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Accident Analysis and Prevention 43 (2011) 329–341
Contents lists available at ScienceDirect
Accident Analysis and Prevention
journa l homepage: www.e lsev ier .com/ locate /aap
afety climate and safety behavior in the passenger ferry context
hin-Shan Lu ∗, Chung-Shan Yang1
epartment of Transportation and Communication Management Science, National Cheng Kung University, No. 1 University Road, Tainan City 701, Taiwan, ROC
r t i c l e i n f o
rticle history:eceived 1 February 2010eceived in revised form 26 August 2010ccepted 1 September 2010
a b s t r a c t
This research empirically evaluates safety climate and safety behavior in the passenger ferry context.Using survey data collected from 155 respondents working for passenger ferry companies in Taiwan,hierarchical regression analysis was used to examine the effects of safety climate on self-reported safety
eywords:assenger ferryafety climateafety behavior
behaviors. Confirmatory factor analysis identified five main dimensions of safety climate as measuredon a passenger ferry safety climate scale: safety policy, safety motivation, emergency preparedness,safety training, and safety communication. Further, safety training and emergency preparedness werefound to positively affect self-reported safety behaviors with respect to safety compliance and safetyparticipation. The study also revealed positive associations among respondents’ age, ferry capacity, andsafety compliance. Implications of the study findings for increasing safety in ferry operations and their
opme
contribution to the devel. Introduction
Many injuries or fatalities occur throughout the world everyear as a result of accidents involving passenger ferries. An investi-ation using accident data from various sources showed that morehan 9000 people had died or been reported missing in the past 25ears due to passenger ferry accidents (Iqbal et al., 2008). For exam-le, the Herald of Free Enterprise accident (193 lives lost) in 1987 andhe much more catastrophic Estonia accident (852 lives lost) thatccurred in 1994 were events that critically shaped the develop-ent of international regulations for ferry design and operation in
000 and beyond (Cowley, 1995; Sekimizu, 1997).While traditional safety efforts have focused on the tech-
ological aspects, relatively few accidents are the consequencef unsafe mechanical or physical conditions. Rather, the major-ty of accidents and injuries appear to result from employees’nsafe behaviors (Wilpert, 1994). Previous transport studies oreports have indicated that 60–90 percent of all accidents atea or in the air can be attributed to the “human factor” (Mars,996; Sherry, 1992; Zohar, 1980). The human factor includesarelessness or recklessness under commercial pressure, a mis-
laced sense of overconfidence, or a lack of either knowledger experience (Talley et al., 2005). Prior researchers have con-ended that the majority of workplace accidents and injuriesan be attributed to the unsafe work practices of employees∗ Corresponding author. Tel.: +886 6 2757575x53243; fax: +886 6 2753882.E-mail addresses: [email protected] (C.-S. Lu), [email protected]
C.-S. Yang).1 Tel.: +886 6 2757575x53271 and 4050; fax: +886 6 2753882.
001-4575/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.oi:10.1016/j.aap.2010.09.001
nt of safety management are discussed.© 2010 Elsevier Ltd. All rights reserved.
rather than unsafe working conditions (Hoyos, 1995; Mullen,2004).
Zohar (1980) suggested that how workers perceive the safetyclimate of their workplace is important. Safety climate is a termused to describe shared employee perceptions of how safety man-agement is being operationalized in the workplace (Zohar, 1980),and is therefore a specific form of organizational climate, definedas “shared perceptions about organizational values, norms, beliefs,practices and procedures” (Guldenmund, 2000; Schein, 1992). Thissuggests that safety climate will affect an individual’s perceptionof safety such that if management is committed to safety then itis likely that employees will also exhibit commitment to safety.Thus, it is critical that researchers and shipping practitioners bet-ter understand the events preceding accidents and injuries, as wellas recognize the importance of safety climate since it may affect anindividual’s safety behavior in the workplace.
A positive safety climate should encourage safe action eitherthrough reward or through principles of social exchange (Griffinand Neal, 2000; Zohar, 1980; Clarke, 2006; Christina et al., 2009).Clarke (2006) demonstrated that safety climate is a meaningfulpredictor of safety performance behaviors (particularly safety par-ticipation). A recent meta-analysis by Christina (2009) found safetymotivation to be strongly related to safety performance behav-iors. Several previous studies have examined the effects of safetyclimate on safety performance or safety behavior within organiza-tions (Cox and Cox, 1991; Neal et al., 2000; Zohar, 2002; Christina
et al., 2009). Zohar (1980) found successful injury control pro-grams to be based on strong management commitment to safety,the high status of safety officers within the organization, workertraining, regular communication between management and work-ers, general housekeeping, and a stable workforce. Management
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ommitment has been the primary focus of much contemporaryafety climate research (Marsh et al., 1998). Smith et al. (1978)ound staff safety training, safety committees and safety officerso be associated with a low accident rate in companies. Simondsnd Shafai-Sahrai (1977) and Cohen and Cleveland (1983) also con-rmed that management commitment to safety is important.
While the antecedents of safety climate have been widely dis-ussed in the safety related literature, an investigation of theerceptions of safety climate and its relationship with safety behav-
or in passenger ferry operations is lacking. Accordingly, this studyims to examine safety climate and its impact on self-reportedafety behavior in the passenger ferry context. Based on theafety climate literature, dimensions such as safety policy, safetyotivation, emergency preparedness, safety training, and safety
ommunication are assessed in this research. Following this intro-uctory section, the next section presents a review of the literaturen safety climate dimensions and safety behavior. An explanationf the methodology employed to fulfill the research aim is providedn the third section. The analysis of data obtained from a question-aire survey is presented in the fourth section. Conclusions drawn
rom the analysis and the associated practical implications are toe found in the final section.
. Literature review
.1. Self-reported safety behaviors
Self-report of safety behaviors and perceptions of safety canffer alternative measures for determining workplace safetyDeJoy, 1994; Hofmann et al., 1995; Janssens et al., 1995). The usef proactive measures of workers’ perception of safety is consid-red to be a most useful indicator of safety performance (Bormannd Motowidlo, 1993). Neal and Griffin (1997) and Griffin and Neal2000) identified two types of safety behavior: compliance andarticipation. Safety compliance is defined as adhering to safetyrocedures and carrying out work in a safe manner, whereas safetyarticipation is a safety-oriented behavior that involves the individ-al participating in safety meetings, setting safety goals, providingafety suggestions within the organization, and expending efforto improve workplace safety (Neal et al., 2000). The term safetyompliance is used in Neal et al.’s (2000) study to describe the corectivities that need to be carried out by individuals to maintainorkplace safety. These behaviors include adhering to standardork procedures and wearing personal protective equipment insafe manner (Broadbent, 2004; Zhou et al., 2008). Safety partic-
pation describes behaviors that do not directly contribute to anndividual’s personal safety but which help to develop an environ-
ent that supports safety. These behaviors include activities suchs participating in voluntary safety activities, helping coworkersith safety-related issues, and attending safety meetings (Neal andriffin, 2002, 2006; Broadbent, 2004).
