Towards improving detection of early warning signals within organizations : an approach to the identification and utilization of underlying factors from an organizational perspective

J. Luyk

Research output: ThesisPhd Thesis 1 (Research TU/e / Graduation TU/e)

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Abstract

In today’s society, there is a strong need for organizations to proactively manage risk given the increasing product, process and business chain complexity they are facing, and the increasingly dynamic and competitive environment in which they are operating. At the same time, these trends add to the difficulty in executing proactive risk management, amongst other things since organizational threats are consequently becoming increasingly unforeseeable nowadays. Within this context, this thesis explores how industrial organizations might potentially improve one particular aspect of their proactive management of risk, namely the detection of early warning signals of potential risks by people within the organization. Review of literature from various risk management disciplines demonstrated that although most disciplines acknowledge the potential of people within an organization to detect early warning signals, structured approaches (tools, methods) on how to conduct or improve this kind of organizational early warning signal detection are currently not available. More insight is hence needed into this type of detection, to learn how an organization could potentially improve its detection ability. For this purpose, a conceptual framework of organizational early warning signal detection was developed as a starting point, based on insights from communication theory, organizational systems theory, and theory on the cognitive processing of warnings by individuals. The framework is characterized by three main elements, the role of which on organizational early warning signal detection was confirmed by case study analysis. More specifically, organizational early warning signal detection requires 1) propagation of the early warning signal(s) or signal estimate(s) across the organization, at all levels (strategic, tactical, operational), 2) individual early warning signal detection in an organizational context resulting in signal-directed behavior or action, both of which are affected by 3) influencing factors that can either positively or negatively affect signal detection in four main categories: ¿ Human factors: factors active on an individual level ¿ Internal environment: factors active on an organizational level, corresponding to factors active in the organizational subsystems Technology, Structure, Culture, and Strategy ¿ External environment: factors active on the interface between an organization and its external environment ¿ Exogenous: factors originating from an organization’s external environment, which are considered outside the scope of this thesis An overview of influencing factors of organizational early warning signal detection is currently missing from literature, though the case study analysis as well as risk management literature (in the form of specific guidelines to early warning signal detection) indicated some potential factors. This thesis aimed to obtain such an overview, as part of the effort to learn how signal detection might be improved. To identify influencing factors, it was determined that the identification approach to be employed had to meet the following criteria, i.e. the approach should be able to capture insights non-specific for any one particular organization, should support an exploratory approach to research, and should utilize both multiple data sources and multiple research methods, the integration of which is captured in a structured framework. Based on these criteria, a general approach to factor identification was proposed consisting of two phases or steps: model development (in order to construct an initial model of influencing factors) and model validation (to further validate the initial model, in order to try to obtain a comprehensive overview of factors). Application of the proposed approach relied on three main data sources: literature on crisis management and resilience engineering, risk management experts assembled in an expertise network transcending industries, and case studies (mainly of major industrial accidents). An initial model of 21 influencing factors in the categories Human factors, Internal environment and External environment was obtained by means of the concurrent application of an extensive literature study and focus group. On the level of individual factors, comparable results were obtained and the focus group was able to confirm the existence of factors obtained from literature. For that reason, it was decided to proceed with model validation. Model validation was performed in two iterations. Results of an internet based survey overall confirmed the relevance of influencing factors in the initial model, but also suggested some minor modifications. The consequent analysis of various case studies did not yield any new insights compared to the post-survey model of influencing factors, and it was hence decided to accept this model as a validated list of influencing factors of organizational early warning signal detection. As such, based on its application, it was concluded that the proposed approach is effective with regard to its intended goal (i.e. factor identification). Moreover, it was found that consultation of risk management experts assembled in an expertise network is a particularly rich source of information in a field of study in which sources of evidence are not widely available. The exploratory insight gained into influencing factors can next be utilized for the purpose of potentially improving organizational early warning signal detection at various levels, ranging from a basic level to more practical means of utilization. At a basic level, such insight can help organizations realize that signal detection largely lies within an organization’s range of control, and hence is the organization’s responsibility to some extent. Also, the overview of factors makes explicit that poor organizational early warning signal detection can not necessarily be attributed to human failure (or in other words, it can not necessarily be attributed to influencing factors in the Human factors category). Furthermore, exploratory insight gained into influencing factors can act as input to additional research into influencing factors, both descriptive (related to factor characteristics) and analytic (related to factor dependencies) in nature. In this thesis, descriptive research into factor relevance (i.e. the degree of influence of a factor on organizational early warning signal detection) was performed, since insight into factor relevance can potentially allow prioritization of influencing factors, which is desirable from an organization’s perspective. Research into factor relevance by means of an internet based survey indicated that what was intuitively expected, namely that some factors have a higher degree of influence on signal detection than other factors. Survey findings moreover suggested that prioritization according to factor relevance would be possible on the level of individual factors, but not on the level of factor categories. Lastly, it was found that differences in factor relevance might exist between industries and organizational levels (strategic, tactical, operational), though survey results were non-conclusive. When trying to improve organizational early warning signal detection in practice, such potential differences between industries and organizations need to be taken into account. In this respect, it is important to realize that the obtained overview of influencing factors can only be considered valid for its intended purpose, i.e. to give an overview of the ways in which early warning signal detection is affected in industrial organizations in general. Also, since both organizations and their environment change over time, the overview of influencing factors can only be considered valid at the time at which the overview was obtained, given its dynamic nature. Consequently, prior to utilizing insight into influencing factors for the purpose of signal detection improvement in a particular industrial or organizational setting, the extent to which factors found are applicable to the organization (and/or industry) in question should be ascertained. One way of meeting the precondition of industry and/or organization specificity was suggested by an existing diagnostic tool for safety enhancement called Tripod-Delta, namely by means of the identification of indicator items for each influencing factor by a syndicate of specialists from the industry and/or organization under consideration. Indicator items identified can next set the stage for utilizing insight into influencing factors in practice. For that purpose, this thesis proposed a diagnostic evaluation tool for organizational early warning signal detection, which allows organizations to learn where problems might be found with regard to their ability to detect early warning signals, in terms of the relative cause of concern of each influencing factor. As such, application of the tool can provide an organization with a sense of direction for improvement, and can contribute to areas such as decision analysis and support, and (organizational) assessment. Although based on an existing diagnostic tool to enhance safety which has been validated in various organizations and industries, the proposed tool itself also needs to be tested and validated. Implementation of the proposed tool in various organizational settings across industries is therefore recommended. This type of research is suggested to be performed together with further descriptive research (direction of effect, factor quantification) and analytic research (factor dependencies) into influencing factors, as part of the effort to allow additional and more practical means of utilizing insights gained for the purpose of signal detection improvement to become feasible.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Industrial Engineering and Innovation Sciences
Supervisors/Advisors
  • Karydas, Dimitrios, Promotor
  • Brombacher, Aarnout C., Promotor
  • Rouvroye, Jan L., Copromotor
Award date31 Aug 2011
Place of PublicationEindhoven
Publisher
Print ISBNs978-90-386-2544-7
DOIs
Publication statusPublished - 2011

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