Knowledge that Transforms

Additive manufacturing (AM) is a process by which three-dimensional products are made via the addition of material in a layer-by-layer fashion. This manufacturing technique is growing in commercial usage, given its advantages in creating very dense or complex geometries as well as highly customizable components. In healthcare, for example, AM can be used to improve patient outcomes by providing timely medical devices (or parts) required for treatments. Minimizing the number of late parts in this context will directly improve the patients’ welfare. This paper studies the nesting and scheduling problem within the AM context and shows that the problem of minimizing the number of late parts is strongly NP-hard even if the nesting of parts into jobs is given. We also develop efficient algorithms to minimize the number of late parts, both when nesting is fixed beforehand and when nesting is part of the algorithm. The theoretical results, including algorithm performance bounds, developed in this paper are new contributions to the literature. An extensive computational study evaluates the performance of both algorithms. The nesting and scheduling algorithm performs within 7% of the lower bound on average and shows an effective way to nest and schedule systems containing challenging problem instances. Providing efficient, high-performing algorithms such as these will allow AM managers to quickly schedule parts for AM production with a minimum number of late parts and consequently improve both customer satisfaction and the profitability of the firm.

Third-party distribution channels or intermediaries have become ubiquitous across a wide range of industries, offering firms access to new prospects and an opportunity to expand their customer base. Although prior work has investigated the short-term aggregate demand implications of intermediaries, the long-term customer relationship perspective has remained unexplored. Using customer-level data from a large U.S. hotel brand, we show that customers acquired via travel intermediaries (such as online travel agents or OTAs) have persistently different behaviors on several dimensions that matter for long-term value. Compared to customers acquired through brand-owned (direct) channels, intermediary-acquired customers spend 4.1% less per stay and purchase 3.8% less frequently. Intermediary-acquired customers, while displaying stronger channel inertia and lower multichannel engagement, purchase across a wider variety of brands (multibrand behavior). When we combine these behavioral estimates into a customer lifetime value (CLV) computation, we find that while intermediary-acquired customers have positive CLV, their CLV is 19.94% lower than customers acquired through brand-owned channels, revealing, for the first time, the long-term implications of such customer acquisition strategies. Through an optimal allocation model we show that, although the CLVs are lower for intermediaries, a firm maximizing customer value typically invests in both channels: the optimal share allocated to intermediaries rising proportionally with the intermediary’s acquisition efficiency and accessible prospect pool, and falling when the hotel is operating at capacity. In sum, our results show that although using intermediaries may be a viable strategy for customer acquisition, the purchase behaviors of these customers are significantly and meaningfully different from customers acquired via brand-owned channels, thus urging managers to adopt a more nuanced ‘frenemies’ approach to building a channel portfolio.

Predictive hotspot mapping is an important problem in crime prediction and control. An accurate hotspot mapping helps in appropriately targeting the available resources to manage crime in cities. With an aim to make data-driven decisions and automate policing and patrolling operations, police departments across the world are moving toward predictive approaches relying on historical data. In this paper, we create a nonparametric model using a spatiotemporal kernel density formulation for the purpose of crime prediction based on historical data. The proposed approach is also able to incorporate expert inputs coming from humans through alternate sources. The approach has been extensively evaluated in a real-world setting by collaborating with the Delhi police department to make crime predictions that would help in effective assignment of patrol vehicles to control street crime. The results obtained in the paper are promising and can be easily applied in other settings. We release the algorithm and the dataset (masked) used in our study to support future research that will be useful in achieving further improvements.