.2. Safety climate and safety behavior
Zohar (1980) defined safety climate as the coherent set of per-eptions and expectations that employees have regarding safetyn their organization. Safety climate is a specific form of organiza-ional climate, which describes individual perceptions of the valuef safety in the work environment (Neal et al., 2000). Safety cli-ate has been measured in various industrial sectors including
onstruction (Dedobbeleer and Beland, 1991; Gillen et al., 2002),anufacturing (Zohar, 1980), airport ground handling (Diaz and
abrera, 1997), and health care (DeJoy et al., 2000). In traditionalndustries, the concept of safety climate is a key organizationalharacteristic in understanding how safety rules and procedures
d Prevention 43 (2011) 329–341
affect the organization’s safety performance (Zohar, 2002). How-ever, safety climate has rarely been studied in the passenger ferrycontext. Prior research has examined the dimensions of safety cli-mate (e.g. Hofmann et al., 1995; Siu et al., 2004; Huang et al., 2006),however, consensus on other dimensions is still lacking. For exam-ple, Zohar (1980) identified eight dimensions of safety climate,namely: management attitudes, effects of safety conduct on pro-motion, pace of work, and the status of safety officers. Dedobbeleerand Beland (1991) identified two dimensions of safety climate:management commitment to safety and worker involvement insafety activities. Other studies have obtained a wide range of fac-tor structures incorporating constructs such as individual attitudestowards, safety communication, safety policy, and safety motiva-tion (Lu and Tsai, 2008; Griffin and Neal, 2000; Wu et al., 2007).
The fact that ferry vessel carry diverse groups of people meansthat their officers, staff and crew need a clear understandingof human responses in emergencies and an ability to deal withcrowds. In emergency situations, the crew has responsibility forthe safety of passengers might lead and direct other people, assessthe situation and provide an effective response and recognize spe-cific behaviors of passengers and other personnel. The successof the above can be achieved by the formal safety policy andmotivation, adequate training, specializing on the proper commu-nication with the passengers and emergency preparedness (Lois etal., 2004). Therefore, this study focused on five aspects of safety cli-mate dimensions which have been consistently discussed in priorstudies, namely: safety policy (Lu and Tsai, 2008; Lu and Yang,2010), safety motivation (Zohar, 1980; Griffin and Neal, 2000; Luand Shang, 2005), safety communication (Andriessen, 1978; OSHA,1996; Clarke, 1999; O’Dea and Flin, 2001; Zohar, 2002; Wu et al.,2007), emergency preparedness (Hayes et al., 1998; Vredenburgh,2002; Lu and Shang, 2005), and safety training (Zohar, 1980;Campbell et al., 1993).
2.2.1. Safety policyMuch of the literature has shown that safety policy can help
to create and significantly influence workers’ safety behaviors (e.g.Barling et al., 2002; Mullen, 2004; Fernandez-Muniz et al., 2007; Luand Tsai, 2008; Lu and Yang, 2010). Safety policy refers to the extentto which a firm creates a clear mission, responsibilities and goals inorder to set standards of behavior for employees, and establishesa safety system to correct workers’ safety behaviors (Lu and Yang,2010). Development of a safety policy demonstrates the organi-zation’s commitment to safety, and formally expresses objectives,principles, strategies and guidelines to follow with respect to safetybehavior in the workplace (Fernandez-Muniz et al., 2007). Anorganization should provide a clear and meaningful statement ofits safety policy, which should reflect the organization’s safetymanagement, including the ultimate goal of ‘zero’ accidents andmeeting safety objectives as established by the authorities (Santos-Reyes and Beard, 2002). Written safety policies and safety rules areessential parts of safety climate. In the maritime area, for example,the International Safety Management Code (ISM Code) is an instru-ment that has been developed in order to provide an internationalstandard for the safe operations of ships (Ek and Akselsson, 2005).As an organization’s safety management is developed, applied, andrefined, workers’ safety behavior also changes and takes on newforms. An interrelationship will therefore exist between safety cli-mate and safety behavior. Accordingly, this study hypothesizedthat:
H1. Safety policy will be positively related to workers’ safetybehavior in terms of safety compliance.
H2. Safety policy will be positively related to workers’ safetybehavior in terms of safety participation.
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C.-S. Lu, C.-S. Yang / Accident Anal
.2.2. Safety motivationZohar (1980) indicated that an individual’s safe work behav-
or is influenced by safety motivation. People can be motivated toodify their behavior to conform to a cultural norm if it is per-
eived that compliance will lead to a desired outcome (O’Dea andlin, 2001; Vredenburgh, 2002). Safety motivation reinforces work-rs’ safety behaviors, encourages workers’ participation in safetyeetings and setting safety goals, and encourages workers to pro-
ide safety suggestions that enhance safety performance (Griffinnd Neal, 2000). Informational (feedback, self-recording), socialpraise, recognition), and tangible reinforcements (trading stamps,ash bonuses) have been used in safety motivation as well as non-onetary privileges (Komaki et al., 1978). Well-designed safetyotivation offers recognition, which can help to modify safety
ehavior. For example, the National Safety Council Motivation andecognition Programs acknowledge employee safety achievements
n the workplace, motivate employees to adopt safe practices, andeward them for staying committed (NSC, 2009). A key characteris-ic of successful safety motivation is its high level of visibility withinhe organization. Participants must be able to comprehend whathe motivation program is designed to accomplish and how theirerformance will be measured (Halloran, 1996). Accordingly, thisesearch posited that:
3. Safety motivation will be positively related to workers’ safetyehavior in terms of safety compliance.
4. Safety motivation will be positively related to workers’ safetyehavior in terms of safety participation.
.2.3. Emergency preparednessIt is extremely important that a passenger ferry operator is
ble to immediately respond to emergencies. Emergency prepared-ess forms the basis for a coordinated plan of action that utilizesmergency response networks to provide rapid and comprehensivessistance. The goal of emergency preparedness is to be prepared toake the most appropriate action in the event that a hazard becomesreality so as to minimize its effects and, if necessary, to transferersonnel from a location with a higher risk level to one with a
ower risk level (Wang, 2002). Not surprisingly, companies withell thought-out emergency preparedness that hold regular train-
ng drills are better equipped to handle large-scale emergencieshan those that are not prepared (O’Brien, 2003). While emergencyreparedness is developed and implemented to meet the needs thatrise from ferry accidents, periodic simulations should be carriedut to check emergency preparedness competencies and clearlydentify the emergency response role of employees. Thus, this studyypothesized that:
5. Emergency preparedness will be positively related to work-rs’ safety behavior in terms of safety compliance.
6. Emergency preparedness will be positively related to work-rs’ safety behavior in terms of safety participation.
.2.4. Safety trainingThe basic difference between safety conscious employees and
hose who frequently get hurt is that safety conscious employeesan recognize hazards and hazardous actions and understand theonsequences (Vredenburgh, 2002). Pfeffer and Veiga (1999) haveointed out that training is an essential component in any orga-ization because organizations rely on frontline employees’ skillsnd initiatives to identify and resolve problems, to initiate changes
n work methods, and to take responsibility for safety. When atti-udes towards safety improve, safe behaviors are likely to followAjzen, 1991). In order for employees do the job correctly ando be active participants in a safety program, they must receiveccupational safety training. Such training is the process wherebyd Prevention 43 (2011) 329–341 331
shortfalls in skills or knowledge that may impact on safety are metby providing information and assisting individuals to practice, in asupportive learning environment, the skills necessary to carry outactivities safely. When employees are well trained with regard tosafety precautions, rules and procedures, their safety performanceimproves (DeJoy et al., 2000; Harvey et al., 2001; Zohar, 2002).Roughton (1993) described that safety training as the acknowl-edged means for making incidents more avoidable. To improve thequality of safety for all employees, organizations should institute asystematic, comprehensive safety training program for them. Ferrycompanies should also institute a system of continual re-educationand retraining of employees in current safety and ferry operations.Since workers who have safety training will have better safetybehavior and work practices than those who have not received suchtraining. Thus, this research posited that:
H7. Safety training will be positively related to workers’ safetybehavior in terms of safety compliance.
