aberrant, and therefore negative, behaviours. Focusing on negative behaviours is well justified in terms of traffic safety. Everyday driving, on the other hand, involves other behaviours that cannot be described as negative (Özkan & Lajunen, 2005). These behaviours neither have to be based on coded rules/regulations nor primarily take safety into account. The main intention in these behaviours is to take care of the traffic environment or other road users, to help and to be polite towards them with or without safety concerns. For example, drivers may care about the (traffic) environment (e.g. avoid causing air pollution or congestion) or other road users (Özkan & Lajunen, 2005). Positive driver behaviours include both passive (e.g. avoid causing delays or annoyance to other drivers) and active behaviours (e.g. moving to right side of the lane to ease overtaking, thanking by hand gesture). In order to extend the DBQ towards an omnibus measure of driver behaviour, Özkan and Lajunen (2005) added to the DBQ a scale for measuring positive driver behaviour and obtained a clear three-factor solution; violations, errors and positive behaviours. It is of course convenient to include only the negative ‘errors’ and ‘violations’ factors of the DBQ into a study investigating safety climate and driver behaviour relationship, as it is done by Wills, Watson, and Biggs (2006), for instance. However, in the present study, positive driver behaviours were particularly included in addition to those two negative behaviour categories. In that, traffic settings include both types of behaviours and examining whether any of the negative or positive driver behaviour categories is related to organizational safety climate would provide additional information concerning safety in professional driving. 1.2.2. Driver performance Driver performance was differentiated as technical (i.e. quick and fluent car control, traffic situation management) and defensive driving skills (i.e. anticipatory accident avoidance skills) by Spolander (1983) who developed a self-report instrument to measure driving skills. Through this self-report instrument, drivers were asked to take an external reference and compare themselves with ‘an average driver’ in 13 aspects of driving. Later, Hatakka, Keskinen, Laapotti, Katila, and Kiiski (1992) changed this external reference into an internal one due to a well known finding that the majority of the drivers assess themselves as better than average drivers in their skills (Näätänen & Summala, 1976; Svenson, 1981). This time the drivers were asked to assess their own abilities in different aspects of driving skills. Lajunen and Summala (1995) extended the contents of the Hatakka et al. (1992) scale to find a solution to the model. They argued that safety related motives should be

