The stressor–strain relationship in diary studies: A meta-analysis of the within and between levels

Daily diary studies use the same set of measures repeatedly for several days. Within the work stress domain, these studies are able to isolate the effects of daily exposure to stressors within people from the general level of stressors between people. This meta-analysis investigated both content-related and methodological aspects of workplace stressor–strain relationships in diary studies. Results from 55 unique samples (a combined sample size of 5409) indicated that the magnitude of the stressor–strain relationship was stronger at the between-person level than the within-person level. Further, when the stressor was measured prior to the strain (within the same day), the relationship was somewhat stronger than when stressor and strain were measured concurrently. This suggests that stressor–strain effects might take some time to fully manifest. Differences were also detected among types of strains: affective strains had stronger relationship with stressors than behavioural strains. There were also differences in the stressor–strain relationship depending on both the type of strain and the timing of their respective measurement (concurrent versus predictive), suggesting that certain strain responses require more time to manifest. Overall, this meta-analysis elucidates important considerations in the design and interpretation of diary studies on occupational stress.     

High value manufacturing (HVM) in the UK: case studies and focus group insights

This paper studies the internal mechanisms that allow organisations to become high value manufacturing (HVM). Using a qualitative methodology, three UK manufacturing companies formed in-depth case studies with semi-structured interviews, observations and historical data. The HVM value matrix of Martinez and co-workers is used to categorise each organisation’s value proposition. Wider benchmarking of the three organisations was carried out against a focus group with an additional seven manufacturing organisations. Thus, data from 10 manufacturing organisations are included in this research. The cases follow the ‘customer intimacy’ HVM discipline. The business processes supporting these value propositions were identified. Interestingly, each organisation’s desired value proposition differs from their current one. ‘Technological integrators’ predominantly rely on new product development (NPD) and Strategy processes, whereas ‘Socialisors’ rely predominantly on Strategy and Customer Relationship processes. Companies can use the findings to better understand their current HVM value proposition and, where necessary, plan their transition to a future desired HVM value proposition.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Interactive profit-planning systems and market turbulence: A dynamic capabilities perspective

This article describes, theorizes and empirically investigates the concept of interactive profit-planning systems (PPS) through the lens of the dynamic capabilities logic. With this conceptualization: interactive PPS capabilities comprise budgeting, forecasting and results-reporting routines, in which top and middle managers interact to create knowledge for sensing, seizing, and business model reconfiguring (to manage strategic business change). Survey data from 331 Australian firms is analyzed employing partial least squares structural equation modeling. The data provides support for two hypotheses: (1) greater market turbulence strengthens the positive effect of interactive PPS capabilities on business unit performance; and (2) greater market turbulence strengthens the positive effect of flexibility values (of organizational culture) on interactive PPS capabilities. Our findings show that interactive PPS capabilities function according to the salient tenets of the dynamic capabilities logic, and clarify the beneficial roles of formal cybernetic control systems and the intertwined involvement of top and middle managers in using dynamic capabilities.     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.     

Let's Call it Quits: Break‐Even Effects in the Decision to Stop Taking Risks

“Chasing” behavior, whereby individuals, driven by a desire to break even, continue a risky activity (RA) despite incurring large losses, is a commonly observed phenomenon. We examine whether the desire to break even plays a wider role in decisions to stop engaging in financially motivated RA in a naturalistic setting. We test hypotheses, motivated by this research question, using a large data set: 707,152 transactions of 5,379 individual financial market spread traders between September 2004 and April 2013. The results indicate strong effects of changes in wealth around the break‐even point on the decision to cease an RA. An important mediating factor was the individual's historical long‐term performance. Those with a more profitable trading history were less affected by a fall in cash balance below the break‐even point compared to those who had been less profitable. We observe that break‐even points play an important role in the decision of nonpathological risk takers to stop RAs. It is possible, therefore, that these nonpathological cognitive processes, when occurring in extrema, may result in pathological gambling behavior such as “chasing.” Our data set focuses on RAs in financial markets and, consequently, we discuss the implications for institutions and regulators in the effective management of risk taking in markets. We also suggest that there may be a need to consider carefully the nature and role of “break‐even points” associated with a broader range of nonfinancially‐focused risk‐taking activities, such as smoking and substance abuse.     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission

Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.     

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.     

Making sense of pragmatic and charismatic leadership stories: Effects on vision formation

For some time, it has been argued that stories articulated by leaders are an important vehicle for exercising influence, but stories of leadership might also serve as a means for developing leadership potential. One critical activity involved in leadership is vision formation, which involves constructing and communicating a future state that guides followers in “making sense” of complex organizational events. Like leader visions, analyzing stories also, by nature, evokes sensemaking processes. As a result, analyzing stories of leadership may provide a natural means for practicing the art of sensemaking. In the present investigation, undergraduates were asked to read six short stories about incidents of either pragmatic or charismatic leadership in business settings. After reading each story, questions were asked to encourage sensemaking of story events, causes, and emotions. Participants were subsequently asked to formulate visions for leading a secondary school –– a transfer task. It was found that stronger visions were produced when participants were asked to analyze both story events and the causes of these events. The implications of these findings for the use of leadership stories in leader development initiatives are discussed.