We study how bug bounty programs (BBPs) shape software vendors’ security and release choices. Vendors invest in internal assurance before release to reduce residual vulnerabilities, and after launch they must manage vulnerability discovery, disclosure, and remediation. We develop a game-theoretic model in which a vendor chooses release timing and severity-contingent bounties, anticipating effort by ethical and malicious hackers in a winner-take-all discovery race. The model highlights two linked mechanisms: an incentive channel that shifts first discovery of severe vulnerabilities away from malicious exploitation and toward ethical reporting, and a governance channel in which coordinated disclosure changes how vulnerability information is managed while remediation is underway. We derive closed-form optimal bounties and characterize a feasibility region that sustains positive bounties and interior success probabilities. Within this region, a BBP strictly increases the vendor’s expected profit by reallocating first-discovery probability on severe vulnerabilities from malicious to ethical hackers and by converting part of severe-loss exposure into bounded, pay-for-results expenditures. For private programs, we also solve for the optimal invited set of ethical hackers and show that this optimal set is strictly smaller than the expected number of malicious attackers. Higher bounties raise ethical hackers’ effort and first-discovery probabilities but also increase program cost, and they interact with reputational (non-monetary) incentives. Finally, in the baseline model, BBP adoption conditionally reduces the marginal value of additional pre-release delay and therefore conditionally implies earlier release relative to the no-BBP benchmark. This timing result is a within-model conditional implication; its practical relevance depends on operational readiness, triage throughput, and the vendor’s ability to validate and safely deploy fixes once a valid report arrives. Managerially, BBPs should be viewed as a post-release governance layer that complements strong internal assurance rather than as a substitute for it. Policymakers can support responsible use of BBPs by encouraging timely remediation, transparent post-patch disclosure, and reporting standards that reduce information asymmetry and triage frictions.

Geographic dispersion is routine in new product development, yet it remains unclear whether employee geographic distance (EGD) slows project execution. We develop a coordination cost perspective in which EGD is postulated to increase project delays because it raises attention allocation and information search costs. We test this framework by using a longitudinal employee–project–month panel from a large manufacturing firm that spans 9,729 observations across Europe, North America, and Asia. We find that greater geographic distance is associated with longer project delays, and that the effect is contingent on employee and project characteristics. The EGD–delay link is attenuated by employee familiarity and project similarity, consistent with lower attention allocation costs, and for specialist employees, consistent with differences in information search costs. We also find that the link is amplified for cross-functional geographic dispersion. Additional analyses document that the association between EGD and project delay is concentrated at later project gates and in high-risk or highly innovative projects. A difference-in-differences test around daylight-saving-time transitions indicates that temporal misalignment alone does not account for the EGD–delay relationship. Taken together, the results inform the literature on new product development, project management, and global work by demonstrating when geographic distance is most consequential for project delay.

As consumer demand for transparency and accountability in product sourcing grows, traceability-enabled technologies are increasingly adopted across supply chains. This paper explores the diverse impacts of traceability on product quality and supply chain welfare, particularly on pricing and responsibility sharing among stakeholders. We develop a multi-agent game-theoretic model to investigate how effective traceability systems are in enhancing product quality and supply chain members’ welfare. We find that traceability operates as a double-edged sword: it does not always improve quality or benefit all parties, and its effectiveness depends critically on how pricing power and responsibility for quality failures are allocated between the buyer and suppliers. Under buyer pricing, traceability raises quality and benefits the buyer when quality improvement is cost-efficient, whereas it benefits suppliers when quality improvement is costly. Under supplier pricing, traceability always benefits suppliers, but it improves quality only when responsibility is exogenously set or when quality improvement is cost-efficient, and it benefits the buyer only when responsibility is endogenously chosen or when quality improvement is cost-efficient. These findings provide important managerial implications for practitioners and offer guidance for policymakers and firms in designing traceability systems that enhance product quality and overall supply chain welfare.

Organizations lose billions of dollars due to inadequate customer service. To improve service, and enhance customer satisfaction, frontline employees’ (FLEs) use of relational communication may be key. During online customer service chats, FLEs provide key information and offer solutions, but they also can build customer relationships through conversations. In this article, we establish how relational perceptions get evoked in conversations and what influences they have for the outcomes of customer service interactions. Accordingly, we present an empirical field study that illustrates how FLEs influence customer satisfaction by mirroring or complementing four key themes, in line with relational communication theory: intimate communication, task orientation, assertiveness, and composure. Our results indicate that FLEs should mimic customers’ use of intimate communication and task orientation, complement their assertiveness, and exhibit high levels of composure. Moreover, FLEs should emphasize their task orientation at the conversation's outset, gradually incorporate more intimate communication as it progresses, and adopt assertiveness late in the service chat. These insights, corroborated by four experimental studies, underscore the significance of FLEs’ relational communication. Our findings highlight the value of training FLEs to tailor their word choices adeptly, and leverage the potential benefits of text-monitoring tools, which can help FLEs increase relational perceptions and satisfaction among their customers.

