Knowledge that Transforms

Frontline health workers are an indispensable asset in the healthcare ecosystem of low- and middle-income countries (LMICs). They not only provide clinical care to patients but also frequently shoulder nonclinical responsibilities such as inventory management. However, many lack the necessary skills to effectively manage inventories, leading to the pervasive problem of health commodity stock-outs. To address this problem, several LMICs have launched training programs aimed at enhancing the inventory management skills and capabilities of frontline health workers. Leveraging fine-grained data over a five-year period (2015–2019) from approximately 17,000 health facilities across Indonesia—where the public health supply chain operates as a hybrid push–pull model—we evaluate the impact of a large-scale inventory management training program on contraceptive stock-outs. Applying difference-in-differences estimations, we find that the training program is associated with a significant reduction in stock-outs (i.e., 6.52 percentage points, or approximately 30%). Our numerical calculations suggest that the training program prevents, on average, more than 800 unintended pregnancies and saves 4.25 maternal and newborn lives per 100,000 women of reproductive age within the catchment area of a treated health facility. A counterfactual benefit–cost analysis shows that on-site delivery of the training program (i.e., on the job within a health facility) yields operational and public health benefits that significantly exceed those achieved through off-site delivery (i.e., outside the health facility in a classroom). Our research highlights the practicality and scalability of human-capital-focused interventions in improving health commodity availability and public health outcomes in LMICs.

We study the equity factor zoo using a duration-matching return-decomposition approach that adjusts factor returns by subtracting returns on duration-matched government bond portfolios. By doing so, we remove the component of factor returns attributable to interest rate movements while preserving shocks to expected growth and risk premia observed in the data. Among commonly used factors, the value, investment, and profitability premia increase after duration matching, while the market and size premia decrease, over the post-1981 sample period. Furthermore, the effect of duration matching on mean factor returns depends importantly on the interest rate environment, consistent with our return decomposition framework.

We examine whether U.S. media sentiment on foreign countries influences domestic investors’ international asset allocation. Using flows to country-specific mutual funds as a proxy for investor demand, we find that negative media sentiment, measured by the interaction of negative tone and media attention given to foreign countries, is strongly associated with reduced investor flows into funds targeting those countries. This relationship is driven, at least in part, by media sentiment that is unrelated to the economic fundamentals of the targeted countries. We employ The Wall Street Journal’s acquisition by News Corp to support the causal interpretation of our findings. Our results highlight the perception-shaping role of media narratives in driving global investment decisions.

We use career backgrounds of initial employees from a full cohort of U.S. startups to measure founding teams’ skillsets. Startups with more diverse prior industry experience grow faster than same-industry local peers. One-standard-deviation-higher diversity is associated with 16% (10%) higher five-year employment (sales) growth, controlling for demographic diversity, prestartup wages, and other observables. Startups in innovative industries and facing lower coordination costs among team members drive the results. To address endogeneity, we exploit shocks in which teams suddenly lose members with unduplicated skills. Overall, combining individual specialization with team-level diversification allows teams to act as “jacks of all trades and masters of each.”

We return to the theoretical foundations of absorptive capacity and test the idea that personal experience in a field makes it easier for a firm’s inventors to recognize and build upon knowledge in that field from other local firms. We propose a new empirical model of localized knowledge diffusion, which (1) measures a firm’s absorptive capacity by its inventors’ prior experience in a field, (2) uses a death instrument to exogenously vary the availability of knowledge of the same collaborative patent in different regions, and (3) estimates the difference in citation likelihood from all subsequent inventors across both regions as a function of a potentially citing inventor’s prior experience in the field. Consistent with the original theory of absorptive capacity, firms whose inventors have prior experience in a field are more likely to use locally available interpersonal knowledge from other firms; furthermore, this effect declines monotonically with distance. Although the effects strengthen for more recent experience and multidisciplinary knowledge, the greatest benefits accrue to firms in the interaction—those whose inventors have more recent experience and that seek to absorb multidisciplinary knowledge. Interpersonal absorptive capacity within firms does not appear to localize.

Consumer experiences are inherently dynamic. When engaging in a sequence of activities, consumers are influenced by past experiences in two ways: negatively by their objective quality, through quality contrast; and positively by their subjective appreciation of them, through affect assimilation. How should experience curators sequence activities to maximize consumer satisfaction in the presence of such intertemporal effects? We formulate an experience curator’s problem as a dynamic optimization program. We show that, because of affect assimilation, the best activity may be scheduled at the beginning or in the middle of an experience, in contrast to the common peak-end rule—this provides a rationale for the saying that “first impressions matter.” Under uncertainty, it may be valuable to save the best activity as a “wild card” to recover from bad outcomes. We calibrate our model to four distinct experiential contexts (namely, watching movies, reading books, visiting touristic attractions, and eating out) and consistently find the presence of both quality contrast and affect assimilation. Through a counterfactual study in the context of touristic tours, we show that experience curators may significantly benefit from offering different fixed sequences to different types of consumers, but they tend to gain little from dynamically adjusting them.

