Who Reaps the Benefits,Who Bears the Risks? Comparative Optimism,Comparative Utility,and Regulatory Preferences for Mobile Phone Technology

Although the issue of risk target (e.g., self, others, children) is widely acknowledged in risk perception research, its importance appears underappreciated. To date, most research has been satisfied with demonstrating comparative optimism, i.e., lower perceived risk for the self than others, and exploring its moderators, such as perceived controllability and personal exposure. Much less research has investigated how the issue of target may affect benefit perceptions or key outcomes such as stated preferences for hazard regulation. The current research investigated these issues using data from a public survey of attitudes toward mobile phone technology (N= 1,320). First, results demonstrated comparative optimism for this hazard, and also found moderating effects of both controllability and personal exposure. Second, there was evidence of comparative utility, i.e., users believed that the benefits from mobile phone technology are greater for the self than others. Third, and most important for policy, preferences for handset regulation were best predicted by perceptions of the risks to others but perceived benefits for the self. Results suggest a closer awareness of target can improve prediction of stated preferences for hazard regulation and that it would be profitable for future research to pay more attention to the issue of target for both risk and benefit perceptions.     

Incentives,Contracts, and Markets: A General Equilibrium Theory of Firms1

This paper takes steps toward integrating firm theory in the spirit of Alchian and Demsetz (1972) and Grossman and Hart (1986), contract theory in the spirit of Holmstrom (1979), and general equilibrium theory in the spirit of Arrow and Debreu (1954) and McKenzie (1959). In the model presented here, the set of firms that form and the contractual arrangements that appear, the assignments of agents to firms, the prices faced by firms for inputs and outputs, and the incentives to agents are all determined endogenously at equilibrium. Agents choose consumption—but they also choose which firms to join, which roles to occupy in those firms, and which actions to take in those roles. Agents interact anonymously with the (large) market, but strategically within the (small) firms they join. The model accommodates moral hazard, adverse selection, signaling, and insurance. Equilibria may be Pareto ranked.     

Competitive and Cooperative Positioning in Supply Chain Logistics Relationships*

Cooperative logistics relationships require the sharing of information, which must be enabled by the integration of disparate information systems across partners. In this article, we theorize business‐to‐business logistics relationships should be managed using cooperative and competitive postures. Based on data from 91 dyadic relationships using interorganizational information technology (IT), we find that performance gains accrue when parties share strategic information and customize IT; mutual trust enables IT customization and strategic‐information flows and equitable relationship‐specific investments positively impact IT customization, mutual trust, and performance. Among other scholarly and practical implications discussed, partners should compete on resources for IT customization and cooperate to share strategic information. Managers tend to think of relationships with firms as polar opposites and view them as entirely cooperative or entirely competitive. Our results support active balancing and understanding of both competitive and cooperative stances. Such an approach enables conditions for participation symmetry that yields greater performance gains.     

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.     

Media Disaster Reporting Effects on Public Risk Perception and Response to Escalating Tornado Warnings: A Natural Experiment

Previous research has evaluated public risk perception and response to a natural hazards in various settings; however, most of these studies were conducted either with a single scenario or after a natural disaster struck. To better understand the dynamic relationships among affect, risk perception, and behavioral intentions related to natural disasters, the current study implements a simulation scenario with escalating weather intensity, and includes a natural experiment allowing comparison of public response before and after a severe tornado event with extensive coverage by the national media. The current study also manipulated the display of warning information, and investigated whether the warning system display format influences public response. Results indicate that (1) affect, risk perception, and behavioral intention escalated as weather conditions deteriorated, (2) responses at previous stages predicted responses at subsequent stages of storm progression, and (3) negative affect predicted risk perception. Moreover, risk perception and behavioral intention were heightened after exposure to the media coverage of an actual tornado disaster. However, the display format manipulation did not influence behavioral responses. The current study provides insight regarding public perception of predisaster warnings and the influence of exposure to media coverage of an actual disaster event.     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

The Effect of Demand–Supply Mismatches on Firm Risk

A supply chain management (SCM) system comprises many subsystems, including forecasting, order management, supplier management, procurement, production planning and control, warehousing and distribution, and product development. Demand–supply mismatches (DSMs) could indicate that some or all of these subsystems are not working as expected, creating uncertainties about the overall capabilities and effectiveness of the SCM system, which can increase firm risk. This article documents the effect of DSMs on firm risk as measured by equity volatility. Our sample consists of three different types of DSMs announced by publicly traded firms: production disruptions, excess inventory, and product introduction delays. We find that all three types of DSMs result in equity volatility increases. Over a 2‐year period around the announcement date, we observe mean abnormal equity volatility increases of 5.62% for production disruptions, 11.19% for excess inventory, and 6.28% for product introduction delays. Volatility increases associated with excess inventory are significantly higher than the increases associated with production disruptions and product introduction delays. Across all three types of DSMs, volatility changes are positively correlated with changes in information asymmetry. The results provide some support that volatility changes are also correlated with changes in financial and operating leverage.     

Sequencing and Scheduling Appointments with Potential Call‐In Patients

This paper studies appointment scheduling for a combination of routine patients who book well in advance and last‐minute patients who call for an appointment later that same day. We determine when these same‐day patients should be scheduled throughout the day, and how the prospect of their arrivals affects the appointment times of the routine patients. By formulating the problem as a stochastic linear program, we are able to incorporate random and heterogeneous service times and no‐show rates, ancillary physician tasks, and appointment delay costs for same‐day patients who prefer to see the doctor as early as possible. We find that the optimal patient sequence is quite sensitive to the no‐show probabilities and the expected number of same‐day patients. We also develop two simple heuristic solutions to this combinatorial sequencing problem.