H8. Safety training will be positively related to workers’ safetybehavior in terms of safety participation.
2.2.5. Safety communicationThe role of communication in employees’ performance is critical
because behaviors resulting in industrial accidents are not typicallynew occurrences (Vredenburgh, 2002). Providing risk identificationand safety information to employees through safety communica-tion and replying quickly to safety related problems are passengerferry operators’ responsibilities. In order for organizations to fostera climate where employees are alert to hazards, they must pro-vide and communicate risk and safety information (Pidgeon, 1991;Fernandez-Muniz et al., 2007). Regular feedback on safety perfor-mance can be communicated to employees through posted chartsand a review of behavioral data at safety meetings (Roughton,1993). Hoffmann and Stetzer (1998) found that safety communi-cation significantly influences accident attributions. Accordingly,this study hypothesized that:
H9. Safety communication will be positively related to workers’safety behavior in terms of safety compliance.
H10. Safety communication will be positively related to workers’safety behavior in terms of safety participation.
3. Methodology
3.1. Sample
This research was based on passenger ferry operators, specif-ically operating in Taiwan and other smaller islands of thearchipelago under the jurisdiction of the Republic of China, namely,Orchid Island (Lan Yu) and the Penghu/Makung islands, and thetiny islets of Green Island (Lu Tao) and Hsiao Liuchiu (see Fig. 1).The islands of Kinmen (Quemoy), Matsu, and Wuchiu across theTaiwan Strait are also administered by the Republic of China. Ferryand air are the main transportation services between Taiwan andthe small islands. Thirty passenger ferry operators provide part ofthese transportation services. Most are small companies employing31 persons or less.
Data collected for this study was based on a questionnaire sur-vey. A questionnaire was sent to 600 passenger ferry workers onApril 20, 2009. The initial mailing elicited 108 usable responses.A follow-up mailing was sent 2 weeks after the initial mailing.
Subsequently, an additional 47 usable responses were returned.A total of 155 usable questionnaires were therefore collected,which represented 25.8% of the target sample. The possibilityof non-response bias was checked by comparing early and laterespondents’ responses for all of the constructs using ANOVA. No
332 C.-S. Lu, C.-S. Yang / Accident Analysis and Prevention 43 (2011) 329–341
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Fig. 1. Ferry services between Taiwan and smaller islands o
ignificant differences were found (Armstrong and Overton, 1977).hus, it was concluded that there was no evidence of non-responseias.
.2. Measures
Several previous studies have indicated that measuringmployee attitudes towards safety can be a useful approach foreasuring safety behavior (Schroder, 1970; Zohar, 1980; Hayes
t al., 1998; Griffin and Neal, 2000; Siu et al., 2004; Huang et al.,006; Lu and Tsai, 2008; Luria, 2010), contended that the moreature the safety attitudes of employees, the more likely they will
earch for safer workplaces and decrease unsafe behavior (Glendonnd Litherland, 2001). Ojanen et al. (1988) found that measuringafety climate can indicate changes in organizational safety behav-or and is therefore be useful for evaluating safety programs. Inheir view, any effort to improve safety should be perceived as suchy employees, and that the only way to measure this is by using auestionnaire. Traditional measures of safety performance rely pri-arily on some form of accident or injury data (Chhokar and Wallin,
984). The main problems are that such data are insufficiently sen-itive, of dubious accuracy, retrospective, and ignore risk exposureGlendon and Litherland, 2001). Several researchers have suggestedhat behavior observation data are superior to accident statistics ashey focus on unsafe behavior prior to accidents occurring (Reber et
archipelago under the jurisdiction of the Republic of China.
al., 1993; Shannon et al., 1999; Cooper and Phillips, 2004). However,the disadvantages of behavior observation data include the con-siderable expense associated with this method (Grimaldi, 1970).Moreover, a behavior observation method would not be allowedby ferry operators because of safety concerns and potential reper-cussions against the company. Hence, the current study utilized aquestionnaire survey to examine safety climate and safety behaviorfrom the employees perspective, a method used by prior researchon safety climate (Zohar, 1980; Siu et al., 2004; Huang et al., 2006;Lu and Tsai, 2008; Luria, 2010). The information gathered in thissurvey was treated in the strictest confidence. No individual personor company could be identified from the survey form.
Although the focal constructs in this study were, to a largeextent, stimulated by previous research, the scales were developedspecifically for this study. Multi-item scales were generated basedon a review of the literature and interviews with practitioners inpassenger ferry industry. When determining questionnaire items,it is essential to ensure the validity of their content, since this isan important measure of a survey instrument’s accuracy. Contentvalidity is the extent to which an instrument measures what it is
intended to measure (Cooper and Emory, 1995). Interviews withexperts resulted in minor modifications to the wording of, andexamples provided in, some measurement items, which were allfinally acknowledged to possess content validity. Each item in thequestionnaire was assessed by means of a five-point Likert scale,
C.-S. Lu, C.-S. Yang / Accident Analysis an
Table 1Prior research on measurement scales for safety climate and safety behavior.
Prior studies
Safety climate attributesSafety policy Glendon and
Litherland (2001),Barling et al. (2002),Mullen (2004),Fernandez-Muniz etal. (2007), Lu andYang (2010)
My company has written safety policies.My company has established a safety
responsibility system.My company has set up a work safety rule.Safety motivation Zohar (1980),
Halloran (1996),Griffin and Neal(2000), O’Dea andFlin (2001),Vredenburgh (2002),Fernandez-Muniz etal. (2007), NSC (2009)
My company motivates workers’ safetybehaviors.
My company encourages workers’ participationin safety decision-making.
My company encourages workers to providesafety suggestions.
My company has set up a safety incentivesystem.
Emergency preparedness Hayes et al. (1998),Vredenburgh (2002),Lu and Shang (2005),Wang (2002), O’Brien(2003),Fernandez-Muniz etal. (2007)
My company has set up an emergency plan.My company informs all workers about the
emergency plan.My company has implemented its own
emergency plan.My company carries out periodic simulations to
check the efficacy of the emergency plan.Safety training Roughton (1993),
Pfeffer and Veiga(1999), DeJoy et al.(2000), Harvey et al.(2001), Zohar (2002),Fernandez-Muniz etal. (2007)
My company provides sufficient safetyeducation.
The design of safety training programs is good.Safety training programs have been adopted in
my workplace.My company provides enough safety training
programs for new employees.Safety communication O’Dea and Flin (2001),My company provides workers with safety
related information.Pidgeon (1991),OSHA (1996),Hoffmann and Stetzer(1998), Glendon andLitherland (2001),Vredenburgh (2002),Zohar (2002),Fernandez-Muniz etal. (2007), Wu et al.(2007)
My company informs workers about risksassociated with their work.
My company responds quickly to safety relatedproblems.
My company holds regular job safety meetings.
Safety behavior attributesSafety compliance Borman and
Motowidlo (1993),Neal et al. (2000),Neal and Griffin(2002, 2006), Clarke(2006), Christian et al.(2009)
I maintain safety awareness at work.I comply with safety rules and standard
operational procedures.I do not neglect safety, even when in a rush.I wear personal protective equipment at work.Safety participation Neal and Griffin
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Almost 9% of survey participants were captains or chief engi-neers, and nearly 66% were service crew on ship or onshore. Only3.9% of respondents’ employing companies had a ferry capacity of601 or more persons. As regards respondents’ companies’ number
Table 2Profiles of respondents (N = 155).
