Knowledge that Transforms

Recent empirical studies suggest that human behavior in queues causes workload-dependent service times. We investigate the translation of empirical service times into state-dependent queueing models. To this end, we identify two types of state-dependent models, static and dynamic, and two types of corresponding behavioral mechanisms. For example, we view customer early task initiation as a static mechanism and social speedup pressure as a dynamic mechanism. For each model type, we discuss behavioral mechanisms consistent with the model assumptions and indicate how empirical service times can be translated into model input parameters. We illustrate how translating service times into dynamic models can result in invalid service rates, which provides evidence against dynamic mechanisms. For dynamic models, we find that mean service times are in general not the inverse of service rates, the directional change in service rates is not always the opposite of the directional change in mean service times, and workload measurement timing can drastically impact mean service time patterns. We provide closed-form equations to convert service times into service rates and vice versa, and find conditions under which monotonic mean service times imply monotonic service rates and vice versa. Our results provide guidelines for researchers to select and specify an appropriate state-dependent queueing model from service time data, and expand the scope of previously published analytical results.

Peer-to-peer (P2P) platforms facilitate the direct exchange of goods, services, or financial transactions between individuals without the involvement of intermediaries. To maintain trust among their user bases, these platforms must implement stringent access controls to determine which users are eligible to participate in their digital marketplace. We argue that when a platform transitions from private to public ownership, it may be incentivized to strategically lower its access standards and admit users who might otherwise have been deemed unqualified. Lowering standards before an initial public offering (IPO) can enable platforms to rapidly increase their user base—and, consequently, enhance their perceived valuation—which could appeal to stock investors and positively influence the IPO price. While this strategy may bolster short-term growth, it could be costly to the platform’s user base. We support this hypothesis using data from two major P2P lending platforms—one that went public and one that remained private. Using a difference-in-differences analysis, we find that the platform preparing for an IPO admitted borrowers who exhibited higher risk levels. Additionally, lenders, in some cases, did not effectively screen out these subpar borrowers and ended up issuing loans to them, leading to higher default rates and lower returns for the lenders. This effect was particularly evident for a specific segment of lenders—namely, those who brokered small-valued loans and loans for necessary purchases (e.g., health emergencies). By contrast, lenders who screened for large-valued loans or loans for discretionary expenditures (e.g., vacations or weddings) were more successful in screening out these subpar borrowers and not issuing loans to them. These results fill a gap in the operations management literature on platform governance by integrating capital-raising objectives into the discussion of access control and operational screening. Our study highlights the nuanced trade-off between quality control and user expansion during capital-raising events, emphasizing both the opportunities and potential inefficiencies that arise in this context.

Dynamic treatment regimes improve health outcomes by tailoring each treatment to a patient’s evolving condition, but they also allow providers to learn and game the system over time. How should insurers pay? We study this new class of reimbursement problems, where the provider can privately learn and manipulate the progression of the patient’s condition. (i) We characterize the optimal payment policy: it internalizes two intertemporal effects of each treatment, and rewards provider honesty with incentive pay; moreover, it admits a simple implementation of risk-adjusted cost-sharing policy. (ii) We show that, ignoring dynamic learning and gaming, the existing payment models may have overestimated the harm of information asymmetry. Using the optimal policy, insurers only need to pay for initial private information; they can exploit provider uncertainty and elicit future private information at no cost. (iii) Our study informs U.S. healthcare payment reform with new insights; using two sets of real data, our study also quantifies when and why the optimal policy outperforms the existing ones. By highlighting the critical role of dynamic learning and gaming, this study advances our understanding of healthcare payment theory and practice.

Platforms provide great opportunities for independent sellers to experiment with new products. By facilitating transactions between trading parties, platforms can gather a huge amount of information about successful products and introduce their own versions of competing products. This phenomenon of platform encroachment has received attention from various stakeholders, and concerns have been raised about how it may marginalize independent sellers and hinder the development of the ecosystem. At the same time, platforms expedite the diffusion of information about successful products and facilitate learning and imitation from other independent sellers, which has received little attention in the literature. In this article, we explicitly account for this feature and consider a dynamic model to study the impact of platform encroachment on sellers’ incentives to experiment with new products, when both the platform and independent sellers can imitate and introduce competing versions of products offered by the successful experimenter. We show that when a seller with successful experimentation holds a competitive advantage in the product market, platform encroachment may enhance the incentives to carry out experimentation. This enhancement effect is stronger when information diffuses faster on the platform. We further discuss the implications for the platform’s optimal encroachment strategy and regulatory policies.

