Safety-culture in a Norwegian shipping company

Journal of Safety Research 36 (2005) 441 – 458 www.elsevier.com/locate/jsr

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Safety-culture in a Norwegian shipping company Jon Ivar Ha˚vold * A˚lesund University College, N-6025 A˚lesund, Norway Norwegian University of Science and Technology (NTNU), N-7034 Trondheim, Norway Received 11 April 2005; received in revised form 15 July 2005; accepted 30 August 2005

Abstract Problem: Although there has been considerable interest in safety culture and safety climate in many industries, little attention has been given to safety culture in one of the world’s riskiest industries, shipping. Method: Using both self developed items and items from published research on safety culture, safety climate, and quality and management style, a 40-item safety culture questionnaire was developed. The questionnaire was distributed in a self-administered form to sailors onboard 20 vessels and to officers attending a seminar in Manila. A total of 349 questionnaires were collected (total response rate, 60%). Results, discussion and impact on industry: Principal component analysis (PCA) revealed 11 factors when the Kaiser eigenvalue rule was used and four factors when the scree test criterion was used. The factor structure in the material confirmed structures found in other industries. The relative importance of the factors from the factor analysis on ‘‘level of safety’’ measures was tested by canonical correlation analysis and regression analysis. The results confirmed previous research and showed that the most important factors were influential across industries. To determine weather differences existed between nationalities, occupations, and vessels the factors from the PCA was subjected to Multiple Discriminant Analysis. Significant differences between occupations, nations, and vessels were found on one or more of the factors from the PCA. D 2005 National Safety Council and Elsevier Ltd. All rights reserved. Keywords: Safety; safety at sea; safety-culture; safety-climate and national culture

1. Introduction This introduction briefly discusses characteristics of the seafaring environment, gives a short review on safety climate and culture research, discusses organizational, professional and national cultures influence on safety culture, establishes a link between attitudes and behavior, and outlines the hypotheses that will be tested. 1.1. The seafarer environment The shipping industry differs from other industries in several ways. It is a 24/7 society and the ship can be seen as a "closed" social milieu where all the needed competence is aboard. The work is based on shift and the vessel is split in

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three different functional areas, (deck, engine, and catering) with different competence and different tasks. The crew is organized hierarchical from master downwards. Even if the relative manning on a vessel has been reduced drastically the last 25 years, the organizational structure has remained much the same. Other characteristics of the shipping industry are their rotating systems. The sailors have contracts with the shipping companies on how many months or weeks they have to stay aboard, and how much leave they get before they have to muster onboard again. A normal contract for a Norwegian might be 8 weeks on and 8 weeks off, while a sailor from the Philippines might have a 9-month contract. The shipping industry is in its nature international, which a headline in the weekly Independent, London 22. February 1996 p.1 on the Sea Empress oil spill illustrates: Built in Spain, owned by a Norwegian registered in Cypress; managed from Glasgow; chartered by the French; crewed by Russians; flying a Liberian flag;

0022-4375/$ - see front matter D 2005 National Safety Council and Elsevier Ltd. All rights reserved. doi:10.1016/j.jsr.2005.08.005

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J.I. Ha˚vold / Journal of Safety Research 36 (2005) 441 – 458

carrying an American cargo; and pouring oil into the Welch coast. . . . BUT WHO TAKES THE BLAME? Norwegian shipping companies employ people from many countries and almost 50% of the total crew of 34,338 onboard in Norwegian registered vessels are foreigners. The countries with the largest number of crew onboard vessels registered in Norwegian ship registers by December 31, 1999 are Philippines (8,313), India (1,838), Poland (1,720) and Russia (1,202) (Maritime Statistics, 1999). Seafaring has been one of the world’s most dangerous occupations; a study by the British Board of Trade showed a mortality rate at 113 deaths per thousand seafarers in 1894. This was nine times that of railway employees, and 147 times that of factory and shop employees (Larsson & Lindquist, 1992). That seafaring still is among the world’s most dangerous occupations is confirmed by Drever (1995) and Li (2002). Hanson (1996) carried out a survey revealing that fatal injuries and drowning among Danish seafarers were 11.5 times higher than average rates among the Danish male workforce ashore from 1986 to 1993. A vessel is a floating factory often far away from doctors and hospitals, with complex and dangerous machinery in a limited space, surrounded and often made worse by heavy sea and bad weather. Ship casualties like collisions, capsizing, and fires/explosions and also personal accidents, homicide, suicide, and diseases can be added to the dangers for seafarers. 1.2. Safety culture/Safety climate The last couple of decades the shift of focus has been driven from technical failures as the prime cause of accidents to organizational, managerial, cultural, or human factors. Research aimed to measure attitudinal, organizational, cultural, and social aspects relevant to occupational safety have been carried out in many countries and many industries (Zohar, 1980; Rundmo, 1992, 1998; Lee, 1993; Coyle, Sleeman, & Adams, 1995; March et al., 1998; Mearns, Whitaker, Flin, Gordon, & O’Connor, 2000; Flin, Mearns, Fleming, & Gordon, 1996; Flin, Mearns, O’Connor, & Bryden, 2000; Glendon & Litherland, 2001; Seo, Torabi, Blair, & Ellis, 2004; Cooper & Phillips, 2004). Zohar (1980) used factor analysis on an Israeli sample and developed the first measure on safety climate/culture. His results included dimensions covering workers perceptions of: the importance of safety training, management’s attitude toward safety, effects of safe conduct on promotion, level of risk at workplace, effects of work pace on safety, status of safety officer, effects of safe conduct on social status, and status of the safety committee. Brown and Holmes (1986) replicated Zohar (1980) on an American sample, using confirmatory factor analysis. Their result did not support all factors found in Zohar’s research,

and they ended up with a three factor model: employee perception of management concern about their well being, management activity in responding to problems with their well being, and their own physical risk. Dedobbeleer and Beland (1991) tested the three-factor model developed by Brown and Holmes (1986) on construction workers and found that it was supported by their data, but a two-factor model was superior to the three-factor model. The two factors were interpreted to be management commitment to safety and workers involvement in safety. These two factors are also found to be the most important factors in more resent research (Flin et al., 1996, 2000; March et al., 1998; Glendon & Stanton, 2000, Glendon & Litherland, 2001). For more comprehensive reviews of safety climate/ culture research, several recently published papers can be recommended (Guldenmund, 2000; Flin et al., 2000; Glendon & Litherland, 2001). 1.3. Safety culture/Safety climate research in maritime settings Industries like nuclear reprocessing and engineering, gas and oil production, air transport, chemical plants, manufacturing plants, airports, mining and construction have been covered in reported safety culture/climate research. A literature search (Bibsys, 2004; ISI, 2002) indicates that no such research has been done in shipping. Ha˚vold (2000) found that no safety climate/culture research had been reported in the maritime sector, and a more recent SINTEF report (2003) commissioned by the Norwegian Research Council confirmed Ha˚vold’s conclusions: ‘‘We have not found any papers reporting research on safety culture aboard ships. It is known that Norway has used CRM-training (Crew-Resource Management) for crew on ships, identical to crew on aeroplanes, but this is not identified by scientific publications.’’ SINTEF report, 2003, p. 22). However some research on safety culture at sea has been done recently; at Lund University Centre for Risk Analysis and Risk Management a program ‘‘MARSAFSafety organisation, safety culture, risk management and maritime safety’’ has been running since 1999 (Ek, Olsson, & Akselson, 2000) and a Ph.D thesis on safety culture at sea was defended at Norwegian University of Science and Technology (NTNU) in the Autumn of 2004 (Soma, 2004). 1.4. Safety, professional culture, organizational culture, and national culture A crews behavior and thereby safety can be influenced by professional culture, organizational culture, and national culture (Helmreich & Wilhelm, 1999).

