Knowledge that Transforms

We investigate fan cultivation and monetization with network effects for competing influencers. Influencers are asymmetric in content-creation ability, and each influencer cooperates with a brand. They monetize their fame by charging a commission fee for selling the cooperating brand’s products to consumers. Influencers compete for fans by adjusting content quality in the fan-cultivation stage, and brands determine product prices in the monetization stage. We first study influencers maximizing only current sales profits and then incorporate long-term benefits through the word-of-mouth effect. We characterize the equilibrium of the quality-pricing game between influencers and brands and reveal how commission rates, content-creation abilities, network effect levels, and word-of-mouth strength affect influencers’ and brands’ equilibrium behavior and profits. We find that a higher revenue share of a brand does not always benefit the brand. Improving an influencer’s ability always helps the influencer-brand channel, whereas raising the commission rate may hurt the channel’s profit, depending on whether the influencer is advantaged (i.e. having a higher content-creation ability or charging a higher commission rate). From the social welfare perspective, cooperating with low-commission-rate influencers is recommended. Moreover, conducting influencer marketing with influencers who have a greater difference in content-creation ability improves social welfare. Allowing for the word-of-mouth effect, strengthening the effect improves the advantaged influencer’s content quality and may lower the disadvantaged influencer’s. Surprisingly, a stronger word-of-mouth effect may make either influencer worse off. We extend the base model by investigating a brand’s influencer cooperation strategy, cooperation with multiple influencers under a commission-and-slotting-fee (a fixed payment made to an influencer) mode, fans with correlated content preferences, and substitutable products with correlated consumer preferences sold by competing brands. These extensions confirm the robustness of the base-model findings and bring new insights. Specifically, as the network effect strengthens, the brand tends to cooperate with a more advantaged influencer. When a brand can cooperate with multiple influencers with a slotting fee, cooperating with one influencer is non-optimal when the slotting fee is low or the network effect is weak. Direct competition between correlated products may make a higher commission rate of the advantaged influencer contribute to more social welfare.

The long drug approval process in the United States imposes significant development costs on firms and delays access for patients, especially when a therapy that addresses a condition with no existing treatments shows early promise. To shorten approval timelines, the Food and Drug Administration (FDA) has instituted expedited pathways that are designed to bring the most promising therapies to patients more quickly. In this paper, we study the safety implications of the newest of the four expedited pathways: The Breakthrough Therapy Designation (BTD) program. We also examine the effectiveness of Risk Evaluation and Mitigation Strategies (REMS) and the Boxed Warnings (BW)—two pharmacovigilance tools that the FDA uses to mitigate the safety risks. To conduct our study, we compile a unique dataset of 327 drugs approved by the FDA between 2012 and 2019. We measure drug safety as the number of serious adverse events associated with a drug in a year. Results from the instrumental variable analysis show that BTD drugs incur 1,722 additional serious adverse events in a year (equivalent to 2.7 times the average annual serious adverse events) compared to non-BTD drugs. We find that REMS substantially reduces this safety gap, but BW does not. Specifically, BTD drugs with REMS record 875 fewer serious adverse events per year than BTD drugs without REMS. By revealing the BTD program’s detrimental effects on drug safety and showing which pharmacovigilance tools effectively mitigate these hazards, we contribute to quality and new product development literatures, and provide actionable guidance to policymakers. We propose that the FDA should consider requiring REMS for all drugs approved with BTD status.

A developer's popularity plays a crucial role in their success within open source software (OSS) communities and their access to sponsorship opportunities. This study seeks to answer the question: which signals have the most predictive power for popularity and sponsorship volume on social coding platforms? Using algorithm-supported abductive theory generation supplemented by qualitative insights from observations and interviews, we arrive at a theory of peer evaluation in OSS communities. We examine a large number of signals and categorize them. The two categories are signaling via self-disclosure through profile signals and signaling via contribution quantity and quality through behavioral signals. The large amount of data available to us allows us to use machine learning techniques to arrive at top-ranking predictors within each category. We generate our theory by finding robust patterns and test our theory using a hold-out sample. Our findings indicate that easily observable credibility-enhancing and approachability-related developer profile signals hold greater predictive importance in shaping popularity. However, harder to observe and more complex behavioral signals show greater predictive importance for sponsorship volume. These results signify that OSS social coding platforms are not meritocratic, as developer self-disclosure significantly influences popularity. In contrast, sponsorship decisions, due to their high cost and irreversibility, depend on within-platform contribution-related signals. This research contributes to a deeper understanding of popularity and sponsorship within peer-to-peer followership networks in OSS communities. Through our research, platforms are better informed about the predictors of popularity and sponsorship and can introduce measures to enhance the meritocratic nature of these communities. Developers who seek influence and sponsorship on the platform can be more strategic about information disclosure and their contributions.