Characteristics Frequency %
Years of working experience 5 years or less 68 43.96–10 years 54 34.811–15 years 21 13.516–20 years 4 2.621 years or more 8 5.2
Age 30 years or below 23 14.831–40 years old 50 32.341–50 years old 46 29.751–60 years old 22 14.261 years or more 14 9.0
Job title Captain/chief engineer 14 9.0Officer/engineer 51 32.9Service crew on ship 18 11.6Manager or above 10 6.5Supervisor onshore 11 7.1Service crew onshore 51 32.9
Company ownership Public 36 23.2Joint-venture 6 3.9Private 113 72.9
Number of employees 30 persons or less 94 60.631–60 persons 36 23.2
(2002, 2006), Clarke(2006), Christian et al.(2009), Lu and Yang(2010)
I actively participate in setting safety goals.I actively provide safety improvement
suggestions.I actively participate in safety meetings.
nchored by a level of agreement ranging from “1 = strongly dis-gree” to “5 = strongly agree”.
A large number of studies analyzing and measuring safety cli-ate contain the key dimensions: safety policy, safety motivation,
mergency preparedness, and safety training (Zohar, 1980; Griffinnd Neal, 2000; O’Dea and Flin, 2001; Mearns et al., 2003; Flin etl., 2000; Glendon and Litherland, 2001; Mullen, 2004; Fernandez-uniz et al., 2007; Lu and Yang, 2010). However, no consensus
as been reached regarding the specific dimensions making theafety climate. The measurement scales of safety climate and safety
ehavior used in this study for the passenger ferry sector are sum-arized in Table 1. The 19 safety climate items were based onspoused values and underlying assumptions of safety climateound in previous studies (Zohar, 1980; Halloran, 1996; Griffin andeal, 2000; O’Dea and Flin, 2001; Mearns et al., 2003; Flin et al.,
d Prevention 43 (2011) 329–341 333
2000; Glendon and Litherland, 2001; Barling et al., 2002; Mullen,2004; Fernandez-Muniz et al., 2007; Lu and Yang, 2010). Safetybehavior was measured by means of two dimensions adapted fromBorman and Motowidlo (1993), Neal et al. (2000), Neal and Griffin(2002, 2006), Clarke (2006), and Christian et al. (2009). These twodimensions were safety compliance and safety participation. Pos-sible confounding effects were controlled for by including relevantcontrol variables, such as respondents’ age, years of working expe-rience, job title, ferry company ownership, number of employees,and ferry capacity.
3.3. Data analysis methods
First, descriptive statistics and exploratory factor analysis wereused to reduce the safety climate attributes smaller, manageablesets of underlying factors or dimensions. Confirmatory factor analy-sis was then used to examine the unidimensionality and convergentvalidity. Hierarchical regression analysis was subsequently used toexamine the effect of safety climate on safety behavior. ANOVAanalysis was also used to evaluate the relationship between safetyclimate, safety behavior and respondents’ characteristics. All anal-yses were carried out using SPSS 12.0 and AMOS 5.0 software(Arbuckle, 1999).
4. Results of empirical analyses
4.1. Participants’ demographics
The majority of respondents (62.0%) were aged between 31 and50 years (Table 2). Twenty three percent were aged 51 or above,and 14.8% were aged 30 or below. As regards ownership, 23.2%and 72.9% of respondents’ employing companies were publicly andprivately owned, respectively, while 3.9% were joint-venture com-panies.
61 persons or more 25 16.1Ferry capacity 200 persons or less 49 31.6
201–400 persons 49 31.6401–600 persons 51 32.9601 persons or more 6 3.9
334 C.-S. Lu, C.-S. Yang / Accident Analysis and Prevention 43 (2011) 329–341
Table 3Respondents’ agreement with safety climate attributes.
Ranking Safety climate attributes Mean SD
1 My company informs workers about risksassociated with their work.
4.22 0.60
2 My company provides workers with safetyrelated information.
4.21 0.55
3 My company responds quickly to safety relatedproblems.
4.19 0.63
4 My company has set up a work safety rule. 4.17 0.695 Safety training programs have been adopted in
my workplace.4.17 0.65
6 My company has implemented its ownemergency plan.
4.14 0.70
7 My company has established a safetyresponsibility system.
4.12 0.74
8 My company has written safety policies. 4.11 0.769 My company provides enough safety training
programs for new employees.4.11 0.70
10 My company informs all workers about theemergency plan.
4.11 0.62
11 My company has set up an emergency plan. 4.09 0.6212 My company provides sufficient safety
education.4.08 0.65
13 My company carries out periodic simulationsto check the efficacy of the emergency Plan.
4.07 0.81
14 The design of safety training programs is good. 3.99 0.7015 My company holds regular job safety meetings. 3.99 0.8416 My company has set up a safety incentive
system3.93 0.83
17 My company encourages workers to providesafety suggestions.
3.74 0.77
18 My company encourages workers’participation in safety decision-making.
3.71 0.77
19 My company motivates workers’ safety 3.69 0.76
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Table 4Respondents’ agreement with self-reported safety behavior attributes.
Ranking Safety behavior attributes Mean SD
1 I comply with safety rules and standardoperational procedures.
4.47 0.53
2 I do not neglect safety, even when in a rush. 4.47 0.553 I maintain safety awareness at work. 4.45 0.534 I actively participate in setting safety goals. 4.37 0.655 I actively provide safety improvement
suggestions.4.35 0.66
behaviors.
ote: The mean scores are based on a five-point scale (1 = strongly disagree to= strongly agree); SD = standard deviation.
f employees, 60.6% employed 30 persons or less, 23.2% employedetween 31 and 60 persons, and 16.1% employed 61 or more. Overhree-quarters of respondents (78.7%) had had working experiencef 10 years or less, whereas 7.8% had had working experience of 16ears or more.
.2. Perceptions of safety climate and self-reported safetyehavior
Passenger ferry workers’ perceptions at the individual level ofach safety climate attribute revealed their level of agreement withll 19 attributes was at the upper end of the five-point Likert scale,here 1 represented strongly disagree and 5 signified strongly
gree. Table 3 shows respondents’ level of agreement with safetylimate attributes in descending order of agreement. Notably, thereere two safety climate attributes with which respondents agreedost (their mean scores were over 4.2): my company informsorkers about risks associated with their work and my companyrovides workers with safety related information. These two safetylimate attributes are safety communication-related dimensions.heir high mean scores suggest that respondents in general hadisk and safety-related information from their companies. Respon-ents least agreed with the safety climate attributes: my companyotivates workers’ safety behaviors, my company encourages workers’
articipation in safety decision-making, and my company encouragesorkers to provide safety suggestions (their mean scores were allelow 3.9). These three safety climate attributes are safety moti-
ation related dimension. Their low scores implied respondentserceived safety motivation from their companies were insuffi-ient.Respondents were also asked to indicate their level of agree-ent with 6 safety behavior attributes. Respondents’ perceptions
6 I actively participate in safety meetings. 4.32 0.737 I wear personal protective equipment at work. 4.29 0.56
Note: The mean scores are based on a five-point scale (1 = strongly disagree to5 = strongly agree); SD = standard deviation.
at the individual level of each safety behavior attributes revealedtheir level of agreement with all 6 safety behavior attributes wasat the upper end of the five point Likert scale (the mean scoreswere 4.3 or above). Table 4 shows respondents’ level of agreementwith safety behavior attributes in descending order of agreement.Notably, there were three safety behavior attributes with whichrespondents agreed most (their mean scores were all over 4.4): Icomply with safety rules and standard operational procedures, I do notneglect safety, even when in a rush, and I maintain safety awarenessat work.
