Confluence and contours: reflexive management of environmental risk

Government institutions have responsibilities to distribute risk management funds meaningfully and to be accountable for their choices. We took a macro-level sociological approach to understanding the role of government in managing environmental risks, and insights from micro-level psychology to examine individual-level risk-related perceptions and beliefs. Survey data from 2,068 U.K. citizens showed that lay people's funding preferences were associated positively with beliefs about responsibility and trust, yet associations with perception varied depending on risk type. Moreover, there were risk-specific differences in the funding preferences of the lay sample and 29 policymakers. A laboratory-based study of 109 participants examined funding allocation in more detail through iterative presentation of expert information. Quantitative and qualitative data revealed a meso-level framework comprising three types of decisionmakers who varied in their willingness to change funding allocation preferences following expert information: adaptors, responders, and resistors. This research highlights the relevance of integrated theoretical approaches to understanding the policy process, and the benefits of reflexive dialogue to managing environmental risks.Department of Environment Food and Rural Affairs, EPSRC, NERC, ESR

The influence of individual differences and decision domain in the consistency of risk preferences.

The research presented in this thesis considers the question of whether individual-level risk preferences are consistent or inconsistent across decision domains. For example, do people make the same decisions with respect to work, health and finance? Some previous authors have suggested that risk preferences are inconsistent, e. g. Kahneman and Tversky (1979), while others have put forward the idea that people have generalised tendencies to take or avoid risks, e. g. Sitkin and Pablo (1992). The work of Sitkin and Pablo was drawn upon to develop hypotheses concerning the conceptualisation and construction of risk propensity. Risk propensity was operationalised as the degree of consistency of cross-domain risk preferences. It was proposed that a propensity to take or avoid risks is associated with whether individuals have consistent tendencies across different decision domains, that personality will be a key predictor of risk propensity, and that inconsistent cross-domain risk preferences will be associated with risk domain-specific cognitive and emotional aspects of decision making. A survey measure was developed to assess risk and decision preferences both across and within the domains of work, health and finance. Biographical and personality factors were also measured. The sample comprised 360 participants drawn from five sample groups chosen to capture a range of risk preferences. The results showed that risk propensity can be conceptualised and measured in terms of the consistency of cross-domain risk preferences. People who were consistent in their risk preferences were characterised by the personality traits of emotional stability, low extroversion, low openness and high agreeableness. Additionally, consistent risk preferences were associated with relative consistency of attention to situational information and perceived risk. The majority of participants, however, had different risk preferences in different domains, and showed variability in their decision preferences. The implications of the research for understanding risk propensity and risk management are discussed

The relationship between information processing style and information seeking, and its moderation by affect and perceived usefulness: analysis vs. procrastination

We examined the relationship between information processing style and information seeking, and its moderation by anxiety and information utility. Information about Salmonella, a potentially commonplace disease, was presented to 2960 adults. Two types of information processing were examined: preferences for analytical or heuristic processing, and preferences for immediate or delayed processing. Information seeking was captured by measuring the number of additional pieces of information sought by participants. Preferences for analytical information processing were associated positively and directly with information seeking. Heuristic information processing was associated negatively and directly with information seeking. The positive relationship between preferences for delayed decision making and information seeking was moderated by anxiety and by information utility. Anxiety reduced the tendency to seek additional information. Information utility increased the likelihood of information seeking. The findings indicate that low levels of anxiety could prompt information seeking. However, information seeking occurred even when information was perceived as useful and sufficient, suggesting that it can be a form of procrastination rather than a useful contribution to effective decision making

Shared leadership in tertiary care: design of a simulation for patient safety decision-making in healthcare management teams

Introduction: Simulation-based training (SBT) on shared leadership (SL) and group decision-making (GDM) can contribute to the safe and efficient functioning of a healthcare system, yet it is rarely incorporated into healthcare management training. The aim of this study was design, develop and validate a robust and evidence-based SBT to explore and train SL and GDM. Method: Using a two-stage iterative simulation design approach, 103 clinical and non-clinical managerial students and healthcare professionals took part in an SBT that contained real-world problems and opportunities to improve patient safety set within a fictional context. Self-report data were gathered, and a focus group was conducted to address the simulation's degree of realism, content, relevance, as well as areas for improvement. Results: Participants experienced the simulation scenario, the material and the role assignment as realistic and representative of real-world tasks and decision contexts, and as a good opportunity to identify and enact relevant tasks, behaviours and knowledge related to SL and GDM. Areas for improvement were highlighted with regard to involving an actor who challenges SL and GDM; more preparatory time to allow for an enhanced familiarisation of the content; and, video debriefs to reflect on relevant behaviours and team processes. Conclusions: Our simulation was perceived as an effective method to develop SL and GDM within the context of patient safety and healthcare management. Future studies could extend this scenario method to other areas of healthcare service and delivery, and to different sectors that require diverse groups to make complex decisions