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Professional culture reflects the attitudes and values associated with an occupation, like the values proclaimed by the professional associations of engineers, marketers, doctors, pilots, through members of their own organizations. This might lead to clusters of similarly trained professionals with the same values and attitudes in the same organization. According to Merritt and Helmreich (1996), the professional culture of pilots is associated with pride in the profession and a liking for the job, but it is also linked with an unrealistic denial of vulnerability to the multiple stressors of the occupation. Schein (1992) emphasizes language as a differentiator between sub cultures. Using language expresses membership and belonging. Organizational culture implicates that the values and norms of an organization are important when it comes to priorities and behavior (Schein, 1992). In other words, if an organization has established a safety culture, either good or bad, new members of the organization will be socialized into it. Merritt and Helmreich (1996) indicate also that organizational culture does have a strong impact on safety and directly influences behavior in the cockpit of airplanes: ‘‘The cockpit is a microcosm of the organization- it reflects the organizations norms’’ (p.5). National culture influences behavior, values, and beliefs across nations. A survey conducted by a consulting firm in Europe has found that cultural differences are the biggest source of difficulties in integrating European acquisitions (Schneider & Barsoux, 1997), so national cultures and sub cultures are important to be aware of. Hofstede (1991) developed a study using a large existing databank of IBM employees that covered populations from 64 countries and included 116,000 survey questionnaires. The data used for studying differences in national cultures were an unintended by-product of the study, but are really what make it famous. The study showed national and regional patterns and revealed four largely independent dimensions of differences among national value systems. These dimensions were labeled ?power distanceX (large vs. small), ?uncertainty avoidanceX (strong vs. weak), ?individualismX vs. ?collectivismX, and ?masculinityX vs. ?femininityX. All 53 countries were scored in all four dimensions. Later in a follow-up study using student populations from 23 countries, the four dimensions were confirmed, but a fifth meaningful dimension independent of the four others was found (Hofstede & Bond, 1988). This fifth dimension was labeled ?Confucian dynamismX and contrasted a long term to a short-term orientation in life and work. To find out the extent to which Hofstede’s dimensions have relevance in regards to safety research, Merritt (1998) from the University of Texas replicated his study with regard to safety on 9,000 male commercial airline pilots in 18 countries, and concluded that national culture can and should be added to the list of influences upon a pilots work style and preferences.

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1.5. The link between, attitudes and behavior There are many theories linking attitudes and behavior (Aizen, 1991; Fishbein & Aizen, 1975; Hanish, Hulin, & Rosnowski, 1998), and attitudes and safety behavior are not likely to be exceptional to these theories. According to many scientists, attitude measurements are an upstream measurement to accidents, and a very good predictor for safety performance (Zohar, 1980; Advisory Committees for Safety in Nuclear Installations [ACSNI], 1993). If used properly, a safety attitude questionnaire could then be a useful quantifiable diagnostic tool that can be applied on different organization levels. Attitudes are argued to be a central factor and part of safety culture together with other perceptions, beliefs, values, and norms (Mearns & Flin, 1999; Lee, 1993; Cox & Flin, 1998). This leads one to expect that safety attitudes and safety behavior will be positively correlated. One might think that people who hold positive attitudes should engage in behavior that approaches, supports, or enhances safety, and people who hold negative attitudes should engage in behaviors that avoid, oppose, or be negligent to safety. 1.6. Hypotheses Hypothesis 1. The PCA analysis of the data from the shipping company will produce a factor structure that will confirm findings in industries other than shipping. This since it is unlikely that shipping will be unique and this research will find dimensions/factors that nobody has discovered before. The factor structure obtained from this data will confirm previous research in other industries and companies. Hypothesis 2. The relative importance of the factors obtained will confirm previous research in other industries. Some of the factors/dimensions obtained in the factor analysis will influence more of the variation in outcome measures than other dimensions. Hypothesis 3. National culture. The perception of the importance of safety issues across nationalities will be shared. The shipping industry is in its nature international with multiethnic crews. Does nationality influence safety? Hypothesis 4. Organizational sub culture: Occupation. The perceptions of the importance of safety issues across hierarchical levels and subgroups onboard the vessel will be shared. A ship consists of several groups of people based on different functional areas and hierarchy. Hypothesis 5. Organizational sub culture: Vessels. The perception of the importance of safety issues across vessels will be shared. In this survey, data from 15 vessels in the same shipping company is collected. Even if the company might have a total safety culture, each vessel is a small society that might have its own sub-culture.

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(SMC) on their vessels, which all vessels had well before the deadline of July 1, 2002.

2. Method ˚ lesund Two nautical students doing a BSc thesis at A University College collected the material for this paper. The author found the collected material interesting and got permission from the students and the shipping company to use the data for further analysis. 2.1. The sample The ship’s names were drawn from a fleet-list given by the ship-owner. In addition, a survey of officers attending a seminar on the Philippines was carried out. The research in this paper is based on data collected from employees in a large Norwegian shipping company with multiethnic crew. An overview of the sample’s size, response rates, nationality, manning, age, and size and type of vessels can be seen in Table 1. A self-completion survey was carried out among the employees on the vessels and at a seminar in Manila. The vessels in the sample are mainly Bulk/Container ships from 39,000 (Dead Weight Tonnes) DWT to 51,000 DWT and built from 1982 to 1998, with a total crew of 25 sailors on each vessel (except vessel E). The company’s long term objective is to provide firstclass, competitive, and professional ship management services with emphasis on reliability, regularity, and safety at sea. In 1994, the company began implementation of a safety and quality management system to reach their objectives and the company has implemented and are certified according to the ISM-code and the ISO-9002 quality standard. In relation to the ISM-code the company is holder of a Document of Compliance (DOC) and is presently implementing Safety Management Certificates

2.2. Questionnaire Initial development of the questionnaire was based on review of the literature. Two items were selected from a questionnaire measuring concentration of authority (Aiken & Haige, 1968) and 17 items from safety questionnaires (Rundmo & Hale, 1999; Mearns et al., 2000; Cox & Cheyne, 2000; Lee & Harrison, 2000; Grote & Ku¨nzler, 2000). Some of the items were slightly changed to fit the hierarchical structure onboard a vessel; for example supervisor was changed to officer. An additional 21 new questions were developed. A pilot study (n = 6) was carried out before the study. The questionnaire was produced only in the English language because the working language in the company is English. The items included in the questionnaire can be seen in Appendix A. All 40 items used a 6-point Likert scale ranging from strongly disagree to strongly agree. 2.3. Procedure The questionnaire was sent by e-mail to 20 ships (N = 486), where the shipmaster made copies and handed it out to the entire crew. The first page of the questionnaire emphasized that replies were anonymous, that respondent participation was voluntary, and that they should answer it honestly. Once the question form was completed, it was collected by the shipmaster and returned by mail. The data were collected at the end of March and the beginning of April 2001. Fifteen out of 20 ships answered the question-

Table 1 Overview of sample size, response rates, nationalities on each vessel. Age, size and type of vessels Vessel

N

Response Rate (%)

Demography

Vessel Built

Size of vessel:1000 DWT in 1000

Type of vessel

A B C D E F G H I J K L M N O Total on vessels Seminar Manilla

25 25 25 25 11 25 25 25 25 25 25 25 25 25 25 361 62

76 80 92 68 91 96 64 48 68 84 84 80 100 80 88 80

All crew from Philippines (Ph) incl. master Master + 2 crew Croatia (Cro); 1 UK, 16 Ph All crew from India incl. master All crew from India incl. master All crew from Norway (No) incl. master All crew from India incl. Master 3 Cr, 1 No,1 UK and 11 Ph Master No, 11 Ph 16 Ph Master + 4 crew Poland (Po), 15 Ph 6 Po, 1 Cr, 14 Ph Master + 2 crew Cr, 1 Other, 16 Ph 1 Po, 1 Cr, 1 Other, 19 Ph Master + 2 Cro, 17 Ph Master Cro, 6 Po, 14 Ph

1985 1986 1984 1982 1977 1997 1998 1992 1997 1985 1982 1997 1985 1985 1985

42 42 39 39 6 51 51 42 48 42 39 51 42 42 42

B/C B/C B/C B/C CC B/C B/C C/F B/C C/F / L B/C B/C B/C B/C C/F / L

3 Masters Ph, 1 Master UK, 1 Master Other, 53 crew Ph, 3 crew Po DWT = Dead Weight Tonnes; B/C = Bulk/Container; CC = Cement carrier; C/F/L= Container/Forestry products/Liquid; C/F = Container /Forestry products.

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naire; for the vessels that answered the questionnaire response rate varied from 48% to 100%, with an average of 80% (Table 1). When the five ships that did not answer are taken into consideration, the response rate is 60%. In

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addition, the questionnaire was handed out by one of the students at a seminar for officers that the ship owner held in Manila. A total of 349 questionnaires were collected, 287 from the ships and 62 from the seminar in Manila.