4.3. Exploratory factor analysis results
Factor analysis was used to reduce the 19 safety climateattributes to a smaller, manageable set of underlying factors(dimensions). This helped to detect the presence of meaningfulpatterns among the original variables and extract the main fac-tors. Principal component analysis with VARIMAX rotation wasemployed to identify safety climate dimensions as shown in Table 5.Prior to the analysis, seven cases were removed based on the cri-terion of having missing data greater than 10%. All other missingvalues were replaced with appropriate mean values. The case-to-variable ratio was 8:1. Gorsuch (1974) and Hair et al. (2006) suggesta minimum ratio of 5:1.
The data were deemed appropriate for analysis according to theKaiser–Meyer–Olkin sampling adequacy value of 0.9 (Hair et al.,2006). The Bartlett Test of Sphericity was significant [�2 = 2480.15,p < 0.01], indicating that correlations existed among some of theresponse categories. In interpreting factors, a decision has to bemade as to which factor loadings are worth considering. Accordingto Hair et al. (2006), factor loadings of 0.30 are considered to meetthe minimal level of factor loading; loadings of 0.40 are consideredmore important; and if loadings are 0.50 or greater, they are con-sidered practically significant. The larger the absolute size of thefactor loading, the more important the loading is in interpretingthe factor matrix. The first analysis yielded a six-factor solution,which accounted for 74.5% of the variance (see Table 5). How-ever, the interpretability of this solution was rendered problematicbecause of four items which loaded on two factors. These items,which were removed from further analysis, were: my company hasset up a safety incentive system, my company provides enough safetytraining programs for new employees, my company holds regular jobsafety meetings, and my company has set up an emergency plan.
Subsequent analysis of the 15 remaining items yielded five fac-tors or dimensions with eigenvalues greater than one (Churchilland Iacobucci, 2002). The percentage of variance for each of thefive dimensions identified is shown in Table 5. The total variance
percentage can be used to indicate how well a particular factoraccounts for what all the variables together represent. Factor anal-ysis showed that approximately 83.7% of the total variance wasrepresented by the information contained in the factor matrix, thuscould be said to represent all the safety climate attributes (Hair et
C.-S. Lu, C.-S. Yang / Accident Analysis and Prevention 43 (2011) 329–341 335
Table 5Exploratory factor analysis of safety climate attributes.
Safety climate attributes F1 F2 F3 F4 F5
X1: My company has written safety policies. 0.80 0.16 0.28 0.29 0.13X2: My company has established a safety responsibility system. 0.83 0.17 0.23 0.24 0.18X3: My company has set up a work safety rule. 0.78 0.18 0.15 0.18 0.33X4: My company motivates workers’ safety behaviors. 0.15 0.90 0.15 0.16 0.17X5: My company encourages workers’ participation in safety decision-making. 0.16 0.91 0.10 0.16 0.16X6: My company encourages workers to provide safety suggestions. 0.13 0.92 0.14 0.15 0.19X7: My company informs all workers about the emergency plan. 0.24 0.10 0.78 0.12 0.27X8: My company has implemented its own emergency plan. 0.15 0.15 0.85 0.18 0.22X9: My company carries out periodic simulations to check the efficacy of the emergency plan. 0.22 0.16 0.82 0.23 0.16X10: My company provides sufficient safety education. 0.20 0.27 0.30 0.76 0.15X11: The design of safety training programs is good. 0.22 0.21 0.22 0.81 0.23X12: Safety training programs have been adopted in my workplace. 0.28 0.08 0.09 0.77 0.27X13: My company provides workers with safety related information. 0.14 0.34 0.22 0.27 0.73X14: My company informs workers about risks associated with their work. 0.25 0.16 0.26 0.19 0.78X15: My company responds quickly to safety related problems. 0.26 0.16 0.25 0.24 0.78
Eigenvalues 7.71 1.77 1.17 1.09 1.03Percentage variance 51.40 11.80 7.86 6.41 6.09Cumulative variance 51.40 63.30 71.20 77.60 83.70
aioc
(
(
(
(
(
Cronbach alphaMeanStandard deviation
l., 2006). To aid interpretation, only variables with a factor load-ng greater than 0.50 were extracted, a conservative criterion basedn Kim and Muller (1978) and Hair et al. (2006). These five safetylimate factors (dimensions) were labeled and are described below:
1) Factor 1, a safety policy dimension, consisted of three items,namely, my company has written safety policies, my companyhas established a safety responsibility system, and my companyhas set up a work safety rule. Most of these items are related toorganizational safety policy. Factor 1 accounted for 51.4% of thetotal variance. My company has established a safety responsi-bility system had the highest factor loading on this dimension.
2) Factor 2, a safety motivation dimension, comprised three items,namely, my company motivates workers’ safety behaviors, mycompany encourages workers’ participation in safety decision-making, and my company encourages workers to provide safetysuggestions. These items are safety motivation related activitiesin passenger ferry operations. My company encourages work-ers to provide safety suggestions had the highest factor loadingon this factor, followed by my company encourages workers’participation in safety decision-making, and my company moti-vates workers’ safety behaviors. Factor 2 accounted for 11.8% ofthe total variance.
3) Factor 3, an emergency preparedness dimension, consisted ofthree items: my company informs all workers about the emer-gency plan, my company has implemented its own emergencyplan, and my company carries out periodic simulations to checkthe efficacy of the emergency plan. These items are emergencypreparedness related activities. My company has implementedits own emergency plan had the highest factor loading on thisfactor. Factor 3 accounted for 7.86% of the total variance.
4) Factor 4, a safety training dimensions, comprised three items:my company provides sufficient safety education, the design ofsafety training programs is good, and safety training programshave been adopted in my workplace. These items are related tosafety training activities. The design of safety training programsis good had the highest factor loading on this dimension, fol-
lowed by my company provides sufficient safety education, andsafety training programs have been adopted in my workplace.Factor 4 accounted for 6.41% of the total variance.5) Factor 5, a safety communication dimension, consisted of threeitems, namely, my company provides workers with safety
0.90 0.96 0.88 0.87 0.874.13 3.71 4.10 4.07 4.200.66 0.73 0.64 0.59 0.52
related information, my company informs workers about risksassociated with their work, and my company responds quicklyto safety related problems. This factor accounted for 6.09% ofthe total variance, slightly less than factor 4.
A reliability test based on Cronbach alpha value, was used totest whether these dimensions were consistent and reliable. TheCronbach alpha value for each dimension is shown in Table 5.The reliability value of each factor was well above 0.8, indicat-ing adequate internal consistency (Nunnally, 1978; Churchill andIacobucci, 2002).
Table 5 also shows respondents’ agreement levels with theimportance of each safety climate dimension in the current situa-tion. The results indicate they considered the safety communicationdimension (mean = 4.20) the most important (factor 5), followedby the safety policy dimension (factor 1) (mean = 4.13), emergencypreparedness dimension (factor 3) (mean = 4.10), safety trainingdimension (factor 4) (mean = 4.07), and safety motivation dimen-sion (factor 2) (mean = 3.71).
