Risk perception and safety on offshore petroleum platforms — Part II: Perceived risk, job stress and accidents

Safety Science, 15 (1992) 53-68 Elsevier

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Risk perception and safety on offshore petroleum platforms - Part II: Perceived risk, job stress and accidents Torbjprrn Rundmo Institute of Transport Economics, P.O. Box 6110, Etterstad, 0602 Oslo, Norway (Received 9 April 1991; accepted 13 January 1992)

Abstract Rundmo, T., 1992. Risk perception and safety on offshore petroleum platforms, Part II: perceived risk, job stress and accidents. Safety Science, 15: 53-68. In this paper relations between perceived risk, job stress, and frequency of accidents and near accidents are analyzed. The analysis was based upon a self-completion survey among petroleum personnel (n =915) on the Norwegian Continental Shelf. The survey, conducted in spring 1990, drew respondents from five companies and eight installations. The response rate was 92%. Physical working conditions, safety and contingency factors, and assessment of the respondents’ risk were found to exert influence on the number of accidents and near accidents. Results are presented and implications discussed.

R&urn6 Dans ce document les relations existantes entre le risque pergu, le stress du travail et la frequence des accidents ou des presqu’accidents sont analydes. L’analyse est bake sur une etude en r&e directe parmi le personnel petrolier de la Norwegian Continental Shelf (n =915). L’Ctude, men&e au tours du printemps 1990, a design4 des interlocuteurs dans cinq compagnies et sur huit installations. Le taux de response ktait de 92%. 11s’est averk que les conditions de travail physique, la securite, les facteurs d’urgence, l’dvaluation des risques par les interlocuteurs ont une influence sur le nombre d’accidents et les presqu’ac~idents. Les r&&ats sont present& et les implications discutees.

Zusammenfassung In diesem Artikel wird der Zusammenhang analysiert, der besteht zwischen dem RisikobewuRtsein, dem Arbeitsdruck und dem MaRe, in dem Unfalle stattfinden oder fast stattfinden. Diese Analyse basiert auf einer eigenen Untersuchung durch Arbeitnehmer in der Erdiilindustrie (n=915) auf dem norwegischen Schelf. An dieser Un~rsuchung, die im Friihjahr 1990 angestellt

0925-7535/92/$05.00

0 1992 Elsevier Science Publishers B.V. All rights reserved.

54 worden ist, nahmen ftinf Betriebe und acht Anlagen teil. 92% von ihnen haben reagiert. Wiihrend dieser Untersuchung ist festgestellt worden, da& die physischen Arbeitsbedingungen, die Sicherheitsfaktoren und die Faktoren, die mit Vorkehrungen gegen unvorhergesehene Ereignisse zusammenhangen, Einfluf3 auf die Anzahl der Unfdle oder Fast-Unfalle haben. Die Ergebnisse werden wiedergegeben und die Implikationen werden besprochen.

1. Introduction

Sutherland and Flin (1989) reviewed literature available on working conditions, strain, health and safety in offshore petroleum and fishing industries. They concluded that “more research is needed in the offshore sector before the extent of stress, possible causes, and strategies for minimizing any harmful effects can be properly determined”. Another review of literature about working conditions, health and safety in the offshore oil industry (Mykletun and Mellem, 1988) resulted in the same conclusion. Understanding the relations between risk perception, job stress, and occupational accidents may provide a basis for proposing measures to enhance industrial safety. Up till now, no studies have been especially aimed at analyzing these relations in the offshore petroleum industry. Perceived risk, i.e. a person’s subjective assessment of risk sources, may be quite different from objective risk, i.e. the risk which exists whether we are aware of it or not and regardless of whether we are concerned about it (Risk Research Committee, 1980). Defects in the perception of risk may be a causal factor in occupational accidents. Success in avoiding accidents will depend on the processing of cognitions associated with a situation. Likewise, value systems and motivation are important factors. Defects in perception of objective risk can be caused by incorrect estimation of objective risk and over-estimation of one’s own abilities in mastering situations with objective risk. An extensive number of theories have focused on how human malfunctions and human information processing can cause human error (see e.g. Reason, 1987; Norman, 1981; Rasmussen, 1982,199O; Hale and Glendon, 1987). Wilde’s (1982,1988) theory of risk compensation and Naatanen and Summala’s ( 1974) theory of risk behaviour are examples of theories which have focused behavioural adaption and coping responses. (See e.g. Rundmo ( 1990a) for a review of accident theories and models.) The underlying motivation for studying defects in subjective assessments is the assumption that “correct” perception of risk will prevent accidents from taking place because “handling risk in an emergency situation requires a realistic assessment of the situation” (Marek et al., 1985a). An accident happens when the subjective evaluation of potential risk sources does not correspond to the real situation. When risk perception is in accordance with the “real” situation human actions will be taken to avoid accidents if necessary.