Table 2 Result of the rotated factor analysis (varimax) using eigenvalue 1 criteria, showing name of each factor, factor loadings, communalities, percentage of variance accounted for by each factor, the internal consistency between items of each factor (Cronbach alpha) F1 Factor 1: Knowledge (Explained variance 10.2%, alpha 0.84) I know well the purpose of the ISM code I know well the purpose of Quality Management System. (ISO) I know what a SAFIR report is I am very familiar with the company’s safety policy. Factor 2: Management attitude to safety (Explained variance 9.3%, alpha 0.78) Officers do all they can to prevent accidents onboard. Officers often discuss safety issues with ratings. Officers are aware of the main safety problems onboard. Employees are given enough training to do their work tasks safely. I can locate the nearest fire apparatus wherever the fire should break out. XXXX can be friendlier to the environment.

F2

F3

F4

F5

F6

F7

F8

F9

F10

F11

,86 ,85 ,71 ,70

Com ,77 ,78 ,64 ,62

,68 ,66 ,63 ,53 ,46 ,44

Factor 3: Safety behavior (Explained variance 6.1%, alpha 0.69) There is always an extra person in addition to the mate on the bridge when sailing in low visibility. Everybody always use helmet during mooring and cargo operations. All enclosed spaces are tested with an oxygen analyzer before entered. Factor 4: Attitude to safety rules/instruction (Explained variance 5.9%, alpha 0.84) I find difficulties in understanding the purpose of the safety instructions. Safety instructions are in general hard to understand.

,61 ,59 ,55 ,50 ,51 ,49

,77

,69

,68 ,49

,64 ,54

,79 ,76

Factor 5. Employees satisfaction with safety and quality (Explained variance 4.9%, alpha0.64) XXXX ships are better maintained than ships from other companies We always have enough spare parts Onboard XXXX ships safety has improved a lot since I started in the company Factor 6: Concentration of authority (Explained variance 4.3%, alpha 0.40) Even small matters on the job always have to be referred to the Master. We always report accidents/incidents. In this organization no action s are taken without the approval of an officer Factor 7: Training experience (Explained variance 4.2%, alpha 0.40) We do have realistic drills at least once a month. All drills are unannounced. Factor 8: Quality experience. (Explained variance 4.0%, alpha 0.54) We always have enough spare parts We always deliver undamaged cargo XXXX A/S will never choose time saving and economic actions before safety. Factor 9: Stress experience (Explained variance 3.7, alpha 0.45) I sometimes lie awake because I’m thinking about problems at work. I often experience stress at sea. Factor 10: Actions after an unsafe act. (Explained variance 3.64%, alpha 0.25) A worker who acts unsafe is disciplined. A SAFIR report is written with every unsafe act. Factor 11: Environmental systems (Explained variance 3.4%) XXXX need Environmental Management Systems in the future. Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 15 iterations.

,71 ,72

,73 ,57 ,52

,65 ,60 ,62

,74 ,49 ,43

,62 ,50 ,49

,76 ,54

,64 ,51

,40 ,71 ,63

,60 ,64 ,57

,79 ,65

,80 ,64

,68 ,58

,61 ,63

,80

,68

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3. Results The results section shows the result of the following analyses: exploratory analysis, factor analysis/principal component analysis, canonical correlation, multiple regression, and multiple discriminant analysis. 3.1. Exploratory analysis The crew that responded were from several countries: 63% from the Philippines, 19% from India, 7% from Poland, 5% from Croatia, 4% from Norway, 1% from UK, 1% from other countries. The average age was 36.5 years, ranging from 19 to 61 years. Average experience as a seaman was 12.4 years, ranging from less than a year to 46 years. All types of occupations on board the vessel were represented in the study, 38% ratings, 25% deck officers, 20% engine officers, 12% galley section, and 5% masters. 3.2. Factor analysis Principal component analysis (PCA) was used to identify underlying organizational and social factors. First the correlation matrix was inspected for extreme multicollinearity or for variables that did not correlate with any other variables or very few variables so they could be found and considered for exclusion. The conclusion was that on this stage there was no need to eliminate any questions. The response pattern for each question was analyzed and histograms and plots indicated that several of the items were skewed and four of the items had a relatively high degree of kurtosis. Transformations were considered but were not performed since factor analysis is generally robust to non-normality (Hair, Anderson, Tatham, & Black, 1995; Brewer & Hills, 1969). Spector (1992) recommends a minimum value of 0.30 to 0.35 for considering an item to load on any factor but the cutoff point in this analysis was set to 0.4, which is suggested by Hair et al. (1995), and the chosen rotation was Varimax. The 40 Likert type questions were subjected to an item evaluation. The adequacy of the sample correlation matrix for factor analysis was examined using Barlett’s test of sphericity and the KMO test that showed 0.851, this measure indicates that the data were appropriate for factor analysis (Hair et al., 1995). The case to variable ratio was higher than 5:1, which Hair et al. (1995) suggests as a minimum. The analyses were first performed by Kaiser’s eigenvalue rule (latent root criterion) requiring the eigenvalue to be at least 1 (Nunally, 1978). The latent root criterion using an eigenvalue of 1 gave as a result that 11 factors were drawn from the data. The plot for the scree test (Catell, 1966) was then examined. The scree plot showed a small elbow after the 4th factor and an even less distinct after the 6th factor. After factors had been interpreted from several trial solutions based on the number of factors suggested by the

latent root criterion, percentage of variance criterion and scree test criterion, the Kaiser’s eigenvalue rule and the scree test solution was considered as equally good. Both alternatives were quite easy to interpret and name and the results from the two principal component analyses are given in Tables 2 and 3. The results from the 11-factor alternative are reported so factor structures can be compared with surveys in other industries (Hypothesis 1), however, for further use in this paper the 4-factor alternative is chosen for two reasons: simplicity and because internal consistency was at an unacceptable low level for some of the factors in the 11-factor alternative. The 11-factor alternative accounted for 59.4% of the variance and the 4-factor solution accounted for 37.1% of the variance. Both results are evaluated as acceptable. 3.2.1. Kaiser eigenvalue rule (11-factor alternative) The 11-factor solution in Table 2 showed that the factors knowledge, management attitude to safety, safety behavior, attitudes to safety rules/instructions, and employee satisfaction with safety and quality were the most important factors. 3.2.2. The scree test criterion (4-factor alternative) If the simplified 4-factor alternative shown in Table 3 is compared with the 11-factor alternative we can observe that factor 1 in Table 3 includes all items from factor 2, 3 and 5 from Table 2; factor 2 is equal to factor 1, factor 3 is equal to factor 4, and factor 4 includes factor 5 and 8. The four factors were named: (a) employee and management’s attitude to safety and quality, (b) knowledge, (c) attitudes to safety rules/instructions, and (d) quality and safety experience. Internal consistency measured by Cronbach alpha was satisfactory for the three first factors but somewhat low for factor four. 3.3. Canonical correlation Two canonical correlations were performed. In both the performed canonical correlations the relationship between three variables measuring ‘‘level of safety’’ [(1) I am very satisfied with the safety onboard; (2) I am very satisfied with the quality work onboard and (3) Port State Control Ratio1] is used as the dependent set (DV). 1 Port State Control (PSC). The International Maritime Organization has been helping to develop regional PSC organizations. PSC starts with a check on the ship’s certificates and the Port State Control Officer (PSCO) should try to get a general impression of the overall condition of the vessel. He does some spot-checks in certain areas and if necessary he will carry out a more detailed inspection. If the vessel is showing deficiencies that are endangering the safety of the vessel and it’s crew, or that constitutes an unacceptable risk for the marine environment, then he has the right to stop and detain the vessel until the deficiencies are rectified. It is in the concept of port state control that the maritime community worldwide has seen a possible solution to the problem of the substandard ship. Port State Control Ratio is: Number of Non-conformities in % of number of Port State Controls.