Factor analysis was used to detect the presence of meaning-ful patterns among the 7 safety behavior attributes. Initial factoranalysis resulted in two factors (dimensions). These two factorsaccounted for 78.08% of the total variance. Since two items, namely,I wear personal protective equipment at work and I do not neglectsafety, even when in a rush had factor loadings greater than 0.5on two factors, these two items were removed in the subsequentanalysis. As shown in Table 6, two factors were identified whichaccounted for 82.6% of the total variance. They are labeled anddescribed below.
Factor 1 was designated the safety participation factor as it com-prised the following three items: I participate in setting safety goals,I actively provide safety improvement suggestions, and I actively par-ticipate in safety meetings. These items represent attributes thatare related to safety participation. Factor 1 accounted for 65.1% ofthe total variance. The item I actively participate in safety meetingshad the highest factor loading on this factor, followed by I partici-pate in setting safety goals, and I actively provide safety improvement
suggestions.Factor 2 was called the safety compliance factor since it con-sisted of the following three items: I maintain safety awarenessat work, I comply with safety rules and standard operational pro-cedures, and I do not neglect safety, even when in a rush. These
336 C.-S. Lu, C.-S. Yang / Accident Analysis an
Table 6Exploratory factor analysis of self-reported safety behavior attributes.
Safety behavior attributes SP SC
Y1: I actively participate in setting safety goals. 0.81 0.37Y2: I actively provide safety improvement
suggestions.0.78 0.41
Y3: I actively participate in safety meetings. 0.71 0.26Y4: I maintain safety awareness at work. 0.25 0.87Y5: I comply with safety rules and standard
operational procedures.0.36 0.85
Eigenvalues 3.81 1.12Percentage variance 62.60 18.40Cumulative variance 62.60 82.00Cronbach alpha 0.86 0.84
N
imodwa
sentr1b(Stm1
4.5. Hierarchical regression analysis results
TC
N
Mean 4.19 4.32Standard deviation 0.62 0.49
ote: SP = safety participation; SC = safety compliance.
tems represent attributes that are related to safety compliance. Iaintain safety awareness at work had the highest factor loading
n this factor, followed by I comply with safety rules and stan-ard operational procedures, and I do not neglect safety, evenhen in a rush. Factor 1 accounted for 17.5% of the total vari-
nce.Traditional methods employed to develop and evaluate mea-
urement scales include exploratory factor analysis (EFA) andstimations of reliability using Cronbach’s alpha. While these tech-iques are useful in the early stages of empirical analysis, whereheoretical models do not exist and the basic purpose is explo-ation, they do not, however, assess unidimensionality (Segars,997; O’Leary-Kelly and Vokurka, 1998), nor can unidimensionalitye demonstrated by either mathematical or practical examinationsAnderson, 1987; Anderson and Gerbing, 1988; Koufteros, 1999).
everal researchers have therefore suggested the use of confirma-ory factor analysis (CFA) with multiple-indicator measurementodels to assess unidimensionality (Anderson et al., 1987; Segars,997; Lu and Yang, 2010).
able 7onfirmatory factor analysis.
Construct/item Standardizedfactor loading
Standard error
Safety policyX1 0.88X2 0.91 0.07X3 0.81 0.07Safety motivationX4 0.93X5 0.92 0.05X6 0.96 0.04Emergency preparednessX7 0.80X8 0.87 0.10X9 0.87 0.12Safety trainingX10 0.83X11 0.91 0.09X12 0.75 0.09Safety communicationX13 0.81X14 0.83 0.10X15 0.85 0.10Safety participationY1 0.84Y2 0.88 0.08Y3 0.87 0.09Safety complianceY4 0.78Y5 0.80 0.11
ote: AVE = average variance extracted. Fit indices: �2 = 170.7, �2/df = 1.638, GFI = 0.908, A** p < 0.01.
d Prevention 43 (2011) 329–341
4.4. Confirmatory factor analysis
In order to test the validity of the constructs i.e. safety pol-icy, safety motivation, emergency preparedness, safety training,safety communication, and safety behaviors and the relationshipsbetween them, confirmatory factor analysis (CFA) and correlationanalysis were conducted in this study. The results are shown inTable 7 and Table 8, respectively. The model indices were as follows:�2 = 170.2, p < 0.01, df = 104, normal fit index (NFI) = 0.923, com-parative fit index (CFI) = 0.968, goodness-of-fit index (GFI) = 0.905,adjusted goodness-of-fit index (AGFI) = 0.857, root mean squareresidual (RMR) = 0.015 and root mean square error of approxi-mation (RMSEA) = 0.065, which indicate that the model had anacceptable fit level.
Further, all item loadings were significant. The GFI and CFIexceeded or equaled the recommended 0.9 threshold level (Hairet al., 2006). A RMSEA < 0.05 is considered a good fit (Steiger, 1989;Browne and Mels, 1990; Hair et al., 2006) while a RMSEA > 0.05and <0.08 is considered a fair fit (Bollen, 1989; Kaplan, 2000;Whang, 2002; Min and Mentzer, 2004). The overall goodness-of-fit of the constructs therefore lent sufficient support for theresults to be deemed an acceptable representation of the hypoth-esized constructs. As shown in Table 7, the construct reliability(CR) and average variance extracted (AVE) were as follows:safety policy (CR = 0.90, AVE = 0.75); safety motivation (CR = 0.96,AVE = 0.88); emergency preparedness (CR = 0.88, AVE = 0.72); safetytraining (CR = 0.87, AVE = 0.70); safety communication (CR = 0.87,AVE = 0.69); safety behavior (CR = 0.86, AVE = 0.75). Accordingly, theresults indicated that the six constructs demonstrated satisfactorylevels of internal consistency and convergent validity.
Hierarchical regression analysis was utilized to test thehypotheses. As shown in Table 9, the analysis was conducted in fourstages. First, the control variables, i.e. respondents’ years of working
Critical ratio R2 Constructreliability
AVE
0.90 0.750.77
14.99** 0.8212.59** 0.65
0.96 0.880.87
20.83** 0.8523.58** 0.92
0.88 0.720.65
12.09** 0.7612.00** 0.75
0.87 0.700.69
13.08** 0.8210.45** 0.57
0.87 0.690.66
11.23** 0.6911.54** 0.72
0.90 0.750.70
13.47** 0.7713.20** 0.75
0.77 0.630.61
9.82** 0.63
GFI = 0.862, RMSR = 0.016, RMSEA = 0.068.
C.-S. Lu, C.-S. Yang / Accident Analysis and Prevention 43 (2011) 329–341 337
Table 8Analysis of correlation among constructs.
Variable 1 2 3 4 5 6 7
1. Safety policy 1.002. Safety motivation 0.43** 1.003. Emergency preparedness 0.55** 0.38** 1.004. Safety training 0.61** 0.46** 0.54** 1.005. Safety communication 0.60** 0.50** 0.59** 0.61** 1.00
0.63**
0.68**
eecstsispaew1
pe(Hpchtrf
te(hmpnW
TH
N
6. Safety compliance 0.55** 0.40**
7. Safety participation 0.52** 0.46**
** p < 0.01.
xperience, age, job title, company ownership, number of employ-es, and ferry capacity were entered into the regression on safetyompliance (Model 1). Second, the safety climate dimensions (i.e.afety policy, safety motivation, emergency preparedness, safetyraining, and safety communication) were entered into the regres-ion as a block (Model 2). Third, the control variables were enterednto the regression on safety participation (Model 3). Lastly, theafety climate dimensions were added to the regression on safetyarticipation (Model 4). To investigate multicollinearity, the vari-nce inflation factors (VIF) were examined for each of the regressionquations. The maximum VIF within the models was 2.8, which wasell below the rule-of-thumb cut-off value of 10 (Neter and Kutner,
990).Regarding the effect of safety climate factors on safety com-
liance (see Table 9), Model 2 shows that the coefficients formergency preparedness (ˇ = 0.35, p < 0.001) and safety trainingˇ = 0.201, p < 0.05) were positive and significant. Thus, hypotheses5 and H7 were supported. However, for safety policy (ˇ = 0.10,> 0.05) and safety communication (ˇ = 0.05, p > 0.05), the coeffi-ients for safety compliance were positive but not significant. Thus,ypotheses H1 and H9 were not supported in this study. Regardinghe effect of safety motivation on safety compliance, the Model 2esult (ˇ = −0.07, p > 0.05) was negative and not significant, there-ore, hypothesis H3 was not supported.
