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.     

Marketing as an Investment in Shareholder Value

We present resource‐based and capability‐based arguments of marketing investment intensity to offer a strategic view of marketing as an investment in shareholder value. We find that marketing investment intensity has a U‐shaped quadratic effect on shareholder value creation (Tobin's q) that calls for marketing investment to be protected and increased, not surrendered. We show how marketing investments interact with investments in R&D, human capital and operations to reveal how strategic co‐investments can alter the shareholder value of marketing. Finally, we show how competitive intensity and failings in the firm's investment productivity (its ability to convert investment expenditure into sales) point to malaise in the firm's own strategic architecture as a fault for perceived poor returns from marketing investments. Our findings suggest that marketing investment should not be scapegoated when its contributions to shareholder value are not as expected. When invested in strategically and in combination with other investments, marketing can unlock exciting improvements in shareholder value.     

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.     

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.     

Synergy from configuration of global production networks: drivers,mechanisms, and outcomes

Abstract

This paper explores the network synergies available from the configuration of highly dispersed, global production networks (GPNs). Synergy theory from the literature on intra-firm and inter-firm GPNs is examined in terms of drivers, mechanisms and outcomes. An explanatory model is developed and enriched via a series of extended case studies of firms undertaking re-configuration of their GPNs during the period 2003–2014. The primary finding is that firms create synergy in their GPNs by selecting specific configuration mechanisms, in response to a certain set of external drivers, in order to achieve certain synergistic outcomes. The paper classifies the main types of drivers, mechanisms and outcomes in terms of key variables and discusses their inter-connections and relationships.     

Competitive analysis for make-to-order scheduling with reliable lead time quotation

This paper makes extended studies on the discrete problem of online scheduling and reliable lead time quotation (discrete Q-SLTQ) introduced by Keskinocak et al. (Manag. Sci. 47(2):264–279, 2001). We first relax the assumption on revenue function from a linear decreasing function to any decreasing function. We present an online deterministic strategy which is optimal in competitiveness for concave revenue functions. The above results are further extended to the continuous Q-SLTQ model where orders are released at arbitrary time points. For the discrete Q-SLTQ problem, if orders are with nonuniform lengths, we prove the nonexistence of online strategies with bounded competitive ratios; otherwise if orders are with unit length but various weights, we present an optimal online strategy.