J.I. Ha˚vold / Journal of Safety Research 36 (2005) 441 – 458

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Table 3 Result of the rotated factor analysis (varimax) using scree test criteria, showing name of each factor, factor loadings, communalities, percentage of variance accounted for by each factor, the internal consistency between items of each factor (Cronbach alpha) F1

F2

F3

F4

Com

Factor 1: ‘‘Employee and management’s attitude to safety and quality (Explained variance 13,5%, alpha 0.85) Officers do all they can to prevent accidents onboard. ,63 XXXX ships can be friendlier to the environment ,63 Officers often discuss safety issues with ratings. ,59 Employees are given enough training to do their work tasks safely. ,58 Officers are aware of the main safety problems onboard. ,55 Everybody always use helmets during mooring and cargo operations ,53 Onboard XXXX ships safety has improved a lot since I started in the company. ,51 All new crewmembers get proper safety training before they starts working ,49 I can locate the nearest fire apparatus wherever the fire should break out ,48 There is always an extra person in addition to the mate on the bridge when sailing in low. . . ,39 All enclosed spaces are tested with an oxygen analyzer before entered ,37 Factor 2: ‘‘Knowledge." (Explained variance 10,6%, alpha 0.84) I know well the purpose of Quality Management System. (ISO) I know well the purpose of the ISM code I know what a SAFIR report is. I am very familiar with the company’s safety policy

,46 ,41 ,47 ,40 ,37 ,44 ,42 ,39 ,37 ,30 ,37

,82 ,82 ,71 ,66

Factor 3: Attitudes to safety rules/instructions (Explained variance 6,6%, alpha 0.84) Safety instructions are in general hard to understand. I find difficulties in understanding the purpose of the safety instructions.

,71 ,72 ,60 ,51

,75 ,71

Factor 4: Quality and safety experience (Explained variance 6.1%, alpha 0.58) We always have enough spare parts A SAFIR report is written with every unsafe act XXXX will never choose time saving and economic actions before safety We always deliver undamaged cargo XXXX ships are better maintained than ships from other companies

,67 ,60

,60 ,54 ,46 ,43 ,43

,43 ,36 ,30 ,29 ,36

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 7 iterations.

The first independent set (IV-1) is 38 items from the questionnaire (Table 5), and the second set (IV-2) is 4 factors from the PCA analysis shown in Tables 3 and 7. The assumptions of canonical correlation were evaluated and found satisfactory with one exception; 11 outliers were detected and deleted from the sample. The numbers of observations was above the 10 cases recommended by Tabashnick and Fidell (1996) as a minimum needed for each variable included in the analysis. SPSS was used to perform the canonical correlation. Even if the SPSS menu does not offer canonical correlation there are two ways of doing canonical correlation in SPSS, both requiring use of syntax. One is to use the Canonical correlation.sps.macro and the other is to use MANOVA with DISCRIM subcommand. Both analyses were run, but the Canonical correlation.sps.macro was used because of easier interpretation of the output. 3.3.1. First canonical correlation analysis (38‘‘safety’’items and three ‘‘level of safety’’ variables) All three variables from set 1 were found to be correlated with the 38 items from the safety questionnaire. Tables 4 and 5 shows that canonical loadings produced 3 functions, but only canonical function 1 with a canonical correlation of 0.787 is selected for further analysis. This is because

redundancy indexes of set 1 and set 2 are both greater than 1.5% and the significance level is 0.000. The redundancy coefficient shows that 39.8% of the variance in set 1 (DV) is predicted from set 2 (IV) and 9.5% of the variance in set 2 (IV) is predicted from set 1(IV). According to Hair et al. (1995), the redundancy Table 4 Overall results of canonical correlation analysis between set 1 (DV) and set 2 (IV) Function 1

Function 2

0.787 0.172 260.789 0.000

0.605 0.452 117.677 0.001

Variance traced (%) Set 1 (DV) Cumulative Set 2 (IV) Cumulative

64.3 64.3 15.4 15.4

28.4 92.7 2.2 17.6

7.3 100 1.8 19.4

Redundancy (%) Set1 (DV) Cumulative Set2 (IV) Cumulative

39.8 39.8 9.5 9.5

10.4 41.2 1.0 10.5

2.1 43.3 0.5 11.0

Canonical correlation Wilks lambda F value (Chi – square) Significance (p <)

Function 3 0.536 0.713 50.098 0.059

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Table 5 Canonical loadings and cross loadings analysis for Function 1, set 1 and set 2

Set1 (DV ’’safety level’’): Portstat (Portstate control ratio) Saetysat (I am very satisfied with the safety onboard) Qualisat (I am very satisfied with the quality work onboard) Set2 (IV-1): I am familiar with the company’s safety policy. I know well the purpose of the ISM code. I know well the purpose of ISO and ISO9002. I know what a SAFIR report is. The level of oxygen in a tank has to be higher than 25% before entering I know why we have a designated person in the company. I know which work operations onboard that require a checklist. All new crewmembers get proper safety training before they start working. All drills are carried out in a realistic manner. We do have realistic drills once a month. All drills are unannounced. I sometimes lie awake because I_m thinking about problems at work. I can locate the nearest fire apparatus wherever the fire should break out onboard the ship. Officers often discuss safety issues with ratings. Officers are aware of the main safety problems onboard. A worker who acts unsafe is disciplined. Officers do all they can to prevent accidents onboard. Communications about safety issues are good in our company. Employees are given enough training to do their work tasks safely. I sometimes have to turn a blind eye to the strict safety rules to get the job done on time. I find difficulties in understanding the purpose of the safety instructions. Safety instructions are in general hard to understand. I am afraid of asking questions related to safety. We always report accidents/incidents. XXXXA/S will never choose time saving and economic actions before safety We always deliver undamaged cargo. We always have enough spare parts. XXXX ships are better maintained than ships from other companies. All enclosed spaces are tested with an oxygen analyser before entered. A SAFIR report is written with every unsafe act. There is always an extra person in addition to the mate on the bridge when sailing in low visibility, (such as night-time, foggy weather, etc). Everybody always use helmet during mooring and cargo operations. Onboard XXXX ships safety has improved a lot since I started in the company. I often experience stress at sea In this organisation no actions are taken without approval of an officer Even small matters on the job have to be referred to the master XXXX ships can be friendlier to the environment XXXX need Environmental Management Systems in the future. a b

Canonical loadings of function 1

Canonical cross loadings of function 1


0.401a
0.981a
0.900a


0.311b
0.772b
0.709b


0.335
0.281
0.277
0.410a 0.177
0.399a
0.296
0.377
0.498 a
0.186
0.334
0.040
0.384
0.516 a
0.532 a
0.007
0.611a
0.642a
0.455a 0.083 0.271 0.353 0.168
0.175
0.350
0.257
0.534a
0.590a
0.493a
0.301
0.472a


0.263
0.222
0.218
0.322b 0.139
0.314b
0.233
0.297
0.385b
0.146
0.263
0.032
0.302b
0.406b
0.419b
0.005
0.520b
0.506b
0.358b
0.066 0.213 0.278 0.133
0.138
0.275
0.202
0.420b
0.465b
0.388b
0.237
0.372b


0.567a
0.649a 0.147
0.149
0.077
0.593a
0.226


0.446b
0.511b 0.116
0.117
0.060
0.467b
0.178

Indicates variables with relation over 0.40 in canonical loadings are accepted for correlation explanation within the same set. Indicates variables with relation over 0.30 in canonical cross-loadings are accepted for cross-loading correlation between two sets.

coefficient is equivalent to the more known R2 from regression analysis. Canonical loading measures the linear correlation between the variables and their respective canonical variate. Canonical cross loadings correlate each observed independent or dependent variable with the opposite canonical variate. The criteria of selecting canonical loadings over 0.4 and cross loadings over 0.3 (9% variance) were used (Tabashnick & Fidell, 1996). Canonical loadings revealed from function 1 individually and by the subset itself (DV) revealed that ‘‘Port state control

ratio’’ (- 0.401), ‘‘I am very satisfied with the safety onboard’’ (- 0.981) and ‘‘I am very satisfied with the quality work onboard’’ (- 0.900) were all significantly correlated with 15 independent variables, providing a substantive contribution as key predictors of the outcome dimension. 3.3.2. Second canonical correlation analysis ( four‘‘safety’’ factors and three ‘‘level of safety’’ variables) (Tables 6 and 7) Canonical loadings produced 3 functions but only canonical function 1 with a canonical correlation of 0.672 is selected for further analysis since redundancy indexes of