As regards the effect of safety climate factors on safety par-icipation (see Table 7), Model 4 shows that the coefficients formergency preparedness (ˇ = 0.43, p < 0.001) and safety trainingˇ = 0.31, p < 0.001) were positive and significant. Accordingly,
ypotheses H6 and H8 were supported. However, for safetyotivation (ˇ = 0.10, p > 0.05) and safety communication (ˇ = 0.06,> 0.05), the coefficients for safety participation were positive butot significant. Thus, hypotheses H4 and H10 were not supported.ith regard to the effect of safety policy on safety participation, theable 9ierarchical regression analysis results (standardized ˇ coefficients).
Model 1 (SC) M
Control variablesYears of working experience −0.17* −Age 0.35**
Job title 0.05 −Company ownerships 0.25**
Number of employee 0.08Ferry capacity 0.07Independent variablesSafety policySafety motivation −Emergency preparednessSafety trainingSafety communication
F-value 9.33** 1R2 0.27�R2
ote: SC = safety compliance, SP = safety participation.* p < 0.05.
** p < 0.01.
0.50** 0.49** 1.000.63** 0.55** 0.72** 1.00
Model 4 result (ˇ = −0.03, p > 0.05) was negative and not significant,therefore, hypothesis H2 was not supported.
With respect to the effects of the control variables, i.e. respon-dents’ years of working experience, age, job title, companyownership, number of employees, and ferry capacity on self-reported safety behavior, including safety compliance and safetyparticipation, Model 1 results showed that the coefficients forage (ˇ = 0.33, p < 0.001) and ferry capacity (ˇ = 0.30, p < 0.01) werepositive and significant. These results suggest that older respon-dents whose companies’ had higher ferry capacity had higherself-reported safety compliance. The Model 1 result indicated thatthe effect of years of working experience (seniority) on safety com-pliance was negative and significant (ˇ = −0.22, p < 0.01), implyingthat respondents who had longer years of working experienceshowed low safety compliance in ferry operations. As regards theeffects of the control variables on safety participation, Model 3results revealed that the coefficients for age (ˇ = 0.25, p < 0.01) andownership (ˇ = 0.23, p < 0.05) were positive and significant. Thissuggested that respondents who worked for older and private ferryoperators had higher self-reported safety participation.
The hierarchical regression analysis results indicate that onlysafety training and emergency preparedness are major predictorsfor safety behavior in the context of passenger ferry operations.As can be seen in Table 8, most safety climate dimensions arehighly significantly related to safety behavior in the correlationresults. This probably reflects a suppressor effect in the hierar-chical regression analysis (Cohen and Cohen, 1983). For example,the standardized path coefficient and t-value for hypothesis H3
showed that the relationship between safety motivation and safetycompliance (ˇ= −0.08, p > 0.05), thus H3, was not supported. Thisunexpected negative influence of safety motivation on safety com-pliance may be explained by a suppressor effect (Cohen and Cohen,1983). A suppressor variable is basically defined as a variable thatode 2 (SC) Model 3 (SP) Model 4 (SP)
0.10 −0.13 −0.030.24** 0.25** 0.100.01 0.04 −0.030.12 0.23* 0.100.06 0.00 −0.020.12 0.09 −0.03
0.04 −0.030.08 0.100.33** 0.43**
0.22** 0.31**
0.02 0.06
2.16 5.87** 19.96**
0.48 0.16 0.570.21** 0.41**
338 C.-S. Lu, C.-S. Yang / Accident Analysis an
Table 10Regression results example to identify a suppressor effect.
Dependent variable: safety compliance
Independent variable Parameterestimates
p-Values R2
Regression 1: 0.382Safety policy 0.152 0.092Safety motivation 0.055 0.473Emergency preparedness 0.361 0.000Safety training 0.180 0.050Safety communication −0.002 0.980
Regression 2: 0.115Safety motivation 0.339 0.000
Regression 3: 0.255Safety motivation 0.162 0.039Safety policy 0.414 0.000
Regression 4: 0.333Safety motivation 0.148 0.040Emergency preparedness 0.504 0.000
Regression 5: 0.257Safety motivation 0.144 0.070Safety training 0.425 0.000
Regression 6: 0.212Safety motivation 0.162 0.050
iaCTce0tf
way analysis of variance was performed. There were no statistically
TC
N
TC
N
Safety communication 0.358 0.000Regression 7: 0.241
Safety training 0.491 0.000
ncreases the predictive validity of another variable (or set of vari-bles) by its inclusion in a regression equation (Conger, 1974;ohen and Cohen, 1983). According to the correlation matrix (seeable 8), safety motivation and safety compliance were signifi-antly correlated (0.40, p < 0.01), however, other correlations were
ven higher (safety policy: 0.55, p < 0.01; emergency preparedness:.63, p < 0.01; safety training: 0.50, p < 0.01; safety communica-ion: 0.49, p < 0.01). A series of regressions were performed tourther examine the suppressor effect (see Table 10). Darlingtonable 11omparison of differences of safety climate and safety behavior dimensions based on ow
O1 O2
Mean SD Mean
Safety climateSafety policy 3.65 0.60 4.50Safety motivation 3.09 0.26 4.28Emergency preparedness 3.82 0.49 4.39Safety training 3.68 0.57 4.39Safety communication 3.99 0.39 4.44Safety behaviorSafety compliance 3.96 0.38 4.67Safety participation 3.75 0.48 4.67
ote: O1: public, O2: joint-venture, O3: private.* p < 0.05.
** p < 0.01.
able 12omparison of differences of safety climate and safety behavior dimensions based on num
N1 N2
Mean SD Mean
Safety climateSafety policy 4.03 0.63 4.43Safety motivation 3.49 0.62 4.31Emergency preparedness 4.04 0.61 4.40Safety training 4.00 0.56 4.35Safety communication 4.10 0.48 4.48Safety behaviorSafety compliance 4.23 0.47 4.58Safety participation 4.11 0.62 4.54
ote: p < 0.05, **p < 0.01; N1: less than 30 persons, N2: 31–60 persons, N3: 61 persons or
d Prevention 43 (2011) 329–341
(1968) has defined a negatively suppressed variable as a variablethat has a positive correlation with the dependent variable butcan have negative beta weights in a regression equation. Resultsrevealed safety motivation was positively correlated with safetycompliance (ˇ = 0.055, p > 0.05) in regression equation (1). Whensafety motivation was the only variable regressed to safety com-pliance, regression equation (2) showed a significant positive betaweight (ˇ = 0.339 p < 0.01). Regression equations (3)–(6) were thenrun with each independent variable with safety motivation toidentify which was a suppressor (Cohen and Cohen, 1983). Inregression equation (5), safety motivation showed a positive betaweight (ˇ = 0.144, p > 0.05) with safety training, which suggestedthat safety training was a suppressor, suppressing the effect ofsafety motivation on safety compliance. When the only indepen-dent variable was safety training, the regression result indicatedthat a strong prediction power for safety compliance in regressionequation (7) (R2 = 0.241, ˇ = 0.491). Because safety motivation wasnot significant (ˇ = 0.144, significance 0.070), the safety trainingbeta showed a strong influence on safety compliance (ˇ = 0.425).Accordingly, safety motivation did not have a positive effect onsafety compliance. Under the suppressor effect, the level of influ-ence of safety motivation on safety compliance will be reduced bysafety training.
