Factors affecting the classes of causal explanations by industrial safety specialists

Transcription

1 Factors affecting the classes of causal explanations by industrial safety specialists Kathryn Woodcock, Ph.D., P.Eng. Assistant Professor, Industrial and Manufacturing Engineering Rochester Institute of Technology, Rochester NY Alison Smiley, Ph.D. University of Toronto & Human Factors North Inc. 118 Baldwin St., Toronto ON M5T 1L6 Causal explanations determine how industry acts to prevent accident recurrence. Little is known about industrial accident cause-finding practices. Sixteen practicing safety specialists performed three common tasks: walkthrough accounts of subjects' own experiences, non-interactive exercises and interactive simulated investigations to elicit schemas. Analysis examined the relation of individual differences in knowledge sources and organizational factors to the causal concepts retrieved and the process of reasoning. Particular interest was taken in influences on the retrieval and use of worker-related causal factors and management or design-related causal factors. No evidence was observed of overattribution to worker-centered explanations, nor did the investigation approaches resemble attribution judgements. The predominance of management/design causal explanations does reflect current explanatory fashions, although the self-serving bias is the one that best describes the observed effects. Status and to a lesser degree operations values and operations pressures influenced the proportion of factors retrieved as well as the class of factors used for starting and concluding investigations. 1 Introduction Causal explanations determine how industry acts to prevent accident recurrence. A previous paper [1] discussed the complexity of the concept of bias as applied to the understanding of causal explanations. Little is known about industrial accident causefinding practices. Consequently, whether these practices are biased is a matter of speculation. The present study aimed to examine causal reasoning among a sample of actual practitioners, using a variety of knowledge elicitation tasks.

2 2 Method Sixteen subjects were recruited from a local chapter of practitioners. All subjects had completed self-report questionnaires describing their educational and continuing education backgrounds, their work history and current position, as well as several questions about work beliefs. As the interviews were conducted at a central research site, a sample of convenience was required, introducing a self-selection factor. However, the sample reflected a range of characteristics both in the observed independent variables (experience, education, status, etc.) and in other characteristics. All subjects performed the same tasks. First, they recounted a couple of investigations they had experienced, in the chronological order of their own information acquisition. Secondly, six accident descriptions were provided, and subjects were asked to state the information they would initially seek if asked to determine the cause. Third, they performed two interactive simulated investigations. Subjects were provided with the initial accident report. Additional facts and observations were provided on request. The researcher impersonated any character in the accident story that the subject wished to question. The additional information was standardized across subjects by reference to a hidden story tree. There were no limits imposed on hypothesis revision and closure. Subjects could ask as few or as many questions as they wished and could take as long as they needed. World view statements were also obtained from subjects, asking them generally to state why accidents occur in the workplace. These world views dictated three classes into which factors were grouped: worker-centered, management/design-centered, and fatalistic. The data were reduced through a process of open coding of the verbatim interview transcripts (recorded by a certified real-time court reporter). Coded factors were tabulated in the same classes as the world views. A large proportion remained ambiguous; although the subject may have had an unambiguous intent, their way of stating their thoughts was not clearly worker-related or management-related. Analysis examined the relation of knowledge factors (education, experience, peer interaction) and organizational factors (status, satisfaction, powerlessness, operations values) to the concepts retrieved and the process of reasoning. Particular interest was taken in influences on the retrieval and use of worker-related causal factors and management or design-related causal factors. This paper reports only on the factors influencing the predominant classes of causal explanations. Other variables measured included the total number of factors elicited from each subject with the standard set of experimental material, and the revision of hypotheses during the simulated investigations. These will not be reported here. 3 Results The method of interview was well accepted by the practitioners. Only one indicated that his approach to the simulated investigation differed from his usual practice, specifically that he would usually investigate much less thoroughly. Others indicated that they approached it essentially the same way as they would in the field. (Verification of this impression was achieved by comparing data from the simulated investigations with the data provided in reports of past experiences, but an independent source of actual investigation was not sought.) 3.1 Over-attribution to worker-centered causes? The number of different codes considered in each exercise was significantly greater than 1 (p<.001) describing causal reasoning that differs from attributional thinking. The ratio of