J.I. Ha˚vold / Journal of Safety Research 36 (2005) 441 – 458 Table 6 Overall results of canonical correlation analysis between set 1 (DV) and set 2 (IV) Function 1

Function 2 0.250 0.905 1.02 0.007

Function 3

Canonical correlation Wilks lambda F value (Chi – square) Significance (p<)

0.672 0.497 14.28 0.000

0.186 0.965 0.50 0.044

Variance traced (%) Set 1 (DV) Cumulative Set 2 (IV) Cumulative

61.9 61.9 45.0 45.0

26.9 88.8 22.5 67.5

11.2 100 12.8 80.3

Redundancy (%) Set1 (DV) Cumulative Set2 (IV) Cumulative

27.9 27.9 20.3 20.3

1.7 29.6 1.4 21.7

0.4 30.0 0.4 22.1

set 1 and set 2 are both greater than 1.5%, and the significance level is 0.000. The redundancy coefficient (R2) shows that 27.9% of the variance in set 1 (DV) is predicted from set 2 (IV) and 20.3% of the variance in set 2 (IV) is predicted from set 1(IV). Canonical loading and canonical cross loading analysis was performed to investigate any correlation between variables within and between independent variables and dependent variables. Canonical loadings of function 1 revealed that all three variables from set 1 were found to be correlated with the same independent variable set as well as the dependent set. The criteria of selecting canonical loadings over 0.4 and cross loadings over 0.3 were used (Tabashnick & Fidell, 1996). Judging from the result of the canonical loadings individually by the subset itself (DV), it was found that ‘‘Port state control ratio’’ (- 0.512), ‘‘I am very satisfied with the safety onboard’’ (- 0.977), and ‘‘I am very satisfied with the quality work onboard’’ (-0.801) were all significantly correlated with the subset measuring ‘‘safety level.’’ Two of the four PCA factors from the factor analysis counts for most of the variance in the dependent set (relations over 0.40 in canonical loadings), ‘‘Employee and management’s attitude to safety and quality’’ and ‘‘Safety and quality experience;’’ but the two remaining PCA factors were very close to reach the canonical loading and cross loading cut off point of 0.40 and 0.30, respectively. The above indicates that the same set of underlying factors that accounts for most of the variation within variable sets are also important in determining relationships across the variable sets. 3.4. Multiple regression analysis Port state control ratio is chosen as the dependent variable in the regression analysis, because this variable is

449

not a part of the survey. To evaluate the relationship between port state control ratio and the independent variables measured by the factors from the factor analysis, a standard multiple regression analysis was conducted. To avoid level of research problems, all the data were converted to vessel level before the analysis was undertaken. Table 8 shows correlation between the variables in the multiple regression analysis. Employee and management attitude to safety and quality is the factor correlating most to the other variables, while attitude to safety rules/instructions do not correlate significantly with any of the other variables. Table 9 shows the descriptive statistics for the variables in the ‘‘ecological’’ multiple regression analysis. Several assumptions of regression analysis were tested, linearity, normality, homoskedasity, and multicollinearity. Regression is not greatly affected by minor deviations from the assumption of linearity, and by inspecting bivariate scatterplots of the variables of interest no significant curvature was found. Regression assumes that variables have normal distributions. Non-normally distributed variables (skewed, kurtosis, or variables with substantial outliers) can distort relationships and significance tests (Osborn & Waters, 2002). Outliers especially have to be identified and dealt with, but not necessarily deleted according to Hair et al. (1995); on the other hand, analyses by Osborn and Waters (2002) show that removal of univariate and bivariate outliers can reduce the probability of Type I and Type II errors, and improve accuracy of estimates. One influential observation was detected and deleted from the material. Plots, histograms, and steam and leaf plots were inspected and found satisfactory. By using the K – S (Lilliefors) test, the hypothesis of normality could be rejected; but according to Norusis (1993) it is almost impossible to find data that are

Table 7 Canonical loadings and cross loadings analysis for Function 1, set 1 and set 2

Set1 (DV ’’safety level’’): Portstate control ratio I am very satisfied with the safety onboard I am very satisfied with the quality work onboard Set2 (IV-2): Employee and management_s attitude to safety and quality Knowledge Attitudes to safety rules/instructions Safety and quality experience

Canonical loadings of function 1

Canonical cross loadings of function 1


0.512a
0.977a
0.801a


0.344b
0.656b
0.538b


0.967a


0.650b


0.428a 0.388
0.728a


0.288 0.261
0.489b

a Indicates variables with relation over 0.40 in canonical loadings are accepted for correlation explanation within the same set. b Indicates variables with relation over 0.30 in canonical cross-loadings are accepted for cross-loading correlation between two sets.

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J.I. Ha˚vold / Journal of Safety Research 36 (2005) 441 – 458

Table 8 Correlations (Pearson) between vessels on the four factors from the factor analysis and Port State Control Ratio

Employee and management attitude to safety and quality Knowledge Attitudes to safety rules/instructions Quality and safety experience Port state control ratio

F1

F2

F3

F4

Port state control ratio

1,000 ,856**
,113 ,811** ,547*

1,000
,086 ,716** ,444

1,000
,165
,171

1,000 ,444

,444 1,000

* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

exactly normally distributed and if the sample size is large almost any goodness of fit test will result in rejection of the null hypothesis. Hetroskedasity was checked by visual examination of a plot of the standardized residuals and found satisfactory. According to Berry and Feldman (1985) and Tabashnick and Fidell (1996) slight heteroscedasticity has little effect on significance tests. The correlation matrix was inspected and the VIF calculated and found satisfactory. According to Hair et al. (1995) multicollinearity is not a problem when the tolerance is above 0.19 that equals a VIF less than 5.3. Several regression models were calculated using backward elimination. The final model (Table 10) shows that the factor ‘‘employee and managements attitude to safety and quality’’ explain 50% of the variance on ‘‘port state control.’’ Yi ¼
20:291 þ 26:355Xi1 þ e; R ¼ 0:734; R2 ¼ 0:538; Adjusted R2 ¼ 0; 500 3.5. Multiple discriminant analysis From the discriminant analysis we can assess the independent variables relative importance to dependent variables. The analysis was carried out on the whole sample in order to see which independent variable (factor) contributes most while discriminating between the dependent variables. The goal of discriminant analysis is to predict group membership from a set of predictors. In this paper three analyses with the dependent variables (nations, occupation, and vessels) will be performed. Table 9 Descriptive statistics for variables included in the multiple regression analysis Variable

Explanation

N Min Max Mean SD

Dependent Y Port State Control Ratio

14 8

Predictors X1

14 1.22

X2 X3 X4

50

24.4

13.2

Discrimination analysis is robust, and tolerates deviations from its assumptions without loss of efficiency, accuracy, or misclassification according to Lachenbruch (1975). According to Tabashnick and Fidell (1996) there are no special problems posed by deviations from assumptions or unequal sample size in groups, but the sample size of the smallest group should exceed the number of predictor variables. Tests for multivariate and univariate outliers were run for each group separately and four outliers were eliminated. 3.5.1. Nationalities A stepwise analysis was performed using nationalities as discriminant variables. Results of discriminant analysis indicate that ‘‘Employee and management’s attitude to safety and quality’’ and ‘‘Safety and quality experience’’ were significant discriminators based on their Wilks’ Lambda (p = 0.000). This provides strong support for the discriminant function’s ability to discriminate group membership on the basis of the used variables. The proportional chance criterion was computed from the output titled Prior Probabilities for Groups. The proportional chance criteria including a 25% margin (recommended by Hair et al., 1995) were 23% [(.2)^2  5 = 0.20  1.25 = 0.23]. Our hit ratio of 39.2% is 16.2 points higher than the required standard of 23%. Particularly, classification of sailors from India was high (73.6%). The structure matrix showed that Function 1 was very close to ‘‘Employee and management’s attitude to safety and quality’’ (correlation 0.979) and Function 2 close to ‘‘Safety and quality experience’’ (correlation 0.778). Fig. 1 show that Function1 is the factor that discriminates best. The most important factor in Function 1 is employee and management’s attitude to safety and quality, where low Table 10 The ‘‘final’’ regression-model: Unstandardized Coefficients B

Employee and managements attitude to safety and quality Knowledge Attitudes to safety rules/instructions Quality and safety experience

2.47

1.82

.32

14 1.34 14 1.68

2.70 5.85

1.82 4.86

.29 .98

14 1.46

2.98

2.32

.44

Standardized t Coefficients

Sig.