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included in the assessment of driving skills because a driver’s view of himself/herself as a safe or dangerous driver may influence his/her driving style. As a result, they developed an instrument named as the Driver Skill Inventory (DSI) to further assess both general perceptual-motor performance and safety concerns and verified the two-factor structure of the DSI as perceptual-motor and safety skills. Lajunen and Summala (1995) suggested that the distinction between perceptual-motor (i.e. perception, decision making, motor control related skills) and safety skills (i.e. anticipatory accident avoidance skills) is essential because a driver’s internal balance between these skills reflects her/his attitude to safety. A consistent factor structure and high reliability of the DSI were obtained in different studies (e.g. Lajunen, Corry, Summala, & Hartley, 1998). 1.3. Organizational safety climate, driver behaviours and driver performance Previous literature on the relationship between safety climate and safety criteria evidenced the strength and stability of this relationship (Zohar, 2010). Ostrom, Wilhelmsen, and Kaplan (1993) stated that organization’s socially transmitted beliefs and attitudes towards safety affect safety performance. Similar to this, Varonen and Mattila (2000) found that company’s positive attitudes to safety and its safety precautions are negatively related to accident rate. Compliance with safety rules is related to lower levels of work-related injuries and accidents (Probst & Brubaker, 2001). Rundmo (2000) indicated acceptance of rule violation as a strong predictor of risky behaviour and showed that perceived management priority of safety over production is a significant predictor of nonacceptability of rule violations. According to Wiegmann, Zhang, Von Thaden, Sharma, and Gibbons (2004) in the organizations with a well established safety culture, beliefs, attitudes and practices should emphasize minimizing the exposure of employees to hazards. In other words, any type of application including training, selection, scheduling work and use of equipment should be organized by taking employees’ safety into account. In a more recent study, Christian, Wallace, Bradley, and Burke (2009) stated that positive safety climate enhance safety knowledge through on-thejob discussions and formal trainings, positively influences safety performance and behaviours through safety knowledge and motivation. The results of their meta-analytic study revealed that both person and situation are important workplace safety related factors in such a way that if the workers are selected trained and supported to maximize safety motivation and safety knowledge, an increase in safe behaviours and decrease in frequency of accidents and injuries are observed. Öz, Özkan, and Lajunen (2010) used a general organizational culture scale (i.e. Hofstede’s organizational culture scale) to investigate the relationship between organizational culture and/or climate and driver behaviours (i.e. errors, violations and positive behaviours) among professional drivers. It was found that the highest frequency for violation was reported in the case of low scores of work orientation (i.e. low organizational importance on the work being done, rules and regulations, etc.) and low score of employee consideration (i.e. the employees are given less consideration for their presence in and adaptation to the organization, etc.) The lowest violation frequencies were reported when work orientation scores are high but employee consideration scores are low (i.e., high organizational importance on the work being done, rules and regulations, etc., but the employees are given less consideration for their presence in and adaptation to the organization, etc.). More specifically, the link between organizational safety climate and professional driving has been supported by road safety researches. Walton (1999), for example, found that drivers reported to be less safe if they believe their employers have less regard for their safety and were less concerned about the number of hours that they drive. Wills et al. (2006) found that ‘safety rules’ is the only significant predictor of overall self-reported driver behaviour, traffic violations like speeding and aggressive driving. According to Wills et al.’s (2005) research, organizational safety culture in transportation settings covers three important aspects; safety behaviours of professional drivers, the way in which the management practices impact on driving and the value of driver safety within the organization. In their study investigating the relationship between organizational safety climate and work-related driver fatigue, Strahan, Watson, and Lennonb (2008) indicated that safety climate is a predictive of self-reported fatigue-related driver behaviour and near misses after controlling for several individual factors. In the present study, depending on the support from the previous study, it was aimed to investigate transportation companies’ safety climate in relation to driver behaviours and driver performance for a sample of professional drivers. 2. Method 2.1. Participants and procedure A total of 354 professional drivers were approached to participate in the study across from eight different public and private people/good transportation organizations in Ankara, Turkey. With the response rate of 65%, a total of 230 professional drivers agreed to participate in the study. Five participants were removed from the sample prior to data analyses due to their incomplete data. Because of the nature of the industry and the organizations involved in the study, there were only two female driver participants of the study. Depending on the findings from the previous literature indicating that sex have significant effects on driver behaviours and performance (e.g. Lajunen & Summala, 1995; Lajunen et al., 1998) and because of the very few number of female participants of the study, the female participants were kept out of the analyses. All the professional drivers were recruited first by contacting their companies/employees for permission, and then, they were contacted individually and asked to participate in the study. The participants were assured about confidentiality and not compensated for their participation in the study. The mean age of the drivers was 39.16 years (SD = 7.96), and the average annual mileage (the distance has been driven) was 100.37 km (SD = 48.65, range = 12.000–190.000 km). The participants had driven 17.7 years in average.

