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For experience goods (products or services), given the uncertainty about their actual quality and the growing popularity of social media, potential customers nowadays depend on experiences of peers who have used the goods previously to learn about their quality. In this paper, we study how such customer learning affects a firm's (credible) quality disclosure strategy as well as other relevant decisions. To model such learning, we adopt the anecdotal reasoning framework, which we show to be rational and a special case of the Bayesian framework. There are two main insights that we glean from this study. First, we find that the incorporation of the learning behavior significantly alters the optimal disclosure strategy from its single threshold structure in the extant literature to a multi‐threshold policy. Specifically, firms with high‐ or low‐quality goods prefer not disclosing quality information in order to utilize the pricing flexibility that such a strategy affords; on the other hand, a medium‐quality firm might disclose its quality level, even though this hinders its pricing flexibility, so that customers are confident about it when purchasing the product. Second, we show that the change in the disclosure strategy impacts the optimal pricing decision, which can be non‐monotone in the quality level. Our results suggest that when disclosure is expensive, high‐quality firms are better off educating potential customers through advertising or social media, rather than disclosing their quality levels. They also suggest to policymakers that mandatory quality disclosure may not be socially optimal as more customers obtain quality information through peer learning. Our findings are robust and hold true under quite general customer valuation distributions, in capacitated settings and even when price can be used as a signal of quality level by firms.

We consider the capacity allocation problem for a retailer with multiple suppliers and multiple demand classes. The retailer offers one seasonal product and reserves capacity from multiple suppliers. Customers in different classes are charged with different selling prices for the same product. We analyze the optimal capacity allocation policies with the following three types of customers: (i) patient customers, (ii) impatient customers, and (iii) customers with limited patience. To empower our analysis, we propose a new preservation property of decomposition under a maximization operator. Based on the preservation property, we show that the value function in each period is decomposable for each type of customers. We then characterize the optimal capacity allocation policy for each type of customers and develop an efficient algorithm to obtain the respective optimal policy by exploiting decomposition. We also numerically investigate the optimal policy and show its value against a counterpart static heuristic policy. Finally, we extend our results to systems with multiple products, new capacity additions, etc.

We consider a periodic review, joint inventory and pricing control problem for a firm that faces general random price‐dependent demands. Any unsatisfied demand can be either backordered or lost immediately. The objective is to maximize the expected profit over a finite selling horizon by coordinating the inventory and pricing decisions in each period. For both the backorder model and the lost sales model, we derive some quite general sufficient conditions to ensure the optimality of a base‐stock list price (BSLP) policy based on the strict monotonicity of demand functions in the realizations of random noises. We are among the first to utilize the strict monotonicity of demand functions in the realizations of random noises for deriving the sufficient conditions. We derive the sufficient conditions in both the backorder model and the lost‐sales model by utilizing the new concept of upper‐set and lower‐set decreasing properties (USDP/LSDP), which is a generalized version of the first‐order stochastic dominance. This study reveals that the optimality of a BSLP policy is robust to more general business environments than what we previously thought. Finally, we also apply the USDP/LSDP in other inventory management problems.

This study examined randomized controlled trial data for blonanserin and risperidone in Chinese schizophrenia patients (N = 264). Data related to historical changes in the Positive and Negative Syndrome Scale (PANSS) were used to successfully construct a longitudinal Emax model. Results: (a) The efficacy of the two drugs was similar after week 8, showing a small difference in PANSS reduction. (b) Using the model, we predicted that each 5-point increase in the baseline FPOS (positive score in PANSS five-factor subscales) leads to a decrease in the PANSS total scores at week 8 for 2 points in patients administered blonanserin. (c) The effect of blonanserin on prolactin (PRL) elevation was less. The model was used to predict that prolactin elevations in patients administered risperidone were 2.41-fold of those in patients administered blonanserin. (d) Model quantitation showed that gender is a risk factor for prolactin elevation. Prolactin elevations in female patients were 2.95-fold of the value in male patients administered the same drug. The model demonstrated Blonanserin has similar antischizophrenic efficacy and less serum prolactin rising compared to risperidone in Chinese patients.

We provide an exact myopic analysis for an N-stage serial inventory system with batch ordering, linear ordering costs, and nonstationary demands under a finite planning horizon. We characterize the optimality conditions of the myopic nested batching newsvendor (NBN) policy and the myopic independent batching newsvendor (IBN) policy, which is a single-stage approximation. We show that echelon reorder levels under the NBN policy are upper bounds of the counterparts under both the optimal policy and the IBN policy. In particular, we find that the IBN policy has bounded deviations from the optimal policy. We further extend our results to systems with martingale model of forecast evolution (MMFE) and advance demand information. Moreover, we provide a recursive computing procedure and optimality conditions for both heuristics which dramatically reduces computational complexity. We also find that the NBN problem under the MMFE faced by one stage has one more dimension for the forecast demand than the one faced by its downstream stage and that the NBN policy is optimal for systems with advance demand information and stationary problem data. Numerical studies demonstrate that the IBN policy outperforms on average the NBN policy over all tested instances when their optimality conditions are violated.

We analyze the benefit of production/service capacity sharing for a set of independent firms. Firms have the choice of either operating their own production/service facilities or investing in a facility that is shared. Facilities are modeled as queueing systems with finite service rates. Firms decide on capacity levels (the service rate) to minimize delay costs and capacity investment costs possibly subject to service-level constraints on delay. If firms decide to operate a shared facility they must also decide on a scheme for sharing the capacity cost. We formulate the problem as a cooperative game and identify settings under which capacity sharing is beneficial and there is a cost allocation that is in the core under either the first-come, first-served policy or an optimal priority policy. We show that capacity sharing may not be beneficial in settings where firms have heterogeneous work contents and service variabilities. In such cases, we specify conditions under which capacity sharing may still be beneficial for a subset of the firms

Prior research documents the value of network relationships to firm behavior but is relatively silent on how networks influence opportunism in distribution channels. Focusing on a common type of distribution networks in which multiple distributors serve a single, dominant supplier, this study moves beyond a dyadic view to examine how a focal distributor's relational and structural embeddedness in such a distribution network influences its opportunism toward the dominant supplier. In particular, we postulate that a distributor's relational embeddedness in the network curbs its opportunism, whereas its network centrality, as a form of structural embeddedness in the network, promotes its opportunism. Moreover, we propose that relational embeddedness magnifies the role of a focal distributor's dependence on the supplier in suppressing the distributor's opportunism, whereas network centrality buffers such a role. We first empirically test these hypotheses using data collected from car dealers in China; the results provide support for the hypotheses. We then develop an analytical model to validate and further explain the underlying mechanisms of the network effects. Our analytical results not only validate the empirical results but also provide guidance for managers on controlling opportunism in distribution networks