Explainable artificial intelligence (AI) models have been proposed to mitigate overreliance and underreliance on AI, which reduce the effectiveness of human-AI collaborative tools. Yet, empirical evidence is mixed, and the impact of explainable AI on the cognitive effort and fatigue of a decision maker (DM) is often overlooked. This paper offers a theoretical perspective on these issues. We develop an analytical model that incorporates the defining features of human and machine intelligence, capturing the limited but flexible nature of human cognition with imperfect machine recommendations. Crucially, we represent how AI-based explanations influence the DM’s belief in the algorithm’s predictive quality. Our results indicate that explainable AI has varying effects depending on the level of explainability provided. Although low explainability levels have no impact on decision accuracy and reliance behavior, they lessen the cognitive burden on the DM. In contrast, higher explainability levels enhance accuracy by improving overreliance but at the expense of increased underreliance. Further, the relative impact of explainability is higher when the DM is more cognitively constrained, when the decision task is sufficiently complex, or when the stakes are lower. Importantly, higher explainability levels can escalate the DM’s cognitive burden (and hence, overall processing time and fatigue) precisely when explanations are most needed (i.e., when the DM is pressed for time to complete a complex task and doubts the machine’s quality). Our study clarifies how explainability affects decision outcomes and cognitive effort, informing the design of effective human-AI systems across decision environments.

This paper presents the multinomial logit model with repeated customer interactions. In each period, the same customer selects a product from the recommended assortment or opts out. From the seller’s perspective, the choice probability in the current period is updated based on the purchase history of the customer. We derive this conditional choice probability and study the adaptive assortment recommendation strategy. Although the problem is generally intractable, we discover the structures of the optimal assortment when the customer interacts with the seller for two periods and the available products to recommend are identical in the two periods. For a general number of periods, we find that the optimal fixed assortments that are not adapted to the purchase history can achieve 50% of the optimal expected revenue, and the approximation ratio increases to 68.47% when the available products across periods are disjoint. Using real-world data sets, we demonstrate that the model with repeated customer interactions can better predict the purchase behavior and generate higher revenues. This paper was accepted by Chung Piaw Teo, optimization. Funding: The authors gratefully acknowledge the funding support from Meituan. C. Wang’s research was supported by the National Science Foundation of China [Grants 72495133, 72172104] and China Postdoctoral Science Foundation [Grant 2025M780760]. P. Gao’s research was supported by the National Natural Science Foundation of China [Grants 72522026, 72201234, and 72192805], Collaborative Research Funding [Grant C6032-21G] of the Hong Kong Research Grants Council, and the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001]. Y. Wang’s research was supported by the National Natural Science Foundation of China [Grant 12371513] and the Major Program of National Fund of Philosophy and Social Science of China [Grant 23&ZD135].

Normative theories assume that people have stable preferences across logically equivalent decision contexts. Preference reversals, cases where preferred options vary across decision contexts, violate this assumption and suggest that preferences are constructed during the decision-making process. We contribute to this literature by identifying a boundary condition for these context effects. We theorize that monetary preference elicitations, such as willingness to pay (WTP) and willingness to accept (WTA), evoke comparisons to external out-of-context standards, such as the market price of similar goods. These out-of-context comparisons should weaken the influence of the local context. Our theory predicts that context effects will be weaker for (1) monetary versus nonmonetary preference elicitations (e.g., WTP/WTA versus choice) and when (2) options differ on attributes highly correlated with market prices of similar goods and services. Across 22 studies, we find support for these predictions using three canonical decision context effects (intertemporal tradeoffs, framing effects, and attraction effects), incentive-compatible and stated preferences, and different response formats (binary, continuous, and mixed). Preferences for a wide range of goods and services—including baked goods, apartments, and lotteries—exhibit these patterns. Our theory explains substantial variance in context effects and offers testable predictions for when context effects will arise in research and practice.

Since 2015, global progress in improving energy efficiency has lagged behind the targets set by the United Nations Sustainable Development Goals, in part due to behavioral barriers to such improvement. Rising renewable energy adoption may impact energy efficiency improvement by raising or lowering these barriers. The well-documented rebound effect—where renewable energy adoption may increase energy consumption—could hinder efficiency gains, whereas renewable energy adoption may also raise awareness of energy use, thereby driving efficiency improvement. This paper examines whether and how renewable energy adoption influences energy efficiency improvement. Using data from 183 sites of a multinational industrial conglomerate from 2015 to 2020, we estimate the impact of changes in renewable energy usage and procurement methods on energy efficiency improvement. We find that using renewable energy to satisfy an additional 10% of a site’s energy demand caused an additional 2.8%–6.1% improvement in energy efficiency. However, the impact varies significantly depending on the procurement approach. Sourcing off-site renewable energy led to energy efficiency improvement, whereas installing on-site renewable energy generation had either no effect or a negative effect. To understand the mechanism, we surveyed site managers and conducted further analysis by leveraging variations in on-site generation costs. Our results indicate that the rebound effect prevailed when sites adopted renewable energy with low ongoing costs. For corporations expanding renewable energy usage, we offer evidence of additional energy efficiency benefits, but capitalizing on these benefits requires careful consideration of the procurement approach. For policymakers, we provide guidance on prioritizing certain procurement methods to accelerate progress toward net-zero.