4.6. One-way analysis of variance
To evaluate the relationship between safety climate dimensionsand respondents’ characteristics (i.e. years of working experience,age, job title, number of employees, and company ownership), one-
significant differences found for years of working experience, age,and job title at the 5% significance level. However, some safety cli-mate dimensions were found to statistically significantly differ fornumber of employees and company ownership.
nership.
O3 F ratio
SD Mean SD
0.55 4.27 0.62 15.32**
0.39 3.88 0.74 22.27**
0.49 4.18 0.67 5.05**
0.49 4.19 0.55 12.89**
0.50 4.26 0.55 4.34*
0.52 4.41 0.48 14.55**
0.52 4.30 0.61 14.51**
ber of employees.
N3 F ratio
SD Mean SD
0.63 4.11 0.76 4.88**0.67 3.69 0.78 19.98**0.58 3.93 0.73 5.43**0.60 3.99 0.60 5.21**0.59 4.21 0.49 7.50**
0.49 4.22 0.52 8.83**0.54 3.97 0.63 8.37**
more.
ysis an
otfjcFwtNihsmeois
5
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5
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C.-S. Lu, C.-S. Yang / Accident Anal
Perceived differences between safety climate dimensions basedn ownership groups are shown in Table 11. The mean scores ofhe five safety climate dimensions statistically significantly dif-ered using one-way analysis of variance. Results indicated thatoint-venture ferry companies had higher mean scores for safetylimate dimensions than private ferry and public ferry companies.urther, as shown in Table 12, the five safety climate dimensionsere found to statistically differ at the p < 0.05 significance level for
he three categories based on number of employees (N1, N2, and3). A comparison of the mean scores showed that respondents
n companies whose number of employees was N2 (31–60 people)ad highest mean scores on safety communication, followed byafety policy, emergency preparedness, safety training and safetyotivation. However, respondents in companies whose number of
mployees was N1 (less than 30 persons) had lowest mean scoresn the safety motivation dimension. These results are not surpris-ng since small ferry operators have limited resources to encourageafety behavior.
. Conclusion and discussion
The importance of safety climate for workers’ safety behaviorsas long been recognized (Cox and Cox, 1991; Flin et al., 2000;eal et al., 2000; Zohar, 2002; Mearns et al., 2003; Clarke, 2006;ernandez-Muniz et al., 2007; Christian et al., 2009). The pas-enger ferry operation is one of the most risky operations in theervice industry. Although passenger ferry operators attempt tonsure work safety, they are not completely successful in elimi-ating accidents. This study examined safety climate and safetyehavior in the passenger ferry context and empirically evaluatedafety climate dimensions, namely: safety policy, safety motivation,mergency preparedness, safety training, and safety communica-ion. In the course of the study, several important questions haveeen answered with regard to safety climate and safety behavior
n this context, namely: what are workers’ perceptions of safetylimate in the passenger ferry context and do their perceptionsf safety climate dimensions differ according to the nature ofhe company for which they work, its number of employees, anderry capacity, and individual characteristics? To the best of ournowledge, this is the first study to provide answers to these ques-ions and empirical evidence of the importance of safety climaten explaining workers’ safety behaviors in passenger ferry opera-ions.
.1. Implications of the study findings
Several implications can be drawn from the key findings ofhis study. First, safety climate is an important factor influencingelf-reported safety behavior in passenger ferry operations andust be taken into consideration by passenger ferry managers.
y understanding the differences between safety climate dimen-ions, passenger ferry managers and officers can develop effectiveraining programs and emergency plans to reduce unsafe behav-or and human error in passenger ferry operations. Second, withespect to safety climate dimensions, older respondents and com-anies with large ferry capacity were positively associated withafety behavior in terms of safety compliance. These findings areot surprising since older workers and large firms are likely tolace greater emphasis on management policies, goals and sys-ems which stress workers’ safety behaviors. Third, the study’s
ndings indicated that safety training and emergency prepared-ess are positively associated with safety behavior, including safetyompliance and safety participation, whereas safety policy andafety communication have a positive but not significant influ-nce on safety compliance. Such findings suggest that greaterd Prevention 43 (2011) 329–341 339
safety climate will lead to better safety behavior and furtherreduce accident occurrences. They are consistent with findingsreported in the studies of Zohar (1980), Campbell et al. (1993),Hayes et al. (1998), Vredenburgh (2002), and Lu and Shang(2005).
5.2. Limitations and future research
This section discusses various limitations of the study, pro-vide meaningful directions for future research. First, self-reporteddata on safety behaviors and respondents’ perceptions of safetyclimate in passenger ferry operations may have been subject tobias in terms of workers’ willingness to respond and report accu-rately. Workers may have been reluctant to report actual safetybehavior because of potential personal repercussions and an inter-est in avoiding lawsuits brought against them by the company.Hence, further research might measure workers’ safety behaviorsby actual observation. Second, this study focused on safety cli-mate and was limited to five safety climate dimensions based onthe prior studies of Zohar (1980), Campbell et al. (1993), Griffinand Neal (2000), Hayes et al. (1998), Vredenburgh (2002), and Luand Shang (2005). Although these five dimensions are importantand explain safety behavior, safety climate is a complex construct.Future studies could consider other variables such as safety leader-ship (O’Dea and Flin, 2001; Barling et al., 2002; Wu et al., 2007;Christina et al., 2009; Lu and Yang, 2010), preventive planning(Reniers et al., 2005; Fernandez-Muniz et al., 2007), internal control(Hurst et al., 1996; Fernandez-Muniz et al., 2007), and supervisormanagement (O’Dea and Flin, 2001; Barling et al., 2002; Flin andYule, 2004). Third, the results could not be expected to be pos-sible to generalize in the safety culture context. There is muchdebate in the safety literature regarding the similarities and dif-ferences between safety climate and safety culture (Guldenmund,2000). Berends (1996) views culture as simply a replacement termfor climate. In the safety culture and safety climate research field,however, many restrict themselves to the term “safety climate”and consider this to be the “psychological” or attitudinal climatewith regard to safety within an organization (Donald and Canter,1994; Niskanen, 1994; Guldenmund, 2000; Lu and Tsai, 2008). Sincethis study seeks capture workers’ feelings and attitudes towardssafety behavior issues, specifically in the passenger ferry context,the term “safety climate” is used in this study. Fourth, futureresearch could seek to explain how the safety climate dimen-sions influence safety performance outcomes, such as accidents orinjuries. Finally, this study’s findings reflected the situation regard-ing safety at a particular moment in time. Future studies mightbe conducted using the longitudinal approach to investigate theshort- and long-term effects of safety climate on passenger ferryoperations.
Acknowledgements
This research was sponsored by the National Science Council,Taiwan, R.O.C. under NSC 96-2415-H-006-005-MY3. The authorsalso would like to thank anonymous reviewers and Dr. Yueng-Hsiang Huang who provided detailed and constructive commentsof this research.
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