Std. Error Beta

(Constant)
20.291 11.666 X1 Employee and 26.355 7.050 managements attitude to safety and quality

.734

Dependent Variable Y: Port State Control Ratio.


1.739 .108 3.738 .003

J.I. Ha˚vold / Journal of Safety Research 36 (2005) 441 – 458

1

451

4. Discussion 4.1. General

Philippines

Nationality

Croatian

0

Group Centroids

Indian

Indian

Function 2

Polish Norwegian

Polish Croatian Norwegian Philippines

-1 -2

-1

0

1

2

3

Function 1 Fig. 1. Centroides of five nations on the two functions derived from the data.

scores shows a more positive attitude and as such could the factor be named safety attitude. The most important factor in Function 2, safety and quality experience, is also scaled so the lowest scores represent the most positive experience, and could be named experience. Both ‘‘Knowledge’’ and ‘‘Attitudes to safety rules/ instructions’’ were excluded from the functions. By inspecting the national group statistics (mean and standard deviation) one could see that the reason could be found in more equal country means and higher standard deviations on the two factors. 3.5.2. Occupations A stepwise analysis was performed using occupations (master, deck officer, engine officer, galley section, ratings) as discriminant variables. Results of discriminant analysis indicate that ‘‘Knowledge’’ was a significant discriminator based on their Wilks’ Lambda (p = 0.000). The proportional chance criteria including a 25% margin (Hair et al., 1995) were 23%. Our hit ratio of 21.3% is 1.7 points lower than the required standard of 23%. 3.5.3. Vessels A stepwise analysis was performed using occupations of the 15 vessels as discriminant variables. Results of discriminant analysis indicate that employee and managements’ attitude to safety were a significant discriminator based on their Wilks_ Lambda (p = 0.000). The proportional chance criteria including a 25% margin (Hair et al., 1995) were 7.8%. Our hit ratio of 8.3% is 0.5 points higher than the required standard of 7.8%.

An aspect with these scales of safety culture/climate is that many of the items in the item pool reflected a high degree of consensus among respondents no matter of nationality, vessel, or occupation. Many of the responses in the item-pool were skewed to the ‘‘good’’ safety side, however, Helmreich and Merritt (1998) reported the same from their survey from airlines. A possible explanation for this result is that the skewedness reflects the general belief or stereotypes about safety share in most of the workforce; another explanation is that the items were not worded in a way that measured the individual differences good enough. Yet another explanation might well be that all the vessels belonging to this shipping company really had good safety behavior, good safety attitudes, and are ‘‘high standard vessels.’’ The last explanation is supported by Port State Control data; the ratio of reported non-conformities to the number of inspections is ranging from 0.08 to 0.5, with an average of 0.22 for the vessels in this survey. On average, there is one non-conformity registered every fifth Port State Control on vessels belonging to this ship owner. 4.2. The PCA analysis of the data from the shipping company will produce a factor structure that will confirm findings in industries other than shipping. (Hypothesis 1) The factor analysis using Kaisers eigenvalue rule generated 11 factors (Table 2); the strongest factors were knowledge (explained variance 10.2%), management attitude to safety (explained variance 9.3%), safety behavior (explained variance 6.1%), attitude to safety rules/instructions (explained variance 5.9%), and employees_ satisfaction with safety and quality (explained variance 4.9%). The factor analysis using the scree-test criterion (Table 3) generated four factors: employee and management’s attitude to safety, quality and safety behavior (explaining 13.5% of the variance), knowledge (explaining 10.6% of the variance), attitudes to safety rules/ instructions (explaining 6.6% of the variance), and safety and quality experience (explaining 6.1% of the variance). Senior management attitudes/influence on safety is the factor appearing most frequently in papers dealing with safety climate/culture. Flin et al. (2000) reports that senior management as a factor influencing safety appears in 13 out of 18 reviewed papers. To know is not the same as putting knowledge into practice. Knowledge/competence might be necessary for safe behavior but not enough for safe behavior. In a review paper (Flin et al., 2000), competence was reported to appear in 6 out of 18 safety climate/culture studies. The employees’ experience with the company’s safety and quality is the third strongest factor and this factor can be grouped under the heading ‘‘Safety system.’’ Perception of the state of the

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J.I. Ha˚vold / Journal of Safety Research 36 (2005) 441 – 458

Table 11 Result of discriminant analysis (Nationalities) Dependent Variable

Independent variable

Wilk’s lambda

Significance

Discriminate function coefficient

Nationality

Attitude Experience

0,756 0,951

0,000 0,003

0,875 1,102

Eigen-value

Canonical cor.

Wilk’s lambda

Chi-Square

Significance

Hit-ratio

0,309

0,453

0,756

79,78

0,000

39,2%

safety systems is according to Flin et al. (2000) included as a factor in 12 out of 18 reviewed papers. The factor attitude to safety rules/instruction or ‘‘procedures/rules’’ emerged only in 3 of the 18 studies reviewed in Flin et al. (2000), but Guldenmund (2000) identified this as one of the most frequently occurring themes in his review. Coyle et al. (1995) argued that safety climate factors were not universally stable, and that no universal set of safety factors exists; on the other hand others (e.g. Glendon & Litherland, 2001; Flin et al., 2000) suggest that at least some safety climate factors might be stable across both industries and cultures. Conclusion: The factor structure from this research seems to confirm factors identified in other studies, and thereby support Hypothesis 1. 4.3. The relative importance of the factors obtained will confirm previous research in other industries (Hypothesis 2) It has been strongly argued that many safety problems have their origins in the poor attitude of management and employees toward occupational health and safety. Several papers on the subject verify that poor attitudes almost always precede accidents and unsafe performance (Glennon, 1982; Dedobbeleer & Be´land, 1991; Rundmo, 1992; Coyle et al., 1995; Diaz & Cabrera, 1997; Mearns et al., 2000; Seo et al., 2004; Cooper & Phillips, 2004). On the other hand, do Glendon and Litherland (2001) contradict the above findings when they failed to find any relationship between safety climate and behavioral observation measures of safety-performance? According to them a plausible reason could be that the other studies have used self-reporting components of safety performance as outcome variables, while they in their last study used observational measures. In this paper canonical correlation and regression analysis is used to see if some of the found factors /items are more influential on the ‘‘level of safety.’’ Canonical correlation is in some ways a form of scale development, as the dependent and independent variates represent dimensions of the variable set similar to the scales developed with factor analysis. The primary difference is that the dimensions are developed to maximize the relationship between them, whereas factor analysis maximize the explanation (shared variance) of the variable set. The canonical correlation between the three ‘‘level of safety’’ variables and the 38 safety items showed that the 14 significant items (Table 5) grouped themselves into three

of the four factors of PCA scree-test solution (two items on knowledge, nine items on employee and managements attitude to safety and quality, and three items on quality and safety experience) The items with strongest canonical correlation belonged to the management attitude to safety and quality group. This finding is confirmed by the second canonical correlation using the four factors from the PCA scree test solution as independent variables (Table 7). The regression analysis showed that 50% of the variation in the dependant variable Port State Control Ratio could be explained by one independent variable/factor included in the final model (Table 10). The importance of employee and management’s attitude to safety and quality is also found in other studies confirming studies as the most important factor influencing safety behavior (Zohar, 1980; Dedobbeler & Beland, 1991; Glennon, 1982; Lee & Harrison, 2000; Flin et al., 2000). Conclusion: The two canonical correlations indicate a significant relationship between’’level of safety’’ and 14 items and 3 factors (Tables 5 and 7). This indicates that some of the most important factors obtained in other industries also are among the most important in shipping. The regression analysis (Table 10) indicates a significant relationship between reported Port State Control and employee and management’s attitude to safety and quality as the most important factor. The findings support Hypothesis 2. 4.4. Variations across nationalities: ‘‘National Cultures’’ (Hypothesis 3) The multiple discriminant analysis (Table 11) shows significant difference between nationalities on two of the factors found in the factor analysis: employee and management’s attitude to safety and quality, and safety and quality experience are the two factors that discriminated significantly. According to Merritt and Helmreich (1996), Merritt (1998), and Helmreich and Merritt (1998) two of Hofstede’s national culture dimensions, power distance (PDI) and collectivism/individualism (IDV) influence safety.2 To see whether one can indicate correlation between the two 2 The PDI and IDV values used in Tables 14 and 15 are country scores calculated by Hofstede in his original survey study or later research. Hofstede’s country scores is the most widely used measure of national cultures, and he is amongst the 10 most cited European on the Social Science Citation Index SSCI. For more information on the questionnaire and formula for calculating the indexes see his book Cultures Consequences (Sage Publications 2001; 2nd edition) in Appendix 1 to 4 (p 467 – 499).