Under what conditions should traditional firms transform into digital platforms? While adding developers can enhance value through externalizing value creation, it also entails investment risk. We show that when transformation entails high risk and the value of network effects is low, firms should avoid transforming into a platform and retain their traditional form. By contrast, low transformation risk or high value of network effects make digital transformation profitable. Interestingly, when firms choose to transform, we show that inviting rivals onto the platform can raise profits. Indeed, the platform can even pay rivals to join its platform in certain cases. We find that the benefit of enhancing network effects through demand aggregation can be more profitable than competing as separate platforms. Further, inviting rivals onto a proprietary platform lowers the rival’s competitive aggressiveness. This is a novel strategic rationale for inviting rivals onto the platform elicited in this article. Yet, when the value of network effects is very high and investments are nearly certain, the platform chooses to foreclose rivals’ participation. We offer guidelines for managers seeking to transform and for regulators seeking to intervene to boost market efficiency. We use real-world examples to illustrate our theory.

We study an agricultural supply chain in an emerging economy, where farmers make land allocation decisions among alternative crops and have the option to sell their harvest either in a side market or directly to a processing plant, and where a government-mandated minimum price guarantee (MPG) is in place. Our study examines a range of pricing strategies that the processing plant can employ to favor a protected crop and the utilization of the processing plant. We begin by considering the Benchmark strategy, in which the plant strictly adheres to the MPG. Subsequently, we investigate two distinct pricing strategies: The Ex-ante strategy, in which the plant commits to an optimal predetermined final price at the start of the growing season; and the Responsive strategy, in which the optimal final price is determined at the end of the season after yield and side-market uncertainties have been resolved. Additionally, we propose the Hybrid strategy, which integrates both ex-ante pricing as a preseason commitment and ex-post price adjustments in response to realized uncertainties. We develop two-stage stochastic program models to analyze the farmer allocation and plant pricing decisions. Among other results, our study suggests that the ex-post pricing flexibility inherent in the Responsive approach may leave the plant worse off, even though price adjustments are plant-profit optimizing. We refer to this phenomenon as the ex-post pricing flexibility paradox. Due to this phenomenon, the Hybrid strategy, while consistently outperforming the Responsive approach, does not always dominate the Ex-ante approach. We propose execution variants of the Hybrid strategy that can guarantee outperformance on the Ex-ante benchmark. In long-term repeated interactions between the plant and farmers, the Hybrid strategy with historically credible probabilistic after-harvest price adjustments, proves effective. For short-term practices, defining a contingency region of relevant outcomes (e.g., yield realizations, side-market attractiveness, and processing margins) that triggers after-harvest price adjustments enables the Hybrid strategy to outperform. These findings urge caution when the plant considers adjusting after-harvest prices, and highlight the importance of building long-term and stable relationships in agri-chains for effective execution of pricing strategies that align incentives and optimize profits for all involved parties.

The widespread prevalence of opioids has prompted governments to implement targeted interventions aimed at reducing overdose mortality, with naloxone accessibility emerging as one of the most prominent policies. Naloxone, a potent opioid antagonist, is highly effective in reversing overdoses, yet its expanded availability introduces complex trade-offs, particularly in the presence of moral hazard. We develop a dynamic compartmental model that captures transitions between susceptible individuals and those with opioid use disorder, allowing us to evaluate the impact of naloxone accessibility on overdose mortality and to derive the optimal accessibility policy. We show that full naloxone accessibility is optimal in the absence of moral hazard or when its effect is small. However, when moral hazard is significant—where greater access to naloxone encourages riskier opioid use—expanded accessibility can paradoxically increase overdose deaths. Extending the model to incorporate peer-driven contagion in opioid misuse, we find that the structure of the optimal policy remains robust, preserving the bang–bang nature and the reversal induced by moral hazard. Two additional insights emerge under this interaction-based model. First, in epidemics primarily driven by prescription-induced opioid use, full accessibility remains optimal. In contrast, when opioid use spreads socially—especially as the effectiveness of naloxone declines due to potent synthetic opioids like carfentanil—limited accessibility may become preferable. Second, the relationship between naloxone accessibility and overdose mortality may become nonmonotonic, exhibiting an inverted U-shape in which moderate increases in accessibility can initially worsen outcomes. A calibrated case study based on U.S. data suggests that under current epidemic conditions, full accessibility remains optimal—a finding that aligns with existing regulatory policies. However, our results highlight that shifts in epidemic dynamics, such as increased opioid potency, may fundamentally alter this conclusion. These findings underscore the need for continuous reevaluation of naloxone distribution policies as the opioid crisis evolves.

