Knowledge that Transforms

Problem definition: We consider an entrepreneur designing a fixed funding rewards-based crowdfunding campaign for an innovative product. Product quality is known to the entrepreneur but unknown to some backers. We study how the entrepreneur can signal quality to backers via the design of the crowdfunding campaign, including the price of the reward and the funding target. Academic/practical relevance: Crowdfunding is a new and popular way of funding innovative products. Despite numerous advantages, there are challenges to this model, one of the most significant being credibly signaling information about product quality to a pool of small, uninformed investors. We explore how an entrepreneur might accomplish this and overcome a key obstacle to crowdfunding. Methodology: We employ a game theoretic model of signaling between an entrepreneur and campaign backers. Results: We find that the entrepreneur should signal high quality by setting a high target that is distorted above the full information optimal level. While a separating equilibrium always exists, a pooling equilibrium can only occur under very specific circumstances. We show that the high target affects the quality choice of entrepreneurs and may deter unique, high-quality projects. In addition, we discuss how the entrepreneur should modify the signaling strategy when a high target potentially deters backers from pledging because of the cost of participating in a failed campaign. Managerial implications: We show how entrepreneurs can effectively design their crowdfunding campaign to signal high quality, thus providing guidance to creators listing products on crowdfunding websites. We also show information asymmetry and signaling affect product quality decisions by creators, which in turn is of interest to platform designers seeking to solicit high-quality products for their platforms.

Problem definition : We analyze a contract in which a supplier who is exposed to disruption risk offers a supply-flexibility contract comprising of a wholesale price and a minimum-delivery fraction (“flexibility” fraction) to a buyer facing random demand. The supplier is allowed to deviate below the order quantity by at most the flexibility fraction. The supplier’s regular production is subject to random disruption, but she has access to a reliable expedited supply source at a higher marginal cost. Academic/practical relevance : Despite the prevalence of supply-flexibility contracts in practice, to the best of our knowledge, there is no previous academic literature examining the optimal design of supply-flexibility contracts. As such, the level of flexibility in practice is usually set on an ad-hoc basis, with buyers typically reluctant to share risk with suppliers. Our analysis of supply-flexibility contracts informs practice in two ways: First, using analytically supported arguments, it educates managers on the effects of their decisions on the economic outcomes. Second, it shows that the supply-flexibility contract benefits both the supplier and the buyer, regardless of which player chooses how supply risk is allocated in the supply chain. Methodology : Non-cooperative game theory, non-convex optimization. Results : We derive the supplier-led optimal contract and show that supply chain efficiency improves relative to the price-only contract. More interestingly, even though the buyer lets the supplier decide how the two share supply risk, profits of both the players increase by the introduction of flexibility into the contract. Further, supply flexibility may be even more valuable for the buyer compared with the supplier. Interestingly, the flexibility fraction is not monotone in supplier reliability and a more reliable supplier may even prefer to transfer more risk to the buyer. The robustness of these findings is established on two extensions: one where we study a buyer-led contract (i.e., the buyer chooses the flexibility fraction) and the other where the expedited supply option is available to both the supplier and the buyer. Managerial implications : The supply-flexibility contract is mutually beneficial for both players and yet retains all the advantages of the price-only contract—it is easy to implement, it requires minimal operational and administrative burden, and there is evidence of the use of such contracts in practice. While our focus is not on supply chain coordination, we note that the combination of two mechanisms—the supply-flexibility contract derived in this paper to share supply risk and a buyback contract to share demand risk—yields a coordinating contract.

Problem definition: Firms developing novel and innovative products regularly face a canonical product development and introduction problem: introduce a proven and immediately available product or delay product introduction until the successful development of an advanced version. Academic/practical relevance: Limited access to resources for the development of an advanced version adds another wrinkle to this problem, particularly for cash-constrained start-ups. For such start-ups, the introduction of an on-hand product can generate additional funds to support the development of an advanced product. However, the lower performance of the on-hand product can negatively impact the perception of the firm’s future products and lower future profitability. Methodology: We study the trade-off between revenues that an on-hand product generates for research and development funding and the negative effect it has on future profits. We characterize the optimal introduction timing of the on-hand product as a function of the financial resource constraints, the interdependence between these sequential products and the cost of development. Results: We identify important differences between the optimal product introduction strategies of a start-up and an established firm. Specifically, although it is always optimal for an established firm to accelerate the launch of a better-quality on-hand product, a start-up might find it optimal to delay its launch. The impact of technological failure and different forms of learning on the optimal strategy of the start-up are also explored. We translate our analytical findings into a managerial framework and illustrate these results using examples from the pharmaceutical and medical devices industries.

