Humans and artificial intelligence (AI) algorithms increasingly interact on unstructured managerial tasks. We propose that tailoring this human-AI interaction to align with individuals' cognitive preferences is essential for enhancing performance. This hypothesis is examined through a field experiment in a multinational pharmaceutical firm. In the experiment, we manipulated four contextual parameters of human-AI interaction-work procedures, decision-making authority, training, and incentives-to align with sales experts' cognitive styles, categorized as either adaptors or innovators. Our results show that tailored interaction significantly improves sales performance, whereas untailored interaction results in negative treatment effects compared with both the tailored and control conditions. Qualitative evidence suggests that this negative outcome arises from role conflicts and ambiguities in untailored interaction. Exploring the mechanisms underlying these outcomes further, a mediation analysis of AI login data reveals that human-AI interaction tailoring leads sales experts to adjust their AI utilization, which contributes to the observed performance outcomes. These findings support a human-centered approach to AI that prioritizes individuals' information-processing needs and tailors their interaction with AI accordingly.
Agricultural extension programs often train a subset of farmers and rely on social networks for knowledge dissemination. We evaluate this approach through a two-stage experiment of an agronomy training program among Rwandan coffee farmers. The first stage randomized trainee concentration at the village level; the second randomly selected participants within villages. The program increased knowledge and self-reported adoption of the taught practices. It also reshaped farmers’ social networks by creating new social ties primarily among co-trainees. At first glance, the intervention appeared modestly effective, with treated farmers exhibiting higher yields than control applicants within the same village, although this difference is not statistically significant. However, knowledge did not diffuse, and control farmers with more treatment friends at baseline reduced audited adoption and input use. Villages with high trainee concentrations showed suggestive evidence of negative spillovers, consistent with reallocation of labor and other shared inputs toward treated farmers, as formalized in a simple model in which social ties shape access to inputs. Relative declines in the yields of control farmers in these villages account for treatment-control differences, raising concerns both about this dissemination strategy and estimates that fail to consider potential negative spillovers.
Despite significant efforts by humanitarian actors, initiatives, and donors to improve coordination among humanitarian organizations during disaster response, the challenge of insufficient coordination persists. Drawing on practical considerations, we develop a stylized non-cooperative game-theoretical model to examine the coordination dynamics between large international and small local humanitarian organizations in the aftermath of a disaster, comparing both pooled and partitioned coordination models. Our findings reveal that bureaucratic delays commonly associated with coordination efforts not only deter collaboration but can also result in coordination levels that are detrimental to overall relief system performance. This analysis underscores the importance of rethinking coordination structures to better reflect the specific context of relief efforts. In alignment with calls for increased localization, we demonstrate that an efficiently designed partitioned coordination model outperforms a pooled model, which often marginalizes smaller local actors. This partitioned approach proves particularly effective in emergency settings, bringing actors’ decisions closer to the optimal outcome for the entire system.
Purpose This paper examines donor interventions aimed at improving the performance of underdeveloped Pathogen Genomic Sequencing (PGS) supply chains in Sub-Saharan Africa. Specifically, we investigate in-kind donations and supply chain management (SCM) capability-building at laboratories performing PGS. In-kind donations have historically been the primary tool used by donor-led initiatives to scale up PGS capacity, while SCM capability-building represents a more recent, complementary strategy. Design/methodology/approach We develop a system dynamics model of the PGS supply chain, grounded in extensive empirical data, to analyze the short- and long-term impacts of each type of intervention. Findings The results reveal a core trade-off: while in-kind donations can mitigate acute shortages, frequent use risks creating dependency and suppressing learning. In contrast, SCM capability-building supports sustainable improvements, particularly when targeted at labs that are unlikely to improve without external support. Research limitations/implications We derive six testable propositions from the analysis and offer a decision framework to support donors in allocating resources more effectively, balancing immediate shortage mitigation with longer-term supply chain improvements. Originality/value By applying a system dynamic modeling approach tailored to the development of PGS supply chains, we capture the nuanced interactions between donor interventions and lab performance, that is: the ability of labs to timely meet disease surveillance needs in their catchment areas. By evaluating both short- and long-term performance impacts of donor interventions, we identify contexts in which each intervention is most effective.
The aim of this note is to introduce two new generic Banach lattice couples based on weighted spaces of continuous functions, and weighted Radon measures on the positive real-line, denoted by ->-C and--> M respectively. This leads to a new approach, based on these couples, of the Sedaev-Semenov result regarding the Calder & oacute;n-Mityagin property for weighted L1 spaces. As a consequence is obtained a formal equivalence between the concept of K divisibility and the relative Calder & oacute;n-Mityagin Property between--> M and general Banach couples.
