Teacher–Child Relationship Quality and Children's Peer Victimization and Aggressive Behavior in Late Childhood

This investigation examines the extent to which characteristics of the teacher–child relationship (closeness, dependency, and conflict) are predictive of changes in children's peer victimization and aggressive behavior over the course of a school year. Relational and physical forms of victimization and aggression were studied, and changes in peer acceptance and number of friendships were tested as possible mediators. Longitudinal data from 410 fourth‐ and fifth‐grade students (193 boys; 217 girls) and their teachers (N = 25) were analyzed. Whereas dependency on the teacher predicted heightened victimization from peers, a close relationship with the teacher forecasted less physical aggression toward peers. Moreover, decreases in number of friendships partially mediated the link between dependency on the teacher and heightened relational victimization for boys. These findings have implications for understanding the continuing influence of teacher–child relationships on children's social development in late childhood and for identifying interpersonal risk factors associated with involvement in peer harassment.     

Does It Pay to Be Moral? How Indicators of Morality and Competence Enhance Organizational and Work Team Attractiveness

Based on a social identity analysis, the authors argue that people are attracted to teams and organizations with positive features. Such features can refer to the competence and achievements of the organization, or to its moral values and ethical conduct. However, in work contexts, ethics and achievements do not necessarily go together. The paper reports three studies that examine the relative and combined impact of perceived competence vs morality of a team or organization on its attractiveness to individuals. Study 1 (n = 44) reveals that students prefer to seek employment in a moral rather than a competent organization, when forced to choose between these organizational features on a bipolar scale. Study 2 (n = 100) replicates these findings in a design where the competence and morality of a fictitious organization were manipulated orthogonally. Study 3 (n = 89) examines responses to experimental task teams that systematically differed from each other in their competence and morality. Results of all three studies converge to demonstrate that the perceived morality of the team or organization has a greater impact on its attractiveness to individuals than its perceived competence. The authors discuss the theoretical and practical implications of these findings.     

Supplier Encroachment and Investment Spillovers

It is conventional wisdom that a manufacturer's encroachment into retail space will likely hurt an existing retailer. In contrast to this conventional belief, current research indicates that a retailer may welcome a manufacturer's encroachment despite the new competition in the final market. The encroachment may help the manufacturer have some “skin in the game” at the retail level, which will cause the manufacturer to make a selfish cost‐reducing investment that spills over to the retailer as a lower wholesale price. Such a spillover effect enhances the retailer's profit as long as the encroachment does not result in extreme retail competition by a certain degree of product differentiation, and ultimately generates Pareto gains in the supply chain. The spillover effect is so robust that the retailer's benefit from the encroachment remains even after considering potential mitigating factors such as selling costs, a nonlinear form of cost reduction, decentralized encroachment, additional retail competition, price competition, and a negotiation between the manufacturer and the retailer.     

Building Supply Chain Resilience through Virtual Stockpile Pooling

Stockpiling inventory is an essential strategy for building supply chain resilience. It enables firms to continue operating while finding a solution to an unexpected event that causes a supply disruption or demand surge. While extremely valuable when actually deployed, stockpiles incur large holding costs and usually provide no benefits until such a time. To help to reduce this cost, this study presents a new approach for managing stockpiles. We show that if leveraged intelligently, stockpiles can also help an organization better meet its own regular demand by enabling a type of virtual pooling we call virtual stockpile pooling (VSP). The idea of VSP is to first integrate the stockpile into several locations’ regular inventory buffers and then dynamically reallocate the stockpile among these locations in reaction to the demand realizations to achieve a kind of virtual transshipment. To study how to execute VSP and determine when it can provide the most value, we formulate a stylized multi‐location stochastic inventory model and solve for the optimal stockpile allocation and inventory order policies. We show that VSP can provide significant cost savings: in some cases nearly the full holding cost of the stockpile (i.e., VSP effectively maintains the stockpile for free), in other cases nearly the savings of traditional physical inventory pooling. Last, our results prescribe implementing VSP with many locations for large stockpiles, but only a few locations for small stockpiles.     