Problem definition: We conduct a controlled human-subjects experiment in a two-tier supply chain where a supplier’s per-unit production cost may be private information while bargaining with a buyer. Academic/practical relevance: Academically, supply chain studies often assume full-information or highly structured bargaining. We consider private information with dynamic, unstructured bargaining. In practice, a buyer may not know its supplier’s cost exactly and interact with its supplier in a back-and-forth bargaining environment. Thus, understanding how a supplier’s private cost information affects both supply chain outcomes and bargaining is new to the literature and relevant to practice. Methodology: We employ insights from mechanism design to generate restrictions on the space of agreements and solve for a specific bargaining solution under private information to generate precise predictions. These predictions are then tested through a human-subjects experiment. Results: In our experiment, theory predicts that all supplier types should earn at least 50% of total profits when their cost information is private. However, we find that high-cost suppliers earn a disproportionately low share of total profits under private information, 20.16%. We show that this is because buyers, under private information, act as if they are bargaining with the lowest-cost supplier and suppliers do not appear to blame buyers for behaving this way. Based on these findings, we conduct an additional experiment where suppliers have the ability to communicate their private costs to buyers and observe that verifiable disclosure significantly increases profits for high-cost suppliers. Managerial implications: High-cost suppliers actually suffer from having their costs as private information, which runs counter to theory. However, if high-cost suppliers can credibly disclose their costs to buyers, they can significantly increase profits. Lastly, although private information does not lead to more disagreements, negotiations do take longer, which can be costly to firms.

Problem definition: The shortage of inpatient beds is a major cause of delays and cancellations in many hospitals. It may also lead to patients being admitted to inappropriate wards, resulting in a lower quality of care and a longer length of stay. Academic/practical relevance: Investment in additional beds is not always feasible. Instead, new and creative solutions for a more efficient use of existing resources must be sought. Methodology: We propose a new configuration of inpatient beds, which we call the clustered overflow configuration. In this configuration, patients who are denied admission to their primary wards as a result of beds being fully occupied are admitted to overflow wards, with each designated to serve overflows from a certain subset of specialties and providing the same quality of care as in primary wards. We propose two different formulations for partitioning and bed allocation in the proposed configuration: one minimizing the sum of average daily costs of turning patients away and nursing teams, and another minimizing the numbers turned away subject to nursing cost falling below a given threshold. We heuristically solve instances from both formulations. Results: Applying the models to real data shows that the configurations obtained from our models compare very well with the other configurations proposed in the literature, provided that patients’ willingness to wait is relatively short. Managerial implications: The proposed configuration provides the combined advantages of the dedicated configuration, wherein patients are only admitted to their primary wards, and the flexible configuration, in which all specialties share a single ward. On the other hand, it restricts the adverse impacts of pooling and minimizes cross-training costs through appropriate partitioning and bed allocation. As such, it serves as a viable alternative to existing inpatient configurations.

Problem definition : We examine a dynamic assignment problem faced by a large wireless service provider (WSP) that is a Fortune 100 company. This company manages two warranties: (i) a customer warranty that the WSP offers its customers and (ii) an original equipment manufacturer (OEM) warranty that OEMs offer the WSP. The WSP uses devices refurbished by the OEM as replacement devices, and hence their warranty operation is a closed-loop supply chain. Depending on the assignment the WSP uses, the customer and OEM warranties might become misaligned for customer-device pairs, potentially incurring a cost for the WSP. Academic/practical relevance : We identify, model, and analyze a new dynamic assignment problem that emerges in this setting called the warranty matching problem. We introduce a new class of policies, called farsighted policies, which can perform better than myopic policies. We also propose a new heuristic assignment policy, the sampling policy, which leads to a near-optimal assignment. Our model and results are motivated by a real-world problem, and our theory-guided assignment policies can be used in practice; we validate our results using data from our industrial partner. Methodology : We formulate the problem of dynamically assigning devices to customers as a discrete-time stochastic dynamic programming problem. Because this problem suffers from the curse of dimensionality, we propose and analyze a set of reasonable classes of assignment policies. Results : The performance metric that we use for a given assignment policy is the average time that a replacement device under a customer warranty is uncovered by an OEM warranty. We show that our assignment policies reduce the average uncovered time and the expected number of out-of-OEM-warranty returns by more than 75% in comparison with our industrial partner’s current assignment policy. We also provide distribution-free bounds for the performance of a myopic assignment policy and of random assignment, which is a proxy for the WSP’s current policy. Managerial implications : Our results indicate that, in closed-loop supply chains, being completely farsighted might be better than being completely myopic. Also, policies that are effective in balancing short-term and long-term costs can be simple and effective, as illustrated by our sampling policy. We describe how the performance of myopic and farsighted policies depend on the size and length of inventory buildup.