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Reason (1988, 19891, and Wagenaar and Reason (1990) show that elimination of risk factors connected to working conditions frequently have much more impact on the risk level than measures aimed at reducing unsafe actions. In addition, if risk perception exerts influence on the number of accidents and a person maintains exposure to the risk source, the reason could be that he has made a conscious decision to stay exposed. The cause could be that sustained exposure gives benefits which are not possible to achieve otherwise. Moreover, the person can be aware that his ability to cope with dangers is limited. Consequently, he feels unsafe. When people feel unsafe in a situation their behaviour is affected. This in turn increases the probability of an accident. Then, physical and organizational working conditions exert influence on perceived job stress and strain, as well as on the perception of risk. This in turn affects the employees’ capacity to cope in situations with objective risk. Physical and organizational working conditions as well as perceived job stress and risk among offshore personnel have been assessed in a number of studies (Marek et al., 1985a,b; Norman and Brebner, 1985; Tangenes et al., 1985). However, correlations between these factors and accidents have so far not been analyzed to the same extent as correlations between job stress, strain, performance, and health (see e.g. Eide et al., 1985; Cohen, 1980; Person and Sjijberg, 1978; Caplan et al., 1975; Margolis et al., 1974; French et al., 1974; Kahn, 1973). The Norwegian Petroleum Directorate concluded that there was a need for research on specific offshore related issues in these matters. The specific aims of this study were to determine the relations between: ( 1) Job stress, perceived risk, and the frequency of reported accidents and near accidents; (2 ) Physical and organizational working conditions, and subjective evaluations of risk and job stress; (3) Physical and organizational working conditions, and the frequency of accidents and near accidents. If perception of risk and job stress, i.e. employees’ subjective evaluations of objective risk and organizational workload, mirror physical and organizational working conditions, exert influence on strain, i.e. physiological and psychological responses of individuals to these conditions, it is reasonable to expect these factors also to affect the reported numbers of accidents in a workplace. We would expect working conditions to affect the workload factors (perceived risk and job stress). In addition, workload exerts influence on the number of occupational accidents and near accidents. If so, the subjective evaluations are realistic. No measures need to be directed at changing perception of risk and other subjective evaluations. Measures should be directed at changing the physical and organizational factors determining subjective assessments. These assessments can also be influenced by personality factors, for example sensation-seeking tendencies (Zuckerman, 1979). We expect personality fac-

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tom to be of minor importance for job stress and risk, This is due to our assumption that the personnel’s subjective assessment of risk is realistic.

2. Method 2.1. Sample All platforms on the Norwegian Continental Shelf were stratified and a sample of installations was drawn. The sample is described in part I of the study (Rundmo, 1992 ). 2.2. Contests of the questionnaire The questionnaire asked respondents to evaluate potential sources of risk, job stress factors, physical working conditions, and safety and contingency factors. The questionnaire contained seventeen potential sources of risk, ten indicators of job stress, five items describing physical working conditions and thirteen test items for safety and contingency aspects. Test items measuring specific sources of risk and work tasks involving danger were reported in part I. The remaining indexes and their items are listed in Table 1. The evaluations included ratings on a five-point rating scale for each test item. The scale for risk perception ranged from “very safe” to “very unsafe.” The job stress scale ranged from “very satisfied” to “very dissatis~e~‘. Physical working conditions were likewise measured on a five-point scale ranging from “very exposed” to “not at all exposed”. The scale for safety and contingency aspects ranged from “very ideal” to “not at all ideal”. The questionnaire was pre-tested before the main study started. In the pretest thirty petroleum employees filled in the questionnaire and made proposals which improved the questionnaire. 2.3. Statistical procedures Causal relations were analyzed by the LISREL system. LISREL analysis (analysis of linear structural relationships by the method of maximum likelihood; Jiireskog and Soderbom, 1979) is well suited for estimating relations between latent or unobserved variables (normative concepts), which each has one or more indicators (test items) that are measured (Ringdal, 1987). In the LISREL analysis several estimation methods can be used. In this study the maximum likelihood (LISREL-AL) method was used. The analysis was carried out using a LISREL software package especially designed for such analysis (Jijreskog and Siiderbom, 1989). The system is a general approach providing