While sports are enjoyed by several billion fans globally, the business of sports has evolved into a multi-billion dollar industry. Multi-disciplinary research on developing competitive strategies for drafting players and making on-field decisions is growing rapidly. However, there remains a notable gap in operations management research focusing on the business aspects of the sports industry. This paper addresses this gap in the literature to encourage future research in this domain. We highlight new opportunities for operations management researchers, emphasizing five research themes that are of importance to the sports industry: (1) Revenue management, (2) new technologies in sports business, (3) betting, (4) rule and competition changes, and (5) service operations. We identify important and relevant open-ended research questions and discuss related trade-offs to address the research questions. This paper provides a basis for future operations management research to address the unique challenges faced by sports organizations.

Subscription sharing, where users share premium family plans with non-family members via platforms like Together Price and Sharesub, has become increasingly common. This raises a key question: should providers still offer discounted family plans alongside individual ones? Our research explores this issue for a monopolistic provider facing this sharing threat. We analyze the optimal pricing strategy and the effects of subscription sharing on profits, plan offerings, consumer surplus, and social welfare. We find that offering both plans is at least as profitable as offering individual plans only, and sharing can sometimes supercharge profits. However, platform fees reduce these benefits by narrowing the price gap between plans, weakening the market expansion effect. Our numerical results show that sharing often improves social welfare. Overall, these insights suggest that subscription sharing is not always as harmful to providers, and the platform service fees play a key role in pricing strategies.

This paper studies the impact of product sharing on competing manufacturers under a platform’s different quality entry barrier strategies. We build a game-theoretic analytical model to examine the strategies of two manufacturers: A high-quality manufacturer producing a high-quality product and a low-quality manufacturer producing a low-quality product. We explore three markets: The N-S market where the sharing market does not exist, the L-S market, and the H-S market, where the platform sets low and high entry barriers, respectively. We study these three markets because they bracket the most common platform entry policies in practice—no sharing, low-barrier (open access), and high-barrier (quality-gated) participation. Our findings indicate that while product sharing makes it easier for the high-quality manufacturer to survive, it may not necessarily improve the survival likelihood of the low-quality manufacturer. Furthermore, we demonstrate that the existence of the sharing market may negatively impact both manufacturers. To the best of our knowledge, this study is the first to investigate how competing manufacturers, platforms, and consumers can cope with the sharing phenomenon. We show that, while the high-quality manufacturer prefers a low cost of quality in all three markets, the low-quality manufacturer, interestingly, prefers a high cost of quality in both product-sharing markets. Additionally, we illustrate that the platform should not always set a low entry barrier to allow more product types to join. Surprisingly, our results show that although product sharing always benefits social welfare, it could potentially harm consumers.

On-demand platforms such as ride-sharing services rely heavily on economic incentives to attract, retain, and manage independent workers who have significant discretion over whether and where to work. Using an analytically tractable spatial model, we explore the impact of different pricing and commission strategies on customer demand, driver entry and retention, and their location choices. Our model yields several unique results and actionable insights. We find that flexible commission policies are more effective than fixed commission policies in allocating drivers efficiently across locations, reducing bottlenecks, and improving driver retention. We also show that commission-based interventions are more effective than price interventions in responding to labor market changes, as they directly affect driver incentives without distorting customer demand. Finally, if fairness-sensitive customers are prevalent in the market, then fixed pricing, combined with flexible commissions, becomes the optimal rule. Simulations based on actual ride patterns from New York City and Los Angeles confirm our insights.