J.I. Ha˚vold / Journal of Safety Research 36 (2005) 441 – 458

453

Table 12 Result of discriminant analysis (Occupation) Dependent Variable

Independent variable

Wilk’s lambda

Significance

Discriminate function coefficient

Occupation

Knowledge

0,890

0,000

1.0

Eigen-value

Canonical cor.

Wilk’s lambda

Chi-Square

Significance

Hit-ratio

0,124

0,332

0,890

34,28

0,000

21,3%

expected to be told what to do, and hierarchy in organizations reflects the existential inequality between higher up and lower downs; while in low PDI societies, teachers and pupils are equal, subordinates are expected to be consulted, and hierarchy in organizations means an inequality of roles established for convenience. People in low IDV societies act in the interest of their in-group, show belief in collective decisions, and evaluate direct appraisal of performance as a threat to harmony; while in high IDV societies employees are supposed to act as ‘‘economic (rational) men,’’ show belief in individual decisions, and evaluate direct appraisal of performance as improvement of productivity. One could speculate that the cultural difference might lead to, for instance, that people living in societies with high PDI and low IDV (high degree of collectivism) like the Philippines and India will be more likely to answer what they believe the management wants to hear (espoused values) than Norwegians and British nationals with low PDI and high IDV. Conclusion: The above discussion does not support Hypothesis 3, that the importance of safety issues across nationalities is shared.

national culture dimensions and attitude factors from the factor analysis are the PDI and IDV factors compared with the attitude factors: employee and management attitude to safety and quality and attitude to preventive safety work in Table 14. The rankings of Hoftede’s factors and the attitude scores are very close and seem to confirm Helmreich and Merritt (1998) safety culture research on airline pilots. Helmreich and Merritt (1998) also found a link between national culture, organizational culture, and safety that indicates why PDI and IND are the most important national culture dimensions when it comes to safety. In situations where national and organizational culture are in harmony there are no stress factors that can influence safety, but in situations where the values in the national and the organizational culture are in conflict, this might lead to stress and influence safety. Authors like Mooji (2000), Perry (2002), and Smith (2004) have suggested that a dimension interpreted as ‘‘development’’ can explain the variation in Hofstede’s dimensions. Three ‘‘development’’ indicators: (a) Gross National Income (GNI) per capita, (b) number of telephones and (c) PC’s per 1,000 inhabitants representing a country’s ‘‘development’’ are included in Table 14 and 15, showing strong correlation both to Hofstede’s dimensions and the factors from the factor analysis. By splitting the countries into three distinct groups representing development levels (India and the Philippines representing low, Poland and Croatia representing medium, and UK and Norway representing high level of development) the rankings follow almost exactly the grouped attitude scores and Hofstede’s dimensions. However, another explanation for the correlation between Hofstede’s dimensions and the attitude factors can be produced. Individual values influence behavior, priorities, and thereby answers given in a survey (Schein, 1992). Trompenaars and Hampden-Turner (1997) use examples in his book ‘‘Riding the Waves of Culture’’ showing how differences in national culture influence answers to questions. According to Hofstede, people in high PDI societies teach obedience at school, subordinates in work organizations are

4.5. Variations across occupation: Organizational sub cultures (Hypothesis 4) Is safety culture a mix of several sub cultures or a single culture? Some evidence suggests that safety culture differs conceptually for different groups of workers in the organization (Sinclair & Haines, 1993; Clarke, 1999). Mearns, Flin, Gordon, and Flemming (1998) found significant differences in attitudes toward safety between staff at different levels in the offshore oil industry (managers vs. workers), but far less difference was found between operators who were contractors compared to those who were permanent staff. This latter finding might be explained by professional subcultures shared across the industry (same education, training, roles, etc.). McDonald, Corrigan, Daly, and Cromie (2000) found in their research in four aircraft maintenance organizations a strong professional subculture,

Table 13 Result of discriminant analysis (Vessels) Dependent Variable

Independent variable

Wilk’s lambda

Significance

Discriminate function coefficient

Vessels

Attitude

0,756

0,000

0,875

Eigen-value

Canonical cor.

Wilk’s lambda

Chi-Square

Significance

Hit-ratio

0,320

0,492

0,758

81,39

0,000

8,3

454

J.I. Ha˚vold / Journal of Safety Research 36 (2005) 441 – 458

Table 14 Variation in means and ranking across nationality for management and employee attitude to quality and safety compared with values for PDI and IDV dimensions from Hofstede and three measures representing ‘‘development’’ Nationality

Mean and (rank) Employee and Management Attitude to Safety and Quality

Power Distance (PDI)

Collectivism/ Individualism (IDV)

GNI per capita (US$) (2001)*

Telephones per 1000 inhabitants (2001)*

PC_s per 1000 inhabitants (2001)*

Philippines (Ph) Norwegian (N) British (UK) Croatian (Cro) Polish (Po) Indian (Ind)

1.61 2.40 2.02 1.65 1.89 1.24

94 31 35 72 55 77

32 69 89 33 55 48

1030 32620 25120 4410 4340 470

192 1545 1358 742 555 43

22 508 366 85 85 6

(2) (6) (5) (3) (4) (1)

(1) (6) (5) (3) (4) (2)

(1) (5) (6) (2) (4) (3)

(2) (6) (5) (4) (3) (1)

(2) (6) (5) (4) (3) (1)

(2) (6) (5) (3) (3) (1)

ing a view that each vessel is a small society, with its own sub culture. All vessels show factor scores on the positive side of the scale, indicating a positive overall safety culture in the company. Attitude (employee and management’s attitude to safety and quality) was the discriminator between vessels (Table 13), and by inspecting the items behind the factor one could indicate that the safety culture of the master and officers could be influential on the outcome. Another aspect to consider is the fact that the differences in scores between the vessels might be rooted in the nationality of the crew. The three vessels with an all-Indian crew showed the lowest mean factor score (highly positive safety culture), and the vessel with all-Norwegian crew showed the highest mean score (least positive safety culture). Conclusion: The finding above does not support Hypothesis 5, that the perception of safety issues across vessels is shared.

which was independent of the organization in addition to a significant difference between occupational groups. From the result of this study it is apparent that the different occupational groups share the perception that safety is important. However, significant differences emerged in the relative influence assigned to only one of the factors: knowledge (discriminant analysis) (Table 12). The officers and especially the masters registered showed a better knowledge than the rest of the crew and the galley section and ratings registered knowledge somewhat less positive than the rest of the crew. An explanation to this might be that as a master you have the total responsibility of everything onboard the vessel and you want everything to be in ship shape; and if you work in the galley safety issues might not be as focused as for the sailors working on deck and in the engine. This difference might be a real, but attribution theory can also explain this in terms of selfserving attributions by management who inherently are answerable for safety matters in their positions of higher authority (Dake, 1992; Weisz & Jones, 1993). Conclusion: The above discussion does not support Hypothesis 4, that the importance of safety issues across occupations is shared.

4.7. Relevance and usefulness for injury prevention In order to manage error proactively within an organization, management must be able to locate the causes of error. Management needs to ask where they are making their errors and what can be done to minimize their occurrence and impact. By asking the employees, one gets to know how well the company’s safety culture has penetrated through the organization. Using surveys to ask members of the organization to share their perceptions on safety related issues provides data

4.6. Variations across vessels: ‘‘Organizational Subcultures’’ (Hypothesis 5) The discriminant analysis shows significant difference between vessels on factors from the PCA analysis support-

Table 15 Correlation (Pearson) between nations on the four factors from the factor analysis and three ‘‘development’’ measures

F1: Employee and managements attitude to safety and quality F2: Knowledge F3: Attitudes to safety rules/instructions F4: Quality and safety experience PDI: Power Distance Index IND: Individualism/Collectivism GNI per capita Telephones per capita PCs per capita & Correlation is significant at the 0.05 level (2-tailed).