57 TABLE 1 List of test items Area

Test item

Job stress

“The tasks I carry out have been planned in detaif by others” “My superior issues differing and contradicting orders” “I have sufficient amount of freedom to decide on my pace of work” “It is easy to predict the expectations put on me by others” “I can take short breaks whenever I wish without to take account of other people” “I know what I can expect from others” “I can do my work independently and according to my own views” “I can decide when and how each individual work-task shall be implemented” “I have a fair opportunity of influencing the decisions to be made by my superiors” “My immediate superiors ask for my advice before making their decisions” “I am satisfied with the way I am kept informed of what takes place on the platform”

Physical work. conditions

“Ergonomic workload” ‘~Noise/vibrations” ~‘Draught/cold/humidity” “Cold/hot working climate” “Vapor, inflammable health-hazardous

The safety and contingency

substances”

measures “Control and inspection routines in the safety work” “Work instructions” “Safety instructions” “Follow up and measures taken after injuries and accidents have taken place” “First aid training” “Contingency training” “Safety training” “Order and cleantiness at the place of work” “Access to emergency exits/escape routes” “Protection and safety devices on machines and equipment” “Marking and sign posting” “Availability of personal safety equipment” “Use of personal safety equipment”

a number of possibilities for testing causal models. Path analysis and a complete LISREL analysis were carried out. Path analysis can be used for comparing the magnitude of direct and indirect effects between a set of variables. The higher the path coefficient, the stronger is the effect that one variable has on another variable. Path coefficients vary between - 1 and + 1, being analogous to standardized partial regression coefficients. The standardized regression coefficient expresses the change, measured in relation to standard deviation, 0, in t,he expected value of the effect variables y, when the cause variables x are changed according to their standard deviation s,. Error terms in the path model are termed e. Complete LISREL analysis consists of two parts; the structural equation model and the measurement model. The structural equation model specifies causal relationships among the latent variables and gives a description of the causal effects. The latent, endogenous variables (dependent variables) in a model are designated as eta-variables (rf), and the exogenous variables (independent variables) as k-variables (l). Effects of an r/-variable on another ?Ivariable are expressed in the beta-matrix (p), which designates endogenous effects, i.e. effects which are explained by the model. The effects of a <-variable on an q-variable are expressed in the gamma matrix (;t). Also presented are models where several directly observed x-variables effect one unobserved lat,ent variable. The latent endogenous variable is, however, measured by several ~8indicators. An assignment of the explained as well as the unexplained variance is also given. R2 indicates the proportion of variance in the effect variable that is explained by the cause variables. The error term or the variation in an endogenous variable, which cannot be attributed to other variables in the model, is designated <. The measurement model specifies how latent variables depend upon the observed variables. The measurement model for the y-variables, which are indicators for t,he unobserved endogenous variables, is designated by the lambday matrix (2, f . Parameters for the x-variables, which are indicators for the latent exogenous variables, are expressed in the lambda-x: matrix (Ax 1. Correlations between each indicator and the theoretical concept it measures are interpreted in the same manner as ordinary factor loadings. As a rule, the indicators contain measurement errors, i.e. unexplained variances which cannot be att,ributed to the latent variables. Delta (6, ) designates measurement errors in the AX-indicators and epsilon (t,,) measurement errors in the J.,-indicators. Theta-epsilon (0, ) is a co-variance (correlation ) matrix for measurement errors in the y-variables. Theta delta (8<$) is the corresponding matrix for measurement errors in the x-variables. Phi (@) and psi f w) are co-variance matrixes for correlations between r-variables and between the error terms of endogenous variables zeta (<). Together, these symbols and the eight matrices form the LISREL model. The LISREL model can be used to search for causal relationships only in-