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2.2. Measures 2.2.1. Transportation Companies’ Climate Scale (TCCS) The newly developed scale consists of 33 items measuring three organizational safety climate dimensions (general safety management, specific practices and precautions, work and time pressure). The drivers were asked to evaluate each item on a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree). Cronbach’s Alpha for internal consistency scores for the general safety management, specific practices and precautions and work and time pressure dimensions was 0.92, 0.82 and 0.78, respectively. 2.2.1.1. Scale development (i.e. the TCCS). In the process of development of the TCCS, a comprehensive literature search was conducted to find out the studies on safety culture/climate, and the studies targeting different sectors were reviewed to figure out the main dimensions of safety culture/climate in general, and statements/items placed under those dimensions. Among these statements/items, the ones that can be adapted into the safety culture/climate scale to be used for professional drivers were selected by the last two authors of the present study. Previously determined main dimensions were differentiated as safety climate and safety culture dimensions, and the selected items were placed under the dimension that they are belong to. After that, the listed safety climate dimensions and items were also evaluated in an expert panel including two professional drivers and their manager in terms of items’ importance and frequency in relation to the task (i.e. driving as a professional driver in a company). The evaluators in the panel were asked to add new items if they consider it necessary to do so. As a result, sixty-one items were indicated to be the important ones for measuring transportation companies’ climate. After that, the same evaluators were asked to determine the dimensions which are important for transportation companies climate, and this process culminated in the following dimensions: safety management and organizational commitment to safety, job security and safety concerns, specific prevention strategies for safety, work and time pressure, safety communication in trip, passengers/customers’ commitment to safety, drivers’ commitment to safety in trip, reward system for safe trip, selection of drivers for safe trip, training of drivers for safe trip, and control/check points during the trip. On the next step, the evaluators of the expert panel group were asked to classify these dimensions into categories in terms of their relevance to the main tasks of drivers and companies’ ‘ways of doings’ in transportation. Two main categories were obtained and named as Policy-focused Safety Orientation (PfSO) and Transport/Trip-focused Safety Orientation (TfPO). The former category included the first four of the eleven dimensions listed above. The items related to safety related policies, precautions and applications were included in these dimensions. The latter category included the last seven of the eleven dimensions listed above and included the items specifically focusing on such concepts like mechanical check-ups, controls and trainings on technical or mechanical aspects of vehicles. Then, the expert panel evaluators were asked to classify the sixty-one items within one of the PfSO or TfPO categories. Lastly, they were asked to decide which item should be placed under which dimension within the category. In the present study, the PfSO dimensions including thirty-three items were included because the aspect of safety climate investigated in the study is congruent with the content of those dimensions. 2.2.2. The Driver Behaviour Questionnaire (DBQ) Only the violation and error scales of the DBQ were used, because slips and lapses are not critical for safety and are mostly relevant only for elderly drivers (Parker, McDonald, Rabbitt, & Sutcliffe, 2000). Positive Driver Behaviours Scale (Özkan & Lajunen, 2005), which was developed to measure driver behaviours conducted with positive intentions, was also used together with the DBQ. The drivers were asked to evaluate each item on a 6-point Likert-type scale (1 = never, 6 = always). Cronbach’s Alpha for internal consistency scores for the scales was as follows for the present study: 0.92 for violations (13 items), 0.89 for errors (eight items) and 0.92 for positive driver behaviours (eight items). 2.2.3. The Driver Skill Inventory (DSI) The short version of the DSI which was used in the present study is a 10-item self-report measure of perceptual-motor (including five items; ‘Fluent driving’) and safety skills (including five items; ‘Avoiding unnecessary risks’) (Lajunen & Summala, 1995). Drivers were asked to rate each item on a 5-point Likert-type scale (0 = very weak, 4 = very strong). For the present study, Cronbach’s Alpha for internal consistency scores for the perceptual-motor skills and safety skills dimensions were 0.61 and 0.70, respectively. 2.2.4. Demographic information form Age, sex, annual mileage (km) and organization information were recorded. 3. Results 3.1. Managing and analyzing the data Before conducting the statistical analyses, the data were checked for outliers. Missing data were kept as system missing in the analyses. The statistical assumptions for the regression analyses were checked. After evidencing that the data can be used for further analyses, determined statistical analyses were applied to the data. In order to figure out the factor structure of the