The mismanaged soul: existential labor and the erosion of meaningful work

Meaningful work has been defined as work that is personally enriching and that makes a positive contribution. There is increasing interest in how organizations can harness the meaningfulness of work to enhance productivity and performance. We explain how organizations seek to manage the meaningfulness employees experience through strategies focused on job design, leadership, HRM and culture. Employees can respond positively to employers' strategies aimed at raising their level of experienced meaningfulness when they are felt to be authentic. However, when meaningfulness is lacking, or employees perceive that the employer is seeking to manipulate their meaningfulness for performative intent, then the response of employees can be to engage in “existential labor” strategies with the potential for harmful consequences for individuals and organizations. We develop a Model of Existential Labor, drawing out a set of propositions for future research endeavors, and outline the implications for HRM practitioners

Understanding health management and safety decisions using signal processing and machine learning

Background: Small group research in healthcare is important because it deals with interaction and decision-making processes that can help to identify and improve safer patient treatment and care. However, the number of studies is limited due to time- and resource-intensive data processing. The aim of this study was to examine the feasibility of using signal processing and machine learning techniques to understand teamwork and behaviour related to healthcare management and patient safety, and to contribute to literature and research of team working in healthcare. Methods: Clinical and non-clinical healthcare professionals organised into 28 teams took part in a video- and audio-recorded role-play exercise that represented a fictional healthcare system, and included the opportunity to discuss and improve healthcare management and patient safety. Group interactions were analysed using Recurrence Quantification Analysis (Knight et al., 2016), a signal processing method that examines stability, determinism, and complexity of group interactions. Data were benchmarked against self-reported quality of team participation and social support. Transcripts of group conversations were explored using the topic modelling approach (Blei et al., 2003), a machine learning method that helps to identify emerging themes within large corpora of qualitative data. Results: Groups exhibited stable group interactions that were positively correlated with perceived social support, and negatively correlated with predictive behaviour. Data processing of the qualitative data revealed conversations focused on: (1) the management of patient incidents; (2) the responsibilities among team members; (3) the importance of a good internal team environment; and (4) the hospital culture. Conclusions: This study has shed new light on small group research using signal processing and machine learning methods. Future studies are encouraged to use these methods in the healthcare context, and to conduct further research on how the nature of group interaction and communication processes contribute to the quality of team and task decision making

Deadly combinations: how leadership contexts undermine the activation and enactment of followers’ high core self-evaluations in performance

Employees with high core self-evaluations (CSE) generally perform well in their jobs. The enactment of CSE in performance occurs within contexts, and leadership is one form of context that influences the activation and expression of CSE. Drawing on theories of CSE and leader–member exchange (LMX), we characterized the leadership context as the interaction between leader CSE and LMX quality. Examination of 173 followers and their 31 leaders in a manufacturing organization showed a positive association between follower CSE and performance when the context comprised high leader CSE and high LMX. Conversely, leadership contexts comprising high leader CSE and low LMX, or low leader CSE and high LMX, resulted in a negative relationship between follower CSE and performance. We also show that low CSE followers have relatively high performance under some circumstances. Thus, we contribute to understanding how some leadership contexts undermine high CSE followers’ performance and promote low CSE followers’ performanc

Human Reliability Analysis: A Review and Critique

Few systems operate completely independent of humans. Thus any study of system risk or reliability requires analysis of the potential for failure arising from human activities in operating and managing this. Human reliability analysis (HRA) grew up in the 1960s with the intention of modelling the likelihood and consequences of human error. Initially, it treated the humans as any other component in the system. They could fail and the consequences of their failure were examined by tracing the effects through a fault tree. Thus to conduct a HRA one had to assess the probability of various operator errors, be they errors of omission or commission. First generation HRA may have used some sophistication in accomplishing this, but in essence that is all they did. Over the years, methods have been developed that recognise human potential to recover from a failure, on the one hand, and the effects of stress and organisational culture on the likelihood of possible errors, on the other. But no method has yet been developed which incorporates all our understanding of individual, team and organisational behaviour into overall assessments of system risk or reliability

Human reliability analysis: A critique and review for managers

In running our increasingly complex business systems, formal risk analyses and risk management techniques are becoming a more important part of a manager's tool-kit. Moreover, it is also becoming apparent that human behaviour is often a root or significant contributing cause of system failure. This latter observation is not novel; for more than 30 years it has been recognised that the role of human operations in safety critical systems is so important that they should be explicitly modelled as part of the risk assessment of plant operations. This has led to the development of a range of methods under the general heading of human reliability analysis (HRA) to account for the effects of human error in risk and reliability analysis. The modelling approaches used in HRA, however, tend to be focussed on easily describable sequential, generally low-level tasks, which are not the main source of systemic errors. Moreover, they focus on errors rather than the effects of all forms of human behaviour. In this paper we review and discuss HRA methodologies, arguing that there is a need for considerable further research and development before they meet the needs of modern risk and reliability analyses and are able to provide managers with the guidance they need to manage complex systems safely. We provide some suggestions for how work in this area should develop