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directly, by eliminating or modifying poor models that give predictions which are inconsistent with the data. Correlations between model and data are examined by means of the chi-square test and tests of general adaptability (GFI) and modified adaptability (AGFI). A non-significant x2 shows that there are no significant differences between the predicted and observed co-variance matrixes, i.e. that the theoretical model is in accordance with the observed data. The models presented in this article all satisfied the demand for non-significance (p>O.lO). Factor analysis was used as an auxiliary method for detecting structures underlying direct evaluations. Principal component analysis was used. Missing values were excluded listwise. Single test items were replaced by indexes in the models and defined as observed variables for new theoretical, unobserved dimensions. This was necessary due to the large number of items used to measure, for example, risk perception and job stress. For each individual the scores on test items belonging to a certain index were added without differential weighting. To test the reliability of the dimensions Cronbachs’ (Ywas used (see Norusis, 1988).

3. Results 3.1. Job stress, perceived risk, and reported accidents/near accidents Job stress and perceived risk could both be regarded as workload factors. Workload can lead to an increased strain and a reduction of the ability to cope in danger situations. It was, therefore, expected that in conjunction they would also affect the liability to injuries and human errors. The hypothesis to be tested had the structural equation

and the measurement model equations for the variables were y1 =L$;‘q+El, x1=3L$;)t1 61,

yz =3L&‘v+ta, x2+1$;‘&

+&,

x3=A&‘&

+cJ,

x4=A:;‘~2

+cY4.

Factor analysis showed that the job stress questionnaire items consisted of two underlying dimensions (factor loadings given in parentheses): (a) Time independence: I can do my work independently and according to my own views (0.76)) have sufficient amount of freedom to decide on my pace of work (0.74)) can decide when and how each individual work task shall be implemented (0.66), can take short breaks whenever I wish without having to take account of other people (0.59) Cronbachs’ cy=0.745); (b) participation and cooperation: I know exactly the expectations of me held

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by others (0.62)) my immediate superiors ask for my advice before making their decisions (0.60)) I know what I can expect from others (0.56 ), I have the opportunity to influence the decisions to be made by my superiors (0.54)) my superior issues differing and contradicting orders (0.53), I am satisfied with the way I am kept informed of what takes place on the platform (0.53) (Cronbachs’ cy = 0.727). In addition, Cronbachs’ cy was found to be satisfactory for each single personnel group in both indexes (Rundmo, 1990b). Perceived risk was analyzed in the same way as job stress. Three reliable factors emerged: (a) Subjective evaluations of safety related to disasters and major accidents, such as fire, explosion and blow-out (Cronbachs cy=0.873); (b) ordinary occupational accidents, e.g. the danger of a fall to a lower level, injuries from machines or machine parts, slippery surfaces and falling objects (Cronbachs cy= 0.852); (c) post accident measures, such as evacuation possibilities, reliability of the alarm systems and escape routes (Cronbachs’ CY= 0.813 ). The structure of the risk perception factors is described in part I. Evaluation of reliability of post accident aspects was not included in the model. This was due to inconsistency between the theoretical model and the data when this factor was included. Post-accident aspects are not supposed to be a causal factor in accidents. However, they affect perception of risk. Dependent variables in the model were self-reported accident frequency/ injury frequency, and experience of near accidents/human errors. The first variable, y,, was measured on a two-point scale and the second, yZ, on a threepoint scale. The supposition for multivariate normality in LISREL-ML was not fulfilled. However, the distributions were not extremely skewed. According to Colbjornsen et al. (1984) LISREL-ML can be used under such conditions. The effect is probably that measurement error is overestimated (Hauser et al., 1983). The indicators will then not be as reliable as they really are. The survey included minor injuries, which did not result in absence from work, as well as more serious injuries. Of a total of 355 injuries 40% occurred during the execution of planned interventions, 30% when the victim carried out handling tasks. 10% during the conduct of operational tasks, 4-5% happened during the execution of catering functions and the remaining 5-6% of the accidents was due to auxiliary activities. A large number of the injuries resulted in repatriation from the installation (54% ). Only 12% of the injuries entailing absence from work were treated on the platform. The remaining 34% were minor accidents not resulting in absence into the next shift. The ratio between reported accidents and near accidents was 1: 3. Self-reported accident frequency and the Norwegian Petroleum Directorate’s annual accident figures gave range values separately for four personnel groups. The groups were maintenance and construction personnel operators,