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Transportation Companies’ Climate Scale, an explanatory factor analysis was conducted. With the purpose of examining whether organizational climate is related to driver behaviours and driver skills, hierarchical regression analyses were conducted. It should be noted that, before conducting regression analyses in the present study, the appropriateness of the data for multilevel analyses was tested by using Hierarchical Linear Modelling 6.8 software (HLM 6.8 – Raudenbush, Bryk, & Congdon, 2004) because the nature of the data was multilevel. Preliminary analyses were conducted with a fully unconditional model, which means that no employee or organization characteristics are considered. This step revealed low intraclass correlation (ICC – i.e. lower than 5% of the total variance in the outcome is associated with differences between organizations). According to some researchers (e.g. Kreft & De Leeuw, 1998), this finding shows that it is not needed to continue with multilevel analyses. However, some others (see Nezlek (2008) for a discussion) argue the opposite and state that even though the ICC is low, multilevel modelling should be kept in analyses as the nature of the data requires so. In the present study, despite the low ICC, further analyses were done in HLM; however, the analyses did not reveal interpretable results most probably because of low observations at level 2. As it has been stressed by the previous researches (e.g. Hox, 1998; Nezlek, 2008; Richter, 2006), for a two level data, there might be problems of using multilevel modelling when the observations at level two are limited (i.e. organizational level for the present study). As a result, the data were analyzed by using hierarchical regression analyses. 3.2. Factor structure of the Transportation Companies’ Climate Scale Factor analyses (principal axis factoring with promax rotation) were performed for 33 items of the scale. Number of factors was determined by taking eigenvalues (eigenvalues >1.0 were acceptable) and scree plot as the basis. Factor loading value of 0.30 was determined as the cut-off score, and the items loaded to a factor with a value lower than 0.30 were not included into that factor. According to the results, the best solution was the three-factor solution (see Table 1). The items of ‘There are clear and easy to follow rules and regulations in the organization I am working for’ and ‘The organization I am working for indulges risky driver behaviours’ were left out from the final solution because of conceptual irrelevance of these items with the factors they were loaded. As a result, the 31-item scale was obtained. The first factor was named as ‘general safety management’ because the content of the items were about safety commitment within the organization in a general and at a broader level. The drivers’ general perceptions about the applications, rules and regulations which might constitute base for the more detailed ones were mentioned. This factor included 16 items and explained 30.35 of the total variance having an eigenvalue of 9.7. The second factor was named as ‘specific practices and precautions’. The content of the items emphasized more detailed and specific safety related applications and prevention strategies within the organization. This factor included eight items, which accounted for 10.13% of the total variance with an eigenvalue of 3.2. The third factor was named as ‘work and time pressure’. The items loaded in this factor were directly related to the pressure that the drivers felt on the work being done and being on time. That is, specifically work and time pressure aspects of safe driving were emphasized with this dimension. This last factor included seven items, which accounted for 6.52 of the total variance with an eigenvalue of 2.1. Thus, the mentioned three factors of the scale explained 46.73% of the total variance, which might be accepted as a modest value. None of the items that is loaded in any dimension and having the selected loading value of 0.30 or a higher value cross-loaded on more than one factor in the factor analysis. 3.3. Correlation analyses As presented in Table 2, there was a positive relationship between age and specific practices and precautions. Annual mileage was positively related to specific practices and precautions and general safety management. There were positive relationships among work and time pressure, general safety management and specific practices and precautions. Work and time pressure dimension was negatively related to violations and errors; violations were also negatively related to general safety management dimension. Positive driver behaviours were positively related to both general safety management and specific practices and precautions. Safety skills were positively related to age, annual mileage, general safety management, specific practices and precautions and perceptual-motor skills. As Table 2 presents, the significant correlation coefficients obtained in this present study were generally weak-moderate in nature. 3.4. Hierarchical regression analyses Five different hierarchical regression analyses were conducted by using violations, errors and positive driver behaviours and perceptual-motor and safety skills as dependent variables. In order to control for the statistical effects of age and annual mileage, these two variables were entered into the model in the first step. In the second step, general safety management, specific practices and precautions and work and time pressure safety climate dimensions were entered into to the model. The results of the regression analyses revealed significant results with modest overall variance explained by the proposed regression models. As presented in Table 3, the results revealed that only work and time pressure was related to violations and errors. Violations and errors were committed less frequently by the drivers of the organizations where work and time pressure is given high importance.

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B. Öz et al. / Transportation Research Part F 16 (2013) 81–91 Table 1 Factor structure of the Transportation Companies’ Climate Scale. Items*

Item-total correlation

Safety rules are applied without conflicting with work-related demands, like being on time There are clear and certain rules preventing overtime work Organization promotes safe driver behaviours more than being on time Work load is arranged without taking the employees’, customers’ and road users’ safety into account Sometimes, I have to drive for another task without any rest There is no problem with not obeying safety rules and regulations in case of a time pressure I remember that the organization forced me to drive when there is no other driver with me to help Accidents and violations are used/benefited for prevention purposes Organization exerts effort to prevent accidents Management immediately intervenes with the situations effecting safety Management takes into consideration the passengers’ complaints about the drivers’ unsafe behaviours Driver reports kept during the trip (the information about the vehicle, trip or driving) are used to develop preventive activities and arrangements Drivers might lose their job as a result of risky driving Drivers might lose their job as a result of passengers’ complaints about their risky driver behaviours Drivers might lose their job as they are not on time Drivers might lose their job as they did not obey the general traffic rules and company regulations Is it possible to lose the job for the drivers if they have an accident Safety has the primary importance for the organization Organization encourages the drivers for safer driving The drivers who are not driving safely are disciplined in the organization Management gives high importance to safety of drivers and passengers Being on time and safe driving are equally important for the organization Many drivers in the organization are anxious as they now that they will be accepted as guilty in case of any rule violation, risky behaviour or accident involvement Safety has secondary importance to the organization during emergency When it is needed, the organization ignores the safety related issues As compared to other organizations, the one I am working for gives more importance to safe driving I know with whom to contact in the organization in the case of an accident Organization gives importance to time tables and obeying speed limits Annual goal of the organization is ‘zero accident’ Organization made me drive safer It is obligatory to use some equipments (like tachometers) so that the organization can control driver behaviours of the drivers