A common strategy of extending the lifetime of an aging system is to reduce its workload below the normal operating level, a practice known as derating. While derating can slow the deterioration process, it often comes at the expense of reduced performance. Thus, derating involves a trade-off between performance and deterioration. Central to the optimal derating strategy is the relationship between deterioration and workload, also referred to as the pd-relationship. In practice, however, this relationship is rarely known a priori. We consider the workload optimization when the pd-relationship can be adaptively learned through sequential experimentation, or active learning. We show that the workload not only influences the performance and deterioration but also controls the speed of learning. The decision-maker must therefore account for the complex interplay between performance, deterioration, and information in real time. We formulate this problem as a partially observable Markov decision process and characterize the optimal policy. A key structural insight is that the optimal workload is always less than the myopic load. We further propose an efficient algorithm based on the fast Gauss transform to compute the optimal policies. The model is validated with vibration data and the performance of the optimal policy is compared against several heuristic policies.

Maintaining a high service level with customers involves a fundamental tradeoff between investing in inventory and investing in expediting the shipping of the additional units needed to achieve that service level. We use a multi-method approach to show how and when ordering decisions are influenced by different levels of expediting costs and target service levels. First, we derive a mathematical model that provides a closed-form solution to this tradeoff. Second, we run a behavioral study to show how increments in the expediting cost and the target service level impact buyers’ ordering behavior. Results show that ordering decisions are influenced mainly by the expediting cost. Our econometric estimations provide a generalization of the pull-to-center effect to a setting with expedited shipping. Moreover, we find that buyers adjust their orders only when facing a high target service level. To reduce the observed behavioral biases, we propose that managers can increase the salience of key performance metrics in the buyers’ decision-making process. We test for the role of salience with a second behavioral study and a behavioral model. Results show that the extent to which salient information helps improve buyers’ ordering decisions depends on the level of the expediting cost. Interestingly, our behavioral model highlights how ordering decisions improve not by eliminating people’s biases but by amplifying some of those biases. We contribute to the literature on expedited shipping and behavioral operations, and provide practical recommendations for how managers can improve ordering decisions.

Disturbances in production along with volatile demand have raised concerns over shortfalls in the global supply chain and prompted the need to build a more diversified supply chain with competitive suppliers. This research investigates the impact of disturbances on a two-tier supply chain network with asymmetric competing firms. We establish the equilibrium in a unique structure that represents the maximum set of profitable upstream supply paths achievable through competition and exhibits stability under specific conditions. We evaluate the efficiency of the supply chain configuration by a shortfall problem and solve it with an adapted pseudoflow algorithm that efficiently identifies the mismatches between shortfalls and capacity surpluses in the multitier network. The parametric analysis reveals that the disturbance loss can be significantly offset by supplier competition, although the marginal benefit of competition decreases rapidly with the number of suppliers. Furthermore, shortfalls could be magnified by network asymmetries that increase configuration inefficiency, and supply chain performance could be improved by pushing high-cost firms to cease production. Simulation results indicate that the supply chain with a moderate level of competition and a balanced configuration can be robust against disturbance and demand volatility.

Merchants use the rolling intrinsic policy to value commodity storage assets and monetize the values they attribute to these assets. A recent study ascribes to informational inconsistency two discrepancies between its partial monetization findings and the known excellent valuation performance of this method: (i) the rolling intrinsic policy can perform worse than the intrinsic policy and (ii) it appears to be far from optimal. This paper contends that this claim generally confounds informational inconsistency and the risk adjustment that underpins the common no-arbitrage valuation of this policy. In particular, lack of risk adjustment in that investigation explains at least the first difference and informational inconsistency cannot be associated with the second dissimilarity in about <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>4</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> of its instances. Furthermore, this work establishes that equipping the rolling intrinsic policy with forward trading rules out the former disparity, whereas the latter one in general can persist. Finally, it uses known results to argue that in realistic settings this extended policy incurs small monetization losses on average for storage assets acquired at a fair price and outlines alternative monetization methods with potentially improved effectiveness.

This paper investigates the interaction between cheap-talk advertising and credible third-party product reviews to inform customers about product quality. We find that cheap-talk advertising can be informative when the firm’s private information helps predict a credible product review. A more informative credible product review has two effects on cheap-talk advertising. First, a credible product review plays a disciplinary role that enables the firm to provide informative advertising. Second, it reduces the incremental value of cheap-talk advertising. We find that, in equilibrium, whether or not the advertising is consistent with a credible product review is informative about product quality. The results also imply that an overly informative product review can reduce the total information available to customers by deterring the firm from providing informative advertising.