F1

F2

F3

F4

PDI

IND

1,000 ,749
,481 ,696
,843* ,666 ,885* ,888* ,902*

1,000
,489 ,186
,484 ,326 ,598 ,449 ,622

1,000 ,042 ,117 ,187
,034
,159
,084

1,000
,693 ,802 ,767 ,789 ,744

1.000 ,888*
,897*
,905*
,899*

1,000 ,808 ,744 ,783

GNI per capita

Telephones per capita

PCs per capita

1,000 ,951* ,998*

1,000 ,957*

1,000

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on a number of topics. In some instances, a problem might be identified as employees ignorance of the safety reporting procedures, in other cases the problem can be identified as lack of feedback or communication from management, and in others training can be identified as failing to meet the needs. Effective safety cultures rely on continuously updated information. Not only must the status of operations and opinions be monitored, data should be collected on trends in several indicators (factors). By monitoring the vessels it might be easier for management to prescribe the right medicine to improve safety. With minor modifications this research finding can be directly incorporated into training curricula, providing the necessary scientific basis for recommended actions. Companies can use indicators to benchmark performance between organizational units like vessels or factories or between organizations. Such safety indicators can also be integrated as key performance indicators (KPI’s) into organizational performance measurement systems like the balanced scorecard (Kaplan & Norton, 1996; Mearns & Ha˚vold, 2003). Both the canonical correlation and the multiple regression analysis showed significant relationships between factors from the factor analysis and ‘‘level of safety.’’ However the most important factor to monitor and include in a balances scorecard as a KPI seems to be ‘‘employee and management’s attitude to safety and quality.’’ National culture seems also to influence how people behave in safety matters, and must be taken into consideration. The multiple discriminant analysis (Table 11) showed that function 1 ‘‘Employee and management’s attitude to safety and quality’’ together with function 2 ‘‘Safety and quality experience’’ discriminated best between countries in this survey. Every national culture seems to have its strengths and weaknesses with regards to optimal ‘‘vessel management.’’ By understanding the cultural perspectives of other nationalities ratings and officers can be introduced to different ways to determine which ‘‘best practice’’ can be developed. On the ideal bridge the seafarers will follow standard operating procedures (SOP), yet retain the expertise to know when deviations from this SOP might be necessary and will know when to follow orders and when to question them. Bridge resource management (BRM) involves human to human interactions in which culture is transmitted. To be better prepared for training, instructors and management should study the research literature on the different national cultures like Hofstede (1991), Triandis (1994), Trompenaars and Hampden-Turner (1997), Helmreich and Merritt (1998), Helmreich and Willhelm (1999), and Smith (2004). The literature shows differences between national cultures amongst other values, communication styles, methods of conflict resolution, decision-making, and organizational behavior. According to Triandis (1994), individuals (in this case seafarers) from individualistic countries are more likely to define their professional norms by reference to their own personal standards, whereas individuals from collectivistic

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cultures are more likely to define their personal standards by reference to the group norm.

5. Limitations and future research directions The findings in this study should be viewed with consideration to limitations that are shared by most correlational studies that rely on self-reported data obtained from questionnaires. First, the design of this study was cross-sectional and all measures were collected during the same time period, so establishing sequential relationships between predictors and outcomes is difficult. This could be avoided by using longitudal data in further research. Second, all our measures were self reported, thus introducing the possibility of common method bias based on language and culture since many nationalities were represented in the sample. Third, the study was conducted in one shipping company in one industry, thus the results may not generalize to other shipping companies or other industries. Despite these limitations, the study revealed findings of theoretical and practical importance. The results suggest that factors influencing safety culture might exist across organizations and industries confirming previous research (Williamson, Feyer, Cairns, & Biancott, 1997; Mearns et al., 2000; Harvey et al., 2002). This study indicates an empirical link between a set of safety culture perceptions and safety behavior (Port State Control), however, more research has to be undertaken on the relationship between actual safety performance and safety culture making use of a variety of outcome variables. The challenge is to find reliable and valid outcome variables: accidents are fortunately too few in most industries and subject to random fluctuations, and are therefore not very reliable; near misses are difficult to collect, staff are reticent enough about reporting accidents; accident free period can suppress accident reporting. A number of safety climate and safety culture instruments exist, so one future research direction is to launch an international study including milieus from several countries to agree on a general instrument measuring safety culture making it possible to benchmark safety culture and test for differences within organizational sub cultures, between organizations within an industry sector, between industry sectors, and between countries or cultures. Several safety and quality managers working in shipping companies have said that they have a ‘‘gut feeling’’ that the safety culture is following the vessel, not the crew. With this in mind it might be interesting to find out if the safety culture is most influenced by the top management ashore, the vessel, the master, the officers, or the crew. This study indicates that the power distance and collectivism/individualism factor from Hofstede’s national culture measure correlates with officers and employees’ attitudes to safety and quality. A survey replicating Hofstede’s questionnaire together with questions measuring elements of safety culture might be an interesting follow up.

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It might also be interesting to see if some nationalities have better working relations and cooperate better than other nationalities in a working group, and thereby influence safety.

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Appendix A. Questionnaire 1. 2. 3. 4. 5.

What is your age? What is your nationality? How long have you been a seaman? How long have you been working for this company? How long have you been working on this ship altogether? 6. What is your present occupation? 7. What ship(s) have you been working on in your career? Mark all the ship(s) you have been working on 8. I am familiar with the company’s safety policy. 9. I know well the purpose of the ISM code. 10. I know well the purpose of ISO and ISO9002. 11. I know what a SAFIR report is. 12. The level of oxygen in a tank has to be higher than 25% before entering. 13. I know why we have a designated person in the company. 14. I know which work operations onboard that requires a checklist. 15. All new crewmembers get proper safety training before they start working. 16. All drills are carried out in a realistic manner. 17. We do have realistic drills once a month. 18. All drills are unannounced. 19. I sometimes lie awake because I_m thinking about problems at work. 20. I can locate the nearest fire apparatus wherever the fire should break out onboard the ship. 21. Officers often discuss safety issues with ratings. 22. Officers are aware of the main safety problems onboard. 23. A worker who acts unsafe is disciplined. 24. Officers do all they can to prevent accidents onboard. 25. Communications about safety issues are good in our company. 26. Employees are given enough training to do their work tasks safely. 27. I sometimes have to turn a blind eye to the strict safety rules to get the job done on time. 28. I find difficulties in understanding the purpose of the safety instructions. 29. Safety instructions are in general hard to understand. 30. I am afraid of asking questions related to safety. 31. We always report accidents/incidents. 32. XXXXA/S will never choose time saving and economic actions before safety. 33. We always deliver undamaged cargo. 34. We always have enough spare parts. 35. XXXX ships are better maintained than ships from other companies. 36. All enclosed spaces are tested with an oxygen analyser before entered. 37. A SAFIR report is written with every unsafe act.

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38. There is always an extra person in addition to the mate on the bridge when sailing in low visibility, (such as night-time, foggy weather, etc). 39. Everybody always use helmet during mooring and cargo operations. 40. Onboard XXXX ships safety has improved a lot since I started in the company. 41. I often experience stress at sea 42. In this organisation no actions are taken without approval of an officer 43. Even small matters on the job have to be referred to the master 44. XXXX ships can be friendlier to the environment 45. XXXX need Environmental Management Systems in the future.

46. I am very satisfied with the safety onboard 47. I am very satisfied with the quality work onboard Jon Ivar Ha˚vold BSc, BBA, MBA, is currently a PhD candidate at the Norwegian University of Science and Technology in Trondheim, Norway. ˚ lesund University Jon Ivar Ha˚vold teaches safety management courses at A College for nautical students. He has worked for five years as a metallurgist, for four years as a scientist with the Norwegian Telecommunication Authorities Research, and for 15 years in the insurance industry as a manager responsible for underwriting, claims and sales ˚ lesund University (including insurance of vessels), before he joined A College. His research has appeared in journals such as Maritime Policy and Management, TQM Magazine, Zagadnienia Eksploatacji Maszyn (Polish Academy of Science Quarterly) and Policy and Practice in Health and Safety.