Factors Factor 1 (general safety management)

Factor 2 (specific practices and precautions)

0.43

0.32

0.33 0.49 0.56

0.31 0.42

Factor 23 (work and time pressure)

0.61

0.51 0.56

0.63 0.66

0.46

0.52

0.55 0.71 0.68 0.63

0.60 0.82 0.84 0.62

0.70

0.72

0.63 0.58

0.55 0.50

0.44 0.68

0.62

0.49 0.78 0.70 0.64 0.71 0.45 0.34

0.49 0.68 0.56 0.60 0.60 0.55 0.55

0.54

0.50 0.49 0.69

0.55 0.49 0.69

0.67 0.77 0.71 0.66 0.71

0.69 0.77 0.72 0.65 0.72

Note: Items were sorted according to their order in the scale. Reverse scoring has been done for the negative items.

*

Table 2 Descriptive statistics concerning the variables of interest.

1. Age 2. Annual km driven 3. General safety management 4. Specific practices and precautions 5. Work and time pressure 6. Perceptual-motor skills 7. Safety skills 8. Errors 9. Violations 10. Positive driver behaviours * **

p < 0 .05. p < 0.01.

Mean

SD

39.16 100.37 3.94 3.92 3.47 3.46 3.42 1.55 1.70 4.67

7.69 48.65 0.83 0.82 0.94 0.52 0.56 0.87 0.91 1.45

1

2 0.02 0.07 0.19** 0.02 0.09 0.19** 0.02 0.11 0.06

3

0.28** 0.24** 0.08 0.00 0.17* 0.14 0.09 0.15

4

0.57** 0.19** 0.03 0.32** 0.12 0.17* 0.25**

5

0.22** 0.01 0.20** 0.09 0.09 0.18**

6

0.02 0.04 0.32** 0.39** 0.09

7

0.29** 0.10 0.01 0.08

8

0.02 0.10 0.08

9

0.84** 0.04

0.05

10

88

B. Öz et al. / Transportation Research Part F 16 (2013) 81–91

Table 3 Hierarchical regression analyses (TCCS and the DBQ scales). Step

Independent variables

Errors as the dependent variable 1 Age Annual km driven 2 General Safety Management Specific preventions and precautions Work and time pressure Violations as the dependent variable 1 Age Annual km driven 2 General safety management Specific preventions and precautions Work and time pressure Positive driver behaviours as the dependent variable 1 Age Annual km driven 2 General safety management Specific preventions and precautions Work and time pressure

R2

Adj R2

R2 change

F

df

0.01

0.02

0.01

0.85

2

0.13

0.10

0.12

4.05**

5

0.01

0.01

0.01

0.63

2

0.19

0.16

0.18

6.38***

5

0.06

0.04

0.06

4.03*

2

0.12

0.09

0.06

3.61**

5

R2

Adj R2

b 0.03 0.11 0.05 0.04 0.33*** 0.07 0.06 0.16 0.07 0.40*** 0.17* 0.17* 0.18 0.05 0.13

*

p < 0.05. p < 0.01. *** p < 0.001. **

Table 4 Hierarchical regression analyses (TCCS and the DSI dimensions). Step

Independent variables

Perceptual-motor Skills as the dependent variable 1 Age Annual km driven 2 General safety management Specific preventions and precautions Work and time pressure Safety Skills as the dependent variable 1 Age Annual km driven 2 General safety management Specific preventions and precautions Work and time pressure **

R2 change

F

df

0.01

0.01

0.01

0.44

2

0.01

0.03

0.00

0.18

5

0.08

0.07

0.08

5.96**

2

0.21

0.18

0.13

7.06**

5

b 0.08 0.02 0.01 0.00 0.01 0.02** 0.00 0.24** 0.03 0.03

p < 0.001.