Distributionally Robust Optimization of Two‐Stage Lot‐Sizing Problems

This paper studies two‐stage lot‐sizing problems with uncertain demand, where lost sales, backlogging and no backlogging are all considered. To handle the ambiguity in the probability distribution of demand, distributionally robust models are established only based on mean‐covariance information about the distribution. Based on shortest path reformulations of lot‐sizing problems, we prove that robust solutions can be obtained by solving mixed 0‐1 conic quadratic programs (CQPs) with mean‐risk objective functions. An exact parametric optimization method is proposed by further reformulating the mixed 0‐1 CQPs as single‐parameter quadratic shortest path problems. Rather than enumerating all potential values of the parameter, which may be the super‐polynomial in the number of decision variables, we propose a branch‐and‐bound‐based interval search method to find the optimal parameter value. Polynomial time algorithms for parametric subproblems with both uncorrelated and partially correlated demand distributions are proposed. Computational results show that the proposed models greatly reduce the system cost variation at the cost of a relative smaller increase in expected system cost, and the proposed parametric optimization method is much more efficient than the CPLEX solver.     

Traffic‐Based Labor Planning in Retail Stores

Staffing decisions are crucial for retailers since staffing levels affect store performance and labor‐related expenses constitute one of the largest components of retailers’ operating costs. With the goal of improving staffing decisions and store performance, we develop a labor‐planning framework using proprietary data from an apparel retail chain. First, we propose a sales response function based on labor adequacy (the labor to traffic ratio) that exhibits variable elasticity of substitution between traffic and labor. When compared to a frequently used function with constant elasticity of substitution, our proposed function exploits information content from data more effectively and better predicts sales under extreme labor/traffic conditions. We use the validated sales response function to develop a data‐driven staffing heuristic that incorporates the prediction loss function and uses past traffic to predict optimal labor. In counterfactual experimentation, we show that profits achieved by our heuristic are within 0.5% of the optimal (attainable if perfect traffic information was available) under stable traffic conditions, and within 2.5% of the optimal under extreme traffic variability. We conclude by discussing implications of our findings for researchers and practitioners.     

Experimental Results Indicating Lattice‐Dependent Policies May Be Optimal for General Assemble‐To‐Order Systems

We consider an assemble‐to‐order (ATO) system with multiple products, multiple components which may be demanded in different quantities by different products, possible batch ordering of components, random lead times, and lost sales. We model the system as an infinite‐horizon Markov decision process under the average cost criterion. A control policy specifies when a batch of components should be produced, and whether an arriving demand for each product should be satisfied. Previous work has shown that a lattice‐dependent base‐stock and lattice‐dependent rationing (LBLR) policy is an optimal stationary policy for a special case of the ATO model presented here (the generalized M‐system). In this study, we conduct numerical experiments to evaluate the use of an LBLR policy for our general ATO model as a heuristic, comparing it to two other heuristics from the literature: a state‐dependent base‐stock and state‐dependent rationing (SBSR) policy, and a fixed base‐stock and fixed rationing (FBFR) policy. Remarkably, LBLR yields the globally optimal cost in each of more than 22,500 instances of the general problem, outperforming SBSR and FBFR with respect to both objective value (by up to 2.6% and 4.8%, respectively) and computation time (by up to three orders and one order of magnitude, respectively) in 350 of these instances (those on which we compare the heuristics). LBLR and SBSR perform significantly better than FBFR when replenishment batch sizes imperfectly match the component requirements of the most valuable or most highly demanded product. In addition, LBLR substantially outperforms SBSR if it is crucial to hold a significant amount of inventory that must be rationed.     

Equilibrium Joining Strategies and Optimal Control of a Make‐to‐Stock Queue

We consider a make‐to‐stock, finite‐capacity production system with setup cost and delay‐sensitive customers. To balance the setup and inventory related costs, the production manager adopts a two‐critical‐number control policy, where the production starts when the number of waiting customers reaches a certain level and shuts down when a certain quantity of inventory has accumulated. Once the production is set up, the unit production time follows an exponential distribution. Potential customers arrive according to a Poisson process. Customers are strategic, i.e., they make decisions on whether to stay for the product or to leave without purchase based on their utility values, which depend on the production manager's control decisions. We formulate the problem as a Stackelberg game between the production manager and the customers, where the former is the game leader. We first derive the equilibrium customer purchasing strategy and system performance. We then formulate the expected cost rate function for the production system and present a search algorithm for obtaining the optimal values of the two control variables. We further analyze the characteristics of the optimal solution numerically and compare them with the situation where the customers are non‐strategic.     

RISK AVERSION RELATES TO COGNITIVE ABILITY: PREFERENCES OR NOISE?

Recent experimental studies suggest that risk aversion is negatively related to cognitive ability. In this paper we report evidence that this relation may be spurious. We recruit a large subject pool drawn from the general Danish population for our experiment. By presenting subjects with choice tasks that vary the bias induced by random choices, we are able to generate both negative and positive correlations between risk aversion and cognitive ability. Our results suggest that cognitive ability is related to random decision making rather than to risk preferences. (JEL: C81, C91, D12, D81)