x, Time

v, Near Accidents

---_)Coopeiation

C---‘r,=

R2=.25 r =75

s,= 13

----+

Ordinary 0ccupat10nal accidents

X2=8.94, d f.=6. f% 175 Goodness of Fit Index (GFI)=.997 Adjusted GFI= 989 n=765-915

Fig. 1.Effectsof job stressandperceived riskon objectiverisk.

drillers, and technical-mechanical personnel. Self-reported range values were identical to statistical figures. This indicated that there was reason to have confidence in the self-reported data on accidents. The test items in each dimension were added and the four indexes were included in the model. The model is shown in Fig. 1. The results of the analysis were: - Job stress and perceived risk contributed significantly to injuries and human errors (R 2= 0.25 ) . Contributions from the two exogenous factors on the endogenous factor were R” X 100, i.e. 25%. y1-0.34 and y2= 0.27, which indicates the effects of each endogenous dimension on the number of accidents and near accidents. - The correlation between perceived risk and job stress ((p) was 0.33. The analysis confirmed that job stress and perceived risk affected injury liability and human errors. 3.2. Physical working conditions, safety and contingency aspects, individual characteristics and workload Physical working conditions, safety and contingency aspects, and individual characteristics (e.g. sensation seeking) can affect perception of risk and job stress. Therefore, it was expected that variations in these factors would influence both the degree of perceived risk and job stress. The hypotheses to be tested are shown in the model in Fig. 2. It had the structural equation

83

62

2 ‘~3.23, Goodness Adjusted n=809

Fig. 2. Effects of physical working conditions, acteristics on perceived workload.

d.f =2, ~=.I99 of ht index GFI=

(GFI)=.999

989

safety and contingency

factors, and individual char-

The measurement model equations for the y-variables were

The model consists of a single unobserved endogenous latent variable. Test items for physical working conditions were selected from an earlier published study on living conditions in Norway, and included ergonomic, climatic, and chemical working conditions, noise and draught/high temperature/cold (Colbjornsen and Hernes, 1983; Arnesen, 1983). Safety and contingency aspects included employee evaluations of inspections and audits, safety instructions, training, evacuation possibilities, and personnel equipment (see e.g. Rundmo and Saari, 1988). Individual characteristics included test items from Zuckermann’s sensation-seeking scale (SSS) and from Johnson’s BOREdom scale (Zuckermann, 1979; Johnson, 1967) (see Table 1). The three scales were measured with five, thirteen and eight test items (see Table 1) . Scale reliabilities were satisfactory. Cronbachs’ cy for physical work

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environment was 0.829, for safety and contingency aspects 0.908, and for individual characteristics 0.688. The latent variable was measured by means of two y-indicators, the indexes job stress and perceived risk. ‘?he analysis showed the following: (ii Physical working conditions, safety and contingency aspects as well as individual characteristics explained almost half the variance in workload (job stress and perceived risk) (R2=0.45). (2) Safety and contingency aspects were found to be very important. They contributed considerably to workload ( y2=0.63). As seen in the figure, evaluations of safety and contingency measures exerted a stronger effect on risk perception than on job stress. safety and contin(3) The analysis confirmed that physical job environment, gency aspects, and individual characteristics (selected SSS- and BOREindicators) affected perceived risk and job stress. The purpose of the model in Fig. 3 was to clarify how much of the variation in perceived risk could be explained by job stress, strain, physical working conditions, safety and contingency aspects, and personal factors. The model is an ordinary path model which proved to be well suited for clarifying both direct and indirect relations between individual differences, strain and perception of risk. Strain was measured through items for physical and psychological wellbeing (Cronbachs’ CY= 0.875). The results of the analysis were:

Y, kl2

stram

x”4.96, Goodness Istics

Ad]usted

d.f.=4,

-

p=.292

of Fit Index GFI=.991

n=691

Fig. 3. Path diagram showing cause factors of job stress, strain and perceived risk.

e= 86

(GFI)=l.OO

64

(1) Perceived risk was primarily a consequence of job stress and strain (R2=0.26). (2) Good safety and contingency conditions caused an increase in perceived lack of safety. (3 ) Physical working conditions had primarily an indirect effect on perceived risk. The same applied to individual characteristics, via their effect on strain. 3.3. physical wormingconditions, safety and contingency factors, strain, and accidents/near accidents The purpose of the model in Fig. 4 was to clarify whether variation in physical working conditions, safety and contingency factors, and strain could explain why people were exposed to accidents. The model to be tested had the structural equation

The measurement model equations for the y-variables were y1 =A;:‘~+C,,

y~=A~‘Y/+C,

Fig. 4 shows the factors which affected injuries and human errors (R” = 0.23 f . Their effects were:

Near accidents

+----

k

82

I,

49

x ‘:, 24. d f =2. p: 537 Goodness of Fbi Index (GFI)Adjusted GFI= 996 n=765-915

Fig. 4. Effects of physical working conditions, act,eristics on objective risk.

safety and contingency

999

factors, and individual char-

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(1) Bad physical work conditions increased the probability of injuries and human errors. (2) Strain also increased the probability of injuries. (3) The more satisfied employees were with safety and contingency conditions, i.e. the more satisfactory these were, the fewer injuries they experienced. Individual characteristics, i.e. a desire for risk and sensation-seeking behaviour, were included as a fourth exogenous factor, y, (x2 = 1.99, d.f. = 3,~ = 0.58). However, this did not improve the model significantly ( Ax2 = 1.99 - 1.24 = 0.75, d.f. = 3 - 2 = 1). A 0.75 improvement is not significant, indicating that individual characteristics primarily had an indirect effect via strain, as shown in Fig. 3.

4. Discussion The results show that risk perception and strain contributed considerably to the frequency of injuries and human errors. Furthermore, good safety and contingency conditions were particularly important in risk perception. Physical working conditions were among the factors causing an increase in perceived lack of safety and in strain. Safety and contingency factors, physical working conditions and a tendency towards sensation-seeking also directly affected the frequency of reported accidents and human errors. Focal area efforts should therefore primarily be directed towards: (1) Job-stress factors, in particular the pa~icipating influence in actual job execution and the predictability of what can be expected from others. These items proved to be important job-stress factors affecting both risk perception and safety. (2) Safety and contingency factors, because they strongly affected perceived workload. Efforts should be directed at improving these factors. (3) Physical working conditions, such as ergonomic, climatic, and chemical working conditions should be further evaluated because they directly affect the frequency of reported accidents and near accidents. In every method for estimating objective risk, accident frequency is an important element. In this study the relation between risk perception and the frequency of accidents and near accidents was considered to be important. When a person’s risk perception deviates from objective risk, his actions in critical situations can lead to an accident. Risk perception can be decisive in handling such situations. In accordance with this, risk perception was found to influence objective risk (see Fig. 1). In this study two indicators for objective risk were used: personal experience of accidents and near accidents. Employees’ evaluations of physical working conditions, safety and contingency aspects, and sensation-seeking tendencies were taken as real indicators

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of these characteristics. Such evaluations have in general proved t,o be a useful source of information concerning work conditions on oil platforms (Marek et. al., 1987). This was especially found to be true for the most experienced personnel groups on the Statfjord Field (Marek et al., 1985a). The majority of respondents in our study had extensive experience of offshore petroleum production. Risk perception was not independent of physical and organizational working conditions (see Figs. 2 and 3 ). Bad physical conditions and safety and corningency measures resulted in an increased number of respondents who did not feel safe (see Fig. 4). Risk perception was found to mirror the true working conditions and status of the accident preventive work on the platforms, as perceived by the personnel. These evaluations were realistic. Subjective evaluations are therefore an important, source of information when decisions concerning accident, prevention measures are to be taken. To change perception of risk, safety measures should therefore be directed at improving physical and organizational working conditions.