As presented in Table 4, only general safety management was related to safety skills. When organization’s commitment to safety is high, and it is performed into the general safety management practices, drivers reported to have stronger safety skills. 4. Discussion The results of the present study indicated that the Transportation Companies’ Climate Scale had a very clear structure with a three-factor solution (i.e. general safety management, specific practices and precautions, and work and time pressure), high item loadings and internal consistency scores. On the other hand, the factor solution was not exactly the same with the original one, which was ended up as a result of the expert panel classification done in the scale development phase. It seems that the professional driver participants of the present study conceptualized the expert panel classification of ‘job security and safety concerns’ under the ‘general safety management’ factor. It should be noted that, however, the rest of the classification of the factors and the items between the expert panel and the data set collected from drivers was mainly overlapped. Regression analyses revealed that when professional drivers perceive the organization they are working for as arranging work load and dealing with job related time pressure by giving priority to safety, they reported fewer violations and errors. That is, if safety rules and regulations are strictly applied even in the case of time pressure, and work load is arranged by taking safety of employees and passengers into account, fewer violations and errors appear to be committed. In general, these results of the present study were in line with some earlier findings indicating that organizational culture has an impact on employee behaviour (e.g. Schein, 1984; Öz et al., 2010; Øgaard, Svein, & Einar, 2005). For instance, Vredenburgh (2002) stated that if organization’s ‘cultural message’ is that production – not people or safety – is the priority, and employees might

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perceive loose organizational safety, which, in turn, may affect their safety performance negatively. More specifically, the results of the study were also in congruence with the previous literature evidencing the link between organizational safety climate and driver behaviours (e.g. Strahan et al., 2008; Wills et al., 2006). Results also indicated that general safety management was related to safety driving skills of the professional drivers. The regression results showed that if professional drivers perceive management as committed to create a safe work environment in general, if the management’s general understanding within the organization is safety focused, the drivers reported higher safety skills and, hopefully, behave accordingly. Walton’s (1999) results provide support for this finding of the study by showing that as the drivers report that high importance is given to their safety within the organization and to the number of hours that they work for the organization, they reported to be less safe. Hence, it might be possible to argue that in such organizations, personnel related decisions like employment, termination, selection, rewarding, career development and training should emphasize hiring drivers with stronger safety skills and trying to make the employees working for the organization to have stronger safety skills through training. Two significant regression results mentioned about above are in line with the previous literature indicating that some aspects of organizational safety climate might be more related to safety outcomes, as compared to some other aspects (Wills et al., 2006). Thus, such findings should be used as an opportunity by the researchers and management of the organizations to determine and focus on the development and change opportunities with the aim of constructing safer organizations and having employees displaying safer behaviours. Additionally, by ending up such findings, the present study supports the previous literature indicating that organizations may directly influence the safety behaviour of their employees via organizational safety climate. Thus, organizations should realize their role in influencing driver safety and creation of positive safety climates (Strahan et al., 2008). Considering the results of the present study, it should also be noted that the mentioned significant regression results were relatively weak. It means that the some other possible factors to explain the remaining variances in driver behaviours and performance should be investigated as well. For example, as Öz (2011) mentioned about in her tentative model describing relationships among safety climate, human factors, driver stress and accident involvement; the structure of the organization and organizational processes might be investigated in terms of their relationship with driver behaviours. Some structural characteristics, for instance, might have direct effects on safety related behaviours without any additional influence coming from safety climate of a particular organization under investigation (Antonsen, 2009). The hierarchical regression analyses did not reveal significant results for organizational safety climate’s relationship with positive driver behaviours and perceptual-motor skills. It could be claimed that positive driver behaviours and perceptualmotor skills might be related to some other factors like personality, attention capacity and information processing, rather than being related to organizational climate. Theory of Planned Behaviour (TPB – Ajzen, 1985, 1991) which has been successful in predicting a variety of driver behaviours (Parker, Manstead, Stradling, Reason, & Baxter, 1992) might provide information on how drivers might be motivated to display positive driver behaviours. There are studies like the one conducted by Wills, Watson, and Biggs (2009) including the TPB constructs (general attitudes towards driver safety, perceived behavioural control and subjective behavioural norms) as indicators of person-related influences on work-related driving. Future research in understanding professional driving might include such constructs in addition to some organizational factors like safety climate. There have been some methodological issues in the literature related to the way organizational safety climate has been studied. Two of these issues are specifically related to the present study as well. First of all, factor analysis is typically used to identify the factor structure of the concept of organizational climate, and different studies have produced many different factor structures (see Flin, Mearns, O’Conor, & Bryden, 2000; Guldenmund, 2000) although there are some factors found to be replicated across studies, like ‘management’s commitment to safety’. This fact makes it difficult to find evidence for a common set of core features of this concept. Sorting the items into a factor structure that has been published previously might be a way to end up with a set of core factors. However, as Flin, Mearns, O’Conor, and Bryden (2000) argued, direct comparison among the factor labels found in the previous studies might be problematic because of both methodological inconsistencies (i.e. differences in content, style, statistical analysis, sample composition) and cultural and language differences across countries and industries. As Flin et al. (2000) pointed out, for the construct validity to be obtained, different climate scales administered to the same workforce could be compared. As the TCCS has the distinctive characteristics of being developed specifically for the professional drivers from the scratch, it can provide a basis for such a comparison to be made in the future. Secondly, as Raudenbush and Bryk (2002) indicated one of the most common conceptual and technical difficulties in organizational research is aggregation bias that can occur when a variable has different meanings and consequently different effects at different organizational levels. The solution to this problem might be the use of multilevel modelling (Nezlek, 2008). The nature of the data collected for the present study was multilevel, such that the individuals (level 1) were nested within the organizations (level 2). For this reason, the effects of organizational level climate measures should be taken into account in addition to the individual level measures to see whether the organizational level climate measures have an influence. Thus, multilevel analyses were conducted; however, the results were not significant most probably because of the low subject number at organizational level (level 2). Although conceptually multilevel models might reveal more comprehensive description of the relationships than do conventional models, there might be some other points as well to consider before using multilevel models in analyses. As Guldenmund (2010) indicated, it is doubtful that the attitude objects of individuals are the same as those for organizations. Aggregating the individual level data to organizational level does not guarantee that