3 references to the two types of codes (worker-centered to management/design-centered) was computed for each subject. The mean ratio was 0.68 worker-centered codes per management/design-centered code. However, there was extreme variation among subjects. The ratio ranged from 0.31 through 1.29, signifying that some subjects generated predominantly management-centered explanations while others generated many more worker-centered explanations. The 95% confidence interval from clearly lies on the side of management or design-centered causal explanations predominating. The tendency to start with worker-centered factors also differed widely among subjects. Individual subjects started with a worker-centered line of inquiry in anywhere from 0% to 71% of their exercises. However, both initial codes and conclusions were significantly more likely to refer to management/design factors than to worker-centered factors (p<.001 and p<.05 respectively). These findings are contrary to the expectation of overattribution to worker-centered causes. Rather, the results seem to support the observation that management and design oriented explanations for accidents are currently more fashionable than blaming the injured worker [2]. In addition, worker-related factors comprised less than half of all codes generated from all the transcripts taken together. Overall, there was no indication that overattribution to worker-related explanations occurred. 3.2 What increases consideration of worker-centered explanations? Correlational methods obviously do not permit establishment of causality. The observations made here pertain to association between the subject characteristics and the explanations they used. An increase in the proportion of worker-centered explanations mentioned by a subject was associated with higher job status. Holding the top safety job in the organization (as indicated by the subject s self-reported title and superior s title) increased the proportion of worker-centered explanations among factors considered. Of all the interpersonal variables and combinations, status alone was the best predictor of ratio of factors mentioned (B=0.52. R 2 =.20, p=.08). Among the factors which did not appear to be correlated with the ratio of worker-centered to management/design-centered factors were experience, safety education, and practitioner-group activity, operations values, satisfaction with recognition, indications of perceived powerlessness, world view of accident causation, or perceived role in the investigation context. Expression of operations values, in responses to the questionnaire, and length of work experience, along with status, combine to predict the likelihood of beginning an investigation with an worker-centered explanation (R 2 =54.6%, F 3,12 =4.8, p=.02). Status, length of work experience, and expressions of powerlessness (in questionnaire responses) combined to predict the likelihood of concluding at a worker-centered explanation (R 2 =61.3%, F 3,11 =5.81, p=.012). These results suggest that practitioners who identify with management tend to favour worker-centered explanations over management or design centered explanations, and both begin and end investigations with these preferences. The preferences are not apparently conscious, however: world views were not associated with either start or conclusion class. Rather than supporting a cognitive error explanation such as overattribution, the results appear to describe a self-serving bias that is quite rational within the corporate survival environment. World view, or the explanation given by the subject for the causes of accidents in general, was significantly correlated with the operations pressures reported by the subject (r=.52, p=.04; a worker-centered world view was associated with greater operations pressures). Operations pressures were considered to be responsibility for compensation claims management (keep claims costs under control) and the company s use of

4 compensation cost as a measure of safety program effectiveness. These pressures would tend to make worker-centered explanations more attractive, since a worker-centered explanation might relieve the company of responsibility for the injury claim. 3.3 Mechanisms As already mentioned, it appears improbable that a cognitive attribution error accounts for biases in causal reasoning among practitioners. World view was not associated with either starting point class or the ratio of worker to management factors. However, world view did correlate with the class of the conclusion (r=.55, p=.03) suggesting that there may be some confirmation bias, or self-fulfilling prophecy involved with causal reasoning. A self-serving bias explains the observations better than alternative mechanisms considered. The prominent role of status in regressions for class ratio, starting point, and end point seems to support the idea of incompatibility between a management role and management-centered causal explanations. Self-serving bias was also consistent with results obtained for search length and hypothesis revision, results not fully reported here. 4 Discussion No evidence of the fundamental attribution error was observed, nor did the investigation approaches resemble attribution judgements. Generating and using management/design causal explanations more than worker-oriented explanations does reflect current explanatory fashions, although the self-serving bias is the one that best describes the observed effects. Safety specialists are conscious of the values of company operations and their accident investigations show its effects. Holding the top safety position and responsibility for managing workers compensation are associated with increased affinity to workeroriented explanations and investigation characteristics that favour them. Other variables observed in the study included the number of factors elicited using standardized materials, and length of causal search and number of hypotheses generated in the interactive exercises. Those variables, which cannot be discussed here, also showed similar effects of status and operations values. 5 Further study The greatest concern about the data obtained is the high proportion of factors mentioned which were not unambiguously classifiable by the researchers [3]. There is no reason to suspect that the factors which seem to be ambiguous were intended by their users to refer significantly more often to one class over the other, but resolving this ambiguity is a primary objective. Referring the elicited collection of causal schemas to classification by a larger sample of practitioners would be a desirable next step.

5 6 References [1]Woodcock, K. (1995) Bias in real-world accident cause-finding. Advances in Industrial Ergonomics and Safety Bittner, A.C. & Champney, P. (Eds.) London: Taylor & Francis [2]Rasmussen, J Diagnostic reasoning in action. IEEE Transactions on Systems, Man, and Cybernetics 23(4): [3] Woodcock, K. & Smiley, A.M. (1996) Causal explanation knowledge base of occupational safety practitioners. Proceedings of the Human Factors Association of Canada. Mississauga, Ontario: HFAC