Acknowledgements This study was initiated and financed by the Norwegian Petroleum Directorate (NPD ). The author especially wishes to thank Mrs. Ingrid Arstad, Senior Engineer, and Mr. Per Saltro, Manager of Safety Department at NPD, for their support during the work and the participating petroleum companies for their cooperation and interest. Professor Dr. Julius Marek at the University of Bergen, who so suddenly died at. Christmas 1991,gave me the benefits of his great experience and insight into the field of safety research.

References Arnesen. C..&., 1983. Employment,. In: I. Stangeby (Ed.), Social Survey 1983. Central Bureau of Statistics of Norway, Oslo: 91-126 (in Norwegian). Caplan, RD., Cobb, S., French, J.R.P., Harrison, R. and Pinneau, S.R., 1975. Job Demands and Worker Health: Main Effects of Occupational Differences. Washin~on DC. HEW Publicat‘ion No. (NIOSH): 75--160. Cohen, S., 1980. After-effect.s of stress on human performance and social behavior: a review of research and theory. Psycholog. Bull., 88: 82--108. Colbjemsen, T. and Hernes, G., 1983. Class differences in work environment. Tidsskr. for Samfunnsforsk., 24: 441-468 (in Norwegian I. Colbjsmsen, T., Hermes, G. and Knudsen, K., 1984. Causal analysis in the social sciences: An introduction to LISREL. Tidsskr. for Samfunnsforsk., 2.5: 5-34 (in Norwegian). Eide, R., Hellesey, O.H. and Gogstad, AC., 1985. Associations between experiences of environmental factors and health. In: O.H. Hellesery (Ed.), Work Environment Statfjord Field: Work

67 Environment, Health and Safety on a North Sea Oil Platform. Universitetsforlaget, Oslo: 317324. French, J.R.P., Rogers, W. and Cobb, S., 1974. Adjustment as per~n-environment fit. In: D.A. Coello, D.A. Hamburg and J.E. Adams (Eds. ), Coping and Adaption. Basic Books, New York: 97-106. Hale, A.R. and Glendon, A.I., 1987. Individual Behaviour in the Control of Danger. Elsevier, Amsterdam. Hauser, R.M., Tsai, S.L. and Sewell, W.H., 1983. A model of stratification with response error in social and psychological variables. Psychol. and Education, 56: 20-46. Jiireskog,K. and Soderbom, D., 1979. Advances in Factor Analysis and Structural Equation Models. Cambridge, MA, Abt. Books. Jiireskog, K. and Siiderbom, D., 1989. LISREL 7. Users Reference Guide. Software Inc., Moresville. Johnson, SC., 1967. Hierarchical clustering schemes. Psychometrica, 32: 242-254. Kahn, R.L., 1973. Conflict, ambiguity, and overload: three elements in job stress. Occup. Mental Health, 3: 2-9. Marek, J., Tangenes, B. and Hellesey, O.H., 1985a. Experience of Risk and Safety. In: O.H. Hellesey (Ed.), Work Environment Statfjord Field: Work Environment, Health and Safety on a North Sea Oil Platform. Universitetsforlaget, Oslo: 142-174. Marek, J., Tangenes, B. and Hellesey, O.H., 1985b. Organizational and Social Aspects of Jobs. In: O.H. Hellesrny(Ed.). Work Environment Statfjord Field: Work En~~nment, Health and Safety on a North Sea Oil Platform. Universitetsforlaget, Oslo: 198-229. Marek, J., Iversen, E. and Hellesey, O.H., 1987. Risk, organization and safety on an oil platform. In: W.T. Singleton, J. Hovden (Eds.), Risk and Decisions. Wiley, New York: 123-138. Margolis, B.L., Kroes, W.H. and Quinn, R.P., 1974. Job stress: An unlisted occupational hazard. cl. Occup. Med., 16: 10-15. Mykletun, R.C. and Mellem, J.T., 1988. Safety, risk perception and stress: pre-study report. Rogalandsforskning, Stavanger. Report no. RF 21 l/88 (in Norwegian ) . N&t%-ren, R. and Summala, H., 1974. A model for the role of motivational factors in drivers’ decision-making. Accid. Anal. and Prevention, 314: 243-261. Norman, D.A., 1981. Categorization of action slips. Psychol. Rev., 88: 1-15. Norman, N. and Brebner, J.A., 1985. The Offshore Health Handbook: A Practical Guide to Coping with Injury and Illness. Martin Dunnitz, London. Norusis, M.J., 1988. SPSS/PC+: Advanced Statistics. SPSS Inc., Chicago, IL. Norwegian Petroleum Directorate, 1985-1988. Annual Reports. NPD, Stavanger. Person, L.O. and Sjoberg, L., 1978. The influence of emotions on information processing. Psychological Reports, University of Gothenburg, Gothenburg. Rasmussen, J., 1982. Human errors: a taxonomy for describing human malfunction in industrial installations. J. Occup. Accid., 4: 311-333. Rasmussen, J., 1990. Human error and the problem of causality in analysis of accidents. In: DE. Broadbent, J. Reason, A. Baddeley (Eds.), Human Factors in Hazardous Situations. Clarendon Press, Oxford: 1- 12. Reason, J.T., 1987. A framework for classifying errors. In: J. Rasmussen, K. Duncan, J. Leplat (Eds.), New Technology and Human Error. Wiley, New York: 5-14. Reason, J.T., 1988. Resident pathogens and risk management. Paper presented at The First World Bank Workshop on Safety Control and Risk Management, Washington, DC, October 1988: 18-20. Reason, J.T., 1989. The human contribution to ‘organizational accidents’. Draft Paper for The Second World Bank Workshop on Safety Control and Risk Management, Karlstad, November 1989: 18-20. Ringdal, K., 1987. Causal Analysis in the Social Sciences. Norwegian University Press, Oslo (in Norwegian).