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one would get information about attitude object existing at that level of aggregation. Other critical issue to be noted about the levels at which safety climate is investigated is emphasized by Zohar and Luria (2005). The researchers argued that within an organization, there is a variation in departmental safety climates. In order to avoid level discrepancy errors in safety climate measurement, the practice of mixing items associated with divergent levels of analysis must be discontinued in the scales to measure safety climate. Because the employees develop level specific climate perceptions (i.e. different perceptions concerning supervisor and senior manager) in the future studies, level specific subscales should be encouraged for the sake of measurement sensitivity. Another methodological issue might be related to the way the data were collected. All the data focused on self-reports of the drivers which is vulnerable to biases. The participants might not fully and/or correctly remember the information they are supposed to remember. They might be hesitant to report errors, violations and give misleading or socially desirable answers. Lajunen et al. (1998) indicated that an individual’s need for social approval and avoidance of social disapproval influences self-reports of driving. Impression management, the tendency to give others favourable self-descriptions, should always be controlled for in investigating driving style by self-reports. For the perceptual-motor skills, high positive correlation was found with self-deception, but not with impression management. Depending on this finding, it can be argued that as a measure of a person’s perception of his/her perceptual-motor skill orientation, perceptual-motor skills of the DSI is prone to biases, such that the drivers with high trust of their vehicle handling skills actually over-rate their perceptual-motor skills. That is a person may over-trust his/her motor skills and misinterpret the negative feedback in driving. This may cause the drivers to have serious problems especially when the actual skills are insufficient. As a result, the literature shows that besides/apart from collecting self-report data, some other ways of data gathering like checking the company records and archives might reveal more, additional and various information on the variables of interest. It is already clear that many factors (e.g. types of goods, time schedule, working shifts and hours, route choice) determine why, when and where professional drivers drive. However, the role of organizational safety climate in professional drivers’ driving (i.e. driver behaviour and driver performance) has remained mainly unexamined so far. This study might provide a small but considerable contribution to understanding professional driving in relation to safety climate. 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