Live-streaming e-commerce has gained tremendous success as a new form of business model over the past few years. Nevertheless, there has been scarce research examining the effect of exogenous stimulus-driven factors (i.e., social cues such as promotion coupons) on the profit of the live-streaming platform. We model the continuous evolvement of the aggregate viewer involvement level with geometric Brownian motion and investigate the optimal timing and depth of promotion insertion policy. Our findings reveal that the optimal promotion insertion policy for the live-streaming room is a threshold policy that consists of a start-to-promote threshold and an involvement target when the trend of the aggregate involvement level is not large and the promotion cost is not high. Under some scenarios, the promotion planning process is constrained by various business rules, such as the promotion depth being limited by a discrete set (e.g., integral multiples of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>D</mml:mi> </mml:math> ). Although this problem involves more complex constraints, we analytically show that a variation of the abovementioned threshold policy is the optimal strategy among all feasible policies, i.e., when the aggregate viewer involvement drops below a certain level ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>P</mml:mi> </mml:math> ), the live-streaming host promotes with the depth of the least integral multiple of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>D</mml:mi> </mml:math> to raise the viewer involvement level above <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>P</mml:mi> </mml:math> . Finally, to examine the effectiveness of our proposed policy, we compare it with prevalent industry practices. The result shows that the proposed promotion insertion policy outperforms prevailing ones significantly in generating profit for the live-streaming room.

Livestream commerce has become an important sales channel with billions of revenue potential. A major challenge that brands often face in managing livestream sales in practice is negotiating sales prices and commission rates with key opinion leaders (KOLs) who promote products in such sales. On the one hand, KOLs can increase consumer reach, enhance consumer valuation of a product, and improve profitability of a sale. On the other hand, KOLs—who typically promise low sales prices in livestream sales—are subject to reputation damage risks if their followers find cheaper prices of the same product elsewhere. As a result, KOLs often bargain for deep discounts, which lead to price reduction in negotiation and reduce brands’ profit margins. In this article, we examine this tradeoff based on a Nash bargaining game framework. We show that price reduction may not be the brand’s optimal negotiation strategy to mitigate the KOL’s reputation concern. Instead, increasing the commission rate to the KOL may be more effective especially when product availability in the livestream sale is low. We further show that the presence of consumer price search can either improve or undermine the profitability of a livestream sale depending on the competitive features of the sale. Our study indicates that the negotiation dynamics in livestream sales can have significant profit impact for the brand, and the brand should be mindful about the operational features of livestream sales and stakeholder incentives such as KOLs’ reputation concerns and consumer price search when developing the optimal negotiation strategy.

Apparel retailers face increasing pressure to communicate the corporate social responsibility (CSR) performance of their supply chains. While communicating positive CSR performance is common, communicating less-than-perfect performance is challenging and has only recently gained attention. To inform retailers’ communication strategies, we examine consumers’ responses to different levels of supply chain CSR performance and the mechanisms underlying these responses. We propose a mediation framework in which CSR performance influences purchase intentions through two interrelated evaluations: perceived price fairness and product desirability. We further test how these evaluations are moderated by two aspects of price context: consumers’ historical price paid and the retail price. We conduct two controlled experiments in an online shopping context. A non-incentivized study examines the proposed mediation mechanisms and develops additional hypotheses about price context, which we test in an incentivized study. Across both studies, we find that communicating less-than-perfect CSR performance does not uniformly elicit negative consumer responses. Mediation analyses identify the indirect effects of CSR performance on purchase intentions, with price fairness emerging as a particularly robust mediator. Moreover, the strength of these effects depends on price context. Historical price paid shapes how consumers interpret CSR information when forming price fairness perceptions: consumers who typically pay a lower (higher) price respond more strongly to positive (negative) CSR performance. A low retail price then amplifies the influence of price fairness on product desirability. Overall, consumer responses to CSR performance communication are not uniform but depend on price fairness and product desirability evaluations and the price context.