Problem description: Measuring quality in the service industry remains a challenge. Existing methodologies are often costly and unscalable. Furthermore, understanding how elements of service quality contribute to the performance of service providers continues to be a concern in the service industry. In this paper, we address these challenges in the restaurant sector, a vital component of the service industry. Academic/practical relevance: Our work provides a scalable methodology for measuring the quality of service providers using the vast amount of text in social media. The quality metrics proposed are associated with economic outcomes for restaurants and can help predict future restaurant performance. Methodology: We use text present in online reviews on Yelp.com to identify and extract service dimensions using nonnegative matrix factorization for a large set of restaurants located in a major city in the United States. We subsequently validate these service dimensions as proxies for service quality using external data sources and a series of laboratory experiments. Finally, we use econometrics to test the relationship between these dimensions and restaurant survival as additional validation. Results: We find that our proposed service quality dimensions are scalable, match industry standards, and are correctly identified by subjects in a controlled setting. Furthermore, we show that specific service dimensions are significantly correlated with the survival of merchants, even after controlling for competition and other factors. Managerial implications: This work has implications for the strategic use of text analytics in the context of service operations, where an increasingly large text corpus is available. We discuss the benefits of this work for service providers and platforms, such as Yelp and OpenTable.

Problem definition : This paper explores the impact of competition between platforms in the sharing economy. Examples include the cases of Uber and Lyft in the context of ride-sharing platforms. In particular, we consider competition between two platforms that offer a common service (e.g., rides) through a set of independent service providers (e.g., drivers) to a market of customers. Each platform sets a price that is charged to customers for obtaining the service provided by a driver. A portion of that price is paid to the driver who delivers the service. Both customers’ and drivers’ utilities are sensitive to the payment terms set by the platform and are also sensitive to congestion in the system (given by the relative number of customers and drivers in the market). We consider two possible settings. The first one, termed “single-homing,” assumes that drivers work through a single platform. In the second setting, termed “multihoming” (or “multiapping,” as it is known in practice), drivers deliver their service through both platforms. Academic/practical relevance : This is one of the first papers to study competition and multihoming in the presence of congestion effects typically observed in the sharing economy. We leverage the model to study some practical questions that have received significant press attention (and stirred some controversies) in the ride-sharing industry. The first involves the issue of surge pricing. The second involves the increasingly common practice of drivers choosing to operate on multiple platforms (multihoming). Methodology : We formulate our problem as a pricing game between two platforms and employ the concept of a Nash equilibrium to analyze equilibrium outcomes in various settings. Results : In both the single-homing and multihoming settings, we study the equilibrium prices that emerge from the competitive interaction between the platforms and explore the supply and demand outcomes that can arise at equilibrium. We build on these equilibrium results to study the impact of surge pricing in response to a surge in demand and to examine the incentives at play when drivers engage in multihoming. Managerial implications : We find that raising prices in response to a surge in demand makes drivers and customers better off than if platforms were constrained to charge the same prices that would arise under normal demand levels. We also compare drivers’ and customers’ performance when all drivers either single-home or multihome. We find that although individual drivers may have an incentive to multihome, all players are worse off when all drivers multihome. We conclude by proposing an incentive mechanism to discourage multihoming.

Problem definition : Is teamwork better than working alone for creating breakthrough inventions? We challenge the widely accepted affirmative answer to this question. Academic/practical relevance : Extant research has consistently found that lone inventors significantly underperform teams in creating breakthroughs; thus it extols the benefits of teamwork while neglecting the role of single inventors. This paper offers an important counterweight to those empirical results by identifying a fundamental contingency under which teams might or might not outperform lone inventors: the degree of decomposability of the invention. By ignoring this contingency, past literature has systematically underestimated the role that lone inventors can play for companies. Methodology : We use utility and design patent data for 1985–2009 to compare the effect—on the probability of creating a breakthrough—of working alone versus working with a team. Results : For utility patents, we do find that working alone reduces the likelihood of achieving a breakthrough. Yet this disadvantage of lone inventors is not evident for design patents. We theorize that the nearly nondecomposable nature of design is a major factor contributing to lone designers’ relative efficacy of achieving breakthroughs. This theory is then tested in the context of utility patents, where we can observe variation in inventions’ decomposability. We find that technology inventions that are difficult to decompose also relatively advantage lone inventors compared with teams, and we demonstrate that this finding reflects greater coordination costs when such inventions are attempted by teams. If one takes a myopic view of collaboration’s role, then our results suggest that working with others does not help develop outstanding nondecomposable inventions. Yet taking a long-term view reveals that lone inventors benefit more than do teams from having collaborated with others in the past. In fact, we find that past collaborations can help lone inventors outperform teams with regard to developing nondecomposable inventions. Managerial implications : Past research has suggested that collaboration is universally beneficial in creating breakthrough inventions. However, such efforts have ignored crucial contingencies: we show why inventors should explicitly consider both the targeted invention’s decomposability and their own history of collaboration when deciding whether or not to work with a team on a given innovation.