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.

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.

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.

It is a common practice that business and economy compartments share the seating capacity of a short or medium-haul European flight, with the middle seats in the business compartment kept vacant to enhance comfort and privacy for business passengers. The two compartments are separated by a movable curtain whose position determines the capacity allocated to each compartment. By postponing the decision on curtain position until departure, airlines gain extra flexibility in their capacity allocation. We formulate an airline’s problem of dynamically determining the curtain position and the subset of fare products to offer in each compartment to maximize the combined revenue from business and economy passengers. We show that the airline should offer a business (resp., economy) fare product if and only if its fare exceeds a business (resp., economy) threshold or bid price. We then establish monotonicity properties which are useful in expediting the computation of the optimal policy. An extended model where the airline has the option to offer a free upgrade (from economy to business class) to a new arriving economy passenger is presented, and a necessary condition on when to offer upgrades is provided. Using a large-scale and real-life case study based on a proprietary dataset, we demonstrate significant revenue improvements from postponing the decision on curtain position, compared to the current practice of fixing it at the beginning of the booking horizon. Furthermore, we provide guidance for the implementation of our policy, taking into account passengers’ seat selection and substitution towards lower fare classes. We also discuss the implications of our work in practice.

Hospital-physician integration has been increasingly considered a potential solution for the underlying challenges hospitals face as they are adapting to value-based healthcare services. We adopt an activity-based measure of integration (ABI) to investigate the relationship between integration and care outcomes, namely in-hospital mortality risk, length of stay (LOS), and 30-day readmission risk. ABI is measured based on a group of physicians who handle a specific procedure, coronary artery bypass graft (CABG), that is, CABG physicians, and is operationalized as the proportion of cases handled by the CABG physicians who single-site (or concentrate all their activity) at a focal hospital. To test hypotheses that posit a U-shaped relationship between ABI and patient care outcomes, we utilize patient-visit level information for Florida patients who underwent cardiac surgery performed by CABG physicians during 2011–2014. We find that ABI has a U-shaped relationship with both mortality risk and LOS, such that patient mortality risk and LOS are minimized at ABI tipping points of about 55%. In contrast, 30-day readmission risk continues to decrease as ABI increases. We also find that hospital teaching status and bed utilization moderate the relationship between ABI and LOS, such that the U-shaped relationship is flatter, basically linear in teaching and/or high-utilization hospitals. Our results suggest that a medium level of integration could be desirable, since a strategy of high integration trades off potentially higher patient volumes and revenues for suboptimal care outcomes. Overall, this study offers new insights for theory and practice, as the non-linear association between integration and care outcomes has not been investigated previously.

In this paper, using data from indirect auto lending and a structural model of external sales representative (ESR) behaviour, we investigate (1) the role of commissions as a potential tool to influence ESRs’ pricing decisions under limited authority, (2) the impact of optimized commissions on firm profitability, and (3) the implications for customer welfare. The results provide strong evidence for ESRs being strategic (vs. myopic) in their pricing and effort decisions; and in both cases, strategic behaviour is inversely proportional to customer risk. Moreover, once optimized, commissions are an effective tool for firms to bridge the profitability gap between centralized pricing and pricing delegation. Our analyses on social justice and fairness reveal that customer groups along the dimensions of customer risk, income class, and gender, which have been traditionally marginalized in society, suffer from inequities in the indirect-lending ecosystem. While, the optimization of commissions does not intensify these biases, we found females to be the exception, and that the inequities due to gender bias not only persist in the optimized regime, but also deepen. Through counterfactual simulations, we propose two policies for firms to minimize social inequity, which helps them balance immediate profit-maximizing goals with responsible AI initiatives.

Self-interested customers’ form of reasoning and its consequences for system performance affect the planning decisions of service providers. We study procedurally rational customers—customers who make decisions based on a sample containing anecdotes of the system times experienced by other customers. Specifically, we consider procedurally rational customers in two-station service networks with open routing, that is, customers can choose the order in which to visit the stations. Because some actions may be less represented in the population, a given customer may not succeed in obtaining anecdotes about all possible actions. We introduce a novel sampling framework that extends the procedurally rational framework to incorporate the possibility that a customer may not receive any anecdotes for one of the actions; in this case, the customer uses a prior point estimate in lieu of the missing anecdotes. Under this framework, we study the procedurally rational equilibrium in open routing. We show first that as the sample size grows large, customers’ estimates become more accurate, and the procedurally rational equilibrium converges to the fully rational equilibrium (which is also socially optimal). We then uncover two main findings. First, we obtain bounds on the distance between the procedurally rational and fully rational equilibrium, aiding operational planning and showing the rate of convergence to the fully rational outcome as the sample size of anecdotes of each individual customer grows. Second, if customers obtain anecdotes of both actions with high probability, then the equilibrium will approximate the fully rational outcome, despite the sampling error inherent to procedural rationality.

Three-dimensional (3D) printing technology has opened up possibilities for product design collaborations between device providers and customers. To enable an environment of cocreation, device providers are now renting 3D printers via the 3D-as-a-Service (3DaaS) model. Although prior research has examined pricing and quality issues in the traditional manufacturing setup, these studies have not analyzed such decisions in the 3D printing supply chain setting, where end users possess the ability to customize product designs. Therefore, several important questions remain unanswered from the perspective of the 3D printing device provider. For example, what is the appropriate pricing model for providing 3DaaS? How do factors such as the extent of design customization and the complexity influence the pricing strategy of the 3DaaS firm? Our analysis shows that if the customers’ impact on the product quality is relatively high or low, the pay-per-build pricing model generates a higher profit than the fixed-fee pricing model. Interestingly, we also find that if customers frequently print highly intricate product designs, the firm might choose the pay-per-build pricing model, only if the likelihood of design failure for these complex structures is low. Otherwise, the firm might opt for the fixed-fee pricing model.