68 Risk Research Committee, 1980. Accidents in Norway: how do we perceive and handle risk? Royal Norwegian Council for Scientific and Indust.rial Research (NTNF), Oslo. Rundmo, T., 1990a. Safe Behaviour: Accident Theories, Modets and Psychological Methods. Trondheim. SINTEF-report no. STF75 A90007 (in Norwegian). Rundmo, T.. 1990b. Risk Perception and Safety. Norwegian Petroleum Directorate. Trondheim/ Stavanger. Research Report no. STF75 A90035 (in Norwegian). Rundmo, T., 1992. Risk perception and safety on offshore petroleum platforms -- Part I: Risk perception. Safety Sci., 15: 39-52. Rundmo. T. and Saari, J., 1988. Safety program factors. In: J. Hovden (Ed,), Safe Behaviour: Accident Preventive Measures. Tampere University of Technology, Department of Mechanical Engineering. Uccupational Safety Engineering, Tampere. Report no. 50: 39-88 (in Finnish 1. Sutherland, K.M. and Flin, R.H., 1989. Stress at sea: A review of working conditions in t.he offshore oil and fishing industries. Work and Stress, 3: 269-285. Tangenes, B., Marek, J. and Hellessy. O., 1985. The physical work environment. In: O.H. Hellesery (Ed. 1.Work Environment Statfjord Field: Work Environment, Health and Safety on a North Sea Oil Platform. Universtetsforlaget, Oslo: 175-197. Wagenaar, W. and Reason, J.T., 1990. Types and tokens in road accident causation. Leiden liniversity/Universit,y of Manchester: paper, not published. Wilde, G.J.S., 1982. The theory of risk homeostasis: implications for safety and health. Risk Anal., 4: 209-225. Wilde. G,3.S., 1988. Risk homeostasis theory and traffic accidents: propositions. deductions and discussion of recent commentaries. Ergonomics, 31: 441-468. Zuckermann, M., 1979. Sensation Seeking: Beyond the Optimal Level of Arousal. Lawrence Erlbaum Associat,es Publishers. NJ.